Metabolomics is a well-established tool in systems biology, especially in the top–down approach. Metabolomics experiments often results in discovery studies that provide intriguing biological hypotheses but rarely offer mechanistic explanation of such findings. In this light, the interpretation of metabolomics data can be boosted by deploying systems biology approaches.This review aims to provide an overview of systems biology approaches that are relevant to metabolomics and to discuss some successful applications of these methods.We review the most recent applications of systems biology tools in the field of metabolomics, such as network inference and analysis, metabolic modelling and pathways analysis.We offer an ample overview of systems biology tools that can be applied to address metabolomics problems. The characteristics and application results of these tools are discussed also in a comparative manner.Systems biology-enhanced analysis of metabolomics data can provide insights into the molecular mechanisms originating the observed metabolic profiles and enhance the scientific impact of metabolomics studies. The pioneering experimental work of Mamer and Horning  or unsupervised tools . Instead, we focused on systems biology approaches like network inference and metabolic modelling.An important aspect underlying most if not all the methods for the analysis of metabolomics data that we will address in the next sections is to properly identify the metabolites in the MS or NMR spectra and map them within the metabolic context of the organism. Often the peaks detected in experiments are assigned based on reference spectra contained in large chemical database. However, the analytical methods used in metabolomics do not allow coverage of the whole range of small molecules produced by an organism, introducing possible biases in the interpretation of whole-organism metabolism. Although this is a very broad theme, in this section we will try to summarize the features of some tools for metabolite mapping that can be exploited in the context of systems biology approaches.http://procyc.westcent.usu.edu/cgi-bin/MetaboSearcher.cgi) to directly search genome-constructed metabolic databases. Its aim is to enhance the identification of MS data by using compound databases derived empirically. Incorporating information on genome-encoded metabolism facilitates the identification of MS peaks that may not be present in standard chemical databases. Only the compounds that the organism of interest is able to produce, based on its genome, are investigated for potential matches. The output metabolites are mapped also to known metabolic pathways.Metabolome Searcher  addresses the annotation of putative metabolites by providing a hypothesis-driven approach to interpret MS data. MassTRIX processes the submitted list of raw mass peaks by comparing the input experimental masses against all chemical compounds of the Kyoto Encyclopedia of Genes and Genomes (KEGG) database , the user can interpret the metabolic state of the organism in the context of its actual or potential enzymatic capacities.The MassTRIX web server  is the Polyomics integrated Metabolomics Pipeline (PiMP)   allows users to combine datasets from heterogeneous multiple-omics sources. This tool visualizes and combines datasets from transcriptomics, proteomics, and metabolomics on user defined metabolic pathway maps. ProMeTra supports pathway maps designed and annotated by the users.The web-based ProMeTra system  using dedicated experiments. In this review, we focus on the application of network approaches to analyze metabolomics data that usually have not been gathered with the aim of reconstructing entire metabolic networks. For the latter purpose, the typical starting point is genome data  and their relationships (described by links or edges). It can be conveniently described using a matrix, termed adjacency or connectivity matrix Table ρ = 0.0243, Spearman). Instead, valine and methionine are strongly correlated (ρ = 0.951) even if they are not metabolic neighbors  based on some similarity measure: in metabolomics the similarity between metabolites, and thus their association, is usually expressed using Pearson or Spearman’s correlation indexes. Consequently, the elements of the corresponding adjacency matrix are in the interval   and glucose in high and low risk cardiovascular risk networks. They applied a combined method, by analysing association networks with a multivariate approach to highlight differences among networks pertaining to different risk phenotypes  can be affected by data pre-treatment  such as scaling, transformation and normalization. Such pre-treatments are routinely applied to metabolomics data in order to correct for systematic and unwanted variation such as sample-to sample to variability induced by dilution effects (e.g. in the case of urine) or differences in experimental settings (like different sample titration or different number of scans in NMR experiments). The literature on the topic is huge: we refer the reader to Bijlsma et al. , Goodacrwij of TOM is defined asmi and mj denote metabolite i-th and j-th, ki and kj denote the connectivity of metabolite i-th and j-th. The dissimilarity is defined as 1 − wij, which is a measure of interaction between metabolites weighted by the strength of their correlation. The parameter Weighted gene correlation network analysis (WCGNA) is a systems biology method for describing the correlation patterns among genes across microarray samples. WCGNA can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the so-called module eigengene, i.e. a representative gene summarizing the expression profile of the module  and the clustering coefficient C(k) to follow a power law. The authors found P(k) to follow a power law but not C(k), indicating the absence of modular structure in the network of urinary metabolites. They suggested that this may be caused by (i) urine being a waste product in which little to no active metabolism occurs and (ii) the limited number of metabolites considered (n = 60) which is less than the content of the full urinary metabolome. A further hypothesis put forward in the same work was that networks constructed from metabolite profiles derived from biological samples that are metabolically active, such as blood or tissue, may exhibit power law (i.e. a few metabolites connected with many metabolites) behaviour in both connectivity and clustering coefficients. However, in contrast to gene regulatory network, expression networks or metabolic networks, the metabolite correlation networks have not been fully characterized in terms of network topology (i.e. the patterns of interconnection among the nodes). Therefore, it is not very clear what are the expected or more likely network properties . We refer the reader to , the mutual information MI between A and B is defined asp is the joint probability distribution function of A and B, and p(ai) (respectively p(bj)) indicates the probability that A = ai (respectively B = bj). It should be noted that the mutual information between two variables is not independent from correlations, since, under some conditions, the two variables can be functionally related  and pruned, i.e. set to 0, if the difference between the two lowest weights is above a cut-off value ξ. In practice, the following two conditions are evaluated for each triplet i, j, k:ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks)  between two metabolites by defining a null model that considers all the possible MI values [MIi] and [MIj] for metabolites i and j. The following equations define the likelihood fµi and σi are, respectively, the mean and the standard deviation of the distribution of the [MIi] values: a weighted adjacency matrix is built with entries f.The CLR algorithm  to analyse and interpret untargeted liquid chromatography–mass spectrometry (LC–MS) data from lipidomics and phosphoproteomics experiments in a cell-line model of Huntington’s disease. Grounding on database information, the algorithm infers the identity of unassigned metabolites corresponding to features and the molecular mechanisms underlying their dysregulation. This innovative approach helps to reduce the bias towards well-studied metabolites typical of targeted metabolomics. The algorithm takes as input a list of LC–MS peaks that differ between two different conditions and searches for them in a databases containing over 42,000 nodes (either proteins or metabolites) connected by over one million weighted edges representing interactions between proteins as well as enzymatic and transporter reactions. The output is a subnetwork of the database representing metabolic pathways that are dysregulated under the conditions considered.Pirhaij et al.  used theThe metabolism is a network structure that can be approached as a system of interdependent variables that enable mathematical modelling through kinetic models. These models are defined as systems of ordinary differential equations describing the time course of metabolite concentrations as a function of rate laws that account for enzyme catalysis. The development of these models requires to know both the network structure and the reaction kinetics and parameters   of transformation or transport. Mechanistic changes at the level of the metabolism are likely to produce changes in the distribution of fluxes. Intracellular fluxes are not directly measurable, but the use of stable isotope-enriched nutrients, such as 1,2-13C) relies upon a combination of different methods, going from the direct interpretation of the labeling patterns to computational model-based approaches . Instead, the entry into the non-oxidative pentose phosphate pathway results in the synthesis of ribose phosphate molecules that contain two 13C atoms . The subsequent entry of M+1 pentose-phosphate into glycolysis contributes to the synthesis of triose phosphate and lactate molecules with one 13C atom (M+1). An approximate estimation of the relative importance of oxidative versus non-oxidative pentose phosphate pathway fluxes can be inferred from the M+1/M+2 ratio of the RNA-derived ribose. During the last years, the use of this and other isotopic labelling tracers have been applied to unveil the different metabolic pathways activated in cancer cells . Progressively, these products are enriched in 13C, with concomitant decrease in M0. Isotopic steady state   where one checks whether a group of differentially expressed genes is enriched for a pathway or ontology term by using overlap statistics such as the cumulative hypergeometric distribution . As the first step of the analysis, metabolites are assigned to specific sets based on one or more reference databases. A group of metabolites are assigned to the same set if they are known to be: (i) involved in the same biological processes   of p metabolites are measured, of which m metabolites belonging to the same pathway are selected. The question whether these metabolites behave differently in the two conditions being compared can be translated into the question whether the metabolite levels are predictive for the outcome  testing (e.g. Bonferroni). The Globaltest detects consistent differences in patterns of metabolite levels between two conditions. It does not test in which direction a pathway is regulated (up or down), nor it determines how many metabolites have changed concentration levels between two conditions. If the tested pathway is activated or inhibited by the tested condition , the differences in metabolite levels will result in a large Q-score and a small P-value. However, the results may change, depending on which metabolites are included, i.e. on the completeness of the database(s) from which prior knowledge has been obtained. If the correlation of the missing metabolite(s) with the outcome is almost equal to the average correlation between the outcome and the metabolites included in the pathway, this has almost no effect on the Q-score. Instead, if a metabolite that has a much higher or lower correlation to the outcome than average is missing then the Q-score will change upon its inclusion. This is an aspect inevitably intrinsic to the MSEA strategy. Databases contain metabolites from only a limited number of pathways, compared to the whole metabolic network of an