Escherichia coli proteome by mass. Genome-scale models of Metabolism and macromolecular Expression (ME) compute proteome allocation linked to metabolism and fitness. Using proteomics data, we formulated allocation constraints for key proteome sectors in the ME model. The resulting calibrated model effectively computed the “generalist” (wild-type) E. coli proteome and phenotype across diverse growth environments. Across 15 growth conditions, prediction errors for growth rate and metabolic fluxes were 69% and 14% lower, respectively. The sector-constrained ME model thus represents a generalist ME model reflecting both growth rate maximization and “hedging” against uncertain environments and stresses, as indicated by significant enrichment of these sectors for the general stress response sigma factor σS. Finally, the sector constraints represent a general formalism for integrating omics data from any experimental condition into constraint-based ME models. The constraints can be fine-grained  or coarse-grained  as demonstrated here. This flexible formalism provides an accessible approach for narrowing the gap between the complexity captured by omics data and governing principles of proteome allocation described by systems-level models.Integrating omics data to refine or make context-specific models is an active field of constraint-based modeling. Proteomics now cover over 95% of the  Genome-scale models have been used to conduct systems-level studies of cellular metabolism for over 15 years348910et al. recently published a proteomics data ResourceE. coli genes (>95% by mass) under 22 experimental conditions. Using genome-scale models, we show how such proteomics resources can be used to reveal principles underlying proteome allocation. ME models compute growth optimal proteomes consistent with laboratory evolved strains accuratelyE. coli allocate a fraction of the proteome to non-growth related functions. Collectively, such allocation can be viewed as “hedging” against unknown enviromental challengesSchmidt E. coli in a variety of different growth environments. We then compare the “optimal” versus the “generalist” proteomes to reveal principles underlying proteome allocation.Elucidating trade-offs between multiple cellular objectives is an active field of systems biology research16E. coli proteome has been shown to exhibit relatively simple relationships when proteins were grouped into meaningful “sectors” We first computed optimal proteome allocation that maximized growth rate. Measured proteome allocation differed notably from these computed optimal proteomes. While the interactions between thousands of individual proteins is complex, the We then identified proteome sectors whose measured mass fractions were greater  and smaller  compared to the optimal proteomes across growth conditions . For ourE. coli. To this end, we added new “sector constraints” to the ME model (constraint (5) in Methods). Here we constrained the sum of protein mass fractions within each of the six sectors; however, the formulation is general in that any individual protein or different sector definition (coarser or finer-grained) can be used. It is important to note that while our sector constraints involved 966 of the 1678 genes in the iJL1678 ME model, only six actual constraints were added to the model–only the sum of mass fraction of each sector was constrained. Therefore, the individual protein mass fractions were still computed by the ME model. Because our objective was to develop a generalist ME model, we used these coarse-grained sector constraints to prevent over-fitting the proteome to specific conditions.We hypothesized that by constraining the ME model to more accurately allocate proteome to these large sectors, we could better predict growth rate and metabolism of the wild-type, generalist The addition of the six sector constraints led to markedly improved agreement in growth rate predictions across all conditions, with 69% lower sum of squared error (SSE), overall . We alsoσS  and macromolecular expression (E) model subsystem . Note thmeasured .P < 0.05) fluxes in 5 of 8 of the subsystems for glucose , and all 8 metabolic subsystems for acetate . Interestingly, the Generalist model showed a clear and significant negative correlation between total proteome size and growth rate, whereas the Optimal model did not  data by Gerosa keff). To this end, we randomly perturbed effective rate constants by ±50% of their nominal values and maximized growth rate. Due to the considerable computational burden of many ME simulations, we limited the sensitivity analysis to glucose and acetate conditions. We ended up with 80 simulations that were still feasible after perturbations. We found that on average, the sensitivity to keff uncertainty was greatest at the proteome mass fraction level, decreased for metabolism, and was smallest for the growth rate. For protein mass fractions, the coefficient of variation (CV) of proteins across randomly perturbed simulations ranged between 0.073 to 3.9 (median of 0.31) and 0.086 to 8.9 (median of 0.62) for the Optimal and Generalist models, respectively . Metabolic fluxes showed similar variability with CVs ranging from 0.29 to 4.8 (median of 0.38) and 0.28 to 9.7 (median of 0.61) for the Optimal and Generalist models, respectively . Finally, growth rates varied with median CVs of 0.28 and 0.31 for Optimal and Generalist models, respectively (P = 1). Therefore, we conclude that while uncertainty in keff can lead to wide variability in the expression of individual proteins, the effects are attenuated for metabolic fluxes and eventually growth rate.Due to proteome constraints, ME models exhibit essentially no flux variability at the proteome level at maximum growth rateectively . The Optectively . Again, ectively , which wE. coli strains17E. coli.One of the primary uses of ME models in previous studies has been to model optimal −1, up to 95% of the generalist proteome was not beneficial for growth. Much of the apparently wasted proteome was related to general stress response and “hedging” against environmental uncertainty. By integrating proteomics data into a ME model, we revealed the cellular cost of dedicating resources to maintaining this generalist proteome. We showed that proteomics data can be used to identify key proteome sector constraints and calibrate ME models. This approach can be extended in future work using sectors other than COGs, or determining novel sectors using the ME model itself. In particular, here we defined sector constraints capturing broad trends across many conditions, rather than fitting individual conditions. Yet, nearly all of the 15 minimal media examined showed improved predictions in terms of proteome allocation, growth rate, and metabolic fluxes. In future work, the sector constraints may be extended to include known regulation. For example, transcriptional regulation of carbon transport and utilization pathways has been well-studied for phosphotransferase system (PTS) sugarsThe ME model indicated that at growth rates as low as 0.1 het al.Sσ. Thus, new data sets that describe other experimental conditions or environmental pressures could be used to place additional constraints on the proteome using a ME model. Such experiments might measure the proteome across multiple nutrients under various stresses such as low or high pHFurther, we believe that the efforts here provide a framework moving forward for using novel data sets to tailor models to represent cellular objectives other than maximum growth rate. For example, with the various growth conditions in the data provided by Schmidt One of the biggest challenges facing systems biology is to integrate new data types into genome-scale mathematical models to provide biologically meaningful phenotypic predictions. ME models provide mechanistic understanding of how metabolic flux states are linked to protein expression. Integrating data into a structured framework thus leads to an improved understanding of systems-level properties of organisms, suggesting a combined experimental and modeling approach to meet the “Big Data to Knowledge” challenge.E. coli (iJL1678)102635−20. These tight tolerances were necessary because ME fluxes can vary by 15 orders of magnitudeWe used the latest published ME model of li iJL16781034, weThe generalist ME model includes “sector constraints” in addition to the standard ME model formulation. The complete formulation of the optimization problem associated with the “generalist” ME model is the following:g(μ) is a function of growth rate μ, p is the total simulated proteome mass, Translation is the set of translation fluxes, nsector is the number of constrained sectors, Sector(k) is the index set of translation reactions in sector k, wj is the molecular weight for protein (in g/mmol) j, vj is the translation flux for protein j (in mmol/gDW/h), ϕk is the mass fraction of sector k, and v, l and u are the vectors of reaction fluxes, lower and upper flux bounds, respectively. “Optimal” ME models are formulated similarly, except without constraints (4) and (5).where Constraint (5) is the “sector constraint”, which constrains the summed mass fraction of a proteome sector to reach the specified amount. In this case, we constrained each sector by an inequality so that allocation to the constrained sector was greater than or equal to the measured mass fraction. The constraint is derived from the relation,v are the rates of reactions that synthesize protein. The macromolecule Expression (E) matrix in the ME model enables the explicit computation of protein synthesis rate.noting that the translation fluxes et al.Our formulation can also be considered a more generalized extension of work by O’Brien keff) were kept identical for both the Optimal and Generalist ME models, and were the same values as the original iJL1678 ME modelkeff and fluxes v are related as v = keffE, where E is the enzyme concentration. Therefore, the maximum flux of a reaction is affected by proteome allocation across conditions.Effective catalytic rate constants  of each protein by its molecular weight (mi): fi = mivi/∑j∈Translationmjvj, where Translation is the index set of translation reactions.Computed growth rates P (grams protein/gram dry weight). Because total protein synthesized was diluted by cell division, we haveME models compute the protein fraction of dry cell weight, wj is the molecular weight of protein j, vjtrsl, is the translation flux of protein j, and μ is the growth rate (h−1). Therefore, P = ∑jwjvjtrsl,/μ.where keff). We (uniformly) randomly perturbed these keff by ±50% of their original values in the published iJL1678 modelWe analyzed the sensitivity of proteome mass fraction, metabolic flux, and growth rate predictions to uncertainty in 1322 effective rate constants , and 11 minimal media . We chose the six sectors having at least 5% mass fraction across all conditions. The constrained mass fraction for each sector (ϕi in constraint (5)) was the minimum mass fraction across all conditions.In this study, we used proteomics data from 15 minimal media conditionsHow to cite this article: Yang, L. et al. Principles of proteome allocation are revealed using proteomic data and genome-scale models. Sci. Rep.6, 36734; doi: 10.1038/srep36734 (2016).Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The explosive growth of microbiome research has yielded great quantities of data. These data provide us with many answers, but raise just as many questions. 