Milnesium tardigradum were dehydrated by removing the surrounding water to induce tun formation. During this process and the subsequent rehydration, metabolites were measured in a time series by GC-MS. Additionally expressed sequence tags are available, especially libraries generated from the active and inactive state. The aim of this integrated analysis is to trace changes in tardigrade metabolism and identify pathways responsible for their extreme resistance against physical stress.Tardigrades are multicellular organisms, resistant to extreme environmental changes such as heat, drought, radiation and freezing. They outlast these conditions in an inactive form (tun) to escape damage to cellular structures and cell death. Tardigrades are apparently able to prevent or repair such damage and are therefore a crucial model organism for stress tolerance. Cultures of the tardigrade In this study we propose a novel integrative approach for the analysis of metabolic networks to identify modules of joint shifts on the transcriptomic and metabolic levels. We derive a tardigrade-specific metabolic network represented as an undirected graph with 3,658 nodes (metabolites) and 4,378 edges (reactions). Time course metabolite profiles are used to score the network nodes showing a significant change over time. The edges are scored according to information on enzymes from the EST data. Using this combined information, we identify a key subnetwork  of concerted changes in metabolic pathways, specific for de- and rehydration. The module is enriched in reactions showing significant changes in metabolite levels and enzyme abundance during the transition. It resembles the cessation of a measurable metabolism (e.g. glycolysis and amino acid anabolism) during the tun formation, the production of storage metabolites and bioprotectants, such as DNA stabilizers, and the generation of amino acids and cellular components from monosaccharides as carbon and energy source during rehydration.The functional module identifies relationships among changed metabolites (e.g. spermidine) and reactions and provides first insights into important altered metabolic pathways. With sparse and diverse data available, the presented integrated metabolite network approach is suitable to integrate all existing data and analyse it in a combined manner. Milnesium tardigradum was analysed during tun formation, which was induced by dehydration. In tardigrades few metabolites have been analysed including carbohydrates that stabilize the membrane in the dry state )The differences between the tests  are shown for simulated time series in Figure et al. [Π-upper value of 0.4448 measures the amount of noise in the data, vice versa denoting that 55.52% of the metabolites change significantly in their profiles. The computed node scores range from -0.871 to 4.122, whereby significant p-values lead to a positive score, while non-significant p-values lead to a negative node score. Unmeasured metabolites were assigned the average of the negative scores (-0.473) which corresponds to a score derived from the noise in the data. Similarly, other methods handle unmeasured metabolites by calculating random p-values and computing scores from these [The p-values from the Umbrella test were subsequently used to score the nodes of the metabolite network. To convert the p-values to scores for the network and calculate functional modules the approach by Dittrich et al.  was usedom these ,34.The ESTs were subdivided into two data sets, EST data with EC number mappings from previous studies and differential ESTs (dESTs) for the active and inactive stage (see Methods). The EC number allowed the identification of corresponding tardigrade-specific enzymes and therefore reactions. The dESTs were mapped, as described in the following, to KEGG ko identifiers using KAAS  and ther2 ratio of active na to inactive nicounts of dESTs was used for edge scoring: The dESTs were clustered into 4,422 clusters using CD-HIT-EST (see Methods). Out of each cluster a representative was used to map KEGG ko identifiers with the KEGG Automatic Annotation Server (KAAS) . 898 clut≥0 to adjust the sensitivity/specificity of Sesimilarly to the FDR used for the node scoring .with threshold heinz[A functional module was calculated with an exact algorithm termed heinz,35 usingheinz algorithm identified the maximum-scoring subnetwork, which aggregates metabolites and reactions that are likely to be present, over the positive scores.First an enrichment step reduced the metabolic network to a tardigrade-specific subnetwork of 868 nodes and 1127 edges. Since the complete network was created from the KEGG reference pathways, it contains all possible metabolic reactions and metabolites of which some might not occur in tardigrades. Therefore a subnetwork was extracted based solely on the presence of enzymes and metabolites measured in tardigrades. Identified enzymes from EST data were used to score the edges and a positive node score (+1) was used for measured metabolites. Using these scores the changes in metabolites and differentially abundant ESTs. A functional metabolic module was calculated based on the node scoring from the metabolic profiles with an FDR of 0.2 (see Equation 2) and the log ratio score for the edges from the dESTs with t=1 (see Equation 1). The resulting edge score lay between -1 and 1, giving these edges a 100-fold higher weight than with just enzyme information . By integrating the different sources of information, a module was obtained representing the significant trend changes in metabolites between the dehydration and rehydration process as well as changes in EST abundance, connected by reactions for which enzymes were identified in the tardigrades.The second step used the enriched subnetwork to score the During dehydration the metabolism of tardigrades slowly reduces, up to a complete cessation of measurable metabolism in the tun stage. The recovery time during rehydration is probably a function of metabolic activities linked to repair of damage caused by dehydration and to restoration of metabolic pathways. The resulting module Figure  reveals The inverse metabolic trend with a peaked shape is less common and would be expected for storage metabolites or bioprotectants. The module includes 6 metabolites with significant changes in this direction: sn-glycerol 3-phosphate, putrescine, spermidine, aspartate, citrate and D-glycerate. Sn-glycerol 3-phosphate could potentially increase during dehydration to produce triglyceride as a highly efficient energy storage or even more likely glycerol as membrane stabilizer. Putrescine is a diamine created by the decarboxylation of ornithine. The addition of two propylamine residues yields spermidine, an essential growth factor. Other probable biological functions of spermidine are the stabilization of DNA by association of the amino-groups with the phosphate residues of the DNA, increase of RNA synthesis and enhancement of stability of tRNAs and ribosomes .changes in reactions between metabolites are identified, e.g. the link between the pentose phosphate pathway to glycolysis by the transaldolase reaction: sedoheptulose 7-phosphate + glyceraldehyde 3-phosphate ⇌erythrose 4-phosphate + fructose 6-phosphate. Using solely the metabolite data (node scores) during the module identification these reactions would not be taken into account (data not shown). However, they seem to be highly important for tardigrade metabolism since they show strong differences in EST counts between the active and inactive state. At present only few data on the abundance of the enzymes from dESTs is available. The integrated analysis will certainly gain from the creation of additional stage-specific data to identify further important reactions and pathways.Especially the information on differentially abundant enzymes is valuable, by which M. tardigradum) neither the genome is known nor the complete transcriptome or proteome, and up to now only quite incomplete analyses exist on a molecular level. This study represents a first attempt to use the existing data in an integrative approach to complement each other. By integrating metabolic profiles and transcript data into a metabolic network created from KEGG pathways, a module of concerted changes in metabolism during the process of de- and rehydration is identified.Here we present an integrated approach to analyse sparse, heterogeneous data from a largely unknown organism. For tardigrades  our fraIt has to be noted, that changes in transcript abundance and metabolite levels correlate only to a certain extent. A clear relationship is evident where the metabolites are end-products . The relationship is less transparent in those cases where the metabolites are intermediates. This has different reasons, importantly, the use of metabolic data on changes (if any) of intermediary metabolites to infer changes in metabolic rate is an intractable (possibly insoluble) problem beyond the scope of our methodology. In fact, our approach applies only statistical reasoning on more or less strongly correlated changes for different network nodes. Hence, the interpretation of the changes for the intermediary metabolites has to be done with special care. The significance levels given for our estimates are only a guiding indicator here.Furthermore, it by now only uses a small set of measured metabolites (84) to score the nodes in the network, which is due to the limitation of current technologies. Nonetheless, is the algorithm capable of handling ’omics scale metabolic data sets, which will unquestionably be available in future studies.M. tardigradum and more comprehensive metabolic profiles, this kind of integrated network analysis will certainly provide a powerful tool for deeper and more detailed insights into tardigrade metabolic processes and adaptations.A clear benefit of the presented integrative method is the identification of key processes and pathways changing during the transition phases, despite the sparsity of available data for tardigrades