Clostridium beijerinckii co-cultures as representative examples, models predict cross-fed metabolites will simultaneously modify multiple internal pathways, evident by different internal metabolic network structures. Differences in degree and betweenness centrality of hub precursor metabolites were correlated to C. beijerinckii metabolic outputs, and thus demonstrate the potential of co-cultures to differentially direct metabolisms to useful products.Food waste represents an underutilized resource for commodity chemical generation. Constituents of the human gut microbiota that are already adapted to a food waste stream could be repurposed for useful chemical production. Industrial fermentations utilizing these microbes maintain organisms in isolation; however, microbial consortia offer an attractive alternative to monocultures in that metabolic interactions may result in more efficient processes with higher yields. Here we computationally assess the ability of co-cultures vs. monocultures to anaerobically convert a Western diet to commodity chemicals. The combination of genome-scale metabolic models with flux-balance analysis predicts that every organism analyzed can benefit from interactions with another microbe, as evidenced by increased biomass fluxes in co-culture vs. monoculture. Furthermore, microbe combinations result in emergent or increased commodity chemical production including butanol, methane, formaldehyde, propionate, hydrogen gas, and urea. These overproducing co-cultures are enriched for mutualistic and commensal interactions. Using  The U.S. Army alone generates tons of food waste each day at contingency operating bases2. Standard remediation practices involve combustion and disposal in landfills, thereby perhaps wasting energy dense substrates and generating pollution. Though no longer palatable to humans, food waste is a rich nutrient source for microbial growth. Exposed to the environment, microbes will colonize the waste in diverse consortia, as observed in composting5. This process will be a complex interplay of microbes cooperating and competing and not just monoculture growth that we are accustomed to in the laboratory. If we can understand the assembly of microbial consortia, then we may be able to direct complex microbial metabolisms to not only efficiently degrade waste streams, but also to direct the products toward commodity chemicals.Food waste generation in the United States is a widespread remediation problem. A recent study estimated that 0.615 pounds of food waste was produced per person per day2, CH4, polymer precursors, and fertilizers7. The combinatorial potential of gut microbes’ collective metabolic action is even greater and has remained unexplored for commodity chemical production.Though environmental microbes can degrade food waste, we need not look farther than our own digestive tracts to find a diverse suite of microbes and metabolisms (collectively known as the gut microbiota) that aid digestion of our food. The majority of research has centered on health effects, but these microbes could be used in a new context: food waste remediation to commodity chemicals. Gut microbiota species encode powerful degradative enzymes such as cellulases, hemicellulases, pectinases, and amylases; and produce a plethora of useful compounds including short-chain fatty acids (SCFAs), alcohols, H9. A diversity in metabolisms also results in high niche occupancy, thus reducing the chance of invasion10. There are natural examples of the efficacy of microbial consortia for accomplishing tasks, including fecal microbiota transplants to resolve recurrent Clostridium difficile infections11 and microbial communities to treat wastewater12. Consortia can also work usefully in vitro: electrosynthesis of methane, acetate, and hydrogen gas provides an excellent example13.Consortia are advantageous because complex tasks are divided amongst members, leading to reduction of intermediates14. GSMMs and FBA have proven remarkably effective in recapitulating single organism metabolisms in vitro17. This type of framework has also been broadened to model organism interactions within consortia under steady-state and dynamic conditions22. These multi-species metabolic modeling methods allow for computational evaluation of ecological interactions and production potential of microbial consortia, which can inform experimentation.The use of microbial consortia for chemical production is still rare due to difficulty in prediction, and thus control, of community assembly. Furthermore, the number of combinations of species from which to build a consortia is near infinite. A predictive framework is desired to screen and choose members and combinations to produce useful chemicals. Genome-scale metabolic models (GSMMs), combined with flux balance analysis (FBA), provides an attractive framework. To create a GSMM, an annotated genome is the only requirement, which makes this framework advantageous for organisms for which we do not have cultured isolates23 used GSMMs to determine seed metabolites (compounds that cannot be endogenously produced) for gut microbes, then compared the overlap of these compounds for co-occurring species in gut microbiome surveys24. Based on this analysis, co-occurring species generally exhibited a high competition index indicating that habitat filtering is likely to be the major mechanism that structures gut microbiotas23. Zelezniak et al.25 also observed this phenomenon, but further identified subcommittees that displayed a high degree of cross-feeding and cooperativity. Klitgord and Segre26 further explored ecological interactions’ reliance on nutrients by computationally finding media components that could push pairwise interactions to be mutualistic. Mutualistic interactions generally involved cross-feeding a growth promoting nutrient or removal of an inhibitory metabolite26. Though these studies have broadly informed consortia design, they did not assess the abilities and efficiencies of these communities to produce compounds of interest.Metabolic modeling of consortia has suggested different ecological strategies of assembly of natural microbial communities. Levy and Borensteinet al.27 computationally identified that pairwise growth of gut microbes can result in emergent metabolite secretion (metabolites not produced by either member in monoculture). Building on these findings, algorithms have been developed to design non-natural consortia for conversion of user specified inputs to targets29. In particular, the CoMiDA algorithm not only identifies the possible combinatorial paths from input to output, but also minimizes the number species included in that path28. However, if one species can produce the output in monoculture, the program will not return other consortia options that may be more efficient or generate higher yields.There still remains the question of whether microbial consortia provide additional advantages over monocultures for commodity metabolite production. Chiu et al.30. These models have all been equally refined and curated to predict anaerobic growth in media based on a Western diet, which we use as a proxy for food waste. As in Magnusdottir et al.30, we simulated all monocultures and co-cultures, but here we focus on co-cultures that produce commodity chemicals and dissect the metabolic and ecological interactions that contribute to these phenotypes. We find that for every isolate model, there exists a co-culture that increases its biomass production. Co-cultures can produce emergent metabolites, as observed before27, and also commodity chemicals in greater than additive ways compared to monocultures. These overproducing co-cultures are enriched for mutualistic and commensal interactions. The number of cross-fed metabolites is also higher for these co-cultures and network analysis indicates that these metabolites simultaneously modify multiple pathways in the internal metabolic networks of each member. Overall, this screen provides the first step towards repurposing human gut microbes to remediate food waste to useful chemicals.Given this challenge, we sought to computationally assess pairs of microbes for ability to not only produce emergent metabolites, but also overproduce compounds of interest. For this screen, we leveraged a large bank (773) of gut microbe GSMMs graciously made available by Magnusdottir et al.30 built and refined GSMMs for 773 different gut microbe isolates and kindly made them publicly available. We sought to computationally build consortia with these models, and to make the simplest first step: going from one organism to two. As a proxy for food waste, we used the “Western diet” outlined in Magnusdottir et al.30, combined with FBA simulations of every pairwise combination of the AGORA models  to generate a rich dataset. We asked whether we could re-purpose human gut microbes for commodity chemical production and whether co-culturing could boost production of useful chemicals.In this study, we sought to computationally screen microbial consortia for anaerobic remediation of food waste to commodity chemicals. We modeled interactions between microbes with GSMMs combined with FBA. We leveraged research from the human gut microbiome, which has the most sequenced genomes by far from any environment. Recently, Magnusdottir et al.30 using slightly different methods. Instead of the COBRA31 Toolbox in MATLAB, we used the sybil32 package in R. We used the publicly available GLPK solver instead of CPLEX . Finally, we have implemented the AGORA v1.01 model developers’ recent reaction constraint modifications that differ with the published conditions. Given these discrepancies, we still observed good correlation of monoculture biomass fluxes with the anaerobic Western diet . The correlation of co-culture biomass fluxes with the same diet was lower .We first confirmed that we could re-capitulate the results from Magnusdottir et al.30 (see Methods). We further classified the interaction type for each co-culture (see Methods) and counted the types for all co-culture simulations for each microbe (Supplementary Table 4). This table is useful for selection of strains that are likely to be mutualistic. The model with the most mutualistic co-culture simulations was Enterococcus saccharolyticus DSM 8903, followed closely by Eubacterium desmolans ATCC 43058 (Supplementary Table 4). These results indicate that co-culturing can be an effective method to boost a microbial biomass production. Furthermore, predicting mutualistic co-cultures is useful to guide future experimental screening of stable, robust consortia.To gain insights into overall production of co-cultures, we assessed biomass flux effects in monoculture versus co-culture. For each microbe model, we observed increased biomass production when simulated in the presence of another microbe Fig. . We clas2O) , methane and hydrogen (combustible gases), urea (nitrogen fertilizer), propionate (SCFA and polymer precursor), and formaldehyde (resin precursor). We compared export chemical fluxes for all monocultures, all theoretical additive monoculture fluxes, and the simulated co-culture fluxes Fig. , and obs2O) Fig. . Therefop < 2.2e-16; commensal p < 2.2e-16; neutral, parasitic, amensal, competitive p = 1). We filtered for overproducing co-cultures that were neutral, commensal, or mutualistic because these interaction types are likely to be more long-lived in a dynamic bioreactor scenario. We examined the frequency of transfer metabolites between models in these co-cultures and observed that mutualistic overproducing co-cultures exchanged more metabolites compared to neutral or commensal co-cultures . These transferred metabolites fall into different transfer types: “End product removal”; “Cross