16S rDNA—the backbone of microbiome analyses—allows us to assess α-diversity, β-diversity, and microbe-microbe associations, which characterize the overall properties of an ecosystem. However, we are still unable to use 16S rDNA data to directly assess the microbe-microbe and microbe-environment interactions that determine the broader ecology of that system. Thus, properties such as competition, cooperation, and nutrient conditions remain insufficiently analyzed. Here, we apply predictive community metabolic models of microbes identified with 16S rDNA data to probe the ecology of microbial communities.Desulfovibrio piger is affected by, and affects the growth of, other members in a simplified gut community under metabolic conditions suggested to be determinant for their dynamics.We developed a methodology for the large-scale assessment of microbial metabolic interactions (MMinte) from 16S rDNA data. MMinte assesses the relative growth rates of interacting pairs of organisms within a community metabolic network and whether that interaction has a positive or negative effect. Moreover, MMinte’s simulations take into account the nutritional environment, which plays a strong role in determining the metabolism of individual microbes. We present two case studies that demonstrate the utility of this software. In the first, we show how diet influences the nature of the microbe-microbe interactions. In the second, we use MMinte’s modular feature set to better understand how the growth of By applying metabolic models to commonly available sequence data, MMinte grants the user insight into the metabolic relationships between microbes, highlighting important features that may relate to ecological stability, susceptibility, and cross-feeding. These relationships are at the foundation of a wide range of ecological questions that impact our ability to understand problems such as microbially-derived toxicity in colon cancer.The online version of this article (doi:10.1186/s12859-016-1230-3) contains supplementary material, which is available to authorized users. Advances in sequencing technology have culminated in an explosion of 16S rDNA–based microbiome projects, both small  and largEfforts to understand complex microbial communities range from inference based on 16S rDNA sequences  to the uBridging the gap between association and mechanism in microbe-microbe interactions requires an approach centered on mechanistic principles. One avenue to deciphering the role of a microbe in a community is through the use of a predictive modeling approach , 23. MetThe use of metabolic modeling to understand community dynamics is a thriving area of study, as demonstrated by the variety of tools being developed by different groups –37. NetCMMinte (pronounced /‘minti/) is an integrated pipeline that allows users to explore the pairwise interactions (positive or negative) that occur in a microbial network. From an association network and 16S rDNA sequence data, MMinte identifies corresponding genomes, reconstructs metabolic models, estimates growth under specific metabolic conditions, analyzes pairwise interactions, assigns interaction types  to netwoMMinte consists of minimally overlapping functions that come together to perform a single task. In designing it, our goal was to facilitate code re-use by focusing on modularity, allowing the user to streamline the parts presented here for other purposes. Indeed, we do not view MMinte as a single-purpose code, but as a set of widgets that can be repurposed for multiple queries, ranging from testing interactions between a set pair of species to reconstructing a community metabolic network. The user is required to provide a file with a measure of association between OTUs in Widget 1 that will define the pairwise interaction that will be analyzed. However, if the user wants to simulate all pairwise analyses between a list of sequences independently of a pre-assumed association, the user can start such an analysis in Widget 2.www.github.com/mendessoares/MMinte) for use by the community, and the authors welcome contributions to further its development.The web browser interface creates a point-and-click experience that allows the user to perform complex analysis on large data sets without programming expertise. For those seeking more control or to implement their own pipelines using MMinte widgets, MMinte functions can also be run in a command-line environment. Because all of the code is provided to the user, it can be changed to fit a particular need. Finally, MMinte is under continuous development, it is publicly available on Github  that will not be used in future analyses, based on an existing list of OTU associations. For plotting of the network in Widget 7, MMinte first creates a network based on the information on the file listing the associations between pairs of OTUs to be analyzed. If pairs of OTUs are not listed in the file as having some level of association, no edges between them are represented. This network is then used as the basis for the depiction of the kind of interactions that are predicted to occur between the OTUs (see description of Widget 7).(1) 16S rDNA sequences of all representative OTUs .Using BLAST, the 16S rDNA sequences are matched with 16S rDNA sequences from publicly available, complete genomes in NCBI . Importa16S rDNA sequences of OTUs.(1) Genome IDs and (2) percent similarity table.This widget uses the web based ModelSEED  frameworGenome IDs.Single-species metabolic models.Using COBRApy , this fu(1) Species-species associations  and (2) single-species metabolic models in the Systems Biology Markup Language (SBML) format.Two-species metabolic models.This step estimates the growth rates for each species under defined nutrient conditions, in isolation and in the presence of another species, by running a flux balance analysis in COBRApy , 47 and (1) Two-species metabolic models and (2) choice of metabolic conditions to be used from media file .Growth-rate predictions.Using previously calculated growth rates, this function quantifies the effect of pairwise interactions and assigns an interaction type to each pair, following Heinken and Thiele . The intGrowth-rate predictions.Quantitative effect of interaction and interaction type predicted.Widget 6). The shading of a node reflects the percent similarity between OTUs and genomes (Widget 2).This function generates a color-coded interaction network using the D3.js  visualiz(1) Association table between pairs of OTUs, (2) percent similarity table, and (3) quantitative effect of interaction and interaction type predicted.Metabolic interaction network see Fig. .Fig. 2NeBelow we present two case studies that exemplify how MMinte can be used to predict microbial interactions under user defined metabolic conditions. The user can choose to run the default example representing a mock community created by the authors as a test for the performance of the software. All files used in these examples, and a full tutorial on how to perform these analyses, can be found in the Additional file With just two user-provided files, one containing correlations between pairs of OTUs and the other representative sequences from a microbiome study, MMinte creates an association network in which the color of the links represents the types of predicted interactions between pairs of OTUs in a microbial community.http://hmpdacc.org/HMQCP/, uncompressed files from rep_set_v13.fna.gz and otu_table_v13.txt.gz) [In Case Study 1, we use data from the Human Microbiome Project (HMP) to demonstrate MMinte’s potential for exploratory research studies that focus on the dynamics of host-associated microbial communities. The HMP is a multi-institutional collaboration that allowed the description of the microbial diversity of several sites of the human body in healthy individuals. The study was reviewed by each participating institution’s Institutional Review Boards. Full information can be found in . To demo.txt.gz) , 5, allo.txt.gz) . The redThe number of positive and negative interactions in a community influences its level of stability and consequently its resistance to invasion by pathogens . With MMleft panel). We then reran Widget 5 with different metabolite availability conditions. Figure right panel) shows the same correlation network with a metabolite availability that is 10 times lower, resulting in different predicted interactions (58 positive and 651 negative interactions between OTUs and 45 pairs lacking any interaction). This result is consistent with the prediction that lower nutrient availability will favor more competition between organisms.We ran the full MMinte pipeline by clicking the “run all” tab on MMinte’s introductory page and providing two files, corrs.txt (which contains the associations between OTUs) and seqs.txt (which contains representative sequences) using the default setting of “complete” for the metabolite availability condition, which represents a condition with 380 metabolites available in large amounts. MMinte predicted 252 positive and 323 negative interactions between pairs of OTUs in addition to 179 pairs lacking any type of of interaction  are likely to promote more stability. In addition, if we assume that stronger positive correlation values between pairs of species indicate positive interactions [The results of this analysis highlight some possible characteristics of the community that could not be inferred solely from association data. For instance, assuming stability, and thus protection against pathogen invasion, is greater in communities with more competitive interactions , then thractions , the netDesulfovibrio piger in the guts of gnotobiotic mice, in the presence of eight other bacterial species and under different nutritional conditions. D. piger is the most commonly found sulfate-reducing bacterium in healthy adults, and is thought to shape the responses of the gut microbiota to dietary changes [In the following example, we use data from Rey et al. , who inv changes . However changes .D. piger and other members of the gut microbial community have been shown to influence the level of H2S in the gut. However, D. piger has a variety of potential metabolic pathways, only some of which will lead to the production of H2S. The role of interactions in determining the metabolic niche of D. piger in the gut is both important and not fully understood. Using MMinte, we explored the types of interactions that are predicted to occur between nine different species of microbes that co-occur in the human gut and whose interactions are believed to be metabolically based [The interactions between ly based , 53. We ly based .D. piger: model 411464.8, Bacteroides thetaiotaomicron: model 226186.12, Bacteroides caccae: model 411901.7, Bacteroides ovatus: model 28116.7, Eubacterium rectale: model 657318.4, Marvinbryantia formatexigens: model 478749.5, Collinsella aerofaciens: model 411903.6, Escherichia coli: model 83333.113, and Clostridium symbiosium: model 742740.3). After reconstructing the individual species metabolic models and creating two-species communities (Widget 4), we predicted species growth rates in the presence and absence of another species in the community by running Widget 5 under 17 different metabolic conditions, listed in Additional file We started by providing a list of species IDs to Widget 3 (D. piger in the presence and absence of other species in the community shows that this species is likely to benefit from the presence of each of the other species in the community under “Complete” metabolic conditions. D. piger is consistently predicted to grow under aerobic conditions, but under anaerobic conditions, growth is only predicted to occur if either B. ovatus, B. thetatiotaomicron, B. caccae, C. symbiosium, or E. coli are present. Thus, using the models reconstructed using ModelSEED, MMinte predicts an obligate association between D. piger and these species in anaerobic environments (Additional file D. piger impaired the growth of most species it was paired with under all conditions except “Complete” (Additional file E. coli and E. rectale; the magnitude of the effect of D. piger on their growth depended on the flux conditions for oxygen, chondroitin sulfate, and sulfate (Additional file A look at the predicted growth rate of Overall, the number of each type of interaction changed depending on metabolite availability, but not linearly Fig. . For insMMinte bridges the gap between association and mechanism in microbe-microbe interactions by assessing the metabolic influence that two microbes have on each other. Our predictive modeling approach involves reconstructing the pairwise metabolic community models that make up the basic unit of interaction within a community. More specifically, MMinte advances microbiome research by assigning functional interactions instead of simply calculating associations or correlations based on abundance . This alThe metabolic interactions that MMinte identifies can be used to understand the broader ecological features of a biological system. Dynamic ecological features such as stability and robustness are linked to competitive-cooperative interactions and the nature of the positive-negative feedback loops they engender . For exaD. piger and other organisms commonly found in the gut. In vivo experiments have shown that mice colonized solely with D. piger have significantly increased levels of H2S, which is a genotoxic metabolite that may