Metaboloepigenetics is a newly coined term in biological sciences that investigates the crosstalk between epigenetic modifications and metabolism. The reciprocal relation between biochemical transformations and gene expression regulation has been experimentally demonstrated in cancers and metabolic syndromes. In this study, we explored the metabolism-histone modifications crosstalk by topological analysis and constraint-based modeling approaches in the budding yeast. We constructed nine models through the integration of gene expression data of four mutated histone tails into a genome-scale metabolic model of yeast. Accordingly, we defined the centrality indices of the lowly expressed enzymes in the undirected enzyme-centric network of yeast by CytoHubba plug-in in Cytoscape. To determine the global effects of histone modifications on the yeast metabolism, the growth rate and the range of possible flux values of reactions, we used constraint-based modeling approach. Centrality analysis shows that the lowly expressed enzymes could affect and control the yeast metabolic network. Besides, constraint-based modeling results are in a good agreement with the experimental findings, confirming that the mutations in histone tails lead to non-lethal alterations in the yeast, but have diverse effects on the growth rate and reveal the functional redundancy. Saccharomyces cerevisiae, the budding yeast, as a model for investigating the global influences of epigenetic modifications on metabolism. The first budding yeast GSMM, iFF708, Biological systems contain many highly interconnected processes that function in a coordinated fashion to produce cellular behavior. Therefore, understanding the biological networks and their crosstalk properties is important to put an accurate interpretation on the complex nature of biological systems. Metabolism and epigenetics are two central biological processes that are vital to the organisms' survival and reproduction. Metabolism is a complex network of dynamical biochemical reactions empowering organisms to grow, reproduce and maintain their integrity S. cerevisiae, Yeast_6.01, as a scaffold, consisting of 889 genes, 1889 reactions (150 reactions are irreversible), 1456 metabolites and the standard biomass equation. Then we analyzed the nine gene expression profiles of four different states of mutated histones tails  extracted from Gene Expression Omnibus (GEO) In this study, we used the GSMM of S. cerevisiae extracted from GEO database. The GSE accession numbers and their categorizations are listed below. The GSMM of yeast in SBML format (Systems Biology Markup Language), a representative format for mathematical models of biological processes such as metabolic network, was obtained from http://www.comp-sys-bio.org/yeastnet. The metabolic states of yeast have been studied by integration of gene expression data into the Yeast_6.01 GSMM as a scaffold model.In this study, we used microarray gene expression profiles and RNA-Seq data of mutated histone tails GSE1639: Consists of gene expression profiles of H3 and H4 mutated tail. Group 1: H3 deleted tail.Group 2: H3 substituted tail, lysine substituted with glutamine residues in histone 3 tail.Group 3: H4 substituted tail, lysine substituted with glutamine residues in histone 4 tail.Group 4: H4 deleted tail.GSE3806: Consists of gene expression profiles of H2B mutated tail. Group 5: H2B deleted tail.Group 6: H2B substituted tail, lysine substituted with glutamine residues in histone 2B tail.GSE7337: Consists of gene expression profile of H2A deleted tail. Group 7: H2A deleted tail.GSE7338: Consists of gene expression profile of H2A substituted tail. Group 8: H2A substituted tail, lysine substituted with glutamine residues in histone 2A tail.GSE29293: Consists of gene expression profile of H3 depletion. Group 9: H3 depletion.2-normalized data . The differential expression of a gene defined significant, if p-value <0.01 and the negative fold change value was indicative of the down-regulated genes. The SBML file of Yeast_6.01 was converted into COBRA  model structure by COBRA toolbox for subsequent analysis and the lowly expressed reactions determined according to gene-protein-reaction relationship (GPR) in the COBRA model of Yeast_6.01.Microarray gene expression data analysis for each given group has been done by GeWorkbench 2.4.0 software FBA, a constraint-based modeling approach, calculates the flow of metabolites through a metabolic network. This method allows predicting the rate of production of a metabolite or the growth rate of an organism. FBA includes delineating constraints on the network, based on environmental, physicochemical, regulatory, enzyme capacity and thermodynamics principles for shrinking the solution space. Integration of transcriptomic data into GSMM is a way to generate better predictive computational models through adding an extra biologically meaningful constraint and limiting the solution space of the GSMM. Blazier and Papin in a comprehensive review, summarized the differences, limitations and advantages of all integration algorithms Yeast_6.01) to identify the reaction’s fluxes (which matches fluxes observed in vivo) and established the boundaries of the WT model according to the reaction fluxes of the initial model p-value as a threshold for identification of highly and lowly expressed genes. Finally, for not reducing the gene expression data into binary or three states, we used fold change values as a quantitative parameter to constraint the fluxes. Totally, there are two main classes for creating condition-specific models; switch- and valve-based approaches. In a switch-based approach, the lowly expressed genes removed from constrained model by adjusting their corresponding reactions boundaries (lower and upper bounds) to zero, while in a valve-based approach, the activity of lowly expressed genes decrease by reducing the corresponding reaction boundaries according to expression values Moreover, we used the standard biomass equation as an objective function of the yeast growth rate. To investigate the comprehensive metabolic properties of the models, several COBRA utilities have used. All GSMMs are available in the Yeast_601 using CytoHubba plug-in in Cytoscape Centrality analysis has been carried out on the undirected enzyme-centric network optimizeCbModel function in the COBRA toolbox, four main output structures are built: f, x, y, and w, where f is optimal objective value, x is containing reaction fluxes of each reaction in the model, y is a vector of shadow prices, and w is a vector of reduced costs.Among various software for calculating the FBA, we used the well-known MATLAB toolbox, COBRA, and a standard objective function (maximization of biomass equation) for evaluating the metabolic models. After running the x file of each model extracted after performing FBA.Then, we calculated the number of carrying-flux reactions (negative and positive carrying-flux reactions) according to the Flux Variability Analysis (FVA) was carried out on all GSMMs to identify and compare the range of possible flux values (flux capacity) resulting in the same optimal objective value (maximum and minimum possible fluxes through a particular reaction). We compared the flux capacity of each reaction of a mutated histone tail models with the corresponding reaction of WT model. So, positive values indicate increased flux capacity in mutated histone tail models in comparison to corresponding WT model, whereas negative values indicate decreased flux capacity in mutated histone tail models in comparison to corresponding WT model. Finally, the increased and decreased ranges of flux values categorized in the yeast metabolic subsystems.Single Gene Deletion used to compute the essential genes, which are important for the growth of the each model.Parsimonious FBA (pFBA) used to categorize the metabolic reactions to the six groups according to their importance in FBA.SingleGeneDeletion function is implemented by constraining the flux of deleted gene to zero and then the flux distribution and maximal growth for the new phenotype simply calculated by FBA. If the maximal growth of the new phenotype is reduced, the gene will be essential. The pFBA method is a modified of FBA in which an extra constraint is added. In this approach, after maximizing the growth rate, the net metabolic flux through all gene-associated reactions will be minimized The Then, we calculated the percentage of the lowly expressed enzymes of the nine constructed GSMMs in each category of pFBA and Single Gene Deletion. . Results as indicated in Biological systems often contain redundancies that contribute to their robustness. FVA is a valuable method to examine the redundancy of metabolic network by calculating the full range of the numerical values (maximum and minimum possible fluxes) for each reaction. FVA was carried out for each model and subsequently compared to the WT model. Then, increased and decreased flux range of each reaction within the metabolic subsystems of yeast were determined. SingleGeneDeletion function and pFBA analysis.pFBA was used to label all metabolic genes based on their ability to contribute to the optimal growth rate predictions. As results in Cell metabolism is dependent on different factors. Among those, some are related to the external metabolites such as nutrient cultures, while some are internal regulatory factors Do the modifications of histone tails  affect the whole metabolism of yeast? Moreover, if so, to what rate? In addition, whether the GSMMs are able to explain these changes?Is the nucleocytoplasmic acetyl-CoA, which is the main metabolite pool for acetylation of histones, under the control of histone tail modifications? Moreover, whether the epigenetic modifications can control the main source of acetylation?To answer the first question, we used GSMM of yeast and nine gene expression profiles of the mutated histone tails. Determination of structural properties of a network and its nature is the first step to analyze the given network File S1A: Centrality definitions used in this study. B: Definition of constraint-based analysis performed in the study.(DOCX)Click here for additional data file.File S2Genome-Scale Metabolic Models (GSMMs) in MATLAB structure format.(RAR)Click here for additional data file.File S3Up- and down regulated genes and their corresponding reactions according to GPR in nine mutated histone tail models.(DOCX)Click here for additional data file.File S4The lowly expressed enzymes among the 100 top ranked centrality indices.(XLSX)Click here for additional data file.


