Here, we used high-resolution 33S and 13C tracing technology with NMR and LC-MS to access direct measurement of metabolic fluxes in S assimilation, when photosynthesis and photorespiration are varied via the gaseous composition of the atmosphere . We show that S assimilation is stimulated by photorespiratory metabolism and therefore, large photosynthetic fluxes appear to be detrimental to plant cell sulphur nutrition.Intense efforts have been devoted to describe the biochemical pathway of plant sulphur (S) assimilation from sulphate. However, essential information on metabolic regulation of S assimilation is still lacking, such as possible interactions between S assimilation, photosynthesis and photorespiration. In particular, does S assimilation scale with photosynthesis thus ensuring sufficient S provision for amino acids synthesis? This lack of knowledge is problematic because optimization of photosynthesis is a common target of crop breeding and furthermore, photosynthesis is stimulated by the inexorable increase in atmospheric CO Cyril Abadie and Guillaume Tcherkez tested whether the metabolic flux associated with sulfur assimilation effectively scales with photosynthesis by measuring metabolic fluxes in sulfur assimilation. This study suggests that photorespiratory metabolism stimulates sulfur assimilation. Also, grain S content is essential to form protein disulfide bridges and dictates flour processing properties (such as dough extensibility) in bread, pasta or biscuit industry3. Sulfur fertilization of crops represents a demand of ≈15 Mt S and an expenditure of ≈$25B each year globally4. It is often assumed that S assimilation positively correlates with photosynthesis, because sulfate incorporation is stimulated by sugars in plant roots5 and inhibited under non-physiological conditions where CO2 is omitted from the atmosphere (CO2-free air)6. Nevertheless, the relationship with photosynthesis has never been tested experimentally and thus it is presently uncertain as to whether S assimilation can be impacted by environmental conditions that affect photosynthetic metabolism.Sulfur is a crucial microelement for plant nutrition, required by the biosynthesis of sulfolipids, antioxidants, cofactors, secondary metabolites, and amino acids that are strictly or conditionally essential for human nutrition (cysteine and methionine)2 mole fraction in Earth’s atmosphere inexorably increases and favours plant photosynthesis, and also current metabolic engineering aims to increase photosynthesis by suppressing photorespiration8. In the past 20 years, it has been found that plants cultivated in a CO2-enriched atmosphere contain less elemental S13 including in grains, thereby affecting flour quality15. Accordingly, cultivation under CO2-enriched conditions has been found to alter the content in S-containing antioxidant (glutathione)17. Therefore, present atmospheric changes (probable CO2 doubling by 2100) seem to be detrimental to S metabolism. This problem may worsen in the near future because of limited available S in the environment, due to decreased pollution-driven SO2 emissions and lower utilization of S-containing fertilisers (such as superphosphate). However, possible mechanisms explaining the lower S content when plants are grown at high CO2 have not been studied.As mentioned above, solving the question of a possible correlation between photosynthesis and S metabolism in the long term is critically important because CO18. In principle, changes in S metabolism when photosynthesis varies could stem from metabolic interactions with photorespiration. The key enzymatic activity of photorespiratory metabolism is the conversion of glycine to serine  as a cofactor to transfer the one-carbon unit19. One-carbon (C1) metabolism is thus directly involved in photorespiration (due to H4F requirement) and this is visible in, e.g., mutants affected in formyl tetrahydrofolate deformylase (which regenerates H4F) that have growth defects and accumulate glycine to very high levels under photorespiratory conditions20. Also, C1 metabolism is essential not only for photorespiration but also for many one-carbon requiring reactions of metabolism, including methionine synthesis21. The involvement of N-containing compounds  in photorespiration explains why photorespiration is intimately linked to enzyme activities of N assimilation and how it may stimulate nitrate reduction23. There is no such direct relationship with sulfur but presumably, photorespiration may stimulate S assimilation because serine and one-carbon units are the building blocks used to synthesize S-containing amino acids methionine and cysteine , and aspartate (for methionine synthesis) may in principle benefit from photorespiration28. When photorespiration is low, N and S assimilation is believed to be downregulated in leaves because there is an increase in nitrate reduction in roots resulting in a generally lower N/C ratio in leaves and higher xylem transport of organic N from roots to shoots29 and furthermore, C3−C4Flaveria interspecies grafts suggested that in C4 plants (where photorespiration is suppressed), glutathione is preferentially synthesized in roots30. Conversely, the response to S-deficiency includes an increase in photorespiratory transcripts and metabolites31 and an increase in the photosynthetic CO2 compensation point32, suggesting that photorespiration participates in S homeostasis.The question of a possible correlation between photosynthesis and S assimilation in the short term is also important since in the field, photosynthesis can vary considerably depending on environmental conditions, and thus plant S nutrition could be affected accordingly. Furthermore, S metabolism is involved in electron consumption and redox metabolism in illuminated leaves and thus can affect leaf photosynthetic capacity33. Here, we used a low S-demanding C3 crop, sunflower, and isotopic methods to probe directly the response of the S assimilation flux from sulfate to cysteine and methionine, in vivo. Leaves from plants cultivated in S-sufficient conditions were labelled with both 13CO2 and 33S-sulfate under controlled atmospheric O2:CO2 environment, including situations that favour or disfavour photorespiration compared to ambient conditions. Here, we used the 33S isotope to allow simultaneous analysis by nuclear magnetic resonance (NMR) and mass spectrometry (34S is not visible using NMR and 35S is radioactive). We took advantage of quantitative 33S-NMR to measure precisely the amount of sulfur incorporated, high-resolution liquid chromatography/mass spectrometry (LC-MS) to determine populations of isotopic species (isotopologues), and gas chromatography/mass spectrometry (GC-MS) for metabolic profiling. Because of the relatively low cysteine and methionine amount in leaves, the modest sensitivity of 33S-NMR analyses and also the fact that sulfur assimilation represents a small metabolic flux , analyses required a considerable acquisition time, representing a total of nearly 3000 h for isotopic measurements.Still, there is presently no answer to the question of short-term metabolic interactions between photorespiration and S assimilation13C-enrichment regardless of gas-exchange conditions, including at low photorespiration (inlet air at 0% O2)  when all C-atoms are averaged. However, LC-MS analyses with fragmentation showed that the intramolecular isotopic distribution was highly heterogeneous, with a strong 13C enrichment in the methyl group and very little 13C in other C-atoms  as a background gas  Fig. . Methionrly Fig. . Also, t33S-enrichment in cysteine strictly followed the 13C-enrichment  and decreased at low photorespiration. Measurements of the 33S absolute content clearly showed that at high photorespiration (low CO2 or high O2), there was a significant increase in 33S-methionine and 33S-cysteine content  was then used to compute the sulfur assimilation flux . There was a very clear linear positive relationship between the relative photorespiration rate (vo/vc) and sulfur assimilation and a negative linear relationship between sulfur assimilation and photosynthesis  at high photorespiration. Also, the probability of finding 33S-molecules amongst the population of 13C-labelled cysteine and methionine molecules was significantly lower at low photorespiration , the small flux to S assimilation is explained not only by low photorespiration but also the downregulation of the aspartate pathway of amino acid synthesis (and thus methionine production) under hypoxic conditions35. A stimulating effect of photorespiration has also been found on N assimilation, with less nitrate reduction at high CO223 and higher de novo glutamate synthesis as photorespiration increases36. In the case of nitrogen, this effect is partly driven by the increased demand in glutamate to sustain photorespiratory metabolism due to both the direct involvement of 2-oxoglutarate/glutamate cycling and the slight incompleteness of glycine-to-serine production, which must be compensated for by an increased N assimilation37. By contrast, sulfur metabolism is not part of the photorespiratory cycle and thus the stimulating effect of photorespiration must be due to other mechanisms. Since our experiments demonstrate an effect in the short term, it is unlikely that drastic changes in transcriptional activity and therefore in enzyme quantities (such as methionine synthase or cysteine synthase complex) are involved—although we recognize that the transcription of genes encoding adenosine 5′‐phosphosulfate reductase (APR) can vary significantly within 2 h, as shown for the dark-to-light transition38. Rather, rapid metabolic effects must cause the stimulation of sulfur assimilation by photorespiratory conditions. High photorespiration leads to an increased availability in cysteine precursor, serine. In addition, 5,10-methylene tetrahydrofolate formed by photorespiratory metabolism is more easily interconvertible to 5-methyl tetrahydrofolate because photorespiration generates NADH in the mitochondria, thereby facilitating methionine synthesis21. Photorespiration is also associated with antioxidant metabolism and in fact, glutathione tended to be less abundant under oxygen-depleted atmospheres , cysteine and glucose are recognized effectors under S-deficiency39. Here, high photorespiration was associated with a significant increase in leaf glucose-to-sucrose ratio and a decrease