be involved in the development of colorectal cancer [D. piger can help inform dietary interventions aimed at reducing the abundance of this species in the gastrointestinal tract.Case Study 2 highlights the modular nature of MMinte and the ability of the user to explore the effect that changes in the availability of a particular metabolite may have on the interactions between organisms. The results give us an important window into the role of the environment, specifically the presence or absence of oxygen, chondroitin sulfate, and sulfate, on the interactions between l cancer , 60, comDesulfovibrio vulgaris and Methanococcus maripaludis. The model accurately predicted the ratio of the two species during cell growth and the flux of several metabolites, thus demonstrating the utility of this modeling approach to understand the mechanistic bases of interactions [Constraint-based metabolic models were first used to explore the mechanistic bases of interactions between a sulfate-reduced and a methanogen , 62. In ractions . Furtherractions  were ablLike all algorithms, MMinte has potential limitations. For example, the predictions created by MMinte are only as accurate as the metabolic models used. These metabolic models are linked from 16S in a multi-step process that involves identification of genomes and metabolic network reconstruction using ModelSEED . This auMMinte is a tool that predicts the type of interactions occurring between organisms in a complex microbial community under defined metabolite conditions based on the metabolic models of each species. A full run takes data of an association network and 16S rDNA sequences, identifies the genomes, reconstructs metabolic models, and estimates the effect of being in a two-species community for each species under user defined metabolic conditions. The predicted interactions are then plotted in an interaction network. Additionally, the widgets that make up MMinte can be run independently allowing the user to perform specific tasks and bypass some of the steps of the analysis. We have incorporated the design principles of clear modularity, usability, and open access into the development of MMinte. In our view, part of the value of MMinte to the development of predictive community metabolic modeling is the potential for integration into other analytical platforms. We view the ability to build on existing development efforts as critical to expanding systems biology tools to wider and broader scales of ecology and data . MMinte


An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota. Microbial communities are abundantly present throughout nature and form many beneficial symbiotic interactions with different eukaryotic hosts. In many cases, the host acquires novel functionalities with mutualists that can contribute to host fitness and well-being  by, e.g.While the symbiosis between the human gut ecosystem and its host is less well understood than other symbioses, intestinal microbial communities have been associated with human well-being . In partA loss of microbial and metabolic diversity can lead to various microbiota-associated diseases, such as obesity , type 2 With the advent of high-throughput sequencing technologies, we can now enumerate and describe the microbial and functional diversity of the human gut with respect to different diseases and conditions. These analyses have unraveled a complex microbial ecosystem that is influenced by diet and environmental factors . FurtherTo understand the ecological mechanisms that drive the human gut microbiota better, it is important to go beyond the descriptive nature of high-throughput data analysis. This can be achieved by formulating knowledge- or data-driven models, which can describe the underlying ecosystem and give mechanistic insights for hypothesis generation and designing subsequent experiments. A recent review described the relevance of such models in giving novel causal relationships in a field that is dominated by data and correlation . Here, wNetwork-based approaches are used to identify relevant microbes or metabolites of the human gut microbiota. Networks are usually represented by interactions in the form of edges that connect biological components in the form of nodes. With respect to the human gut microbiota, these components can represent species that interact  or metabSpecies cooccurrence networks can be used to investigate ecological interactions between microbes. Based on high-throughput data analyses, simple networks can be constructed with the species abundance information for different patients . Such neTo overcome these limitations, metabolism-based networks have been used to assess the functional aspect of the human gut microbiota. Therefore, metabolic functions or enzymes annotated from high-throughput sequencing of different human individuals can be used to identify pathway or metabolite differences . By connIn the bottom-up network approach, biochemical knowledge is used to generate specific networks, e.g., biochemical networks, from information provide in databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) , MetaCycIn another study, the metabolic exchange between microbes has been represented in a global interaction map, which helped the identification of metabolic deficiencies in certain diseases . On the In a complementary approach, genome-scale metabolic reconstructions of organisms are constructed based on genomic and biochemical data to represent comprehensively their metabolism to investigate the metabolic potential. To facilitate the reconstruction process, available automatic reconstruction pipelines, such as ModelSEED , can be While the automatic reconstruction process provides a good approximation of a species’ metabolism, the manual curation of reactions is essential for metabolic modeling. On the basis of biochemical knowledge of reaction directionality and substrate uptake, reactions are refined within the metabolic reconstruction to be more congruent with experiments . This mavB, which is subject to S × v = 0, and vi,min ≤ vi ≤ vi,max, where v represents the flux values  through all model reactions i while maximizing the flux vBthrough the metabolic objective reaction B. The steady-state assumption implies that metabolites cannot accumulate and the total flux into the network must equal the total outflux, and it is represented by S × v = 0, where S is the aforementioned stoichiometric matrix. The constraints, represented as vi,min ≤ vi ≤ vi,max, can reflect medium conditions, in which the uptake of metabolites via exchange reactions is limited or limitations of internal reaction fluxes that come from experimental data  is based on genome-scale metabolic reconstructions, which are formalized as metabolic models to simulate biological relevant physiological states. By applying condition-specific constraints, reconstructions are converted into metabolic models . Often, tal data . The biotal data  and can tal data . Constratal data –45 or coal data ––50. Compl data ––, facilitAn advantage of the COBRA approach is that it can assess the spectrum of metabolism for an organism . The steCompartmentalized microbial community models can be constructed by integrating individual microbial metabolic reconstructions via their S-matrices . In thisPairwise models of microbial communities can give new insights in terms of the metabolic interactions that occur in the human gut. In a recent study, we applied pairwise community models to the human gut microbiota by modeling all possible pairs of more than 700 microbes and found strikingly mostly negative metabolic interactions, e.g., competition for resources, of different microbes , which uPopulation-based COBRA modeling can be used to analyze temporal dynamics of metabolic interactions within microbial communities. With the optimization of biomass growth, FBA intrinsically models growth of microbe populations. The combined S-matrix approach can also be used to model population dynamics with respect to a temporal scale . EssentiPopulation-based COBRA modeling can give new insights in the dynamic change of metabolic interactions of human gut microbial communities. A recent approach applied population-based metabolic models to a simple community of six microbes . The resThe metabolic interactions between distinct microbial organisms can be modeled by combining the COBRA approach with individual-based modeling (IBM). IBM is a method also used in classical ecology  to modelIndividual-based COBRA modeling can give insights into the spatial and temporal community structure of the human gut microbiota. For instance, a recent study has explored the effects of antibiotics on a two-species community model of human gut microbes, represented by a combination of individual-based and kinetic modeling . SimilarThe COBRA approaches for analyzing microbial communities provide promising tools for investigating the metabolic effect of the human gut microbiota. Several studies have been conducted in this context, mostly focusing on small communities representative of the intestinal microbiota . These sA challenging aspect in community modeling of the human gut microbiota is horizontal gene transfer (HGT). HGT seems to occur frequently within the human gut microbiota , 79 and in vitro (One of the most striking hallmarks of the human gut microbiota is its species diversity, which poses challenges to the model simulations that need to be addressed. Simplified microbiota models of less than 10 species are relevant for studying metabolic interactions in general and can be used to simulate experiments that are conducted with small microbial communities in gnotobiotic animals  or in viin vitro . Howeverin vitro , we creain vitro , 83. By in vitro , 81, demin vitro  and are Model complexity also poses the problem of subsequent data analysis and visualization. By simulating large-scale microbiota models with a high number of variables, it becomes difficult to identify the explaining pathways. The large amount of simulations and the complexity of the models further require data analysis approaches to find the most relevant parameters influencing the modeled system with the aim to propose an experimentally testable hypothesis. State-of-the art machine learning approaches have already been used to aid the analyses of COBRA simulation results , 85 but High-throughput omics data can be used to contextualize genome-scale metabolic models , 87. SimExperimental validation of community simulation results plays an important role to assess the predictive potential of the microbial community models. While community models can give interesting novel hypotheses with their simulation, particular attention should be paid to the biological relevance of the predictions. It is therefore important to relate and validate at least part of the simulations with experimental values that come from existing knowledge or direct experiments. Existing knowledge can be used to validate the predictions of community models, e.g., by comparing simulated with measured metabolite concentrations . This wiThe introduced COBRA community modeling approaches are promising tools to give novel insights and hypotheses of microbial consortia in the human gut. The models allow for the integration of multidimensional omics data and go beyond the descriptive nature of more traditional high-throughput data analysis approaches with the potential to unravel novel mechanisms. The different discussed modeling paradigms rely on various assumptions that need to be assessed before starting an analysis. On the basis of this assessment, the model that is least complicated and most explanatory for a specific research question should be chosen. It could also be fruitful to combine different approaches to see their consistency or potential differences for a specific problem. For example, it would be interesting to model single isolated species and communities separately to identify beneficial or detrimental effects on the single species level. Further comparisons can be made between network-based and modeling approaches. Can, for example, the negative and positive interactions in a coexistence correlation network be explained by the underlying metabolic interactions? Comparisons between algorithms could give further hints on candidate experiments that can be performed for validation. This can drive further research on the human microbiota, mechanistically predicting different treatments for various diseases.