Since the fundamentals of electron transport between electrodes and microbes have not been fully uncovered yet, we propose different options and discuss their impact on biomass and product yields.Microbial electrosynthesis and electro fermentation are techniques that aim to optimize microbial production of chemicals and fuels by regulating the cellular redox balance via interaction with electrodes. While the concept is known for decades major knowledge gaps remain, which make it hard to evaluate its biotechnological potential. Here we present an para-hydroxybenzoic acid and para-aminobenzoic acid. Furthermore, it is shown that the way of electron transport has major impact on achievable biomass and product yields. The coupling of electron transport to energy conservation could be identified as crucial for most processes.For the first time 20 different valuable products were screened for their potential to show increased yields during anaerobic electrically enhanced fermentation. Surprisingly we found that an increase in product formation by electrical enhancement is not necessarily dependent on the degree of reduction of the product but rather the metabolic pathway it is derived from. We present a variety of beneficial processes with product yield increases of maximal 36% in reductive and 84% in oxidative fermentations and final theoretical product yields up to 100%. This includes compounds that are already produced at industrial scale such as succinic acid, lysine and diaminopentane as well as potential novel bio-commodities such as isoprene, This study introduces a powerful tool to determine beneficial substrate and product combinations for electro-fermentation. It also highlights that the maximal yield achievable by bio electrochemical techniques depends strongly on the actual electron transport mechanisms. Therefore it is of great importance to reveal the involved fundamental processes to be able to optimize and advance electro fermentations beyond the level of lab-scale studies.The online version of this article (doi:10.1186/s12859-014-0410-2) contains supplementary material, which is available to authorized users. Metabolic redox limitations can be a crucial factor determining the viability of an industrial biotechnology process . It coulBefore general process design steps can be approached, a better understanding of the overall net benefits of possible target processes is needed ,9. TheseE. coli.The research field of microbial fuel cells studies microbe-electrode interactions for many years. While key reactions could be identified a thorough understanding of the metabolic response to electrical enhancement has not been achieved yet ,11. The Geobacter and Shewanella two main mechanisms are identified: direct electron transfer, which is performed by direct contact between the electrode surface and cellular components of the outer membrane , and indirect EET, which includes all forms of electron transfer between electrode and organisms mediated by soluble electron carrier molecules [For anodic EET of electrogens such as olecules ,13. Firsolecules ,14-17. Bolecules ,18,19.Actinobacillus succinogenes Park and Zeikus observed an electron flow from the cathode into the product succinate [A. succinogenes induced proton translocation and therefore increased ATP synthesis [A. succinogenes is most likely solely responsible for the reported increase in proton flux through the ATPase complex, nowadays the theory about cathodic EET generally assumes that all electrons supplied by EET enter the cytoplasm as negative charge and catalyse intracellular, proton consuming reductions. Simultaneously, the proton consumption would lead to a proton gradient across the inner membrane that drives ATP synthesis [Regardless of the major carbon metabolic pathway the effect of electrical enhancement is typically assumed to result in an increase or decrease of intracellular redox factors such as NADH or NADPH ,11. Elecuccinate . Simultaynthesis . While tynthesis . But is ynthesis ,18, whicin silico approach to calculate the metabolic impact of different electron transport routes during electrically enhanced fermentation enables the evaluation of different mechanisms while current knowledge gaps remain. Pandit et al. recently presented a first computational approach that characterized the general role of bio electrosynthesis in chemical production using a genome scale metabolic model of E. coli [+ to NADH. Analogous to the theory discussed before the authors precariously assume the creation of a proton motive force that drives ATP synthesis even though the fumarate reductase of E. coli is, unlike the one of A. succinogenes, a non-proton-pumping enzyme [Aim of this work is to present a useful analysis tool, which is able to identify beneficial production processes for microbial electrosynthesis, and at the same time enables insight into the energy conservation possibilities during anaerobic electrically enhanced fermentation. Using  E. coli . Within g enzyme . Not surMediator oxidation occurs on outer membrane cytochromes that transfer the electrons into the organism and finally onto NAD. Charge-imbalance creates driving force for 1 proton per electron to enter the cytosol and drive ATP-synthase .The mediator transfers its electrons and protons directly onto NAD without creating a driving force for ATP synthesis. This could happen by diffusion into the cytoplasm or catalyzed by enzymes such as hydrogenases.Electrons from the Quinone-pool are transferred to the mediator molecule by membrane-bound cytochromes of the respiratory chain. Running the electron transport chain via Quinones creates a proton gradient that drives ATP synthesis.The anode acts like an electron sink by directly accepting electrons from NADH. This could happen either catalyzed by membrane-bound enzymes such as hydrogenases or by diffusion of the mediator into the cytoplasm.Note that apart from the mediator diffusion model all models could theoretically also happen as direct electron transfer between the electrode surface and the cellular membrane.elementary flux modes (efms) [elementary flux mode pictures the proposed cellular metabolism in steady-state conditions and together all efms span the complete solution space for each network. Within this solution space we can determine maximum yields for certain products and reconstruct carbon fluxes within the network for example to study changes in by-product formation. The advantage of EMA over other modelling approaches is the calculation of ALL solutions rather than only one best solution . Thereby we can not only assess theoretical maximum yields for production and biomass formation but are also able to compare all possible metabolic flux distributions, which presents a more holistic view of the impact of each EET model [We created core networks of metabolic carbon pathways to determine the effect of electrical enhancement through the different EET ways on production. The tool chosen for the metabolic analysis is elementary mode analysis (EMA), which determines all possible solutions of the metabolic matrix by calculating a unique set of so called s (efms) . Each elET model .in vivo yields will lie inside the determined solutions space. However they will usually be lower than the theoretical maximum yield and will depend on many factors such as thermodynamics, enzyme kinetics, gene regulation and product toxicity, which are not taken into account here.Elementary mode analysis is based purely on stoichiometry of the reaction equations and steady-state conditions of the organism. Therefore the solution space can be regarded as outer boundaries of the metabolic possibilities. Here we use this effectively to determine the maximum theoretical possible advantage of EET on production. Actual Escherichia coli as model organism for industrial biotechnology. Tools for its genetic modification are well established which makes it an attractive host for the production of various compounds. Even though E. coli does not show a comparable electrical activity to Geobacter or Shewanella species, it was shown to be able to exchange electrons with electrodes via soluble mediator molecules [Shewanella oneidensis into E. coli and therefore suggest that the microbe could be modified for optimized electron exchange mechanisms [The presented metabolic analysis was implemented exemplarily for the central carbon metabolism of olecules -32. Furtchanisms ,34.2 and provide all electrons by an electrode. But a disadvantage of using CO2 as sole substrate is the extremely high electron demand and energy limitation by the strictly anaerobic pathways such as the Wood Ljungdahl pathway. Hence many approaches regard the conversion of organic molecules from waste streams, such as acetate, lactate or glycerol by non-acetogenic organisms as more beneficial [2, mainly glucose and glycerol, a process which is often referred to as “electro fermentation” [E. coli carbon network including all product pathways. A full list of all maximum product yields, with and without biomass formation as well as the number of computed elementary flux modes for each substrate and product combination can be found in Additional file In the following sections we present calculated carbon yields for the production of biomass and various valuable compounds including carboxylic acids, alcohols and aromatics via electrically enhanced fermentation. The initial idea of microbial electrosynthesis was to start from the fully oxidized substrate COneficial ,36. Withntation” ,37. Sugantation” . As a sentation” . Its morntation” . A currentation” . We implAn1). On the other hand providing even more electrons through a cathode cannot significantly increase biomass yields. In fact the addition of NADH to the anaerobic network leads to considerably less elementary flux modes as the network has fewer options to distribute carbon fluxes while retaining its redox balance  the biomass yield can be slightly increased, from maximal 26.5% to 32.5%, while the cathodic model that only supports NADH formation results in a minor yield decrease of about −0.6% (Cat2). The network with the anodic model An2 acting as a pure redox sink is still ATP limited with a maximum achievable biomass yield of about 29.9%. However the anodic model that supports the creation of a proton motive force, An1, has the power to enhance biomass production to a maximal yield of 64.1% which equals an increase of about 37.5% and is close to the theoretical maximal biomass yield under aerobic conditions (71.5%).The maximal carbon yields for biomass production that are achievable with the different electron transport pathways under anaerobic conditions in A. succinogenes. Activity of its fumarate dehydrogenases complex creates a proton motive force and therefore ATP synthesis while this cannot be translated to other organisms such as E. coli [E. coli grown anaerobically on fumarate was determined to be 13.6% for non-enhanced conditions while cathodic EET causes an increase to 19.3% and 14.2% for Cat1 and Cat2, respectively. To create the amounts of ATP and NADPH needed for biomass formation from fumarate the metabolism is required to produce NADH. Without EET the NADH is mainly produced by the malic enzyme and pyruvate decarboxylase so that in both cases one carbon is “lost” in the form of CO2. NADPH is also created by a malic enzyme under CO2 release  and acetate (13%). The assumption that cathodic EET results in an increase of available NADH (Cat2) reduces the by-product spectrum to carbon dioxide and succinate only. With fumarate as final electron acceptor the electrons from NADH can enter the first step of the electron transport chain and create a proton motive force, which can drive the highly efficient ATPase. NADPH is created by the membrane bound transhydrogenase driven by proton gradient. This reaction consumes 0.33 ATP equivalents per transhydrogenation .The use of fumarate as substrate was investigated as it is excessively used in literature that studies enhancement of succinate production by EET ,43. This E. coli . The maxAn2 increases the maximal achievable biomass yield on glycerol from 10.0% to 26.4%. Again An1 results in a major increase of the max biomass yield up to 69.5% by providing additional ATP.Biomass yields calculated for growth on a further reduced substrate such as glycerol cannot be improved by providing additional electrons or protons as the breakdown of glycerol is highly limited by the availability of an electron acceptor. Usually anaerobic growth with glycerol as sole substrate is coupled to the production of hydrogen or 1,3-propanediol as this includes pathway branches that consume NADH created during biomass formation ,44. If tThe here presented metabolic benefits of increased ATP availability and improved redox balance offered by an anode might be an explanation for the observed thick biofilms on anodic electrodes and poor growth of cathodic cultures ,18.The decision if microbial electrosynthesis will become an important technique in bio industry will strongly depend on the product yield increase that it can trigger. Therefore it is important to understand the effects of different electron transport routes and energy conservation mechanisms that might happen during electrical enhancement. The degree of reduction (DoR) of a product is often used to describe the electron demand of its production. In fact this is only useful for