Corynebacterium glutamicum and a newly created model of Phaeobacter inhibens.Genome-scale metabolic models are of high interest in a number of different research fields. Flux balance analysis (FBA) and other mathematical methods allow the prediction of the steady-state behavior of metabolic networks under different environmental conditions. However, many existing applications for flux optimizations do not provide a metabolite-centric view on fluxes. Metano is a standalone, open-source toolbox for the analysis and refinement of metabolic models. While flux distributions in metabolic networks are predominantly analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization also allow a metabolite-centric view on flux distributions. In addition, we present MMTB (Metano Modeling Toolbox), a web-based toolbox for metabolic modeling including a user-friendly interface to Metano methods. MMTB assists during bottom-up construction of metabolic models by integrating reaction and enzymatic annotation data from different databases. Furthermore, MMTB is especially designed for non-experienced users by providing an intuitive interface to the most commonly used modeling methods and offering novel visualizations. Additionally, MMTB allows users to upload their models, which can in turn be explored and analyzed by the community. We introduce MMTB by two use cases, involving a published model of  Genome-scale metabolic models are important tools for systems biology. They are used in various fields, e.g. in metabolic engineering, the identification of potential drug targets, and as knowledge libraries to understand the behavior of biological systems in detail . In thesFlux balance analysis (FBA) allows the prediction of the steady-state condition of the metabolic network of an organism under different environmental conditions . MetabolAs most applications for flux optimization focus on a reaction-centric point of view, they pay little attention to metabolite fluxes. Only a small number of previous studies showed the benefit of analyzing models from a metabolite-centric point of view, e.g. for the discovery of new drugs ,7. TherePhaeobacter inhibens DSM 17395, an ideal model organism for basic research and elucidation of stress responses because of its metabolic versatility. Experimental data for degradation pathways of different carbon sources based on metabolome and proteome measurements have been determined for P. inhibens DSM 17395 in previous studies by us and others [P. inhibens DSM 17395 are applicable for metabolic modeling. Metabolic modeling is used for a variety of applications, ranging from biotechnological applications to ecological questions . A well-d others ,12,13,14d others ,16. Protd others ,17,18. Dd others . More red others ,21. ThusCorynebacterium glutamicum DSM 20300, a widely used biotechnological production strain that was originally isolated as an l-glutamate-producing bacterium [C. glutamicum has been metabolically engineered for the production of other amino acids, organic acids, alcohols, and polymers [The second model system of this study is acterium  and is uacterium . Furtherpolymers . In this study, we present Metano, an open-source command-line toolbox for metabolic modeling, and MMTB, a web-based interface to Metano that comes with an integrative database of biochemical reactions and genome annotations. While Metano was developed for a more versed audience, MMTB was designed especially for inexperienced users or users without a strong background in bioinformatics. C. glutamicum to verify the accuracy or capability of the toolbox. In a second step, we used the TDA-negative mutant strains of P. inhibens DSM 17395, with optimal growth on three different amino acids to reconstruct a metabolic model for P. inhibens DSM 17395, based on genomic and experimental data. FBA was performed to analyze the carbon flux in the wild-type strain compared to the TDA-negative mutant strains with respect to carbon loss and growth inhibition by TDA. MMTB methods provided a metabolite-centric view on the model to determine changes in the intracellular carbon flux under different growth conditions.We applied Metano methods to the published model of Metano is a Python-based command-line tool for the analysis of stoichiometric models. Several features can be accessed online via the MMTB. Prominent examples of constraint-based metabolic modeling software include COBRA , FASIMU While flux distributions in metabolic networks are conventionally analyzed from a reaction-centric point of view, the Metano methods of split-ratio analysis and metabolite flux minimization (MFM) allow a metabolite-centric view on flux distributions . Split-rThe Metano toolbox includes several features for flux optimization and is optimized for processing speed. In addition to the common FBA algorithm, Metano implements the fastFVA algorithm for FVA . MetabolIn addition to the widely used SBML format, Metano models can be represented by simple lists of reactions that are readable by humans and can be easily modified by the user with any text editor. An additional parameter file sets the constraints for a simulation scenario so that the variables, e.g., substrate boundaries and objective function, are detached from the model itself. Easy exchange of models is ensured by offering export and import tools for the widely accepted standard format SBML .Metano Modeling Toolbox (MMTB) is a web-based tool for the generation and analysis of metabolic models. MMTB closes the gap between BKMS-react  and EnzyOn the one hand, MMTB assists during model creation. The key feature is an intuitive search tool that gives access to reaction and metabolite information. The user can search for reactions by either entering parts of the biochemical reaction, EC numbers, database IDs, or by selecting a BRENDA, MetaCyc, or KEGG pathway. The resulting reactions originate from the integrative database BKMS-react  that incOn the other hand, MMTB assists during model analysis via an interface to the Metano toolbox. The user can upload a metabolic model and apply one of the following analysis methods: (1) FBA, (2) FVA, (3) dead-end analysis, (4) split-ratio analysis, (5) metabolite flux minimization, (6) a metabolite-centric visualization that shows the network as an interactive bipartite graph with consuming and producing metabolite fluxes, (7) a model verification tool where all metabolites in the uploaded model are replaced by their recommended names from BKMS-react. Because misspelled metabolites or synonyms are not treated as identical metabolic nodes, this unique method helps to avoid mistakes. Additionally, MMTB has a model exchange platform, where users can upload and publish their own models. These models can be viewed, analyzed, visualized, and downloaded, thus promoting the reuse of models that are already published. A model conversion tool for SBML and JSON models allows the use of models that were generated with other software. C. glutamicum model iMG481 that consists of 550 reactions [The Metano modeling toolbox was evaluated using the published eactions . The onl−1, as well as key fluxes described in the paper. Furthermore, the MFM method revealed that of a total of 408 metabolites, 195 were not essential for biomass production of 95% of the optimal value. When the biomass function is restricted to 10%, 205 metabolites are non-essential. By using the Metano toolbox, we were able to reproduce the in silico growth on glucose as it was described by Graf and colleagues. When using maximal biomass production as objective function, Metano was able to reproduce the growth rate of 0.337 hThe model iPin571 was manually assembled with the assistance of the MMTB database of metabolic reactions and the integrated EnzymeDetector annotation . The modl-phenylalanine as the precursor for TDA biosynthesis, (2) l-alanine as an amino acid with immediate access to the central carbon metabolism, and (3) l-leucine as an amino acid with a more complex degradation pathway, slower growth, and a reduced TDA production compared to l-alanine. TDA-negative strains (Δ262-kb plasmid-cured and tdaE transposon mutant strains) were cultivated as a reference to compare optimal growth with inhibited growth, and furthermore, to analyze the influence of carbon loss due to secreted metabolites. Nine of the proteinogenic amino acids were previously analyzed with respect to growth physiology and degradation pathways . For theThe model iPin571 was able to simulate growth for the TDA-negative mutants on different carbon sources and the wild-type strain on leucine . l-phenylalanine, indicating reduced carbon yields due to TDA biosynthesis , 1.3-fold (l-leucine), and 1.6-fold  higher compared to that of the wild-type strain. The tdaE transposon mutant strain showed almost the same growth yields as the Δ262-kb mutant strain, indicating that the lower growth yields of the wild-type strain were connected to the inhibitory effect of TDA with a necessity for an increased respiratory activity resulting in more carbon loss by CO2 production [The wild-type strain showed the lowest growth yields on each of the amino acids as a nutrient, especially on ynthesis . Growth n source . The grooduction ,21.2 , and cells were transferred at least for one adaptation cycle in the respective amino acid before inoculating the main cultures. All main experiments were conducted with three biological replicates in 500 mL Erlenmeyer flasks with three baffles at 150 rpm and 28 °C in an orbital incubator as previously described [g for 5 min. Cells were re-suspended in 3.7% sodium chloride and split to get 1–3 mg cell dry weight (CDW) for DNA, RNA, protein and polyhydroxybutyrate (PHB) each, 10–30 mg for lipid and 10–30 mg for determination of CDW  as previously described . For detn of CDW .iMG481 was obtained from the The metabolic model iPin571 was reconstructed based on genome and experimental data [The metabolic model tal data ,17,18,46tal data . We inclMetano is a software toolbox for metabolic modeling implemented in the Python programming language. Metano is easily extensible by prospective