Acinetobacter baumannii is a clinical threat to human health, causing major infection outbreaks worldwide. As new drugs against Gram-negative bacteria do not seem to be forthcoming, and due to the microbial capability of acquiring multi-resistance, there is an urgent need for novel therapeutic targets. Here we have derived a list of new potential targets by means of metabolic reconstruction and modelling of A. baumannii ATCC 19606. By integrating constraint-based modelling with gene expression data, we simulated microbial growth in normal and stressful conditions (i.e. following antibiotic exposure). This allowed us to describe the metabolic reprogramming that occurs in this bacterium when treated with colistin (the currently adopted last-line treatment) and identify a set of genes that are primary targets for developing new drugs against A. baumannii, including colistin-resistant strains. It can be anticipated that the metabolic model presented herein will represent a solid and reliable resource for the future treatment of A. baumannii infections. Acinetobacter were long considered harmless, environmental organisms, but from the 1960s onward, an increasing number of reports have documented the emergence of Acinetobacter strains of this genus among severely ill, hospitalized patients. These strains showed unusually high levels of resistance to antibiotics that could be used at the time. Also, they gave rise to cross-infections and outbreaks among patients1. Recently, resistance to antibiotics in A. baumannii has risen to worrisome proportions .Bacteria of the genus A. baumannii is colistin, a positively charged molecule that, by interacting with the lipid A moiety of lipopolysaccharide (LPS), causes disorganization of the outer membrane. Unfortunately, colistin resistance in A. baumannii has also been reported, thus highlighting the urgency of finding new molecules to face this threat4. Although careful monitoring, antimicrobial stewardship and measures to prevent spread in health care institutions are important for controlling A. baumannii infections, new antimicrobial agents and/or strategies are urgently required to eradicate antibiotic resistant strains from affected patients.One of the last-line treatments for MDR A. baumannii is required. Metabolic network reconstruction and its conversion to a mathematical framework has become a cornerstone for studying the systems biology of metabolism5, allowing the examination of the connection between phenotype and genotype and driving biological discoveries. In particular, constraint-based tools  enable the estimation of the rate that metabolites’ flow through a metabolic network and to compute cellular phenotypes for various growth conditions6. Interestingly, by identifying those genes whose deletion is predicted to impair cellular growth, this in silico technique can be used to predict essential genes (EGs) at a genome-scale. Following a metabolic modelling approach, several EGs datasets have already been derived for important pathogens such as Helicobacter pylori7, Pseudomonas aeruginosa8, Mycobacterium tuberculosis9, and Staphylococcus aureus10. Usually, such predictions are performed simulating growth in an arbitrarily defined medium, accounting for the main nutrients used by the microbe and without imposing any additional constraint to the model. Indeed, the search space of essential genes predicted can be narrowed by imposing additional constraints on the model. One possibility consists of modulating the flux admissible across each reaction on the basis of the expression values of the corresponding genes. By doing so, it is possible to generate context-specific models that reflect the actual set of reactions employed11. This approach promises to reduce i) the gap between the predicted and real cellular metabolic landscapes, and ii) the number of false positives/negatives in EGs predictions. Additionally, it might reveal hints for the synergistic use of antibiotics and, in particular, to the possible additional targets that might arise from the adaptation/response of a microbe’s metabolism to a single antibiotic. Indeed, changes in gene expression might redirect the cellular metabolic fluxes in such a way that novel and untapped essential reactions may emerge, representing good candidates for a synergic antibiotic. Despite that the use of antibiotics in combination is sometimes questionable, this approach can be considered in cases of severe infections and it has been shown to be effective in the case of Pseudomonas and Acinetobacter spp.13.To address new possible solutions, a system-level study of antibiotic-response in A. baumannii exposure to colistin. We integrated gene expression data during exposure to colistin14 with a newly reconstructed genome scale metabolic model, allowing for constraint-based modelling of the type strain ATCC 19606. Our data revealed the metabolic reprogramming that occurred in this strain following the establishment of a stressful condition such as the presence of an antibiotic. Furthermore, the metabolic reconstruction provided here represents an important resource for the future understanding of A. baumannii metabolism and for the detection and identification of novel drug targets.Here, we explored the system-level metabolic consequences of A. baumannii ATCC 19606 metabolic model was obtained through the Kbase server (http://kbase.us). This was manually curated as described in Methods. Afterwards, we used previously published large-scale phenotypic data15 to validate our reconstruction over a large set of experimental tests. Manual curation was performed by comparing FBA outcomes with such auxotrophies data (determined through Phenotype Microarray (PM) technology). During this process, the capability of our model to represent the observed phenotypes was tested.A preliminary draft reconstruction of the in silico in simulated Simmons minimal medium  under aerobic conditions by iteratively probing each C-source used in PM plates. During these simulations, biomass optimization was selected as the model objective function (O.F.). Results of the simulations  were compared with the activity directly measured during an experimental phenotype microarray experiment, and discrepancies identified between the in silico and experimental data were manually adjusted as possible (such as by filling in missing transport reactions or metabolic gaps).Growth rates were firstly estimated in silico and experimental data: out of the 67 in silico screened metabolites, 24 were correctly found to be carbon and energy sources for A. baumannii ATCC 19606 (true positives) and 32 not (true negatives) while only 10 disagreements remained, 5 false negatives and 5 false positives. All data are briefly summarized in Fig. Following this procedure, we reached an overall agreement of about 84% between the A. baumannii ATCC 19606 genome-scale metabolic model (named iLP844 according to the current naming convention16) contains 1628 reactions (162 exchange reactions), 1509 metabolites, and 844 genes , A. baylyi ADP1 model, iAbaylyi18 (22%), and A. baumanni AYE model, AbyMBEL89119 (17%). A COG classification of all the genes embedded in the model is provided in Table A. baumannii ATCC 19606 model is available as Supplementary Material in SBML validated format and embedding cross-references to several databases , by imposing the necessary constraints to the metabolic reconstruction. Accordingly, screens for EGs were performed for multiple specific conditions: starting by simply changing the set of available nutrients  and then by simulating stressful situations such as antibiotic exposure and mutations (using available expression data in such conditions).As already mentioned, the identification of EGs is one of the key-steps in a drug discovery pipeline. Indeed, both general and condition-specific EGs can be distinguished19. To do so, we set the lower bound of exchange reactions as described in methods. Then we performed in silico gene deletions for each gene in the model. Accordingly, each gene was defined as essential if its elimination destroyed the network’s ability to synthesize at least one key biomass molecules (i.e. the model predicts no-growth); otherwise, the gene was considered to be dispensable. Gene essentiality analysis was performed through both FBA and MOMA approaches (see Methods), which both lead to the identification of 67 EGs. Fig. ratio, see Methods). The complete list of EGs and their functions is reported in Supplementary Material As we were interested in modelling the system in a gradually constrained manner, we initially simulated an arbitrary rich medium, allowing our model to have virtually all the nutrients needed – as likely happens inside of a hosti.e. in Simmons medium, Fig. Next, we repeated EGs prediction by simulating growth in a minimal medium , Wang et al.21 labelled 481 genes as essentials for that strain. However, not all of them were comparable with our predictions since a large fraction was neither metabolic or possessed an orthologous gene in A. buamannii ATCC 19606. In both cases these genes are absent in iLP844. For the same reason, not all the 67 EGs found through our simulation were comparable with the reported experiment. After performing all these necessary restrictions, we reached the result shown in Fig. i.e. 42 genes have been predicted to be essential by both approaches (in silico and wet-lab) for the two A. baumannii strains considered  medium. Specifically, we compared our EGs set to that obtained by Dorsey et al. through insertional mutagenesis experiments with A. baumannii ATCC 1960623, where the metabolic deficiency of insertion derivatives was subsequently confirmed, identifying essentiality of 10 disrupted genes. Repeating the assay in silico, our model correctly represented the phenotypes of the A. baumannii mutants, with 8 out of the 10 genes predicted as essential by Dorsey and colleagues also shown to be essential in iLP844  by adding to the minimal medium the metabolite(s) whose production was affected by the mutation.Comparisons were also carried out between our 844 Fig. . Additioscenario into the genome-scale model.Although a large fraction of the predictions was supported by previous experimental data, a possible source of error, using the methodology described above, stems from the observation that not all the reactions of the model will be active during growth in a given physiological condition. In particular, changes in gene expression are likely to influence the activity rate of the corresponding cellular metabolic reactions, leading to the observation that a given reaction can be considered ‘turned on’ or ‘off’ on the basis of the expression levels of the encoding