Models are important tools in microbial ecology. They can be used to advance understanding by helping to interpret observations and test hypotheses, and to predict the effects of ecosystem management actions or a different climate. Over the past decades, biological knowledge and ecosystem observations have advanced to the molecular and in particular gene level. However, microbial ecology models have changed less and a current challenge is to make them utilize the knowledge and observations at the genetic level. We review published models that explicitly consider genes and make predictions at the population or ecosystem level. The models can be grouped into three general approaches, i.e., metabolic flux, gene-centric and agent-based. We describe and contrast these approaches by applying them to a hypothetical ecosystem and discuss their strengths and weaknesses. An important distinguishing feature is how variation between individual cells  is handled. In microbial ecosystems, individual heterogeneity is generated by a number of mechanisms including stochastic interactions of molecules , stochastic and deterministic cell division asymmetry, small-scale environmental heterogeneity, and differential transport in a heterogeneous environment. This heterogeneity can then be amplified and transferred to other cell properties by several mechanisms, including nutrient uptake, metabolism and growth, cell cycle asynchronicity and the effects of age and damage. For example, stochastic gene expression may lead to heterogeneity in nutrient uptake enzyme levels, which in turn results in heterogeneity in intracellular nutrient levels. Individuality can have important ecological consequences, including division of labor, bet hedging, aging and sub-optimality. Understanding the importance of individuality and the mechanism(s) underlying it for the specific microbial system and question investigated is essential for selecting the optimal modeling strategy. Microcystis aeruginosa can bloom and produce toxins that make the water unsafe to drink   polymerase chain reaction  fixation by the cyanobacterium a L−1) and the effect of a nutrient on the growth rate using an equation developed 75 years ago (Monod model). Likewise, most models of the gut aggregate species into functional groups based on metabolic pathways  rather than empirical models. One aspect in which the existing approaches differ is their representation of microbial individuality. The second part of our review will use examples to explain why including individuality is important.In this section we describe three modeling approaches that have been used to bridge the gap between genes and ecosystems, including metabolic flux, gene-centric and agent-based modeling (ABM). We illustrate each approach using a hypothetical ecosystem, where two microbial species grow and interact via three metabolites Figure . We thenThe review is focused on the use of gene-level models for advancing understanding and making predictions of microbial ecosystems. To keep the scope of the review manageable, we included only quantitative models, which are required for predictions, although qualitative models may be sufficient to advance understanding. We applied the following selection criteria: (1) model uses a mechanistic  approach, (2) model explicitly considers at least one actual gene or protein; (3) model includes some form of direct or indirect interaction among microbes; (4) model includes multiple microbial species (or strains) or phenotypes in different locations; and (5) model makes predictions at the population level. We therefore exclude empirical models that correlate observed gene distributions to environmental factors and function  is a vector of metabolite concentrations, S is the stoichiometric matrix and v (mmol gDW−1 h−1) is a vector of reaction rates for uptake, excretion, internal metabolism and growth. Typically, a steady-state is assumed so the derivatives are zero. The stoichiometric matrix (S) for species 1 of the hypothetical ecosystem is presented in Table where EF or ADF). Computational methods are available that decompose the stoichiometric matrix into unique sets of functional units (pathways) such as elementary modes or extreme pathways  and extracellular metabolites (C). The microbes grow according to a growth rate (μ) and consume/produce metabolites according to specific consumption/production rates (V). Those values are calculated from the metabolic flux models, which are optimized to maximize the growth yield subject to a number of constraints, including a maximum consumption rate for each metabolite based on its concentration. A simulation will proceed in a step-wise manner: (1) Calculate the constraints based on all C. (2) Optimize the metabolic model of each species, which yields μ and V. (3) Calculate the new X for both species based on μ. (4) Calculate the new C for both metabolites based on V from both species. Repeat. When the metabolic model does not lead to a viable solution, a simple death routine can be invoked  and ammonia concentrations in each grid element. Those growth rates and fluxes are then used by the reactive transport model to compute the Geobacter biomass, acetate, Fe(III) and ammonia concentrations, as well as other processes like U(VI) reduction. The new ambient concentrations are then again used by the FBA model to compute the growth rate and fluxes at the next time step and so on. Due to computational constraints, the FBA calculations were done a priori for 1,000 combinations of metabolite concentrations and stored in a look-up table, rather than a dynamic coupling between the models. One of the main advantages of the FBA-based approach is that it allows for variable substrate utilization and growth yields, which is not supported by conventional models. The model was able to make predictions of similar quality as the previous reactive transport model , but it did so without the need to calibrate rate parameters , but only Geobacter can reduce U(VI), rendering it less soluble and therefore contributing to the clean-up of the site. The FBA models of the two species calculate growth and metabolite production/consumption rates, which are used to integrate biomass and metabolite concentration state variables. The model predicted that, under low-ammonia conditions, Rhodoferax is outcompeted by Geobacter, which can fix nitrogen, and that this promotes respiration (vs. biomass production) and associated U(VI) reduction, which are patterns consistent with observations.Zhuang et al.  developeGeobacter to differentiate their functions: planktonic cells reduce U(VI) and attached cells reduce Fe(III). Attachment and detachment rates were used to transfer biomass among these two fractions. This illustrates one approach by which heterogeneity can be simulated in these types of models.Zhuang et al.  expandedE. coli in colonies on agar. The model was able to simulate the small-scale environmental heterogeneity in dissolved oxygen and nutrient concentrations, and the resulting phenotypic differentiation of the bacteria . Other multi-species, environmentally coupled metabolic flux models were presented by Salimi et al. . By constraining the metabolic network with gene expression data of P. falciparum, they were able to predict metabolic fluxes for different life cycle stages of the pathogen.Huthmacher et al.  generateZomorrodi and Maranas  developeOther multi-species, directly linked metabolic flux models were produced by Taffs et al. , BizukojTaffs et al.  applied Tobalina et al.  applied 2 fixation pathways, which were not identified for these systems previously.Cerqueda-García and Falcón  applied The FBA approach can directly utilize molecular data, genomics, transcriptomics, proteomics and metabolomics, from pure laboratory cultures and the environment  as long as annotations are available, which is increasingly the case.Actinobacteria ac1 and Alphaproteobacteria LD12 tribes had no overlap in their substrate acquisition spectra. The concept of dissolved organic carbon (DOC) as a common currency for heterotrophic microbes is too simplistic. One of the main applications of FBA has been to understand complex substrate uptake patterns.The approach is comprehensive in terms of functions and metabolites. This is likely to be increasingly useful, as recent observations from a number of environments suggest that bacteria have a high substrate specificity  model of Benyamini et al. , is typically ignored .In the gene-centric or functional gene approach, the model is built based on genetic information, as in metabolic flux modeling, but focused on capturing the dynamic behavior of specific genes or gene activities in the system. Thus, the biogeochemical fluxes are based on the genetic composition of the microbial community. Microbes are grouped based on specific functional or proxy genes and tracked using corresponding concentration state variables. This is similar in spirit to modeling functional groups  represent species 1 and 2, respectively. The reactions mediated by these gene products are assumed to be the limiting reactions along the pathway, but exchange with extracellular metabolites requires accounting for the input and output of the entire pathway. For prdF, the overall reaction is:The following description is based on Reed et al. , but adas Figure . Thus, smetC genes as a result of metabolism associated with the metC gene  is:The production rate of metCg (no. L−1) is the metC gene concentration. FT,metC is a thermodynamic potential factor, which accounts for the chemical energy available to drive the metabolism, and can be estimated from the energy yield of the associated reaction. μmax,metC (d−1) is the maximum specific growth rate. CC (molC L−1) and Km,metC,C (molC L−1) are the extracellular concentration and half-saturation constant for metabolite C. The production rate of prdF genes (RprdF) is:The last fraction accounts for inhibition by metabolite C. The mass balance equation for extracellular metabolite B is (transport and other reactions are omitted for clarity):prdF,B and γprdF,A are