fed”, “End product removal and cross fed”; and “Other” (see Methods). We observed that “Cross fed” metabolites are enriched in overproducing co-cultures, particularly with mutualistic interactions , we decided to focus on these “hub precursors” for downstream analyses.Based on the extracellular exchanges outlined above, we queried how these metabolites influence internal pathways thus resulting in modified outputs. We hypothesized that these changes would likely manifest in changes to hub metabolite network statistics. Therefore, we created networks for each organism (see Methods). To find hub metabolites, we calculated the mean degree and betweenness centrality for each metabolite over all monocultures and defined hubs as having the highest values of each metric Fig. . Twelve Clostridium beijerinckii NCIMB 8052. This microbe is predicted to produce hydrogen gas in monoculture, and some co-cultures result in greater than additive production. To uncover the internal metabolic shifts that result in these phenotypes, we analyzed macroscopic network flux shifts for C. beijerinckii simulated in monoculture . We thus advocate for the use of GSMMs to screen and identify consortia of interest. These co-cultures then require in vitro experimentation to evaluate prediction accuracy and aid in the decision to invoke non-metabolic interactions in further modelling.Though GSMMs can remarkably predict single and multi-species phenotypes38, DMMM18) and dFBA with spatial components  could be used to further evaluate greater than additive co-cultures identified in our screen. These methods can further inform experimentation parameters, and provide kinetic frameworks that could also model non-metabolic interactions. The steady-state assumption may convolute predictions of long-term species interactions, especially species with large differences in growth rates39. A recently developed FBA method, SteadyCom, provides a framework in which to model consortia with time-averaged constant growth rates of all members, thus providing community stability39. Given this point, we filtered our combinations of interest to mutualistic and commensal interactions. These interaction types are more likely to promote long-term multi-species co-existence40.The choice of FBA to evaluate combinations of GSMMs is another limitation. We assumed that consortia are at a steady-state with no spatial structure or dynamic interactions, clearly not the case in nature. However, single point FBA allowed us to screen all pairwise combinations of 773 models, which would have taken exponentially more time if considering multiple spatial factors over time. Dynamic FBA  for reactions in co-culture. Though we modeled the same microbe combinations in Magnusdottir et al.30, the correlation of biomass fluxes was good, but not perfect. Our use of pFBA likely did not bias our findings, since the biomass fluxes are first maximized with FBA. However, we used a different linear solver (GLPK vs. CPLEX) and we did not constrain lower and upper biomass reaction bounds as was done in Magnusdottir et al.30. Experimental co-cultures have demonstrated that microbes can be completely inhibited by co-culturing, so we chose not to constrain lower biomass bounds above zero. Also in contrast to Magnusdottir et al.30, we only simulated microbes with a Western and High-Fiber diet under anaerobic conditions , which sets the biomass flux to the maximum obtained with FBA, and subsequently minimizes the total absolute flux. The assumption with pFBA is that organisms seek to minimize enzyme production, which has been shown to be an appropriate assumption in recapitulating single organism growthThe microbe combinations identified in this study with the ability to overproduce commodity chemicals represent numerous avenues of future research. From an ecological standpoint, we found an overrepresentation of mutualistic and commensal interactions, and the mutualistic interactions generally involved more cross-fed metabolites Fig. . The mutC. beijerinckii demonstrated the complex metabolic changes that occur in response to different microbes. The combination of transferred metabolites coordinate to modify the internal network structure. Microbes can metabolically alter outputs from other microbes by transferring metabolites that will exert effects through hub precursor metabolites. From an industrial perspective, these predictions highlight the potential of co-culturing as an alternative to genetic manipulation for modifying and optimizing target chemical production. Typically, industrial fermentations involve highly controlled monoculture conditions with genetic modifications to increase flux through desired metabolic pathways. Co-culturing offers a means to redirect metabolism for microbes that do not have established genetic modification methods, and thus opens the door to microbes with unique metabolisms for industrial commodity chemical production.Interestingly, the case study of 42. This approach also scales up to test the effects of more complex consortia (3+), but obviously the computations increase exponentially. Overall, this study predicts that consortia can outproduce monocultures. The benefits of consortia have been observed in several case studies43, but we predict that by selectively choosing different combinations of microbes, we can direct the same food waste nutrients to different useful products.This work represents the first step in designing agile and efficient consortia. These simulations now provide a framework to understand multi-species interactions after bringing these isolates into the laboratory for experimentation. The study also demonstrates the power of the human gut microbiota in a new context, food waste remediation to commodity chemicals. We also envision this approach for the design of probiotic combinations of microbes to direct a set of prebiotic nutrients to desired SCFAs, for example. Note that consortia can also overproduce detrimental metabolites such as TMAO Fig. , which h30. As noted on their website, models were modified after publication30. These modifications included gap-filling for 17 models that could not grow under anaerobic conditions, replacement of unlikely reversible reactions with irreversible reactions, and changes to exchange flux bounds for diet components. The lower and upper exchange bounds for the Western and High-Fiber diets are included in Supplementary Table Genome-scale metabolic models for 773 gut microbes (AGORA v 1.01) were downloaded from http://vmh.uni.lu 44 with plots created using ggplot2 v 2.2.145. All custom scripts are included in Supplementary Information. Briefly, SBML models were uploaded with sybilSBML v 3.0.146 package, and the sybil v2.0.432 package was used for model manipulations and FBA. Exchange reaction bounds were modified according to Supplementary Table glpkAPI package v 1.3.047). The maximum biomass flux was then used to run parsimonious enzyme usage FBA  so that the total absolute flux was minimized. All resulting single model biomass fluxes are included in Supplementary Table −1 were used.All simulations and analyses were performed in R v3.4.0et al.30. The COBRA31 MATLAB script, createMultipleSpeciesModel.m found at: https://github.com/opencobra/cobratoolbox, which is based on the FBA implementation in Klitgord and Segre26, was used as a template. This script was converted to work in R with the sybil package (Supplementary Information) and creates a common environment for metabolites to be exchanged between models, but does not create a host compartment as in the COBRA implementation. After combining the models, exchange fluxes were updated for the Western diet, and pFBA was used to simulate growth, simultaneously maximizing each model’s growth and minimizing the total absolute flux. In contrast to Magnusdottir et al.30, the lower biomass flux bound was set to 0 and the upper bound set to 1000. All simulations were not able to be solved (given a 5 min timeout) resulting in solutions for 297549 of 298378 combinations. All pairwise biomass fluxes are included in Supplementary Table −1 were used.Growth for all pairwise combinations of the 773 models were simulated using the Western diet described above. For each combination, models were combined in a similar manner to Magnusdottir et al.30. If the co-culture biomass flux was at least 10% greater than the monoculture biomass flux, a positive effect was assigned (+1). If the co-culture biomass flux was at least 10% less than the monoculture biomass flux, a negative effect was assigned (−1). Otherwise, a neutral effect was assigned (0). The following interaction types were then assigned:  neutral;  commensal;  mutualistic;  competitive;  amensal;  parasitic. Transferred metabolites were classified as follows: “Cross fed” if taken up by the microbe in monoculture and co-culture simulations, “End product removal” if output for one monoculture and input for the other microbe in co-culture, “End product removal and cross fed” if both classifications applied, and “Other” if neither classification applied.Ecological interaction types were assigned as in Magnusdottir +, H2O, ATP, ADP, AMP, NAD+, NADH, NADP+, NADPH, Pi, PPi, CoA, O2, NH4+, H2O2, SO42−, SO32−, NO2−, NO3−, H2S, H2CO3, NO, S, Fe2+, Fe3+, Co2+, HCO3−, and H2. Note that metabolite abbreviations are included in Supplementary Information. Currency metabolite pairs were also removed according to Hamilton et al.48. Graphs were further pruned according to simulated FBA fluxes. Networks were pruned to the strongest connected component and any edges corresponding to a reaction with absolute flux less than 1e-05 mmol/gDCW/h were removed. The igraph package v 1.1.249 was used to construct the networks and calculate normalized degree and betweenness for each metabolite.To create network graphs for each GSMM, species specific reactions and any reactions involving external metabolites were removed. These reactions included those with the following labels: “Transport, extracellular”, “Exchange/demand reaction”, “Exchange”, “Fatty acid synthesis”, “Cell wall biosynthesis”, “Nucleotide interconversion”, “Terpenoid backbone biosynthesis”, “Lipopolysaccharide biosynthesis”, and “Glycerophospholipid metabolism”. For each remaining reaction, edges were created for each reactant to product and product to reactant if the reaction is reversible. Highly connected metabolites were then removed, including Het al.30, Pearson’s product-moment correlation  function in R) was used to test the alternative hypothesis that the true correlation was not equal to zero. To test the overrepresentation of ecological interaction types in overproducing co-cultures vs. all co-cultures, Fisher’s exact test  function in R) was used on the contingency table of interaction types. To test the significance of the differences in number of transfer metabolites in co-cultures by interaction type, ANOVA was used (aov function in R). To find metabolic inputs and network statistics that correlate with a specific microbe’s output metabolites in co-cultures, we used canonical correspondence analysis (CCA) to calculate multivariate correlations between combinations of metabolic inputs and network statistics with outputs in co-cultures. The response matrix of metabolic output fluxes by co-culture (log10 + 1 transformed for fluxes >1 mmol/gDCW/h) was used as the community matrix for the cca function in vegan. The following explanatory matrices were separately used to constrain the community matrix: transfer metabolite fluxes into the model (log10 + 1 transformed for fluxes >1 mmol/gDCW/h), normalized betweenness centrality of hub precursor metabolites, and normalized degree of hub precursor metabolites. To emphasize correlations in CCA plots, a scaling factor of four was used for arrows. Mantel tests were used to test the correlation of distance matrices of output metabolite fluxes (log10 + 1 transformed) by co-culture with distance matrices of transfer input fluxes (log10 + 1 transformed) by co-culture, normalized betweenness centrality of hub precursor metabolites centrality by co-culture, and normalized degree of hub precursor metabolites by co-culture (mantel(method = “spearman”) function in vegan with Euclidean distance matrices calculated with dist function in R). Significance was assessed by 999 permutations.To test the relationship between biomass fluxes calculated in this study and those calculated in Magnusdottir Supplementary informationCodeDataDataset 1Dataset 2Dataset 3Dataset 4