functional extensions via well-documented interfaces in a structured object-oriented framework. This allows the adaptation of the software to different requirements by users with basic programming skills. Additionally, all functionalities of Metano can be accessed by standalone Python programs via the command line, without the need to write programs. The Metano toolbox implements many well-established analysis tools, including FBA, FVA, MOMA, knockout simulation, and dead-end analysis . AdvanceMFM first performs a standard FBA and then adds a variability analysis comparable to FVA by the following algorithm: (1) Set a constraint for the objective function (default 95% of FBA flux). (2) Generate coefficient vectors expressing the (producing) metabolite fluxes as linear combinations of the reaction fluxes. (3) Perform linear programming with the linear objective functions defined by these coefficient vectors to successively minimize each objective function.Split-ratio analysis computes the split ratios for the fluxes entering and leaving each metabolite as totals fluxes and percentage values. This method is used to calculate the network and label edges and nodes for the visualization available at MMTB.github.com/JuliaHelmecke/metano (accessed on 9 Febraury 2021).All methods are described in more detail on the software website and in the Metano includes interfaces to numerous freely available solvers for mathematical optimization problems. For linear programming, the supported solvers are lp-solve and the GNU Linear Programming Kit (GLPK), interfaced to Python via Swiglpk and PyMathProg, respectively. Quadratic programming problems are solved by using the quadratic cone program solver of CVXOPT, interfaced via CVXPY.mmtb.brenda-enzymes.org (accessed on 9 Febraury 2021)). Metano does not rely on commercial software packages and can be installed on various Linux distributions via the Python Package Index (PyPI) and the related package installation system pip. A current version of Metano is also available for OS-independent use as a Docker image on Docker Hub.The software is published under the GNU General Public License version 3 and is available via the MMTB website (


Our study improves the current understanding of microbial interactions. Because “assistant” microbes showed importance in communities in addition to the functional microbes, our findings also suggest a useful “assistant-microbe” principle in the design of microbial communities for either bioremediation or chemical production.Many natural and synthetic microbial communities are composed of not only species whose biological properties are consistent with their corresponding communities but also ones whose chemophysical characteristics do not directly contribute to the performance of their communities. Even though the latter species are often essential to the microbial communities, their roles are unclear. Here, by investigation of an artificial two-member microbial consortium in  Dietzia sp. strain DQ12-45-1b and Pseudomonas stutzeri SLG510A3-8, had a synergistic effect on hexadecane (C16 compound) biodegradation, even though P. stutzeri could not utilize C16 individually. To gain a better understanding of the roles of the alkane nonconsumer P. stutzeri in the C16-degrading consortium, we reconstructed a two-species stoichiometric metabolic model, iBH1908, and integrated in silico prediction with the following in vitro validation, a comparative proteomics analysis, and extracellular metabolomic detection. Metabolic interactions between P. stutzeri and Dietzia sp. were successfully revealed to have importance in efficient C16 degradation. In the process, P. stutzeri survived on C16 metabolic intermediates from Dietzia sp., including hexadecanoate, 3-hydroxybutanoate, and α-ketoglutarate. In return, P. stutzeri reorganized its metabolic flux distribution to fed back acetate and glutamate to Dietzia sp. to enhance its C16 degradation efficiency by improving Dietzia cell accumulation and by regulating the expression of Dietzia succinate dehydrogenase. By using the synergistic microbial consortium of Dietzia sp. and P. stutzeri with the addition of the in silico-predicted key exchanged metabolites, diesel oil was effectively disposed of in 15 days with a removal fraction of 85.54% ± 6.42%, leaving small amounts of C15 to C20 isomers. Our finding provides a novel microbial assembling mode for efficient bioremediation or chemical production in the future.Biodegradation of alkanes by microbial communities is ubiquitous in nature. Interestingly, the microbial communities with high hydrocarbon-degrading performances are sometimes composed of not only hydrocarbon degraders but also nonconsumers, but the synergistic mechanisms remain unknown. Here, we found that two bacterial strains isolated from Chinese oil fields, IMPORTANCE Many natural and synthetic microbial communities are composed of not only species whose biological properties are consistent with their corresponding communities but also ones whose chemophysical characteristics do not directly contribute to the performance of their communities. Even though the latter species are often essential to the microbial communities, their roles are unclear. Here, by investigation of an artificial two-member microbial consortium in n-alkane biodegradation, we showed that the microbial member without the n-alkane-degrading capability had a cross-feeding interaction with and metabolic regulation to the leading member for the synergistic n-alkane biodegradation. Our study improves the current understanding of microbial interactions. Because “assistant” microbes showed importance in communities in addition to the functional microbes, our findings also suggest a useful “assistant-microbe” principle in the design of microbial communities for either bioremediation or chemical production. Afterward, we did in vitro cultivation, comparative proteomics analysis, and extracellular metabolomics analysis based on the in silico prediction of the metabolic exchange between the two strains on hexadecane (C16) to understand the mechanism of communication between the two strains. Our finding on the mutualistic interaction between the leading and supporting members reveal a novel assemblage strategy to construct synthetic microbial consortia with high-performing functions.In this study, we constructed a simple microbial consortium consisting of an alkane-degrading bacterial strain, l fields –22. Dietradation . Among te strain –26. P. sse genes , indicatronments –30, its Dietzia sp. strain DQ12-45-1b and P. stutzeri SLG510A3-8 were aerobically cocultivated on C16. When P. stutzeri was exposed to C16 individually, the percentage of C16 removed by P. stutzeri was low in a 12-day culture period (5.21% ± 2.88%)  was significantly higher than those by each individual strain (P < 0.05). On the other hand, both P. stutzeri and Dietzia sp. in the coculture system grew much better than those in monocultures, even though the cell density of P. stutzeri was an order of magnitude less than that of Dietzia sp.  than in its monoculture system (0.71 ± 0.16 pg day−1 CFU−1) (P < 0.05). Based on the efficient performance in C16 degradation by the coculture of P. stutzeri and Dietzia sp., a synergistic effect on C16 biodegradation through metabolic communication was proposed for the coculture system, in which P. stutzeri might survive on the by-products of Dietzia sp. under the C16 exposure condition and, in return, secretory metabolites of P. stutzeri are able to induce a higher C16 recovery efficiency of the Dietzia strain.Bacterial strains ± 2.88%) , and theow level . This inl on C16 , with 79oculture . Neverthtzia sp.  and c. BP. stutzeri and Dietzia sp., two-species metabolic network reconstruction and consortium-level flux balance analysis (FBA) were carried out. First, we reconstructed the single-species GEM of Dietzia sp. DQ12-45-1b, namely, iBH925 , and the GEM of P. stutzeri SLG510A3-8, namely, iBH983 . The accuracy of these two models was validated by the consistency between in vitro experimental data and in silico prediction of growth rates and available carbon source spectra . Afterward, iBH925 and iBH983 were reconciled to unify the metabolites and reaction formats, followed by network integration. The resulted model for the microbial consortium, namely, iBH1908, contained 1,908 genes, 1,273 metabolites, and 2,670 reactions . In iBH1908, the metabolic networks of Dietzia sp. and P. stutzeri were connected to each other through 68 shared metabolites (see Table S1 in File S1). Details of the modeling and analyzing processes of iBH925, iBH983, and iBH1908 are described in File S1.To elucidate the interspecific metabolic communication between Dietzia sp. and P. stutzeri was simulated for individual growth or coculture in C16-exposed minimal medium. A maximum growth rate μ0.0718 = 2.38 × 10−2 h−1 (see Table S2 in File S1) for Dietzia sp. in monoculture was predicted by FBA on Dietzia sp. GEM iBH925 at the C16 uptake rate of 7.18 × 10−2 mmol g−1 h−1, which was determined by batch culture . Similarly, with the same nutrient constraints, a maximum growth rate of 0 h−1 for P. stutzeri was predicted by FBA on P. stutzeri GEM iBH983. In contrast, FBA on the two-species GEM iBH1908, in which the Dietzia sp. growth rate was constrained to be 2.38 × 10−2 h−1 while the C16 uptake rate was fixed at 8.00 × 10−2 mmol g−1 h−1 based on our previous measurement (File S1), predicted a maximum growth rate of 5.60 × 10−3 h−1 for P. stutzeri. Taken together, these predictions were in agreement with our previous findings that no P. stutzeri cell grew on C16 individually but its cell density gradually increased in the presence of Dietzia sp.  was almost unaffected when formate and glycolaldehyde uptake rates were both constrained to 0 mmol g−1 h−1. However, the drops were significant when each of the other three compounds was constrained, suggesting that R-3-hydroxybutanoate, α-ketoglutarate, and hexadecanoate were key compounds secreted by Dietzia sp. to support P. stutzeri growth in the C16-exposed minimal medium. Similarly, the 10 