stoichiometric coefficients. Here, 2 mol B are consumed for every 1 mol A, so γprdF,B = 2 and γprdF,A = 1. qprdF  is the intracellular concentration of prdF genes, which depends on the number of gene copies in the genome. YprdF (gDW molA−1) is the yield, which depends on the energy yield of the associated reaction. For metabolite A, we have to consider consumption by both species:γmetC,A = γsmetC,C = 1. Metabolite C is consumed by the reaction associated with metC and produced by the reaction associated with prdF:Here, A and C are consumed in equal amounts, so γprdF,C = 2 and γprdF,A = 1. The mass balance for gene prdF is:Here, 2 mol C are produced for every 1 mol A consumed, so γkd (d−1) is the death rate. The first term in the gene mass balance equation accounts for the production of the gene due to the growth associated with its reaction and the second term accounts for mortality.where narG, nitrite reductase, nirK), aerobic ammonia oxidation  and anaerobic ammonium oxidation , as well as relevant metabolites, including dissolved oxygen (O2), ammonium  may also be difficult to model with this approach.The extension of the method to mRNA and proteins  as functional genes.The method supports multiple co-occurring genes .ABM or individual-based modeling (IBM) involves simulating microbes as individuals. This is in contrast to the traditional population-level approach, where microbes are simulated as concentration state variables. ABM is already an established modeling technology in microbial ecology  threshold is reached, the DNA polymerase is induced, which synthesizes DNA. Once that is complete, cell division is induced and the cell divides, which involves division of all intracellular components. The approach entails explicitly modeling genes, transcripts and proteins. However, typically only a handful of representative genes are simulated using a coarse-grained approach  basis, but carbon and volume can also be used. The uptB1 transcript  mass balance equation is:To illustrate the approach, we present the equations for kS,T (bp RNAP−1 s−1) is the transcription rate, LDNA (bp) is the total DNA length, RNAP1 (mol protein gDW−1) is the RNA polymerase level, yuptB1 is the uptB1 expression level (may depend on various factors), uptB1g is the number of uptB1 gene copies, kd,T (s−1) is the mRNA decay rate and μg (s−1) is the specific growth rate. The UptB1 (mol gDW−1) protein mass balance is:where kS,P (nt RPT−1 s−1) is the translation rate, TxL (mol mRNA gDW−1) is the total mRNA, RPT1 (nmol protein gDW−1) is the ribosome level and kd,P,UptB1 (s−1) is the UptB1 decay rate. The UptB1 rate  is:where kUptB1 (molB molUptB1−1 s−1) is the UptB1 catalytic rate constant, Km,UptB1 (molB L−1) is the half-saturation constant and Ki,UptB1 (molB gDW−1) is the inhibition constant. The intracellular metabolite B  mass balance is:where VPrdF1 (molF gDW−1 s−1) is the PrdF1 reaction rate  carried by viruses that infect the marine cyanobacterium Prochlorococcus. The idea is that these genes help to maintain the host photosynthesis apparatus during the latent period, increasing energy to support the replication of the virus. The model simulates individual viruses and host cells and explicitly resolves mechanisms of gene expression, protein synthesis, photosynthesis and events associated with infection at the molecular level. The model was calibrated to observations of virus and host gene transcript and protein levels and then used to simulate population dynamics in the water column of the Sargasso Sea. Modeled populations were diverse, including multiple virus types (different combinations of psbA and hli copies) and cells with different light histories, cell cycle phases and infection stages. Using competition experiments between virus strains that have different combinations of psbA and hli, and evolution experiments , the model predicted an optimal gene content that matched that of the wild-type.ABM was used by Hellweger  to exploSynechococcus and its circadian clock was constructed by Hellweger (Prochlorococcus model described above. A new feature was the explicit simulation of the concentration of proteins with different phosphorylation states and their interaction. The modeled population includes cells at different phases in their cell and circadian cycles and gene expression levels (psbAI luminescence) were compared to observations at the individual level.An ABM of the cyanobacterium ellweger . The modE. coli cells in a three-dimensional microfluidics chamber. The model simulates a heterogeneous population of individual, motile cells, each with a number of genes  and associated proteins, which communicate via a diffusible substance. They showed that autoinducer oscillations on the population level do not follow simply from synchronizing single cell oscillations. Single cells can switch between a state of constant signal concentration and oscillations, depending on the parameters of the positive and negative feedback loops in the gene regulatory network. Yet in a population of these cells, only the oscillatory state is stable—once cell density exceeds a threshold.Mina et al.  used an Saccharomyces cerevisiae and used it to explore the fitness effect of age-correlated stress resistance. The model explicitly simulates the regulation of the proteins Tsl1 and Tps3, which synthesize the stress protectant trehalose. Their expression is modeled using constant, age-dependent and stochastic terms. The population is diverse consisting of cells in different phases of their cell cycles as well as different ages, damage and Tsl1/Tsl3 expression levels. The modeled heterogeneity was compared to observations obtained using flow cytometry. Comparison of the various expression strategies showed that age-correlated stress resistance can be beneficial under some conditions.Hellweger et al.  built anAnabaena—nitrogen interactions was developed by Hellweger et al. . For example, on/off control of a gene by light can be modeled simply using an “if” statement . It is much more difficult to incorporate this into a stoichiometric matrix or differential mass balance equation.This approach is relatively complex and difficult to apply. Although it can theoretically be extended to the whole-genome scale, past models have focused on a handful of genes, transcripts, proteins and metabolites. This is due to the limited availability of rate formulations and parameters, and the difficulty of calibrating a model with numerous non-linear feedbacks.The models reviewed above are characterized along six different dimensions, including space, time, function, heterogeneity, species diversity, and genes Figure . These dA key distinction between the modeling approaches reviewed above is their consideration of individuality and heterogeneity. It is now well established that microbial populations in the environment and laboratory exhibit substantial heterogeneity in properties and behaviors. There are probably cases where this individuality averages out and is of no consequence to the ecology or biogeochemistry of the system . However, there are also cases where individuality has been shown to critically affect the fitness of a population. A thorough understanding of individual heterogeneity and its potential ecological consequences is critical for selecting the most appropriate modeling strategy.In this part, we review the importance of individuality. There are a number of mechanisms that produce heterogeneity in a population, which we refer to as sources. Once heterogeneity is introduced, it can be maintained and amplified in a number of ways. Finally, there are a number of important ecological consequences of heterogeneity Figure .From a modeling perspective, the distinction of sources, amplifiers and consequences is important. Specifically, sources of heterogeneity are included in the design of the model. In other words, there are equations or parts of the model code that produce heterogeneity. For example, stochastic cell division asymmetry can be included in an agent-based model by randomly varying the daughter biomass from the perfect 50/50 split after division. Amplifiers are mechanisms that operate at the individual level and change the cell's properties. The resulting additional heterogeneity is not prescribed in the model design, but it emerges from running it . For example, heterogeneity in birth sizes may lead to heterogeneity in generation times without any added equation or code. Consequences are also not included in the design of a model, but they emerge as population- or ecosystem-level properties rather than individual-level properties.There are many mechanisms that can produce and maintain individual heterogeneity. Here we consider a mechanism that would lead a colony growing up from a single cell to become heterogeneous to be a “source of heterogeneity.” Of course these mechanisms can also operate and produce/modify heterogeneity in other scenarios.Desulfovibrio vulgaris cells varied by as much as 50-fold in a sample of 30 individual cells  are often low. For example, natural populations have on average less than one transcript per gene  and carbonylated proteins, which are retained preferentially by the mother cell during division by binding to special cellular compartments  or active , can act differently on individuals within a homogenous population. Water flow velocities tend to be larger near the center of conduits . Even for a uniform flow field, advection is generally associated with diffusion causing individuals from one location to be transported to different locations. On leaf surfaces, cells can be dispersed to different locations and grow into microcolonies, followed by detachment and colonization elsewhere   and growth rate  Figure . This maSaccharomyces cerevisiae, 800 genes are cell-cycle regulated  because size can be a major checkpoint for various cell cycle phases. Since the cells perform different tasks at different phases in the cell cycle, this translates into a population with heterogeneous behavior. For example, in E. coli and new-pole cells with less damage have a 4% higher specific