gene(s). Using available computational methodologies, it is possible to modulate the flux across each reaction on the basis of the expression values of the corresponding genes. This permits to take a picture of the current metabolic state and tightening up the predictive capabilities of the model itself. Accordingly, as the dynamic changes of metabolic reprogramming are likely mirrored by changes in gene essentialities, a possible solution for avoiding or reducing false positives is to merge transcriptomics data of the tested A. baumannii strains is the exposure to antibiotics and to colistin in particular14. Importantly, both the (metabolic) consequences and the occurrence of targets to be used in a synergic treatment are, currently, almost untapped. In order to study the dynamic changes of the metabolic network following antibiotic exposure and to derive a more realistic picture of gene essentiality patterns in a real scenario (antibiotic treatment), we used available transcriptomic data for A. baumannii ATCC 19606 in response to colistin treatment14. Up-regulation and down-regulation ratios  of genes were combined with the iLP844 by using MADE 24. Briefly, MADE uses statistically significant changes in gene expression measurements to determine binary expression states (highly and lowly expressed reactions) i.e. reactions are turned on and off depending on the changes in mRNA transcript levels. Thus, by mapping gene expression data into the model, the in silico metabolic predictions are more consistent with the actual physiological state of the cell.Arguably, one of the most interesting physiological conditions of et al.14, A. baumannii was grown in two different media, i.e. with and without 2 mg/L of colistin, and then sampled at 15 and 60 minutes after exposure. Following the described approach, we integrated the available transcriptomic data regarding all the metabolic genes embedded in our in-silico reconstruction (i.e. about 80 genes). Accordingly, we obtained four distinct models, each representing the predicted functional metabolic state of the cell at both 15 and 60 minutes, treated and untreated with colistin. These models differ in that some of their reactions are (completely) ‘turned on’ or ‘off’ according to the measured levels of their corresponding genes. Afterwards, optimization of the four models was performed, allowing the analysis of flux distribution in the network and the occurring metabolic reshape.In the experiment by Henry ratio, see Methods) of treated vs. untreated models, for all the reactions. However, FBA only provides one of the possible optimal solutions out of many alternative (and feasible) cellular flux distributions. Hence, in order to correctly predict metabolic changes following antibiotic exposure we restricted the feasible solution space by performing Flux Variability Analysis (FVA)25. This approach allows estimating the minimum and maximum flux admissible across each reaction (under the same constraints as in FBA) and hence it can be used to estimate the correctness and accuracy of FBA predictions (see Methods).In order to highlight changes in the overall metabolic behaviour and to identify changes on the metabolic rewiring occurring after antibiotic exposure, we compared flux distributions at the two time-points by calculating the flux ratio (RF−1 in some cases), revealing the lack of accuracy in some of FBA-derived predictions. We here used FVA outcomes (as described in Methods) to filter out those reactions whose fluxes display little variation. In other words, each reaction was considered for downstream analyses only if both the maximum and minimum FVA predicted fluxes did not differ from the FBA predicted flux by more than 20%. Consequently, we were left with 901 reactions at 15 minutes and 970 reactions at 60 min. It is worth noting that several intervals of admissible flux ranges were tested and we report in Supplementary Materials vs. treated condition.As shown in Supplementary Material Both qualitative and quantitative flux changes were analysed by dividing the reactions into three categories  at both 15 and 60 minutes after exposure to colistin. As for the analysis involving nutrient availability, shifts in gene essentiality emerged following antibiotic stress. The complete sets of the predicted EGs for each condition have been reported in Supplementary Material A. baumannii ATCC 19606. Interestingly, however, some genes switch from the ‘essential’ condition to the ‘non-essential’ one, following the exposure to colistin.The same outline has been depicted in the second time-point condition (60 minutes): we identified 12 and 5 condition-specific EGs in the absence and in the presence of the antibiotic, respectively, see Table in vivo experiments could impact the set(s) of predicted EGs. Specifically, the robustness of the number of predicted EGs in each of these conditions (i.e. treated vs. untreated samples) in respect to possible variations in the medium composition was assessed via random permutation. We tested up to 1,000 different nutritional compositions as described in detail in Supplementary Material The two new sets of EGs show how changes in gene expression induced by the presence of the antibiotic might influence gene essentiality patterns in the strain ATCC 19606 and provide additional, nontrivial targets for drug design in such an organism. We also performed additional robustness analyses in order to test whether nutrients depletion occurring in treated and untreated samples during the A. baumannii specific or have orthologs in Homo sapiens, meaning that they would not represent good candidates for antibiotic treatment development, the sequences of the identified potential EGs were used to probe the human genome. Based on this BLAST26 search (see Methods), we excluded from further studies those genes presenting more than 30% sequence identity with their human counterparts. Targeting of such genes is non-ideal since they may cause potential side-effects by perturbing critical components in the human body. All BLAST results are reported in Supplementary Material Further, with the aim of discriminating whether the products of all the identified hypothetical EGs are A. baumannii ATCC 19606 infections. However, it is relevant to remark that, while these 46 general EGs could have been detected in several conditions, the other 4 condition-specific EGs are the result of specific constraints integrated in the model (gene expressions data). Hence, as already mentioned, they are nontrivial detections and they could represent a suitable horizon in the field of colistin-coupled treatment. The four genes, named HMPREF0010_00949, HMPREF0010_02972 and HMPREF0010_03445, HMPREF0010_01216 respectively encode a malonate decarboxylase (epsilon subunit), an arginine succinate synthase, a 3-4-dihydroxy-2-butanone-4-phopsphate synthase and a quinolate synthase. Interestingly, malonate decarboxylase epsilon subunit has already been characterized in the closely related organism Pseudomonas putida and labelled as an indispensable component of the enzyme for the cyclic decarboxylation ofmalonate27. However, to the best of our knowledge, no therapies targeting this protein have been developed to date. The product of 3-4-dihydroxy-2-butanone-4-phopsphate synthase is an intermediate in the biosynthesis of riboflavin. The enzyme requires a divalent cation, preferably Mg2+, to be active. The step becomes essential after colistin treatment as the antibiotic is predicted to cause an increase in flux through this pathway, probably following the shutdown of other parts of the network due to the down regulation of the corresponding genes. The arginine succinate synthase, is an enzyme catalysing the penultimate step in arginine biosynthesis (urea-cycle): the ATP-dependent ligation of citrulline to aspartate in order to form arginino-succinate, AMP, and pyrophosphate. The last EGs encodes for a bacterial specific enzyme that catalyses the second step in the de novo biosynthesis of NAD+ from aspartate.Among all the queries, we identified 46 (out of 73) general EGs and 4 (out of 12) condition-specific EGs that do not have any human orthologous. Thus, the 46 EGs represent valuable targets for further development of brand new drugs against Up to now, we have presented how metabolic reconstruction and mathematical modelling can be used to explore the strain’s metabolic response during colistin treatment and how it can lead to the identification of novel potential drug targets. Our last attempt is now to illustrate how, starting from the same available experimental data, the model can be employed as a ready-to use blueprint in order to test new hypothesis.et al.28, the mechanism responsible for colistin resistance is linked to LPS. Specifically, mutations in the lpxA, lpxC, and lpxD genes have been reported as the main cause of LPS loss, thus abolishing the initial charge-based interaction with the antibiotic. Hence, to simulate an A. baumannii LPS (LPS−) deficient and colistin-resistant strain, we removed this component from the biomass formulation in our genome-scale model. Then, to determine which genes are central for the cell’s survival in such a condition, we used the transcriptomic data of the mutant strain in the presence/absence of colistin at 60 minutes14 and mapped the data onto the new LPS− model. After this, we repeated the EGs prediction pipeline described above.As it was reported by Moffatt The analysis yielded a total of 55 and 72 EGs in the untreated and treated condition, respectively. Even in this case, the two sets share several elements (54 EGs) that remain mandatory for the cell in the two conditions  with the results obtained from experiments found in the literature. Some of these genes, although not yet experimentally validated, might represent primary targets for future research on the treatment of both the wild type and LPS-mutant (i.e. colistin resistant) strains. Our results have practical implications for the identification of new therapeutics as the identified essential genes can be used in drug-design pipelines. Moreover, we showed that the sequences of predicted EGs for the type strain ATCC 19606 are shared by most of the members of A. baumannii species, encouraging further research to check whether they are valuable drug targets for a larger number of strains than currently known. Finally, it can be anticipated that the iLP844 model illustrated herein represents a reliable and solid platform for further developments and the system-level understanding of the physiology of A. baumannii representatives and for the treatment of their infections.In this work, we have reconstructed and validated a genome-scale metabolic model of