a direct conversion. The DoR is calculated by the formula given in Table glucose = 4) one would expect a benefit from additional electron supply for the production of all compounds with a DoR higher than 4, such as primary alcohols (e.g. DoRethanol = 6) or some carboxylic acids (e.g. DoRbutyric acid = 5). In fact we observe an overall limited predictive power of the DoR as many products with a higher degree of reduction than the substrate show no increased yield with increasing availability of redox equivalents (e.g. ethanol). Contrary we could also find substrate-product-combinations that benefit from extracellular electron supply even though their reductive state is equal (e.g. 3-hydroxy-propionic acid from glucose). Furthermore it was observed that the production of two isomers of the same compound can benefit from opposing redox interference: While the production of 2,3-butanediol is increased in presence of an anode, 1,4-butanediol production benefits from additional electron supply by a cathode . A full list of all maximum yields for growth coupled production of each substrate-product-combination is given in Additional file 2 and acetate are detected in more than 85% of the 1,965 efms of Cat2. Whereas none of the 2,840 efms that use Cat1 produces acetate as the influx of electrons and protons results in sufficient ATP production. Emde and Schink reported similar results for in vivo fermentation of Propionibacterium freudenreichii, in which they observed an increased production of propionic acid in presence of reduced mediators while acetate formation was inhibited [Cat1 since the shift in product spectrum suggests that the cathode supports an alternative ATP source to acetate production. These results demonstrate that electrical enhancement could be a suitable technique to boost propionic acid production from glucose by reducing by-product formation. But they also highlight the importance of unveiling the actual connection between extracellular electron transport and energy metabolism for the viability of microbial electrosynthesis.The elementary mode analysis could identify several carboxylic acids and alcohols that show increased production from glucose and glycerol under extracellular electron supply by a cathode. Propionic acid and adipic acid are both derived from the tricarboxylic acid cycle intermediate succinate and show maximum achievable carbon yields of 100% on a cathode if produced from glucose . Reported yields of these glucose based fed-batch fermentations with engineered E. coli are around 60% [i) such as ATP or PEP for the phosphorylation of sugar.i + 2 NADH + 2 H+ → 1,3-PDO + H2O + 2 NAD+ + Pi,½ glucose + ~P+ → 1,3-PDO + H2O + NAD+.glycerol + NADH + HThe second group of products that benefit from additional electron supply by a cathode are compounds derived directly from glycerol or the upper branch of glycolysis such as 3-hydroxypropionic acid, 1,2-propanediol and 1,3-propanediol  displays for both substrates the most beneficial option    triggers significant higher yield increases in many cases . It also shows reverse behaviour to its isomer 1,4-butanediol that benefits from a cathode, which is due to the different production pathways  is 66.7% from glucose and glycerol, respectively  is an interesting example to study as the production is increased by an anode even though the substrates glucose and glycerol are further oxidized (DoRpara-aminobenzoic acid (pABA) and para-hydroxybenzoic acid (pHBA) on an anode benefits strongly from the proposed mechanism An1 whereas a pure NADH-redox-sink (An2) results in a significantly smaller yield increase  while other products even though further reduced than the substrate are limited by redox surplus and/or energy requirements .To understand benefits and limitations of microbial electrosynthesis a detailed understanding and analysis of the involved metabolic processes is needed. The presented analysis is the first published approach to methodically screen bio-production processes for their potential benefit from electrical enhancement and could successfully identify 18 target products with possible product yield increases between 7% and 84%. Even though The accumulation of NADH during anaerobic growth could also explain the poor growth performance observed for cathodic cultures. Our analysis shows that the presence of an anode promotes biomass formation while electron supply by a cathode limits the metabolic options of the organism during growth. This limitation could possibly be turned into a benefit by coupling growth to production as explained for the example of the anaerobic conversion of glycerol to 1,3-PDO on a cathode.The elementary mode analysis does not only identify target processes but also highlights the major importance of electron transport mechanism and its coupling to energy conservation. For the majority of products a crucial dependence of maximum achievable product yield and ATP availability was detected. Therefore it is important to direct the focus of current research in the microbial electrosynthesis community towards fundamentals of electron transport as these are needed to be understood to design processes that approach the full potential of microbial electrochemical techniques.E. coli core model used for the in silico analysis includes: Embden–Meyerhof–Parnas pathway/glycolysis, glycerol degradation, Entner–Doudoroff pathway, pentose phosphate way, tricarboxylic acid cycle, glyoxylic shunt, anaplerotic reactions, anaerobic fermentation, electron transport chain, import and export reactions and interaction with a soluble electron carrier  and we calculated up to 215,000 efmtool (version 4.7.1) [in vivo yields will very likely be lower. Nevertheless, it represents the most reliable estimate of the capacity of a network. The operational mode that shows maximum possible carbon flux from substrate into product is referred to as top mode. Maximal theoretical yields for biomass or a certain product are obtained by calculating the carbon balance of all carbon containing substrates entering the network and the carbon containing products leaving the network:The stoichiometric analysis of metabolic networks was performed based on the elementary mode analysis framework introduced by Schuster et al.  The javan 4.7.1)  was usedfluxproduct is the reaction rate for products leaving; fluxsubstrate the reaction rate for substrate uptake and carbonproduct and carbonsubstrate the number of carbon atoms in the product and substrate molecules, respectively.Where


A major roadblock in the effective treatment of cancers is their heterogeneity, whereby multiple molecular landscapes are classified as a single disease. To explore the contribution of cellular metabolism to cancer heterogeneity, we analyse the Metabric dataset, a landmark genomic and transcriptomic study of 2,000 individual breast tumours, in the context of the human genome-scale metabolic network. We create personalized metabolic landscapes for each tumour by exploring sets of active reactions that satisfy constraints derived from human biochemistry and maximize congruency with the Metabric transcriptome data. Classification of the personalized landscapes derived from 997 tumour samples within the Metabric discovery dataset reveals a novel poor prognosis cluster, reproducible in the 995-sample validation dataset. We experimentally follow mechanistic hypotheses resulting from the computational study and establish that active serotonin production is a major metabolic feature of the poor prognosis group. These data support the reconsideration of concomitant serotonin-specific uptake inhibitors treatment during breast cancer chemotherapy. A major challenge in breast cancer diagnostics and therapy is the heterogeneous nature of tumours, whereby multiple molecular landscapes are classified as a single disease. These diverse tumour phenotypes are unlikely to respond in a similar manner to therapeutic intervention, often leading to sub-optimal patient response. Effective patient stratification and treatment is currently hindered by the lack of clear consensus on breast tumour classification2In a landmark study towards the molecular stratification of breast cancer, Metabric  collected over 2,000 clinically annotated, fresh-frozen breast cancer specimens from biobanks in the UK and Canadaet al. have previously analyzed breast cancer transcriptome data in the context of Recon 1 GSMN12et al. did not generate metabolic landscapes for all individual Metabric samples, and were hence unable to relate personalized features of tumour metabolism to clinical information available for each of the tumours. This was due to the limitation of their computational approach requiring information about tumour cell growth rate, which is readily available for cell lines, but not clinical samples. The advances in this field have been comprehensively reviewed in the recent article of Yizhak and colleaguesIn the original Metabric publication6789Here, we for the first time analyze the entire Metabric dataset in the context of the human GSMN Recon 2 and generate sample-specific GSMNs for all 2,000 transcriptomic datasets. Classification of these personalized metabolic landscapes derived from the Metabric discovery set reveals a novel poor prognosis subgroup, which is reproduced in the independent validation dataset. Analysis of the metabolic landscapes belonging to this subgroup provides several mechanistic hypotheses that we experimentally investigate. We demonstrate that active serotonin production is a major metabolic feature of the poor prognosis subgroup, and provide a potential molecular mechanism by which serotonin may promote cell viability in breast cancer cell lines. The association between active serotonin production and poor patient prognosis warrants further clinical examination, including a re-examining of the use of selective serotonin re-uptake inhibitors (SSRIs) in this group of patients.To identify cellular metabolic activities associated with poor patient prognosis we have analyzed the transcriptome of the 997-sample discovery dataset in the context of the human GSMN Recon 2. The Recon 2 model represents a complete set of reactions encoded by the human genome. In a healthy tissue or tumour only a subset of all metabolic enzyme genes is expressed and only a subset of metabolic reactions active. Thus, for each transcriptome sample in the discovery set, we have created a sample-specific version of Recon 2, where transcriptome data were used to determine which metabolic reactions are not active in a particular tumour. We next applied K-means clustering to classify these 997 personalized metabolic landscapes derived from the Metabric discovery dataset. This classification resulted in clusters of patients grouped by the activities of 7440 metabolic reactions; of these, the activity state of 3016 reactions varied between the clusters, adding considerable biological constraint compared to analysis of the raw transcriptome data alone. Since clinical follow-up data were available for each of the patients, we calculated a survival curve for each cluster. Alternative classifications with a range of clusters from 5 to 10 were examined, with eight clusters determined optimal for patient survival stratification (see Methods). As demonstrated in Within the Metabric study, two fully independent sets of clinical samples were used to generate the discovery and validation datasets. Each data set contains data from approximately 1000 patients, and were used for independent validation of study findingsHaving identified a cluster of personalized metabolic landscapes with significant association to poor patient prognosis, we next examined metabolic features of this emergent poor prognosis phenotype. Using the discovery dataset samples, we compared the activity of reactions in the personalized metabolic landscapes obtained for all patients within the poor prognosis cluster (134 patient samples) with all other tumours (863 patient samples). This approach identified 632 Differentially Activated Reactions (DARs) with an adjusted p-value < 0.05 . These DThe three DAPs most significantly divergent between the poor prognosis samples and all other samples are listed in 15Enhanced degradation of casomorphin would lead to an increase in N-acetyl-seryl-aspartyl-lysyl-proline peptides, which are casomorphin degradation products. It is thus pleasing to note that the second DAP identified in As casomorphins act as opioid peptides it has been suggested that they exhibit anti-proliferative and anti-tumorigenic properties1718212425Further examination of metabolic pathways differentially regulated between poor prognosis tumours and all other tumours reveals a dysregulation in serotonin production: 82/134  of the poor prognosis cluster patient samples were predicted to exhibit an active serotonin production pathway, compared to only 102/863  in the remaining discovery set patient samples. This is due to the active state of DOPA decarboxylase -mediated reactions in all poor prognosis sample-specific GSMNs, the final step in the conversion of tryptophan to serotonin. Up-regulation of DDC was confirmed at the transcript level within the Metabric discovery dataset .33–35). However, no effect of serotonin was observed on MDA-MD-231 cells. Importantly, the concentrations used herein are within previously reported physiological ranges37In breast tissue development, serotonin