organism. Consequently, it is possible to test only a relatively small number of pathways and this is an inherent limitation of MSEA.Different strategies exist for performing MSEA depending, among others, on the statistical test applied. In the popular ome Fig. . In othen et al.  dealt win et al. , which iF-test is applied to test the null hypothesis and a P-value is computed using permutations. A correction for multiple testing is also used. Differently from the Globaltest, GlobalANCOVA evaluates the impact of group membership on the observed metabolite concentration patterns. In other words, GlobalANCOVA practically tests the null hypothesis that the information on the group level does not improve the fitting. The GlobalANCOVA approach allows the inclusion of time-dependent information in a straightforward manner constructed . GlobalANCOVA exploits linear logistic regression and Analysis of Variance (ANOVA) in the framework of a global assessment for a group of metabolites. GlobalANCOVA aims to evaluate the relationship between the metabolite concentrations and the phenotypic covariates. In particular, the aim of GlobalANCOVA is to prove the relevance of certain covariates in explaining the observed metabolite concentration patterns, called covariates of interest. Therefore, two models are compared: the full model (FM), which contains all covariates and the reduced model (RM), which does not have the covariates of interest. The null hypothesis is that both models explain the data equally well. The relevance of the covariates of interest in explaining the observed pattern is proven if the full model explains the observation better than the reduced model. To do so, a squared error is computed for the fitting of the concentration levels of each metabolite. Subsequently, the residual sum of squares (RSS) over all metabolites in the group is computed. Finally, a multivariate test statistic is built based on the RSS values for the full and reduced models  terms are often used for this purpose. The annotation terms passing the enrichment P-values). The analysis focuses on whether common terms tend to occur towards the top or the bottom of the list , and this can make difficult to grasp potential interrelationships of relevant terms. In addition, the quality of the pre-selected metabolite lists has a deep influence on the enrichment analysis, making the output unpredictably sensitive to changing statistical methods or cutoff thresholds. In particular, it is inappropriate to use all the metabolites of the metabolite set library as the reference metabolome, because there is no analytical platform that can measure all these metabolites with the same probability. Thus, the choice of the platform rather than the experimental conditions may cause the observed metabolite enrichment. To tackle this problem, the user may upload a platform-specific reference metabolome. This is an option provided, for example, in the implementation available in MetaboAnalyst  (Hulsegge et al. t-test or by ANOVA. PAPi can provide information regarding the impact of environmental conditions and stimuli on metabolite uptake and intracellular metabolic overflow. Metabolic pathway activity is directly related to metabolic flux distribution and thus this kind of analysis can tie directly to fluxomics.PAPi allows users to compare the activity of metabolic pathways under different experimental conditions (Aggio et al. The systems biology approach to the interpretation of metabolomics has the potential to unravel the causative mechanisms leading to the observed metabolomics profile. In this way, there is a paradigm shift from the chemometrics framework that makes metabolomics a hypothesis-generating research field to a framework where metabolomics can provide insights into the biological properties of cell and organism functioning. This shift will unlock the potential of metabolomics and related omics disciplines, such as fluxomics and lipidomics, to fully contribute to the advancement of our understanding of health and disease. In this review, we addressed approaches based on association networks and on pathway analysis. These are useful tools to grasp the complexity of metabolomic profiles; however, they are not sufficient to understand fully the intricacies of the metabolism without dedicated experiments.Many of the methods described here exploit the lessons learned in other, more mature omics, mainly genomics and transcriptomics, e.g. regarding the validation of their theoretical frameworks. As mentioned several times, a major caveat in untargeted metabolomics is the impossibility of measuring all metabolites in the sample, whose consequences are very difficult to predict.


Paulinella chromatophora by in silico modeling. We asked whether genome reduction is driven by metabolic engineering strategies resulted from the interaction with the host. As its widely known, the loss of enzyme coding genes leads to metabolic network restructuring sometimes improving the production rates. In this case, the production rate of reduced-carbon in the metabolism of the chromatophore.Genome degradation of host-restricted mutualistic endosymbionts has been attributed to inactivating mutations and genetic drift while genes coding for host-relevant functions are conserved by purifying selection. Unlike their free-living relatives, the metabolism of mutualistic endosymbionts and endosymbiont-originated organelles is specialized in the production of metabolites which are released to the host. This specialization suggests that natural selection crafted these metabolic adaptations. In this work, we analyzed the evolution of the metabolism of the chromatophore of P. chromatophora CCAC 0185 and a close free-living relative, the cyanobacterium Synechococcus sp. WH 5701. We found that the evolution of free-living to host-restricted lifestyle rendered a fragile metabolic network where >80% of genes in the chromatophore are essential for metabolic functionality. Despite the lack of experimental information, the metabolic reconstruction of the chromatophore suggests that the host provides several metabolites to the endosymbiont. By using these metabolites as intracellular conditions, in silico simulations of genome evolution by gene lose recover with 77% accuracy the actual metabolic gene content of the chromatophore. Also, the metabolic model of the chromatophore allowed us to predict by flux balance analysis a maximum rate of reduced-carbon released by the endosymbiont to the host. By inspecting the central metabolism of the chromatophore and the free-living cyanobacteria we found that by improvements in the gluconeogenic pathway the metabolism of the endosymbiont uses more efficiently the carbon source for reduced-carbon production. In addition, our in silico simulations of the evolutionary process leading to the reduced metabolic network of the chromatophore showed that the predicted rate of released reduced-carbon is obtained in less than 5% of the times under a process guided by random gene deletion and genetic drift. We interpret previous findings as evidence that natural selection at holobiont level shaped the rate at which reduced-carbon is exported to the host. Finally, our model also predicts that the ABC phosphate transporter (pstSACB) which is conserved in the genome of the chromatophore of P. chromatophora strain CCAC 0185 is a necessary component to release reduced-carbon molecules to the host.We reconstructed the metabolic networks of the chromatophore of Paulinella chromatophora natural selection at the holobiont level played a prominent role in shaping the metabolic specialization of the chromatophore. We propose that natural selection acted as a “metabolic engineer” by favoring metabolic restructurings that led to an increased release of reduced-carbon to the host.Our evolutionary analysis suggests that in the case of The online version of this article (doi:10.1186/s12862-017-0947-6) contains supplementary material, which is available to authorized users. Paulinella chromatophora is an amoeba dispensed with phototrophic nutrition that contains blue-green photosynthetic organelles of cyanobacterial origin termed chromatophores [Paulinella that have been described [tophores , 2. Thesescribed  and wereescribed .P. chromatophora , revealChromatophores are genetically integrated with their host. More than 30 nuclear encoded genes of chromatophore origin have been identified , 8. And P. chromatophora nutrition relies on the reduced-carbon photosynthetically assimilated by the chromatophore [Buchnera aphidicola [B. aphidicola revealed a high degree of genetic degradation, while genes necessary for the syntrophic relationship with its host have been retained [atophore . This enatophore . Particuhidicola . Sequencretained .Ne) and a concomitant relaxation of natural selection [Prokaryotic endosymbionts evolve small genomes mainly by the combined action of genetic drift and negative selection –16. In helection –17. The election , 17. As election .Something that should be considered is that, differing from free-living relatives, the metabolism of mutualistic endosymbionts is specialized in the production of metabolites that are released to their host as nutrients , 20. ThiDuring early stages of organellogenesis, the cyanobacteria that evolved into the chromatophore, had access to metabolites provided by the host. It is likely that the availability these metabolites render of some metabolic routes dispensable in the endosymbiont. The loss of these biosynthetic pathways in the endosymbiont led to restructurings and changes in the remaining metabolic fluxes. Taking into consideration all these modifications experienced by the chromatophore and the nutrient dependency of the holobiont for the photosynthetic function of the chromatophore, we made the analogy of natural selection acting as a “metabolic engineer” directing the strategies for the metabolic specialization of the chromatophore. In general, the objective of metabolic engineering is the directed improvement of metabolic capabilities through the deletion of metabolic genes or the introduction of new ones . By usinPaulinella chromatophora and the cyanobacteria Synechococcus sp. WH 5701. We inquired into the metabolic capabilities of the chromatophore; the possible metabolic interaction of the chromatophore with its host; and in silico simulate the process of metabolic evolution experienced by the chromatophore in host-restricted conditions.In this work, we reconstructed the genome based metabolic models of the chromatophore of Synechococcus sp. WH 5701. We compared against Synechococcus sp. WH 5701 because is the closest free-living cyanobacterium with a sequenced genome and it is likely to be similar in gene content to the ancestor of the chromatophore. To assess the statistical significance we used a hypergeometric distribution.Our first objective was to determine to what extent genetic loss affected functional metabolic categories in the chromatophore  when compared to the genome of As is shown in Fig. P. chromatophora CCAC 0185 [Synechococcus sp. WH 5701, the closest free-living cyanobacterium with a sequenced genome. The rationale behind this is to use Synechococcus sp. WH 5701 as a proxy of the ancestral cyanobacterium that evolved into the chromatophore.To better understand the role in the symbiosis played by remaining genes in the chromatophore, we reconstructed two metabolic models. One for the chromatophore of Synechococcus sp. WH 5701 was done by identifying orthologs to those protein-coding genes reported in the metabolic model of Synechocystis sp. PCC 6803 (iJN678) [iCV498) comprised 743 metabolic reactions with 698 metabolites and 498 protein-coding genes. Metabolic model reconstruction of the chromatophore was done by identifying those genes in the genome of the chromatophore of P. chromatophora CCAC 0185 that are orthologous to the free-living metabolic model (iCV498). The metabolic model of the chromatophore (iCV265) comprised 627 reactions, 615 metabolites and 265 protein-coding genes. Because Synechococcus sp. WH 5701 is a close free-living relative of the chromatophore, it could be considered that 158 reactions were lost along genome reduction in the chromatophore (Table Metabolic model reconstruction of the free-living cyanobacterium (iJN678) . The resSynechocystis sp. PCC 6803 [iCV498 and the iCV265 metabolic models with Flux Balance Analysis (FBA). Biomass production was set as objective function. In silico growth was simulated under autotrophic conditions. CO2 and photons uptake were set to 3.7 mmol × gDW−1 × h−1 and 100 mmol × gDW−1 × h−1 respectively and set as constraining metabolites as in [By using the biomass equation of the cyanobacterium PCC 6803 , we testiCV498, almost every metabolic pathway for biomass production is complete. The exceptions were 9 reactions for which no orthologous exist in Synechococcus sp. WH 5701 when compared to iJN678  did not show in silico growth. This was obviously due to the reduced metabolic capabilities caused by the genomic reduction process experimented by the photosynthetic endosymbiont. Genome reduction has affected the metabolic capabilities of the chromatophore in two ways: a) some biosynthetic pathways were completely lost; while b) some other were partially lost.Under these conditions, the metabolic model of the chromatophore (Synechocystis sp. PCC 6803 riboflavin is synthesized by four genes that perform six reactions by using Guanosine 5′-triphosphate (G5P) and D-Ribulose 5-phosphate (R5P) as precursors metabolites [For example, in abolites . All theiCV265 in Additional file Some other biosynthetic pathways are truncated in the chromatophore because single gene coding enzymes were lost. For example, in the biosynthetic pathway of leucine, most gene coding enzymes are present in the chromatophore except for the gene coding for 3-isopropylmalate dehydrogenase. In this case, we assumed that host encoded enzymes complement the pathway in the endosymbiont. Either by importing host encoded enzymes to the chromatophore or by exchanging intermediated metabolites between the symbionts. Similar situations have been proposed for other host-endosymbiont systems . For thiiCV265 for which no orthologous genes exist with the free-living model iCV498 but are essential for in silico growth were assumed to be present  after a genetic deletion. Under phototrophic conditions, model iCV498 showed 333 genes (66.86%) to be essential because its deletion decreases the biomass production over 99%  and fumarase. Of these three, the last enzyme is the only one decreasing the in silico growth rate in iJN678 when it is deleted (data not shown). In the iCV265 model, all these 3 genes were lost. In addition, the non-essential gene in iCV498 coding for an enzyme with arginase activity is the only one whose deletion decreases the growth rate in the iCV265 chromatophore model. This suggests that genome reduction leading to iCV265 caused metabolic restructuring because deletion of this enzyme with arginase activity in iCV498 has no effect.Interestingly, we found that there are 3 non-essential genes in the metabolic model Synechococcus sp. WH 5701, we simulated in silico the gene loss. We evaluated the impact of intracellular conditions (metabolite availability) on the evolution of the chromatophore. In particular, we asked whether the set of metabolites predicted to be provided by the host in the iCV265 model   and assigned as available nutrients under intracellular conditions , 52.2% are strictly conserved in the 500 simulations and 26.7% are dispensable in all of them. In Randomized Nutrients simulations, reduced networks have an average size of 326.8 (± 5.26) genes and 54% and 16.2% are conserved and dispensable in the 500 simulations, respectively.Simulations under the Proposed Nutrients conditions resulted in reduced metabolic networks with 295.1 (± 2.63) genes on average. In these reduced networks, of the 498 genes present in the free-living ancestor . Surprisingly, the most extreme case is that of the genes participating in photosynthetic activity. In Proposed Nutrients 77.6%, 18.3% and 4.1% are predicted to be essential, variable and dispensable, respectively. While for Randomized Nutrients none was predicted to be essential nor dispensable because 100% of them were variable.As is shown in Fig. real chromatophore model (iCV265). This was done by measuring sensitivity and specificity as in [iCV265 is present in more simulations than a gene which has been lost.Genetic concordance was evaluated between these simulated minimal networks and the ty as in . In Fig.del iCV26. This wap-value < 0.001, Chi-square test of homogeneity).The area under the curve shows the contribution of the nutrients available in intracellular conditions explaining the evolutionary history experimented by the chromatophore. Accordingly, the accuracy obtained under the Proposed Nutrients condition was 77.4%, while that of the Randomized Nutrients was 59.8%. The difference between the areas under the curve from both conditions is statistically significant  , 32.−1 which, as stated previously, is the growth rate of Synechocystis sp. PCC 6803 metabolic model under autotrophic growth conditions [To study how the potential rate of carbon exportation evolved, a hexose export reaction was added to the metabolic models. This reaction was defined as objective function. To ensure biomass components production, the biomass reaction was fixed to 0.0884 hnditions .iCV498 model. However, in the iCV265 chromatophore model, the potential rate of hexose exported without affecting the in silico growth rate was 0.2689 mmol × gDW−1 × h−1. In Fig. Under these conditions, there is no exportation of reduced-carbon in the Fluxes calculated for production of metabolites precursors used to produce biomass components are produced in less quantity in the chromatophore’s model Fig. . This isSaccharomyces cerevisiae compared to Zymomonas mobilis, this was the result of the specialization of the microorganism to produce specific metabolites [2, ATP and NADPH) required to produce hexose  could have been possible under a random model of evolution or were the consequence of natural selection for metabolic specialization of the chromatophore and its positive impact at the holobiont. To tests this, we simulated the metabolic reduction process with hexose export and biomass production as evolutionary restrictions in a random model where hexose exported must be greater than zero. It means that every gene affecting the in silico growth rate of 0.0884 h−1 and impairing hexose export was considered as essential, while the hexose export rate could always vary while being greater than zero (purifying selection restriction for hexose export).We then inquired about the evolutionary forces that determined genetic conservation and metabolic functionality in chromatophores. Specifically, we wanted to infer if these metabolic capabilities of the iCV265 are conserved in all the simulations. Conversely, there are 45 genes predicted to be essential in all these 500 simulations which are not conserved in model iCV265. The metabolic networks from these 500 simulations are different in gene content and show different hexose export rates however they are equivalent in biomass production  of the genes conserved in model % of the −1 × h−1). This suggests that the probability of obtaining a potential rate of hexose exported similar to that of iCV265 under a random model is less than 5%. We got a similar result by varying the growth rate constraint of 0.0884 h−1 under plausible biological values . This shows that the small percentage of simulations (2.6%) showing a potential rate of hexose exported equal or greater than that of the chromatophore (0.2689 mmol × gDW−1 × h−1) is not due simply to smaller size of the simulated metabolic networks.Although variable, our simulations evolved metabolic networks that have approximately the same number of reactions than iCV265 is unlikely to be the outcome of only genetic drift and purifying selection . This suggests that the potential rate of hexose exported was the result of a process of functional specialization in which the increasing rate of hexose exportation was favored by natural selection due to the positive impact at the holobiont level.These in silico experiments suggest that the potential rate of hexose exported in model iCV265). Conservation of this phosphate transporter allows the simulated network to get the phosphate necessary to be able to export fixed carbon. Without this transporter most of fixed carbon is oxidized in the pentose phosphate pathway releasing only a small amount to the extracellular compartment (data not shown).Interestingly all these 2.6% of in silico evolved chromatophores have in common the conservation of a phosphate transporter via ABC system which is also present in the chromatophore model  or impaired hexose exportation, we considered this gene as essential and we restored it to the model. In this way we analyzed the selective impact caused by gene loss due to the addition of a single transporter and the concomitant relaxation of natural selection for retention of specific biosynthetic pathways. Once we analyzed every gene in the model, we randomly assigned a second transport and then we repeated the gene loss simulation mentioned above. Simulation stops when in silico chromatophore has access to the 13 nutrients  is less than 5% in 500 simulations, in agreement with our previous result .iCV265. The evolutionary landscape becomes smaller as evolution proceeds.These changes in frequency distributions during the process of metabolic integration can be interpreted in terms of the functional specialization of the chromatophore. As metabolic integration proceeds (with the addition of more transporters), the chromatophore increased its capacity to provide fixed carbon to its host. However, continued gene loss led to a simplified metabolic network and a smaller fraction of in silico evolved chromatophores can export as much fixed carbon as iCV265) is highly dependent on phosphate consumption. In addition, the growth rate of the chromatophore is coupled to the host’s growth rate. As shown above, the potential rate of hexose exported is unlikely to be the outcome of a random evolution.As shown above, the potential rate of hexose exported in the chromatophore model . This contrasts with the chromatophore model (iCV265) where, for a given growth rate, only a specific consumption of phosphate is necessary to sustain hexose release. In addition, the capacity of hexose export in the iCV265 model for a determined growth rate restriction is greater than the iCV498 model.To test the impact of these two restrictions, we analyzed the metabolic properties of models P. chromatophora is different to the metabolic network of its free-living relative Synechococcus sp. WH 5701. We suggest that these differences evolved by natural selection. Gene-loss and carbon flux redirection guided by natural selection led to metabolic specialization of the chromatophore as a reduced-carbon provider.In this work, we show that the metabolic network of the chromatophore of Buchnera aphidicola [Our analysis showed that some metabolic pathways have been preferentially conserved in the chromatophore Fig. . These phidicola . In thishidicola , 35 are P. chromatophora  showed CAC 0185 .It has been suggested that host-level selection prevents the fixation of deleterious mutations in endosymbionts thus lowering the chances of a mutational meltdown resulting in extinction , 37. AndSynechococcus sp. WH 5701 allowed us to inquire into the evolution of the metabolic interaction of the chromatophores with its host. For example, several metabolic pathways in the chromatophore are incomplete. It is likely that the host supplies these metabolites as nutrients to the chromatophore. Metabolic pathway sharing is a hallmark of endosymbiotic organisms. For example Wolbachia, which are endosymbionts of many animal species, show a degraded genome [Comparison of the metabolic models of the chromatophore and the cyanobacterium d genome , 41 whosd genome . In turnd genome . Equallyd genome –46.Escherichia coli were used for metabolic analysis of Buchnera aphidicola [Sodalis glossinidius [Blattabacterium cuenoti [Synechocystis sp. PCC 6803 as a starting point to model the metabolism of the chromatophore and Synechococcus sp. WH 5701 [To study the metabolic capabilities of the chromatophores we used FBA. This stoichiometric approach can predict cellular phenotypes in specific environmental conditions. Generally, biomass production