P. stutzeri secreted metabolites in iBH925. Addition of the 10 compounds induced a cell growth rate rise from 2.38 × 10−2 to 5.53 × 10−2 h−1. Acetate and l-glutamate were found to be the key metabolites responsible for the significant rise, as the maximum predicted growth rate of Dietzia sp. decreased to 2.56 × 10−2 h−1 if the two metabolites were excluded.By using FBA, the flux distribution in tzia sp. . The glolization . P. stutP. stutzeri SLG510A3-8 were inoculated in the minimal medium supplemented with R-3-hydroxybutanoic acid, α-ketoglutaric acid, or hexadecanoic acid. As shown in P. stutzeri biomass accumulated on each of the three compounds after 24 h of incubation, of which α-ketoglutaric acid was preferred by P. stutzeri more than the other two compounds. The in vitro result confirmed that in silico-predicted key exchange compounds were able to be utilized by P. stutzeri and suggested that the three compounds might be the reason why P. stutzeri could survive in the coculture system when C16 was the sole exogenous carbon source.Cells of 16 biodegradation by Dietzia sp. strain DQ12-45-1b, Dietzia sp. cells were cultivated in the C16-exposed minimal medium with the addition of low concentrations of sodium acetate or sodium glutamate, respectively. Higher Dietzia sp. biomass and lower residual C16 contents in the cultures supplemented with acetate and glutamate  , on Dietzia sp. cell growth and C16 biodegradation. The specific C16 removal rates in Dietzia sp. monoculture systems with the addition of these three compounds were not significantly different from those in the monoculture system without any additives (in vitro data mentioned above. This result indicated that C16 biodegradation by Dietzia sp. could be enhanced through the in silico-predicted P. stutzeri secretions but not by some other compounds.In order to test if acetate and glutamate promoted Clutamate  indicate < 0.05) , which wdditives , which wDietzia sp. and P. stutzeri from their monoculture to the coculture states, Dietzia sp. and P. stutzeri cells were separately inoculated in each side of a two-chamber bioreactor , cultivated aerobically on C16-exposed minimal medium, and harvested at the mid-exponential phase for proteomics analysis, with the monocultures of each strain as the controls. The results showed that a total of 1,541 Dietzia sp. proteins and 1,292 P. stutzeri proteins were detected from trypsin-digested protein mixtures of the coculture and monoculture samples, with a false-discovery rate (FDR) of <0.05. As shown in P. stutzeri proteins were significantly upregulated compared with those in P. stutzeri monoculture system (P < 0.05), among which 31 proteins showed at least a 2-fold increase in expression values and were termed high-level upregulated proteins, while the remaining 37 proteins were designated low-level upregulated proteins. On the other hand, a total of 91 and 39 P. stutzeri proteins were significantly high-level and low-level downregulated, respectively, in the coculture system (P < 0.05). Similarly, we found that 59 and 30 Dietzia proteins were high- and low-level upregulated in the coculture system, respectively, while 74 and 54 Dietzia proteins were high- and low-level downregulated, respectively . Among the differentially expressed proteins, nine proteins in Dietzia sp. and 13 proteins in P. stutzeri were involved in the metabolism of the six compounds; the proteins and their fold changes are listed in Tables S3 and S4. As shown in Dietzia sp. and P. stutzeri in the in silico-predicted directions. Generally, the central metabolism of P. stutzeri became relatively less active in the coculture system than in its monoculture system. In contrast, the tricarboxylic acid (TCA) cycle of Dietzia sp. turned out to be more active when the cells were incubated together with P. stutzeri. This indicated that the P. stutzeri cells grown on Dietzia sp. secretions might reorganize the flux distribution to produce more acetate and l-glutamate for their partner via stripping their intracellular active metabolism unrelated to acetate and l-glutamate biosynthesis down to essentials while expressing more enzymes relating to acetate and l-glutamate biosynthesis.Among the differentially expressed proteins, we focused on proteins relating to the metabolism of Cin silico-predicted five key exchanged compounds were all detected in their original or derivatized forms in the supernatant from the coculture system, which was in agreement with above-described proteomic analysis . Among the 43 metabolites, the analysis . Some otediction . HoweverDietzia alkane hydroxylases AlkW1, AlkW2, and CYP153 showed no significant difference between the monoculture and coculture systems (P ≥ 0.05) (see Table S6 in File S1), indicating that the synergistic C16 biodegradation did not depend on the expression level of Dietzia alkane hydroxylases. The key metabolic pathways affecting the C16 consumption rate of Dietzia sp. in the coculture system should be among the 10 metabolic reactions which were catalyzed by the nine differentially expressed Dietzia sp. enzymes , in which the biomass growth rate μ0.0718 (2.38 × 10−2 h−1) (Table S2) was present; after that, the flux rates of the 10 metabolic reactions mentioned above were constrained to 2-fold (for high-level upregulation), 1.5-fold (for low-level upregulation), 1/1.5 (for low-level downregulation), or 1/2 (for high-level downregulation) of the responding flux values in V0.0718; next, the C16 uptake rate in iBH925 was modified to be equal to that in iBH1908 (8.00 × 10−2 mmol g−1 h−1) as mentioned above; and finally, the maximum predicted biomass growth rate μ0.08 was computed via FBA again, and μ0.08 was compared with μ0.0718. If μ0.08 with some special reaction flux constraints was similar to μ0.0718, the enzymes which catalyzed the special reactions would be considered the key enzymes for the synergistic C16 biodegradation in the synthetic microbial consortium. It was found that the value of μ0.08 (2.43 × 10−2 h−1) was similar to that of μ0.0718 when the flux rate of Rxn13 catalyzed by succinate dehydrogenase was constrained, but the value was obviously different from that of μ0.0718 when the flux rate modification was operated on any one of the remaining nine metabolic reactions. This suggested that the synergistic C16 biodegradation in the synthetic microbial consortium might be due to the higher-level expression of succinate dehydrogenase induced by the substrates produced from P. stutzeri. The potential molecular mechanism under which the succinate dehydrogenase positively affects C16 biodegradation by Dietzia sp. was then examined.According to our proteome data, the relative amounts of the known  enzymes . FBA on 16 but also included the synthetic n-alkane mixture of C14, C16, and C28 , and the synergistic effect of the consortium on various n-alkanes was proved in silico to be enhanced by the sustained supply of acetate and glutamate from P. stutzeri (see Table S7 in File S1). Here, we inferred that the microbial consortium of Dietzia sp. and P. stutzeri might have synergistic biodegradation capability on diesel oil, a specific fractional distillate of petroleum composed mainly of alkanes with chain lengths between C8 and C25 , taking the Dietzia sp.-P. stutzeri coculture without or with the additives as two positive controls  and cell-free culture and the P. stutzeri monoculture as two negative controls . All the cultures were harvested on day 15 for Dietzia sp. cell density measurement and determination of the diesel removal percentage. As shown in Dietzia sp. had a significantly higher increase in the coculture systems than in the Dietzia sp. monoculture system, especially in treatment 6. As shown in P. stutzeri was likely incapable of diesel biodegradation, while Dietzia sp. was effective for diesel removal. Notably, the coculture system with the addition of slight amounts of acetate and glutamate had the highest removal percentage (85.54% ± 6.42%), which was over 10% higher than that of the Dietzia sp. monoculture (73.53% ± 3.27%). Afterwards, we analyzed the hydrocarbon compositions of the disposed diesel oil and found that the composition profile in the coculture system with additives was much simpler than those in the cell-free culture and Dietzia sp. monoculture . According to the composition profiles, n-alkanes and the isomers with chain lengths between C12 and C14 were completely removed from the diesel samples in treatments 3 and 6, suggesting a very effective removal of the medium-chain-length n-alkanes and their isomers by Dietzia sp.; for the n-alkanes with chain lengths between C15 and C25, they were partially removed from the diesel sample in treatment 3 but were completely disposed of in treatment 6, leaving small amounts of C15 to C20 isomer residues. Our results suggested that the strategy using the synergistic microbial consortium plus in silico-predicted interspecific regulators was very efficient for diesel disposal.Accordingly, the synergistic biodegradation capability of the microbial consortium was not limited to C and C25 , and thaoculture , as wellDietzia sp. DQ12-45-1b, and a potential alkane nonconsumer, P. stutzeri SLG510A3-8, was investigated. Surprisingly, the microbial consortium was found to have a greater C16 removal rate than Dietzia sp. alone. The synergistic effect was not limited only to C16 degradation but also included degradation of synthetic and natural n-alkane mixtures. This finding was in agreement with cases of bioaugmentation with fungal-bacterial consortia, in which the bacterial members acted as the pollutant degraders while the fungal members did not   under accession numbers 1.10709 and 1.15316, respectively. After the two strains were grown in GPY  and LB  media, respectively, at 150 rpm and 30°C until the mid-exponential phase, they were centrifuged and washed with the minimal medium  of 0.1. The culture without inoculum was taken as the control. As described in File S1 in the supplemental material, cocultivation of the two strains was also done in minimal medium supplemented with a synthetic n-alkane