so far. As more data become available, including the entire genome sequence of Milnesium tardigradum Doyère 1840,  which was originally collected in Tübingen, Germany and is since a decade as a well established laboratory culture available. The animals were cultured on petri-dishes  with a layer of agarose (3%)  covered with a thin layer of Volvic-water  at 20°C. The animals were fed bdelloid rotifers, Philodina citrina , which had been raised on the green algae of the species Chlorogonium elongatum . For this study M. tardigradum starved over two days before harvest to avoid contamination with food-organisms. After repeated washing with clean water, animals were transferred into microliter tubes . By using a micropipette, the animals surrounding water was reduced by careful aspiration to approximately 1.5 +/- 0.5 μL. The open microliter tubes were then exposed for dehydration to 85% relative humidity (RH) in a small chamber containing a saturated solution of KCl . The first time point of the dehydration period was sampled and frozen in liquid nitrogen after 1 h. Further samples were taken after 720 min, 900 min, 1020 min, 1080 min, 1110 min, 1140 min, 1170 min, 1185 min and finally after 1200 min after which the cryptobiotic tun formation was completed in all individuals. To produce different states of rehydration we added 10 μLVolvic-water into each microliter tube. The first rehydration state was immediately sampled and frozen in liquid nitrogen. Further samples during rehydration were taken 5 min, 10 min, 15 min, 20 min, 60 min, 90 min, 150 min, 210 min and 270 min after adding the water.The study was carried out with the eutardigrade M. tardigradum individuals were homogenized by sonification on ice with 150 μL 100% methanol which was pre-cooled to −20°C. Homogenized samples were extracted with an internal standard [μL CHCl3 for 5 min at 37°C, mixed with 200 μL water and centrifuged 5 min at 14000rpm to separate liquid phases and to remove debris. The upper methanol/water phase was collected and dried in a speed vac. Metabolite extracts were stored and shipped as was described previously [http://gmd.mpimp-golm.mpg.de[Metabolic analysis was performed using GC-MS based metabolite profiling  of a whostandard  for 15 meviously . The drieviously . The GC-eviously . Four telm.mpg.de. Retentilm.mpg.de. Averagelm.mpg.de. Relativhttp://waterbear.bioapps.biozentrum.uni-wuerzburg.de[M. tardigradum (dEST) available at NCBI  and the nematodes Caenorhabditis elegans , Caenorhabditis briggsae and Brugia malayi . Finally, 932 KEGG identifiers could be annotated to the clusters.Two EST libraries were used, first with EC numbers annotated ESTs from a current study  and ails see , raw traails see ,50. Adapails see  and trimails see . The dESails see . Represeails see . The dESH0:G1=G2=…=Gk, where Gidenotes either the distribution function or the median of the i-th of k ordered samples. The alternative hypothesis states: H1:G1≤G2≤…≤Gk, where at least one of these inequalities strictly holds.The Jonckheere-Terpstra (JT) test is a non-parametric, rank-based test to analyse monotonic trends between groups ,39. The H0 states that all samples were drawn from the same population Giwith identical distribution: H0:G1=G2=…=Gk. The alternative hypothesis states: H1:G1≤G2≤…≤Gl≥Gl + 1≥…≥Gkwhere l is the peak position. For the JT test the implementation from the R-package SAGx was used [The Umbrella test is a generalisation of the JT test by Mack and Wolfe , which iwas used , while thttp://www.genome.jp/kegg/xml/KGML_v0.7.1in December 2010. The KEGG pathways were loaded with the R package KEGGgraph [α- and β-D-glucose to D-glucose) containing all edges of the previously distinct nodes. Certain metabolites were removed which form non-specific shortcuts between the modules in the network. The excluded metabolites are: H + , H2O, P, ATP, NAD + , NADH, ADP, CO2, CoA, NADP + , NADPH, NH3, PP. Many of these metabolites serve as cofactors and they represent connections that are not involved in C-transfer, therefore they are less relevant for this purpose. They also correspond to highly-connected metabolites, whose concentrations are well-buffered and not likely to be informative about specific areas of metabolism. The considered metabolites are based on experiences from other analyses and we do not claim to provide an exhaustive list. We abstained from further preprocessing steps to reduce the model by eliminating unmeasured metabolites, as mentioned in [The complete KEGG reference pathways were downloaded as XML files  from httEGGgraph  and procEGGgraph  and igraEGGgraph . All pationed in ,57. In c2ratios of dESTs. The analysis and search for optimal modules was performed as described in Dittrich et al. [heinz (heaviest induced subgraph). Briefly, the distribution of raw p-values from a statistical t-test can be considered as a mixture of signal and noise, where the signal component is modelled to be Beta distributed [τdepending on a pre-selected false discovery rate (FDR) Integrated network analysis was performed by scoring the nodes and edges of the metabolite network derived from the trend test of the metabolites and logh et al.  with an tributed , whereashttp://bionet.bioapps.biozentrum.uni-wuerzburg.de/ and the Bioconductor project [Based on the node score, functional modules can be identified by finding the maximum-scoring subnetworks. Using an exact approach based on integer linear programming provably optimal and suboptimal solutions can be detected . Additio project .et al.[Correspondence analysis (CA) is a multivariate statistical method to visualize high dimensional data . It was et al.. For laret al..For functional characterization of genes corresponding to the enzymes contained in robust components of the modules a gene ontology (GO)  term enrThe funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.DB, TM, MD, GK did statistical and computational network analysis. TM and MD led and guided the study. GK, JK and ROS conceived and designed the experiments for the metabolite analyses. The GC-TOF-MS based metabolite profiles were performed and processed in the laboratory of JK. Tardigrades were sampled, extracted and shipped by SH in the laboratory of ROS. MAG and MF contributed the EST data. MAG performed base calling, pre-processing and clustering of EST sequences from the active and inactive stage. TD provided expertise on metabolic pathways. All authors participated in writing and approved the final version of the manuscript.Normalized metabolic profiles based on GC-MS for M. tardigradum.Click here for file


Minimal Cut Sets (MCS) to the ihsTLR v1.0 model of the TLR pathways to determine sets of reactions whose knockouts disrupt these pathways. We decomposed the TLR network into 34 modules and determined signatures for each MCS, i.e. the list of targeted modules. We uncovered 2,669 MCS organized in 68 signatures. Very few MCS targeted directly the TLRs, indicating that they may not be efficient targets for controlling these pathways. We mapped the species of the TLR network to genes in human and mouse, and determined more than 10,000 Essential Gene Sets (EGS). Each EGS provides genes whose deletion suppresses the network's outputs.The Toll-Like Receptors (TLRs) are proteins involved in the immune system that increase cytokine levels when triggered. While cytokines coordinate the response to infection, they appear to be detrimental to the host when reaching too high levels. Several studies have shown that the deletion of specific TLRs was beneficial for the host, as cytokine levels were decreased consequently. It is not clear, however, how targeting other components of the TLR pathways can improve the responses to infections. We applied the concept of  Signal transduction pathways, such as the Toll-Like Receptors (TLR) signaling pathways, are an essential component of the innate and acquired immune response C. rodentiumP. gingivalisPhlebovirusMany studies have established the significant role TLRs play within the immune system: detection of pathogenic particles ihsTLR v1.0 model is a stoichiometric representation of the human TLR signaling pathways that follows six outputs: AP-1, CREB, IRF3, IRF7, Reactive Oxygen Species (ROS), and NF-The and NF-B  [10]. ThE. coli and human metabolic models The stoichiometric reaction format has been used extensively to represent metabolic ihsTLR v1.0 model. We encountered 2,669 MCS showing that each output uses different components of the TLR network. We partitioned the model in modules, which group reactions having similar functions. We determined which modules the MCS targeted, allowing to identify epistatic relations between components of the network. It appeared from our analysis that the TLRs were not the primary targets of the MCS, implying the TLR pathways are better manipulated by disabling targets downstream of the TLRs. We extended the MCS to the notion of Essential Gene Sets (EGS). EGS are sets of genes whose deletion ensures the knockout of an output. We assessed the impact of these deletions over the network, in term of species, reactions, and modules perturbed. We generated more than 10,000 EGS for both human and mouse. The EGS provide valuable information when designing knockout experiments by identifying gene deletions that have minimal impact on the network. They also identify essential genes that have a key role in the activation of a particular output of the TLR pathways.In this study, we generated MCS for the ihsTLR v1.0 model comprises 781 species involved in 963 reactions. We extended the model to 1,956 reactions to account for irreversibility and sink reactions. The over-approximation flux cone The  outputs   as the number of modules an MCS hits. e.g. Common metabolites, ROS production, TICAM) and the ones that are targeted with other modules ). Interestingly, the cardinality of the MCS does not seem to be a good indicator of the cardinality of the signature (i.e. the number of modules targeted simultaneously). Particularly for the CREB, ROS(2), NF-Each reaction