in OAS content   or small (glutathione)  and promote photorespiration. If the stimulating effect of photorespiration we see here also applies during drought conditions, it should be associated with an increased S assimilation18. In fact, an increase in methionine content has been found in water-stressed leaves44 and it correlates to drought tolerance in wheat45. Our results also raise the question of whether S assimilation could be linked to the photosynthetic metabolic type  since it directly impacts on photorespiration activity. Can the beneficial effect of photorespiration on S assimilation contribute to explaining photorespiration persistence in the C3 lineage? In fact, despite its negative impact on plant carbon balance, photorespiration has not been eliminated during plant evolution, except in species that contain a carbon-concentrating mechanism such as C4 plants. While the histological location of S-assimilating enzymes in C4 plants is rather controversial47, recent data on transcript levels of genes encoding for APR suggest that the flux of S assimilation is actually higher in C4 species compared with their C3 relatives. This might be due to the higher demand in reduced S caused by recurring oxidative stress in dry and warm habitats where C4 plants grow naturally48.Of particular significance is the lower ine Fig. a, d and 2:CO2 ratio). Also, the interaction between photorespiration and sulfur metabolism opens the questions of whether S-assimilation has to be examined more closely in plants where photorespiration is manipulated by molecular engineering8, or whether changes in S-fertilization practices have to be anticipated due to future Earth’s atmosphere composition.Taken as a whole, in illuminated leaves, the S assimilation flux appears to be dictated by metabolic fluxes in photosynthesis and photorespiration, which are directly determined by the gaseous composition of the atmosphere. The metabolic interaction we described here has consequences for plant physiology since drought and high temperature are common situations that may happen in the field and impact on atmospheric and/or intercellular gaseous composition  were sown in potting mix and after 14 days, plantlets were transferred to 15 L pots filled with Martins potting mix  at 0.21% S . That way, plants had a sufficient available sulfate content to grow. On average, mature leaves used in this work contained 2.9 mmol m−2 free SO42–. Plants were grown in the greenhouse under 24/18 °C, 60/55% relative humidity, 16/8 h photoperiod (day/night), with natural light supplemented by Lucagrow 400 W sodium lamps . Plants were watered every 2 days supplemented once a week with 1.5 g L−1 nutrient solution Peters® Professional Pot Plant Special  with a N/P2O5/K2O composition of 15/11/29  and trace elements, but not containing sulfur. Plants were used for experiments 50 d after sowing (DAS), thus 36 d after transfer to experimental soil conditions. We used leaves of rank 5 to 7, which are the mature source leaves with maximum photosynthetic capacity at this developmental stage.Sunflower seeds  and having soft walls allowing instant sampling by liquid nitrogen spraying, as described previously36. The leaf chamber was adapted to individual leaves with a surface area of about 100 cm2. Light was supplied by an LED panel RGBW-L084 . Gas-exchange conditions were: 400 µmol m−2 s−1 photosynthetically active radiation (PAR), 80% relative humidity, gas flow 35 L h−1, and 21–23 °C air temperature. Isotopic labelling was performed using 13CO2  and 33S-suphate  for 2 h after 1 h of photosynthetic induction to reach the photosynthetic steady-state. For all O2/CO2 conditions, two series of experiments were done: with 13CO2 + 33SO42−, and with natural CO2 and sulfate. Performing experiments with natural compounds was strictly required for calculations of isotopic enrichment using NMR data. The five O2/CO2 conditions presented here are : 100/380, 21/140, 21/380, 21/800, 2/380 and 0/380. In a separate experiment, leaves were labelled with 13C-5-methionine (99% 13C on the methyl group) instead of 13CO2 and 33S-sulfate, in order to examine methionine redistribution, that is, to check whether methionine utilization  was small within 2 h in the light. Results are shown in Supplementary Fig. 13CO2/33S-sulfate experiments, seven biological replicates  were done for each gaseous O2/CO2 condition. For 13C-methionine labelling  so as to avoid potential diel cycle effects. Gas-exchange under controlled Og, 15 min), the pellet was re-extracted with 3 mL perchloric acid 2% and centrifuged. The two supernatants were combined, the pH was adjusted to 5 with potassium hydroxide, a 10 µL aliquot was collected for LC-MS analysis and the sample was frozen-dried. The frozen-dried extract was resuspended in 1.6 mL MilliQ water and centrifuged, and the pH was adjusted to 7. For 13C-NMR (13C-methionine labelling experiment), the sample was mixed with D2O and analysed. For 33S-NMR (sulfate labelling experiment), the sample was oxidized first. To do so, 600 µL of the sample was mixed with 650 µL H2O2 (30% v-v) with 0.5 mg of methyltrioxorhenium VII as the catalyst. This step oxidized quantitatively methionine to methionine sulfone and cysteine to cysteic acid. Oxidized forms of methionine and cysteine are much more easily visible by 33S NMR than methionine and cysteine themselves. After 3 h at ambient temperature, the sample was vacuum-dried to remove excess H2O2 and MTO (which volatilizes under vacuum). The dry extract was then resuspended in 550 µL phosphate buffer (pH 7) and 50 µL D2O was added. 25 µmol taurine (100 µL at 0.25 M) was added . The sample was vortexed, and poured in a 5-mm NMR tube . 33S-NMR analyses were performed at 298 K (25 °C) without tube spinning, using a standard pulse program (zg) with 90° pulses for 33S (15 µs). Acquisition parameters were: 0.09 s acquisition time, 12.8 k size of FID, and a relaxation delay (D1) of 10 ms. 300,000 scans were done, representing about 12 h analysis per sample. Since the responses of the S atoms at different chemical shifts were not perfectly identical, calibration curves were done with known concentrations of standard metabolites . Signals obtained by NMR were deconvoluted as Lorentzian curves to calculate 33S amounts. 13C-NMR analyzes were performed at 298 K (25 °C) without tube spinning, using an inverse-gated pulse program (zgig) with 90° pulses for 13C (10 µs). Acquisition parameters were: 1.3 s acquisition time, 114 k size of FID, and a relaxation delay (D1) of 15 s. 2600 scans were done, representing about 10 h analysis per sample.Samples were extracted with perchloric acid in liquid nitrogen. Briefly, the sample was ground with 900 µL perchloric acid 70% and 500 µL maleic acid 0.5 M . The powder was poured in a 50-mL centrifuge tube and then 10 mL MilliQ water were added. After centrifugation  and then in the AIF mode  with an HCD (Higher energy Collision-induced Dissociation) set at 35% (mass scan range 50–750 m/z). The following source settings were used for both scan modes: source voltage 3500 V, resolution 70,000, AGC target 1·106, mass scan range 60–600 m/z, sheath gas 40, auxiliary gas 10, sweep gas 1.5, probe temperature 300 °C, capillary temperature 250 °C and S-lens RF level 50. Equations associated with the calculation of isotopic enrichment are provided in Liquid chromatography was performed after Abadie et al.50, using gas chromatography coupled to mass spectrometry (GC-MS), via methanol:water extraction followed by derivatization with methoxylamine and N-methyl-N-(trimethylsilyl) trifluoroacetamide (MSTFA) in pyridine. Data were extracted using the online processing software Metabolome Express.GC-MS analyses were carried out as in ref. 33S, 13C isotopic data (absolute contents in mmol m−2 and %) were used to compute the flux of leaf sulfate reduction, in µmol m−2 s−1. Equations used and model description are provided in Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Reporting SummaryPeer Review File


Bacillus megaterium is a microorganism widely used in industrial biotechnology for production of enzymes and recombinant proteins, as well as in bioleaching processes. Precise understanding of its metabolism is essential for designing engineering strategies to further optimize B. megaterium for biotechnology applications. Here, we present a genome-scale metabolic model for B. megaterium DSM319, iJA1121, which is a result of a metabolic network reconciliation process. The model includes 1709 reactions, 1349 metabolites, and 1121 genes. Based on multiple-genome alignments and available genome-scale metabolic models for other Bacillus species, we constructed a draft network using an automated approach followed by manual curation. The refinements were performed using a gap-filling process. Constraint-based modeling was used to scrutinize network features. Phenotyping assays were performed in order to validate the growth behavior of the model using different substrates. To verify the model accuracy, experimental data reported in the literature  were confronted with model predictions. This indicated a very good agreement between in silico results and experimental data. For example, our in silico study of fatty acid biosynthesis and lipid accumulation in B. megaterium highlighted the importance of adopting appropriate carbon sources for fermentation purposes. We conclude that the genome-scale metabolic model iJA1121 represents a useful tool for systems analysis and furthers our understanding of the metabolism of B. megaterium. Bacillus megaterium has gained momentum due to its versatile metabolic capabilities and physical properties favorable to biotechnology applications. This bacterium had already been commonly used in biochemical studies before the extensive popularity of Bacillus subtilis2. Large cell size and special physiochemical properties were the main incentives to use B. megaterium as a model to study cell structure, sporulation, and protein localization4. As a Gram-positive bacterium with an aerobic sporulation behavior, B. megaterium inhabits diverse environments, ranging from dried food to soil5. Its ability to grow on a variety of carbon sources has made it amenable for industrial applications6. Numerous strains of B. megaterium have been applied for production of various enzymes, such as penicillin amidase, amylase, amino acid dehydrogenase, and glucose dehydrogenase, as