growth rate , but it may also be a case of division of labor, as the migration to the sediment bed allows the cells to accumulate nutrients . The alternative is to repair or eliminate the damage in some way. The evidence for aging to provide a significant ecological benefit in microbes is elusive, probably because the extent of damage segregation varies between species and environmental conditions. For example, for In the absence of conditions that make heterogeneity advantageous , it is disadvantageous or sub-optimal. For any given set of (constant) conditions, there is only one optimal behavior that maximizes fitness (for one species). This has been explored in the context of nutrient assimilation and the effect on growth. Nutrient quotas of phytoplankton can be quite heterogeneous. This heterogeneity leads to a reduction in growth rate, compared to a hypothetical population with uniform quotas, due to the non-linearity of the underlying process (Bucci et al., From the above review, it is clear that individual heterogeneity can have important effects on the ecology of microbes and the ecosystems harboring them. Any model that is to capture these effects, whether for advancing understanding or making predictions, has to be able to simulate the production and amplification of this heterogeneity. Therefore, when selecting a modeling strategy it is important to understand upfront the role of heterogeneity in the system, and how it is produced and amplified. Then, a modeling approach can be selected. For example, the ecology of infectious bacteria is in many ways controlled by bet hedging, which builds on individual heterogeneity. In some cases, such as persisters that can survive antimicrobial or other stresses, there are only two phenotypes. This type of heterogeneity has been modeled with the metabolic flux approach (Zhuang et al., All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses. The application of large-scale -omics data has revolutionized our understanding of how complex cellular, tissue, and organ systems respond to various perturbations. Advancements in mass spectrometry have now enabled the simultaneous quantification of thousands of proteins ,2  received high scores  was assigned as RG = 1 if a metabolite produced by Ri was consumed by Rj. Computationally, this was performed using the bipartite graph network, from which a vector of all outgoing metabolites from Ri and incoming metabolites to Rj were computed, and if at least one metabolite was common between the two vectors an edge between the two reaction nodes was assigned. For MMA, the edges between two reaction nodes are weighted based on the density of SSMs participating in those reactions. This was done by first assigning a reaction score (RS) to each reaction node in the network as the number of SSMs that are either produced or consumed by that reaction. The edge distance for a reaction pair RG was then assigned as the reciprocal of the sum of the reaction scores, if at least one of the reaction nodes had an RS > 0:MMA performs a systematic partitioning of reactions in a reaction-centric network, where metabolites are treated as a shared resource. Reaction-centric graph networks were constructed using an un-directed square and symmetric adjacency matrix (RG) of size 7440C2, or ~28 million computations, so parallel computing on Amazon EC2 was used to ease the burden of computational run time.For the Human Recon model, this requires 1R,2R] is 0.2 because three SSMs are involved with 1R, two SSMs are involved with 2R and the distance is the reciprocal of that sum. Shorter distances between reaction nodes enable the clustering of reaction nodes that have a high SSM density. The importance of weighting edges to ensure the algorithm detects modules enriched in SSMs is highlighted by an example in This is highlighted using the toy network example in It is important to note that, in metabolism, many bio-transformations are catalyzed by different enzymes and several enzymes are promiscuous and catalyze many different reactions. Recon 2.1 accounts for the former by denoting multiple reaction nodes for the same metabolite conversion of substrate to product, each catalyzed by a different enzyme. Regarding enzyme promiscuity, there are multiple cases in the model where different reactions have the same enzyme commission number listed. In this regard, our analysis is focused on the clustering of reaction nodes, rather than the enzymes themselves.RG), a distance matrix (D) is computed where D is the shortest path distance between Ri and Rj. Using the distance matrix, another matrix, B, is constructed such that reaction pairs  that have a relatively large distance D will receive a negative B and those with a short distance will receive a positive B. More specifically, B is a value from between −1 and 1, computed from a linear regression based on the rank the node-pair distance falls amongst on a list of all node-pair distances the two nodes are involved in. The matrix B is used to determine modularity using Newman’s partition algorithm where reactions from reaction pairs with a large and positive B are placed in the same module and those with large negative B values are binned in separate modules. For each reaction pair , all distances D and D, where x is reaction node index that is not either i or j are stored and sorted in a vector. B is assigned a value between  based on the rank of D in the sorted vector of distances, computed from a linear formula correlating the shortest distance with a B value of 1 and the longest distance with a B value of −1. The B-matrix was also constructed using parallel computing.From the reaction-centric matrix (s of length N (number of reactions), comprising of elements −1 or 1 is selected to optimize a modularity score Q, which Newman showed is approximated using the leading eigenvector of the B-matrix, where negative entries are denoted as −1 and the positive as +1:The MMA approach uses Newman’s binary partition algorithm to identify modules of reactions that are enriched in metabolites that are statistically significant. The overall algorithm is similar to that described in prior work . BrieflyQ score for the module is no longer positive.After each binary partition, each sub-network is divided into its connected sub-networks if the sub-networks that result from the partition are not completely connected. Partitioning is terminated if the resulting modularity N (number of reactions) from the original reaction-centric graph, G, and determined the SSM density (number of SSMs divided by the number of reactions) in that subnetwork. The procedure begins by randomly selecting a reaction node from the G, and the index of that node is added to a vector denoting the reactions that will comprise the sub-network. Next, a random edge from that node is then selected and the reaction node that the edge connects to in G is also added to the sub-network vector. Subsequently, a node is selected at random from the sub-network vector and a random edge attached to that node in G. If the new node connecting to the random edge already belongs to the subnetwork, then that edge is removed from G. Otherwise, if the new node does not belong to the sub-network, it is added to the sub-network vector. The process is iteratively repeated by picking another node in the sub-network at random in order to grow the connected sub-network until length of the sub-network vector is equal to N.To obtain SSM density distributions across a network, we performed random sampling of connected sub-networks of a specified size For each module satisfying the baseline modular criteria, we conducted PEA based on the metabolites in the module to determine if pre-defined KEGG pathways can predict the metabolic interactions discovered by MMA. For a given module, the KEGG ID of each unique SSM in the module is obtained because, in some modules, multiple SSMs may be the same metabolites from different physical compartments. Using the downloaded KEGG metabolite entries downloaded from the server, a list of KEGG-defined metabolic pathways that the metabolite is classified under is obtained. Subsequently, for each unique metabolic pathway represented in the module, we computed the number of modular SSMs that belong to that metabolic pathway. If the number of SSMs belonging to a KEGG pathway is the same as the number of SSMs contained in an MMA-derived module, then it is likely that the metabolic interaction captured by MMA was intuitive based on well-known biochemical pathways. On the other hand, if the maximum number of modular SSMs belonging to the same KEGG pathway is less than the number of modular SSMs, then the interactions captured by the module could not have been determined by just examining classic biochemistry pathways.A module satisfying the baseline modular criteria in the partition of liver X was determined to be conserved in liver Y if a module existed in the partition of Y that contained all the reactions in liver X and also had an SSM density greater than 0.5. If multiple modules in Liver Y exist, the module with the shortest distance to a terminal module in the hierarchical tree was chosen, which will also have the highest SSM density. Indeed, there will always be a module in Liver Y that contains all the reactions comprising a specified module in X since modules close in hierarchical distance to the parent module contain thousands of reactions. However, these modules will have a low SSM density, and, therefore, it is meaningless to claim that the module with a high SSM density in X is conserved in Y based on a module that is close to the parent module. Therefore, we add the constraint that the module in Y must also have a high SSM density. In the first case, this procedure described above was applied to determine all modules in liver X that are conserved in each of the other eight livers. A total of 72 comparisons were made. For the second case, the procedure was applied to determine modules in Liver 1 that were conserved in an un-weighted Recon 2.1 network partition using the metabolomics data for Liver 1.