mixture consisting of C14, C16, and C28 or with diesel oil. Since the colonies of Dietzia sp. were orange and smooth, being significantly different from those of P. stutzeri, which were brown and wrinkled, samples were taken every 1 to 2 days for cell density counting by CFU until the early stationary phase. The collected data were processed using nonlinear curve fitting followed by differentiation in Origin8.5  to obtain the time courses of specific growth rates.l medium  three ti16 in each flask was extracted using 20 ml n-hexane in a separating funnel and then was analyzed using a 5975C gas chromatograph (GC) coupled with a 7890A mass spectrometer (MS) detector . Helium was applied as the carrier gas at a flow rate of 25 ml min−1. The oven temperature was set to 100°C for 1 min and then increased to 260°C at a rate of 20°C min−1 and held at that temperature for 5 min. The external standard method was utilized to obtain the C16 concentrations in each sample. Afterward, the residual C16 concentration data were processed in Origin8.5 to obtain the time courses of specific C16 removal rates. The C16 removal rate was calculated as the difference in the percentage of the residual C16 concentration between the experimental and the control treatments in 0.7734 g liter−1 C16.At the end of the cultivation period, the residual CDietzia sp. DQ12-45-1b and P. stutzeri SLG510A3-8 were reconstructed as described in File S1 and then were modified by unifying the format of reactions and metabolites before being integrated to form the two-species model following the approach of Klitgord and Segrè  [t] = extracellular space of Dietzia sp., (iii) [p] = cytoplasm of P. stutzeri, (iv) [s] = extracellular space of P. stutzeri, and (v) [e] = environment shared by the two strains. FBA was used to calculate the metabolic flux distribution in the reconstructed GEMs, in which the biomass reactions accounting for all known biomass constituents and their fractional contributions to the overall cellular biomass were used as the objective to calculate the flux distribution solution in the feasible flux space for the maximal growth rate. System constraints were defined with the upper bound for each metabolic reaction as 1,000 and the lower bounds as −1,000 and 0 for the reversible and irreversible reactions, respectively, except for the exchange reactions. The lower bounds of exchange reactions relating to oxygen and the medium compounds were set to −1,000, while the remaining ones were set to 0. All calculations were performed under the linear programming algorithm using the COBRA toolbox . The mobile phase was comprised of solvent A  and solvent B (99.9% acetonitrile and 0.1% formic acid). The detection of peptide spectra was as described by Liu et al. . Seeds of u et al. . The prou et al. .Dietzia sp. DQ12-45-1b, P. stutzeri SLG510A3-8, and their mixture were obtained by centrifugation of 2-ml samples at 4°C and 10,000 rpm for 10 min to remove C16-exposed cells. For desalinization, 10 ml methanol was added to each cell-free culture, vortexed for 5 min, and kept at 4°C for 5 h. The mixtures were then centrifuged at 20,000 rpm and 4°C for 10 min, and the supernatant was lyophilized  for 4 h. The lyophilized samples were derivatized as described by Mujahid et al.  and Wiley 9 , with a match threshold of >80 . Relative metabolite abundances were calculated from peak areas (unique mass) of identified metabolites from GC-MS data and then were amended using the peak area of methyl tetradecanoate to minimize instrumental errors.Cell-free cultures of d et al. . GC-MS aDietzia sp. DQ12-45-1b were inoculated into the minimal medium containing 10 g liter−1 diesel oil with or without the addition of P. stutzeri SLG510A3-8 and exogenous addition of 0.045 g liter−1 sodium acetate and 0.037 g liter−1 sodium glutamate. The culture without inoculum and the one inoculated with P. stutzeri SLG510A3-8 alone were taken as the two control treatments. Every treatment was performed in triplicates. All the cultures were incubated at 150 rpm and 30°C. Samples were taken weekly until the stationary phase or at the end of the culture period for Dietzia sp. cell density counting with CFU. At the end of the culture period, the oil removal rates were tested using the weight difference assay as described in EPA method 3500B, and diesel composition profiling was tested using GC-MS. Samples for diesel composition profiling were prepared as described previously. During the GC-MS processing, the oven temperature was set to 50°C for 1 min and then increased to 260°C at a rate of 10°C min−1 and held at that temperature for 5 min, after which it was sequentially increased to 280°C at 10°C min−1 and held at that temperature for 0.5 min.Seeds of in vitro experiments were performed in triplicates and subjected to statistical analysis. The standard deviations were calculated in Microsoft Excel to represent the errors for the triplicate data. The P values between two triplicates were also calculated in Microsoft Excel, and a P value of <0.05 was considered statistically significant. For the proteomics analysis, proteins with a |log2(fold change)| of >1 were defined as high-level regulated proteins, and those with a |log2(fold change)| of <1 were defined as low-level regulated proteins. The significantly changed proteins were annotated according to the KEGG database to identify metabolic pathways in each strain that were influenced by the other one.All CP046567.1, CP046568.1, and CP046569.1 for Dietzia sp. DQ12-45-1b and CP011854.1 for P. stutzeri SLG510A3-8. The mass spectrometry proteomics data reported in this paper have been deposited in the open-access database ProteomeXchange Consortium via the iProX partner repository (The DNA sequencing data are deposited at GenBank under accession numbers pository  with the


C. thermocellum and T. saccharolyticum, two industrially relevant thermophilic bacteria whose metabolism still is not well understood. We report that while the glycolytic pathway in T. saccharolyticum is as thermodynamically favorable as that found in model organisms, such as E. coli or Saccharomyces cerevisiae, the glycolytic pathway of C. thermocellum operates near equilibrium. The use of a near-equilibrium glycolytic pathway, with potentially increased ATP yield, by this cellulolytic microbe may represent an evolutionary adaptation to growth on cellulose, but it has the drawback of being highly susceptible to product feedback inhibition. The results of this study will facilitate future engineering of high-performance strains capable of transforming cellulosic biomass to biofuels at high yields and titers.Thermodynamics constitutes a key determinant of flux and enzyme efficiency in metabolic networks. Here, we provide new insights into the divergent thermodynamics of the glycolytic pathways of  Clostridium thermocellum and Thermoanaerobacterium saccharolyticum are thermophilic anaerobic bacteria with complementary metabolic capabilities that utilize distinct glycolytic pathways for the conversion of cellulosic sugars to biofuels. We integrated quantitative metabolomics with 2H and 13C metabolic flux analysis to investigate the in vivo reversibility and thermodynamics of the central metabolic networks of these two microbes. We found that the glycolytic pathway in C. thermocellum operates remarkably close to thermodynamic equilibrium, with an overall drop in Gibbs free energy 5-fold lower than that of T. saccharolyticum or anaerobically grown Escherichia coli. The limited thermodynamic driving force of glycolysis in C. thermocellum could be attributed in large part to the small free energy of the phosphofructokinase reaction producing fructose bisphosphate. The ethanol fermentation pathway was also substantially more reversible in C. thermocellum than in T. saccharolyticum. These observations help explain the comparatively low ethanol titers of C. thermocellum and suggest engineering interventions that can be used to increase its ethanol productivity and glycolytic rate. In addition to thermodynamic analysis, we used our isotope tracer data to reconstruct the T. saccharolyticum central metabolic network, revealing exclusive use of the Embden-Meyerhof-Parnas (EMP) pathway for glycolysis, a bifurcated tricarboxylic acid (TCA) cycle, and a sedoheptulose bisphosphate bypass active within the pentose phosphate pathway.IMPORTANCE Thermodynamics constitutes a key determinant of flux and enzyme efficiency in metabolic networks. Here, we provide new insights into the divergent thermodynamics of the glycolytic pathways of C. thermocellum and T. saccharolyticum, two industrially relevant thermophilic bacteria whose metabolism still is not well understood. We report that while the glycolytic pathway in T. saccharolyticum is as thermodynamically favorable as that found in model organisms, such as E. coli or Saccharomyces cerevisiae, the glycolytic pathway of C. thermocellum operates near equilibrium. The use of a near-equilibrium glycolytic pathway, with potentially increased ATP yield, by this cellulolytic microbe may represent an evolutionary adaptation to growth on cellulose, but it has the drawback of being highly susceptible to product feedback inhibition. The results of this study will facilitate future engineering of high-performance strains capable of transforming cellulosic biomass to biofuels at high yields and titers. Thermoanaerobacterium saccharolyticum and Clostridium thermocellum are thermophilic, anaerobic bacteria with complementary metabolic capabilities being developed for industrial-scale production of biofuels, such as ethanol, from lignocellulosic biomass   , 5. Meta mobilis –9. In co, and H2 , 10. T. olyticum . Severalmocellum –19.T. saccharolyticum and C. thermocellum possess distinct glycolytic pathways. While genome annotation suggests that T. saccharolyticum uses the canonical Embden-Meyerhof-Parnas (EMP) pathway, the glycolytic pathway in C. thermocellum has been shown to possess several unique characteristics (20–i) rather than ATP hydrolysis (C. thermocellum lacks a pyruvate (Pyr) kinase  and instead converts