is fixed as objective function. In absence of biomass composition, the use of a biomass equation from a related organism is a valid starting point for metabolic analysis –49. In thidicola , 30, Sodsinidius , and Bla cuenoti . In the  WH 5701 .Synechococcus sp. WH 5701. A similar difference in metabolic fragility was found by [B. aphidicola and its free-living relative Escherichia coli where 84% and 19% of genes were essential, respectively. In the same way, the metabolic network of two strains of Blattabacterium cuenoti (Bge and Pam), the obligated primary endosymbiont of cockroaches, were shown to be highly fragile to single gene deletion. It was found that 76.1% and 79.6% were essential genes, respectively [Sodalis glossinidius (the secondary non-obligated endosymbiont in early stages of tsetse flies), 44.54% metabolic genes were found to be essential, compared with its ancestral network where only 25.48% are predicted to be essential [We found that the metabolism of the chromatophore is highly fragile to gene deletions. Approximately 84% of the genes in the model are essential when singly deleted in comparison with ~67% of the genes in found by  when comectively . Finallyssential .iCV265 and the iCV498 models agree with the generalization that metabolic networks of endosymbionts are more fragile than their free-living counterparts. This metabolic fragility of endosymbionts contrasts with theoretical estimations that suggest that, in general, metabolic systems are robust and complex [Synechococcus sp. WH 5701 reflects the transition from a free-living style to a more stable condition inside Paulinella chromatophora.Our robustness analyses of the  complex . However complex  than min complex . The difSynechococcus sp. WH 5701 as a proxy of the ancestor of the chromatophore, we evaluated the impact of the host-metabolic environment in the reduction of the metabolic system of the endosymbiont. The proposed host-metabolic environment (Proposed Nutrients) predicted with 77.42% of accuracy the actual gene content of the chromatophore. This is in contrast with the 59.8% of accuracy obtained when using a randomly set of host-provided metabolites (Randomized Nutrients). This emphasizes the contribution of the intracellular metabolic environment to the evolution of the metabolism in the chromatophore.It has been shown that retention of metabolic genes in endosymbionts is determined by the metabolic requirements and molecular environment of the host , 53. WitE. coli as free-living ancestor predicts with 80% of accuracy the metabolic gene content of B. aphidicola and Wigglesworthia glossindia [Similar reductive simulations have been used to predict the set of essential genes of pathogens located in certain environmental niches (like the bloodstream) within the human body . In the ossindia .raison d’être of the symbiosis) 100% of genes are predicted as “variable” (none of the genes are predicted to be retained in the 500 simulations) while in Proposed Nutrients ~78% are essential. This means that under Randomized Nutrients, photosynthesis function could be useful but not essential and could have been lost in the chromatophore by chance. Clearly, the set of metabolites comprising Randomized Nutrients cannot account for the metabolic gene content of extant chromatophores.Inspection of the proportion of dispensable, variable, and essential genes by in silico reductive simulations (i.e. Proposed Nutrients and Randomized Nutrients) predicts differential gene retention patterns between different metabolic pathways. For example, in Randomized Nutrients simulations, photosynthesis pathway (which is the Chlorella (the photosynthetic endosymbiont of Paramecium bursaria), releases 57% of its photosynthates to its host [Chlorella itself. In the same way, P. chromatophora has phototrophic nutrition. It depends on carbon assimilates which derivate from the endosymbiotic cyanobacterium whose inorganic carbon rate assimilation is the same as a free-living cyanobacteria [P. chromatophora. Considering the above metabolic analysis of the chromatophore, which predict an in silico growth rate of 0.1568 h−1, it is difficult to consider the biomass as the only objective function in chromatophores. Taking into consideration that chromatophores provide the host with reduced-carbon, a reaction simulating hexose export to extracellular compartment was added. This reaction was defined as objective function. And to ensure biomass components production, biomass reaction was fixed to 0.0884 h−1 which is the growth rate of a free-living relative cyanobacterium. Interestingly, under these conditions the metabolic model of the chromatophore predicts a potential rate of hexose exportation of 0.2689 mmol × gDW−1 × h−1. As far as we know, this is the first metabolic reductive evolutionary analysis where metabolic functionality (i.e. hexose export) of the endosymbiont is explored as objective function, differing from previous analyses where biomass is set as objective function of mutual endosymbionts as B. aphidicola [S. glossinidius [B. cuenoti [Maximization of biomass production is regularly used as objective function in FBA analysis. It allows predicting the distribution of fluxes through a metabolic network . The maxits host . This mebacteria . But unlhidicola , 30, S. sinidius  and B. c cuenoti .iCV265) share the ABC phosphate transporter. This Pi-dependency in the chromatophore agrees with that observed in isolated spinach chloroplasts [i in the reaction medium. Also, carbon export from the chloroplast is inhibited [14C fixed being retained in the chloroplast [Paulinella FK01 is compensated by a phosphate transporter coded in the host [All simulations showing a hexose exportation rate equivalent to that of the chromatophore model  first, we simulated host-level purifying selection by requiring that the rate of hexose exportation of the model must be always greater than 0 and biomass is produced at 0.0884 mmol × gDW−1 × h−1; b) next, we performed rounds of single gene deletion until no more genes could be deleted; c) finally, we repeated this process 500 times. By this, we obtained a population of 500 reduced metabolic networks all of them capable of producing 0.0884 mmol × gDW−1 × h−1 of biomass, but differing in hexose rate exportation. Differences in rates of hexose exportation were due to contingency-dependent loss of alternative pathways [iCV265 (0.2689 mmol × gDW−1 × h−1) is easily obtained by host-level purifying selection (hexose exportation >0) and contingency-dependent evolution on random gene deletion. Our evolutionary reductive analyses showed that <5% of simulations were predicted to export hexose at a similar rate as the model iCV265. This suggests that metabolic functionality of iCV265 is unlikely to be determined by genetic drift alone. Therefore, we conclude that natural selection at holobiont level may have contributed to shape metabolic functionality of the chromatophore.Inspection of FBA calculated central metabolic fluxes in the chromatophore and in the free-living cyanobacteria showed that the endosymbiont is better at producing hexose. This is likely a host related adaptation. To investigate whether this and other characteristics of the metabolic model of the chromatophore evolved by natural selection, we simulated in silico reductive evolution with a null model not including positive selection. As a proxy of genome reduction by purifying selection and random genetic drift, we submitted the metabolic model of pathways . With thAccording to the above mentioned, we consider suitable to make the analogy of natural selection as metabolic engineer. Metabolic engineering can be defined as “the directed improvement of product formation or cellular properties through the modification of specific biochemical reactions or introduction of new ones” . One of iCV265) seems to be tailored for the exportation of reduced-carbon; that is, when comparing the export of reduced-carbon between the iCV265 and the iCV498 models (with phosphate as restrictive nutrient) we found that iCV265 shows higher rates of hexose exported than the free-living iCV498 model at the cost of increased consumption of phosphate  promote cooperation between symbionts. PFF requires individuals to be “associated for an extended series of exchanges that last long enough that a feedback operates” . SimilarBuchnera  and Blocchmannia  overprodchmannia . Reductichmannia  showed tOur main objective was to better understand the metabolic changes experienced by the free-living cyanobacteria to become a chromatophore. In addition, we assessed the evolutionary forces driving organellogenesis. We found evidence that certain metabolic pathways are preferentially conserved in the chromatophore. We also found that the pattern of metabolic gene loss strongly depends on the availability of nutrients from its host. The high fragility of the chromatophore network reflects the transition to a more stable environment and, consequently, its simplification. The chromatophore is specialized in producing reduced-carbon which could be released to the host. This specialization was consequence of metabolic restructurings which could not be possible in free-living conditions. We interpret this specialization as consequence of natural selection acting as a metabolic engineer which modifies intrinsic metabolic properties of the endosymbiont impacting positively at the holobiont level. Our in silico simulations allowed us to determine that metabolic specialization of the chromatophore is an unlikely result of purifying host-level selection and genetic drift alone. In this way, computational analysis of biological systems allows to obtain new insights on the evolutionary forces shaping metabolic evolution of mutualistic endosymbionts.Paulinella chromatophora CCAC 0185 [Synechococcus sp. WH 5701) according to the functional categories of KEGG orthology (http://www.genome.jp/kegg/ko.html). Then we carried out the statistical analysis with GeneMerge. GeneMerge is a program written in Perl which allows the identification of overrepresented functions or categories in a sample by using a hypergeometric distribution [To identify metabolic pathways preferentially conserved in the chromatophore we carried out a statistical analysis using the program GeneMerge . First, ribution .Synechococcus sp. WH 5701 and the metabolic model of Synechocystis sp. PCC 6803 (iJN678) [A draft metabolic model was initially reconstructed by identifying orthologous genes between (iJN678) . Because(iJN678) .Synechococcus sp. WH 5701. Synechococcus sp. WH 5701 is the closest free-living relative of the chromatophore with a sequenced genome [The metabolic network of the endosymbiont was reconstructed by identifying orthologs between the chromatophore and The metabolic capabilities of both organisms were tested with Flux Balance Analysis . FBA is Synechocystis sp. PCC 6803 was assigned as objective function in both models. In silico growth was simulated under autotrophic conditions with CO2 and photons uptake set to 3.7 mmol × gDW−1 × h−1 and 100 mmol × gDW−1 × h−1, respectively. These were restrictive metabolites in the systems. Nutrient assignment for metabolic functionality of the chromatophore was based on the literature [The functionality of metabolic models is evaluated by their capacity to produce every metabolite that is necessary for in silico growth. For this, the biomass equation of terature  and metaIn both models, robustness to gene deletions was analyzed by using the function singleGeneDeletion of the COBRA toolbox. If deletion of a single gene decreases the biomass production over 99%, compared with wild type, this gene was consider as essential for biomass production.Synechococcus sp. WH 5701 (iCV498) as a proxy of the free-living ancestor of the chromatophore  and then evaluating the impact of this deletion in the metabolic functionality by using FBA. If in silico growth rate in this network (lacking a gene) was equal to or above the growth rate of a free-living cyanobacteria (≥ 0.0884 h−1), then this gene was considered as non-essential and permanently removed. In contrast, if the growth rate was below 0.0884 h−1 then this gene was considered as essential and retained in the model. This process was repeated until each of the genes in the model was evaluated. The whole process is initiated 500 times which results in a population of 500 reduced metabolic networks.To simulate genome reduction, we used the metabolic model of ore Fig. . GeneticiCV265) was analyzed as in [iCV498 depending on whether the gene is predicted to be conserved or not among the 500 simulations. This allowed us to determine the number of occurrences that a gene is predicted as essential in the 500 simulated reduced networks.Genetic concordance between the 500 reduced metabolic networks and chromatophore model (ed as in . In eachiCV265) and false-positives (fraction of genes predicted by the simulations and not present in iCV265) for every cutoff . Figure iCV265. The area under the curve was empirically calculated as in [Measures of sensitivity and specificity were obtained calculating the fraction of true-positives  and the hexose export. Specifically, the rate of hexose export could vary while being always greater than zero. Otherwise, the gene was defined as essential.We performed this analysis by using the same algorithm used in the simulation of reductive evolution. However, this analysis was performed only in Proposed Nutrient conditions Fig. . In addiiCV498 has access to all 13 nutrients and all genes have been tested for essentiality.In this analysis the model does not have access to all 13 nutrients at the same time from the beginning of the simulation. Instead, we randomly allow the model to have access to one of the 13 Proposed Nutrients Fig.  and subsAdditional file 1:Synechococcus sp. WH 5701 (iCV498). (XLSX 187 kb)Metabolic model of Additional file 2:iCV265). (XLSX 121 kb)Metabolic model of the chromatophore