To further improve their accuracy, recent methodological developments focused on integration of thermodynamic information in metabolic models to assess the feasibility of flux distributions by thermodynamic driving forces. Here we present OptMDFpathway, a method that extends the recently proposed framework of Max-min Driving Force (MDF) for thermodynamic pathway analysis. Given a metabolic network model, OptMDFpathway identifies both the optimal MDF for a desired phenotypic behavior as well as the respective pathway itself that supports the optimal driving force. OptMDFpathway is formulated as a mixed-integer linear program and is applicable to genome-scale metabolic networks. As an important theoretical result, we also show that there exists always at least one elementary mode in the network that reaches the maximal MDF. We employed our new approach to systematically identify all substrate-product combinations in Escherichia coli where product synthesis allows for concomitant net CO2 assimilation via thermodynamically feasible pathways. Although biomass synthesis cannot be coupled to net CO2 fixation in E. coli we found that as many as 145 of the 949 cytosolic carbon metabolites contained in the genome-scale model iJO1366 enable net CO2 incorporation along thermodynamically feasible pathways with glycerol as substrate and 34 with glucose. The most promising products in terms of carbon assimilation yield and thermodynamic driving forces are orotate, aspartate and the C4-metabolites of the tricarboxylic acid cycle. We also identified thermodynamic bottlenecks frequently limiting the maximal driving force of the CO2-fixing pathways. Our results indicate that heterotrophic organisms like E. coli hold a possibly underestimated potential for CO2 assimilation which may complement existing biotechnological approaches for capturing CO2. Furthermore, we envision that the developed OptMDFpathway approach can be used for many other applications within the framework of constrained-based modeling and for rational design of metabolic networks.Constraint-based modeling techniques have become a standard tool for the  OptMDFpathway which is based on the recently introduced concept of Max-min Driving Force (MDF). OptMDFpathway offers various applications, especially in the context of metabolic design of cell factories. To demonstrate the power and usefulness of OptMDFpathway, we employed it to analyze the endogenous CO2 fixation potential of Escherichia coli. While E. coli cannot assimilate CO2 into biomass, net CO2 fixation can take place along linear pathways from substrate to product and we show that thermodynamically feasible pathways with net CO2 assimilation exist for 145 (34) products when choosing glycerol (glucose) as substrate. Our results indicate that heterotrophic organisms like E. coli hold a possibly underestimated potential for CO2 assimilation which may complement existing biotechnological approaches for capturing CO2.When analyzing metabolic networks, one often searches for metabolic pathways with certain (desired) properties, for example, conversion routes that maximize the yield of a product from a given substrate. While those problems can be solved with established methods of constraint-based modeling, no algorithm is currently available for genome-scale models to identify the pathway that has the highest possible thermodynamic driving force among all solutions with predefined stoichiometric properties. This gap is closed with our new approach  Another localized TB occurring in many MDF-optimal pathways with glycerol as substrate is given by the glycerol dehydrogenase reaction (R_GLYCDx) with its driving force upper limit of 7.5 kJ/mol. This limit is reached if the concentrations of cytosolic glycerol and NAD are at their respective maximal values and the concentrations of dihydroxyacetone and NADH at their minimal values. Finally, the localized TB with the smallest positive MDF is given by the malate dehydrogenase reaction (R_MDH) confirming that this reaction is a potential bottleneck for biomass or, as in our application, for product synthesis  which sets an upper MDF limit of 8.6 kJ/mol. Hence, the maximal MDF of all EMs in ECC2 or pathways in cf. with ).distributed TB in ECC2 for products with CO2 fixation with glucose as substrate is composed of the three glycolytic reactions R_TPI, R_GAPD, and R_FBA catalyzed by triose-phosphate isomerase, glyceraldehyde-3-phosphate dehydrogenase, and fructose-bisphosphate aldolase. If these three reactions, which were also identified in [An example of a ified in  as a theThe optimal MDF values of all 62 substrate-product combinations of ECC2 are limited by only 17 different bottlenecks where the largest distributed TB limit was composed of nine reactions  Tables. distributed TB with two reactions in iJO1366 is given by the simultaneous operation of triose-phosphate isomerase (R_TPI) together with glyceraldehyde-3-phosphate dehydrogenase (R_GAPD). If both reactions occur together in a pathway, the MDF of all routes comprising these reactions cannot be higher than 4.5 kJ/mol because glyeraldehyde-3-phosphate is a substrate of R_TPI but a product of R_GAPD. Therefore, its concentration needs to be balanced such that both reactions simultaneously achieve the highest possible driving force. Notably, this distributed TB allows for a higher driving force compared to the three-reaction TB discussed above for ECC2 which contained additionally R_FBA and caused a lower MDF limit of 3.3 kJ/mol. In iJO1366 alternative reactions with favorable thermodynamic properties can substitute for R_FBA making it dispensable and allowing the higher MDF value.The most abundant iJO1366  increases the number of products with CO2 assimilation only slightly. However, for some products the maximal CO2 assimilation yield increases significantly. Formate is one such example: with R_POR5 being reversible, significantly higher product yields of 10.5 mol/mol glucose and 6.1 mol/mol glycerol (compared to 6.14 and 3.38 in iJO1366 with R_POR5 being irreversible) with accompanying molar carbon assimilation yields  reversible in 2 fixation. is not possible in E. coli. However, a much less intuitive result found in the genome-scale is the following: if an electron source is provided that can permanently reduce NAD+ to NADH . It has an MDF of 8.6 kJ/mol and is thus theoretically thermodynamically feasible, however, whether it would really have sufficient capacities to allow growth of E. coli solely from CO2 and a source of reduction equivalents remains to be shown.Not surprisingly, both metabolic models used herein predict that biomass synthesis with net COproaches ), then EOptMDFpathway is either an EM or it uses a superset of reactions active in an MDF-optimal EM. In the latter case, the reaction set being active in the found optimal flux vector can always be reduced to the pathway represented by the optimal EM (where the active reversible reactions in the EM are used in the same direction as in the flux vector). This is a direct consequence of the fact that every flux vector can be written as a conformal sum (sum without cancellations) of EMs [We finally note here an interesting theoretical result highlighting the relationship between the steady-state flux vectors with maximal MDF and the EMs  sets of active reactions). In the smaller network ECC2, where the EMs could be enumerated, we implicitly assumed that MDF-optimal pathways with desired properties can be directly identified from the set of computed EMs. Indeed, also for genome-scale networks, it can be shown that there exists always at least one EM with maximum MDF value. The reasoning is as follows: Consider we know all EMs; this allows us to select the one with optimal MDF value. Adding further (active) reactions to this EM would impose further thermodynamic constraints, hence, the MDF either reduces or, in the best case, remains constant. Since removing a reaction from an EM implies that only the zero vector remains as feasible steady-state flux distribution, no flux vector in the network can exist with higher MDF. This implies that a solution found by ) of EMs . If inho) of EMs ) with op) of EMs  and path) of EMs ,59.E. coli cannot be applied to assimilate significant amounts of CO2. We argue that the potential of CO2 assimilation by endogenous pathways of heterotrophic organisms may have been a so far overlooked component for sustainable bioprocesses consuming CO2. To verify this hypothesis, we systematically identified, for the first time, all combinations of two industrially important substrates and cytosolic carbon metabolites (products) in E. coli which lead to net CO2 fixation. By using a new optimization approach, OptMDFpathway, we ensured not only stoichiometric but also thermodynamic feasibility of the identified pathways.It is usually assumed that heterotrophic organisms like 2 assimilation by investigating CO2 fixation routes that require the constant supply of carbon substrates to which CO2 can be attached. We demonstrated that E. coli can assimilate CO2 into many different metabolites: in principle, 15.3% of all cytosolic carbon metabolites in the E. coli genome-scale model can be synthesized with concomitant CO2 fixation from the considered substrates via thermodynamically feasible pathways. The potential products include the expected metabolites of the left branch of the TCA cycle but also less obvious candidates. We found that 40%  and supports a high MDF of 7.5 kJ/mol. Further, the pathway comprises only 13 enzyme catalyzed reactions whereof four are needed for NADH regeneration. Opposed to the proposed cell-free CETCH-cycle [2 assimilation yield and with the highest molar product yield of all candidates is carbamoyl phosphate synthesis of the respective carbon feedstock as well as the possible CO2 release for growth of E. coli must then be taken into account.Clearly, the predicted thermodynamic feasibility of the identified pathways represent best-case scenarios. Whether the upper limits of the corresponding pathway driving forces can be experimentally established 2 fixation in the genome-scale model of E. coli was only possible with the development of the new OptMDFpathway method. This MILP-based algorithm determines, for a given  phenotype both the optimal MDF and a supporting pathway. Herein we used this approach to assess the thermodynamic feasibility of pathways in E. coli that allow for CO2 net fixation but we envision that it can as well be applied to many more general problems in metabolic network modeling and design. In particular, it can be used as a generic method to identify, in large-scale networks, pathways with desired properties  and with maximal possible driving forces.The assessment of the thermodynamic feasibility of pathways with net COS1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.S8 Table(XLSX)Click here for additional data file.S9 Table(XLSX)Click here for additional data file.S10 Table(XLSX)Click here for additional data file.