acts as an autocrine-paracrine regulator2829Fig. 4bin vitro, being approximately 2000-fold larger than the cellular volume. More likely, any changes in serotonin concentration would only be observed within the (relatively) small and slowly diffusing liquid-membrane interface.To relate the concentrations of serotonin used in these experiments to those produced by the cells under basal conditions, we measured serotonin levels in conditioned medium by ELISA. In medium conditioned by MCF7, SKBR3 or MDA-MB-231 cells, the levels of serotonin were 14.2 ± 1.4nM, 14.1 ± 1.2nM, and 14.7 ± 1.4nM, respectively. Addition of serotonin to the medium caused the expected increase in medium levels, while addition of 25μM carbidopa for 72 hours had no significant effect on serotonin levels in conditioned medium. The latter is perhaps not unsurprising given the large excess of medium used in vitro cell lines are often poorly reflective of the in vivo situation that they are trying to model. Rather, we demonstrate that MCF-7 and SKBR3 may represent mechanistically validated models for examining the biology of this poor prognosis cluster.While we present a potential molecular mechanism underlying our observed effects, it should be noted that this does not exclude other potential mechanisms. Indeed, the discord between the effects of carbidopa and serotonin in MDA-MB-231 cells would support the existence of at least one alternate mechanism. For example, an indirect effect on cell viability may exist through altered tryptophan metabolism, which is important in NAD biosynthesis, and may hence impact on mitochondrial functioningSerotonin supply to tumour cells may occur at the local (i.e. intratumor) or remote (i.e. systemic) levels, with platelets and the intestine major sources of serotonin within the body2829in vitro. However, further work is required to demonstrate any causal relationship between local serotonin production in vivo and tumour proliferation and/or patient prognosis. Our work also supports the concerns surrounding the administration of serotonin-specific re-uptake inhibitors (SSRIs) in cancer patients424344We thus provide an important mechanistic underpinning for the growing body of evidence linking serotonin production to cancer progressionHaving experimentally investigated a major metabolic pathway whose activity is associated with poor patient prognosis, we further examined the remaining DAPs. Only DAPs containing 10 or more reactions were considered, as these represent the major metabolic alterations evidenced by both gene expression changes and stoichiometric coupling into sets of reactions having the same activity in all samples. Four DAPs, consisting of 22, 54, 103 and 30 reactions , contain46In addition to active metabolism of chondroitin sulphate in the poor prognosis cluster, we also observe alterations in the synthesis of cell surface glycans, important determinants of cell-cell adhesion. Three DAPs, consisting of 45, 103 and 30 reactions, are statistically active in the poor prognosis cluster tumours compared to all other tumours. The largest of these DAPs  contains reaction stoichiometry dependent on N-acetylglucosaminyltransferase, a key enzymatic activity in the synthesis of multi-antennary chains within the Golgi, which have been shown to promote cell detachment and invasionIn summary, we conclude that the poor prognosis cluster represents primary tumours in which metabolism has been already transcriptionally reprogrammed towards remodeling of the surrounding ECM and cell surface glycans. These tumours are poised for invasion and are highly likely to become metastatic, consistent with the poor patient survival observed in the clinical follow-up data.In this work we have for the first time used knowledge about the stoichiometry of human cellular metabolism to constrain analysis of the Metabric transcriptome dataset. To evaluate the added value of this approach we have compared our results with analysis of transcriptome data alone. First, we note that association of increased serotonin biosynthesis with poor prognosis was not reported in original Metabric publication, where transcriptome data was analyzed by approaches based on overrepresentation of formal gene function descriptors in the lists of differentially expressed genes. While DDC was detected as a differentially expressed gene, this alone did not result in detection of deregulated serotonin synthesis as an important feature of poor prognosis tumours. Only through the incorporation of the GSMN constraints in our analysis were 21 further reactions stoichiometrically coupled to DDC, leading to the emergence of this behaviour in our analysis. A reason that traditional over-representation approaches fail to identify this important emergent behavior is that all other enzymes in this pathway are constitutively expressed. As such, no over-representation of pathway components exists, but the presence of all 22 coupled reactions within sample-specific GSMNs for the poor prognosis patients is able to have a significant impact on flux through the pathway.We also note that we discover DAPs that represent a set of reactions operating in the context of all other reactions in the metabolic landscape derived by analysis of whole-genome transcriptome data in the context of the GSMN. This includes transport of extracellular substances, reflecting nutritional requirements of the tumour. Thus our DAPs differ from static pathway descriptors such as KEGG pathway names, which are frequently assigned to individual differentially expressed genes even if a pathway cannot actually operate due to, for example, down-regulation of metabolic genes in other parts of the network preventing synthesis of pathway precursors. To confirm that our results could not be obtained without the context (constraints) of the entire GSMN model, we classified tumours in the discovery and validation datasets using the absence/presence calls of metabolic genes alone. This analysis already used Recon 2 information to extract a subset of metabolic genes, but mechanistic modeling of network context was not performed. As shown in To further demonstrate the merits of such integrative analysis we also undertook a standard statistical over-representation analysis of the transcriptomic data to examine if the metabolic features associated with the poor prognosis cluster could be identified through traditional approaches. For this analysis we used DAVID, a well-established resource for functional gene annotation and enrichment analysisIncorporation of mechanistic network knowledge in analysis of -omics data has been previously shown to be a successful approach68We have analysed the Metabric dataset in the context of the GSMN Recon 2, integrating for the first time the largest functional genomics study on breast cancer tumours with the most comprehensive mechanistic model of human biochemistry created to date. We have generated personalized metabolic landscape for all samples included in the Metabric dataset. Clustering of these personalised metabolic landscapes and capitalisation on the linked clinical follow-up information allows discovery of a patient group, confirmed in the independent validation dataset, where global metabolic activity is associated with poor prognosis. In addition, the incorporation of thousands of metabolic network constraints into the analysis of transcriptome data of an unprecedented number of samples highlighted DAPs: sets of stoichiometrically coupled reactions representing differentially regulated pathways that determine the emergent poor prognosis phenotype for these tumours. Experimental investigation of the two top DAPs highlighted active serotonin biosynthesis as a potential molecular mechanism associated with poor patient prognosis. Combined with DAPs predicting ECM and membrane remodelling, this predicts a metabolic landscape consistent with a highly proliferative and invasive tumour, consistent with the poor survival of patients within this cluster.in vivo. Moreover, our metabolic landscape suggests that casomorphin or chondroitin sulphate degradation products could represent prognostic biomarkersOne clinical implication of the association between active local serotonin production and poor patient prognosis is the potential for a patient group where administration of the SSRI class of anti-depressants may have implications for tumour growth and invasion, potentially extending any serotonin-dependent increases in tumour cell viability. Epidemiological evidence on the safety of SSRI use in breast cancer patients is inconclusive, with studies showing both positive and negative risk:benefit ratios424344In summary, our analysis of the Metabric dataset in the context of GSMN Recon 2 reveals a poor prognosis group with differentially activated pathways such as serotonin production that underlie this aggressive tumour phenotype. Our result encourages the incorporation of mechanistic network information into the analysis of other clinical functional genomics datasets.https://www.ebi.ac.uk/ega/home)http://humanmetabolism.org11.We used gene expression data from the discovery set of 997 tumour samples and validation set of 995 tumour samples generated by the Metabric consortium. The data were obtained from from the European genome-phenome archive . Furthermore, we randomly selected 5 samples from the poor prognosis cluster and 5 samples from remaining part of the discovery dataset. We then generated metabolic landscapes for these samples using both the iMAT and GEBRA approaches, and determined DARs. There was a 90% agreement in the DARs determined by the two methods, and both methods discovered the serotonin and casomorphin DARs presented in results section. Thus GEBRA and iMAT analyses lead to the same biological insight, but iMAT is computationally too expensive to generate 2000 metabolic landscapes.To assess the gain of computational efficiency and any loss of accuracy due to simplification of the iMAT algorithm, we have compared reaction classification obtained by iMAT and GEBRA for transcriptome data from the NCI-H23 cell line (part of NCI-60 collection). The GEBRA approach was 80-times faster than the original iMAT method and reproduced iMAT’s results with 92% accuracy  characterized by 3016 reactions states (variables): reactions with the same activity in all tumours were not included as variables as they provide no useful information: This resulted in clustering being undertaken using 3016 and 3471 reactions for the discovery and validation datasets, respectively. A range of clusters from 5 to 10 was investigated, consistent with the 10 clusters previously identified from transcriptome analysis in the original Metabric publication. For each of the clusters, clinical follow-up data were used to calculate Kaplan-Maier curves, plus the associated p-value, with the R-package survival. Classification into 8 clusters yielded the lowest p-values and was used for further analysis.Differentially Affected Reactions (DARs) were identified as those that had significantly different activity in the poor prognosis cluster compared to the remaining samples. Given the very large number of samples, we compared mean reaction activities between groups of samples using a t-test with Bonferoni correction for multiple comparisons. Reactions with adjusted p-values < 0.05 were considered to be DARs, while sets of DARs that had identical activities in all samples were considered as Differentially Active Pathways (DAPs).MCF-7, MCF10A, MDA-MB-231, SKRB and T47D cell lines were purchased from ECACC. All cells were grown in Dulbecco’s modified eagle medium (DMEM) with phenol red and L-glutamine, and supplemented with 10% Fetal bovine serum (FBS) and, 100 units/ml penicillin, 0.1mg/ml streptomycin sulphate, and 0.25μg/ml amphotericin B.Cell viability was assessed by the 3-2,5-diphenyl tetrazolium bromide (MTT) assay: treated cells and controls were incubated with 0.5 mg/mL MTT for 2½ h and the resultant formazan salt dissolved in DMSO and its absorbance measured at 540 nm. Results are expressed as a percentage of vehicle control; each data point represents the mean of a minimum of three independent experiments of 3 wells per experiment, with error bars representing the standard error of the mean (SEM). Statistical significance was examined through a one-way ANOVA with Tukey’s multiple comparison correction, using GraphPad Prism .Total protein extracts were resolved on 8–16% SDS-polyacrylamide gels and then transferred electrophoretically to Hybond ECL nitrocellulose membranes . Membranes were blocked (1 hour) in 5% fat free dried milk and then probed with primary antibodies against ACE2 (ab15348), MOR (ab17934), DOR (ab176324), or KOR (ab137264)  for one hour, followed by anti-rabbit (1:10000) or anti-mouse IgG (1:10000) IRDye 800 CW secondary, as appropriate, for one hour at room temperature. The membrane was then imaged using an Odyssey Family Imaging System (LI-COR Biosciences). Immunoblots were also probed with an antibody against β-actin  for one hour, followed by anti-mouse IgG IRDye 800 CW (1:10000) for one hour to ensure even loading per lane. All antibodies were purchased from Abcam  or Licor , as appropriate.How to cite this article: Leoncikas, V. et al. Generation of 2,000 breast cancer metabolic landscapes reveals a poor prognosis group with active serotonin production. Sci. Rep.6, 19771; doi: 10.1038/srep19771 (2016).