Saccharomyces cerevisiae for the production of succinic acid has progressed dramatically, and a series of high-producing hosts are available. At low cultivation pH and high titers, the product transport can become bidirectional, i.e. the acid is reentering the cell and is again exported or even catabolized. Here, a quantitative approach for the identification of product recycling fluxes is developed.The metabolic engineering of 13C labeled succinic acid was added to the extracellular space and intracellular enrichments were measured and subsequently used for the estimation of metabolic fluxes. The labeling was introduced by a labeling switch experiment, leading to an immediate labeling of about 85% of the acid while keeping the total acid concentration constant. Within 100 s significant labeling enrichment of the TCA cycle intermediates fumarate, iso-citrate and α-ketoglutarate was observed, while no labeling was detected for malate and citrate. These findings suggest that succinic acid is rapidly exchanged over the cellular membrane and enters the oxidative TCA cycle. Remarkably, in the oxidative direction malate 13C enrichment was not detected, indicating that there is no flux going through this metabolite pool. Using flux modeling and thermodynamic assumptions on compartmentation it was concluded that malate must be predominantly cytosolic while fumarate and iso-citrate were more dominant in the mitochondria.The metabolic flux distributions at two time-points of the fermentation process were analyzed. Adding labeled product without changing the extracellular environment allowed to quantify intracellular metabolic fluxes under high producing conditions and identify product degradation cycles. In the specific case of succinic acid production, compartmentation was found to play a major role, i.e. the presence of metabolic activity in two different cellular compartments lead to intracellular product degradation reducing the yield. We also observed that the flux from glucose to succinic acid branches at two points in metabolism: (1) At the level of pyruvate, and (2) at cytosolic malate which was not expected.The online version of this article (doi:10.1186/s12934-017-0702-0) contains supplementary material, which is available to authorized users. For several products, current microbial hosts can reach very high product titers. Examples are succinic acid, with final product concentrations reaching above 100 g/L , lactic E. coli [E. coli is sensitive to bacteriophage infections [Metabolic engineering for succinic acid production has focused on the expression of the reductive TCA cycle, glyoxylate cycle, and succinic acid exporters in different microbial hosts . E. coli –10, an ofections  and needS. cerevisiae [Yarrowia lipolytica [Candida strains [To circumvent the disadvantages of carrying out the fermentation at high pH, companies and research groups have engineered eukaryotic hosts like revisiae , Yarrowipolytica , or Cand strains . These s strains , 15.In this study, we analyze the metabolic flux distribution and putative losses due to transport as well as intracellular cycles under production conditions (fed-batch). Especially, labeled product (succinic acid) is used as tracer and modeling approaches are applied to quantify product degradation and recycling. Identification of product recycling and intracellular cycles, including putative parallel routes in different cellular compartments can further improve metabolic engineering strategies and identify relevant product and energy losses.S. cerevisiae used in this study was derived from DSM strain SUC-632, which is described in patent WO2013/004670 [E. coli fumarase B . The expression of the fumB gene was controlled by the native TDH1 promoter and the TDH1 terminator. The synthetic expression cassette including appropriate restriction sites was synthesized by GenArt . This synthetic fragment was cloned in a vector containing a KanMX marker which allows for selection for growth in the presence of G418. The KanMX marker, flanked by lox66 and lox71 sites [The succinic acid producing strain of 3/004670 , and was71 sites , were re71 sites . The fumTransformants were selected on yeast extract bacto peptone (YEP) 2% galactose plates supplemented with 200 µg G418/mL for selection of transformants containing the KanMX marker, yielding multiple transformants. Presence of the introduced fumB gene was confirmed by PCR.2PO4, 0.5 MgSO4·7H2O, trace element solution 1 g/kg  and 1 g/kg vitamin solution . Additionally, 3 mL of chalk solution (0.1 g/g CaCO3) were added to a total of 300 g of seed pre-culture medium. The pre-culture was finished after 3 days at 30 °C and 220 rpm reaching a final optical density (600 nm) of about 7. All chemicals were obtained from Sigma-Aldrich, with the exception of biotin, pyridoxol hydrochloride, and pantothenate which were obtained from Merck, thiamine from VWR International BV and EDTA was purchased from Roche.The seed-culture was prepared in a shake flask starting from 1.2 mL of glycerol stocks (30%) stored at −80 °C. The medium composition for the pre-culture was : carbon source 20 galactose, 2.3 urea, 3.0 KHThe seed cultivation was performed in a 7 L bioreactor  with a maximal working volume of 4 L. The reactor temperature was kept constant at 30 °C; dissolved oxygen tension (DOT) was monitored online using an oxygen probe . The DO was controlled at a minimum of 20% air saturation via automatic adjustment of the stirring speed. The bioreactor had an overpressure of 0.3 bar, and the aeration rate was 1 vvm (sterile air).4)2SO4, 10 KH2PO4, 5 MgSO4·7H2O, 8 µL trace element stock solution and 8 µL vitamin stock solution (see pre-culture medium). After depletion of the initial carbon source (detected by a decrease in CO2), the feed phase (fedbatch) was started. The feed medium contained 500 g/L glucose and was controlled at an exponential feeding rate of 0.2·e0.1·t (g/kg/h) with a maximum feed rate of 9.2 g/kg/h. After about 48 h, a broth weight of 3.69 kg was reached with a final biomass amount of 53.3 gCDW.The cultivation was started by transferring 78 mL of the seed pre-culture (OD 7) into the bioreactor containing 2.4 L of fermentation medium at pH 5. The medium contained : 2 glucose, 1 (NH3) and 2.56 kg of the production fermentation medium. The medium contained (in g/kg): 1 urea, 1.5 KH2PO4, 0.5 MgSO4·7H2O, 5 iron stock solution  and 0.001 biotin. This production fermentation phase was running for more than 90 h, at 30 °C, gassed with a mixture of 1:1 air:CO2 at a flow of 0.3 vvm. During the production phase the DO was controlled at 10% air saturation. The cultivation feeding rate was initially set to a constant feeding rate of 9.74 g/h of a 500 g/L glucose solution and was manually decreased at predefined time points , and at 76 h (100 mL).The biomass from the seed cultivation was diluted to OD 15 – this was achieved by transfer of 285 mL from the seed fermentation, addition of 200 mL of chalk solution  [Samples for intracellular metabolite quantification were withdrawn using a dedicated rapid sampling device (for bioreactor samples) and quenched in 6 mL −40 °C pure methanol . From tht 95 °C) .Samples from the BioScope were collected for about 2 min (~1.4 mL) at each port, weighted and the complete volume was processed by filtration and extraction as in the case of a bioreactor sample.13C cell extract as internal standard and processed comparable to intracellular samples.Extracellular samples from the BioScope were obtained by vacuum filtration . A vial A model was developed based on the stoichiometry and atom-transitions listed in Additional file f. The variables f_cyt_Succ, f_cyt_Mal, etc. reflect the (amount) fraction present in the cytosol. All intracellular rates are described as rate per amount of biomass dry weight (gCDW) and pools are balanced as amounts per biomass (µmol/g).Compartmentation was taken into account by the introduction of a cytosolic and a mitochondrial metabolite pool (index _cyt and _mit). The simulation will predict separate enrichment patterns. The concentration of each pool depends on its distribution The network consists of 31 balanced metabolites, and 39 net- and 24 exchange fluxes  an extracellular succinic acid concentration of 490 mmol/L was reached, and 590 mmol/L at t = 73 h. These concentrations were used for the bioscope feed medium. For the estimation of intracellular fluxes, (quasi) steady-state was assumed during the labeling experiment. Please note that the concentration as well as the production flux of succinic acid changed less than 2% during the labeling experiment (1.5 h).Earlier findings suggested that there is a high bidirectional flux of succinic acid over the cellular membrane . This obThe distribution of intracellular TCA cycle intermediates between the cytosol and mitochondria cannot be determined by direct measurements. Citrate (Cit), isocitrate (iCit), α-ketoglutarate (aKG), succinate (Succ), malate , fumarate (Fum) and oxaloacetate (Oaa) are present in the mitochondria as well as in the cytosol. The distribution can significantly influence the metabolic engineering strategy . TranspoAssuming that these transporters operate close to equilibrium, the distribution of all TCA cycle intermediates can be calculated  after about 180 s , this indicates a very rapid exchange between intra- and extracellular succinic acid pools. A fast increase in 0 s Fig. . AdditioKeq) of about 4 (for the oxidative direction). To obtain malate from fumarate, the ratio Mal/Fum has to be below 4. The intracellular measurements show values between 150 and 185, for t = 52 h and t = 73 h respectively . In contrast to cytosolic malate, mitochondrial malate will mostly originate from the oxidative TCA cycle, carrying labeling from succinate, but because of the small amount compared to the cytosolic amount, no labeling is observed in the whole cell measurements. In the case of isocitrate, fumarate and α-ketoglutarate there seems to be an accumulation in the mitochondria and therefore 13C enrichment can be observed from the whole-cell measurements.In the cytosol malate is synthesized from glucose, which will have a natural enrichment (1.1% CDW/h),A fast exchange of succinic acid over the plasma- as well as mitochondrial membrane  also renders a putative role of a reverse glyoxylate shunt, because labeling in oxaloacetate could only be obtained from malate. Summarizing, the labeling observations can only be reproduced by a metabolic network that especially features:Specifically, the parameter estimation suggests that malate, succinate and citrate are predominantly ≥95%) cytosolic (in amount). Oxaloacetate, α-ketoglutarate, fumarate