in an MCS belongs to a module and some of these reactions may belong to the same module. Hence, an MCS may target the same module through different reactions. We define the t a time . This obEssential Gene Set (EGS) as a set of genes whose deletion renders a predefined output non-producible. EGS can be seen as the gene version of MCS. Instead of providing reactions, EGS procure the list of genes whose knockout stops the production of an objective, which is a valuable information when designing knockout experiments. We can clearly disable genes in an EGS (e.g. with siRNA), and the corresponding objective reaction, unlike reactions in an MCS . We were also able to confirm a total of 12 EGS through literature searches  failed to produce EGS targeting specifically this output. This is easily explained as disabling AP-1(2) requires to knock out the Jun protein. This protein is however essential for the formation of the Jun/Fos complex , MyD88-independent pathway . The initial EGS we computed contain in general a small number of genes  control genes, i.e. the EGS. In addition, Li et al. focused on single DIOS pathways, while we also describe gene sets capable of disabling multiple outputs.Li et al.IRF3 DIOS pathway. A similar result is observed with IRF7. Four out of the 5 essential reactions in the ROS production DIOS pathway are listed in our MCS. Our EGS list that the knockout of IKKRIP1, NOD1, NOD2, and RIP2/TRIP6/TRAF2 DIOS pathways (two reactions in each pathway). These 4 DIOS pathways each control NF-We observed similarities with the essential reactions as reported in Table S6 by Li et al., metabolites seem to play a crucial role in signal transduction Common metabolite module, which perturbs the normal exchange of metabolites between the extracellular and cytoplasmic compartments, can disable most of the outputs. Interestingly, no other module has such effect, highlighting the pivotal role of metabolite transfers. In many cases, perturbations of cytoplasmic transports are associated with the targeting of the Cytoplasm-Nuclear Transport module  for an objective has two applications. Firstly, this information can be used to disable the objective. Secondly, it can identify key elements to maintain in order to conserve the objective. Given the current literature, we can conjecture on the effect of disabling any of the outputs of the TLR network. NF-Searching essential reactions or genes , and determined lists of genes whose deletion perturbs combinations of the network's outputs. In addition, we assessed the impact of such deletions and provided the EGS that have minimal impact on adjacent pathways.We studied epistatic relations between modules of the i.e.e.g. phosphorylation, transport, etc) take less than 1 sec, while transcriptional regulation and receptor internalization take on the order of et al.ihsTLR v1.0 model were considered active, i.e.producible if and only ifWe represent a mass-balanced signaling network involving he model . An outpi.e.e.g. using FBA) if the maximum flux through the objective reaction is zero. This method becomes rapidly infeasible for large networks and MCS of high cardinality. Alternatively, MCS can be constructed as minimal hitting sets of extreme pathways (EP) E. coli and human metabolism Initially defined for metabolic networks, a cut set for an objective reaction is a set of reactions whose knockout disables that function reaction . MCS canihsTLR v1.0 model. Several combinations of genes may exist as different gene deletions can have the same effect over a reaction. We denote as i.e. no subset of an EGS is itself an EGS. We consider an EGS for a set of outputs as a set of genes whose deletion renders all the corresponding outputs non-producible.We constructed EGS iteratively for each MCS of all outputs. For each reaction in an MCS, we first identified combinations of genes whose deletion disabled that reaction. This was done by searching genes coding for proteins used in that reaction. We initially had to identify these genes in human and mouse through searches in the literature and in the Entrez Gene and MGI databases . SimilarihsTLR v1.0 model does not include such an objective function. Moreover, this approach is relevant for the study of unicellular organisms, but for more complex organisms, the producibility of the biomass is only necessary, but not sufficient for survival. This motivated us to compute several estimates for the impact of the deletion of an EGS: the species, reaction, and module impacts Click here for additional data file.Table S1Modules of the TLR network. Function and description of the modules involved in the TLR network. Only modules hit by MCS are listed.(PDF)Click here for additional data file.Table S2EGS validation. List of the initial EGS obtained from our computation. Experimental validations of the predictions are found it the cited publications.(PDF)Click here for additional data file.Dataset S1GPR associations of the TLR network. List of the genes coding for the proteins of the TLR network. Entrez Gene ID are given for human and mouse.(XLS)Click here for additional data file.Dataset S2EGS with minimal side effect. List of the EGS that target the power set of outputs with minimal side effect in human (first sheet) and in mouse (second sheet). Each set of genes is followed by its respective species, reaction, and module impacts.(XLS)Click here for additional data file.


Methylobacterium extorquens AM1 is a facultative methylotrophic Alphaproteobacterium and has been subject to intense study under pure methylotrophic as well as pure heterotrophic growth conditions in the past. Here, we investigated the metabolism of M. extorquens AM1 under mixed substrate conditions, i.e., in the presence of methanol plus succinate. We found that both substrates were co-consumed, and the carbon conversion was two-thirds from succinate and one-third from methanol relative to mol carbon. 13C-methanol labeling and liquid chromatography mass spectrometry analyses revealed the different fates of the carbon from the two substrates. Methanol was primarily oxidized to CO2 for energy generation. However, a portion of the methanol entered biosynthetic reactions via reactions specific to the one-carbon carrier tetrahydrofolate. In contrast, succinate was primarily used to provide precursor metabolites for bulk biomass production. This work opens new perspectives on the role of methylotrophy when substrates are simultaneously available, a situation prevailing under environmental conditions. Methylobacterium and are known to metabolize methanol but also a limited number of alternative carbon substrates, such as organic acids and alcohols. Plant leaf surfaces release diverse carbon sources, mainly sugars and organic acids, at low amounts (µM range) Methylobacterium and contributes to the epiphytic fitness of the organism Methylobacterium should adapt its metabolism to use additional carbon sources during the rest of the day when methanol emission is low or during the night when stomata are closed and methanol is consequently no longer available in situBacteria often live in environments containing diverse substrates M. extorquens AM1 is a model methylotrophic organism, and a number of novel enzymes and pathways involved in methanol dissimilation and assimilation were shown to operate in this organism M. extorquens AM1 was described to encompass 85 biochemical reactions that are strongly reprogrammed upon adaptation to nutrient changes All of these metabolic pathways are strongly connected via a dense network of reactions that interconvert key C2–C3–C4 metabolites . Due to the occurrence of sequences of reactions that generate substrate cycles, this network of reactions is highly flexible and allows the efficient switching of the central metabolism towards the utilization of alternative substrates M. extorquens AM1 in the presence of a multicarbon compound(s) in addition to methanol has not been thoroughly investigated. Preliminary enzyme activity data in cell extracts indicated that methanol dehydrogenase and enzymes of the assimilatory serine cycle were detectable in M. extorquens AM1 cell extracts when cells were incubated overnight with methanol plus succinate Methylobacterium species, including M. extorquens AM1, has been reported M. extorquens AM1 is able to utilize at least two very different energy sources of inorganic and organic nature.The metabolic profile of M. extorquens AM1 under a mixed carbon substrate condition, i.e., methanol plus succinate, to address whether diauxic growth or co-consumption occurs.In this study, we investigated the metabolism of M. extorquens AM1 to conditions under which both succinate and methanol are available, growth experiments were performed on minimal medium with equivalent C-mol of methanol and succinate, i.e., 60 mM and 15 mM, respectively. A growth rate of 0.18±0.01 h−1 was observed under this condition, which is roughly similar to that under either pure succinate growth (0.20±0.01 h−1) or pure methanol growth conditions (0.17±0.01 h−1)  . Both me.01 h−1)  indicati.01 h−1) . Notably (−1·h−1 . The relnditions . Growth 13C-labeled methanol (>99% 13C) and succinate at a natural abundance of 13C (1.1%). The determination of 13C-CO2 production in the exhaust gas of the bioreactor revealed that the methanol consumed was almost entirely converted to CO2 , which corresponds to the percentage of 13C carbon incorporated into the metabolites, was calculated for each compound . This value reflects the mean proportion of methanol-derived carbon in the molecule: an AL13C of 1.1% means that all carbon atoms originate from succinate and an AL13C of 99% means that all carbon atoms originate from methanol. The AL13C values at the first two timepoints were identical, indicating that the metabolism was stable until the depletion of succinate , phenylalanine (6%), tyrosine (6%), and especially methionine (19%) showed higher AL13C values . Because no significant labeling was found in alanine (a derivative of pyruvate), aspartate  or