well as for production of recombinant proteins11. Moreover, it has been used as an alternative microorganism for production of vitamin B12, pyruvate, and shikimate14. Remarkably, B. megaterium can also be utilized as a cyanogenic bacterium in the bioleaching process, in order to mobilize precious metals from e-wastes15.In recent decades, research on B. megaterium, B. megaterium DSM319 (B. m. DSM319 hereafter) stands out as being extensively investigated. B. m. DSM319 and its available derivatives are commonly used for production of recombinant proteins18 and metabolites22. The whole genome of B. m. DSM319 was sequenced by Eppinger et al.23, which provided a wealth of information on its genotype-phenotype relationships. However, genome data alone was not sufficient for providing a holistic and comprehensive picture of the B. m. DSM319 metabolism. Therefore, there remains a knowledge gap that needs to be filled.Among various strains of 24. Genome-scale metabolic models (GEMs) are used to evaluate and determine the potential of industrial strains and explore their unknown metabolic capabilities25. GEM-based predictions can be used for amelioration of culture medium composition, by identifying key metabolites that need to be added to increase the growth rate. They can also be used for predicting metabolite production fluxes26 and defining gene deletion strategies for metabolic engineering27.Genome-scale metabolic networks together with constraint-based modeling provide a computational framework that can predict physiological features of cells and organismsB. megaterium WSH002 (B. m. WSH002 hereafter), iMZ1055, has been previously reported28. This model included some obsolete annotations and was shown not to be very successful in predicting fluxes29. In this study, a GEM has been developed for B. m. DSM319. This model reconciles iMZ1055 and biochemical data specific to B. m. DSM319, taking into account other Bacillus metabolic models . Fig. B. m. DSM319. It should be emphasized that our model was able to accurately predict the metabolic functions and growth behavior of the strain. Model predictions were in agreement with the growth simulation results from Biolog phenotyping assays, as well as with several experimental data-sets reported in the literature.A GEM for Bacillus species to identify potential reactions that should be present in the GEM of B. m. DSM319. Genome-wide multiple sequence alignment was performed prior to refinement and validation, in order to find orthologs. The detected homologous gene pairs were applied as references for identification of similar gene-protein-reaction (GPRs) associations to reconstruct the draft network. For this purpose, the homologous gene pairs of B. m. DSM319 and B. m. WSH002 were initially identified using Mauve30. Based on the results, 4526 coding sequence (CDS) homologous pairs were determined, which will be referred to as the “COM” genes (see Supplementary Information). Moreover, B. m. DSM319 had 692 CDSs with no obvious homolog in the B. m. WSH002 genome. This set of genes will be called “BMD” genes . Figure B. m. DSM319.Finally, the manual refinement step in the reconstruction of the B. m. DSM319 was found to be capable of growing on 49 out of 69 different carbon sources based on triplicate independent experiments  and d-salicin metabolites, and added the relevant transport reactions to the model. For the other 7 intracellular metabolites, interim hypothetical transport reactions were correspondingly added to the model. A hypothetical transport reaction was preserved in the model only when simulating growth on the particular corresponding carbon source. The addition of those hypothetical transport reactions improved model accuracy in all 7 cases. Our results indicated that 56 out of 69 in silico predictions for growth on different carbon sources were compatible with phenotyping assays. Overall, 14 discrepancies were fixed by changing reaction reversibility or filling the gaps based on literature mining  reaction encoded by BMD_2469. Also, it was reported that the uncharacterized membrane proteins DUF969 and DUF979 are involved in PA transport in B. subtilis34. Based on the protein homology search, we decided to add a transport reaction and assumed that it is encoded by BMD_1100 and BMD_1101. The addition of these two reactions resulted in the model becoming capable of accurately predicting growth on PA as the sole carbon source. By refining and filling in the gaps, the GEM, which will be referred to as iJA1121, reached almost 96% accuracy in predicting substrate utilization (see Supplementary Information for the model in SBML format). It should be noted that iJA1121 was checked using Memote35.We performed Biolog phenotyping experiments to examine the capability of the draft network to predict growth behavior on different carbon sources. iMZ1055, the previous GEM for another strain of B. megaterium, predicted growth for only 38 carbon components, where 21 predictions were not consistent with the phenotyping experimental data. Overall, iMZ1055 could estimate growth for about 70% of carbon sources. Although this outcome could be attributed to metabolic differences between strains and their ability to grow on different carbon sources, it might in part be due to possible deficiencies within iMZ1055. A large number of non-growth/growth (in silico/in vivo) predictions , based on the information in biochemical databases and literature. Among the alterations that were made for the reactions in the “Changed” group that comprised 26% of the reactions, the changes of GPR associations were the most common. This is presumably due to the automatic annotation of genes by the modelSEED pipeline28. In addition, some of the EC numbers of iMZ1055 were obsolete, i.e. they were removed in the most recent release of the KEGG database. The other 32% of the reactions were those that were not included in iMZ1055. Fig. 36. From Fig. We inspected the differences between GEM Fig. : “No-chaB. m. DSM319, as well as that of its derivatives, namely B. m. MS941 (∆nprM), B. m. WH320 (∆lacZ) and B. m. WH323 (XylA1-spoVG-lacZ) .We characterized the growth behavior of ata Fig. ; indicatB. megaterium mutants was carried out by applying the results reported by Wang et al.16 to the analysis of MS941 and WH320 strains under high cell density conditions. To estimate glucose uptake fluxes and growth rates, Eqs. .We simulated these conditions by running FBA in the minimal medium and allowing glucose to enter the system at the flux obtained by Eq. . Simulat40. As a prominent compound in the pharmaceutical industry, shikimic acid is the main precursor for the synthesis of oseltamivir14. It is reported that a ∆aroK mutant of B. megaterium can promote shikimate production41. The aroK gene encodes shikimate kinase, which catalyzes the bioconversion of shikimate to shikimate-3-phosphate in an ATP-dependent phosphorylation reaction.Limited availability and high price of precursors like shikimic acid and quinic acid is the main restraining factor for industrial production of essential aromatic compounds∆aroK by setting the lower and upper bounds of the associated reaction to zero. Then, we analyzed the effect of different carbon sources on the growth behavior of the mutant strain by setting the lower bounds of the medium components and the carbon source to −1000 and −5 mmol/gBM/h, respectively. There was a significant correlation  between biomass flux rate predictions by the GEM and the dry weight cell experiments . This observation suggests that the reactions for assimilation of maltose in the GEM need to be improved.We simulated the nts Fig. . When ma∆aroK strain  is a prominent disinfectant that hinders microbial growth, particularly for Bacillus species, e.g., B. megaterium and B. subtilis44. It was shown that the GEMs generated for Bacillus species are incapable of predicting growth behavior under these conditions29. Herein, we simulated the glucose/formaldehyde co-metabolism using FBA for growth in minimal medium and glucose as the carbon source. For the formaldehyde uptake simulation, an exchange reaction was added to the model. In order to investigate the effect of formaldehyde addition, its flux was raised in a step-wise manner, and a number of FBAs were performed. The same simulations were run for the previous GEMs for B. megaterium (iMZ1055) and B. subtilis (iBsu1103) starting from similar dilution rates. Fig. Bacillus species. While iMZ1055 and iBsu1103 show co-metabolism of glucose/formaldehyde as these GEMs contain pathways for assimilation of formaldehyde as a carbon source. However, iJA1121 was able to predict formaldehyde sensitivity in B. megaterium. From iJA1121 predictions, by increasing the formaldehyde uptake flux, the biomass production rate remained constant.Formaldehyde , one can predict the possible range of each flux under a certain optimal growth condition. Therefore, we ran four FVA simulations to investigate how the simulated fluxes agree with experimentally-measured 13C flux data37. The results are shown in Fig. Results of FBA simulations typically do not reflect the exact metabolic behavior due to the existence of multiple optimal solutionsB. megaterium directly, as isotopic measurements could not identify which glucose uptake pathway was active47. When comparing in silico results with experimental data, suboptimal FVA is more relevant, as it allows the objective function to be within an allowable range 48.Suboptimal FVA simulations were performed by setting the biomass producing reaction to 90% of the maximal value obtained by FBA. In Table 13C fluxes generally fall within the intervals suggested by FVA.In the simulations, it was assumed that glucose is taken up by 37. The occurrence of futile cycles enhances the versatility of the organism to survive in a changing environment. Futile cycles could act as alternative pathways in order to regulate the cellular metabolism of the organism to function optimally49. Overall, there is an acceptable agreement between the in silico results and the 13C labeling experiments ] is catalyzed by [ACP]-S-malonyl transferase, and this initiates the synthesis of fatty acids. Then, malonyl-ACP reacts either with an acetyl-CoA for imitation, or with acyl-CoA primers for elongation, resulting in ketoacyl-ACPs, leading to a process known as the elongation cycle. The elongation cycle includes two NADPH-dependent reduction