The model iATCC19606 may have a significant potential in antimicrobial systems pharmacology research in A. baumannii.Combating antimicrobial resistance has been highlighted as a critical global health priority. Due to the drying drug discovery pipeline, polymyxins have been employed as the last-line therapy against Gram-negative “superbugs”; however, the detailed mechanisms of antibacterial killing remain largely unclear, hampering the improvement of polymyxin therapy. Our integrative modeling using the constructed genome-scale metabolic model  Acinetobacter baumannii has emerged as a very problematic pathogen over the past decades, with a high incidence in nosocomial infections. Discovered in the late 1940s but abandoned in the 1970s, polymyxins  have been revived as the last-line therapy against Gram-negative “superbugs,” including MDR A. baumannii. Worryingly, resistance to polymyxins in A. baumannii has been increasingly reported, urging the development of novel antimicrobial therapies to rescue this last-line class of antibiotics. In the present study, we integrated genome-scale metabolic modeling with multiomics data to elucidate the mechanisms of cellular responses to colistin treatment in A. baumannii. A genome-scale metabolic model, iATCC19606, was constructed for strain ATCC 19606 based on the literature and genome annotation, containing 897 genes, 1,270 reactions, and 1,180 metabolites. After extensive curation, prediction of growth on 190 carbon sources using iATCC19606 achieved an overall accuracy of 84.3% compared to Biolog experimental results. Prediction of gene essentiality reached a high accuracy of 86.1% and 82.7% compared to two transposon mutant libraries of AB5075 and ATCC 17978, respectively. Further integrative modeling with our correlative transcriptomics and metabolomics data deciphered the complex regulation on metabolic responses to colistin treatment, including (i) upregulated fluxes through gluconeogenesis, the pentose phosphate pathway, and amino acid and nucleotide biosynthesis; (ii) downregulated TCA cycle and peptidoglycan and lipopolysaccharide biogenesis; and (iii) altered fluxes over respiratory chain. Our results elucidated the interplay of multiple metabolic pathways under colistin treatment in A. baumannii and provide key mechanistic insights into optimizing polymyxin combination therapy.Multidrug-resistant (MDR) IMPORTANCE Combating antimicrobial resistance has been highlighted as a critical global health priority. Due to the drying drug discovery pipeline, polymyxins have been employed as the last-line therapy against Gram-negative “superbugs”; however, the detailed mechanisms of antibacterial killing remain largely unclear, hampering the improvement of polymyxin therapy. Our integrative modeling using the constructed genome-scale metabolic model iATCC19606 and the correlative multiomics data provide the fundamental understanding of the complex metabolic responses to polymyxin treatment in A. baumannii at the systems level. The model iATCC19606 may have a significant potential in antimicrobial systems pharmacology research in A. baumannii. Acinetobacter baumannii is a very problematic opportunistic Gram-negative pathogen with a high mortality in critically ill patients .patients , 2. It cpatients , 4. A. baterials  or spontaterials , 7. ReceA. baumannii  and magnesium (Mg2+), and OM disorganization  or LPS loss (mediated by mutations in lpxACD)  –13. Seveaumannii , 15. NevA. baumannii, AbyMBEL891 and iCN718 for strain AYE  assembles all biochemical reactions possibly occurring in an organism to predict cellular metabolic functions , delinea20–rain AYE , 24, andrain AYE . Howeverntiality . Models ntiality . Model intiality , 26. Howntiality . MoreoveA. baumannii ATCC 19606 using the literature, genome annotation, and experimental data from our laboratory. Integrative analysis with transcriptomics and metabolomics data revealed that the PPP, glyoxylate shunt, arginine biosynthesis, and LPS and peptidoglycan biosynthesis played key roles in metabolic responses to colistin treatment. The simulation results provide novel mechanistic insights into developing synergistic polymyxin combinations to combat MDR A. baumannii.Here we report the development and validation of a GSMM for A. baumannii AYE, and 630 of them were replaced with their corresponding orthologs in ATCC 19606  Gene contents in models iATCC19606 and iLP844. (C) Prediction of growth on various carbon sources. (D) Predicted essential genes on five different media. (E) Comparison of predicted essential genes with AB5075 mutant library. (F) Comparison of predicted essential genes with ATCC 17978 mutant library. (G) Predicted essential reactions on five different media. (H) Predicted essential metabolites on five different media. (I) Predicted metabolic fluxes under control and 2-mg/liter colistin treatment. (J) Comparison of calculated flux sums with differential regulated metabolites. (K) Colistin MICs of mutants from AB5075 transposon insertion library. (L) Comparisons of reactions in iLP844 and iATCC19606. (M) Nutrient uptake constraints for growth media. Download Data Set S1, XLS file, 2.3 MB.(A) Metabolites and reactions in Copyright © 2019 Zhu et al.2019Zhu et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 10.1128/mSystems.00157-18.2DATA SET S2iATCC19606 in SBML format. Download Data Set S2, TXT file, 1.7 MB.Copyright © 2019 Zhu et al.2019Zhu et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the iATCC19606 predicted bacterial exponential growth at 0.96, 0.71, 2.17, and 1.40 h−1 in M9 medium supplemented with citrate (M9C), M9 medium supplemented with succinate (M9S), and Mueller-Hinton (MH) and Luria-Bertani (LB) media, respectively. The predicted specific growth rates in MH and LB media were higher than those  estimated from early-log (2- to 6-h) growth kinetics (−1 ⋅ h−1]) was initially set on nutrient uptake reactions to test the growth capability without loss of generality. No growth was predicted on M9 medium supplemented with glucose, which was consistent with the previous phenome results .Using flux balance analysis (FBA), kinetics , 32, bec results . The Bio results . Model iIn silico single-gene deletion was conducted, and the specific growth rate for each mutant was calculated. With FBA, 148, 148, 80, 117, and 94 genes were predicted to be essential for bacterial growth on M9C, M9S, arbitrary nutrient, and MH and LB media, respectively, whereas using the minimization of metabolic adjustment (MOMA) approach, 157, 157, 93, 126, and 103 essential genes were determined for the above media, respectively . Ac. AcIn siectively , includiectively  35). Si. SiIn siectively  36). Th. ThIn sinadK  and its associated reaction R00104 were identified by FBA as essential, whereas MOMA discovered that atpIBEFJHAGDC , its associated reaction R920, and the cofactor pair ubiquinol/ubiquinone (C00390_c/C00399_c) were essential under all conditions.Likewise, 244/245, 244/245, 145/145, 149/188, and 168/169 essential reactions were identified by FBA/MOMA when growing on M9C, M9S, arbitrary nutrient, and MH and LB media, respectively ; 292/292GSE62794) from the samples of 2-mg/liter colistin treatment and untreated control at 1 h were employed to constrain metabolic fluxes for modeling using the E-Flux method  and CO2 emission (−3.8%), marginally increased (1.5%) respiration quotient (qCO2/qO2), and impaired the uptake of amino acids and dipeptide nutrients , phosphoenolpyruvate synthase , and malate dehydrogenase  were upregulated under 2-mg/liter colistin treatment, resulting in a 1.5-fold increase of gluconeogenic flux  was upregulated after a 1-h treatment with 2 mg/liter colistin. Notably, the l-ornithine uptake (0.003 mmol ⋅ gDW−1 ⋅ h−1 in the control versus 0.87 mmol ⋅ gDW−1 ⋅ h−1 after treatment) and de novo synthesis were remarkably increased upon colistin treatment. The l-ornithine flux in turn enhanced the production of l-arginine (0.004 mmol ⋅ gDW−1 ⋅ h−1 in the control versus 0.91 mmol ⋅ gDW−1 ⋅ h−1 after treatment)  .nuoABCDEFGHIJKLMN operon (DJ41_959-DJ41_947)  were reduced; however, the ATP generation via complex V  increased by 6% under colistin treatment, owing to the enhanced IM proton potential which possibly resulted from the upregulated cross-IM proton efflux through complex I  increased by 66.8% when cells were treated with 2 mg/liter colistin for 1 h, consistent with the transcriptional upregulation of the J41_947) . The oveomplex I .iATCC19606, and 37 carried nonzero flux sums (Date Set S1J) and were grouped into four categories: (i) metabolites with decreased abundance and flux sums (14 metabolites), (ii) metabolites with decreased abundance but upregulated flux sums (21 metabolites), (iii) metabolites with increased abundance but downregulated flux sums (l-aspartate only), and (iv) metabolites with increased abundance and flux sums   .cis-aconitate and fumarate were both downregulated with the decreased flux sums  was reduced by 70.5% upon colistin treatment. Consistently, the upstream fluxes via 3-deoxy-d-manno-octulosonate 8-phosphate synthase (R03254) and 3-deoxy-d-manno-octulosonate 8-phosphatase (R03350) decreased by 31.6%  and E4P were depleted under 2-mg/liter colistin treatment for 1 h with their flux sums increased by 64.0 and 156.3%, respectively; these results indicated that the metabolite reduction was probably due to the upregulated downstream consuming fluxes toward nucleotide and amino acid biosynthesis -2-isopropylmalate were downregulated, while the associated flux sums were enhanced, probably owing to the increased fluxes toward l-arginine and l-leucine biosynthesis  showed a 4-fold increase of colistin MIC (1 mg/liter versus 0.25 mg/liter for wild-type AB5075). The gene tktB (ABUW_2927) encodes the β subunit of transketolase, which catalyzes the transfer of a two-carbon glycolaldehyde unit from a ketose donor (xylulose 5-phosphate) to an aldose acceptor (R5P and E4P). Inactivation of tktB would prevent the flux from diverging at R5P and increase the flux toward nucleotide biosynthesis. Hence, colistin in combination with a nucleotide synthesis inhibitor might exert synergistic