A. baumannii ATCC 19606 based on the genome annotation using Kbase automated reconstruction method (https://kbase.us/)29. This reconstruction was then thoroughly inspected and refined by integrating data from additional functional databases . Further integration was performed by searching for orthologous genes  in closely related organisms  through a BBH  approach (inParanoid34). Information regarding transport proteins was obtained probing the Transporter Classification Data Base (TCDB35) and transportDB36.We obtained a draft metabolic model of A. baumannii AYE, AbyMBEL89119.In order to predict proper phenotypes, the general biomass producing reaction of Gram negative bacteria automatically generated by Kbase was substituted with a more accurate one that takes into account strain’s specific components, which was recovered from the previously reported model of the related strain 37 and COBRAToolbox-2.038 in MATLAB® R2016a (Mathworks Inc.). Gurobi 6.5.0 (www.gurobi.com) and GLPK 4.32 (http://www.gnu.org/software/glpk/) solvers were used for computational simulations presented. A MATLAB® script to obtain all the results shown in this manuscript is provided as Supplementary Material The reconstructed model was analysed using CoBRApy-0.4.1 COnstraints-Based Reconstruction and Analysis for Pythonin silico simulations:Two growth media were considered during the −1 in order to mimic non-limiting conditions. Carbon sources uptake reactions were set to −100 mmol/g*h−1.Lower bounds of salts uptake reactions were set to −1000 mmol/g*h39  was set to −5.Lower bounds of all uptake reactions accounting for the nutrients present in Simmons mediumi.e. the maximum and minimum values of all the fluxes that satisfy the given constraints) while keeping the same optimal objective value.FVA analysis allows the determination of the span of possible flux variability (FBA), which are notably non-unique. In fact, for any optimal solution found through FBA there may exist alternate flux distribution patterns yielding the same growth rate. Hence, the space of reliable FBA-flux predictions has been restricted by selecting only those that occur in the interval defined as follows:This approach has been used in this work in order to impose bounds to FBA flux predictions  between the simulated knocked out strain growth rate (µKO) and the one predicted for the wild type strain (µWT). Formulated as:Gene essentiality testing was performed by simulating deletion of each gene within the metabolic network and hence setting the associated reactions to carrying no flux (according to the corresponding Gene-Protein-Reaction (GPR) rule). To predict the growth of the mutant strain and determine the set of EGs, we used two different approaches, FBA and MOMAratio = 0, then the knocked out gene is labelled as essential. Conversely, in case GRratio = 1, the removal of the gene has no effect on the growth phenotype. Finally, when 0 < GRratio < 1, the deleted gene was labelled as fitness-contributing gene, i.e. its removal partially affects the capability of the cell to produce biomass.Following this approach, if GR40, we used both approaches to compute essential genes in all the conditions tested in this work. Although no major differences were observed, results obtained with both methods are presented throughout the manuscript.As MOMA and FBA predictions may lead to different essential gene setsvs. the untreated conditions as follows:In order to evaluate the range of the change in the carried flux of each reaction in the model following colistin exposure, we compute the ratio between the predicted flux in the treated ratio equal to one indicate that no changes in the activity of the corresponding reactions were observed when simulating growth in the treated vs. the untreated conditions. Conversely, values of RFratio between 0 and 1 or values greater than 1 will indicate a reduced or increased activity of the corresponding reactions in the treated condition, respectively. Finally, negative values of RFratio will indicate those reactions whose directionality is predicted to change after the treatment.Values of RFA. baumannii ATCC 19606 metabolic model by using MADE 24. The program uses significant statistical changes in gene or gene expression to create functional metabolic models. By adopting an optimization approach that applies Boolean rules, MADE connects reactions to the binary expression states of associated genes. The four arrays of genes to be switched-off yielded by MADE have been reported in Supplementary Material In order to add transcriptional regulatory rules to the metabolic model, we imported the model from COBRA Toolbox into TIGER-1.2.0 0  framework (12). Then, the up- and down-regulation ratios of gene expression were mapped into the A. baumannii ATCC 19606 were probed against the human proteome to test their validity as potential drug target in infections with this pathogen, i.e. to exclude any cross-interactions between the drug used for the treatment and human proteome elements.Protein sequences of the corresponding EGs found in H. sapiens using the default search parameters of the NCBI BLASTP online tool . Results were considered positive (orthologous sequences found) if their sequence identity score value was equal to/greater than 30.Queries were aligned to the protein sequences of A. baumannii species level by probing them against all of sequenced genomes retrieved at NCBI ftp site, i.e. 1099 genomes. Particularly, the focus was centred on EGs found in Simmons medium and in rich medium, as well as for those found after 15 and 60 minutes of colistin exposure. BLAST search parameters and analysis of the results were performed as described above.In addition, the global distribution of EGs was evaluated at the SM1SM2SM3SM4SM5SM6SM7SM8


Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives.E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock.To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.The online version of this article (doi:10.1186/s12859-017-1483-5) contains supplementary material, which is available to authorized users. MCSs, was developed by Klamt and Gilles ,,MILP proN and T matrices have been split into reversible (subscript rev) and irreversible submatrices (subscript irr). Similarly, identity submatrices for reversible and irreversible reactions are represented by the matrices Irev and Iirr respectively. cMCS are directly calculated by finding solutions with minimum number of non-zero entries in vp,vn. Additionally binary indicator variables zp and zn are introduced such that zpi=0 if vpi=0 and zpi=1 if vpi>0 and similarly for zn, vn. Only one direction of v (either vpi or vni) can be active, hence Note that the We set up the following optimization problem ri=0 if zpi=1 || zni=1.with the additional constraint that the flux through a reaction is turned off if it is part of a cMCS, i.e., T,D,t and d, the optimization problem can be stated as With this system it is possible to find cMCS which will result in designs satisfying constraints on yields/fluxes specified by , 4). Ho. Ho4). HIn other words, the problem is to find optimal combinations of {target/desired} yields for all reactions to be optimized. This is not easy for a few reasons. In general, this is a non-linear optimization problem. Non-linear optimization is known to be inherently complex with general deterministic solutions being impossible to find. Secondly, slight adjustments in , 4) cou cou4) coPSO is a metaheuristic inspired by the flocking behavior of birds . In PSO,j-dimensional vectors, where j is the dimensionality of the search space. These represent the current position x, its previous best position p which is the position corresponding to the highest fitness achieved by the particle and the velocity v, Fig. Typically, a particle is made up of three g is the position corresponding to the global best fitness of the entire swarm till the current t. φ1,2 are called “acceleration constants” and determine the relative influence of the particle’s own knowledge and that of the group, both of which are commonly set to 2 [β1,2 are uniformly generated random numbers within the range  which we had previously developed . The modented in . FinallyConsider the network given in Fig. E. coli core network which will ensure high yield of ethanol even in the face of high growth. This network was previously used to design a high yield ethanol producing strain in [We apply PSOMCS to generate designs in an train in . This motrain in , particutrain in . Here weE. coli presented in [zp, zn variables to zero. The machine we used for this test had 24 CPUs, 396GB RAM, Intel Xeon E5-2667 2.90 GHz processor, running on Ubuntu 14.4 LTS. The cMCS cardinality was limited to 5. With 4 particles being processed in parallel, the program was run for 40 iterations. It took 14 iterations (∼ 74 hours) to find the optimal design. One of the designs is shown in Fig. FVA [To test the capabilities of our method we applied it to the genome-scale model of ented in . Our aimented in , 9 to reHere we have presented a method, PSOMCS, to design strains with high minimal product yield using knockouts of minimal possible size. To do this, we employ a PSO together with the direct enumeration of cMCS developed in –9. This There have been other methods with a similar strategy as ours, which is the use of a metaheuristic in combination with another method like linear programming. Most methods have relied on genetic algorithms , 12, 27,As shown by the comparison with OptKnock and RobustKnock in Fig. In heuristic search algorithms, performance comes at the cost of being too specific to the problem being solved . By virtMILPs are more difficult to solve than LPs and may consume large amounts of time as well as memory . During Improvements in run time can be made by forcing PSOMCS to explore only a part of the flux space leading to a smaller solution space to be explored. For instance lets consider the design in Fig. Here we have dealt only with knockout strategies to design better strains. It can easily be extended to include the concept of regulatory MCS introduced in  which coPSOMCS finds the best possible design in metabolic networks given multiple objectives with the corresponding cMCS. We have demonstrated its capability in finding optimal knockouts and designs in genome-scale metabolic networks. It finds competitive designs compared to standard tools and is orders of magnitude faster than EFM based tools in finding the optimal solution. PSOMCS could be used to predict minimal knockouts resulting in optimal yields in industrially important microorganisms. As the size and quality of metabolic models increase, methods like the one presented here will be even more useful.