phosphoenolpyruvate (PEP) to Pyr via one of two routes: (i) pyruvate phosphate dikinase  and (ii) the malate shunt, which converts PEP to Pyr via three steps, phosphoenolpyruvate carboxykinase , malate dehydrogenase , and malic enzyme   per glucose but also can be expected to significantly impact the thermodynamic driving force of individual reactions and of the overall pathway.In addition to the differences outlined above, eristics , 20–23  , 26  . These uin vivo Gibbs free energies (ΔG) of metabolic reactions have been applied to investigate the thermodynamics of glycolytic pathways in a few model organisms and biofuel producers  (+J) to backward (−J) flux ratios (+/J−J) from isotope tracer experiments (R is the gas constant and T is the temperature in kelvin)   and the  kelvin) , 29. The kelvin) –34.2H and 13C metabolic flux analysis to investigate the in vivo reversibility and thermodynamics of the glycolytic pathways and central metabolic networks of T. saccharolyticum and C. thermocellum. We show that glycolysis in C. thermocellum is highly reversible at every step of the pathway and operates close to thermodynamic equilibrium. In comparison, the glycolytic pathway in T. saccharolyticum is highly thermodynamically favorable, comparable to that of anaerobically grown Escherichia coli. We also found that the ethanol fermentation pathway of C. thermocellum was substantially more reversible than that of T. saccharolyticum. Besides revealing differences in pathway reversibility, our analyses also represent the first experimental isotope-based reconstruction of the T. saccharolyticum central metabolic network. We found that this bacterium metabolizes glucose exclusively via the EMP pathway, while the Entner-Doudoroff (ED) pathway and the oxidative pentose phosphate pathway (oxPPP) are inactive. Our analyses also revealed a bifurcated tricarboxylic acid (TCA) cycle and a sedoheptulose bisphosphate bypass within the pentose phosphate pathway (PPP). Finally, we present evidence supporting the activity of several currently unannotated amino acid biosynthetic pathways in T. saccharolyticum.Here, we integrated quantitative metabolomics with 2H and 13C metabolic flux analysis to measure in vivo reversibility and thermodynamics of metabolic reactions requires prior knowledge of the underlying metabolic network topology. Recent studies have used isotope tracers, metabolic flux analysis (MFA), proteomics, or biochemical assays to elucidate the central metabolic network of C. thermocellum , we present a quantitative MFA that corroborates the metabolic network reconstruction presented in this section.The application of mocellum , 35–38. ysiology , 39, 40,T. saccharolyticum has genes encoding all of the enzymes in the EMP pathway, but its genome annotation does not support a complete ED pathway. T. saccharolyticum does not have sequences associated with the ED enzymes phosphogluconolactonase  or phosphogluconate dehydratase , although it does have genes annotated as glucose-6-phosphate dehydrogenase  and ED pathway aldolase  (The Embden-Meyerhof-Parnas (EMP) and the Entner-Doudoroff (ED) pathways are the two most common glycolytic pathways found in microbial species, although several other variants have been described , 41–43. 41–ac_0268) , 23.T. saccharolyticum was grown on [1-13C1]- or [6-13C1]glucose, the glycolytic intermediates 3-phosphoglycerate (3PG) and phosphoenolpyruvate (PEP) both were ∼50% M + 0  and ∼50% M + 1 , which was consistent with their production via EMP glycolysis , consistent with its production solely via the EMP pathway (13C2]Pyr), which would have been indicative of ED pathway activity H] to form ribulose-5-phosphate (Ru5P), losing what was originally the first carbon of glucose as CO2 (T. saccharolyticum has annotated genes for g6pdh (Tsac_1610) and 6pgd (Tsac_0154) genes, but there are no annotated pgl sequences  to 6PG via the G6pdh and Pgl reactions, which are shared with the ED pathway. 6PG then is decarboxylated by 6-phosphogluconate dehydrogenase glucose . Nondete]glucose , 47.T. saccharolyticum, R5P and Ru5P were predominantly M + 2 and M + 4 labeled in glucose and M + 1 and M + 2 labeled in [1-13C1]glucose , a transaldolase reaction , and a second transketolase reaction . Additionally, ribose phosphate epimerase  and ribose phosphate isomerase  interconvert Xu5P, Ru5P, and R5P. In ]glucose  and C, w13C2]- and [1-13C1]glucose that could not be explained by canonical nonoxidative PPP reactions  bypass comprising an SBP aldolase  and an SBP phosphatase   are annotated as both Fbas and Sbas  of M + 0 S7P in both ) , 26. Whiand Sbas , 23, andand Sbas .13C1]- and glucose. Similarly, during [6-13C1]glucose labeling, the large fraction of M + 2 S7P (∼32.5%) was also indicative of SBP bypass activity (This SBP bypass can explain the production of unlabeled S7P from unlabeled DHAP and E4P during growth on glucose labeling indicated that formaldehyde transketolase (Ftkt) was unlikely responsible for the formation of Ru5P and R5P. Dihydroxyacetone phosphate (DHAP) was either unlabeled or M + 2 labeled and can be dephosphorylated to form dihydroxyacetone (DHA). Ftkt condenses the first two carbons of DHA with glyceraldehyde-3-phosphate  to form xylulose-5-phosphate (Xu5P), releasing the third carbon of DHA as formaldehyde. Because DHA is symmetrical, carbons 1 and 3 are equivalent, so a 13C carbon from M + 2 DHA has a 50% chance of being incorporated into Xu5P. Thus, Xu5P produced by Ftkt inherits 0 or 2 13C-labeled carbons from GAP and 0, 1, or 2 13C carbons from DHA. Xu5P is epimerized to form ribulose-5-phosphate (Ru5P), which is isomerized to form ribose-5-phosphate (R5P). Therefore, if Ftkt was involved in the production of pentose phosphates, we would expect Xu5P, Ru5P, and R5P to be approximately 17% M + 0, 17% M + 1, 33% M + 2, 17% M + 3, and 17% M + 4. However, labeling of Ru5P and R5P in cells fed glucose did not match these patterns but were primarily M + 2 and M + 4 labeled, with only small fractions of M + 1 or M + 3. These labeling patterns are better explained by canonical nonoxidative PPP reactions supplemented with an SBP bypass (T. saccharolyticum. (B) glucose labeling also indicated that the reverse ribulose monophosphate (rRuMP) pathway was not responsible for the formation of Ru5P and R5P. The rRuMP pathway isomerizes fructose-6-phosphate (F6P) to hexulose-6-phosphate (Hu6P). Hu6P is split into (Ru5P) and formaldehyde, with carbons 2 to 6 retained in Ru5P. Thus, the carbon lost as formaldehyde is 13C labeled during glucose labeling, leading to the production of M + 1 Ru5P. Ru5P then is isomerized to form R5P. Ru5P and R5P were primarily M + 2 and M + 4 during glucose labeling, with only a very small M + 1 fraction, indicating that pentose phosphate generation via the rRuMP pathway was unlikely. This agrees with the lack of an annotated gene encoding hexulose-6-phosphate synthase (Hps) in T. saccharolyticum. (C) [6-13C1]glucose labeling indicated that phosphoketolase (Pkt) activity was unlikely to be responsible for the formation of Ru5P and R5P. GAP produced by the EMP pathway was M + 0 or M + 1. GAP is further processed to form acetyl-phosphate (AcP), which was M + 0 or M + 1 labeled. GAP and AcP can be condensed by Pkt to form Xu5P, which thus inherits no or one 13C-labeled carbon from GAP and 0 or 1 13C-labeled carbon from AcP. Thus, Xu5P, Ru5P, and R5P produced via Pkt were expected to be ∼25% M + 0, ∼50% M + 1, and ∼25% M + 2. However, we observed approximately equal proportions of M + 0 and M + 1 Ru5P and R5P, with only a small fraction of M + 2, indicating that Pkt was unlikely to be responsible for pentose phosphate generation in T. saccharolyticum. This agrees with the lack of an annotated gene encoding a phosphoketolase in T. saccharolyticum. (D) Labeling of TCA cycle intermediates in T. saccharolyticum fed [1-13C1]glucose. M + 2 AKG production was consistent with production from oxaloacetate and acetyl-CoA via the oxidative TCA cycle. A lack of M + 2 succinate indicates that succinate was not produced oxidatively from AKG and that the TCA cycle was bifurcated. The presence of only M + 0 and M + 1 succinate, malate, and aspartate indicates these metabolites were produced by a reductive TCA. (E) Labeling of TCA cycle intermediates in T. saccharolyticum fed glucose. M + 4 AKG production was consistent with production from oxaloacetate and acetyl-CoA via the oxidative TCA cycle. A lack of M + 3 succinate indicates that succinate was not produced oxidatively from AKG and that the TCA cycle was bifurcated. The presence of only M + 0 and M + 2 succinate, malate, and aspartate indicates these metabolites were produced by a reductive TCA. The increased proportion of M + 0 α-ketoglutarate, pyruvate, succinate, and malate were due to variable extracellular pools of these metabolites that do not participate in metabolism. In addition to its production via Fumh, fumarate may be produced by deamination of aspartate, which takes place in a number of biosynthetic pathways, such as purine biosynthesis. This route of fumarate production would result in the same labeling patterns as its production from oxaloacetate via Mdh and Fumh. CO2 labeling was inferred from the MIDs of citrulline and ornithine, as described in Materials and Methods. The diagrams on the left show predicted metabolite labeling with the specified isotope tracer. Pyruvate formate lyase (Pfl) was omitted from panels D and E for clarity but serves the same role as Pfor. The diagrams on the left show predicted metabolite labeling with the specified isotope tracer. Solid arrows represent a single reaction; dashed arrows represent multiple reaction steps. 