activity against A. baumannii. Together, the integrative genome-scale metabolic modeling with systems pharmacology data generates key mechanistic insights into colistin activity which may help optimization of polymyxin combination therapy.Category II contains the metabolites that were potentially drained off due to the increased consuming requirements under colistin treatment. In the PPP, ynthesis . Likewisynthesis . To test mutants  from theiATCC19606 for the type strain A. baumannii ATCC 19606. This strain was originally isolated from a patient urine sample and has been widely used in microbiological and pharmacological studies  upregulated gluconeogenesis, PPP, and amino acid biosynthesis;  and 67.2% (iLP844) of the total essential gene lists, respectively , catalyzing the conversion from dUMP to deoxythymidine monophosphate, was demonstrated to be essential for A. baumannii growth  was predicted as nonessential in iLP844, whereas our prediction classified cyoD (ABUW_1550 in the AB5075 genome) as essential, which agrees with the absence of a corresponding mutant in A. baumannii transposon libraries  and upregulated by 2.2-fold after 2-mg/liter colistin treatment for 1 h , whereas previous experimental results showed that A. baumannii usually grew at approximately 0.80 h−1 in MH medium  were obH medium . In addiH medium , 49, lacH medium , our modH medium . FurtherH medium  were alsH medium . TherefoiATCC19606 to bridge the gaps between transcriptomics and metabolomics. By imposing the transcriptomic constraints, iATCC19606 allowed simulating reaction flux and metabolic network utilization for untreated and colistin-treated A. baumannii, providing a global view of metabolic responses to antibiotic killing (lpxA (DJ41_250) and 3.3-fold-decreased level of LPS inner core precursor KDO  and acetylglutamate kinase  resulted in upregulated arginine biosynthesis flux and a decreased intermediate metabolite pool of N-acetyl-l-glutamate (3.5-fold) and l-argininosuccinate , which recycles the acetyl group from acetylornithine to glutamate  production of ammonia which mitigates against toxic hydroxyl radicals and (ii) generation of polyamines to attenuate the colistin-LPS electrostatic interaction in the OM  and in cation-adjusted Mueller-Hinton broth (CaMHB) and Luria-Bertani (LB) medium. Tryptic soy broth containing 20% (vol/vol) glycerol was used for stock at −80°C.A. baumannii AYE (GenBank accession numbers CU459137 to CU459141) and ATCC 19606 (GenBank accession numbers ACQB01000000 and JMRY00000000) were obtained from the GenBank database. Reciprocal BLASTp was implemented to determine the orthologs between AYE and ATCC 19606, with sequence identity of >70%, E value of <1E−5, and coverage of >70%  until the turbidity reached 42% transmittance in a turbidimeter . The cell suspension was then diluted 5 times with IF-0 solution and dye (Cell Biosciences) to final 85% transmittance. Biolog phenotype microarrays (PMs) 1 and 2 (Cell Biosciences) were employed to test the utilization of 190 carbon sources, with two independent biological replicates. Bacterial growth was detected after 18 and 24 h of incubation at 37°C by measuring the optical density at 595 nm using an Infinite M200 microplate reader . Readings of ≥1.3-fold of blank medium controls indicated the utilization of nutrients.iATCC19606 was then employed to predict the bacterial growth on a chemically defined medium with 190 individual carbon sources using the FBA method  of <1E−5, and coverage of >70%  for ATCC 19606 growth in CaMHB with the absence (control) and presence of 2 mg/liter colistin (treatment) at 1 h were employed as flux constraints using the E-Flux algorithm  was aerobically grown in CaMHB at 37°C, in the absence (control) and presence of 2 mg/liter colistin (treatment). Intracellular metabolomics data from the control and treatment at 1 h (n = 4) were employed in this study. A total of 1,535 and 1,526 unique metabolites were putatively identified for the control and treatment conditions at 1 h, respectively. One-way ANOVA  was used to determine differentially abundant metabolites, with criteria of fold change of ≥1.5, P of ≤0.05, and FDR of ≤0.1. For each intracellular metabolite in iATCC19606, the relative abundance change after 2-mg/liter colistin treatment at 1 h was correlated with the calculated flux sums  -based untargeted metabolomics data of ATCC 19606  were empr rates) , to analr rates) .φi=∑jn|Sof <0.01 . Metabol


Azotobacter vinelandii DJ and an ammonium excreting mutant, AV3 (ΔNifL), to investigate central carbon metabolism fluxes and central cell bioenergetics in response to ammonium availability and nitrogenase activity. Enabled by the powerful and reliable methodology of 13C-metabolic flux analysis, we show that the respiratory TCA cycle is upregulated in association with increased nitrogenase activity and causes a monotonic decrease in specific growth rate. Whereas the activity of the glycolytic Entner–Doudoroff pathway is positively correlated with the cell growth rate. These new observations are formulated into a 13C-metabolic flux model which further improves the understanding and interpretation of intracellular bioenergetics. This analysis leads to the conclusion that, under aerobic conditions, respiratory TCA metabolism is responsible for the supply of additional ATP and reducing equivalents required for elevated nitrogenase activity. This study provides a quantitative relationship between central carbon and nitrogen metabolism in an aerobic diazotroph for the first time.Diazotrophic bacteria are an attractive biological alternative to synthetic nitrogen fertilizers due to their remarkable capacity to fix atmospheric nitrogen gas to ammonium via nitrogenase enzymes. However, how diazotrophic bacteria tailor central carbon catabolism to accommodate the energy requirement for nitrogenase activity is largely unknown. In this study, we used  The ability of diazotrophs to use dinitrogen gas as the sole nitrogen source confers many ecological merits, but also incurs physiological constraints resulting from the high energy requirements of biological nitrogen fixation. The function of the nitrogenase enzyme requires not only reducing equivalents but also the supply of a minimum of 16 ATP per N2 fixed. Substantial energetic equivalents require a coordinated central carbon metabolism which uses a series of enzymatic pathways (i.e. the Embden-Meyerhof-Parnas (EMP) pathway, the Entner-Doudoroff (ED) pathway, pentose phosphate pathway, tricarboxylic acid (TCA) cycle etc.) to convert organic carbohydrates into metabolic precursors and release electrons and ATP. However, to date, there is little understanding of how diazotrophic bacteria quantitatively tailor central carbon catabolism to nitrogen availability and vice versa.Reduced forms of nitrogen are required by every living organism and are used to make many important biological components including nucleic acids, proteins, pigments, energy carrier molecules, etc. In nature, a diverse group of bacteria known as diazotrophs have a remarkable capacity to fix atmospheric nitrogen to ammonium under ambient conditions. This capacity is catalyzed by the nitrogenase metalloenzyme complexed with accessory proteins for proper cofactor biosynthesis and can be exploited for biological fertilizer production. This process is accomplished on an industrial scale by the chemical process (Haber-Bosch) that requires elevated temperature, high pressure and special catalysts and is considered an energy intensive and major greenhouse gas emitting approache.g. heterocysts in nitrogen-fixing filamentous cyanobacteria). Alternatively, the non-compartmentalized diazotroph, Azotobacter vinelandii, is believed to have a different protection mechanism4 that exhibits high respiratory activity, especially when exposed to high oxygen concentrations. In addition, in environments where a high rate of respiration does not prevail (e.g. A. vinelandii grown in phosphate-limited nitrogen-free chemostat culture), cells produce extracellular alginate, a viscous polysaccharide that leads to the formation of a capsule surrounding the cell that prevents oxygen diffusion into the cell5. In order to adapt to different environmental conditions while accommodating nitrogenase activity and ensuring ammonium availability, intracellular carbon flux may need to be directed toward either respiratory metabolism or alginate biosynthesis.In addition to its substantial energy cost, biological nitrogen fixation is also highly oxygen sensitive. To protect the nitrogenase complex from damage, some aerobic diazotrophs have evolved the ability to form specialized cells that create a microanaerobic environment for nitrogen fixation to occur  that exhibits constant expression of nitrogenase, and thus increased ammonium production and excretion into the surrounding media8. However, this mutant exhibits metabolic mechanisms of nutrient balancing that could favor biomass production and may thus oppose the metabolic engineer’s goal of maximizing ammonia yield. Therefore, understanding how A. vinelandii coordinates the utilization of carbon with the rate of nitrogen fixation is critical for the successful reprogramming of this microbe’s metabolism. Therefore, we aim here to understand how ammonium excretion is adding a metabolic energetic burden to the cells’ metabolism. Using 13C-metabolic flux analysis, by coupling chromatography to mass spectrometry we are able to quantify the isotopomer patterns of metabolites throughout the metabolic network. This method has emerged as a powerful tool for generating the quantitative data needed to probe and model central carbon metabolism and is uniquely applicable to provide novel bioenergetic insights into carbon and nitrogen metabolism12.A. vinelandii DJ wild type (wt) and its ammonium-excreting strain, AV3 (ΔNifL)8. To our knowledge, this is the first study to quantify the relationship between central carbon and nitrogen metabolism in this model aerobic diazotroph.The primary goal of this work is to understand quantitative carbon catabolism tailored to nitrogen availability and in particular in relation to mutations