A universal feature of metabolic networks is their hourglass or bow-tie structure on cellular level. This architecture reflects the conversion of multiple input nutrients into multiple biomass components via a small set of precursor metabolites. However, it is yet unclear to what extent this structural feature is the result of natural selection.E. coli core metabolism if we increase the share of invested resources for switching between different nutrient conditions. Here, hub nodes emerge and the optimal network structure becomes bow-tie-like as a consequence of limited cellular resource constraint. We confirm this theoretical finding by comparing the reconstructed metabolic networks of bacterial species with respect to their lifestyle.We extend flux balance analysis to account for limited cellular resources. Using this model, optimal structure of metabolic networks can be calculated for different environmental conditions. We observe a significant structural reshaping of metabolic networks for a toy-network and We show that bow-tie structure can give a system-level fitness advantage to organisms that live in highly competitive and fluctuating environments. Here, limitation of cellular resources can lead to an efficiency-flexibility tradeoff where it pays off for the organism to shorten catabolic pathways if they are frequently activated and deactivated. As a consequence, generalists that shuttle between diverse environmental conditions should have a more predominant bow-tie structure than specialists that visit just a few isomorphic habitats during their life cycle.The online version of this article (doi:10.1186/s12918-015-0232-5) contains supplementary material, which is available to authorized users. Most engineered multi-task systems show a tradeoff between efficient and flexible design. A common example is the Swiss army knife, which is a flexible tool that can realize many functions but the performance of each tool function cannot reach the efficiency of specialized tools. Here the tradeoff arises from the constraint that a hand tool should not be too big to be useful, resulting in components that are shared between different tool functions. To give a more quantitative example for an efficiency-flexibility tradeoff, imagine two workers that drive between home and their common working place  is a simple but efficient constraint-based model for cellular metabolism . FBA hasIn a first step we define the universe of metabolic reactions , 15 as aThe first physicochemical constraint we account for is flux balance, which is given by m . Here, S is the stoichiometric matrix of the universe of metabolic reactions and i  for the environmental condition k , where M, N and P are the number of metabolites, reactions and environmental conditions, respectively. Note that S is common for all environmental conditions and therefore does not depend on k.for each metabolite φ, in addition to the flux vector, ν, in our model. In this manner, we separate the regulation of an enzyme and the related carrying fluxes. The relations between fluxes and investments are given by For an enzymatic reaction, the related flux of an upregulated enzyme can be zero due to the lack of the corresponding substrate. Therefore, it is convenient to introduce the enzyme investment vector, i runs over all of the enzymatic reactions plus the biomass reaction. For each enzymatic reaction, αi and βi are the catalytic rates of the related enzyme in forward and reverse directions, respectively, where αi,βi>0. It has been shown that the concentrations for most intracellular metabolites are much higher than the KM values of the corresponding enzymatic reactions in E. coli , where σ=0 for Additional file σ=0.1 for Additional file σ=0.9 for Fig. ϕ0=100. However, the exact value of ϕ0 does not change the transition points.For To calculate the fraction of essential reactions we use the standard FBA of COBRA toolbox . We put Equations From The Model SEED database  we just E. coli Core model is taken from [http://systemsbiology.ucsd.edu/Downloads/EcoliCore. All organisms and their living conditions considered in our statistical analysis are listed in (Additional file ken from  and is pOur study does not involve any humans, human data or animals.


Cultured mammalian cells exhibit elevated glycolysis flux and high lactate production. In the industrial bioprocesses for biotherapeutic protein production, glucose is supplemented to the culture medium to sustain continued cell growth resulting in the accumulation of lactate to high levels. In such fed-batch cultures, sometimes a metabolic shift from a state of high glycolysis flux and high lactate production to a state of low glycolysis flux and low lactate production or even lactate consumption is observed. While in other cases with very similar culture conditions, the same cell line and medium, cells continue to produce lactate. A metabolic shift to lactate consumption has been correlated to the productivity of the process. Cultures that exhibited the metabolic shift to lactate consumption had higher titers than those which didn’t. However, the cues that trigger the metabolic shift to lactate consumption state (or low lactate production state) are yet to be identified. Metabolic control of cells is tightly linked to growth control through signaling pathways such as the AKT pathway. We have previously shown that the glycolysis of proliferating cells can exhibit bistability with well-segregated high flux and low flux states. Low lactate production (or lactate consumption) is possible only at a low glycolysis flux state. In this study, we use mathematical modeling to demonstrate that lactate inhibition together with AKT regulation on glycolysis enzymes can profoundly influence the bistable behavior, resulting in a complex steady-state topology. The transition from the high flux state to the low flux state can only occur in certain regions of the steady state topology, and therefore the metabolic fate of the cells depends on their metabolic trajectory encountering the region that allows such a metabolic state switch. Insights from such switch behavior present us with new means to control the metabolism of mammalian cells in fed-batch cultures. Glucose metabolism plays a central role in supplying carbon precursors for cellular energy and biosynthetic needs. Cancer cells have elevated glucose consumption and glycolytic flux in ways similar to the response of tissues to growth promoting signals . CellulaThe high rates of glucose consumption and lactate production seen in cancer cells are also observed in other fast proliferating cells, such as mammalian cell lines in culture. The accumulation of lactate in culture has long been recognized as an inhibitory factor for cell growth and recombinant protein production , 9. In tPreviously, we reported development of a mechanistic mathematical model of glycolysis and the pentose phosphate pathway to examine the dynamic behavior of glucose metabolism . The modWe demonstrated that the combination of isozymes of these three glycolytic enzymes, commonly seen in many rapidly growing cells, give rise to bistable behavior in glycolysis activity . Under pin vivo. In particular the inhibitory effect of lactate on PFK that is relatively minor in most tissues in vivo may become prominent in fed-batch cultures due to its high level of accumulation , 3525, 3The AKT signaling pathway regulates the growth and proliferation of mammalian cells, and stimulates the activity of glycolysis , 3. The Four discrete values of pAKT levels were examined and the steady-state behavior of glycolysis flux at various glucose and lactate concentrations are shown in In a fed-batch culture, glucose is added intermittently to sustain its concentration within a given range. Lactate is produced at a high specific rate in the growth phase, but its production rate is subject to variation when the growth rate decreases in the late stage of fed-batch cell cultivation. In some cases, the culture continues to consume glucose and produce lactate at high rates during the period when cell growth has ceased; while in other cases glucose consumption rate on a per cell basis becomes small and lactate production diminishes, or lactate is even consumed . The pheWe performed a set of transient simulations to demonstrate different scenarios where cultures may have different metabolic fates . Cells aA recombinant CHO cell line was grown in four fed-batch cultures with initial sodium lactate concentrations of 0, 10, 25 and 40 mM. Osmolarity at inoculation was adjusted to 300 mOsm/kg in all four conditions using appropriate amounts of sodium chloride. The concentration profiles of lactate and growth curves are shown along with the specific lactate production rates . Under aLactate concentration also exerted a significant effect on growth rate, thus the difference in the metabolism of the four cultures may not be directly related to lactate concentration. Nevertheless, the results are consistent with the model prediction shown in The effect of lactate concentration on the metabolic behavior of the culture is also evident from the data reported previously . GlucoseCells were next cultivated in two fed-batch conditions  Figs.. EUsing a mechanistic model we have shown previously that with the combination of isozymes typically seen in proliferating cells, the glycolysis flux exhibits classical steady state multiplicity . In the In many fed-batch cultures, cell metabolism switches from a high flux and lactate production state during the exponential growth to a low flux and low lactate production or lactate consumption state when the growth rate diminishes –13. A prIn recent times much progress has been made in advancing our understanding of the interplay between energy metabolism and growth control, and the oncological transformation. Of particular relevance to cell culture bioprocessing is the influence of lactate levels and growth control on glycolysis , 11. To Model simulation demonstrated that increasing lactate concentration  for cell cultivation. The rationale is that continuous processes minimize equipment down time and increase overall productivity. Since multiplicity of metabolic state occurs under some conditions, it is likely that those distinctive metabolic states will also lead to different steady states of the culture: at a high flux state, the vast majority of glucose is converted to lactate resulting in a low viable cell concentration at the steady state reached; conversely at a low flux state, little glucose is diverted to lactate, and a high cell concentration is achieved. Published experimental results of continuous culture did suggest that difference in metabolism gave rise to different cell concentrations –53.The mechanistic metabolic model presented above has provided insights into the possible cause of the somewhat erratic behavior of metabolic shift to a low flux and lactate consumption state often seen in cell culture bioprocessing. The experimental evidence supports the notion that the steady state multiplicity is at play, and may possibly be the root cause of this behavior. With a better understanding of the mechanistic cause for the metabolic shift in culture, one can begin to explore the feasibility of controlling the metabolism in a robust manner and ensuring metabolic shift through external medium components such as enzyme inhibitors or through cell engineering methods.S1 FigTranscript levels of isozymes of (A) PFKFB, (B) HK, (C) PFK and (D) PK were analyzed using CHO microarrays. CHO cell lines and the culture conditions used for the transcript analysis are listed in (TIF)Click here for additional data file.S2 FigFour fed-batch cultures of a recombinant CHO cell line were inoculated with different concentrations of initial lactate including 0mM (◊), 10mM (□), 25mM (Δ) and 45mM (×). The metabolic fate of a culture depends on the initial lactate concentration. The cultures that were exposed to higher lactate concentrations had higher specific lactate production rates in the later stages of the cultures. Specific lactate production rates for growth phase were calculated using data between 41–88h whereas those for stationary phase were calculated using data between 160–183h. (A) Lactate (B) Viable Cell Density (C) Specific Lactate Production Rate.(TIF)Click here for additional data file.S3 FigTime series data of a number of manufacturing runs of the same process with harvest titers in the top 20% (blue) and bottom 20% (red) are plotted. Data points from the same bioreactor run are connected by lines. All cultures proceed from the right hand side of the figure, where glucose is high, towards the left hand side where glucose is low. Cultures with low lactate concentration in the early stage exhibit metabolic shift to lactate consumption in their later stage, while those that continue to produce lactate at high rate in the late stage are those with higher lactate levels in the early stages of the culture.(TIF)Click here for additional data file.S4 FigThe seed culture was same as that used for (TIF)Click here for additional data file.S5 FigThe seed cultures used were prepared using cells from a different frozen vial of the same cell bank at a different point in time, as compared to that used in (TIF)Click here for additional data file.S6 FigIntracellular lactate concentration increases monotonically with increasing extracellular lactate. F16BP and F26BP concentrations are relatively constant at lower extracellular lactate concentrations, but increase sharply at higher extracellular lactate levels. pAKT activity was set constant at 0.25.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S1 FileThe file contains description of mathematical model rate equations, differential equations and stability analysis.(DOC)Click here for additional data file.