13C-labeled carbons are colored red; 12C carbons are colored black. Carbons with an approximately equal chance of being 13C or 12C labeled are represented by a half red and half black circle. Data are averages from 2 to 3 independent biological replicates. Error bars show ±SD. Abbreviations: Dap, dihydroxyacetone phosphate phosphorylase; DHA, dihydroxyacetone; FAL, formaldehyde; Ftkt, formaldehyde transketolase; Hps, hexulose-6-phosphate synthase; Hu6P, hexulose-6-phosphate; Phi, phosphohexuloisomerase; Pkt, phosphoketolase. Download FIG S1, PDF file, 0.6 MB.Alternative routes for pentose phosphate generation and bifurcation of the TCA cycle in P bypass . This agCopyright © 2020 Jacobson et al.2020Jacobson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the T. saccharolyticum genome, including citrate synthase and malate dehydrogenase. Our 13C tracer data indicated that the TCA cycle in T. saccharolyticum functions as a branched pathway to produce essential biosynthetic precursors: α-ketoglutarate (AKG) was produced oxidatively despite the lack of an annotated citrate synthase, while oxaloacetate (OAA), malate , and succinate all were produced reductively. During growth in [6-13C1]glucose, α-ketoglutarate (AKG) labeling patterns were consistent with its production from oxaloacetate and acetyl-coenzyme A (AcCoA) via citrate and isocitrate  Labeling of aspartate, asparagine, and threonine was consistent with all three amino acids originating from oxaloacetate (OAA). OAA can be produced via carboxylation (CO2 addition) of PEP by Pepck or via carboxylation of Pyr by ME to produce malate, followed by its conversion to OAA by Mdh. Because PEP and Pyr were both ∼50% M + 0 and ∼50% M + 1 and CO2 was unlabeled, production of ∼50% M + 0 and ∼50% M + 1 OAA was expected. Aspartate, produced via amination of OAA, had the same labeling pattern as Mal, PEP, and Pyr (∼50% M + 0 and ∼50% M + 1). Aspartate is further converted to threonine (Thr) or asparagine (Asn) without carbon transitions, resulting in Asn and Thr labeling that matched that in PEP and Pyr (∼50% M + 0 and ∼50% M + 1). (B) Labeling of amino acids derived from α-ketoglutarate (AKG) was consistent with canonical synthesis pathways. OAA is formed by the addition of CO2 to PEP by Pepck or to Pyr by the combined action of ME and Mdh. Because PEP and Pyr are both ∼50% M + 0 and ∼50% M + 1 and CO2 is unlabeled, M + 0 and M + 1 OAA are produced. Pyr is also decarboxylated by Pfor, losing an unlabeled CO2 to form acetyl-CoA (AcCoA) that is ∼50% M + 0 and ∼50% M + 1. Citrate synthase combines OAA with AcCoA to form citrate, which is ∼25% M + 0, ∼50% M + 1, and ∼25% M + 2, and has a 50% chance of inheriting one 13C carbon each from AcCoA and OAA. Citrate then is decarboxylated to form AKG, losing an unlabeled carbon in the process, leaving AKG with the same labeling pattern as citrate . AKG is aminated to form glutamate, which serves as the precursor to glutamine, proline, ornithine, and citrulline, all of which are approximately ∼25% M + 0, ∼50% M + 1, and ∼25% M + 2 labeled, consistent with the formation of all of these amino acids from AKG. (C) Labeling of T. saccharolyticum with glucose indicated that valine and leucine were produced via canonical synthesis pathways from pyruvate and acetyl-CoA. Briefly, two pyruvate molecules are condensed to form the intermediate ketoisovalerate (KIV), losing the carboxyl group of one of the pyruvate molecules as CO2. Each pyruvate has a ∼50% chance of being labeled at the carbonyl and methyl groups, meaning KIV should be primarily ∼25% M + 0, ∼50% M + 2, and ∼25% M + 4. KIV is aminated to form valine, and we detect valine labeling that approximates what we expect from KIV. KIV is also used for leucine biosynthesis, where it receives an additional two carbons from AcCoA, itself formed from the carbonyl and methyl carbons of pyruvate and, thus, ∼50% M + 0 and ∼50% M + 2. During leucine synthesis, the carboxyl carbon of KIV  is lost as CO2. Thus, leucine independently inherits 3 sets of 2 carbons, and each set may be both labeled or unlabeled and should be labeled ∼12.5% M + 0, ∼37.5% M + 2, ∼37.5% M + 4, and ∼12.5% M + 6. As expected, leucine labeling is consistent with production from KIV, although it is enriched for the M + 0-labeled form due to incomplete label penetration. The presence of only M + 0 and M + 2 labeling in acetyl group carbons confirms the EMP pathway as the sole glycolytic route, as ED pathway activity would lead to the formation of M + 1 acetyl groups via decarboxylation of pyruvate due to the alternate orientation of carbons in pyruvate formed from the first three carbons of glucose via the ED pathway. (D) [1-13C1]glucose labeling in T. saccharolyticum confirmed the synthesis of aromatic amino acids via the shikimate pathway. Erythrose-4-phosphate (E4P) is produced by transketolase from the bottom 4 carbons of F6P. The reverse activity of the Pfk and Fba reactions in the EMP pathway can result in F6P that is labeled at the 6th carbon. Thus, E4P can be M + 0 or M + 1, although the M + 0 form is much more prevalent. The shikimate pathway condenses E4P with PEP  to form a seven-carbon intermediate, 3-deoxy-d-arabinoheptulosonate-7-phosphate (DAHP), which is processed to become shikimate-3-phosphate (Sh3P). Sh3P can contain 0, 1, or 2 labeled carbons. Sh3P is further processed and combined with another PEP molecule (which is M + 0 or M + 1) and dephosphorylated to form chorismate, which is M + 0, M + 1, M + 2, or M + 3. Chorismate mutase converts chorismate to prephenate, which is decarboxylated, dehydrated, and aminated to form phenylalanine (Phe) or decarboxylated, oxidized, and aminated to form tyrosine (Tyr). The CO2 lost by decarboxylation is unlabeled , meaning the labeling of Phe and Tyr is the same as that of chorismate. Therefore, Phe and Tyr should inherit up to one 13C carbon from E4P and up to one 13C carbon from each of the two PEPs, resulting in M + 0, M + 1, M + 2, or M + 3 Phe and Tyr. The experimental labeling patterns in Phe and Tyr matched these predictions. Pyruvate formate lyase (Pfl) was omitted from panels A and B for clarity but serves the same role as Pfor. The increased proportion of M + 0 α-ketoglutarate and malate in panels A and B was due to extracellular pools of these metabolites that do not become labeled. The diagrams on the left show predicted metabolite labeling with the specified isotope tracer. Solid arrows represent a single reaction; dashed arrows represent multiple reaction steps. 13C-labeled carbons are colored red; 12C carbons are colored black. Carbons with an approximately equal chance of being 13C or 12C labeled are represented by a half red and half black circle. Data are averages from 2 to 3 independent biological replicates. Error bars show ±SD. Abbreviations: Asn, asparagine; Citr, citrulline; Gln, glutamine; Leu, leucine; Orn, ornithine; Phe, phenylalanine; Sh3P, shikimate-3-phosphate; Tyr, tyrosine; Val, valine. Download FIG S2, PDF file, 0.6 MB.Routes for amino acid synthesis. When the EMP pathway is used for glucose catabolism, TCA cycle intermediates have equivalent labeling during glucose confirmed production of serine from both 3PG and glycine. Serine was 56.0% M + 0, while 3PG was 52.3% M + 0 and glycine was 99.8% M + 0, indicating serine production from both 3PG and glycine. Labeling of methionine (5.5% M + 2) while threonine was only M + 0 and M + 1 confirms that the C1 unit in 5,10-methylenetetrahydrofolate (MeTHF) was labeled due to Shmt activity. (B) Labeling of serine when T. saccharolyticum was fed glucose confirmed production of serine from both 3PG and glycine. Serine was 52.3% M + 0, while 3PG was 50.7% M + 0 and glycine was 53.1% M + 0. Additionally, serine was 42.3% M + 2 while 3PG was 50.8% M + 2 and glycine was 1.0% M + 2. Together, these data indicate serine production from both 3PG and glycine. Labeling of methionine (1.5% M + 3) while threonine was only M + 0, M + 1, and M + 2 confirms that the C1 unit in 5,10-methylenetetrahydrofolate (MeTHF) was labeled due to Shmt activity. (C) Labeling of purine nucleotides served as additional confirmation that C1 units in one carbon metabolism were 3C labeled during growth on [6-13C1]glucose. Purines are synthesized de novo via a series of reactions that incorporate ribose-5-phosphate (R5P), glycine (Gly), CO2, and two C1 units from 10-formyltetrahydrofolate (FTHF). During [6-13C1]glucose labeling in T. saccharolyticum, R5P is at most M + 2 labeled and CO2 and Gly are M + 0. The increased proportion of the M + 2 and M + 3 forms of ATP and GTP relative to R5P was due to the addition of labeled C1 units from FTHF during de novo purine biosynthesis. We used the MIDs of threonine and methionine to infer labeling of C1 units in MeTHF (represented in the figure as THF carbon). MeTHF is oxidized by one carbon metabolic reactions to form 5,10-methenyl-THF, which is dehydrated to form FTHF. We combined the inferred MeTHF labeling pattern with the labeling in R5P, Gly, and CO2 to produce an expected labeling pattern of a purine molecule synthesized from these precursors (represented on the figure as predicted purine), resulting in a labeling pattern that matched that of measured purines ATP and GTP. The match between the predicted purine labeling pattern and the measured pattern indicated that the labeling in MeTHF was able to substitute for that of FTHF, indicating the two metabolites are close to isotopic equilibrium. (D) Labeling with an equimolar mixture of [U-13C6] and [U-12C6]glucose confirmed the activity of the citramalate pathway. Production of isoleucine from threonine during labeling with an equimolar mixture of [U-13C6] and [U-12C6]glucose would result in 25% M + 3 isoleucine, but we observe only 1.5% M + 3 isoleucine, suggesting at most 6% contribution of the pathway starting from threonine. The diagrams on the left show predicted metabolite labeling with the specified isotope tracer. Solid arrows represent a single reaction; dashed arrows represent multiple reaction steps. 13C-labeled carbons are colored red; 12C carbons are colored black. Data are averages from 2 to 3 independent biological replicates. Error bars show ±SD. Download FIG S3, PDF file, 0.7 MB.One-carbon metabolism, purine biosynthesis, and the citramalate pathway. (A) Labeling of serine when Copyright © 2020 Jacobson et al.2020Jacobson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 13C1]glucose and [1-13C1]glucose, consistent with its production either via threonine aldolase  of M + 2 methionine when only M + 1 aspartate was present indicated that at least some glycine was produced via reverse Shmt to generate 13C-labeled C1 units, from the third carbon in serine, in MeTHF, which can be converted to 5-methyltetrahydrofolate (MTHF) and used to produce M + 2 methionine from M + 1 aspartate (13C labeling of C1 units (9.2% M + 1 during [6-13C1]glucose labeling) in 10-formyltetrahydrofolate (FTHF) was also apparent from labeling patterns in purine nucleotides  or spartate . 