that yield high levels of nitrogen excretion. In this work, we present a quantitative fluxome analysis showing how central carbon metabolism is reprogramed in relation to nitrogen perturbation in diazotroph A. vinelandii wild type (DJ) and the ammonium excreting strain (AV3) with and without 50 mM ammonium (NH4Cl) added to the culture medium. The kinetics of cell growth, glucose consumption and ammonium levels in the media are shown in Fig. To investigate the influence of ammonium level on central carbon metabolism under different nitrogenase activity and environmental availability, we grew −1 h−1, respectively). Interestingly, in linear and stationary growth phase the AV3 strain seemed to re-assimilate the ammonium it produced earlier, indicating that nitrogenase expression might be down-regulated by other unknown mechanisms, or the ammonium was consumed by spontaneous mutants (Nif−) that were unable to fix nitrogen.In cultures grown without added ammonium Fig. , both thA. vinelandii is forced to fix nitrogen (in diazotrophic growth) due to the unavailability of ammonium, biomass formation and growth rate are significantly impeded. In addition, further increase in nitrogenase activity in the case of AV3, impedes the cells’ growth rate even further.For cultures grown with 50 mM added ammonium Fig. , AV3 mai13C-metabolic flux analysis (MFA). We developed 13C-MFA using isotope tracer and GC-MS to resolve the primary pathways in the carbon metabolism of A. vinelandii. For this purpose, we first validated growth on a minimal medium, modified DSMZ Azotobacter medium, to eliminate potential interference with labeled amino acids from non-labeled amino acids present in complex carbon nutrients  that can be incorporated by the cells. Second, we found the fractional labeling (FL) of the GC-MS-analyzed proteinogenic amino acids to be generally consistent with that of the isotopic substrates when sampled from exponentially grown cultures  pathway. Although the complete gene sets for these pathways are found in A. vinelandii’s genome14, their metabolic contribution to glycolysis is not fully understood. Quantitative estimations of flux ratios through these pathways were based on the labeling patterns of serine and alanine as defined by Mass Distribution Vectors (MDV)15. If serine originates through the EMP pathway, half of the serine will be labeled at position C3 while the other half will be unlabeled , strongly supporting that the ED pathway is the dominant pathway for glycolysis. Interestingly, we found the ED pathway flux corelates with the specific growth rate as the fastest growing cells carried the strongest ED pathway, and vice versa  proportionally by PEP carboxylase as an amphibolic reaction. In comparison, the backward fluxes such as “PEP from OAA”, “Pyr from Mal (lb and ub)” are negligible.We further focused on the respiratory flux from the TCA cycle. This flux can be reflected by the proportion of oxaloacetate (OAA) synthesized from either the TCA cycle or amphibolic pathway. The increased fraction of OAA derived through the TCA cycle in the AV3 culture without added ammonium indicates significantly increased flux through the respiratory TCA cycle. This data aligns with the notion that enhanced nitrogenase activity requires highly efficient respiratory metabolism to produce additional ATP. Interestingly, we also found that the relative respiratory activity of the TCA cycle increased as the growth rate decreased in cultures for different ammonium scenarios, with the most extreme negative relation in the case of diazotrophic growth of the AV3 mutant . The biochemical reactions and biomass equation adopted in our model come from Andres’s work16 and are listed in Supplemental Table A. vinelandii genome but showed marginal activity according to flux ratio analysis as presented in Fig. A. vinelandii.The estimated fluxes from the model fit are presented in Supplemental Table The global fluxomics datasets enable us to estimate the bioenergy and redox production from the stoichiometry of the metabolic network Fig. . We thus13C-tracer studies, we developed a fluxomic model to formulate the quantitative relationship between central carbon fluxes and nitrogen metabolism in A. vinelandii and elucidated the integrated responses of metabolic networks to nitrogen perturbation. Using this fluxomic model framework we were able to probe the bioenergetic basis of a diazotrophic phenotype in the ammonium-excreting AV3 mutant, which allows further refinement of the model to guide strategies toward metabolic engineering goals.Using A. vinelandii, which carries complete gene sets for two critical glycolytic options, the EMP and ED pathways. This work shows that A. vinelandii predominantly utilizes the ED pathway, while the EMP pathway has very low activity. Similar glycolytic flux partitioning is also observed in Pseudomonas18, a genus phylogenetically close to Azotobacter, in which the ED pathway accounts for over 90% of utilized glucose20. These two glycolytic pathways vary in reaction schemes and in how much ATP they produce for each glucose molecule metabolized21. Given the substantial energetic requirements for diazotrophic growth, why A. vinelandii prioritizes the ED pathway for glycolysis represents an intriguing open question. Compared with the EMP pathway, the most energetically efficient glycolytic pathway, the ED pathway oxidizes phosphorylated glucose to 2-keto-3-deoxy-6-phosphogluconate (KDPG), which is cleaved into one pyruvate and one glyceraldehyde-3-phosphate (G3P). Only the latter product supports substrate-level phosphorylation and can be used to produce ATP through subsequent glycolysis. Despite reduced ATP production, the ED pathway contains more exergonic reactions22 and is less thermodynamically constrained. This feature leads to tremendous benefit in enzyme kinetics as a stronger thermodynamic driving force will result in forward reactions being favored, which accordingly requires reduced amounts of catalytic enzymes to generate identical net flux. Indeed, the ED pathway could reduce by several-fold the enzymatic protein synthesis burden but still achieve the same glucose conversion rate as the EMP pathway21. It should be noted that in diazotrophic growth, the cost for protein synthesis can be considerable as it is largely based on the energetically expensive nitrogen-fixing process. From this perspective, natural selection of the ED pathway represents an energy balance consideration geared towards higher conversion rates of carbon substrates per N2 fixed and constrained by protein synthesis demands. The trade-off of this strategy is the decreased production of ATP from glycolysis, which must be replenished through another oxidative pathway as discussed below. Interestingly, our data also showed that the flux ratio of the ED pathway is positively correlated with the cell growth rate constraints by either ammonium availability or nitrogenase activity. The biological mechanism behind this correlation is still unknown. One hypothesis is that the activities of the ED pathway enzymes could be regulated by altered intracellular ammonium levels regardless of transcriptomic, proteomic, or metabolomic levels. Further research in systems biology is required to address this question comprehensively.Our study sheds light on the glycolytic strategy used by diazotroph 13C-fluxomic study also provides new evidence to better understand the role of respiratory metabolism in diazotrophs. Respiratory metabolism was proposed to protect the active nitrogenase against oxygen because the respiratory electron transport system bound to the peripheral cytoplasmic membrane can scavenge O2 and prevent the diffusion of O2 into the cells. This mechanism is believed to explain how nitrogenase can function when cells grow diazotrophically in the presence of O2. Although the respiration-protection hypothesis is widely accepted, it was recently challenged when it was reported that A. vinelandii had almost constant respiration rates and nitrogenase activity at O2 concentrations ranging from 30 to 100% air saturation24. These results hardly support the concept of respiratory protection. As a complementary hypothesis, Oelze suggested that nitrogenase protection also depends on the maintenance of a sufficiently low redox state25. This hypothesis assumes that oxidation of the substrate can warrant the proper supply of reducing equivalents and ATP to maintain nitrogenase in a reduced state. Indeed, our data strongly supports this hypothesis in that the increased fluxes through respiratory TCA cycle and OPP pathway positively correlated with enhanced nitrogenase activity. As shown in Fig. 2 fixation activity. It also supports the notion that respiratory protection arises from stringently coupled respiratory electron transport with the TCA metabolism. Although the way in which diazotrophs increase TCA cycle flux in response to enhanced nitrogenase expression is still a mystery, knowledge of this correlation could lead to lead to new strategies to maximize nitrogenase activity for engineering increased ammonium release by alleviating apparent metabolic bottlenecks. One solution could be the use of alternative carbon substrates (e.g. citrate and acetate) that can increase the activity of the TCA cycle locally, and accordingly increase the supply of energy and reducing equivalents. Previous studies on diazotrophically growing A. vinelandii showed that growth rates obtained on acetate achieved 0.35 h−1, higher than those grown on glucose (0.15 h−1)26. Therefore, addition of direct precursors of TCA cycle will not only supply ATP and reducing power for the highly active nitrogen fixation in the AV3 mutant, but also compensate for the carbon loss in TCA cycle which promises an enhanced biomass accumulation. It is noteworthy that the AV3 mutant showed a higher metabolic flux through the OPP pathway than wt in different ammonium availabilities which also supports the hypothesis in that extra reducing equivalents produced from an enhanced OPP pathway could be used to stabilize the enhanced nitrogenase activity in an AV3 mutant background. Overall, by deciphering the