steps accompanying by a dehydratase reaction51.Acetyl-CoA as the precursor of fatty acids plays an important role in the metabolic network see Fig. . In cell52. We compared the flux-sum values of metabolites in the network and the results are illustrated in Fig. BM/h as the basis. Then, the flux-sum of the metabolites was calculated, as explained in the Materials and Methods section. Fig. 53 and fatty acids55. High turnover rates of amino acids known to be the sources of acyl-CoAs  indicate active biosynthesis of fatty acids56. Besides, growth on glycerol exhibited the highest flux-sum value for acetyl-CoA, which in turn favors lipid accumulation. These findings prompted us to compare the flux-sum values of lipids  and B. licheniformis WX02 (B. l. WX02) containing genomic sequences and annotations were downloaded . Four GEMs, iMZ1055 for B. m WSH00228, iBsu114758 and iBsu110359 for B. subtilis 168 and iWX1009 for B. licheniformis. WX0260 were obtained for comparison and/or were used as the reconstruction templates.In order to identify the homologous gene pairs, GenBank genome (.gbk) files for 30. Accordingly, the related GenBank genome files containing genomic sequences and annotations were downloaded to identify gene orthologous pairs using the ‘progressiveMauve’ tool. Then, based on the results gained by Mauve, the associated reactions in iMZ1055 were identified and used as the basis of the draft network. In the second step, the refinement of the draft network was automatically performed  based on public biochemical databases such as KEGG61, MetaCyc62, UniProt63, and PATRIC64. In the next step, with a similar strategy, we found all of the relevant genes and reactions which were present in iBsu110359, iBsu114758 and iWX100960 and their corresponding GenBank genome files. Then, according to the information on TCDB65, the transport equations were added to the draft network. Finally, the gaps of the model were identified and corrected, based on the phenotyping results as well as the experimental data reported in the literature.As the first step, a homology search was done based on progressive multiple alignments using Mauve version 2.4.0, using its default parameter settings. Mauve is a computational framework for multiple genome alignments in the presence of large-scale evolutionary events67. FBA assumes that no accumulation of metabolites occurs during growth (dc/dt = 0), while a cellular objective, typically biomass production rate, is optimized. Based on the FBA, a linear programming problem is defined as follows:Z is a linear function related to the cellular objective and the coefficients cj determine the weights of the reaction j. Also, vj refers to the flux rate of the reaction j in the network and Sij is the stoichiometric coefficient of metabolite i in reaction j. In this approach, each flux, vi, is constrained to the given minimum and maximum values. To perform FBA, a biomass producing reaction, representing a weighted ratio of components in cell dry weight, is maximized based on the evolutionary assumption69. In this study, the biomass producing reaction of the iMZ1055 model is used, which in turn is based on the biomass composition of the B. subtilis model70. Unless stated otherwise, the biomass production rate was used as the objective function in FBA simulations.FBA is one of the popular methods for analyzing the flux of reactions in metabolic networks under steady-state conditions71. In this study, suboptimal FVA was used to find the minimum and maximum possible fluxes of each reaction while the objective function was bound to 90% of its maximal value (achieved by FBA).FVA is a mathematical tool for finding the minimum and maximum possible fluxes of each reaction, while other constraints are satisfied and the objective function takes the optimal  value72. The flux-sum of metabolite i is defined as the summation of all consumption or generation fluxes as follows52:In order to illuminate the role of metabolite in the network and study turnover rates of the metabolites, we used flux-sum analysis73.The existence of possible alternate optimal solutions can result in ambiguity in the interpretation of FBA solutions. Thus, to identify the most plausible flux distribution, the minimization of the sum of total fluxes makes the calculation B. m. DSM319, the ability of the strain to grow on different carbon sources was tested using the Biolog GEN III microplate. This was used to analyze the strain under 94 phenotyping tests. As specified by the manufacturer, a pure culture of the strain was incubated at 30 °C overnight on an agar plate with 5% sheep blood, and then suspended in a special inoculating fluid at the predetermined cell density (90–98% Transmittance). Then, 100 ml of the cell suspension was inoculated into each well of the GEN III MicroPlate™. The microplate was incubated at 30 °C for 24 hours. Readouts of growth-dependent color change were obtained using a microplate reader and interpreted based on the Biolog protocols.To discern the phenotypic pattern of Bacillus species are commonly used in biotechnology, especially in food, pharmaceutical, and environmental processes. Lack of knowledge about growth behavior of these organisms complicates the design and monitoring of industrial microbial processes. Generating constraint-based models that can predict growth behavior is an important step in addressing this knowledge gap. In this regard, several GEMs for Bacillus species have been constructed. After the reconstruction of the GEM for B. subtilis 168, generated by Oh et al., in silico models were developed for B. m. WSH002 (iMZ1055) and B. l. WX02 (iWX1009). In addition, two other GEMs for B. subtilis 168, iBsu1103 and iBsu1147, were published more recently. It has been reported that existing genome-scale models for B. megaterium could not correctly predict the utilization of amino acids and some carbon sources29. Herein, we present a GEM for B. m. DSM319, iJA1121. The model includes 1709 reactions, 1349 metabolites with 1121 genes. Barring exchange reactions, 91% of the reactions are gene-associated.Aerobic Gram-positive B. m. DSM319 growth on different carbon sources (available on PubMed). FBA and FVA were applied to perform the simulations. Our findings suggested a better agreement of in silico predictions and experimental data for iJA1121. Results of carbon source utilization experiments and the model predictions matched in 96% cases. Moreover, the growth behavior of different mutant strains of B. m. DSM319 was studied and results indicated a very good match between iJA1121 predictions and in vivo data, with very few exceptions. For instance, the simulation of shikimate production in aroK knock out worked well on all tested carbon sources except sucrose. Further investigations were performed by comparing in silico results with 13C labeling experiments. Regarding suboptimal FVA results, the experimental 13C fluxes fell within the predicted intervals. In conclusion, iJA1121 seems to offer a clear improvement over iMZ1055, where the success rate of the previous model in accurately predicting growth behavior in the same type of simulations was only 70%.Following an automated approach, the draft network was curated manually by updating the GPR associations and EC numbers as well as adding several reactions. The genome-scale model was validated using our own experimental data on Biolog phenotyping and published data on Supplementary InformationiJA1121Dataset 1


Models of metabolism are often used in biotechnology and pharmaceutical research to identify drug targets or increase the direct production of valuable compounds. Due to the complexity of large metabolic systems, a number of conclusions have been drawn using mathematical methods with simplifying assumptions. For example, constraint-based models describe changes of internal concentrations that occur much quicker than alterations in cell physiology. Thus, metabolite concentrations and reaction fluxes are fixed to constant values. This greatly reduces the mathematical complexity, while providing a reasonably good description of the system in steady state. However, without a large number of constraints, many different flux sets can describe the optimal model and we obtain no information on how metabolite levels dynamically change. Thus, to accurately determine what is taking place within the cell, finer quality data and more detailed models need to be constructed.In this paper we present a computational framework, DMPy, that uses a network scheme as input to automatically search for kinetic rates and produce a mathematical model that describes temporal changes of metabolite fluxes. The parameter search utilises several online databases to find measured reaction parameters. From this, we take advantage of previous modelling efforts, such as Parameter Balancing, to produce an initial mathematical model of a metabolic pathway. We analyse the effect of parameter uncertainty on model dynamics and test how recent flux-based model reduction techniques alter system properties. To our knowledge this is the first time such analysis has been performed on large models of metabolism. Our results highlight that good estimates of at least 80% of the reaction rates are required to accurately model metabolic systems. Furthermore, reducing the size of the model by grouping reactions together based on fluxes alters the resulting system dynamics.The presented pipeline automates the modelling process for large metabolic networks. From this, users can simulate their pathway of interest and obtain a better understanding of how altering conditions influences cellular dynamics. By testing the effects of different parameterisations we are also able to provide suggestions to help construct more accurate models of complete metabolic systems in the future.The online version of this article (10.1186/s12918-018-0584-8) contains supplementary material, which is available to authorized users. Quantitative modelling of metabolic networks has helped design and improve the production of compounds relevant for the bio-industrial and pharmaceutical sectors , 2. ManyIn mathematical terms, large-scale metabolic networks are represented by x is the vector of metabolite concentrations, S is the stoichiometry matrix of the system detailing how one metabolite converts into another, and v is the flux vector that describes the rate of metabolite