Synechococcus elongatus UTEX 2973 is the fastest growing cyanobacterium characterized to date. Its genome was found to be 99.8% identical to S. elongatus 7942 yet it grows twice as fast. Current genome-to-phenome mapping is still poorly performed for non-model organisms. Even for species with identical genomes, cell phenotypes can be strikingly different. To understand Synechococcus 2973’s fast-growth phenotype and its metabolic features advantageous to photo-biorefineries, 13C isotopically nonstationary metabolic flux analysis (INST-MFA), biomass compositional analysis, gene knockouts, and metabolite profiling were performed on both strains under various growth conditions.Synechococcus 2973 flux maps show substantial carbon flow through the Calvin cycle, glycolysis, photorespiration and pyruvate kinase, but minimal flux through the malic enzyme and oxidative pentose phosphate pathways under high light/CO2 conditions. During fast growth, its pool sizes of key metabolites in central pathways were lower than suboptimal growth. Synechococcus 2973 demonstrated similar flux ratios to Synechococcus 7942 (under fast growth conditions), but exhibited greater carbon assimilation, higher NADPH concentrations, higher energy charge (relative ATP ratio over ADP and AMP), less accumulation of glycogen, and potentially metabolite channeling. Furthermore, Synechococcus 2973 has very limited flux through the TCA pathway with small pool sizes of acetyl-CoA/TCA intermediates under all growth conditions.The 2 assimilatory and biosynthesis in Synechococcus 2973. Comparisons across multiple Synechococcus strains indicate faster metabolism is also driven by proportional increases in both photosynthesis and key central pathway fluxes. Moreover, the flux distribution in Synechococcus 2973 supports the use of its strong sugar phosphate pathways for optimal bio-productions. The integrated methodologies in this study can be applied for characterizing non-model microbial metabolism.This study employed flux analysis to investigate phenotypic heterogeneity among two cyanobacterial strains with near-identical genome background. The flux/metabolite profiling, biomass composition analysis, and genetic modification results elucidate a highly effective metabolic topology for COThe online version of this article (10.1186/s13068-017-0958-y) contains supplementary material, which is available to authorized users. Cyanobacteria are of special biotechnological interest due to their metabolic flexibility, unicellular anatomy, and high photosynthetic efficiency. Additionally, cyanobacteria can use flue gas from power plants to mitigate CO2 emissions that contribute to climate change , whereas the best fit of the SF model could not be completely explained by measurement errors . The fitted model for Synechococcus 7942 in the PBR was statistically acceptable . Biomass composition information and CO2 uptake rate were used to improve the accuracy of the flux maps. Flux-partitioning across conditions and strains was compared through relative flux values that were normalized to CO2 uptake rate and are plotted in Fig. Synechococcus 2973 and 7942 in Fig. 13C-enrichment for key metabolites is presented in Fig. INST-MFA relies on measurement of transient labeling data of central metabolites after a acteria)  and resuacteria)  was usedrange of 97 740], range of 97 740], Synechococcus 2973 PBR and SF cultures and Synechococcus 7942 PBR cultures. Although the cells have significantly different CO2 fixation rates and biosynthesis fluxes , the flux ratios in central metabolism  were similar after normalization to CO2 uptake rate. Regardless of the strain and growth rate, Synechococcus did not exhibit flux through OPPP, and approximately 12–14% of the fixed carbon was directed to the TCA cycle similar to another recent study of Synechococcus 7942 [Synechococcus had no flux from aKG to FUM, resulting in a linear TCA pathway that is consistent with those in leaves or other photosynthetic tissues [Synechococcus 2973 genome, including succinyl-CoA ligase, malate dehydrogenase, α-ketoglutarate dehydrogenase, and GABA shunt genes. Although cyanobacteria are thought to employ a cyclic TCA cycle via GABA shunt for respiratory energy production [Synechococcus strains possibly due to evolutionary non-necessity or evolved energy balancing [Synechococcus synthesizes fumarate from aspartate via purine metabolism, which can then be converted into malate and succinate . Among tested culture conditions, there were noticeable differences in respiratory fluxes. Specifically, photorespiration in Synechococcus 2973 was elevated in the PBR condition possibly to rebalance carbon and dissipate excessive reducing equivalents generated by high light. In contrast, Synechococcus 7942 demonstrated lower photorespiration activity under PBR conditions.Figure  tissues . The flualancing . Based oE. coli. Figure E. coli, cyanobacterial R5P/Ru5P and other sugar phosphates were abundant, whereas the pool sizes of TCA metabolites were significantly low  and similar metabolite pools compared to Synechococcus 7942   relative to SF cultures  in either PBR or SF conditions  reaction. Unlike Synechocystis 6803 that maintains a functional GABA shunt [Synechococcus elongatus apparently does not contain the genes to operate a complete TCA cycle (as annotated by the KEGG Genome database), but based on labeling data presented here may utilize a linear pathway from OAA to make citrate and aKG. The GABA shunt contains one decarboxylating reaction and the absence of this shunt reduces CO2 loss. A succinate dehydrogenase deletion mutant was created, and the mutant showed no growth defects under continuous light conditions, which supports the absence of fluxes from aKG to fumarate. Unlike Synechocystis 6803, Synechococcus 2973 pyruvate is predominately made from flux through pyruvate kinase with little flux through the malic enzyme (MAL → PYR + CO2). Pyruvate kinase is a key step for producing pyruvate, but its activity in some cyanobacterial species can be inhibited by high ATP/ADP ratios during photosynthesis [Synechocystis 6803 [Synechococcus 7942 [Synechococcus 2973 has naturally evolved to overcome this pyruvate synthesis bottleneck, de-coupling pyruvate kinase inhibition from high photosynthesis rates. Furthermore, as shown in Fig. Synechococcus 2973 growth despite its net carbon loss. Synechococcus 2973 exhibits twice the O2 evolution rate of Synechococcus 7942 [ynthesis . For exacus 7942  during scus 7942 , 21. Syncus 7942 . Water scus 7942 .Synechocystis 6803 demonstrated ~ 12% CO2 loss through OPPP under continuous light [Synechococcus does not exhibit significant flux through this pathway. To further test this observation, we constructed Synechococcus 2973 zwf and pgl deletion mutants by disrupting the gene encoding G6P dehydrogenase and 6-phosphogluconolactonase, respectively, so that G6P could not be converted to 6PG  that can enhance its growth [Metabolite concentrations are tied to input/output fluxes and can provide insight into biochemical level regulation , 28. Theol sizes . Converss growth –33 and cs growth .Synechococcus 2973 may benefit anabolic metabolism and cell tolerance to stress conditions [+, ADP, and AMP) inhibiting anabolism, possibly resulting in higher levels of some central metabolites. The PBR grown Synechococcus 2973 showed the highest NADPH level that may reflect enhanced light-harvesting that can facilitate organic carbon assimilation and biomass growth [Synechococcus 2973 adjusts its PSII/PSI ratio throughout growth although it was found to have a slightly lower PSII/PSI ratio than Synechococcus 7942 while exhibiting greater O2 evolution [2 fixation rate is a key factor for differences in phenotypes, CO2 fixation rate is influenced by optimal energy balance, biomass distribution/central carbon fluxes, and compartmentalization of photosynthetic reactions.Additionally, the higher energy charge in the PBR than SF for nditions . Cyanobanditions . In SF cs growth , 37. Synvolution . Additiovolution , which mSynechococcus 2973 PBR cells produce little glycogen . When lSynechococcus 2973 is advantageous for the synthesis of targeted products from sugar phosphate pathways under optimal bioreactor conditions. Recently, researchers have engineered Synechococcus 2973 to produce ~ 9 g L−1 of sucrose under potassium chloride stress [Understanding and exploring native pathways with high metabolic strengths is a promising direction for future microbial cell factories . Our flue stress . This ene stress .13C-bicarbonate pulses [Synechococcus 2973 does not exhibit significant growth advantages in SF conditions.Modeling results indicated the presence of unlabeled pools that required additional dilution factors for isotopic data fitting. For SF cultures, an increase in dilution factors was necessary to improve fitting, still resulting in an unaccepted SF fit. The dilution of the metabolite active pool by unlabeled metabolites infers phenotypic heterogeneity, i.e., the presence of inactive/non-growth cells or the presence of pre-existing sources of carbon that are more slowly turned over after e pulses . Inactive pulses . Inactive pulses . This maE. coli species, glycolysis channeling has been evident [13C-enrichment from 3PGA to downstream metabolites in Synechocystis 6803, it was previously found that certain downstream metabolites could be labeled faster than precursors  [13C-enrichment might not completely follow expected precursor-product relationships  . In thisPGA Fig. . HoweverSynechococcus 2973 demonstrates high photosynthesis, efficient carbon fixation and small fluxes towards carbon loss and transient storage pathways under optimal growth conditions. These metabolic traits suggest this strain may be a promising platform to produce high-value compounds in well-controlled PBR conditions. Under suboptimal conditions, Synechococcus 2973 has inferior photosynthesis and decreased biomass synthesis, causing an accumulation of central carbon metabolites. Moreover, Synechococcus strains with different growth rates maintain similar flux distributions in central pathways. These observations offer insight into flux response to adaptive evolution. From a technological perspective, INST-MFA of cyanobacteria is not only important for genome-to-phenome mapping but also crucial for the rational application of platform photo-biorefineries. Currently, INST-MFA is still challenging, and flux results may be influenced by substrate channeling or subpopulations. Sorting out these factors will require advanced labeling experiments [eriments –52 to coSynechococcus 2973 was grown in BG-11 medium (pH 8.0–8.5) at 38 °C with 3% CO2 aeration (2000 mL min−1) and continuous 