glycine, Me-THF (which condenses with glycine to form serine) can explain the introduction of the label into the gluconeogenesis pathway. Indeed, monolabeled phosphoglycerate was generated, which can lead to the incorporation of the label into phosphoenolpyruvate, the precursor of phenylalanine and tyrosine, and/or via gluconeogenesis into hexose-phosphate. An analysis of the hexose-phosphate mass isotopomer fractions indicated that 20% of monolabeled 2-phosphoglycerate is generated from the serine cycle, i.e., glycine plus Me-THF condensation  and adenosine monophosphate (AMP) (27±2%). Pantothenic acid is generated from one Me-THF and pyruvate, and in the case of AMP biosynthesis, two formyl-THF molecules and one CO2 are incorporated into the purine part of the molecule. To investigate whether the observed AL13C can be explained by the incorporation of C1 compounds originating from methanol, AL13C values were calculated assuming that: i. tetrahydrofolate activated C1 precursors were mainly derived from methanol ; ii. CO2 contains 59% 13C (13C). The calculated AL13C values were 19% (methionine), 11% (pantothenic acid) and 25% (AMP), and in agreement with the experimentally determined values. Thus, these results indicate that the incorporation of 13C is limited to metabolites that require C1 precursors for formation.Two additional metabolites involving the incorporation of C1 precursors showed elevated AL 59% 13C ; and iii13C incorporation increased in all amino acids , indicating that cells began to use methanol as a carbon sources in all biosynthesis processes after succinate depletion.To validate the approach taken and to demonstrate the enhanced assimilation of methanol into intracellular metabolites after succinate depletion, additional samples were taken at 90 minutes after succinate was consumed. Indeed, 13C was found for free Coenzyme A (22%), followed by Acetyl-CoA (20%). All CoA esters of C2 and C4 organic acids had AL13C values of approximately 19%. In addition, all CoA esters showed very similar mass isotopomer distributions  was observed. Thus, it can be deduced that the ethylmalonyl-CoA pathway was not operating during the co-consumption of succinate and methanol, which is consistent with the failure to detect key intermediates of the pathway that are detectable during methylotrophic growth The ethylmalonyl-CoA pathway is critical for providing anaplerotic support to the serine cycle during growth on methanol by glyoxylate regeneration ibutions . The dec13C label from 13C methanol seems to be restricted to metabolites for which biosynthesis requires C1 precursors. We used simulations to determine whether the observed 13C labeling into the CoA moiety also originated from the C1 precursors and was thus consistent with the CoA biosynthetic pathway . The measured AL13C of coenzyme A was 21.6%, indicating that almost all serine molecules might have contained 13C carbon from C1 units. These results support the theory that serine is predominantly produced from glycine and Me-THF during growth in the presence of methanol plus succinate and that one-carbon-units for biosynthetic purposes are produced from methanol.The incorporation of  pathway . The carM. extorquens AM1 2 production rate), indicating that the carbon balance is closed. The calculated methanol and succinate contributions to ATP, NADH, and NADPH flux production for a feasible solution are displayed in −1·h−1), indicating that an excess of NADH may result from co-consumption.The above data showed that methanol and succinate were co-consumed but that their carbon atoms had distinct metabolic fates. To better understand the cell physiology and especially the energetic contributions of both substrates during co-consumption, we performed a flux variability analysis using the genome-scale network available for M. extorquens AM1 co-consumes succinate and methanol under mixed substrate conditions. This result is something of a surprise in light of prior knowledge about the adaptation capacity of this model methylotroph. Indeed, the two pure culture conditions that were studied intensively in the past, methanol versus succinate, rely on the use of specific metabolic pathways on one hand and on driving metabolic fluxes in opposite direction through common enzyme steps, like the serine cycle, on the other hand. Consequently, rerouting the metabolic fluxes in a backward direction through some reactions involves a strong reprogramming of the central metabolism to achieve the metabolic switch between succinate and methanol M. extorquens AM1 co-consumes methanol in addition to succinate, and a specific metabolic network takes place that relies on the previously demonstrated modularity 13C-labeling revealed the partitioning of methanol and succinate to specific metabolic pathways and functions upon co-consumption. Methanol was mainly used to fulfill energy requirements; however, a portion of the consumed methanol entered biosynthetic pathways. The most promiscuous pathways were the biosynthetic pathways linked to one-carbon metabolism, such as purine biosynthesis from tetrahydrofolate derivatives; in addition, few carbons entered gluconeogenesis via C1 precursors and the first step of the serine cycle. In contrast, under mixed substrate conditions, succinate was used primarily to fulfill the carbon requirements of the cell and to supply roughly one-third of the energy requirements. Half of this energy is produced concomitantly with biomass precursor formation.The data reported here show that 2 assimilation via the phosphoenolpyruvate carboxylase and ethylmalonyl-CoA pathways, which represent 50% of the carbon assimilated into the biomass during pure methylotrophic growth conditions et al.M. extorquens AM1 grown in the presence of methanol and succinate was found to be significant, and no catabolic repression of the encoding gene occurred. Secondly, lower activities for the first enzymes of the anabolic serine cycle  compared to methylotrophic growth were found 4F +, acetyl-CoA, and weakly reduced by glyoxylate 4F concentration is expected to increase under methanol utilization, which could enhance C1 precursor biosynthesis; on the other hand, a change in the energetic state of the cell (NADP+/NADPH) M. extorquens AM1, in addition to the high modularity of the central metabolic pathways, is crucial to enable the co-consumption of a C1 substrate (methanol) and a C4 organic acid (succinate).The mechanism allowing the observed partitioning through the central metabolism involved the repression of methanol assimilation, not at the level of C1 activation but at the first step of the serine cycle. In addition, COM. extorquens AM1 rather than the successive use of succinate and methanol. First, it could be a strategy of resource management under environmental conditions. Indeed, methanol is only transiently released by plants during the diurnal cycle Corynebacterium glutamicum exhibits identical growth rates during growth on glucose and acetate and co-consumes both substrates at the same growth rate under mixed substrate conditions Methylobacterium and Corynebacterium reduce the uptake rates of both substrates under mixed substrate conditions. In contrast, Bacillus subtilis, which is able to grow on a mixture of glucose and malate, exhibits a higher growth rate during co-consumption than during pure culture conditions Bacillus subtilis, both glucose and malate catabolically repress several other substrates Escherichia coli and Azotobacter vinelandii, which exhibit different growth rates on glucose and acetate, respectively E. coli consumes glucose first and A. vinelandii switches from acetate to glucose metabolization.The metabolic strategy of co-consumption contrasts with the intensively studied phenomenon of diauxic and catabolic repression M. extorquens could be a strategy to optimize substrate utilization because methanol theoretically has a higher C-mol yield of energy units  as well as C1 units (∼ 150%) compared to succinate but only a slightly higher C-mol yield for other biomass production (115%), based on yield calculations using a genome-scale model Nonetheless, the co-consumption of methanol and succinate in in planta). The results presented here for the co-consumption of methanol plus a C4 organic acid by M. extorquens AM1 might suggest that heterotrophy could be as important as methylotrophy for leaf colonization capacity by Methylobacterium and is in line with the finding that methylotrophic bacteria in the phyllosphere are facultative methylotrophs rather than obligate methylotrophs Addressing the role of co-consumption challenges our current ability to measure cell physiology in situ (13C] methanol (99%) was purchased from Cambridge Isotope Laboratories; all other chemicals were purchased from Sigma . The acetonitrile, formic acid, and ammonium used as HPLC solvents were of LC-MS grade.