conversion. Notably, the fluxes will generally depend on the concentrations of metabolites in the network and the parameters, or kinetic rates, k. Methods to solve Eq. 13C-labelling experiments), can be used to further constrain and validate the model. Second, by using mathematical functions  to describe v with measured or estimated values of k and approximating the system solution numerically ).In this work we have shown how flux-constrained model reduction methods result in smaller systems that have qualitatively different dynamics to the original complete model Fig.  35, 36], 3635, 3This raises the issue of whether methods can be automated that are able to reduce a large-scale metabolic network to maintain specific dynamic requirements, such as a match to time-series data. One could envisage two possible techniques; first, by reducing the differential equations based on time-scale arguments  , and secWithin biological systems, processes occur across a range of time-scales (roughly from femtoseconds to hours). Thus, one is generally interested in understanding what happens at a specific time-scale and ignoring or simplifying those processes that happen too quickly or slowly, consequently reducing the systems complexity . ImportaOne other limitation of our current pipeline is that every reaction is mathematically described by the same reversible Michaelis-Menten approximation, including transport reactions or genetic interactions. In Additional file Furthermore, by allowing a user to easily manipulate and alter mathematical functions, sub-modules of larger networks can be easily replaced by known, more-detailed kinetic models of specific pathways. Importantly, our pipeline allows users to fix parameter values by using narrow prior distributions. Consequently, the resulting models will maintain the dynamics of a more detailed model whilst allowing one to observe the effects of the metabolic pathway on a genome-scale reaction network. Future developments of this computational framework will aim to incorporate these ideas to provide more flexibility to users such that finer details can be added to large-scale models of metabolism.In conclusion, we have developed a modular framework that provides an initial approximation of temporal metabolic changes within a cell, which can easily be extended with additional data as required. Due to the modular approach, individual methods within the framework can be replaced or updated as they are enhanced. Furthermore, by generating more detailed data, we have shown that the accuracy of these dynamic models will improve given the current methods used within the pipeline. In addition to this, through the use of model reduction techniques and compartmentalisation within a cell, individual subnetworks or compartments within the dynamic model can be easily manipulated and replaced as metabolic pathways are studied in more detail. We envision that this framework will be of great use to the metabolic community as attempts continue to unravel the complex relationship between system inputs and physiological outputs that are relevant for the bio-industry sector.DMPy can be found at gitlab.com/wurssb/DMPy along with all computer scripts and models used in this study. DMPy is written in Python, further implementation details can be found in Additional file Progject name: DMPy.Homepage: gitlab.com/wurssb/DMPy.Programming Language: Python.Additional file 1Appendix. Implementation details of computational workflow; pseudo-code for automated parameter search subroutine; methods of constructing prior distributions of kinetic rates; compartmentalisation and regulation; method of generating random reaction networks; Supplementary Figures. (PDF 827 kb)Additional file 2Parameter values. Reaction rates that were obtained by our automated database search for different metabolic systems. (XLSX 510 kb)


Cellular metabolism is a dynamic system in which metabolic nutrients are being constantly consumed and catabolized to generate energy Fig. a. ProlifA major complication in cancer metabolic research is that, unlike the concentration of mRNA, proteins, and metabolites, metabolic flux, which reflects the cellular metabolic phenotype, is not a directly measurable quantity Fig. b. HoweveThe most direct approach for interrogating intracellular metabolic flux in cancer cells is isotope tracing –10. ThisAnother commonly used flux inference approach is COnstraint-Based Reconstruction and Analysis (COBRA), enabling flux assessment through genome-scale metabolic networks. COBRA has traditionally been utilized to model microbial metabolism for biotechnological and bioengineering purposes –15. MoreHere, we provide a brief overview of how COBRA and 13C-MFA work  for which a simulated isotopic labeling pattern of metabolites optimally matches experimental measurements [13CFlux2 [13C-MFA calculations require a metabolic network model consisting of a set of biochemical reactions, with information on the mapping of atoms between the substrate and product metabolites  . When an13C-MFA has been frequently used for investigating cellular metabolic rewiring in response to genetic mutations in cancer, revealing the link between signaling circuitry and cancer metabolism. For example, oncogenic activations of Ras , 44, AktEmploying 13C-MFA to probe flux alterations following genetic silencing of metabolic enzymes provided means to explore enzyme importance and mechanisms: depletion of MTHFD1L, an enzyme in the mitochondrial folate cycle that produces formate, was shown to repress mitochondrial one-carbon metabolism and lead to reduced cancer invasion . DeletioMetabolic rewiring due to non-genetic factors such as the tumor microenvironment has also been investigated utilizing 13C-MFA. Hypoxia promotes tumor cell reliance on reductive glutamine metabolism for lipogenesis , 53 and Identifying and characterizing metabolic rewiring with 13C-MFA in specific cancer cells not only contributes to our understanding of metabolic regulation but can also lead to the discovery of novel targets for anticancer drugs. For example, applied to studying the effect of PHGDH amplification in breast cancer cells, 13C-MFA revealed that de novo serine biosynthesis is responsible for up to half of the total anaplerotic flux of glutamine into the TCA cycle, suggesting that targeting the serine synthesis pathway may be therapeutically valuable in breast cancers with elevated PHGDH expression . LikewisA major limitation of flux inference via isotope tracing coupled with 13C-MFA regards the inference of metabolic flux in specific organelles H). Metabolite uptake and secretion rates across the NCI-60 cancer cell lines collection were used to model fluxes in these cells, exploring different metabolic strategies used by cells to generate energy and redox cofactors and explaining the abilities of different cell lines to support respiration [Several studies have utilized COBRA to explore the production and consumption of central energy (ATP) and redox cofactors (NAD(P)piration . An analpiration . The potpiration . Fan et piration  demonstrSeveral studies used COBRA to explore overflow metabolism in cancer—i.e., excess consumption and non-efficient utilization of metabolic nutrients, including for glucose , glutamiWhile measured metabolite uptake and secretion rates in a given cell line provide readily usable constraints for flux analysis by COBRA, utilizing abundant transcriptomic, proteomics, and metabolomics datasets (available for large collections of cell lines) as input for flux prediction is highly challenging. This is due to metabolic flux being regulated at multiple levels and depending on the concentration of the active enzyme , the concentration of reactants and allosteric regulators, and complex enzyme kinetic mechanisms . Numerous computational techniques have been proposed to generate metabolic network models for specific tumors . Specifically, these methods aim to identify a subset of enzymes from a genome-scale metabolic network that is expected to be active based on the mRNA, protein, and metabolite concentrations, enzyme-specific biochemical or genetic measurements, and known cell line-specific metabolic functions. Various methods such as GIMME , iMAT 8, 90, MBAPredictions of cell line-specific gene essentiality derived with cell line-specific metabolic network models were shown to correlate significantly with measured growth response to CRISPR-based gene knockouts , achieviWhile predicting cell line-specific response to genetic silencing or chemical inhibition is technically difficult, identifying enzymes whose inhibition selectively affects cancer cells while sparing normal cells is even more challenging. This was previously addressed by searching for enzymes whose inhibition would prevent cell proliferation, while not affecting basic metabolic functionality such as ATP production . AdditioAnother appealing approach for identifying selective anti-cancer metabolic targets is based on the concept of synthetic lethality . SpecifiIsotope tracing is widely used to probe intracellular metabolic activities in cancer cells. However, most studies still rely on manual assessment of measured metabolite isotopic labeling to qualitatively infer metabolic activities , while 1While both 13C-MFA and COBRA were demonstrated to be highly useful in cancer metabolic research, there are inherent limitations and complications with each approach. We provide a brief comparison of the two modeling approaches in terms of scope, required experimental data, and possible output . Analyzing genome-scale metabolic networks enables COBRA to reveal non-canonical pathways with an important contribution to some cancer cells. However, it can falsely predict flux through enzymatic reactions that were included in the model based on weak biochemical evidence. Further work by the metabolic modeling community is needed to further refine and extend the existing genome-scale metabolic network reconstructions based on accumulating knowledge of enzymatic activities in human cells. An important future challenge for COBRA methods is improving the reliability of biochemical enzymatic activities that are included in the model. With 13C-MFA, on the other hand, it is challenging to determine the boundaries of the analyzed metabolic system, while reactions that are left out of the model could potentially bias flux estimation. Applying 13C-MFA for larger scale