500 μmol photons/m2s under photobioreactors  100 μmol photons/m2s light, 250 rpm). Synechococcus 7942 was grown in identical conditions, except in its optimal PBR conditions, where the light condition was 300 μmol photons/m2s. Higher light irradiance inhibits Synechococcus 7942 growth [Synechocystis 6803 was grown in identical photobioreactors at 30 °C, 3% CO2 aeration and continuous 300 μmol photons/m2s light. For each labeling experiment, cultures were diluted to an OD730 0.05 in BG-11 medium without citrate (so citrate could not be used a carbon source or affect bulk citrate labeling dynamics) and grown to OD730 0.6  before the experiment was initiated. Prior to the experiment, the 3% CO2 aeration was replaced with air (0.04% CO2) and a 2-mL aliquot of saturated NaH13CO3  was injected into each PBR or shaking flask (SF) for a final concentration of 4 g L−1 of NaH13CO3 to saturate the cell with CO2. The use of 13C-bicarbonate rather than 13CO2 is sufficient because 13C-bicarbonate quickly equilibrates within the media without gas–liquid mass transfer limitations.13C-pulse, each identical culture in the PBR and SF was quenched at different time intervals . These time-courses samples were analyzed to capture labeling dynamics of metabolites. Timed biological duplicates were used to generated standard deviations for experimentally measured MID values; however, a minimum standard deviation of 3% was used in INCA model.After the 13C-pulse, samples were quenched by mixing with ice-cold minimal medium  in a falcon tube, which was immediately bathed in liquid N2 to sustain cold temperature . Then the quenched biomass samples (< 0 °C) were harvested via refrigerator centrifuge . For LC–MS analysis, the pelleted samples were extracted with a chloroform–methanol method [Fast-cooling via cold solvents followed by centrifugation is reliable for recovering intracellular labeled metabolites . After al method . Ion-pail method  at JointE. coli K-12 was cultured with uniformly labeled 13C-glucose and 13C-sodium bicarbonate in an M9 minimal media. The estimation of metabolite pool sizes was performed by mixing a known amount of labeled E. coli biomass with unlabeled cyanobacteria cultures . Then, the mixtures were quenched by a liquid N2 bath and extracted for HILIC-MS analysis . Carbohydrates, lipids, chlorophyll a, and ash were measured by previously reported methods  into the medium with fully labeled NaH13CO3 and unlabeled substrates (6 mM) including acetate, TCA cycle intermediates , and pyruvate in shaking flasks or PBRs for cultivation of 48 h. The labeling in proteinogenic amino acids was then analyzed using a TBDMS method [To confirm the capability of S method .Synechococcus 2973 PBR, SF, and Synechococcus 7942 PBR to obtain fluxes in the assumed cyanobacterial network, respectively. INCA provided a goodness of fit via a Chi square statistical test as well as confidence intervals of all estimated parameters (Model formulation details are included in the Additional file MFA is estimated based on isotope labeling dynamics of the free metabolites. The MFA model included the Calvin cycle, pentose phosphate pathway, TCA cycle, the glyoxylate shunt, anaplerotic pathways, and photorespiration pathway. A list of reactions in the network model with their atom transitions is provided in Additional file ∆zwf mutant was constructed by insertional mutagenesis of the first enzyme of the OPPP, glucose-6-phosphate 1 dehydrogenase. The ∆zwf mutant was characterized via growth rate (Fig. ∆pgl and ∆sdh mutant was constructed by a cpf1 based CRISPR system was used to delete succinate dehydrogenase from start codon to stop codon from the chromosome (Additional file Three mutants were built, which served to independently validate the related aspects of metabolism described by the modeling process. The


Actinobacillus succinogenes is a promising bacterial catalyst for the bioproduction of succinic acid from low-cost raw materials. In this work, a genome-scale metabolic model was reconstructed and used to assess the metabolic capabilities of this microorganism under producing conditions.iBP722, was reconstructed based on the functional reannotation of the complete genome sequence of A. succinogenes 130Z and manual inspection of metabolic pathways, covering 1072 enzymatic reactions associated with 722 metabolic genes that involve 713 metabolites. The highly curated model was effective in capturing the growth of A. succinogenes on various carbon sources, as well as the SA production under various growth conditions with fair agreement between experimental and predicted data. Calculated flux distributions under different conditions show that a number of metabolic pathways are affected by the activity of some metabolic enzymes at key nodes in metabolism, including the transport mechanism of carbon sources and the ability to fix carbon dioxide.The model, The established genome-scale metabolic model can be used for model-driven strain design and medium alteration to improve succinic acid yields.The online version of this article (10.1186/s12918-018-0585-7) contains supplementary material, which is available to authorized users. Actinobacillus succinogenes is a gram-negative facultative anaerobic bacterium and is one of the major natural producers of succinic acid (SA). It can grow on a broad range of substrates, including arabinose, cellobiose, fructose, galactose, glucose, lactose, maltose, mannitol, mannose, sucrose and xylose, producing a mixture of by-products , acetic acid (AA), and ethanol (EtOH)) as the main by-products [− 1 of glucose [Basfia succiniciproducens and Mannheimia succiniproducens and genetically engineered organisms such as E. coli or S. cerevisiae. So far, natural producers appear to outperform most engineered strains, but developments in strain design and fermentative processes are expected to promote the production of bio-based SA by metabolically engineered microorganisms. For instance, M. succiniproducens has been metabolically engineered by removing competing pathways, resulting in an increase in the SA yield from 0.45 to 0.76 g of SA per g of glucose [products , 2. Its  glucose ) and hig glucose , as well glucose , make th glucose –15; howe glucose . However glucose –19 or an glucose –23 has b glucose , BASF [2 glucose  or BioAm glucose , offerin glucose –29. Some glucose .Table 1SA. succinogenes wild-type strain is able to produce up to 98 g.L− 1 of SA with an approximate yield of 90% (w/w) on glucose [2) and/or hydrogen (H2) [− 1 of glucose consumed (YSA/Glc) [Under optimized conditions, the  glucose . The optgen (H2) , 33. Othgen (H2) , 35. YetYSA/Glc) . A comprE. coli or S. cerevisiae has been achieved by applying metabolic engineering strategies supported by in silico modelling of metabolic networks [S. cerevisiae and E. coli to exploit these metabolic pathways [S. cerevisiae, the SA channelling from the mitochondria to cytosol is avoided as TCA cycle enzymes are located in mitochondria. However, if a reductive TCA pathway is used, a 2-fold maximum theoretical yield (2 mol.mol− 1 of glucose) can be achieved compared to the oxidative route (1 mol.mol− 1 of glucose) [E. coli and S. cerevisiae have been tested for SA production under anaerobic conditions using the reductive branch of the TCA cycle [in silico modelling may allow to overcome these disadvantages, both under aerobic and anaerobic conditions. Strategies for increasing energy and/or cofactor pools [Genome-scale metabolic models (GSMMs) have proven to be powerful tools for understanding and re-designing the metabolism of microbial strains. For instance, the optimization of SA production in networks –40. Thernetworks . Typicalpathways , 40. Theglucose) . Many orCA cycle , 42. HowCA cycle . Metabolor pools –45, to oor pools  or to deor pools  are justor pools .A. succinogenes produces SA anaerobically through the reductive branch of the TCA cycle  using fumarate as the final electron acceptor, which makes this metabolic branch highly dependent on the redox state of cultures. Phosphoenolpyruvate (PEP) node controls the amount of flux that is directed towards the C4 and C3 pathways, adjusting the level of fermentative products generated by each pathway. Other metabolic nodes like oxaloacetate (OAA) and malate (MAL) have shown to link the C4 and C3 pathways via decarboxylating enzymes, i.e. oxaloacetate decarboxylase and the NADPH-dependent malic enzyme, respectively. The split of carbon flux at these branching points is largely influenced by various factors, such as the availability of CO2, pH or carbon sources. It has been previously shown that an increase in the concentration of dissolved CO2 in the fermentation broth through the supplementation of magnesium carbonate [2 and bicarbonate (HCO3−), may also contribute to increase CO2 fixation [arbonate  or sodiuarbonate  can promfixation , 49.iBP722) representing a wide range of metabolic capabilities of A. succinogenes 130Z is proposed. The model allows predicting and analysing the impact of stoichiometric and physiological constraints known to apply at steady-state conditions. Although the central carbon metabolism of A. succinogenes 130Z has been comprehensively described [A. succinogenes that can be systematically explored to improve the bioproduction of SA.Here, a GSMM  [The application of homology search tools like BLAST  and HMMEThe computation of “functional scores” based on BLAST and HMMER scores. As the range of enzymatic functions attributed to each CDS can vary from tool to tool, a second score was computed, i.e. so-called functional score, in order to propose the best candidate metabolic functions. Besides BLAST and HMMER scores, a functional score was computed similarly to the method used by Merlin , which iAssignment of putative metabolic function(s). EC numbers associated with the highest functional score are automatically attributed to each CDS, as well as the corresponding metabolic reaction(s) from an internal reactions database.The assignment of metabolic functions to each CDS is manually revised, as automatic assignments may fail when more than one high scoring EC number is found and/or EC number(s) are incorrectly associated in databases. Therefore the user is allowed to inspect all putative assignments and select the most appropriate or modify the functional assignment, which can be based on previous knowledge or other assumptions defined by the user. Furthermore, more than one CDS can be associated to the same EC number, which in many cases consists of subunits of the same multimeric enzyme. The classification of multimeric or monomeric subunits is also defined at this stage using an internal database.After validation of functional