and isocitrate are in the range of 40–65% (Table % cytosol2 (this cannot be discriminated). On the one hand, this can be attributed to stoichiometric coupling, i.e. succinate taken from the TCA cycle has to be replenished. On the other hand, the exchange of malate seems reasonable in view of the high intracellular (cytosolic) concentration  and the substrate glucose is used for (by-)product formation and maintenance only. At the pyruvate branch point about 1/3 of the carbon enters the reductive route Fig. . The oxin Tables , 5. The From the model estimation, the labeling originating from the extracellular labeled succinic acid leads to a high labeling in cytosolic succinate, that interchanges with the mitochondrial pool using the Suc/Pi carrier but also Suc/Fum transporter. The predicted labeling of mitochondrial succinate is 50%. For the downstream metabolites Fum, Mal and Oaa lower enrichments are predicted because of the inflow of unlabeled metabolites from the cytosol, especially the exchange with Fum and Mal.13C model is supported, nevertheless, there is an inconsistency for mitochondrial succinate dehydrogenase (the estimated Fum/Suc ratio is too high). The transport reaction directions are consistent with the flux estimation, except the direction of the Fum/Suc shuttle—for Suc and Fum the largest standard deviations were observed.Unfortunately, but also expected, the parameter estimation has large confidence intervals , in relative amounts about 3.4% which is a slight increase compared to the earlier phase  as well as exchange between the cytosol and mitochondria. Extracellular, labeled succinic acid was imported and labeled carbon atoms were found back in intermediates of the TCA cycle. Thermodynamic considerations, especially with respect to the required oxidative direction of fumarase in the mitochondria and reductive direction in the cytosol, suggests that malate is mostly present in the cytosol. The high intracellular concentration leads to excretion of malate to the extracellular space but also transport of malate into the mitochondria. Because there is correlation between the cytosolic/mitochondrial malate ratio and the exchange flux, the flux estimation has a comparably high standard deviation. Nevertheless, the labeling measurements require an exchange of malate between the cytosol and mitochondria.The metabolic flux analysis of high-producing succinic acid yeast revealed a high recycling of the acid product over the cell membrane  ratio was used. Furthermore, the intracellular fluxes were normalized to whole cell amounts .Compartment specific measurements would facilitate and improve the identification of fluxes and ratios. Similar to previous developed sensor reactions for (cytosolic) concentration measurements –32, sensOn the other hand, the labeling approach can overcome the limitation of direct compartment specific measurements to a certain extend and revealed intracellular product recycling. The measurements clearly show that compartmentation can lead to a condition of bidirectional fluxes, including the catabolism of the desired product. The labeling approach can easily be applied to other production processes and used to identify putative product recycling activity especially for other products derived from the TCA cycle or lactic acid production. Performing the labeling experiment in a secondary reactor like the bioscope furthermore reduces the amount of labeled material required and allows for a series of experiments with different substrate(s) labeling(s). The dilution approach allows to reach quasi steady-state conditions while the labeling state is switched, allowing to identify fast intracellular fluxes.


A number of crucial technical considerations for designing experiments and analyzing data with stable isotope labelling were discussed which included replication, instrumentation, methods of labelling, tracer dilution and data analysis. This report reflects the current viewpoint on the use of stable isotope labelling in metabolomics experiments, identifying it as a great tool with the potential to improve biological interpretation of metabolomics data in a number of ways.This is a perspective from the peer session on stable isotope labelling and fluxomics at the Australian & New Zealand Metabolomics Conference (ANZMET) held from 30 March to 1 April 2016 at La Trobe University, Melbourne, Australia. This report summarizes the key points raised in the peer session which focused on the advantages of using stable isotopes in modern metabolomics and the challenges in conducting flux analyses. The session highlighted the utility of stable isotope labelling in generating reference standards for metabolite identification, absolute quantification, and in the measurement of the dynamic activity of metabolic pathways. The advantages and disadvantages of different approaches of fluxomics analyses including flux balance analysis, metabolic flux analysis and kinetic flux profiling were also discussed along with the use of stable isotope labelling in  In the postgenomic era, metabolomics has emerged as a powerful approach for enabling the study of the intricate biochemistry of cells, organisms, and systems in response to different conditions such as stress, disease, or nutrition. This detailed analysis of metabolite profiles can provide functionally relevant biochemical information about biological processes. It forms one of the pillars of systems biology, which advocates the need for a holistic view for understanding biological systems. Metabolomics relies on identification and measurement of small molecule metabolites in a biological system, and is usually driven by global data acquisition which leads to hypothesis generation for further research. This approach is typically built on readouts of steady-state metabolite levels/concentrations and thus only presents a snapshot of biological activity. While useful in some circumstances, for example, in detecting in-born errors of metabolism, measurement of static metabolite levels is unable to directly determine the activity of metabolic pathways, which are intrinsically dynamic in nature and often experience minimal metabolite (reactant and product) concentration changes ,2. In coin vitro or in vivo [Combining the use of stable isotope labelled metabolic precursors (tracers), and modern analytical techniques (such as high resolution chromatography and mass spectrometry) for separation and detection of chemical species permits characterization of the metabolism of a broad range of biological systems. This can include direct and accurate measurement of turnover of a specific metabolite or even determining entire pathway fluxes  in vivo ,5,7. A t in vivo ,9, paras in vivo ,11, dise in vivo  and plan in vivo  amongst  in vivo ,15,16.Stable isotope labelling is becoming increasingly popular in the field of metabolomics for a number of reasons. Being non-hazardous, stable isotopes clearly have a marked advantage over traditionally-used radioisotopes and have almost completely replaced the use of radionuclides in mass spectrometry and NMR based modern metabolomics. The discussion during the peer session encompassed the following broad areas.Stable isotope labelling can be used for generating reference standards, which can aid in metabolite identification and absolute quantification by stable isotope dilution strategies. Confident identification and accurate quantification are major bottlenecks in global discovery-based experiments owing to problematic matrix effects which contaminate spectra and suppress signal responses. Stable isotope dilution is the gold standard for accurate quantification and stable isotope reference standards are becoming increasingly available. However, for metabolome-wide approaches the use of individual stable isotope standards is neither practical nor economical. An attractive strategy is to apply labelled standards in a metabolome-wide approach by mixing the experimental biological sample extract with a known biological extract grown in the presence of an exclusively labelled single carbon source and analyzing the resulting isotope labelling patterns, thus permitting precise, relative quantification (corrected for matrix effects and response factors) of many hundreds of metabolites simultaneously ,18,19,20The major interest in stable isotope labelling for metabolomics stems from its ability to aid in the measurement of dynamic activity of metabolic pathways, in order to provide mechanistic explanations for the perturbations in steady-state metabolite levels observed in classical metabolomics studies. Stable isotope labelled precursors with uniform or positional labelling of constituent atoms have been used to generate biological samples that allow the study of their metabolic fate throughout the network in an untargeted fashion ,15,16. SFluxomics, in theory refers to the measurement of all the metabolic reaction rates in a biological system, including the directionality of metabolic conversions and import/efflux of metabolites. Like metabolomics, fluxomics requires identification and analysis of metabolites, and in practice, is often limited to known central metabolic pathways involving a subset of metabolites due to an inability to actually measure the entire biological “metabolome” using any single analytical platform. A number of approaches for fluxomics analyses were discussed in the peer session.Owing to the highly dynamic nature of any biological system at a given time, it is difficult to directly measure the metabolic flux. An indirect measurement however can be obtained using the concept of metabolite balancing or flux balance analysis . This trin vivo studies in animals or humans, owing to a much higher degree of complexity which results from an interconnection of different cell and organ systems that simultaneously produce, transfer, and consume multiple substrates.Therefore, for successful application of this approach, a thorough understanding of the biological system being studied is critical in order to accurately reconstruct the metabolic network . This me2H, 13C, 15N, and 34S) and performing labelling experiments over a time-course to observe the dynamic nature of metabolism in a variety of biological systems including unicellular pathogens, plants, and mammalian cells [13C-glucose and 13C-glutamine, as they are metabolized in a cascade of enzymatic reactions can be exploited to undertake isotopomer analysis in order to pinpoint specific adaptations in central carbon metabolism [Flux balance analysis can be improved by implementing a method called metabolic flux analysis and using stable isotope labelling to fill in the gaps in data that arise due to compartmentalization or cycling of metabolites. This can be achieved by using a number of stable isotopes of atoms which occur naturally in biomolecules  has been used to analyze mass isotopomer distributions of metabolites based on their atomic rearrangements in autotrophs. These organisms are sampled before achieving isotopic steady state following abolites ,42. Thisabolites ,44.Kinetic flux profiling is an approach which can provide the most dynamic information in a metabolomics experiment, but requires extensive modelling and specialized experimental methodology. It uses dynamic isotopomer distribution data in order to estimate absolute flux along metabolic pathways. Typically, this approach assumes that the magnitude of measured metabolic flux is proportional to the rate of transmission of labelled isotopes from the precursor to subsequent products. It considers steady state dynamics for influx and efflux of a given metabolite and the sample preparation involves switching from an unlabelled precursor to an isotopically labelled precursor at a given time. These assumed steady state dynamics are true for cells undergoing rapid proliferation, provided nutrient resources are not limiting. Under these conditions, when a precursor is switched from an unlabelled to labelled form, the immediate product of the precursor is replaced by its labelled form. Measurement of concentrations of the labelled forms over time—as the label gradually infiltrates the metabolite pool—can be used to estimate the influx and efflux . Kineticin vivo animal and human experimentation. Due to the unpredictable