glucose metabolism of A. vinelandii, this work exemplifies novel insights into a general strategy that diazotrophs adopt for oxidizing carbon substrates under different conditions. It is our expectation that this quantitative knowledge of central carbon metabolism in terms of environmentally or genetically altered N-status can guide the reprogramming of diazotrophic metabolism geared toward increased technical feasibility of biologically produced ammonium fertilizers for industrial applications.In addition, our Azotobacter vinelandii DJ (wt) and AV3 (ΔNifL) strains were kindly provided by Dr. Leonardo Curatti27 and were maintained on 1.5% agar modified DSMZ (Leibniz Institute DSMZ-German Collection of Microorganisms and Cell Cultures) Azotobacter medium plates without added nitrogen. Our modified version of DSMZ Azotobacter medium consisted of 0.9 g L−1 K2HPO4, 0.1 g L−1 KH2PO4, 0.1 g L−1 MgSO4·7H2O, 0.1 g L−1 CaCl2·2H2O, 5 mg L−1 Na2MoO4·2H2O, 20 mg L−1 FeSO4·7H2O, 20 g L−1 glucose, 50 mM MOPS buffer, and was adjusted with HCl to a starting pH of 7.6. For cultures grown with added ammonium, NH4Cl was added to a final concentration of 50 mM prior to adjusting the pH. The MOPS buffer was included to prevent a drop in pH that truncated cell growth in cultures with added ammonium as has been previously reported28. Media was filter sterilized, and cultures were grown in unbaffled shake flasks at 30 °C and 225 rpm. Larger cultures of 250 mL in 1 L shake flasks were grown in triplicate to measure multiple metabolites during cell growth. U-13C and 1-13C glucose  labeled cultures consisted of 12.5 mL of culture in 50 mL shake flasks and were grown in duplicate. Cell growth was monitored by measuring optical density at 600 nm using a spectrophotometer . Dry cell weight measurements were determined by centrifugation of a known volume of culture and subsequent lyophilization and recording of the pellet weight.29. Culture supernatants were filtered through 0.22 µm filters before being loaded on an Agilent 1100 series HPLC equipped with a refractive index detector (RID)  and an Aminex HPX-87H anion-exchange . The mobile phase consisted of 4 mM H2SO4 at a constant flow rate of 0.6 ml min−1. The column temperature was maintained at 55 °C and the optical unit temperature for RID was held at 45 °C.Glucose was analyzed using a method slightly modified from literature30. After incubating with reagent1 and reagent2 at 37 °C for 30 min, samples were analyzed with a microplate reader .Ammonium was quantified using an assay kit  based on a modified version of the Berthelot method31. Freeze-dried cells (about 3 mg) were weighed and added to 500 μL butanolysis solution (consisting of 90% (w/v) 1-butanol, 10% concentrated HCl and 2 mg mL−1 Benzoic acid as internal standard). The samples were incubated at 110 °C for 3 hrs followed by the addition of 750 μL Milli-Q water and vigorous voretxing for phase separation. An Agilent 7890B GC equipped with an Agilent HP-5 GC column was used to quantify the butyl ester of 3-hydroxybutyrate in the upper organic phase. Samples were analyzed at split ratio of 1:20 and overall flow rate of 1 mL min−1 with the following temperature profile: 90 °C, hold for 2 min; linear temperature gradient of 30 °C min−1 from 90 to 300 °C; 250 °C, hold for 2 min.Intracellular PHB was quantified using a modified esterification method+Cl−) method32. Briefly, culture supernatants were added to a PHMBH+Cl− solution (purchased from BOC Sciences) (0.3% (w/v) in 1% sodium acetate) at volume ratio of 1:2 to form an alginate-PHMBH+ precipitate. After centrifugation at 17000 g for 5 min, absorbance of the supernatant with residual PHMBH+Cl− was measured at 235 nm using NanoDrop One .Alginate was determined according to a polyhexamethylenebiguanidinium chloride (PHMBH15. The cells (OD600 * culture volume ≥5) were harvested at three time points  by centrifugation at 13000 g for 5 min. The specific growth rates (µ) calculated at these time points and used for the subsequent metabolic flux analysis was as follows: 0.022 ± 0.009, 0.016 ± 0.009, 0.043 ± 0.011, 0.025 ± 0.007 for wt and AV3 without added ammonium, and wt and AV3 with added ammonium, respectively. Cell pellets were washed with Milli-Q water and resuspended in 500 uL of 6 M HCl followed by incubation at 105 °C for 16 hrs. The hydrolysate was evaporated down under nitrogen gas flow at 40 °C, which can either be stored at −20 °C or used immediately for derivatization. Derivatization was carried out at 85 °C for 1 hr in a solution containing 80 uL pyridine and 20 uL N-methyl-N-(t-butyldimethylsilyl)-trifluoroacetamide. The derivatized sample was analyzed by GC-MS. The GC-MS parameters used were the same as previously described33.Sample preparation for GC-MS measurement of labeled amino acids followed the standard protocol15:i represents the fraction of the mass isotopomer with i carbon atoms labeled, and n is the number of carbons in the fragment. MDVAA was then corrected for natural abundance of non-carbon atoms in fragment and carbon atom in the derivatization reagent. To prove that the cells were in a metabolic steady state, fractional labeling (FL) was calculated for U-13C glucose fed cells as15:Calculation of metabolic flux ratios was based on mass isotopomer distribution vector (MDV) of amino acid fragments asM) can be further estimated via a least square method due to the convolution reaction of amino acid biosynthesis15, and flux ratios at metabolic nodes were calculated as least square solutions. In this study, flux ratios of serine from glycine, glycine from serine, pyruvate from malate (upper and lower bound), erythrose-4-phosphate from pentose-5-phosphate, oxaloacetate from the glyoxylate shunt, oxaloacetate from the TCA cycle, phosphoenolpyruvate from oxaloacetate, phosphoenolpyruvate from the PP pathway and phosphoenolpyruvate from transketolase were calculated according to the principles proposed by Fischer et al.34. Additionally, the flux ratios of serine from glycolysis, and pyruvate from the ED pathway were obtained using 1-13C glucose. If all G3P molecules originate through the EMP pathway, half of the resulting serine will be unlabeled and the other half will be labeled at position C3, while all serine molecules will be unlabeled if carbon flows through the ED pathway or the OPP pathway (Supplemental Fig. Mass isotopomer distribution of the precursors of amino acids (MDVSimilarly, if all pyruvate originates from the ED pathway, alanine will be 50% unlabeled and 50% labeled at position C1 (Supplemental Fig. All calculations were implemented with Python3.6.A. vinelandii was reconstructed according to García et al.16 and provided in Supplemental Table 35. Determined MDVs and measured growth parameters were used as inputs for the following least-squares minimization problem36:sim and vsim are simulated mass isotopomer distribution and flux as a function of u, while MDVexp and vexp are experimental data; ∑exp is square matrix with measurement variances of MDV elements on the diagonal; varexp is the variance of determined reaction rate; N is the null space of stoichiometry matrix of mass balance. The problem can be solved through a gradient-based optimization algorithm. For flux estimation, the reaction rate of alginate formation was set to zero since alginate was not detected in any conditions. The flux between malate and pyruvate was also set to zero because the measured flux ratio of pyruvate from malate is negligible. The optimization was repeated 10 times for each condition to obtain a global solution, and 95% confidence intervals were calculated using a local linearization method, which is symmetric37. This method decreases computational burden compared to parameter continuation or Monte Carlo methods. All computation was implemented with Python3.6.Primary metabolic network of Fluxomics analysis of Azotobacter vinelandii supplemental information


We evaluated the performance of the Anaerobic Digestion Model No. 1 (ADM1), a thermodynamic black box approach (TBA), and dynamic flux balance analysis (dFBA), to describe the experimental observations. All models overestimated the impact of dilution rate on the apparent microbial biomass yield when using default parameter values. Based on our analysis, a maintenance coefficient value below 0.2 kJ per carbon mole of microbial biomass per hour should be used for the TBA, corresponding to 0.12 mmol ATP per gram dry weight per hour for dFBA, which strongly deviates from the value of 9.8 kJ Cmol h-1 that has been suggested to apply to all anaerobic microorganisms at 37°C. We hypothesized that a decrease in dilution rate might select taxa with minimized maintenance expenditure. However, no major differences in the dominating taxa between the reactors were observed based on amplicon sequencing of 16S rRNA genes and terminal restriction fragment length polymorphism analysis of mcrA genes. Surprisingly, Methanosaeta dominated over Methanosarcina even at a dilution rate of 0.18 days-1, which contradicts previous model expectations. Furthermore, only 23–49% of the bacterial reads could be assigned to known syntrophic fatty acid oxidizers, indicating that unknown members of this functional group remain to be discovered. In conclusion, microbial maintenance was found to be much lower for acetogenesis and methanogenesis than previously assumed, likely due to the exceptionally low growth rates in anaerobic digestion. This finding might also be relevant for other microbial systems operating at similarly low growth rates.For biogas-producing continuous stirred tank reactors, an increase in dilution rate increases the methane production rate as long as substrate input can be converted fully. However, higher dilution rates necessitate higher specific microbial growth rates, which are assumed to have a strong impact on the apparent microbial biomass yield due to cellular maintenance. To test this, we operated two reactors at 37°C in parallel at dilution rates of 0.18 and 0.07 days Anaerobic digestion (AD) is a key technology in environmental biotechnology and renewable energy supply . In AD, The dilution rate is a crucial process parameter