[M. extorquens AM1 contained 1.62 g·L−1 NH4Cl, 0.2 g·L−1 MgSO4, 0.22 g·L−1 K2HPO4, 0.13 g·L−1 NaH2PO4·2H2O, and the following trace elements: 15 mg·L−1 Na2EDTA2·H2O, 4.5 mg·L−1 ZnSO4·7H2O, 3 mg·L−1 CoCl2·6H2O, 0.6 mg·L−1 MnCl2, 1 mg·L−1 H3BO3, 3.0 mg·L−1 CaCl2, 0.4 mg·L−1 Na2MoO4·2H2O, 3 mg·L−1 FeSO4·7H2O, and 0.3 mg·L−1 CuSO4 5H2O. Batch-culture was carried out in a 500-mL bioreactor (Infors-HT) at 28°C and at 1000 rpm, aerated with compressed air at 0.1 L·min−1. The pH was kept constant at 7.0 by the addition of 1 M NH4OH or 0.5 M H2SO4. Cells were grown in 400 mL of medium containing a mixture of 60 mM methanol plus 15 mM succinic acid . The partial pressure of dissolved oxygen was monitored using polarographic oxygen sensors . The methanol concentration was determined by GC-flame ionization detection (GC-FID) . The succinate concentration was determined by HPLC-UV-DAD (column: Phenomenex Rezex ROA-organic acid H+7.8 mm) using tartaric acid as an internal standard. The 13C enrichment of CO2 in the exhaust gas was measured using two infrared sensors , one sensitive to 12C CO2 and the other sensitive to 13C CO2. The calibration of each sensor and the specific correction of 12C and 13C signals were performed as recommended by the company. Cell dry weight (CDW) was determined upon growth on each substrate . The results of 7 cultures were not significantly different, and an overall CDW value was found to be 0.269±0.013 (2σ).The minimal medium used to grow g, 2 min, −5°C), and the supernatant was filtered through a Sartorius Minisart filter (pore size 0.2 µm) before analysis. Amino acids and central metabolites were sampled as described previously CoA-ester sampling was performed as follows: a volume of 1 mL of culture was directly injected into 4.5 mL of −20°C cold acidified acetonitrile containing 0.1 M formic acid on a Vortex LC-MS analysis was performed using a Rheos 2200 HPLC system (Flux Instruments) coupled to an LTQ Orbitrap mass spectrometer (Thermo Fisher Scientific) and equipped with an electrospray ionization probe. CoA-thioesters were separated using a previously described procedure The LC-MS system was equilibrated for 6 min under initial elution conditions between two successive analyses. The LC was coupled to the mass spectrometer. The sheath gas flow rate was 40, the auxiliary gas flow rate was 30, the tube lens was 80 V, the capillary voltage was 35 V, and the ion spray voltage was 4.3 kV. MS analysis was performed in the FTMS positive mode to analyze CoA esters and amino acids and in the negative FTMS mode for all other compounds at a resolution of 60,000 (m/z 400).13C label into metabolites during the 13C-labeling experiment was calculated from the analysis of the mass isotopomer distribution (MID) in the mass spectra. The resolution of 60,000 (m/z 400) allowed the separation of carbon, nitrogen and oxygen mass isotopomers; therefore, only carbon MIDs were considered, and correction for naturally occurring isotopes of the other elements was not required for most metabolites. However, the mass resolution of the Orbitrap decreases with increasing m/z values. In case of co-esters (m/z >800), analysis yielded mass resolution below 42,000, resulting in incomplete separation of the isotopes requiring correction for the contributions of N, O and S to MID. The standard deviations (STD) of the measurements were considered to be at least 2%, higher than found over the 3 technical replicates (average STD in amino acids: 0.53%). This was likely due to systematic error resulting from lower linearity of the LTQ-Orbitrap, which was evaluated to be lower than 2% in the intensity range considered. The average 13C labeling AL13C was calculated as follows:i relative abundance of the monoisotopic mass + i fraction.The incorporation of M. extorquens AM1 M. extorquens AM1 biomass composition determined during methylotrophic growth et al.Feasibility analysis and Flux variability analysis was performed using the genome-scale metabolic network of Figure S1Mass isotopomers distribution of central metabolites measured by LC-MS of M. extorquens13C (>99%) methanol and natural abundance (1.1% 13C) succinate. AM1 upon co-consumption with  Mass isotopomer data in black correspond to samples collected during mid-co-consumption phase Click here for additional data file.Figure S213C labeling in intra-cellular metabolites measured by LC-MS of Average M. extorquens AM1 at 90 minutes after succinate exhaustion (Sampling time 3 on ).(PDF)Click here for additional data file.Figure S3Mass isotopomers distribution of central metabolites measured by LC-MS of M. extorquens AM1 at 90 minutes after succinate exhaustion (Sampling time 3 of ).(PDF)Click here for additional data file.Figure S4Mass isotopomers distribution in CoA thioesters measured by LC-MS during growth of M.extorquens13C (>99%) methanol and natural abundance (1.1% 13C) succinate. AM1 upon co-consumption with  Metabolite quenching, extraction and measurements were performed as described in material and methods. Mass isotopomer data correspond to sample collected during mid-co-consumption phase Click here for additional data file.Figure S5Scheme of the Coenzyme A biosynthesis in M. extorquens AM1. Identified 13C carbon entry points are indicated by colored cycles. Red, C1-precursor from tetrahydrofolate pathway; yellow, CO2; green, C3 carbon of serine.(PDF)Click here for additional data file.Table S1Lists of parameters used for flux balance analysis and flux variability analysis.(PDF)Click here for additional data file.Table S2List of the reactions reduced for flux balance analysis upon co-consumption condition.(PDF)Click here for additional data file.Table S3Fluxes solution of the feasibility analysis.(PDF)Click here for additional data file.Table S4Fluxes solution of the flux variability analysis.(PDF)Click here for additional data file.Table S5Details of energetics calculations from flux balance analysis.(PDF)Click here for additional data file.


Synechococcus elongatus PCC 7942, that allows the identification of a specific function for a particular PGM. The strategy employs multiple parameter estimation runs in high CO2, combined with simulations testing a broad range of kinetic parameters against the changes in transcript levels of annotated PGMs. Simulations are evaluated for a match in metabolic level in low CO2, to reveal trends that can be linked to the function of a particular PGM. A one-isoenzyme scenario shows that PGM2 is a major regulator of glycolysis, while PGM1 and PGM4 make the system robust against environmental changes. Strikingly, combining two PGMs with reverse transcriptional regulation allows both features. A conclusion arising from our analysis is that a two-enzyme PGM system is required to regulate the flux between glycolysis and the Calvin-Benson cycle, while an additional PGM increases the robustness of the system.Phosphoglycerate-mutase (PGM) is an ubiquitous glycolytic enzyme, which in eukaryotic cells can be found in different compartments. In prokaryotic cells, several PGMs are annotated/localized in one compartment. The identification and functional characterization of PGMs in prokaryotes is therefore important for better understanding of metabolic regulation. Here we introduce a method, based on a multi-level kinetic model of the primary carbon metabolism in cyanobacterium  Phosphoglycerate-mutases (PGMs) are a group of non-homologous glycolytic enzymes 2 fixation, in which 3PGA represents the first stable carbon-fixation product of the Calvin-Benson cycle. The majority of 3PGA is used for the regeneration of the Calvin-Benson cycle, CO2 acceptor molecule ribulose-bisphophate. Excess carbon is taken out of the cycle and stored as glycogen or shuttled into the primary carbon metabolism via glycolysis (The preferred substrate of PGM is 3-phosphoglycerate (3PGA). In cyanobacteria, 3PGA is made by photosynthetic COycolysis . The fluSynechococcus elonagtus PCC 7942 (hereafter Synechococcus) indicated that cells shifted to low CO2 availability reduce Calvin-Benson cycle activity, stop glycogen storage, and increase the export of organic carbon through the glycolysis Synechococcus genome (http://genome.kazusa.or.jp/cyanobase/SYNPCC7942) four PGM isoenzymes are annotated . The routine employed for parameter estimation was a hybrid genetic algorithm . The model is available in the supplement .Synechococcus el. PCC 7942 −1 ratio was maintained in the physiological range 0.74–0.76 2 steady states and 2) the biomass production in high CO2 was calculated from growth rate data. On average, Synechococcus shows a 3.4-fold higher growth at 5% CO2  stored if the difference between the simulated and experimental metabolic data was lower than 15%. We also assumed that, in the case of isoenzymes in non-compartmented prokaryotic cell, a change in gene expression in the steady state is equal to changes in protein activity, i.e., minimal or no post-translation modifications. For the unidentified part of carbon metabolism - reaction catalyzed by PGM - four transcriptomic profiles (four PGM isoforms) were tested for combinations of one to four isoforms and the quality of match with metabolic data in low CO2 was stored for each profile.The parameter estimation for up to four annotated isoenzymes of PGM was run in high CO2 as well as low CO2Synechococcus cells were taken from 2 grown cells. The concentration of 2-phosphoglycerate was recalculated on the basis of known ratio to 3PGA Relative transcriptomic and metabolomics data of high COThe implementation of isoenzymes within a kinetic model, when localized in one compartment, deals with two extremes. First, for only one enzyme and the model not being constrained by a lack of data, there are still non-identifiability problems due to multiple sets of parameter value combinations that are equally able to fit the data related to a particular state. Second, considering two or more enzymes, the combinational explosion in the parameter space leads to computational requirements that make it virtually impossible to fit the model to sets of experimental data for different states of the system.Various studies have focused on the response of cyanobacteria to changes in the environment, by considering a range of omics data 2 for a single PGM and used these values in numerical simulations. The quality of the estimates is evaluated by comparing the quality of match of simulations with metabolic data, separately for preferred substrate, 3-phosphoglycerate (3PGA) and product, 2-phosphoglycerate (2PGA), in low CO2 .We started with the simplest scenario, encoding only one PGM, which is a common approach to un-constrained kinetic modeling. Although this approach cannot reveal anything about the regulation of catalyzed reactions in the un-constrained model, it is yet unknown if the outcome based on the constrained model can reveal some additional information about the system regulation or not. We therefore run a set of independent parameter estimations in high COmax in the enzymatic kinetics that describes the PGM. The first trend, exhibiting robustness in the system behavior (eq and kM). This pattern was tested for a broad range of Keq and kM values Click here for additional data file.


Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here.Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction  in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of  Advances in genome sequencing and computational modeling techniques have sparked the construction of genome-scale network reconstructions (GENREs) structural differences . However, existing approaches do not identify functional differences , or explain how structural differences impact the functional states of the network . Instead, models must be analyzed individually, and a number of simulations may be necessary before functional differences arising from structural differences are observed. Additionally, reaction alignment approaches can be time-consuming, since biochemical databases  to identify sets of orthologs in two organisms based on their genome sequences, and then we use CONGA to identify conditions under which differences in gene content (and thus reaction content) give rise to differences in metabolic capabilities. Because orthologs often encode proteins with the same function, we would expect their gene-protein reaction (GPR) associations, and thus their associated reactions, to be similar. Therefore, a gene-level alignment serves as a proxy for a reaction-level alignment. By identifying genetic perturbation strategies that disproportionately change flux through a selected reaction  in one model over another, we are able to functional differences  between the two organisms. Once these functional differences are found, they can be further evaluated to identify structural differences  between the organisms' network reconstructions. By using an MILP approach, we are able to identify these differences directly and in an exhaustive fashion, without manually aligning all reactions in the two networks.We have developed a bilevel mixed-integer linear programming (MILP) approach to identify functional differences between models by comparing network reconstructions aligned at the gene level, bypassing the need for a time-consuming reaction-level alignment. We call this new constraint-based method CONGA, or E. coli metabolism, iJR904 iAF1260 iAF1260 model is an update to the iJR904 model, constructed to more accurately reflect experimental data, including gene essentiality data and growth phenotypes We demonstrate that this approach can be used to study both closely- and distantly-related organisms and to address a variety of biological questions, by applying it to three pairs of organisms with increasing phylogenetic distance. We first examine differences between two published metabolic reconstructions of Synechococcus sp. PCC 7002, which we name iSyp611, by comparing it to the iCce806 reconstruction of Cyanothece sp. ATCC 51142 Synechococcus and Cyanothece.We have also used CONGA to aid in the development of a genome-scale network reconstruction of the photosynthetic cyanobacterium M. tuberculosis H37Rv S. aureus N315 Finally, we applied CONGA to compare the susceptibility of distantly-related human pathogens to loss of metabolic enzymes. We selected published networks of Through these three case studies, we demonstrate that CONGA can be used to rapidly compare metabolic networks regardless of phylogenetic distance. We are also able to show that CONGA has applications in metabolic engineering, model development, and antibiotic discovery. We show that CONGA can facilitate jamboree and network reconciliation efforts by pinpointing those metabolic or genetic differences which give rise to differences in model predictions.E. coli models), model development , and drug discovery (comparison of human pathogen models).We have developed a bilevel mixed-integer linear programming (MILP) approach, called CONGA, to identify functional differences between two networks by comparing network reconstructions aligned at the gene level. We have constructed an illustrative example to demonstrate the types of functional differences CONGA can identify. We then present three case studies and demonstrate how CONGA results have implications in metabolic engineering  and identifies deletions such that the specified flux difference is maximized while both models are simultaneously maximizing biomass .gene deletion set. CONGA can select any genes for deletion, with the restriction that orthologous genes present in both models be deleted simultaneously from both models. We note that while CONGA can calculate the flux difference between any two reactions, we believe that selecting equivalent reactions  provides the most useful objective for comparing models. Via manual investigation of the results, we are able to classify gene deletion sets identified by CONGA as arising due to one of four types of functional network differences:We refer to a solution identified by CONGA as a genetic differences, in which gene-protein-reaction (GPR) relationships differ between models;orthology differences, in which genes encoding enzymes with identical functions cannot be assigned as orthologs ;metabolic differences, where one organism has additional reactions which enable it to carry out unique biochemical transformations; andmixed differences, which arise due to some combination of types 1–3.Using two example networks, we demonstrate the types of functional differences CONGA can identify . Each reBM and P , though BM and P . By applBM and P .We first used CONGA to compute gene deletion sets maximizing h models . Under th models . For insh models . For exaCONGA can also identify how metabolic differences affect cellular phenotypes other than growth rate . In thisBecause production values may not be unique at the maximum growth rate, CONGA can artificially inflate flux differences between models. This can only occur when the fluxes whose difference is being maximzed  differ from the fluxes maximized by each model . In this case, we impose a a tilt on the objective of the inner problem. This tilt forces CONGA to identify deletions such that the specified flux difference is maximized when the individual fluxes through each reaction are at their lowest values that still support maximum biomass production. See E. coli, the iJR904 model iAF1260 model iAF1260 model extends the iJR904 model by compartmentalizing the network (separating the cytoplasm and periplasm), improving the biomass composition, and adding new metabolic reactions. The iJR904 model has been used frequently for metabolic engineering studies iAF1260 model's additional metabolic content affects computationally derived strain designs.We first used CONGA to compare two genome-scale metabolic models of iAF1260 model's larger network, we used CONGA to identify gene deletion strategies for three commonly studied fermentation products–ethanol, lactate, and succinate–seeking identical knockout conditions where the iAF1260 model predicted higher production rates than the iJR904 model, and vice versa. We refer to such strategies as model-dominant strategies. For example, an iAF1260-dominant strategy is one in which the same gene deletion set predicts higher chemical production in the iAF1260 model than in the iJR904 model. Because some of these knockout strategies result in nonunique chemical production rates, model-dominant strategies were identified with respect to the lowest possible production rate consistent with the maximum growth rate.To explore the effect of the iAF1260 model. We thus modified the iJR904 model to reflect these changes and repeated the simulations using the reconciled models. . When these two genes are deleted, ethanol synthesis is no longer possible in the iJR904 model, while the iAF1260 model can synthesize ethanol via a second pathway . In fact, the iAF1260 model requires five gene deletions to obtain yields greater than 10% of the theoretical maximum, while the iJR904 model requires only two gene deletions. These results demonstrate that CONGA can also be used to identify differences in the ease of coupling growth to chemical production in different models or organisms.The CONGA results for model-dominant strategies for the production of lactate and succinate were quite different . Here, 1 pathway . The douiJR904 and iAF1260 account for the production differences associated with each gene deletion set found by CONGA. Of the 46 total model-dominant strategies, 34 (74%) could be attributed to at least one of six metabolic differences between the two models , tpiA, and fsaB are deleted, disrupting glycolysis and the Entner-Doudoroff pathway, the iJR904 and iAF1260 models produce different products. The iJR904 model converts glucose into ribose-5-phosphate (r5p) via the oxidative and non-oxidative branches of the pentose phosphate pathway. The r5p is then converted to deoxyribose-5-phosphate and broken down into glyceraldehyde-3-phosphate (g3p), which enters glycolysis, and acetaldehyde , which gets converted to ethanol. In contrast, the iAF1260 model converts glucose to g3p and dihydroxyacetone phosphate (dhap). As in the iJR904 model, g3p enters glycolysis, while dhap enters the methylglyoxal (mthgxl) pathway. Some of the mthgxl is converted to 1,2-propanediol (12 ppd) via a unique 12 ppd synthesis pathway, while the remaining mthgxl continues through the pathway to make pyruvate.Many of these network differences affect the balance of possible fermentation products . For exahreonine . As notepathways . For exaiJR904 and iAF1260 networks to contain identical representations of each pathway , due to the larger network containing more ways to balance internal fluxes. These hypotheses were not borne out (with the notable exception of coupling succinate production to biomass), as we were able to predict similar production levels using both models. In fact, the production differences we did observe were due to only 21 reactions that represent just 3.5% of the 594 unique metabolic reactions in the iAF1260 model  and the iCce806 model of CyanotheceSynechococcus reconstruction.Having analyzed two models of the same organism, we then sought to analyze two models of closely related but distinct organisms, and to examine organisms less well-studied than iSyp611 model. Some of the gene deletion sets identified by CONGA arose due to missing genes in the draft iSyp611 model. For example, CONGA identified gene deletion sets containing protein synthesis enzymes present only in the iCce806 network. Synechococcus also has these proteins, but they had not been included in the model. Other network differences arose due to incomplete GPR associations in the draft iSyp611 model. For example, the iCce806 model associated HisB with both histidinol-phosphatase and imidazoleglycerol-phosphate dehydratase, while the draft iSyp611 network only associated the protein with histidinol-phosphatase. The original annotation indicated the gene was bifunctional, and the draft iSyp611 model was updated accordingly. This approach increased the size of the iSyp611 model from 