nature of a multi-organ system that is shared by the one continually recirculating blood supply, it is difficult, perhaps even impossible, to model (“constrain”) the entire metabolism of an organ (or organs) in an intact animal or human. For example, when an isotopic tracer is administered  in animals or humans, the tracer can be utilized by one organ where it is metabolized and recycled to another form and released into the circulation, ready to be taken up and recycled again by another organ. This can occur many times, thus resulting in the formation of a range of secondary tracers (metabolites) with “scrambled” labelling patterns when compared to the originally administered tracer [in vivo to provide both qualitative and quantitative information on the fate of substrates across multiple metabolic pathways in different organs. By administering stable isotope labelled metabolic precursors, then rapidly harvesting animal organs, or collecting surgical biopsy/resection specimens from humans, targeted metabolomics analyses have been used to determine the contribution that specific substrates make to pathways of central carbon metabolism in both healthy and diseased states [While metabolic flux analysis has been extensively used in microbial, plant, and—more recently—mammalian cell culture systems ,38, its d tracer . Howeverd states ,55,56,57In this session, peers also discussed the importance of a number of technical issues which should be addressed early to ensure reproducibility and acquisition of high quality metabolomics data for stable isotope labelling experiments. Some of these are discussed below.Any good experimental design incorporates a plan for replication of the experimental conditions and the power of the study is heavily dependent on the reproducibility of the data obtained, irrespective of the approach. Replicates are essential as they provide multiple measurement points for any given metabolite in a sample, and averaging across replicates improves accuracy and data resolution. Furthermore, replication provides a means of identifying anomalies in the experimental procedure as outliers and, most importantly, they provide measurements of the variability in experimental data that are used for determination of significant differences between study factors. As metabolomics experiments involve multiple steps between sample preparation and analysis, the probability of user-induced variation increases with every step. Usually, with validated procedures and good laboratory practice, technical variability can be kept to the minimum and the balance of the resources  should be tipped towards obtaining biological replicates. However, due to the nature of analytical methods used in metabolomics, it may be desirable to capture technical variability either by obtaining technical replicates, or by the addition of batch-wise quality control samples .If internal standardization is achieved in an identical manner for all variables under testing  in stable isotope labelled metabolomics experiments, the technical variance is negligible compared to label-free comparative metabolomics studies as the isotopologue peaks are detected in the same sample as the unlabelled (or other isotopologue) peaks. For this reason, fewer technical replicates can be justified in order to offset the increased costs associated with stable isotopes. Nevertheless, intrinsic biological variance remains in even the most tightly controlled experiments, and some degree of biological replication remains essential in order to obtain statistical significance.2H) labelling but is still apparent for 13C and therefore this shift must be taken into account in data processing. Retention time shifts due to 13C incorporation are insignificant for small molecules, for example TCA cycle intermediates, however a small shift may be observed for larger molecules, such as fatty acids, when using a highly selective GC method and therefore the data processing method must ensure peak areas for each isotopologue are measured accurately [Only MS instruments with resolving powers above 100,000 are able to resolve most co-eluting isobaric metabolite mass signals . Howevercurately .The linear dynamic range of a method is an important consideration while developing quantitative analyses, however, it is often overlooked when adapting methods to different instruments. Increasing sensitivity of MS instrumentation is not necessarily associated with an increase in linear dynamic range. Most modern MS instruments report a within-scan linear dynamic range of 4–5 orders of magnitude, which does not cover the full concentration range of metabolites and isotopologues likely to be found in a complex metabolomics sample. Therefore, sample loading and/or analytical methods must be adapted to ensure that the instrument can measure isotope profiles within its linear range. Without this consideration, large errors in the measurement of isotopologue ratios can occur and the inclusion of standard curves is therefore necessary to validate the linear dynamic range of a method.The procedures for addition of stable isotope precursors must be considered in the context of the biological system under study and the aim of the experiment. In many cases, for metabolic flux analysis and generation of reference standards, an isotopic steady state is desired. The time taken to reach isotopic steady state is dependent on the metabolic flux within the pathways of interest under the specific experimental conditions. Often a pilot study is necessary to determine an appropriate experiment duration.In the case of kinetic flux profiling experiments, a series of samples are generally obtained shortly after addition of the labelled precursor. Meaningful interpretation of these studies require the system to be at metabolic steady state throughout the experiment, which necessitates careful consideration of the method used to introduce the stable isotope labelled precursor. Media exchange techniques that rely on centrifugation or filtration, or washing protocols that utilize nutrient-free media, are likely to significantly perturb metabolism and influence the resulting flux measurements.13C labelled central carbon source such as glucose (or CO2 in plants if a time course study is desired) can be highly informative. However, it is common to perform labelling studies subsequent to the initial hypothesis-generating metabolomics study, and labelled nutrients can be selected based on the published or predicted precursor(s) for the metabolic pathway(s) of interest [The choice of stable isotope labelled nutrient is a critical first step in these studies, and is highly dependent on the research questions ,61,62. Finterest .A critical point that was raised in the peer session discussion was the importance of accounting for tracer dilution from unlabelled precursors. It was noted that even when care is taken to account for any pre-existing unlabelled precursor at the start of such studies, such forms may further originate from recycling of other metabolic intermediates or environmental fixation and may dilute the labelled tracer along the path of synthesis of downstream products. To overcome this issue, determination of precursor dilution by unlabelled metabolites is necessary, and this can often be achieved by quantitative mass isotopomer distribution analysis ,64,65.13C-glucose allows differentiation of the flux in each of the two branches (oxidative vs. non-oxidative) of the pentose phosphate pathway (PPP) in a highly glycolytic cell, which would not be possible from a 100% U-13C-glucose precursor  [13C-glucose for differentiating glycolytic and PPP flux.In some cases, it is informative to study the metabolism of partly-labelled tracer mixtures, due to the complex architecture of metabolic pathways. For example, a 50% labelled/unlabelled mixture of U-y state) ,16. Non-2H and 13C) cannot be differentiated from one another. This isobaric background signal can be minimized by using ultra-high resolution mass spectrometers for data acquisition and post-processing computation which incorporates background correction [Due to the presence of naturally occurring isotopes of metabolites, background subtraction is required prior to isotopologue data interpretation ,66,67,68rrection . Backgrorrection . In some13C glucose, amino acids, acetate, etc.) and observing their metabolic incorporation and/or dilution in biological systems, it is also possible to use other labelling approaches combined with mass spectrometric and/or NMR analysis to understand cellular metabolism and growth characteristics. For example, the use of deuterated “heavy” water (2H2O) has proven to be a particularly powerful approach for measuring the synthesis rates of proteins [de novo lipogenesis, esterification, and chain elongation) [in vivo and in vitro. Furthermore, 2H2O labelling can be used to quantify the pathways used to produce glucose and glycogen, thus providing a way to measure gluconeogenic and glycogenolytic/glycogenic pathway activity [2H2O labelling lies in the fact that many different biomolecules incorporate hydrogen atoms (protons) from body/cellular water via enzyme catalyzed reactions [2H2O into a living system, deuterium (2H) atoms become stably incorporated into C-H bonds of newly synthesized biomolecules, essentially “tagging” them. The degree to which molecules (particularly macromolecules) become enriched with 2H atoms depends on both their rates of synthesis and the pathways used to synthesize them. 2H2O can be administered to animals or humans in drinking water, or directly into the cell culture media in vitro, which results in the enrichment of the body/cellular water pool. A major advantage of using 2H2O is that it can be used to probe multiple metabolic processes simultaneously and can be easily and safely administered for long periods of time in vivo and in vitro, permitting flux analysis in free living conditions or long term culture experiments [2H2O enrichment can have toxic effects on cellular systems due to the slightly stronger bonds that deuterium forms with other atoms when compared to hydrogen, thus creating kinetic isotope effects on chemical reactions [2H2O enrichments of body water/cell culture media to <10%, although the kinetic isotope effect should be considered when modelling and interpreting data from 2H2O labelling [2H2O has also recently been combined with other “omics” technologies including proteomics [in vivo, and imaging mass spectrometry [2H2O labelling can provide complementary information on the synthetic rates of numerous cellular components and their biosynthetic pathways .In addition to administration of isotopically labelled precursor molecules  ,76,77,78ngation) ,80, and ngation) , thus practivity ,85,86,87eactions ,89,90. Teriments ,91. Howeeactions ,93. The abelling ,93. 2H2Ooteomics ,95,96, ptrometry , permittWhether being used as a hypothesis-generating holistic approach, or for hypothesis-driven analysis of specific pathways, stable isotope labelling is a powerful tool that can enhance the biological interpretation of metabolomics data. Stable isotope labelling can help alleviate a number of issues associated with metabolomics data analysis and provide confidence in assigning correct identification to metabolite features. Quantification of metabolites in real time using stable isotope labelling allows accurate measurements for fluxomics studies. For most biological experiments investigating mechanisms of metabolic perturbation, targeted tracer studies or metabolic flux analysis are the preferred methods, due to ease of experimental design and data analysis. These approaches often reveal sufficient information about the metabolic pathways of interest to interpret observed biological phenomena without the complexity associated with detailed kinetic flux analysis.Thanks to stable isotope labelling, we are increasingly advancing from discovery-based global metabolomics approaches and becoming capable of teasing out mechanisms of interplay of metabolic pathways in intricate networks. We hope that the use of stable isotope labelling will become routine in metabolomics experiments in the future and will continue to play a major role in biochemical elucidation of existing pathways along with identification of novel metabolic networks.