Such in silico techniques typically involve the analysis of a metabolic model describing the metabolic and physiological states under various perturbed conditions, thereby identifying genetic targets to be manipulated for strain improvement. More often than not, the activation/inhibition of multiple reactions is necessary to produce a predicted change for improvement of cellular properties or states. However, as it is more computationally cumbersome to simulate all possible combinations of reaction perturbations, it is desirable to consider alternative techniques for identifying such metabolic engineering targets.Rational design of microbial strains for enhanced cellular physiology through Escherichia coli, as case studies, for enhancing ethanol and succinate production, and reducing acetate formation. Interestingly, most of the identified metabolites correspond to gene targets that have been experimentally validated in previous works on E. coli strain improvement. A notable example is that pyruvate, the metabolite target for enhancing succinate production, was found to be associated with multiple reaction targets that were only identifiable through more computationally expensive means. In addition, detailed analysis of the flux-sum perturbed conditions also provided valuable insights into how previous metabolic engineering strategies have been successful in enhancing cellular physiology.In this study, we present the modified version of previously developed metabolite-centric approach, also known as flux-sum analysis (FSA), for identifying metabolic engineering targets. Utility of FSA was demonstrated by applying it to The application of FSA under the flux balance framework can identify novel metabolic engineering targets from the metabolite-centric perspective. Therefore, the current approach opens up a new research avenue for rational design and engineering of industrial microbes in the field of systems metabolic engineering.The online version of this article (doi:10.1186/s12918-015-0198-3) contains supplementary material, which is available to authorized users. This practical utility consequently fueled the reconstruction of over 100 GEMs representing the metabolic organization of various organisms across all three domains of life [Previous microbial engineering for strain improvement was largely based on biological intuition and/or trial-and-error methods, such as random mutagenesis. However, with the recent advent of high-throughput experimental technologies and improved  studies –3. Notab of life , 5.in silico analysis of GEMs enables us to achieve the following goals: (1) interpreting high-throughput omics data, (2) aiding design of metabolic engineering strategies, (3) generating new testable hypotheses to gain knowledge of the biological system, (4) investigating inter-cellular and inter-species interactions and (5) understanding of complex genotype-phenotype relationships leading to discovery of emergent properties [in silico simulation of gene deletions [in silico methods share the common theme of identifying suitable genetically and environmentally perturbed conditions by optimizing the cellular objective, typically cell growth, under mass balance, reaction reversibility and flux capacity constraints. Basically, such a constraints-based technique mimics the wet-lab genetic engineering experiments by imposing relevant constraints on the metabolic reaction fluxes and can thus be considered “reaction-centric”. However, more often than not, these techniques have encountered the computational issue of combinatorial explosion due to consideration of multiple reaction perturbations. Hence, to deal with this limitation, an alternative method is needed to further expand our in silico capability of multiple reaction targets identification.Specifically, operties –9. Amongeletions , 11, to eletions , OptReg eletions , OptStraeletions , OptGeneeletions , flux reeletions , RobustKeletions , flux sceletions , OptForceletions  and mosteletions  for ideneletions  for thorE. coli [Z. mobilis and E. coli [Escherichia coli for increasing succinate and ethanol production, and reducing acetate formation as case studies.Recognizing that many metabolites are involved in multiple reactions, a “metabolite-centric” approach can potentially find individual metabolite targets implicating some combination of reaction flux constraints. In this way, the metabolite-centric flux-sum analysis (FSA) was initially utilized to evaluate the structural and evolutionary properties of cellular metabolism in E. coli , 23. Lat E. coli . NonetheE. coli genome-scale metabolic model, thereby identifying the possible metabolite attenuation/intensification targets which can enhance the production of ethanol/succinate, and reduce the formation of toxic acetate. The list of identified best metabolite targets are summarized in Table In this work, we harness the previously presented computational technique, flux-sum analysis , to idenpta) resulted in improved ethanol production rate [The flux-sums of individual metabolites are attenuated to investigate their effects on ethanol production. The ethanol production profile Fig.  generateion rate , 26. Theion rate .Fig. 1EtPFL) reaction yields acetyl-CoA and formate as the co-product. Another alternative is the pyruvate dehydrogenase (PDH) reaction that generates acetyl-CoA, CO2 and NADH. Therefore, attenuating formate flux-sum promotes acetyl-CoA formation through the PDH reaction, leading to increased NADH generation, which in turn drives the NADH-consuming alcohol dehydrogenase (ALCD) reaction towards ethanol production. Interestingly, it has been reported that a high flux through PDH can increase ethanol yield [PFL-repressed and PDH-overexpressed E. coli strain can be explored in future experimental validation to further improve ethanol production.Another flux-sum attenuation target identified by FSA for the ethanol production is formate. Generally, acetyl-CoA in a cell can be synthesized in two ways. The pyruvate formate lyase  reaction and simultaneously regenerate NADH. The elevated NADH regeneration rate can thus improve ethanol production. However, the in vivo activity of UdhA has been reported to be low [UdhA and flux-sum intensification of PPP metabolites by overexpressing the glucose-6-phosphate dehydrogenase and gluconate dehydrogenase will be a promising strategy to increase ethanol production without the inhibitory effect.Flux-sum intensification analysis identified several target metabolites such as 6-phospho-D-gluconate, erythrose-4-phosphate, ribulose-5-phosphate, sedoheptulose-7-phosphate and xylulose-5-phosphate that can improve ethanol production. Clearly, these metabolites are predominantly active in the NADPH-generating pentose phosphate pathway (PPP). Hence, flux-sum intensification of such metabolites will increase the formation of NADPH, which can be re-oxidized to NADP via the soluble NAD transhydrogenase , a key metabolite in folate metabolism. Examination of metabolic reactions involving MLTHF revealed that the metabolite can only be formed by glycine hydroxymethyltransferase reaction and the glycine cleavage reaction, where the latter reaction is coupled with NADH regeneration. Furthermore, FSA predicts that fluxes through both the reactions can be increased when MLTHF flux-sum is intensified. Thus, such a metabolic perturbation will have a positive effect on the ethanol-producing pathway which requires NADH as the co-substrate.PDC) and alcohol dehydrogenase II genes can significantly improve ethanol production [PDC reaction into the GEM of E. coli, iAF1260 model and increasing ALCD flux, which is also equivalent to intensification of acetaldehyde flux-sum. Our simulation has shown that the intensification of acetaldehyde flux-sum in the wild-type E. coli GEM without the PDC reaction did not result in consistently increasing ethanol production. Thus, we carried out flux-sum intensification on the modified GEM of E. coli with the PDC reaction to further understand how PDC insertion can influence the ethanol production profile.In previous metabolic engineering studies, it has been reported that the insertion of pyruvate decarboxylase . The addition of PDC allows the engineered E. coli strain to directly convert the pyruvate into acetaldehyde directly without forming the acetyl-CoA intermediate. Such direct conversion may positively assist the ethanol production as it circumvents the possible limitations which are associated with CoA regeneration in the ACALD step. Interestingly, intensification of acetaldehyde flux-sum beyond a certain point in wild-type decreases the ethanol production as the excess acetaldehyde gets converted into acetate via aldehyde dehydrogenase and then into acetyl-CoA via phosphotransacetylase (PTA) and acetate kinase (ACK), forming a cycle.The new simulation results indeed show that addition of ion Fig. . In addiyde Fig. . Wild-tySimilar to the ethanol production case, we apply FSA to identify metabolite targets for strain improvement in the aspect of succinate production. The corresponding profile under metabolite flux-sum attenuation upon glucose uptake indicated that pyruvate is the only metabolite which can be targeted for enhancing succinate production Fig. . This reACALD and ALCD reactions, which are part of the mixed acid fermentation pathway. Thus, by examining this pathway, we will attempt to explain how pyruvate flux-sum attenuation can lead to enhanced succinate production. From Fig. PYK) and phosphoenolpyruvate/glucose phosphotransferase system (PTS) competes for the metabolic fluxes that could potentially flow towards succinate formation. Hence, attenuating pyruvate flux-sum favors the redirection of metabolic fluxes at the phosphoenolpyruvate branch towards succinate synthesis. Since pyruvate flux-sum attenuation also causes a concomitant decrease in NAD regeneration by LDH, ACALD and ALCD, the metabolic flux is preferably channeled towards succinate-producing fumarate reductase (FRD), where the NAD shortage can be compensated by the coupling of FRD with the menaquinone-dependent NADH dehydrogenase reaction. Therefore, the substitution of LDH, ACALD and ALCD by FRD as the major NAD regenerator can increase the succinate production under pyruvate flux-sum attenuation as depicted by the right section of the succinate production profile  fluxes. Hence, when ATP regeneration is attenuated below the threshold which affects glucose uptake, we immediately observe a decrease in succinate productivity, thus explaining the triangular shaped succinate production profile under pyruvate flux-sum attenuation.On the other hand, the left section of the profile Fig.  can be eFRD consumes menaquinol to produce menaquinone, flux-sum intensification of such metabolites is expected to directly promote succinate production. It should be noted that the iAF1260 model accounts for two species of menquinones, demethylmenaquinone-8 and menaquinone-8, which were thus detected as potential targets for flux-sum intensification.Flux-sum intensification analysis allowed us to