networks is an experimentally challenging task which requires the measurement of metabolite isotopic labeling outside the central metabolism. Furthermore, it is highly computationally challenging to apply 13C-MFA for genome-scale networks, though some attempts in this direction have been made , 111. FuWhile both 13C-MFA and COBRA rely on measurements of metabolite uptake and secretion rates for flux estimation, 13C-MFA that relies on isotope tracing measurements is more experimentally demanding. Omics data, and specifically genomics, transcriptomics, proteomics, and metabolomics, can be utilized as input by COBRA methods, though this typically relies on simplified heuristics that do not account for the complexity of regulatory and enzyme kinetic mechanisms. A major open challenge in COBRA is developing improved methods for utilizing quantitative proteomics and metabolomics data for flux inference via enzyme-mechanistic models accounting for kinetic and thermodynamic considerations.In terms of the ability to uniquely infer flux, this is typically possible with 13C-MFA applied to analyze flux in central metabolism, rigorously evaluating flux confidence intervals. With COBRA, over-simplified optimality assumptions are typically employed to derive unique fluxes . SubcelIn terms of common applications, both COBRA and 13C-MFA enable the inference of flux in cells based on measurements performed under a specific genetic and cell culture condition. Derived flux maps by these approaches provide a holistic understanding of metabolic processes, while changes in flux due to genetic or environmental perturbations provide means to examine metabolic regulation. The identification of induced flux through specific enzymes in cancer cells reveals the increased dependence on metabolic transformations that could be therapeutically targeted. Note that unlike 13C-MFA, COBRA can further address the more challenging task of predicting how metabolic flux will be rewired in response to genetic or pharmacological interventions in silico, providing means to investigate potential anti-cancer drug targets.Overall, COBRA and 13C-MFA provide complementary capabilities for understanding the rewiring of metabolic flux in cancer. While 13C-MFA analyzes isotopic tracing measurements to provide an accurate quantitative view of flux through central metabolic pathways, COBRA analyzes flux through genome-scale metabolic networks based on physicochemical constraints and ‘omics’ data integration. In some cases, isotope tracing is used to quantify specific fluxes in human tissues under different physiological conditions, while these are used as inputs for COBRA-based flux analysis on a genome scale , 113. In


Several genome-scale metabolic reconstruction software platforms have been developed and are being continuously updated. These tools have been widely applied to reconstruct metabolic models for hundreds of microorganisms ranging from important human pathogens to species of industrial relevance. However, these platforms, as yet, have not been systematically evaluated with respect to software quality, best potential uses and intrinsic capacity to generate high-quality, genome-scale metabolic models. It is therefore unclear for potential users which tool best fits the purpose of their research.Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. We additionally compare draft reconstructions with a model of Pseudomonas putida to further confirm our findings. We show that none of the tools outperforms the others in all the defined features.In this work, we perform a systematic assessment of current genome-scale reconstruction software platforms. To meet our goal, we first define a list of features for assessing software quality related to genome-scale reconstruction. Subsequently, we use the feature list to evaluate the performance of each tool. To assess the similarity of the draft reconstructions to high-quality models, we compare each tool’s output networks with that of the high-quality, manually curated, models of Model builders should carefully choose a tool (or combinations of tools) depending on the intended use of the metabolic model. They can use this benchmark study as a guide to select the best tool for their research. Finally, developers can also benefit from this evaluation by getting feedback to improve their software.The online version of this article (10.1186/s13059-019-1769-1) contains supplementary material, which is available to authorized users. Genome-scale metabolic models (GSMMs) have been a successful tool in Systems Biology during the last decades , 2, largThe reconstruction process that forms the basis of a GSMM is very time-consuming. Usually, this process starts with the annotation of a genome and the prediction of candidate metabolic functions at a genome-scale. The draft reconstruction is then refined by the user in an iterative manner through an exhaustive review of each reaction, metabolite, and gene in the network. After curation, the genome-scale metabolic reconstruction is transformed into a mathematical structure, an objective function is given, constraints are set to account for specific media conditions and the resulting GSMM is evaluated to try to reproduce the experimental data. This iterative process of manual refinement is the limiting step of the whole process because it continues until the GSMM achieves the desired performance determined by the model builder. Hundreds of GSMMs have been reconstructed using this procedure, for which protocols have been described  and reviSeveral genome-scale reconstruction tools have been developed over the last 15 years to assist researchers in the reconstruction process , 9. ThesLactobacillus plantarum [Bordetella pertussis [Pseudomonas putida to confirm our findings.In this work, we installed and applied the most promising genome-scale reconstruction tools to provide a systematic evaluation of their performance and outputs. With each tool we reconstructed draft networks for lantarum  and Bordertussis , represeHere, we provide a brief description of the current reconstruction tools  is a software platform for network drafting and editing. A manually curated model is used as a template to generate a draft network for the species under study. Among the tools inside the toolbox, MrBac [GEMSiRV   , SublimiTo assess the reconstruction tools, we performed both a qualitative and quantitative evaluation. As a first step, we created a list of relevant features for genome-scale reconstruction and software quality and we scored each tool depending on the performance . These features are related to software performance, ease of use, similarity of output networks to high-quality manually curated models and adherence to common data standards. In addition, we evaluated 18 specific features related mostly with the second stage (refinement) of the protocol for generating high-quality genome-scale metabolic reconstructions .Lactobacillus plantarum and Bordetella pertussis, representatives of gram-positive and gram-negative bacteria, respectively. These microorganisms were selected because of three reasons. First, the corresponding GSMMs are not stored in the BIGG database, so tools that are able to use the BIGG database  in the reconstruction process cannot use the specific information for these microorganisms. If Escherichia coli or Bacillus subtilis would have been chosen instead we would have favored these tools because high-quality models for E. coli or B. subtilis already exist in the BIGG database and they would have been used as templates or inputs. Second, we chose these microorganisms because we were fully informed of the quality of the reconstructions as we built them ourselves and they have proven to be able to accurately replicate experimental data [Subsequently, to assess how similar the generated draft networks are to high-quality models, we reconstructed with different reconstruction tools the metabolic networks of two bacteria for which high-quality manually curated genome-scale models already were available. We chose to reconstruct the metabolic network of tal data , 42, 43,tal data , 45. ThiPseudomonas putida, for which four lab-independent genome-scale models have been reconstructed. We compared the draft reconstructions with iJP962 [In addition to the two previous species, we also reconstructed with all the tools draft networks for h iJP962 , a modelh iJP962 .The networks were generated using seven tools: AuReMe, CarveMe, Merlin, MetaDraft, ModelSEED, Pathway Tools and RAVEN. These cover most of the freely available software platforms. The general features of these tools are listed in Table None of the tools got a perfect score for all of the evaluated features and usually, strengths in some tools are weaknesses in others  to automatically curate the network. Although some efforts have been done in this area –51, thisCompliance with the latest SBML standards has been pointed as one of the critical points to share and represent models . ConsequLactobacillus plantarum WCFS1, Bordetella pertussis Tohama I and Pseudomonas putida KT2440 with each reconstruction tool. L. plantarum is a lactic acid bacterium (LAB), used in the food fermentation industry and as a probiotic [B. pertussis is a gram-negative bacterium, and the causative agent of the Whooping cough, a highly contagious respiratory disease [B. pertussis, Pseudomonas putida is also a gram-negative bacterium but the interest in this species relies on its capability as a cell factory to produce a wide variety of bulk and fine chemicals of industrial importance [L. plantarum and B. pertussis are the main subject in the network comparisons, P. putida was used, as a model developed independently from us, to validate the tendencies obtained with the two previous species.We reconstructed draft networks for robiotic –56. Its robiotic , to explrobiotic  and as arobiotic . In cont disease . The metportance . Its metL. plantarum, 27 for B. pertussis, and 27 for P. putida. The specific inputs and parameters for creating each network can be found in Additional file L. plantarum, B. pertussis, and P. putida will be called hereafter iLP728, iBP1870, and iJP962, respectively.In total, 29 networks were created for Genes are the basis from which the genome-scale model is reconstructed. When a gene is included in a metabolic reconstruction, there is at least one biochemical reaction associated