Cinara cedri that harbors two co-primary endosymbionts, Buchnera aphidicola BCc and Ca. Serratia symbiotica SCc, and in which some metabolic pathways are partitioned between different partners. Here we present a genome-scale metabolic network (GEM) for the bacterial consortium from the cedar aphid iBSCc. The analysis of this GEM allows us the confirmation of cases of metabolic complementation previously described by genome analysis  and the redefinition of an event of metabolic pathway sharing between the two endosymbionts, namely the biosynthesis of tetrahydrofolate. In silico knock-out experiments with iBSCc showed that the consortium metabolism is a highly integrated yet fragile network. We also have explored the evolutionary pathways leading to the emergence of metabolic complementation between reduced metabolisms starting from individual, complete networks. Our results suggest that, during the establishment of metabolic complementation in endosymbionts, adaptive evolution is significant in the case of tryptophan biosynthesis, whereas vitamin production pathways seem to adopt suboptimal solutions.Bacterial endosymbionts and their insect hosts establish an intimate metabolic relationship. Bacteria offer a variety of essential nutrients to their hosts, whereas insect cells provide the necessary sources of matter and energy to their tiny metabolic allies. These nutritional complementations sustain themselves on a diversity of metabolite exchanges between the cell host and the reduced yet highly specialized bacterial metabolism—which, for instance, overproduces a small set of essential amino acids and vitamins. A well-known case of metabolic complementation is provided by the cedar aphid  Species coexisting in a determined environment establish a network of interactions molded by biotic and abiotic factors  upgrade the host diet by supplying essential amino acids and vitamins absent in the diet  usually relies on the concept of “metabolic complementation”, for which at least two distinct meanings have been used. First, we can consider the exchange of essential components  between host and endosymbiont  endosymbiont, Candidatus Serratia symbiotica SCc  of two different endosymbionts (B. aphidicola and Wigglesworthia glossinidia) showed that the present gene content of these symbionts can be predicted with over 80% accuracy, from distant ancestors of the organisms and considering their current lifestyle  available in the BioCyc collection in Caspi et al.    of i et al. . The useS. symbiotica SCc, the GEM of E. coli K12 MG1655 iJO1366  and the S. symbiotica SCc genome included in the PGDB SSYM568817 19.0. After the gene sequences were extracted and translated, they were compared using Blastp  as previously done  and J the set of all fluxes; vi is the flux through the transporter i; nci is the number of carbon atoms in the transported molecule; and UB is the upper bound, i.e., the parameter establishing the maximum number of carbon atoms that can be exchanged by the α compartment (one of the endosymbionts) and the extracellular compartment (the host cell). This parameter limits the amount of matter flowing through the membrane in terms of the total carbon atoms and was fixed to 100 carbon atoms. In order to guarantee that the transport fluxes are non-negative, the reversible transporters were split into two irreversible transport reactions with opposite direction  , were combined to create a single model named iBSCc. Although it is known that B. aphidicola BCc population and S. symbiotica SCc are hosted in different bacteriocytes, a more simplistic representation was chosen where both endosymbiont models are embedded in a single compartment, in a similar way as it has been previously done  a compartment representing the S. symbiotica SCc population; and (iii) a single extracellular compartment representing the host cells, where both symbionts are embedded   and they grow necessarily at the same growth rate. This means that they behave essentially as one “big” bacteria. However, if this assumption does not hold, and the growth rate of the two bacteria are the same , new (probably non-linear) constraints should be incorporated to the model  is analyzed in the next section.In order to simulate the growth of the system as a whole, we introduced a combined meta-biomass equation, where each member contributes to the growth of the system with a fixed stoichiometry Figure . Our modB. aphidicola BCc is specialized in the production of amino acids, while S. symbiotica SCc produces nucleotides and a large number of cofactors , respectively , who might provide them to B. aphidicola BCc. Indeed, the aphid A. pisum, as most eukaryotes, is able to synthesize nitrogenous bases  of the genes coded by the endosymbionts are essential in order to sustain the growth of the whole system (see Supplementary Table iBCc98 FBA predicted that 72 out of the 98 metabolic genes (~74%) are essential, while the MOMA analyses identify 76 (~78%) as essential. The dispensable genes are mostly involved in catabolism, affecting the phosphate pentose pathway, glycolysis, respiratory chain and pyruvate fermentation  are predicted to be essential, whereas MOMA predicted 5 additional genes as essentials. The 28 dispensable genes predicted by both methods code for 36 enzymatic reactions involved mostly in biosynthetic pathways , but also in the central carbon metabolism . If we consider the cell wall genes to be dispensable (since they have been repeatedly lost in endosymbiotic bacteria), the number of essential genes drops to 189 (~80%). Additionally, if it is also assumed that the host is who provides the nucleotides and deoxynucleotides, the percentage of essential genes drops to ~70% (data not shown). Although it might seem surprising that these estimates are higher or comparable to those obtained by using the smaller iBCc98 network, the iSCc236 model requires 22 organic compounds to be imported, while iBCc98 requires 29, among which there are nucleotides and cofactors such as NAD+ and coenzyme A. Finally, a study of distributed robustness was performed through the analysis of the synthetic lethal  genes, i.e., pairs of non-essential genes whose simultaneous inactivation yields lethality  were generated by removing from this model every possible combination of genes, from single genes to the most reduced cases where only one copy of each gene remains present . A GLRP is considered viable if, after removing the corresponding reactions, FBA predicts a meta-biomass flux greater than zero. Furthermore, in order to reduce the number of combinations of possible GLRPs, the enzyme subsets (ES), i.e., groups of enzymes that always work together under steady state, were first computed for iBSCcAncest  denotes the loss of the enzyme subset by one of the endosymbionts, indicated by the superscript m, which can be either B or S (B. aphidicola or S. symbiotica). Accordingly, B. aphidicola has lost the ES1. A more complex example would be the case of iBSCc . In this case, the GLRP is coded as follows: Using iBSCcAnc. Figure iBSCc) with a tryptophan production rate almost equal to the one exhibited by the putative ancestor  the individual maximal production rate of tryptophan  imply A deeper insight into the results shows that among the minimal GLRPs, 48 out of the total 128 (~37%) also exhibit a B. aphidicola BCc and S. symbiotica SCc, co-primary endosymbionts of the cedar aphid C. cedri, has allowed, in first place, the revision of the annotation of these organisms' genomes. Indeed, through the manual curation of these networks using the UM approach, it was possible to correct annotation errors in different enzymatic activities, and to identify previously unannotated metabolic genes. Moreover, the simulations performed here allowed the refinement of growth conditions and metabolic capabilities of these endosymbionts as compared to previous inferences from genomic analyses. Our study also indicates that GEMs are a possible way to approach the otherwise inaccessible phenotypic features of uncultivable endosymbionts. On the other hand, the analysis of highly reduced metabolic networks, such as the case of iBCc98, bring into consideration methodological issues such as the case of the proton balance. Previously, a sensitivity analysis by Reed et al. . Although, these results may contradict the idea that the smaller the network, the higher the essentiality of its components, the percentage of essential genes predicted by iSCc236 drastically drops to ~70%, when the cell wall genes are considered dispensable, and that the nucleotides and deoxynucleotides are provided by the host. Moreover, a further 15 and 5% of the genes, respectively, are genes that become essential after the deletion of another non-essential gene, thus displaying only a shallow degree of redundancy. Altogether, the amount of non-essential genes seems to positively correlate with the size of the network  of genes being essential  and Cinara tujafilina (Eulachnini). The former contains a genome of only 650 kb and 495 CDSs  the requirements and exports of the bacterial partners to the host and to each other; (2) the robustness associated to reduced metabolic networks individually and by co-integration; and (3) the evolutionary constraints in the emergence of metabolic complementation designs. We could corroborate previously suggested scenarios for metabolic capabilities based on comparative genomic analyses. We also established that the cedar aphid consortium is composed not only of individual highly-reduced symbionts, but also that it is not far from a complete loss of metabolic redundancy and flexibility, thus making it a highly fragile partnership. Finally, we also showed that the patterns of metabolic complementation in this consortium are nearly minimal, in terms of gene content, and exhibit an almost optimal growth rate, and tryptophan production, with respect to a putative ancestor where the complemented pathways are still completely encoded by each symbiont. Therefore, our results suggest a higher role of adaptive evolution in the emergence of metabolic complementation than previously thought, and more studies in different consortia with both similar and different patterns of complementation designs will be invaluable to confirm the generality of these conclusions.MP-d-L, FM, and JP conceived the work. MP-d-L, DT, and JC-E developed the models. MP-d-L performed the simulations and analyzed the data. All the authors discussed the results and wrote the 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.