for continuous stirred tank reactors (CSTR), which is one of the most prevalent reactor configurations for AD around the world, and particularly in Germany . The dilThe impact of the dilution rate on the microbial biomass concentration is important because the capacity of a digester to convert substrate depends on the amount of available biocatalysts , i.e., tappY). Apparent microbial biomass yield refers to the net microbial biomass increase per substrate consumed. The maximum microbial biomass yield (maxY), in contrast, is a theoretical construct that describes the gross microbial biomass increase per substrate consumed if non-growth related processes are neglected. Non-growth related processes include (a) osmoregulation, (b) defense against oxidative stress, (c) cell motility, (d) synthesis, repair and turnover of macromolecules, (e) energy spilling reactions, (f) shifts in metabolic pathways, (g) changes in storage of polymeric carbon compounds and (h) extracellular losses of compounds not involved in osmoregulation . Final microbial biomass concentrations were compared to predictions from the three models applied in AD research: ADM1 (Herbert Model), TBA (Pirt Model) and a dynamic FBA model . These models all account for microbial maintenance but implemented following different concepts and with different default values, and therefore are expected to lead to different predictions concerning the impact of dilution rate on microbial biomass concentrations. In addition, the composition of the microbial communities was analyzed using 16S rRNA gene amplicon sequencing targeting bacteria and terminal restriction fragment length polymorphism (T-RFLP) analysis of the mcrA gene targeting methanogenic archaea.The aim of this study was to analyze the impact of dilution rate and microbial maintenance on the microbial biomass concentration in acetogenesis and methanogenesis . Two lab-1). Both CSTRs were fed continuously with a synthetic, liquid substrate containing a mixture of VFAs as the only carbon source  with a total concentration of 37.2 gCOD L-1 in a mineral medium containing all necessary trace elements, macronutrients and vitamins , VFA concentrations, total solids content (TS), volatile solids content (VS), and pH were determined as described previously . Purified DNA quantity and quality were determined by agarose gel electrophoresis and by NanoDrop®ND 1000 spectral photometer  and the DNA was stored at -20°C.Samples (1.5 mL) from the lab-scale CSTRs were centrifuged at -7°C and 15,000 × g for 2 min and the supernatant was discarded. The samples did not freeze within the 2 min despite the low temperature. The pellets were stored at -20°C until DNA extraction. DNA was extracted from the pellets using the NucleoSpinmcrA gene amplicons as described previously (MwoI and BstNI (New England Biolabs). Methanogens contain only one copy of the mcrA gene per genome  were performed by LGC Genomics GmbH . The V3-V4 regions of the 16S rRNA genes were amplified using the primers 341f (CCTACGGGNGGCWGCAG) and 785r (GACTACHVGGGTATCTAAKCC) according to  release . Reads w release . For tax release  and the  release . For dow release . Raw de-3 version 5.2 using the taxonomy search function . If no copy number was available at the genus level, the next higher taxonomic level was chosen.The relative abundances of 16S rRNA genes were corrected by the average 16S rRNA gene copy number per genome. The 16S rRNA gene copy numbers per genome for each OTU at genus level were taken from the rrnDB databaseThe Herbert Model describes endogenous metabolism by a negative specific microbial growth rate  and, themax the maximum specific microbial growth rate, S the substrate concentration, SK the half saturation constant and a the specific maintenance rate. This model for maintenance in the form of biomass decay is used in the ADM1 as the constant parameter deck .with μ being the substrate concentration dependent specific microbial growth rate, μA COD-based ADM1 implementation  was usedacY (0.05 gCODX gCODS-1), proY (0.04), c4Y (0.06) and h2Y (0.06), which are the microbial biomass yields of acetoclastic methanogens, propionic acid oxidizers, butyric acid oxidizers and hydrogenotrophic methanogens, respectively, and the microbial decay rate deck (0.02 days-1).Default parameters as introduced by -1, the biomass recycling of acetogens and methanogens only accounts for 0.54% of the respective population biomass , Equation 2 for the apparent yield In the Pirt Model, substrate consumption is divided into substrate for growth and substrate to fulfill maintenance requirements:q being the specific substrate uptake rate, μmax the maximum specific microbial growth rate, S the substrate concentration, K the half saturation constant, maxY the maximum yield and m the maintenance coefficient. In AD research, it has been applied by m has been implemented by with appY, the substrate influent concentration inS, the substrate production rate from other reactions  D, and the substrate concentration S inside the CSTR following Equation 4.For steady state, the biomass concentration in the CSTRs for the TBA can be calculated based on the apparent yield appY, which is derived from the maximum yield maxY diminished by a maintenance coefficient m and the specific microbial growth rate μ (Equation 5a), which can be rewritten to Equation 5b assuming a CSTR in steady state.m for each substrate were calculated based on the Gibbs energy of 9.8 kJ per carbon mole microbial biomass (CmolX) per hour at 37°C based on maxY values were based on the Gibbs energy dissipation method (1.8O0.5N0.2 (Maintenance coefficients n method . Carbon O0.5N0.2 . See secX (g L-1) being fed with a growth limiting substrate S  according to:FBA modeling relies on metabolic stoichiometric networks, which detail the intracellular conversion of metabolites. Assuming that intracellular metabolites are at steady state and that the cell maximizes its growth, this approach allows for the prediction of specific microbial growth rate, intracellular metabolic fluxes and product synthesis rates, given a substrate uptake rate . By restD (d-1) and substrate concentration in the inflow inS (g L-1). Following the dFBA approach, the specific microbial growth rate of the population μ (d-1) and the specific substrate uptake sv (g gDW-1 d-1) were computed by optimizing intracellular flux distributions within metabolic networks with respect to cellular growth. The current maximal specific substrate uptake rate uptakev (g gDW-1 d-1) was computed based on current substrate concentration S in the reactor according to uptakev = max S/(KS + S)v with maximal specific uptake rate maxv (g gDW-1 d-1) and half saturation constant SK (g L-1). To numerically solve the model, an Euler method was implemented in Matlab R2015b , assuming linear dynamics between iteration steps, and using CellNetAnalyzer for FBA computations  or hydrogenotrophic methanogenesis of M. barkeri was simulated . inS values were calculated as acetic acid or hydrogen equivalents based on the experimental mixed feed composition. SK values were taken from maxv values adjusted so that model-predicted maximal specific microbial growth rates matched values reported in the same study. Likewise, a propionic acid-degrading population of Syntrophobacter fumaroxidans was simulated using model iSfu648, in which NGAM is fixed to 0.14 mmol ATP gDW-1 h-1 ], respectively  or for biomass build-up  were adjusted  was 15% higher than in the experiment. Increasing deck to fit the microbial biomass concentrations was therefore the wrong approach, because this further increased the microbial biomass concentration ratio error to 75% (deck fit”). Nevertheless, fitting ADM1 to experimental results by increasing deck has been done several times. deck values for acetogenesis and methanogenesis above 0.02 days-1 . The reason was the high maintenance coefficient value. Setting the maintenance coefficient value to 0 kJ Cmol-1 h-1, i.e., no impact of dilution rate on apparent microbial biomass yield, the simulations deviated by less than 3% from the experimental microbial biomass concentrations  compared to our study (0.07–0.18 days-1) and the other AD studies cited above (<0.17 days-1) (see s-1) see . Therefo-1 corresponds to 0.116 mmol ATP gDW-1 h-1 assuming 70 kJ mol ATP-1 . Given that experiments should be run for at least three HRTs, an experiment would take at least 300 days, during which the substrate should remain the same. Second, it is almost impossible to separate microbial biomass from particulate matter being part of the substrate for an accurate quantification. Replacing the microbial biomass determination by molecular biological methods such as qPCR of 16S rRNA genes or microscopic methods would require a conversion of gene copy numbers or cell numbers to biomass. Such conversion factors would need to consider the number of 16S rRNA genes per genome, the number of genome copies per cell and the biomass per cell .Although only acetic, propionic, and butyric acid were fed, less than half of the bacterial reads belonged to taxa known for the syntrophic oxidation of one of these acids . SyntropThe high abundance of bacteria not known to oxidize fatty acids could be explained by several reasons. First, there might be bacterial species present that are capable of, but not yet known for, syntrophic VFA oxidation. Secondly, bacteria could feed on decaying microbial biomass. However, based on ADM1 simulations using default parameters by Syntrophomonas at the dilution rate of 0.18 days-1 compared to the dilution rate of 0.07 days-1 .The datasets generated for this study can be found in the Supplementary Material FB, HS, SK, and FC designed the reactor experiments. SW implemented the ADM1 model and contributed to the discussion of the simulation results. FB performed the reactor experiments, T-RFLP data analysis, and simulations for the TBA and ADM1. DP and DB performed the 16S rRNA gene sequencing data analysis. FC performed the dFBA simulations. HH contributed to the discussion of the results. All authors prepared, read, and approved 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.