What does it take to convert a living organism into a truly productive biofactory? Apart from optimizing biosynthesis pathways as standalone units, a successful bioengineering approach must bend the endogenous metabolic network of the host, and especially its central metabolism, to support the bioproduction process. In practice, this usually involves three complementary strategies which include tuning-down or abolishing competing metabolic pathways, increasing the availability of precursors of the desired biosynthesis pathway, and ensuring high availability of energetic resources such as ATP and NADPH. In this review, we explore these strategies, focusing on key metabolic pathways and processes, such as glycolysis, anaplerosis, the TCA (tricarboxylic acid) cycle, and NADPH production. We show that only a holistic approach for bioengineering — considering the metabolic network of the host organism as a whole, rather than focusing on the production pathway alone — can truly mold microorganisms into efficient biofactories. The bioproduction of value-added chemicals is gaining momentum, slowly but surely replacing environmentally unsustainable fossil-carbon-based chemical processes. Metabolic engineering of microbes now supports the production of food additives , plasticYet, apart from the properties of the bioproduction pathway itself, as a standalone unit, the endogenous metabolic network of the host organism plays a key role in determining biosynthesis efficiency. There are three primary means by which central metabolism can be modified to enhance a bioproduction process: decreasing and abolishing metabolic flux toward competing pathways that lead to waste byproducts, increasing the availability of the direct precursors of the desired biosynthesis routes, and ensuring high availability of cellular energetic resources, mainly ATP and NADPH. These metabolic strategies are the focus of this review. We argue that engineering of the cell as a whole is crucial for the optimization of any metabolic process. We find the term holistic bioengineering most appropriate to describe this approach.Escherichia coli, Corynebacterium glutamicum, and Saccharomyces cerevisiae — are overlaid . Key pathways are marked by specific colors. Enzymes to which we directly refer below are marked with a yellow background. We divide this review into several sections, each discussing the modifications made to a different central pathway or process with the aim of enhancing a particular biosynthetic flux. As the topic is quite extensive, our review focuses on several primary examples which, we believe, demonstrate the key aspects.E. coli, ethanol by yeasts, and lactate by cancerous cells. This ‘overflow metabolism’ phenomenon is the center of a wide range of research efforts , produce different combinations of compounds. While it is clear that fermentation, in the absence of electron acceptors, results in the conversion of most carbon input into fermentation products, it is important to emphasize that many organisms also produce large amounts of such products under aerobic conditions, e.g. acetate production by ts e.g. –18). For. ForE. cts  lactate dehydrogenase (LDH), to eliminate lactate production; (ii) alcohol dehydrogenase, to eliminate ethanol biosynthesis; (iii) acetate kinase and/or phosphate acetyltransferase (PTA), to eliminate acetate production; (iv) pyruvate formate-lyase (PFL), to eliminate formate production; and (v) fumarate reductase (FRD), to eliminate succinate production. Deletion of the genes encoding these enzymes, or a subset of them, was shown to dramatically increase the production of hydrogen , pyruvat-butanol , branche-butanol , tryptop-butanol , citrama-butanol , and man-butanol ,25,28,29E. coli has not only led to the accumulation of pyruvate but also resulted in the increased activity of the alternative anaplerotic enzyme PEP carboxykinase , as well as an enhanced oxidative phosphorylation, which was required to balance the increased cellular NADH/NAD+ ratio . Si. SiS. celutarate . Intereslutarate .Figure E. coli )In most cases, the rewiring of cellular metabolism was rationally designed based on the knowledge and experience of the authors. In other cases, a computational strategy was taken to systematically explore all possible alternations of central metabolism that could lead to beneficial results e.g. –141). Wh. Wh141])E. coli [We emphasize that while we focused on central metabolism in this review, a truly holistic bioengineering encompasses other aspects of cellular physiology, including amino acid, nucleotide and fatty acid metabolism, assimilation of inorganic elements such as nitrogen and sulfur, passive and active cellular transport, as well as transcription and translation. Each of these cellular processes can be — and should be — modified or rewired as to support the most efficient conversion of feedstock into product. We would like to finish by noting that even if we keep focusing on central metabolism, the challenge of wholly redrawing it — as opposed to mildly rewiring it, as is commonly done — is still open. To what extent can we reinvent key metabolic pathways? Can such dramatic changes be useful for biotechnological applications? Some pioneering studies, e.g. engineering an active Calvin Cycle in E. coli  — may st


Oenococcus oeni is the main responsible agent for malolactic fermentation in wine, an unpredictable and erratic process in winemaking. To address this, we have constructed and exhaustively curated the first genome-scale metabolic model of Oenococcus oeni, comprising 660 reactions, 536 metabolites and 454 genes. In silico experiments revealed that nutritional requirements are predicted with an accuracy of 93%, while 14 amino acids were found to be essential for the growth of this bacterial species. When the model was applied to determine the non-growth associated maintenance, results showed that O. oeni grown at 12% ethanol concentration spent 30 times more ATP to stay alive than in the absence of ethanol. Most of this ATP is employed for extruding protons outside of the cell. A positive relationship was also found between specific consumption rates of fructose, amino acids, oxygen, and malic acid and the specific production rates of erythritol, lactate, and acetate, according to the ethanol content of the medium. The metabolic model reconstructed here represents a unique tool to predict the successful completion of wine malolactic fermentation carried out either by different strains of Oenococcus oeni, as well as at any particular physico-chemical composition of wine. It will also allow the development of consortium metabolic models that could be applied to winemaking to simulate and understand the interactions between O. oeni and other microorganisms that share this ecological niche. This process is primarily responsible for lowering the acidity of wine, and also generates other benefits, such as improving aroma and flavor complexity; as well as increasing the biological stability of the resulting wines , low pH (<3.5), limited nutrient availability and high sulphite concentration (<50 ppm)  content of 38%. Borneman et al.  to provide a tool for simulating the metabolism, nutritional requirements, and specific growth rate of this microorganism under the harsh conditions of winemaking. Here we report the general features of the model, as well as its prediction performance. The resulting metabolic model was employed to assess the metabolic capabilities, limitations and potential of this LAB to successfully accomplish malolactic fermentation in wine.In this work, we constructed the first genome-scale metabolic model of an O. oeni PSU-1. The model was then manually curated consulting scientific literature and the online databases KEGG™Lactococcus lactis and Lactobacillus plantarum respectively, was conducted in order to find missing reactions . Gurobi 6.5We employed FBA to calculate the optimal distribution of metabolic fluxes of an underdetermined system of stoichiometric equations , true negatives (no growth observed neither in vivo nor in silico), false positives (growth in silico but not in vivo) and false negatives (growth in vivo but not in silico).For both sets of experiments, results were classified as true positives  to represent non-growth associated maintenance (NGAM).−1 h−1. At each iteration, the growth rate was maximized and the error between the experimental and predicted growth rate was calculated. The value that minimized the error between the experimental and predicted growth rate was chosen as the ATP required for maintenance of cellular processes.The values of NGAM were determined from the model for each experimental condition. For this purpose, we first fixed the consumption and production rates of different metabolites and then we progressively increased the NGAM from 0 to 5 mmol gDWThe experimental rates included in the model were specific consumption rates of glucose, fructose, citrate, L-malate, L-cysteine, L-serine, L-threonine; it also included specific production rates of D-mannitol, L-lactate, D-lactate, acetate, erythritol, and ethanol. They were calculated from experimental data of two batch cultures containing 0 and 12% ethanol, respectively, run in duplicate (see below).O. oeni PSU-1 preculture was prepared from a frozen stock by inoculating 100-ml Erlenmeyer flasks containing 75 ml MRS  medium  to reach 0 or 12% ethanol concentration (v/v) in each culture.An The adapted cells were inoculated in 50 mL flasks containing 35 mL of a chemically defined, wine-like, culture medium to achieve an initial optical density at 600 nm (OD600) of approximately 0.2. We employed the modified culture medium described by Terrade and Mira de Orduña , at an iThe flasks were incubated at 25°C, without stirring. OD600 was periodically measured to calculate the specific growth rate. At the same time, the content from each flask was centrifuged, the supernatant was collected and an aliquot was injected in a Lachrom L-700 HPLC system  equipped with a Diode Array and a Refractive Index detectors . Organic acids, alcohols and sugars were separated using an Aminex HPX-87H ion exchange carbohydrate-organic acid column  and quantified, as described previously  were inoculated in 50 mL flasks containing 35 mL of the same chemically defined, wine-like, culture medium, but lacking the amino acids evaluated for essentiality , one amino acid per flask, in duplicate. The flasks were incubated at 25°C during 13 days, without stirring, and OD600 was periodically measured to calculate the specific growth rate.An For each optimization using experimental data, non-zero reduced costs were extracted from the solver solution and employed for quantifying the impact of changing a capacity constraint on the objective flux. Scaled reduced costs were calculated as follows:wi represents the reduced cost, qi the flux through exchange reaction i, and μ the specific growth