observe the increase in succinate production when the flux-sum of several metabolites involved in redox reactions and the glyoxylate pathway were intensified. As the succinate-forming ACALD and ALCD in the ethanol production pathway as the major NAD regenerator is replaced by the succinate-producing FRD in the TCA cycle, similar to the metabolic state observed under pyruvate flux-sum attenuation. This strategy has also been demonstrated in a previous experimental study whereby the deletion of iclR, a repressor of the glyoxylate bypass operon aceBAK [In the case of glyoxylate flux-sum intensification, its perturbation effect was understood by examining the TCA cycle with glyoxylate bypass. Under anaerobic condition, the metabolic flux through the TCA cycle is low: energy (ATP) is mainly regenerated by substrate-level phosphorylation rather than oxidative phosphorylation. Thus, increasing glyoxylate flux-sum will force a redistribution of fluxes away from the ethanol production pathway towards the TCA cycle. Consequently, the role of n aceBAK , increasn aceBAK .ackA) or pta, which is equivalent to attenuating acetylphosphate flux-sum, can effectively reduce acetate formation [From the viewpoint of strain improvement, acetate secretion is usually undesirable as it inhibits cell growth and biochemical production , 33. Henormation , 35.On the other hand, for flux-sum intensification, we identified about 30 potential metabolite targets, some of which are shown in Table An interesting and novel flux-sum intensification target for reducing acetate formation is 3-phosphoglycerate (3PG). As 3PG is an upstream precursor required for producing acetate, it is unexpected that increasing its turnover rate will attenuate acetate production. However, further examination of the flux distribution indicated that as the 3PG flux-sum increases, the pathway for glyoxylate catabolic process becomes activated. In short, glyoxylate catabolic process begins the synthesis of glyoxylate from isocitrate lyase and continues through a series of reactions, glyoxylate carboligase, tartronate semialdehyde reductase and glycerate kinase, where glyoxylate is converted back to 3PG. Since 3PG is a precursor for serine biosynthesis, the utilization of the glyoxylate metabolic pathway redistributes the fluxes such that cell growth is lowered. Consequently, the decrease of both energy requirement and resources for acetate synthesis leads to less flux through acetate kinase. Interestingly, a concomitant production of succinate was also observed as a result of glyoxylate bypass utilization. Thus, the intensification of 3PG flux-sum via overexpression of glyoxylate catabolic process can be a desirable metabolic engineering strategy for reducing acetate formation and increasing succinate production.S. cerevisiae was reported [E. coli for enhancing ethanol and succinate production, and reducing acetate formation. In general, this procedure can be simply applied to any other organism of interest whose genome-scale metabolic model is readily available. For example, we also applied the proposed FSA to S. cerevisiae using the iMM904 model [E. coli where the redirection of carbon flux towards glyoxylate cycle instead of the TCA cycle is beneficial for succinate overproduction. Collectively, such results reveal that the utilization of glyoxylate cycle could be a global strategy for succinate overproduction across different organisms.Recently, the application of flux-sum to a strain improvement study for vanillin production in reported . In that04 model  for succPTAr and ACKr, corresponding to the attenuation of acetylphosphate and acetate, respectively. Similarly, the deletion/downregulation of PFL, as identified by OptKnock, OptReg and OptForce also correspond very well with the formate attenuation. Moreover, the overexpression of citrate synthase and aconitase in conjunction with the isocitrate dehydrogenase deletion may be an equivalent strategy to augment the glyoxylate pool as identified by FSA. However, despite such comparable results, the pyruvate flux-sum attenuation for succinate overproduction does not have many equivalent targets in reaction-centric algorithms, except PYK deletion, which is presumably due to pyruvate’s association with multiple reactions. Intensification of menaquinol/menaquinone is unique target identified by FSA which does not have equivalent reaction-centric targets. Such results clearly demonstrate the efficacy of FSA in identifying novel metabolite targets that could only have been found via the multiple reaction deletion analysis. In this sense, FSA can be considered as a useful tool to circumvent the much higher computational cost of perturbing the large number of reaction flux combinations. Nonetheless, the flux-sum analysis method is not completely superior to conventional gene/reaction deletion analysis as we have also identified unique reaction targets using the latter method. Such examples include the downregulation of malate dehydrogenase (MDH) identified by OptForce, which prevents the malate from getting converted into oxaloacetate and redirects the flux towards fumarate, and then to succinate. Hence, we propose FSA as a complementary procedure within the general constraints-based flux analysis framework for strain improvement studies.The metabolic engineering targets identified using the metabolite-centric technique can be equivalent to the reaction targets obtained by the gene deletion-based approach. Thus, we compared the results of current work with that of OptKnock, OptReg and OptForce algorithms for the succinate overproduction case  to exami(zwf) or forcefully reroute excess carbon flux into the desired pentose phosphate pathway by deleting the phosphofructokinase (pfk) or phosphoglucose isomerase (pgi) genes from the parallel glycolytic pathway. In the second approach, more intuitive strategies such as the use of antimetabolites or co-feeding of certain pathway intermediates in the culture media could possibly restrict or enhance the target metabolites’ turnover rates. Although the use of such compounds to manipulate the flux-sum of a particular metabolite has never been attempted before, it could still be an interesting option to explore.While using FSA for metabolic engineering, following the identification of metabolite targets, we envisage two different approaches to manipulate the flux-sum of target metabolites. The first approach could be based on the genetic engineering of genes around the target metabolite in the metabolic network. For example, in order to intensify the 6-phospho-D-gluconate production levels, we can either overexpress the corresponding biosynthetic gene, glucose-6-phosphate dehydrogenase E. coli using the FSA method developed in our previous work [in silico simulation presented results that were highly consistent with previous wet-lab experimental observations, and also novel findings that could be validated in future works. In addition, comparison with reaction targets identified by conventional gene/reaction deletion analysis shows that FSA has the capability to identify unique metabolite targets. Thus, the application of FSA complemented with conventional gene deletion analysis will provide researchers with a wider choice of potential metabolic engineering targets for stain improvement, which will be beneficial to the field of systems metabolic engineering.In this study, we successfully identified potential metabolite targets that can enhance the cellular physiology of ous work . The oriin silico method that simulates cellular metabolism such that a hypothetical cellular objective, usually cell growth, is maximized under stoichiometric and reaction capacity constraints [Constraints-based flux analysis is an straints –41. The Subject to:cj is the coefficient of the desired cellular objective to be maximized, vj is the flux of reaction j and Sij indicates the stoichiometric coefficient of metabolite i involved in reaction j. Values of αj and βj can be specified based on experimental measurements or any hypothetical flux perturbation. Solving Eq.  metabolic flux distribution.where ving Eq.  by settiConventional constraints-based flux analysis does not provide a means to quantify the degree of individual metabolite utilization. Hence, we introduced the flux-sum concept to quantify metabolite turnover rates , 23. Undi.e i, Φi, can be calculated as half the sum of the absolute rate of all reactions consuming or producing the metabolite:Thus, the flux-sum of any intermediate metabolite Unlike the mathematical formulation of the basic constraints-based flux analysis, the flux-sum expression is nonlinear due to the modulus operator and directly imposing constraints on the flux-sum expression will result in a nonlinear problem. Hence, additional constraints are introduced to recast the problem to a linear integer form as discussed previously :\documengij+ and gij−, refer to the generation and consumption components of metabolite i due to reaction j, respectively. Binary variables, Iij+ and Iij−, serve as switches to turn the generation and consumption components on and off such that only one of the component is active, effected by the Iij+ + Iij− = 1 constraint. Big M can be an arbitrarily large flux value, e.g. 1000 mmol/gDCW-hr.The positive variables, To identify the attenuation and intensification metabolite targets which lead to the enhanced production of desired compound, we first need to quantify the flux-sum of all metabolites in the wild-type strain. This can be determined by first solving the constraints-based flux analysis problem Eq.  with bioOnce the reference flux-sum values are established, i.e. wild-type, maxima and minima values, we then solve the below mentioned MILP problem to analyze the effects of perturbing a particular metabolite’s turnover rate on cellular growth.Subject to:ORkatt and kint are gradually varied between 0 and 1 in steps of 0.1 to analyze the effect of metabolite attenuation between minimal and wild-type values, and intensification of metabolite turnover between the wild-type and maximal values, respectively.where constraint (C1) and (C2) is applicable for attenuation and intensification problems, respectively. Parameters Finally, the objective value obtained from the solution of Eq.  is used Subject to:ORBik is the maximum biomass obtainable while solving problem (Eq. ith metabolite at kth attenuation/intensification levels. Here, it should be noted that we problem (Eq. where blem Eq.  for ith blem Eq.  in two siAF1260 metabolic model of E. coli [in silico simulations were carried out based on a glucose uptake rate of 1 g/gDCW-hr, such that all flux values can also be interpreted as yield in mmol/g glucose or g/g glucose. Since by-product formation in E. coli typically occurs under the anaerobic condition, we also constrained oxygen uptake rate to zero. All the optimization problems were solved using the GAMS IDE software version 22.4 [The FSA procedure is applied to the  E. coli  to invesion 22.4  with IBM