assignments and enzyme subunits, the association of metabolic reactions is carried out based on the associated EC number(s) and/or previous knowledge using as a reference an internal reactions database. This step is perhaps the most critical during the reconstruction of the metabolic network, as it will define the set of stoichiometric reactions that characterize a specific organism.The complete genome sequence of _009655)  was usedTo note that this annotation pipeline is flexible enough, such that unassigned CDSs in step (3) can be later reviewed, particularly during the gap filling process.A. succinogenes 130Z.Identify metabolic gaps (or missing reactions) that either consume or produce isolated (or dead-end) metabolites within the metabolic network. Then, each dead-end metabolite was inspected to search for metabolic reactions that consume, produce or transport this metabolite. Typically, MetaCyc or KEGG databases were used for gap-filling, i.e. to identify the sets of biochemical reactions that link each dead-end to a metabolite in the network. When several alternatives are found, a manual inspection is required and sequence-based homology searches using one or more amino acid sequences collected from potential candidates are used to find the most likely reactions set in the metabolic network of Infer and correct the mass and charge balance of biochemical reactions. Stoichiometric coefficients of compounds in reactions are corrected, such that the reaction is balanced for mass and charge, usually by adding missing protons or water molecules.Identify and correct the reversibility of reactions based on their thermodynamic properties and information found in literature. Databases like MetaCyc  and toolA. succinogenes in terms of proteins, DNA, RNA, lipopolysaccharide (LPS), phospholipids, peptidoglycan, glycogen and cofactors and vitamins (CAV). The synthesis of each macromolecule was also represented by individual reactions considering the building blocks molar composition. For instance, the synthesis of one gram of protein was calculated based on the average amino acids composition using the set of encoded proteins of the A. succinogenes 130Z genome, according to the methodology proposed in [Include a biomass reaction representing the basic macromolecular composition of posed in . The synThe GSMM was initially constructed by compiling the annotated metabolic genes and their corresponding coding enzyme(s) and biochemical reaction(s). Additionally, spontaneous and transport reactions from databases like KEGG  and TranA. succinogenes 130Z under varying environmental conditions. Phenotype simulations were performed by maximizing the biomass reaction assuming growth under defined conditions, i.e. defined minimum media containing basic components required for biomass synthesis, such as vitamins, minerals and trace metals and explicit carbon, nitrogen and sulphur sources. Maximum theoretical product yields were calculated by maximizing the target product instead, ignoring the formation of biomass and ATP maintenance requirements, such that the costs of product biosynthesis in terms of carbon, energy and reducing equivalents were properly evaluated.Basic stoichiometric modelling methods, such as parsimonious flux balance analysis (pFBA) and flux variability analysis (FVA), were used to interrogate the metabolic properties and capabilities of the reconstructed GSMM for A. succinogenes 130Z was carried out in three different phases: (1) first, metabolic functions were assigned to genes; (2) then, biochemical reactions and enzymatic complexes (assigning proper gene-reaction relationships) were compiled to build a draft metabolic model, (3) which was thereafter completed and corrected by defining a biomass reaction, identifying network gaps and correcting inconsistencies when comparing with reported information.The construction of the GSMM for iBP722 model consists of 722 unique genes (open reading frame (ORF) coverage − 35%), 1072 reactions and 713 unique metabolites. The model is available as a Systems Biology Markup Language (SBML) file at http://darwin.di.uminho.pt/models and BioModels database [The final database  assignediBP722 model was inspected for the ability to simulate the A. succinogenes growth on different conditions. Model simulations were performed using the OptFlux software [2, NH4+, Pi, H+, vitamins and trace elements were kept unconstrained (i.e. unlimited uptake rates) to provide unlimited basic nutrients for biomass synthesis.The software  applyingA. succinogenes is auxotrophic for three amino acids: L-glutamate, L-cysteine and L-methionine. Glutamate auxotrophy is due to the organism’s inability to synthesize α-ketoglutarate, since the genes encoding for isocitrate dehydrogenase and α-ketoglutarate oxidoreductase enzymes are absent in the genome. A. succinogenes possesses most of the genes encoding enzymes associated with the cysteine biosynthetic pathway, but the absence of an adenylsulfate kinase to assimilate sulfate prevents the synthesis of hydrogen sulfide required for the synthesis of L-cysteine. This organism also lacks several genes for the biosynthesis of methionine as identified during the functional annotation process. Given that, exchange fluxes associated with amino acid auxotrophies in A. succinogenes, such as L-cysteine and L-methionine, were maintained unconstrained, except for L-glutamate that was limited to a minimum flux value to support growth requirements and avoid glutamate consumption as an additional carbon source. Further flux constraints were introduced when inspecting carbon flux distributions in the central carbon metabolism, and are further detailed in Table SI 11 . In silico growth on beta-gentiobiose and D-arabitol was not predicted, as catabolic and transport reactions were not identified in A. succinogenes. Further information on transport activities included in the model is given in Additional file The model predicted accurately growth on more than 90% of the carbon sources, with only two not supporting iBP722 model was further validated by predicting anaerobic production yields and comparing with experimental data from batch and/or chemostat cultures of A. succinogenes 130Z growing on glucose or xylose at different initial concentrations. [− 1.h− 1, and then predicted production rates were used to calculate minimum and maximum FVA yields for biomass, SA, AA, FA and EtOH . FVA spans are given by the difference between the maximum and minimum predicted yields while maintaining 95% of the maximum biomass formation.The rations. , 51, 63 FVA predictions indicate higher yields for C4 by-products (i.e. SA) compared to C3 by-products , which is in good agreement with experimental data; however predicted mass ratios between fermentative by-products, specifically SA/AA and SA/FA, are higher compared with in vivo observations, especially when considering maximum FVA ratios. Yet, FVA spans indicate a significant flexibility for these ratios, which might explain variations in the in vivo observations.A. succinogenes was examined. Given a set of flux constraints , the predicted flux spans for the main reactions in the central carbon metabolism were calculated  reactions, indicating that the NADH-producing FDHmq reaction should be active and CO2 exchange flux should be lower. Changes in flux constraints associated with CO2 uptake (from unlimited to a maximum of 4 mmol.gDCW− 1.h− 1) altered predicted flux spans   were higher compared to glucose , with the consequent accumulation of higher amounts of EtOH. Under glucose conditions most of the carbon flux through the C3 branch is redirected toward the production of AA instead, via acetate kinase (ACKr) with the production of ATP . Interestingly, however, is that predicted FVA yields for SA production hardly change between glucose and D-sorbitol growth conditions when considering the same transport mechanism, i.e. PEP:sugar phosphotransferase system (PEP:PTS). Yet, assuming that both transport mechanisms could be active  for glucose uptake would increase SA minimum and maximum FVA yields from 0.63 and 0.67 to 0.82 and 0.96 mol.mol− 1, respectively.The production of SA in  sources  or the aurces [2 , 33, 48.A. succinogenes [+ to NADPH by ME2, may have a major role in the fermentative metabolism. Although the thermodynamics of this reaction is not conclusive regarding its reversibility, the metabolic network was tested using both the forward and the reverse directions. FVA results , experimental values for biomass yields were used to validate model predictions. Variations in experimental conditions, particularly associated with culture media that is often supplemented with yeast extract, interfering with carbon yields, or the initial sugar concentration that may affect bacterial growth due to substrate inhibition [in silico predictions, especially regarding metabolic requirements to generate biomass contents per unit of substrate . Additional reaction constraints were included in the model to improve model predictions. For instance, flux constraints of L-glutamate uptake and succinyl-CoA synthase were changed to improve carbon-to-nitrogen ratios according to experimental measurements [Further validations included the comparison of in vivo measurements and ons Fig. . Despitehibition , may exphibition , which iurements . Moreove13C-labeling experiments [2 uptake rate  is comparable to those predicted using E. coli or S. cerevisiae models (1.1 and 0.8 g per g of glucose for anaerobic conditions using iJO1366 [+ ratio) or CO2 availability, therefore affecting the SA production in A. succinogenes growing cultures [In silico predicted SA yields are higher with more reduced carbon sources  and higher CO2 availability, favouring carbon flux through the C4 branch, as a consequence of higher reducing power and higher PPCK carboxylation activity. Moreover, flexibility in sugar transport mechanisms or enzymes reversibility (e.g. ME2) may lead to increased levels of SA, as carbon flux partitioning between C3 and C4 pathways was shown to be largely affected  was reconstructed and validated using different sets of experimental data from literature. The reconstruction of the model included the compilation of functionally annotated metabolic genes and the corresponding coding proteins, as well as associated biochemical reactions. The model was complemented with a biomass equation and spontaneous and transport reactions. It was further amended after a gap filling process, including the identification of genetic evidences based on homology searches. Model accuracy to predict growth phenotypes and the production of fermentative by-products was evaluated using FVA and pFBA simulation methods. The ability to