542 to 611 genes, an increase in gene content of 13%. This increase in gene content is comparable to that seen in metabolic network reconciliation Pseudomonas aeruginosa and Pseudomonas putida by 3% and 18%, respectively.We first applied CONGA to the draft iSyp611) was compared again to iCce806 using CONGA. We identified 30 gene deletion sets that are lethal only in the iSyp611 model and 36 gene deletion sets that are lethal only in the iCce806 model (reaction deletion set). For example, we identified six gene deletion sets lethal in the iSyp611 model that all mapped to photosystem II. As a result of these and other redundancies, the 30 gene deletion sets for the iSyp611 model reduced to 20 unique reaction deletion sets, and the 36 gene deletion sets for the iCce806 model reduced to 18 unique reaction deletion sets.After refining the draft model based on these results, the resulting model (06 model . We founiSyp611 model and 10 of 36 gene deletion sets (or 5 of 18 reaction deletion sets) for the iCce806 model  for the 06 model .iCce806 model require deletion of two reactions from both models , which allows it to recover from this double deletion (blue arrows). In the second deletion set , which enables it to produce pyruvate and recover from the double deletion. A search of the Cyanothece genome failed to reveal candidate genes for ASPT and VPAMT, lending support to the hypothesis that they may be true metabolic differences between the two cyanobacteria.Two of the reaction deletion sets which are lethal only in the h models . In the h models , deletiotion set , deletioSynechococcus and Cyanothece share a significant number of pathways, with important differences in central and amino acid metabolism.CONGA reveals differences that can be used to reconcile and improve genome-scale metabolic models of closely-related species. We intend to use the remaining genetic and orthology differences found by CONGA as a starting point in further updating our reconstruction, as they may indicate missing or incorrectly annotated genes. CONGA can also identify differences in metabolic capabilities between models: our analysis here indicates iNJ661 model of M. tuberculosisiSB619 model of S. aureusWhile were able to idenitfy metabolic differences between the two cyanobacteria, many of the differences identified by CONGA were not due to reaction-level differences. We thus sought to use CONGA to explore differences in metabolic capabilities between two dissimilar oganisms, and to exploit those differences to identify organism-specific drug targets. For this application, we applied CONGA to existing models of two phylogenetically distant human pathogens, the iSB619 model and 36 reactions in the iNJ661 model  could be traced in whole or in part to genetic or orthology differences. As these differences made up the majority of identified differences, we manually evaluated the quality of the orthology assignments and the original GPR associations. This analysis resulted in the modification of the GPR associations for 19 reactions in the 61 model . As a reiSB619 reconstruction modeled PDH as an overall reaction, and GCC via its three individual reactions  and pyruvate dehydrogenase system (PDH) in the two models . Both GCeactions . In conteactions . Due to o models . These ciSB619 model and 84 gene deletion sets lethal only in the iNJ661 model  and NAD to produce biomass. Unfortunately, many staphylococci are already resistant to inhibitors of THF synthesis S. aureus uses for NAD synthesis have only recently been identified M. tuberculosis can grow in media lacking THF and NAD iNJ661 biomass equation may reflect a model development choice, rather than a biological difference. We identified M. tuberculosis' unique use of siderophores for iron transport, for which biosynthesis inhibitors have been identified M. tuberculosis-specific antibiotics. Of the remaining organism-specific metabolic functions, two candidate antibiotic targets (nicotinamidase in S. aureus and siderophore synthesis in M. tuberculosis) had not been identified by previous computational studies of these models We also identified a variety of other metabolic functions which antibiotics do not yet target. For example, the By comparing pathogens against each other, we are able to identify essential functions unique to a particular pathogen. This enables the identification of narrow-spectrum antibiotics tailored to individual pathogens. It is believed that the use of such antibiotics can overcome multi-drug resistance through novel mechanisms of action M. tuberculosis and S. aureus genes which were mutual best-BLAST hits of each other, called bidirectional best-BLAST hits. An E-value of Before CONGA can be applied to a pair of metabolic models, a gene-level alignment must be performed. We perform this alignment by identifying the orthologous genes between the two models, and we force CONGA to select ortholog pairs as a single unit. Prior to applying CONGA to the pathogen models, we examined three methods for identifying orthologous genes . The firM. tuberculosis and S. aureus genes were included in the iNJ661 and iSB619 models, respectively. We found that the number and content of ortholog calls depended on the method used  orthologs being involved in vitamin and cofactor synthesis. Many of these 35 genes are involved in the assembly of metal clusters and would not generally be included in a metabolic model. Finally, we observed that metabolic genes are enriched for members of an ortholog pair: 37% (229 of 619) of genes in the iSB619 model had orthologs in the iNJ661 model, while only 21% (528 of 2515) of genes in the S. aureus genome had orthologs in the M. tuberculosis genome  in one network or the other. We demonstrate that our algorithm can be used to identify genetic, orthology, and metabolic differences between reaction networks with applications in metabolic engineering, model development, and antibiotic discovery.In this work, we developed a bilevel mixed-integer programming approach to identify the functional differences between networks by comparing network reconstructions aligned at the gene level. The constraint-based method first identifies a set of orthologous genes based on genome sequence, and then identifies conditions under which differences in gene content give rise to differences in metabolic capabilities. Our gene-centric approach allows for the rapid identification of iSyp611 model was constructed from a draft iCce806 model containing 591 genes. Orthologs for 537 of these genes were copied to the iSyp611 model, representing a 9% gene loss. Of the 54 Cyanothece genes for which a bidirectional best-BLAST search did not identify orthologs in Synechococcus, manual curation identified orthologs for 26 of them. While these orthologs were not bidirectional best-BLAST hits, we decided the genes had sufficiently high sequence similarity and sufficiently similar annotations to be considered orthologs.  S. aureus and M. tuberculosis models yielded 41 false positives . If one model had been created from the other, these genes would have incorrect reactions associated with them. Manual assessment of the cyanobacterial bidirectional best-BLAST hits yielded 35  false positive orthologs in the draft iSyp611 model, which were subsequently removed from the final reconstruction. Thus, false positive ortholog calls represent a significant problem even for closely-related organisms.Increasingly, new genome-scale reconstructions are being created by identifying bidirectional best-BLAST hits against genomes for which models have already been constructed. GPR and reaction annotation information can then be copied into the new model . Additional reaction differences could be picked up by CONGA if other environments , growth conditions , and objective functions  were considered, and if orphan reactions (those without a GPR association) could be deleted as well (since 20 of the 229 unique reactions did not have GPR associations). Despite the inability to identify all structural differences, CONGA can identify those gene (and thus reaction) differences which give rise to differences in predicted growth and production rates, as well as other phenotypes. As a result, we believe that it will be a useful tool to complement existing model reconciliation and comparison efforts, such as jamborees.Our approach represents a significant advance in comparing genome-scale network reconstructions. CONGA is a single instance of a broader approach, in which two different networks are compared and analyzed for functional differences. This represents a significant advance over existing model-comparison approaches iAF1260 model includes a periplasmic space and an explicit (instead of lumped) representation of fatty-acid biosynthesis. Many of the reaction differences between the iJR904 and iAF1260 models arise due to these differences in model abstraction. In such a scenario, a reaction-alignment approach may be challenging, whereas using CONGA we can identify how these abstraction differences affect model predictions. CONGA can also be used to examine abstractions at the level of constraints, by comparing identical models with otherwise different constraints based on gene expression, regulation, or thermodynamics. Finally, we envision our approach being used to examine cellular behavior under different environmental conditions, or to compare evolved and un-evolved cellular phenotypes. Ultimately, a comparative approach such as ours will enable rapid evaluation of the influence of network and model differences on predicted functional states.While this work identified gene deletions pointing to functional metabolic differences, other network perturbations may be equally effective indicators of network differences. Robust algorithms for identifying other types of perturbations have also been developed The CONGA framework employs a bilevel optimization problem to identify genetic perturbations which disproportionately change flux through a selected reaction  in one organism over another . The outThe FBA formulation for each model's inner problem is shown below:j in the set of reactions j has a flux given by c is a vector of weights. We choose to maximize for biomass alone, in which case j consumes and produces some metabolites i in the set of metabolites I, with stoichiometry given by Each reaction y and determined by GPR constraints embedded in the outer problem:On-off reaction states are given by the binary variable g in the set of genes G, protein p in the set of proteins P, and reaction j in the set of reactions J has a corresponding binary variable z, w, and y, respectively, which determines the gene, protein, or reaction's on-off state.  