A report on the Cold Spring Harbor Asia conference on Genome Assisted Biology of Crops and Model Plant Systems Meeting, held in Suzhou, China, April 21–25, 2014. Plant genomes are generally much larger and more complex than those of animals, encoding molecular circuits of high sophistication and robustness to control physiology. Due to the immobility of plants, their large and complex genomes have evolved to encode an organism adapted to a constantly changing environment. In addition to the natural selection process, the genomes of domesticated crops have been selected for desired traits by breeding. Thus far, the adaptability of plants and selective breeding have created crops to match the ever-increasing human demand for food and energy. However, given the explosive growth of the global population and the concomitant decrease in the amount of arable land, current breeding strategies alone will not be able to maintain a sufficient supply of plant-derived food and energy sources for the entire world. Therefore, understanding and engineering molecular networks that control plant phenotypes, as well as the identification of new genomic markers for selection of these phenotypes, will be crucial to begin the next green revolution.Embracing these impending agricultural challenges, over 100 participants gathered at the Cold Spring Harbor Asia conference Genome Assisted Biology of Crops and Model Plant Systems Meeting to discuss recent progress in a diverse array of plant science topics. Four major themes emerged during the meeting, including the extended applications of next-generation sequencing technologies to explore genetic elements and their networks to control plant phenotypes, ways to integrate multiple types of -omics data and genetics data, moving from model plants to crops, and challenges to the quantitative modeling of complex traits.The conference discussions covered many crop species, including rice, maize, foxtail millet, wheat, sorghum, oil palm, tomato and cucumber. Decoding their reference genomes has resulted in a new era of genome-assisted crop genetics. Recently, resequencing of a large number of accessions, followed by population-genomics analysis, has revealed genetic loci for the domestication of and improvements in crops such as rice and maize. Sanwen Huang  demonstrated the feasibility of resequencing-assisted genetics in cucumber, which is also a model for plant vascular biology and sex determination. Deep resequencing of 115 cucumber lines, sampled from 3,342 accessions worldwide, identified 112 domestication sweeps. Interestingly, one of these regions contains a gene for the loss of bitterness, an essential domestication trait of cucumber. Single-nucleotide polymorphism (SNP) markers identified from resequencing are generally tested for their association with traits of agricultural importance, such as plant growth and development, and metabolites are often crucial for such traits. Therefore, genome-wide association of SNPs with metabolic traits should provide new insights about the metabolic pathways essential for plant growth and development. Jie Luo  presented the results of a metabolome-based genome-wide association study (mGWAS) of maize and rice using a new, widely targeted metabolomics strategy that simultaneously detects hundreds of primary and secondary metabolites. The mGWAS successfully identified candidate genes involved in crucial metabolic traits.SHELL gene.Robert Martienssen  introduced a variety of resequencing-assisted genetics strategies to discover key genes for oil palm domestication and breeding. Oil from fruit and seeds provides approximately 45 % of edible oil globally, and palm oil is the most efficient source of biodiesel. Shell thickness controls oil yield, and oil palms with thin-shelled fruit have been selected by breeders. To clone the genes responsible for this trait, Martienssen’s team first mapped the genomic interval for shell thickness from hundreds of inbred lines derived from self-pollination of a thin-shelled accession. Genomic regions with high homozygosity may contain genes selected during domestication and breeding. Given this hypothesis, genomic regions were scored for homozygosity using SNPs from resequencing data. Using the ‘homozygosity mapping by resequencing’ approach, his team successfully cloned the Arabidopsis. In this approach, a mutant population is generated by treatment with ethyl methanesulfonate (EMS), and then mutations that disrupt genes for the trait of interest are identified by resequencing the mutant genomes. Brian Dilkes  demonstrated that a similar approach would also be effective in crops by cloning genes affecting the accumulation of insect defense compounds in sorghum. He also reported the discovery of error-prone regions of the sorghum genome, perhaps due to genome assembly quality. This finding demonstrated the importance of systematic cataloging of variants from a large number of resequenced genomes to improve future applications of resequencing-assisted genetics.Resequencing-assisted forward genetics has proven useful in model organisms, including Lotus japonicus is a model plant that is used to study regulatory networks controlling nitrogen fixation by symbiotic nodulation. Masayoshi Kawaguchi  reported an extended model of a long-distance root-to-shoot-to-root signaling network that controls nodulation. Multi-omics approaches elucidated post-translational modifications for a key signaling molecule that functions in long-distance interactions with other signaling network molecules. Another relatively small but sophisticated model network presented at the conference was for age-dependent cell death signaling in Arabidopsis. Hong Gil Nam  presented data on a core senescence regulatory network mediated by ORE1, in which a highly robust regulatory circuit includes a trifurcate, feed-forward pathway, involving ORE1, EIN2 and miR164. He also introduced his ongoing efforts to identify the functional and regulatory networks for temporal and spatial coordination of the age-dependent developmental transitions by integrating various multi-dimensional transcriptomes of the Arabidopsis leaf during the entire plant life span.Most plant phenotypes are regulated by highly complex and robust molecular networks. Unraveling the network structure and identifying its key components will provide effective targets for crop engineering. During the conference, extended models for many such networks derived from systematic and integrative approaches were presented. Arabidopsis. Andrea Eveland  presented a regulatory network that controls maize inflorescence architecture, reconstructed from the integrative analysis of spatiotemporal gene expression profiles and genome-wide binding loci of key regulators. The elucidated regulatory modules could go on to provide a molecular target for making improvements to organ architecture in cereal crops.Crop yields could be improved by engineering molecular networks that control the development of plant organs. Dirk Inze  discussed a key molecular network in determining the timing of the transition from cell division to cell expansion, which is relevant to final leaf size. Chromatin-remodeling complexes comprise the core network, and their dynamic interactions with transcriptional co-activators were detected by proteomics approaches. He also showed that overexpression of key components of the network can increase leaf size in Setaria viridis (green millet). Using integrative system-level approaches, including high-resolution leaf transcriptome profiling, his team is characterizing systems-level differences between a less-efficient C3 photosynthesis network and a more-efficient C4 photosynthesis network. Information from the study could be used to improve the photosynthetic systems of C3 crops in the future.Larger-scale network modeling efforts were also discussed during the meeting. Thomas Brutnell  presented his recent efforts in the reconstruction of molecular networks for photosynthesis in rice, maize and a new model plant, Arabidopsis and rice and described their use for the discovery of novel gene functions. He also proposed functional gene networks as efficient technical platforms for big-data plant biology to enable the analysis and integration of heterogeneous data from diverse public databases.In contrast to the well-characterized molecular networks of each biological process, a genome-wide predictive network could provide a platform in which to study many pathways and interpathway organization. Such networks could also be used for the functional study of individual genes based on guilt-by-association principles or other network algorithms. Insuk Lee  presented genome-wide functional gene networks for Studies of determining factors in transcriptional programs in plants have been conducted mainly in laboratories or greenhouses, rather than in the field. However, most crops are typically grown in complex, fluctuating environments. Takeshi Izawa  has developed a statistical model for the rice transcriptome in the field, revealing that gene expression dynamics can be predicted for changing temperatures. Such models accounting for environmental factors will help to translate genotype-to-phenotype associations discovered in the laboratory to agricultural traits of crops in the field, reducing gaps between laboratory models and field phenotypes.Another new challenge in crop science is gaining improvements in the flavor quality of fruits. Harry Klee  argued that traditional breeding programs have been biased towards yield, resulting in reduced flavors. He showed how to identify the molecules responsible for flavor quality using consumer-assisted selection programs. He demonstrated that not only taste, but also olfaction, are important for fruit flavor, and hundreds of chemicals can be used to improve flavor. Although the genetics of flavor appears complex, information from this study will help to promote the commercial value of fruits, as well as enhance a healthier diet in the future.Despite a long history of plant genetics, genotype-to-phenotype association mapping is still largely limited in plants owing to their immense genomic space and confounding effects of environmental factors. Advanced sequencing technologies, along with systems-genetics approaches, however, are promising for unveiling such complex relationships between genetic variations and phenotype differences in plants. Next-generation sequencing with improved accuracy and read length has resolved many technical barriers in traditional genetics approaches. In addition, high-throughput molecular profiles and advanced modeling technologies have elucidated the genetic networks around key regulatory genes for a specific phenotype, accounting for the effects of molecular changes on emergent phenotypes. Most, if not all, of the meeting participants agreed that we are entering a golden era of crop science because of advanced genomics technologies.EMS: Ethyl methanesulfonate; mGWAS: Metabolome-based genome-wide association study; SNP: Single-nucleotide polymorphism.The authors declare that they have no competing interests.