predict changes in carbon flux distributions due to environmental perturbations like CO2 limitations or alterations in the redox state was also tested. Predicted SA yields were in good agreement with experimental data, suggesting that the model is able to characterize the fermentative metabolism under various conditions. The increase in CO2 availability showed to have a positive impact in SA yields, which is consistent with reported data [2 availability, the use of more reduced carbon sources like sorbitol or the use of external energy source like H2 [2 fixation activities or the reversibility of the malic enzyme may have an impact in the SA yield. Therefore, it is expected that modifications in the metabolism that would include these activities would further improve the SA yield.In this work, the GSMM of ted data , 49. As  like H2 . Besides like H2 . Model piBP722 model enables a better understanding of the metabolic behaviour and capabilities of this organism, which can be explored to further improve SA productivity. The capacity to consume a wide range of C5 and C6 sugars, as well as other low-cost carbon sources (e.g. glycerol or lactose) and its metabolic flexibility may provide some advantages over other SA-producing strains, like recombinant Escherichia coli or Mannheimia succiniciproducens [.Overall, the roducens .Additional file 1:A. succinogenes 130Z. (DOCX 43 kb)Biomass composition of Additional file 2:iBP722 model. (DOCX 24 kb)Details on the Additional file 3:A. succinogenes growth on different carbon sources. (DOCX 22 kb)Testing Additional file 4:iBP722 model. (DOCX 37 kb)Details on transport mechanisms included in the Additional file 5:Growth predictions on glycerol with and without DMSO. (DOCX 21 kb)


Plasmodium biology hinders the elucidation of resistance mechanisms. In this regard, systems biology approaches can facilitate the integration of existing experimental knowledge and further understanding of these mechanisms.Malaria remains a major public health burden and resistance has emerged to every antimalarial on the market, including the frontline drug, artemisinin. Our limited understanding of P. falciparum parasite to expand our understanding of metabolic changes that support resistance. We identified 11 metabolic tasks to evaluate iPfal17 performance. Flux balance analysis and simulation of gene knockouts and enzyme inhibition predict candidate drug targets unique to resistant parasites. Moreover, integration of clinical parasite transcriptomes into the iPfal17 reconstruction reveals patterns associated with antimalarial resistance. These results predict that artemisinin sensitive and resistant parasites differentially utilize scavenging and biosynthetic pathways for multiple essential metabolites, including folate and polyamines. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistant parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood.Here, we developed a novel genome-scale metabolic network reconstruction, iPfal17, of the asexual blood-stage Using this systems biology approach, we identify metabolic shifts that arise with or in support of the resistant phenotype. This perspective allows us to more productively analyze and interpret clinical expression data for the identification of candidate drug targets for the treatment of resistant parasites.The online version of this article (doi:10.1186/s12864-017-3905-1) contains supplementary material, which is available to authorized users. Plasmodium parasites, and most deaths are associated with human-infective P. falciparum. Without an efficacious vaccine, antimalarials are essential to combat the severity and spread of disease. Combination therapies are implemented to preserve antimalarial efficacy and slow resistance development [Three billion people are at risk for malaria infection globally and treatment approaches are failing. Malaria is caused by elopment –3; despielopment –6.Plasmodium resistance development [Pseudomonas aeruginosa [Typically, resistance is conferred by genomic changes that lead to drug export or impaired drug binding (for example ); howeveelopment –10 and oruginosa  (revieweruginosa ). These P. falciparum Kelch13 gene [PfKelch13 mutations [Plasmodium metabolic genes are better characterized than signalling pathways, as (for example) PlasmoDB identifies 43 3D7 genes associated with the term ‘signalling’ as opposed to 1112 3D7 genes associated with the term ‘metabolism’ [In clinical malaria infections, artemisinin resistance is established in Southeast Asia –15. Thish13 gene , 16, 17 h13 gene –21 and oh13 gene –29. Overh13 gene –36), it utations , 37, 38)utations , 30, 33)utations , 39–42) abolism’ –47. Moreabolism’ , making P. falciparum metabolism. Using constraint-based metabolic modeling, we integrated transcriptomic data from over 300 clinical isolates from Cambodia and Vietnam with varying levels of artemisinin sensitivity. This approach identified innate metabolic differences that arise with or in support of the resistant phenotype, despite large clinical variability, over multiple genetic backgrounds. Additionally, we were able to explore the functional consequences of expression changes by predicting essential enzymes within these distinct metabolic contexts; these enzymes are candidate drug targets for the prevention of drug resistance.Here, we use a systems biology approach to analyze the metabolic profile associated with resistant and sensitive parasites. First, to maximize the accuracy of our predictions, we curated an existing genome-scale network reconstruction of asexual blood-stage In order to investigate the presence of a distinct metabolic phenotype in artemisinin resistant parasites, we analysed a previously published expression dataset of clinical isolates from Southeast Asia (NCBI Gene Expression Omnibus accession: GSE59097). Patient blood samples were collected immediately prior to beginning artemisinin combination therapy, and their relative expression was evaluated via microarray . This daPfKelch13 mutations, a commonly-used genetic marker of resistance . Among metabolic genes specifically, expression differences are small (maximum fold change 0.6 and 1.6) and few are both significant and conserved between data sets  was highly predictive of resistance status with 85.71% sensitivity and 88.91% specificity  to impr66,  to i numbers  (Additio). Importantly, we removed cellular import of pyrimidines from the host erythrocyte, as P. falciparum relies on de novo synthesis  because further research may add connectivity to these network components. iPfal17 contains 303 blocked reactions and 78 dead-end metabolites . For example, 4-pyridoxate (a byproduct of vitamin B6 biosynthesis) is included; production is supported by bioinformatic analyses of the parasite genome, but the metabolite function or excretion pathway is not known. Second, citations are included within iPfal17 to identify the date of discovery and degree of literature support for each reaction . MADE cp-value = 1.47*10−2), glutamate , and selenocysteine . Thus, sensitive models contain more reactions in these pathways than the unconstrained model, resulting from increased expression of these pathways in sensitive parasites , polyamine , redox reactions , and central carbon metabolism . Thus, resistant models have a larger proportion of their total reactions associated with these pathways than the original unconstrained model, whereas sensitive models do not have this enrichment. This indicates that these pathways are upregulated in resistant parasites and may remain important for metabolism in the resistant state  and several transport reactions, which furthers our understanding of condition-specific intra-organellar function  [Toxoplasma gondii, with a genome of 80 MB with 8000 genes [Leishmania major network reconstruction accounts for 1101 metabolites, 1047 enzymatic reactions, 1112 total reactions, and 560 genes [E. coli and S. cerevisiae reconstructions account for 1366 and 910 genes [iPfal17 is similar in functional distribution and scope to other high quality models of apicomplexans, despite its reduced genome size. The . genes) , 75, 76;00 genes , 78, acc00 genes ; with a 60 genes . These p60 genes , 75 and,10 genes , respect10 genes , 83).Plasmodium, require more exchange and transport reactions as they obtain many nutrients from the host environment [P. falciparum does just that: the parasite remodels the host erythrocyte, generating a vesicular network for protein translocation and increasing host cell permeability for nutrient acquisition from the host serum [Intracellular parasites, like ironment , 84–87. st serum –92. Thusst serum , 68, 93.st serum , 94 havest serum , 68, 93.P. falciparum parasites   Fig. . In sens4+) Fig. . Overall4+) Fig. .Fig. 4ArPlasmodium metabolism contains redundancies; for many essential metabolites, the parasite’s genome encodes one or more biosynthetic pathways, while there is also evidence for a parallel host-scavenging pathway [Plasmodium can either scavenge or synthesize putrescine [, Tables We also identify differential utilization of transport pathways from the extracellular environment into the parasite.  