13C lableotides .T. saccharolyticum has annotated genes encoding most of the enzymes required for isoleucine biosynthesis from threonine, but it does not have an annotated threonine deaminase, which is the first step in this pathway. Similarly, most of the enzymes required for isoleucine synthesis via citramalate are annotated, but citramalate synthase is missing  and unlabeled glucose  revealed substantial differences in the reversibility of upper glycolytic reactions between C. thermocellum, T. saccharolyticum, and E. coli  (C. thermocellum (∼23%) versus T. saccharolyticum (∼3%) and E. coli (∼5%) indicates that phosphofructokinase is substantially more reversible in this bacterium (P < 0.005). This observation is consistent with PPi-Pfk in C. thermocellum being less energetically favorable than the ATP-Pfk used by T. saccharolyticum and E. coli (13C1]- and [6-13C1]glucose labeling.Labeling with an equimolar mix of - and [1-13C1]glucose labeling . The fractions of M + 1 DHAP in C. thermocellum, T. saccharolyticum, and E. coli were 49.8%, 46.4%, and 43.5%, respectively, indicating that Tpi is highly reversible in all three organisms. The data indicated that Tpi was significantly more reversible in C. thermocellum than E. coli (P < 0.005), although the difference between C. thermocellum and T. saccharolyticum did not reach statistical significance (P = 0.07).Reversibility of triose phosphate isomerase  was revealed by glucose labeling, [1-2H1]GAP  is produced via Fba. Conversion of [1-2H1]GAP to 1,3-bisphosphoglycerate (BPG), catalyzed by glyceraldehyde-3-phosphate dehydrogenase (GAPDH), removes the deuterium from [1-2H1]GAP and places it on NAD+, generating 2H-labeled NADH. Reverse GAPDH flux then can produce unlabeled GAP if unlabeled NADH is used. The M + 0 GAP produced can propagate to DHAP (via reverse Tpi) and FBP (via reverse Fba), as well as to other upstream glycolytic intermediates. We observed that during growth on [4-2H1]glucose, C. thermocellum displayed a larger loss of 2H labeling across all glycolytic intermediates than T. saccharolyticum and E. coli, which indicated increased reversibility of glycolytic reactions in this bacterium. For example, although we could not reliably measure GAP, higher fractions of M + 0 DHAP and M + 0 FBP in C. thermocellum, i.e., 92% and 72%, respectively, versus 82% and 62% in T. saccharolyticum or 89% and 43% M + 0 in E. coli, suggested greater reversibility of its GAPDH/Tpi and GAPDH/Fba pairs of reactions .As shown in 2H1]glucose, the intermediates GAP, BPG, 3PG, and 2-phosphoglycerate (2PG) become deuterated at the second carbon (2H1]GAP to M + 0 DHAP via reverse Tpi or by the forward phosphopyruvate hydratase reaction (Eno) converting [2-2H1]2PG to M + 0 PEP. Once produced, propagation of M + 0 DHAP or M + 0 PEP to upstream metabolites provides a measure of the reversibility of Tpi, Eno, and other glycolytic reactions. As with [4-2H1]glucose, the greater loss of 2H labeling across glycolytic intermediates in C. thermocellum during [5-2H1]glucose labeling indicated increased reversibility of glycolysis in this bacterium compared to that of T. saccharolyticum or E. coli. For example, taking Tpi reversibility into account (C. thermocellum (98.4%) and T. saccharolyticum (96.6%) suggested increased reversibility of the Pgm/Eno reaction pair in these thermophilic organisms compared to that in E. coli .During growth on glucose and unlabeled glucose, the Pyr initially produced via glycolysis is either M + 0 or M + 3, and its decarboxylation produces acetyl-CoA containing an M + 0 or M + 2 acetyl group. Complete or partial  reversibility of Pfor or Pfl resulted in the interchange of the carboxylic group in Pyr with unlabeled CO2 or formate to produce M + 2 Pyr (E. coli (33.2% M + 2 Pyr) and C. thermocellum (26.5% M + 2 Pyr) than in T. saccharolyticum .When cells are grown in an equimolar mixture of [U- + 2 Pyr  Fig. 5)13C6]glucP < 0.01) in C. thermocellum (10.2% M + 2 PEP) than in E. coli (2.0% M + 2 PEP). Data suggested that PEP-to-Pyr conversion was also more reversible in C. thermocellum than T. saccharolyticum (3.9% M + 2 PEP), but this difference was not significant at P < 0.05. Considering the reported absence of pyruvate carboxylase or oxaloacetate decarboxylase activity in C. thermocellum H→acetaldehyde + NAD(P)+] and then to ethanol [acetaldehyde + NAD(P)H→ethanol + NAD(P)+] by a bifunctional aldehyde/alcohol dehydrogenase, AdhE  (C. thermocellum and T. saccharolyticum in the presence of [1-13C1]ethanol and unlabeled cellobiose (for C. thermocellum) or unlabeled glucose (for T. saccharolyticum). The incorporation of 13C from [1-13C1]ethanol into acetyl-CoA was significantly larger (P < 0.005) in C. thermocellum (67.9% M + 1) than T. saccharolyticum (46.0% M + 1), indicating increased reversibility of the conversion of acetyl-CoA to ethanol in C. thermocellum . We also observed significant propagation of 13C from [1-13C1]ethanol into upper glycolytic intermediates, such as G6P (5.5% M + 1) and FBP (8.8% M + 1), in C. thermocellum (P < 0.05 for both metabolites). However, these metabolites remained essentially unlabeled in T. saccharolyticum (<0.2% M + 1). Overall, this analysis indicated a substantially more reversible ethanol fermentation pathway in C. thermocellum than T. saccharolyticum.Since se, AdhE . T. sacce (AdhA) . To invemocellum . Additioin vivo flux ratios (+/J–J) and compare the thermodynamics of the distinct glycolytic pathways of C. thermocellum and T. saccharolyticum, we integrated data from five 13C and 2H tracer experiments  into a single flux map for each organism  as well as models lacking reactions that we identified as necessary to explain labeling patterns of specific metabolites . The results of this analysis agreed with our previous conclusions and predicted an inactive ED pathway , an inactive oxidative PPP , and a bifurcated TCA cycle with oxidative production of AKG and reductive production of succinate. Although Pepck typically is considered a gluconeogenic reaction , our MFA results supported the activity of a PEP carboxylating enzyme producing OAA from PEP. We also found that the SBP bypass was necessary to explain labeling patterns in PPP intermediates and aromatic amino acids. This analysis also supported qualitative conclusions regarding amino acid biosynthetic routes, such as isoleucine synthesis via the citramalate pathway instead of from threonine. Additionally, the production of serine from 3PG and its consumption by Shmt to form labeled THF intermediates was necessary to achieve a statistically acceptable model fit.For C. thermocellum, our MFA results were also consistent with the previously reported central metabolic network (C. thermocellum.For  network . SpecifiΔG = −RT ln(+/J−J), we estimated ΔG of glycolytic reactions  from MFA-derived flux ratios (+/JJ−) in C. thermocellum and T. saccharolyticum  and was comparable to the glycolytic pathway in anaerobically grown E. coli  (Using the relation olyticum . For a sx ratios . Estimat kJ/mol)  , so we calculated the free energy of the Pfk reaction using our measured intracellular metabolite concentrations. We used our calculated ΔG range for the Pfk reaction and assigned the remainder of the ΔG of the Pgi-Pfk combined reaction to Pgi. Error bars shown represent the 95% confidence intervals of the free energy estimates. Error bars smaller than the icon used to represent the average are not visible. Download FIG S4, PDF file, 0.4 MB.Thermodynamic profile of glycolysis in  E. coli  and Z. m mobilis . T. saccCopyright © 2020 Jacobson et al.2020Jacobson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the C. thermocellum could be attributed in large part to the small driving force of the Pfk reaction, which had a ΔG of only −1.45 kJ/mol compared to −22.57 kJ/mol in T. saccharolyticum and −18.84 kJ/mol in E. coli. The small ΔG of Pfk in C. thermocellum was likely due to the use of PPi instead of ATP as the high-energy phosphate donor. Indeed, the standard free energy for the ATP-dependent Pfk (ΔG = −15.0 kJ/mol) is ∼10.6 kJ/mol more favorable than the PPi-dependent Pfk (ΔG = −4.4 kJ/mol) due to the greater free energy of ATP hydrolysis than PPi hydrolysis (The limited thermodynamic driving force of the glycolytic pathway in drolysis .T. saccharolyticum (−0.70 kJ/mol) and C. thermocellum (−0.47 kJ/mol) but was more thermodynamically favorable in anaerobic E. coli (−3.64 kJ/mol) (T. saccharolyticum (−1.93 kJ/mol) than in C. thermocellum (−0.74 kJ/mol). The ΔG of lower glycolytic reactions (GAPDH to Eno) were close to equilibrium in both C. thermocellum and T. saccharolyticum but were generally more favorable in T. saccharolyticum. For example, the ΔG of the Pgm/Eno pair of reactions was −0.49 kJ/mol in C. thermocellum compared to −0.77 kJ/mol in T. saccharolyticum.Consistent with our qualitative observations regarding its reversibility, the Fba reaction operated close to thermodynamic equilibrium in both  kJ/mol) . Also inC. thermocellum and T. saccharolyticum, most of the reactions in the PPP, including Sba, SBPase, Rpi, and Rpe, as well as the transketolase half reactions TktA (Xu5P→GAP + C2) and TktB (S7P→R5P + C2), all had ΔG no more favorable than −1.3 kJ/mol, putting them close to thermodynamic equilibrium  and also higher than those in E. coli . Higher levels of G6P and F6P in C. thermocellum potentially reflect the need to accumulate larger amounts of upper glycolytic intermediates to drive the PPi-Pfk reaction forward. Indeed, the FBP/F6P ratio was considerably lower in C. thermocellum (∼1) than E. coli (∼15) or T. saccharolyticum (∼3.7). Thus, higher intracellular FBP concentrations maintained by