with that gene. When a gene is not in the reconstruction, either the reconstruction tool could not find an orthologous gene in the reference database or an orthologous gene was found but no biochemical reaction is associated with that gene. Gene sets are interesting to compare because if a gene present in the manually curated model is absent in a draft reconstruction, that could explain why some biochemical reactions are missing in the draft. Alternatively, if a gene is absent in the manually curated model but present in a draft reconstruction, that could explain the presence of reactions that should not be in the reconstruction. Moreover, gene sets are simple to compare among reconstructions because gene identifiers in all the cases are the same (the locus tag in the genome annotation) and so, in contrast to metabolites and reactions, there is no mapping-related bias in the comparison.JD) as well as the ratio between the percentage of covered genes and the percentage of additional genes (R)  and adding a relatively large number of genes not present in the manually curated models, which explains why they had lower values in the Rg.We further analyzed the percentage of genes covered in the manually curated models and the percentage of genes not in the manually curated models to explain differences in L. plantarum we found 1613 different genes in total with all the tools, of which 885 were not present in iLP728. For B. pertussis, 1888 different genes were found, of which 1118 were not present in iBP1870. In addition, 79 genes were correctly predicted in all the draft networks for iLP728; for iBP1870, this was 131 genes. The distribution of metabolic pathways associated to those genes is wide for both species, with carbohydrate metabolism and amino acid metabolism accounting for more than 50% of the metabolic processes  and ferredoxin/thioredoxin related reactions (30%) for B. pertussis. Finally, one gene in L. plantarum, which was associated with riboflavin biosynthesis, was recovered by all the networks but it was not present in iLP729. For B. pertussis, three such genes were found. These genes were associated with alternate carbon metabolism and cell envelope biosynthesis.For Genes and biochemical reactions are connected within a reconstruction through gene-protein-reaction (GPR) associations. However, genes and reactions relationships are ultimately represented in reconstructions as boolean rules known as gene-reaction rules. With the exception of exchange, sink, demand, spontaneous and some transport reactions , each reaction has a defined gene-reaction rule in the reference database used by each reconstruction tool. During the process of reconstruction, if orthologous genes are found that satisfy the gene-reaction rule of a particular reaction, that reaction is included in the draft reconstruction. Other reactions may be added to the draft reconstruction based on others criteria, such the probability of a particular pathway to exist in the microorganism under study or the need to fill particular gaps in the network in order to produce biomass. Nonetheless, we expect that networks which are more similar in terms of genes will also be more similar in terms of reactions.In contrast to genes, however, reactions are labeled with different identifiers in different databases. Thus, the same reaction can be stored with two different identifiers in two different databases. During the reconstruction process, reactions are added from the reference database to the draft reconstruction and tools using different databases will generate reconstructions comprising reactions with different identifiers. We, therefore, used MetaNetX  to map rL. plantarum and B. pertussis, respectively, even when considering partial matches. We classified the reactions that were not recovered in different categories .First, both manually curated models contain a considerable amount of reactions without gene-associations, including spontaneous, transport, exchange reactions, reactions added during the manual gap-filling and biomass-related reactions. For JDr and the Rr to assess how similar the networks were, in this case in terms of reactions. The first observation we made is that, independent of the metric and for both species, each reconstruction was less similar in terms of reactions than in terms of genes, which is consistent with the decrease in coverage. In addition, as in the gene comparison, the order of scores for the Rg and the Rr by magnitude was not the same. If we compare the similarity scores for reaction sets with the ones for gene sets, we see almost the same trend but with one difference. AuReMe and MetaDraft are still the tools with the best similarity scores but now CarveMe goes up in the list of scores and RAVEN goes down . For CarveMe, this is due to the inclusion of transport and spontaneous reactions as well as reactions needed to create biomass (from gap-filling); for Pathway tools, it is because of the addition of reactions to complete probable pathways and spontaneous reactions; and for Merlin, this is solely due to spontaneous reactions.Without considering exchange and demand/sink reactions, 127 and 449 reactions without gene associations  were found in iLP728 and iBP1870, respectivelyIn this work, we reviewed the current state of all the reconstruction tools we could find in the literature and performed a systematic evaluation of seven of them. None of the tools performed well in all the evaluated categories so users should carefully select the tool(s) that suit the purpose of their investigation. For example, if a high-quality draft is required and models are available for a phylogenetically close species, MetaDraft or AuReMe could be selected, reducing thus the time needed to obtain a high-quality manually curated model. Of these, MetaDraft was the most robust for handling models and since it has a graphical user interface, it is also suitable for non-specialists. AuReMe, on the contrary, offered a command-line workspace where the traceability is the priority. Although we were not able to use RAVEN in the template mode , this tool allowed us to automate the generation of several reconstructions, it had a high flexibility with parameters and it offered integration with the KEGG and MetaCyc databases which makes it very appropriate for less-studied species. ModelSEED, CarveMe, and Pathway Tools were the fastest tools to generate reconstructions having a great potential for large-scale studies how it has been proven in previous works , 63. TheThe list of features that we defined not only can be used by model builders to select the best tool(s) but also by developers as a guide for improving them. We highlight four features, which are in accordance with the FAIR guiding principles for scientific data management and stewardship , that shE. coli, S. cerevisiae, and H. sapiens have gone through multiple rounds of improvements during the years [Lactobacillus plantarum that we made with CarveMe and ModelSEED and which were gap-filled using a modified version of CDM  Lactococcus lactis (iNF517). (2) Lactococcus lactis (iNF517), Bacillus subtilis (iYO844), Staphylococcus aureus (iSB619), Clostridium ljungdahlii (iHN637) and Mycobacterium tuberculosis (iNJ661). 3) Lactococcus lactis (iNF517), Bacillus subtilis (iYO844), Staphylococcus aureus (iSB619), Clostridium ljungdahlii (iHN637), Mycobacterium tuberculosis (iNJ661), Escherichia coli (iML1515), Klebsiella pneumoniae (iYL1228), Shigella flexneri (iSFxv_1172), Shigella boydii (iSbBs512_1146), Shigella sonnei (iSSON_1240), Pseudomonas putida (iJN746), Yersinia pestis (iPC815), Helicobacter pylori (iIT341), Geobacter metallireducens (iAF987), Salmonella entérica (STM_v1_0), Thermotoga marítima (iLJ478), Synechocystis sp (iJN678), and Synechococcus elongatus (iJB785).To generate the genome-scale metabolic reconstructions of Bordetella pertussis we used Escherichia coli as a template (iML1515).For Pseudomonas putida we used Pseudomonas putida as a template (iJN746).For https://github.com/cdanielmachado/carveme on August 1st, 2018) to generate the draft reconstructions. Two genome-scale metabolic reconstructions were generated for Lactobacillus plantarum using the universal bacterial template and the gram-positive bacterial template, respectively. For B. pertussis, the universal bacterial template and the gram-negative bacterial template were used. For P. putida, the universal bacterial template and the gram-negative bacterial template were used.We used CarveMe version 1.2.1  to generate the draft reconstructions. For all the networks, we first annotated the genomes with EBI through MERLIN using default parameters. Then, we loaded KEGG metabolic data and integrated the annotation with the model. Finally, we created gene-reaction-protein associations and removed unbalanced reactions to be able to export the network to SBML format.We used Merlin version 3.8  Lactococcus lactis (iNF517). (2) Lactococcus lactis (iNF517), Bacillus subtilis (iYO844), Staphylococcus aureus (iSB619), Clostridium ljungdahlii (iHN637) and Mycobacterium tuberculosis (iNJ661). (3) Lactococcus lactis (iNF517), Bacillus subtilis (iYO844), Staphylococcus aureus (iSB619), Clostridium ljungdahlii (iHN637), Mycobacterium tuberculosis (iNJ661), Escherichia coli (iML1515), Klebsiella pneumoniae (iYL1228), Shigella flexneri (iSFxv_1172), Shigella boydii (iSbBs512_1146), Shigella sonnei (iSSON_1240), Pseudomonas putida (iJN746), Yersinia pestis (iPC815), Helicobacter pylori (iIT341), Geobacter metallireducens (iAF987), Salmonella entérica (STM_v1_0), Thermotoga marítima (iLJ478), Synechocystis sp (iJN678), and Synechococcus elongatus (iJB785).To generate the genome-scale metabolic reconstructions of Bordetella pertussis we used three different set of templates from the BIGG database: (1) Escherichia coli (iML1515). 2) Escherichia coli (iML1515), Klebsiella pneumoniae (iYL1228), Shigella flexneri (iSFxv_1172), Shigella boydii (iSbBs512_1146), Shigella sonnei (iSSON_1240), Pseudomonas putida (iJN746), Yersinia pestis (iPC815), Helicobacter pylori (iIT341), Geobacter metallireducens (iAF987), Salmonella entérica (STM_v1_0), Thermotoga marítima (iLJ478), Synechocystis sp (iJN678), and Synechococcus elongatus (iJB785). 