is maximized with respect to the other (B). So long as growth is unconstrained, an objective of the formThe final formulation results from using equation (6) as the outer objective, and accumulating equations (1)–(5), (7), and (8) as constraints. Equations (1)–(5) and (7) must be imposed for each species:To facilitate the solution process, we reformulated the bilevel program as single-level MILP by replacing the inner maximization problems with their optimality conditions, in accordance with strong duality i must satisfy the mass balance, for which we introduce the unconstrained dual variable This reformulation requires a new variable for each constraint of the inner problem. Each metabolite Constraints (11) to (13) can be implemented using big-M constraints The single-level formulation can then be constructed by using equation (6) as the outer objective, equating the primal and dual objectives (1) and (9) for each network, including constraints (2) to (5), (7), and (10) to (14) for each network, and adding equation (8). Equating the primal and dual objectives of the inner problem givesWe also implemented integer cut constraints E. coli models, we sought gene deletions maximizing chemical production in one model with respect to the other. For these simulations, a few modifications from the previous formulation are required. First, the outer objective, equation (6), was altered to reflect chemical production flux. The vector c was changed to a standard basis vector along the production flux of interest. We denote this objective as To identify model-dominant chemical production strategies in the nonunique phenotype for a particular chemical, in which multiple chemical production values can occur at the maximum growth rate. Under such conditions, CONGA can artificially inflate flux differences between models, by choosing a large production rate in one model and a small production rate in the second. We thus imposed a tilted objective function on each inner problem, which maximizes biomass while imposing a small penalty  differ from the fluxes maximized by each model . We found this formulation is sensitive to the value chosen for We also constrained the dual variables associated with the reaction deletion constraint to be between  to improve solver performance Finally, we constrained both models to have nonzero biomass. The final, single-level formulation can be expressed as:A sample implementation of CONGA for the network in O (equation (8)) and using reaction instead of gene deletions (equation (7)).Finally, we note that CONGA can be rewritten to consider a reaction alignment and reaction deletions, by redefining the set In order to reduce simulation times, we eliminated essential and blocked genes from consideration as possible deletions by CONGA. For each of the six models, we performed single-gene deletions with all possible nutrients provided to the model in order to identify essential genes (those required for cellular growth). Genes whose orthologs were essential in both models as well as essential genes without orthologs were then excluded from consideration by CONGA. Flux-variability analysis (FVA) E. coli models, we performed additional steps in order to improve the run-time performance of CONGA. First, we identified essential and blocked genes (as described above) on glucose media under anaerobic conditions . We also removed from consideration all genes associated with membrane transporters. These two steps forced CONGA to consider only metabolic genes that can be active under the simulation conditions. Finally, we developed a procedure to reduce the number of genes needed to determine the on-off state of each reaction. For a set of isozymes catalying the same set of reactions, all but one isozyme were fixed to the off state, such that deleting the remaining isozyme forced the set of reactions to the off state. Likewise, for a set of subunits which are all components of the same protein, all but one subunit were fixed to the on state, such that deleting the remaining subunit forced the protein to the off state. These sets of isozymes and subunits were identified using a mixed-integer programming (MIP) approach  website iCce806 model to create a draft reconstruction using SimPheny .The model was constructed from a draft version of the Cyanothece  accessed via the General Algebraic Modeling System . Simulations were performed on a Red Hat Enterprise Linux server with 2.66 GHz Intel Xeon processors and 8 GB of RAM. CONGA can identify a lethal gene deletion set containing just one gene in less than a second. Lethal gene deletion sets containing two or three genes took on average 3 minutes to identify. Identifying model-dominant chemical production strategies is more time-consuming, with a model-dominant strategy containing two genes requiring on average 2 minutes. Model-dominant strategies containing three, four, or five genes took an average of 15 minutes, 75 minutes, and 5 hours to identify, respectively. A full summary of differences identified by each simulation can be found in Figure S1Model-dominant production strategies for lactate. (A) Deletion strategies for lactate production. Each bar represents the absolute difference in predicted lactate yields between the iJR904 and iAF1260 models as a fraction of the maximum theoretical yield (2 lactate/glucose). Left side: Strategies for which the iAF1260 model predicts higher production. Right side: Strategies for which the iJR904 model predicts higher production. Corresponding gene deletion strategies involving 3, 4, or 5 genes are given below the figure. Numbers above each bar indicate the fraction of the theoretical maximum yield obtained by each model, with the dominant model listed first. Some strategies have a nonunique lactate production phenotype, in which multiple lactate production values can occur at the maximum growth rate. For these scenarios, the production difference calculated by CONGA is from the lowest expected level of lactate production in each model, and such strategies are indicated in green. Strategies for which the yield of the dominant model meets or exceeds the yield for the third-best OptORF strategy for that model are known as OptORF strategies, and such strategies are indicated in red. OptORF strategies which also have a nonunique production phenotype are indicated in orange. (B) The same gene deletion strategies after reconciliation of the iJR904 and iAF1260 networks with respect to metabolic differences.(PDF)Click here for additional data file.Figure S2Model-dominant production strategies for succinate. (A) Deletion strategies for succinate production. Each bar represents the absolute difference in predicted succinate yields between the iJR904 and iAF1260 models as a fraction of the maximum theoretical yield (1.71 succinate/glucose). Left side: Strategies for which the iAF1260 model predicts higher production. Right side: Strategies for which the iJR904 model predicts higher production. Corresponding gene deletion strategies involving 2, 3, or 4 genes are given below the figure. Genes enclosed in parentheses indicate a deletion unique to the iAF1260 model. Numbers above each bar indicate the fraction of the theoretical maximum yield obtained by each model. Strategies for which the yield of the dominant model meets or exceeds the yield for the third-best OptORF strategy for that model are known as OptORF strategies, and such strategies are indicated in red. (B) The same gene deletion strategies after reconciliation of the iJR904 and iAF1260 networks with respect to metabolic differences.(PDF)Click here for additional data file.Figure S3Differences in metabolic pathways in E. coli GENREs. The text above each map indicates the pathway responsible for the phenotypic difference, the phenotype with which the strategy is associated, and the gene deletion for which the phenotype occurs. (A–C) Schematic views of the flux distributions associated with the indicated gene deletion set. Metabolites are represented in plain text. Metabolites are represented in plain text. Metabolic transformations are indicated via arrows, with thicker arrows indicating higher flux. In some instances, multiple transformations are combined into a single dashed arrow. Fluxes active in the iAF1260 network are in red, fluxes active in the iJR904 network are in blue, and inactive fluxes are in grey. If gene (reaction) deletions occur in the fermentation pathway, they are indicated by black ‘X’s. Fluxes crossing the dashed boundary indicate transport to the extracellular environment. Metabolite abbreviations: 2aobut, L-2-Amino-3-oxobutanoate; actp, acetyl phosphate; athr, allo-threonine; glc, glucose. All other abbreviations match those used in the iSyp611 metabolic model . (A) As a consequence of growth, E. coli produces protons which must be consumed by reactions in the network or secreted into the media. The succinate (succ) antiporter in the iAF1260 model makes succinate transport energetically unfavorable, so flux is instead diverted to ethanol (etoh). (B) This knockout eliminates ATP synthase, necessary for pumping protons into the cytosol. Deletion of ATP synthase forces protons to be consumed via the fermentation reactions. The ability of the iAF1260 model to transport C) Wild-type FBA predicts that both E. coli models uptake glucose via the phosphotransferase system (PTS), and convert the resulting glucose-6-phosphate (g6p) to fructose-6-phosphate (f6p). The deletion of pgi prevents this transformation. The iAF1260 network contains an additional hexokinase which allows it to bypass the deletion by bypassing the PTS. The iJR904 model lacks this reaction and is forced to use PTS. The deletion of edd then forces the iJR904 network to metabolize glucose via the oxidative branch of the pentose phosphate pathway. This process generates electrons, leading to higher levels of fermentation products than in the iAF1260 model.(PDF)Click here for additional data file.Dataset S1SBML version of the iSyp611 model.(XML)Click here for additional data file.Dataset S2Microsoft Excel versions of the iSyp611 model. Description of stoichiometric and annotation changes made to each of the six metabolic models examined over the course of this study.(XLSX)Click here for additional data file.Dataset S3Computational results of all simulations. This Microsoft Excel file contains 1) the simulation conditions for each study, 2) the full results of all CONGA simulations performed in this study, 3) all ortholog assignments used in performing the CONGA simulations, and 4) results from each ortholog calling method applied to the human pathogen models.(XLSX)Click here for additional data file.Text S1Sample implementation of CONGA formulation in GAMS format. A free demo version of GAMS can be downloaded from http://www.gams.com. This file contains the example network described in (GMS)Click here for additional data file.Text S2Variable Reduction Procedure. This section contains additional details about the algorithm used to decrease the number of genes considered by CONGA.(PDF)Click here for additional data file.