rate. Wi, the scaled reduced cost of the exchange reaction i, represents the fractional change in biomass obtained by a fractional variation in compound i. Reactions that showed both, a non-zero reduced cost and a non-zero scaled reduced cost, were further analyzed.Where FVA was carried out by minimizing and maximizing the flux through each reaction, under either unconstrained or constrained conditions. Span range was sorted by magnitude and plotted.Reactions unable to carry flux were considered blocked. For each of these, the cause of the obstruction was investigated by finding dead-end metabolites; these were determined searching for those metabolites that were only consumed or produced in the stoichiometric matrix. Additionally, we determined dead-ends by adding a demand (maximizing the flux) or a sink (minimizing the flux) reaction for each metabolite. Metabolites were considered dead-ends if the model was unable to produce, nor consume them.hit and run , IL1403 (Lc. Lactis), and Yeast 7 (S. cerevisiae) models  . We only left pathway I in the model because it was the most complete, i.e., 9 out of the 10 reactions of this pathway were associated with genes.The iSM454 model contains the whole biosynthetic pathways for 6 amino acids , as arisen from genome annotation , unsaturated fatty acids  and cyclopropane fatty acids (lactobacillate and dihydrosterculate)  associated with a generic acyl fatty acid. We therefore manually included these genes and reactions for the canonical catabolism of the above-mentioned saturated fatty acids.In relation to β-oxidation of fatty acids, Pathway Tools assigned two genes, L. lactis , codified by the l Figure  , and that the 6 remaining ones  were not.The O. oeni. On the other hand, nicotinic acid and pantothenate were predicted to be essential nutrients. Moreover, several vitamins, such as biotin, folic acid, pyridoxine, riboflavin, and thiamin were not essential.Single nucleotide omission experiments of iSM454 showed that these metabolites were not essential for O. oeni was able to grow in 91% of the cases in which growth has been observed experimentally (sensitivity); whereas it identified correctly 96% of the cases where O. oeni did not grow (specificity). Furthermore, 97% of the experiments in which O. oeni was predicted to grow, O. oeni actually grew (precision). Additionally, 90% of the experiments in which O. oeni was not predicted to grow, O. oeni actually did not grow (NPV). The accuracy of the model was 93%, i.e., the proportion of correct results to total predictions. By comparison, the GEM model of L. plantarum presents an accuracy of 86% , pyruvate kinase (EC 2.7.1.40), and phosphoglycerate kinase (EC 2.7.2.3); and subtracting the fluxes through hexokinase (EC 2.7.1.1/E.C 2.7.1.2) and fructokinase (EC 2.7.1.4). On the other hand, by using the usual method for ATP determination through heterolactic fermentation, which consists of adding the total D-lactate and acetate produced, the resulting ATP production rates were 1.38 and 2.12 mmol gDW−1 h−1, for 0 and 12% ethanol-containing cultures, respectively.As expected from the higher carbon flow through the heterolactic pathway in ethanol-containing cultures, the corresponding ATP specific production rate was 3-fold faster than in the absence of ethanol, passing from 0.74 to 1.98 mmol gDWTotal ATP production increases as the concentration of ethanol in the medium increases Figure . The modO. oeni, by conducting a sensitivity analysis  as well as the transport of acids -malate or citrate uptake- , and the export of acetate, L- and D-lactate . The specific uptake rate of cysteine, serine and threonine also showed a positive effect on the specific growth rate . On the contrary, the production rate of D-mannitol and D-erythritol had a negative effect on the specific growth rate, which showed scaled reduced cost of −2.67 and −1.48, respectively.To evaluate the robustness of our results, we employed random sampling and FVA to identify and explore the different phenotypes achieved at optimal specific growth rate.−1 h−1, i.e., a negative value for the logarithm of the flux range in Figure −1 h−1 in the unconstrained network; meanwhile, this value increased to 370 in the constrained network. Moreover, 99% of the reactions in the constrained network showed a flux range lower than 3 mmol gDW−1 h−1, suggesting that the constraints applied (uptake and production rates) strongly delimit the solution space. Thus, applying these constraints, the phenotype is well defined.FVA revealed some important differences between unconstrained and constrained networks Figure . 485 and−1 h−1.Additionally, random sampling in the constrained network revealed that the solution space was tight and alternative pathways were limited. Except in the case of reactions of isomer interconversions which could result in a futile cycle, none of the reaction rates analyzed changed more than 1.1 mmol gDWO. oeni requires oxygen to achieve growth at all ethanol levels. Moreover, the oxygen consumption rates needed for growth increase as the concentration of ethanol increases, ranging from 0.8–1.2 to 4.1–4.2 mmol gDW−1 h−1 for 0 and 12% ethanol, respectively.Interestingly, when constrained by experimental rates, FVA shows that O. oeni were predicted by in silico simulations of reaction knockouts—inhibiting the activity of the enzyme(s) carrying away the respective reaction. This was conducted by further constraining the model, i.e., fixing the flux of the corresponding reaction to zero.The essential reactions of 132 essential reactions were found by reaction deletion analysis, which represent 20% of the 660 total reactions of the model; of these, 28% correspond to fatty acid biosynthesis, and 14% to unsaturated fatty acid biosynthesis. The other main essential pathways include the biosynthesis of peptidoglycan (10%), glycerolipids (8%), and amino acid biosynthesis (8%)  were thoroughly curated in the model.In this work, we reconstructed, curated and validated the first genome-scale metabolic model of OEOE_1176, a gene related to a generic reaction of cyclization of unsaturated fatty acids. We identified dihydrosterculic acid, which derives from oleic acid under stress conditions , cis-vaccenic acid (C18:1 Δ11), lactobacillic acid (C19:0 cyΔ11), and dihydrosterculic acid (C19:0 cyΔ9). Moreover, we discovered that the reactions for generating cyclopropane bounds were associated with O. oeni under stress conditions, such as those prevailing in wine during malolactic fermentation (MLF). Moreover, these polysaccharides have been implicated in ropiness, a wine spoilage process . Likewise, a lactic acid transporter that allows this compound to cross the cytoplasmic membrane was added. Citrate metabolism, another important system of energy generation in O. oeni, was also included. Both processes allow proton extrusion, which relates to ATP synthase, allowing more ATP to be synthesized. These three added metabolic processes were critical for accurate model performance, especially for the prediction of specific growth rates. Thus, simulations strongly suggest that proton transport is the most important process for the survival of O. oeni under these harsh cultural conditions.Another important feature of the model is the representation of different proton extrusion/energy generation pathways employed by O. oeni phenotype by iSM455 model. For this purpose, we ensure that the only reactions for extrusion of protons were those related to heterolactic fermentation (efflux of lactate and carbon dioxide); in those cases, the network properly described the proton motive force needed to generate energy from the ATP synthase located in the cell membrane. Additionally, a careful revision of the metabolism was performed to account for proper cofactor utilization. This feature was recently reported as a critical step in improving phenotypic predictions by genome-scale metabolic models  was on average 0.25 mmol ATP gDW−1, pointing out that under ethanol stress (12%v/v), O. oeni spends in total almost nine times more energy in cell maintenance than under non-stress conditions, i.e., absence of ethanol in the medium.In general, microorganisms colonizing extreme ecological niches need higher energetic requirements for cell maintenance, which is not reflected in biomass formation  to those determined by Aceituno et al. , threonine degradation by aminoacetone:oxygen oxidoreductase (E.C. 1.4.3.21) and spontaneous diacetyl formation by acetoin oxidation.Moreover, a redistribution of intracellular fluxes occurred when ethanol content increased in the culture medium. FVA revealed that o et al.  for the O. oeni is still scarce; the physiological as well as proteomic and transcriptomic responses of the bacterium grown under different environmental perturbations are necessary to further improve the model. These might include determination of biomass composition, exhaustive electronic and proton balancing of stoichiometric equations and integration of transcriptomic and proteomic data. On the other hand, dynamic flux balance analysis (dFBA) has emerged as a promising strategy to study batch cultures of several strains (Sainz et al., Saccharomyces cerevisiae strains in wine-like medium (Vargas et al., O. oeni could be useful to simulate the kinetics of growth and industrial MLF extension.The overall good performance of the metabolic model evidences the correct distribution of metabolic fluxes, with or without 12% ethanol content and pH 4.8. However, it is worthy to mention that the information about E. coli's GEM has been successfully employed as a platform to model metabolite exchange between different organism under different environmental conditions (Jain and Srivastava, O. oeni and other microorganisms that share this ecological niche, as S. cerevisiae and other LAB, like Lb. plantarum, Lb. kunkeii, Pediococcus pentosaceus; and even undesirable and detrimental wine microorganisms, like Brettanomyces spp. or Acetobacter aceti, responsible for acetic acid production spoilage. Consortium metabolic models might predict how each organism develops in a shared, not isolated, scenario (Tzamali et al., O. oeni in coexistence with other species.Finally, a future challenge is the development of a more extended platform, based on the iSM454 model, allowing the simulation and prediction of the biological interactions occurring within the wine microbiome. For example, EA designed the research and provided guide throughout the investigation. EA, SM, and PC coordinated the project. SM and PC are first-authorships and developed and refined the model, and PC developed the OMIX version of the reconstruction. SM and MR performed the model validation and evaluation. AC designed, performed, and coordinated the batch experiments as well as processed the HPLC data. All the authors wrote the paper, read, and approved the final manuscript.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.