the more favorable upper EMP reactions in E. coli and T. saccharolyticum may help drive subsequent reactions in glycolysis.Intracellular concentrations of glycolytic intermediates across the bacteria in this study reflected the thermodynamic profile of their glycolytic pathways see . For exaC. thermocellum  than E. coli (66.0 and 42.4) and T. saccharolyticum (59.9 and 61.4). The higher ATP/ADP ratios in E. coli and T. saccharolyticum likely contribute to the favorability of the ATP-Pfk reaction and other reactions in upper glycolysis in these organisms. In contrast, the low ATP/ADP and GTP/GDP ratios in C. thermocellum may help make ATP/GTP-generating reactions in lower glycolysis, such as GAPDH and Ppdk, more favorable but may also slow biosynthetic reactions that rely on ATP/GTP hydrolysis, such as amino acid, protein, fatty acid, nucleotide, and mRNA synthesis.Interestingly, ATP/ADP and GTP/GDP ratios were considerably lower in ΔG = −RT ln(+/J−J), enzyme efficiency  is directly proportional to the drop in Gibbs free energy (ΔG) of a biochemical reaction (−J) increases exponentially as ΔG approaches equilibrium, decreasing the net reaction rate (+J– −J)  , 65. Thi(J+– J−) , the smaΔG→0) and effectively stops further substrate utilization and product formation. Therefore, it is likely that the limited thermodynamic driving force of the C. thermocellum glycolytic pathway represents a key contributor to its low ethanol titer (25 g/liter) (T. saccharolyticum (70 g/liter) (S. cerevisiae (75 g/liter) (Z. mobilis (>85 g/liter)   comparedg/liter) , S. cereg/liter) , and Z. g/liter) , 68, 69.olyticum  g/liter olyticum , 16, 70.C. thermocellum is to replace PPi-Pfk with an ATP-dependent Pfk. In addition, replacing the malate shunt and Ppdk routes for conversion of PEP to Pyr with Pyk also may increase flux and ethanol titers by providing a single more thermodynamically favorable route. Indeed, C. thermocellum strains expressing an exogenous Pyk achieve higher ethanol titers  and Ppdk (−6.92 kJ/mol) reactions than the values we report in this study . This discrepancy may be due to in vivo constraints not accounted for in the MDF analysis, such as limits on enzyme abundance and the need to maintain adequate metabolite pools to drive biosynthetic pathways .The previously reported MDF-based analysis of ycolysis . Our in pathways , 33, 34.C. thermocellum mirrors that of C. cellulolyticum, another cellulose-degrading organism that also utilizes PPi-Pfk, Ppdk, and the malate shunt (i-Pfk (which uses PPi in place of ATP) and Ppdk (which produces ATP from AMP and PPi), by cellulolytic microbes represents a potential mechanism for increased energy yield. However, recent research suggests that the PPi needed for glycolysis is in excess of what can be generated as a byproduct of biosynthetic pathways and is instead generated by ATP-consuming glycogen cycling reactions , which is transcribed and expressed in C. thermocellum (+ oxidoreductase (Rnf)  , 77, witse (Rnf) , 18. Thu−1 hour−1) or the cell yield (grams of cells × gram substrate−1), the product of which is the specific growth rate (per hour). For microbes growing on cellulosic biomass, however, the specific substrate consumption rate is highly constrained, leading to strong selective pressure for cellulolytic bacteria to maximize the cell yield by increasing glycolytic ATP yield. Conversely, the use of highly thermodynamically favorable pathways with lower ATP yield, such as the ED pathway in Z. mobilis, likely represents an adaptation to glucose-rich environments  were purchased from Cambridge Isotope Laboratories . All other chemicals were purchased from Sigma-Aldrich .Tracers (glucose and then extracting intracellular metabolites into solvent containing known concentrations of unlabeled standards for metabolites of interest. The solvent containing 13C-labeled intracellular metabolites and unlabeled standards was analyzed by LC-MS, and the ratio between the 13C and 12C peak intensities was used to determine intracellular metabolite concentrations -N′-ethylcarbodiimide hydrochloride (EDC) solution and 10 μl 6 M aniline solution was added. Samples were vortexed for 2 h, and then 5 μl triethylamine was added to stop the reaction. Samples were centrifuged to remove debris and then subjected to LC-MS analysis as described above.To enhance chromatographic separation and detection of some metabolites, we performed an aniline derivatization protocol for some samples used for metabolite quantitation . After tN-acetyl-glucosamine-6-phosphate.Positional labeling in pyruvate was determined by comparing the labeling patterns in ornithine (Orn) with those in acetylornithine (AcOrn). These compounds differ by a single acetyl group, received from acetyl-CoA, which is itself produced by decarboxylation of pyruvate. Thus, the acetyl group always contains only the 2nd and 3rd carbons of pyruvate, and the gain of labeled carbons by Orn during acetylation to AcOrn can be used as a readout of the position of labeled carbons in pyruvate. Acetyl group labeling was also measured by comparing the labeling patterns of glucosamine-6-phosphate to those in 13C labeling, unlabeled CO2 may come from two sources: (i) glucose dissimilation and (ii) carryover from inoculation. For glucose dissimulation, in cultures fed an equimolar mixture of [U13C6]glucose and unlabeled glucose, ∼50% of CO2 released during glucose fermentation to ethanol or acetate is expected to be unlabeled; the decarboxylation of pyruvate to form acetyl-CoA likely releases most of the cell-produced CO2. In cultures fed [1-13C1]- or [6-13C1]glucose, almost all CO2 released by the culture would be unlabeled. The 13C carbon under these conditions becomes the methyl carbon of pyruvate , which differs from Orn by incorporation of a single CO2 molecule. Thus, the difference between the labeling patterns of the two metabolites is due to the labeling of CO2. We used CO2 labeling derived this way solely to assist in our qualitative analysis of metabolism; CO2 was not assigned an MID during MFA.To determine the labeling of intracellular CO2, into a single hypothetical MID for purines. This simulated MID showed agreement with measured labeling of purines ATP and GTP (THF intermediate labeling was measured by comparing the labeling patterns of threonine (Thr) to the labeling pattern of methionine (Met). Met is formed from threonine by the addition of a single carbon from 5-methyltetrahydrofolate (MTHF), derived from the same pool as FTHF. Thus, the difference between Thr and Met labeling allowed us to determine FTHF labeling. Labeling of “mock purine” was produced by combining the labeling patterns of all precursors to purine formation, ribose-5-phosphate (R5P), glycine (gly), 2 THF intermediates, and CO and GTP .T. saccharolyticum metabolic model was constructed from the KEGG genome-scale metabolic model, manually curated  equals forward flux (+J) minus backward flux (−J), and exchange flux (Jexch) equals min. Using the optimal solution, we calculated 95% confidence intervals for all estimated fluxes by performing a parameter continuation, which varies each flux to determine how sensitive the optimal SSR is to that flux. Upper and lower bounds are assigned to each flux by finding how far they can be varied before the SSR is perturbed past a critical point, corresponding to a chi-square distribution with a single degree of freedom.MFA was performed using the INCA software suite . INCA isisotopes . We rest10.1128/mSystems.00736-19.10Table S613C and 2H labeling data in C. thermocellum and T. saccharolyticum used for MFA analysis. Download Table S6, XLSX file, 0.05 MB.Copyright © 2020 Jacobson et al.2020Jacobson et al.Creative Commons Attribution 4.0 International license.This content is distributed under the terms of the 2, which was modeled by allowing intracellular CO2 to exchange with a pool of unlabeled CO2. We used a previously published biomass equation  and the derived free energies across them. We found a <1.0% cumulative difference in free energy of glycolytic reactions between the two models, with the single most affected reaction having only a 5.5% difference in free energy. Thus, the omission of malic enzyme had a negligible effect on ΔG measurements, and this model was used for all flux and free energy calculations presented in the text and figures.Measured uptake and excretion fluxes for biomass, lactate, ethanol, acetate, and glucose in t titers , 39. Our2 test was used to determine whether the estimated fluxes adequately describe the measured data. The optimized SSR of a correct model and data set is a variable with a χ2 distribution with degrees of freedom equal to the number of fitted measurements (n) minus the number of estimated independent parameters (p). Fitted measurements are fitted external fluxes, namely, uptake, excretion, growth rates, and all non-zero MIDs. Estimated parameters are all free fluxes, including net, exchange, and dilution fluxes. We required that our models pass the χ2 test with a critical threshold of 0.05 (95% confidence), meaning the optimized SSR fell between C. thermocellum, the acceptable SSR range was 422.1 to 543.7, and the SSR after convergence was 499.2. For T. saccharolyticum, the acceptable SSR range was 434.3 to 557.5, and the SSR after convergence was 507.3.A χΔG = −RT ln(+/J−J). For two reactions (Pfk and Pyk) in the glycolytic pathway of T. saccharolyticum, the 95% confidence interval lower bound for the reverse flux was 0, implying the reaction free energy is unbounded in the negative direction . For these reactions, we supplemented our flux-based ΔG values with those calculated from metabolite concentrations using the relation ΔG = ΔG°′ + RT lnQ, where Q is the ratio of the concentrations of products to reactants, R is the gas constant, and T is temperature in kelvin. Reaction standard free energies (ΔG°′) were retrieved from the online tool eQuilibrator, using a pH of 7 and ionic strength of 0.1 as settings (E. coli (Reaction free energies were calculated from MFA-derived flux measurements using the relation settings , 88, 89.(E. coli  combine (E. coli .