pathway  . Strescine , 108; Fi, Tables ; therefoPlasmodium [Interestingly, recent metabolomics studies demonstrate that intra-parasitic putrescine levels are decreased upon artemisinin treatment . Furtherasmodium , 112. Inasmodium  and signasmodium . In the asmodium , 30, 33 asmodium , 19. As asmodium , 115.Our systems biology approach also identifies metabolic weaknesses of resistant parasites; these weaknesses can be used to identify drug targets for combination therapies Fig. . For exa, Fig. Additionally, we identified serine hydroxymethyltransferase (SHMT) and thiamine diphosphokinase as potential drug targets of resistant parasites . In sensitive parasites, the essential metabolites can be generated by SHMT or the mitochondrial glycine cleavage system, given the reversible nature of these enzymes [Although iPfal17 represents our best understanding of intra-erythrocytic tes Fig. left. In enzymes , 119. Th enzymes , 121. ThPlasmodium [Similarly, model integration reveals that protein localization influences essentiality predictions. We predicted that the cyclical oxidization and reduction of glutathione, a key regulator of oxidative stress –125, andasmodium , and artasmodium . Howeverasmodium ), these P. falciparum metabolic network reconstruction, iPfal17, and investigation of the metabolic differences between artemisinin sensitive and resistant parasites. Antimalarial resistance is a major public health problem and we demonstrate that constraint-based modeling can be used to reveal metabolic shifts that arise with or in support of the resistant phenotype and discrepancies between otherwise incomparable datasets. We find inherent differences in artemisinin resistant and sensitive parasite metabolism, even before artemisinin treatment. Artemisinin resistant parasites have major metabolic shifts in the mitochondria and in the synthesis of folates and polyamines, indicating incomplete transition to the metabolic state most appropriate for the blood-stage environment. These findings generate areas of future research to elucidate Plasmodium biochemistry, understand the evolution of artemisinin resistant parasites, and tackle antimalarial resistance.Here, we have presented both a novel blood-stage-specific Normalized preprocessed data was obtained from GEO (GSE59097) . Probes p-value were incorporated into our curated model using the Metabolic Adjustment for Differential Expression (MADE) algorithm. MADE utilizes statistical significance of gene expression changes along with network context to assign binary gene states (‘on’/‘off’) to each metabolic gene. This constrains the network by limiting flux through reactions mapped to ‘off’ genes while maintaining growth, or a similar objective. An 80% growth threshold was used given that there is no reported evidence that resistant and sensitive parasites produce variable biomass as measured by the size of ring-stage parasites; while varying this threshold affects sensitive parasite biomass yield, it does not affect essentiality predictions (data not shown). Essential genes were predicted for the resultant condition-specific models  is an approach to explore metabolic phenotypes in silico . FBA simWe simulated in vitro experiments and in vivo data to evaluate the model; these are our metabolic ‘tasks’ that the reconstruction should pass. We simulate in vitro growth requirements by modifying media components or access to particular metabolites. Metabolite import or production was eliminated from the reconstruction, and subsequent biomass production was observed. Effects of enzyme inhibition, gene knockouts, and metabolite production were also used to evaluate the model. Lethal modifications were defined as changes that resulted in no production of biomass; growth-reducing modifications were defined as producing less than 90% of unconstrained flux value , 134.The COBRA Toolbox 2015, Tiger Toolbox (version 1.3.1), and MATLAB R2013b were used for model generation and flux simulations.P. falciparum metabolic network reconstruction [Plasmodium-specific databases  [Manual curation of an existing truction  was condB, MPMP) , 136–138In order to assess gene essentiality, we used a biomass reaction as the modeling objective function. Thus, flux through this reaction, simulating cellular growth, was maximized for all in silico experimental procedures. We used the biomass reaction from a previous study  with modWe predicted essentiality by performing single deletion studies with both genes and reactions and double gene deletion studies in our curated model and each expression-constrained sensitive and resistant models. All simulations were performed in an in silico red blood cell environment  and Vietnam (B) with significantly differentially expressed genes in red. Fold change is the ratio of mean expression in resistant parasites to mean expression in sensitive parasites, for each respective country. (PDF 820 kb)Distribution of genome-wide expression data demonstrates moderate differential expression between sensitive and resistant parasites. Fold change values from differential expression between sensitive and resistant parasites from Cambodia  or metadata (B), including patient and parasite features (see Methods), we can classify samples as artemisinin sensitive or resistant by Random forest analysis. Of the top 25 most important variables (gene probes) in the expression classifier, 12 encoded exported proteins, four genes of complete unknown function, three encoded a putative kinase and putative phosphatases, one encoded a component of dynein, four were uncharacterized genes though to be involved in protein folding or trafficking, and one encoded a transcription factor. Abbreviation key:  in generating iPfal17. Table S3. Predicted lethal reactions in wild-type blood-stage Plasmodium falciparum. Table S4: Predicted lethal genes in wild-type blood-stage Plasmodium falciparum. Table S5. Consensus predicted lethal reactions across 4 expression-constrained models. Table S6. PlasmoGem orthologous results. Table S7: Extended Table Table S8. Microarray platform blast results. Table S9: Metabolites in the in silico extracellular environment. (XLSX 1104 kb)Modifications and reaction additions in iPfal17 curation. Additional file 4: Figure S3.red/blue, with genes removed from expression-constrained models in white. (TIFF 1795 kb)Functional differences in data-driven sensitive and resistant models. Gene states from four condition-specific models, the results of MADE integration, cluster by sensitivity not by location. Active genes in Additional file 5: Figure S4.p < 0.001, grey = p < 0.01, light grey = p < 0.05, white = non significant. Abbreviation key: Aminosugars = amino sugar metabolism; ArgPro = arginine and proline metabolism; AsnAsp = asparagine and aspartate metabolism; BiosynthesisCytochrome = biosynthesis of cytochromes; CoABiosynthesis = coenzyme-A biosynthesis; Dolichol = dolichol metabolism; Exchange = exchange reactions; FattyAcidSynthesis = fatty acid synthesis; FolateBiosynthesis = folate biosynthesis; Glu = glutamate metabolism; Glycolysis = glycolysis; GlySer = glycine and serine metabolism; GPIAnchorBiosynthesis = GPI anchor biosynthesis; Hemoglobin = hemoglobin degradation (including hemozoin formation); InositolPhosphate = inositol phosphate metabolism; Isoprenoids = isoprenoid metabolism; LeuIleVal = leucine, isoleucine, and valine metabolism; Lys = lysine metabolism; MannoseFructose = mannose and fructose metabolism; MetPolyamine = methionine and polyamine metabolism; MitochondrialElectronFlow = mitochondrial electron transport chain; MitochondrialTCACycle = mitochondrial tricarboxylic acid cycle; NicotinateNicotinamide = nicotinate and nicotinamide metabolism; Nitrogen = nitrogen metabolism; PentosePhosphateCycle = pentose phosphate cycle; PheTyr = phenylalanine and tyrosine metabolism; Phosphatidylcholine = phosphatidylcholine metabolism; PhosphatidyletanolaminePhosphatidylserine = phosphatidyletanolamine and phosphatidylserine metabolism; Porphyrin = porphyrin metabolism; Propionate = propionate metabolism; Purine = purine metabolism; Pyrimidine = pyrimidine metabolism; Pyruvate = pyruvate metabolism; Redox = redox metabolism; RedoxMitochondrialAntioxidantSystem = mitochondrial redox metabolism; Riboflavin = riboflavin (vitamin B2) metabolism; Selenocysteine = selenocysteine metabolism; ShikimateBiosynthesis = shikimate biosynthesis; SphingomyelinCeramide = sphingomyelin and ceramide metabolism; Terpenoid = terpenoid metabolism; Thiamine = thiamine biosynthesis; Transport = transport reactions; tRNA = tRNA and protein synthesis; Trp = tryptophan metabolism; Ubiquinone = ubiquinone metabolism; UtilizationPhospholipids = utilization of phospholipids; VitB6 = pyridoxal (vitamin B6) metabolism. (PDF 6 kb)Artemisinin sensitive and resistant parasites utilize different metabolic genes and pathways. Enrichment analysis of gene utilization in sensitive and resistant parasite models demonstrates functional differences in expression data integration. Consensus gene utilization from resistant and sensitive models (both Cambodian and Vietnamese datasets) were used and compared to unconstrained model. Black = Additional file 6:iPfal17 in SBML format. (XML 2000 kb)


In constraint-based metabolic modelling, physical and biochemical constraints define a polyhedral convex set of feasible flux vectors. Uniform sampling of this set provides an unbiased characterization of the metabolic capabilities of a biochemical network. However, reliable uniform sampling of genome-scale biochemical networks is challenging due to their high dimensionality and inherent anisotropy. Here, we present an implementation of a new sampling algorithm, coordinate hit-and-run with rounding (CHRR). This algorithm is based on the provably efficient hit-and-run random walk and crucially uses a preprocessing step to round the anisotropic flux set. CHRR provably converges to a uniform stationary sampling distribution. We apply it to metabolic networks of increasing dimensionality. We show that it converges several times faster than a popular artificial centering hit-and-run algorithm, enabling reliable and tractable sampling of genome-scale biochemical networks.https://github.com/opencobra/cobratoolbox.Bioinformatics online. The linear equalities constrain the system to a steady state where fluxes into and out of every node are balanced. Nonequilibrium steady-states are enabled by including metabolite sources and sinks, collectively known as exchange reactions, at the boundary of the system with the environment. The inequalities arise from physicochemical constraints such as thermodynamics, as well as environmental constraints such as nutrient availability. Fluxes can be further constrained to the optimal value A constraint-based model of a metabolic network, with Uniform sampling of constraint-based models . ACHR isS, l, u and c from CHRR consists of rounding followed by sampling see . The rouWhen sampling the feasible set of a constraint-based model, it is important to run the sampling algorithm until the sampling distribution converges to a stationary distribution of fluxes over Ω. Otherwise, the sampling distribution is likely to be misrepresentative, leading to incorrect conclusions about the model see . It is gWe compared the convergence time of CHRR to the COBRA toolbox implementation of ACHR . We founCoordinate hit-and-run with rounding makes uniform sampling of genome-scale metabolic networks tractable and reliable. The compatibility of our implementation with the COBRA toolbox should facilitate widespread utilization by the constraint-based metabolic modelling community.HSH and IT were supported by the Luxembourg National Research Fund (FNR) through the National Centre of Excellence in Research (NCER) on Parkinson’s disease. BC and SV were supported in part by NSF awards CCF-1217793 and EAGER-1415498. RMTF was funded by the Interagency Modeling and Analysis Group, Multi-scale Modeling Consortium U01 awards from the National Institute of General Medical Sciences, award GM102098, and U.S. Department of Energy, Office of Science, Biological and Environmental Research Program, award DE-SC0010429.Conflict of Interest: none declared.Supplementary DataClick here for additional data file.