3) Escherichia coli (iML1515), Klebsiella pneumoniae (iYL1228), Shigella flexneri (iSFxv_1172), Shigella boydii (iSbBs512_1146), Shigella sonnei (iSSON_1240), Pseudomonas putida (iJN746), Yersinia pestis (iPC815), Helicobacter pylori (iIT341), Geobacter metallireducens (iAF987), Salmonella entérica (STM_v1_0), Thermotoga marítima (iLJ478), Synechocystis sp (iJN678), Synechococcus elongatus (iJB785), Lactococcus lactis (iNF517), Bacillus subtilis (iYO844), Staphylococcus aureus (iSB619), Clostridium ljungdahlii (iHN637), and Mycobacterium tuberculosis (iNJ661).To generate the genome-scale metabolic reconstructions of Pseudomonas putida, we used three different set of templates from the BIGG database: (1) iJN746. (2) iJN746 - iML1515 - iYL1228 - iSFxv_1172 - iSbBS512_1146 - iSSON_1240 - iPC815 - STM_v1_0 - iIT341 - iAF987 - iLJ478 - iJN678 - iJB785 iJN746 - iML1515 - iYL1228 - iSFxv_1172 - iSbBS512_1146 - iSSON_1240 - iPC815 - STM_v1_0 - iIT341 - iAF987 - iLJ478 - iJN678 - iJB785 - iNF517 - iYO844 - iSB619 - iHN637 - iNJ66.To generate the genome-scale metabolic reconstructions of Lactobacillus plantarum and B. pertussis. Version 2.4 was used to generate the draft reconstructions for Pseudomonas putida. Models were created using different template models. No media was specified to create the models.We used ModelSEED version 2.2 web service on August 16st of 2018 to generate the draft reconstructions of We used Pathway Tools version 22.0 to generate the draft reconstructions. Four networks were created with the Desktop mode using different cutoff values for pathways prediction and one was made with the Lisp-console with default parameters. All the networks were exported manually with the Desktop mode.https://github.com/SysBioChalmers/RAVEN, to generate the draft reconstructions. Different models were created using different databases (KEGG and MetaCyc) and different values in the parameters for orthology searches.We used RAVEN version 2.0.1, which was downloaded from We pre-processed the manually curated networks in order to compare them with the draft networks. We semi-automatically changed metabolite and reaction identifiers to match those of the BIGG database. Also, we removed duplicated reactions (those with the same reaction equation). Before the deletion of a duplicated reaction, the associated gene-reaction rule was transferred to or merged with the gene-reaction rule of the reaction that was kept in the network.We define the union of all the unique genes found in a particular metabolic network as the gene set in that network. We compared gene sets from each draft network with those in the corresponding manually curated model by case sensitive string comparison.Each metabolic network contains a set of metabolites. For those networks generated with reconstruction tools using the BIGG database , we compared metabolites just by string comparison. For other reconstruction tools , we mapped the metabolites using MetaNetX version 3.0 . As metaEach metabolic network contains a set of reactions. Reaction sets were compared using two complementary methodologies. First, by using reaction identifier MetaNetX mapping and second, by using reaction equation comparison.In the first approach, as a pre-processing step, we removed duplicated reactions (those reactions with the same MetaNetX identifier even if the reaction equation is different). For those networks generated with reconstruction tools using the BIGG database  reactions identifiers were compared by direct case sensitive string comparison. For other reconstruction tools, MetaNetX was used to map reaction identifiers, which also were compared by string comparison.In the second case, as a pre-processing step, we first removed duplicated reactions (those with the same equation even if they had different identifiers) and empty reactions (those with an identifier but with no reactants and products). Then, reaction equations were compared by comparing each metabolite and its stoichiometry individually. For those networks generated with reconstruction tools using the BIGG database , we directly compared reaction equations. For those networks generated with reconstruction tools using a database different from BIGG , we first converted metabolite identifiers to BIGG by using MetaNetX version 3.0 and our own dictionary  was calculated to compare reconstructions in terms of genes, reactions and metabolites. For two any sets of elements, Si and Sj, the JD is calculated as JD = 1 −  ∣ Si ∩ Sj ∣ / ∣ Si ∪ Sj∣. We called JDg, JDr and JDm to the JD calculated in terms of genes, reactions and metabolites, respectively. Thus, JDg, JDr, and JDm were calculated as:The Jaccard distance .JDr = 1 −  ∣ Ri ∩ Rref ∣ / ∣ Ri ∪ Rref∣, Ri being the reactions set of the generated draft network i and Rref being the reactions set of the reference network .JDm = 1 −  ∣ Mi ∩ Mref ∣ / ∣ Mi ∪ Mref∣, Mi being the metabolites set of the generated draft network i and Mref being the metabolites set of the reference network .R) between the coverage and the percentage of additional elements was calculated to assess how similar a particular draft network was to the manually curated reconstruction. We called Rg, Rr, and Rm to the R calculated in terms of genes, reactions, and metabolites, respectively. Thus, Rg, Rr, and Rm were calculated as:The ratio .Rr =  ∣ Ri ∩ Rref ∣ / ∣ Ri − Rref∣, Ri being the reactions set of the generated draft network i and Rj being the reactions set of the reference network .Rm =  ∣ Mi ∩ Mref ∣ / ∣ Mi − Mref∣, Mi being the metabolites set of the generated draft network i and Mj being the metabolites set of the reference network .Lactobacillus plantarum with CarveMe version 1.2.1 and ModelSEED version 2.4, using different media compositions for the gap-filling procedure that is carried out internally in these tools. Since the models were not able to generate biomass with the original media composition of CDM, PMM7, and PMM5 [We created three models of and PMM5 , we modi; True negatives as no growth in vivo and in silico; False positives as no growth in vivo and growth in silico; False negatives as growth in vivo but no growth in silico. CDM-like media was used as a basal media for the single omission experiments. For both in vivo and in silico experiments, growth rates below 10% of the growth rate obtained in CDM-like were considered as no growth.A set of 34 single-omission experiments  were useMetrics to evaluate the performance were calculated as follows:https://github.com/SystemsBioinformatics/pub-data/tree/master/reconstruction-tools-assessment [https://github.com/SystemsBioinformatics/pub-data/tree/master/reconstruction-tools-assessment/supplementary%20material/lpl and https://github.com/SystemsBioinformatics/pub-data/tree/master/reconstruction-tools-assessment/supplementary%20material/bpe, for L. plantarum and B. pertussis, respectively. The code is distributed under a General Public License (GPL), an open-source license compliant with OSI (http://opensource.org/licenses).All the reconstructions used as well as the MATLAB functions to generate the models (when possible) and to compare them are available at sessment . In partAdditional file 1:Supplementary figures, tables and files. (RAR 559 kb)Additional file 2:Review history. (DOCX 2969 kb)


Clostridium thermocellum, a model cellulolytic bacterium which represents as an attractive platform for conversion of lignocellulose to dedicated products. We uncover that the Embden–Meyerhof–Parnas (EMP) pathway is the predominant glycolytic route whereas the Entner–Doudoroff (ED) pathway and oxidative pentose phosphate pathway are inactive. We also observe that C. thermocellum's TCA cycle is initiated by both Si- and Re-citrate synthase, and it is disconnected between 2-oxoglutarate and oxaloacetate in the oxidative direction; C. thermocellum uses a citramalate shunt to synthesize isoleucine; and both the one-carbon pathway and the malate shunt are highly active in this bacterium. To gain a quantitative understanding, we further formulate a fluxome map to quantify the metabolic fluxes through central metabolic pathways. This work represents the first global in vivo investigation of the principal carbon metabolism of C. thermocellum. Our results elucidate the unique structure of metabolic network in this cellulolytic bacterium and demonstrate the capability of isotope-assisted metabolite studies in understanding microbial metabolism of industrial interests.Cellulolytic bacteria have the potential to perform lignocellulose hydrolysis and fermentation simultaneously. The metabolic pathways of these bacteria, therefore, require more comprehensive and quantitative understanding. Using isotope tracer, gas chromatography-mass spectrometry, and metabolic flux modeling, we decipher the metabolic network of  Clostridium thermocellum is among the most attractive CBP host microbes.Lignocellulosic biomass is the most abundant bio-renewable resource on earth. It has the potential to substitute the current sugar feedstocks for sustainable biofuels and biochemicals production. The use of lignocellulosic biomass has significant merits including improving net carbon and energy balances, reducing production cost, and avoiding the competition between food vs. fuel   has provided essential information and blue prints in terms of metabolic pathways, elaborate experimental validation is still required to fully realize its biotechnological applications.Applications of 13C-tracer and metabolic flux analysis has emerged as a powerful tool for validating genome annotation and this technique has enabled us to identify a unique CO2-fixing one-carbon metabolic pathway in C. thermocellum  pathway is the predominant glycolytic route whereas the Entner–Doudoroff (ED) pathway and oxidative pentose phosphate pathway are inactive. We also observe that C. thermocellum's TCA cycle is initiated with both Si- and Re- citrate synthase and it is broken between 2-oxoglutarate and oxaloacetate in the oxidative direction. C. thermocellum uses a citramalate shunt to synthesize isoleucine; and the one-carbon metabolism and the malate shunt are active in this bacterium.Recently, To gain a quantitative insight into the newly identified metabolic activities, we constructed a fluxomic model that enables us to quantify the metabolic fluxes through the EMP pathway, the nonoxidative pentose phosphate pathway, the TCA cycle, the malate shunt, and amino acid biosynthesis pathways. This study represents the first global characterization of the central carbon metabolic pathways in a model cellulolytic species. It also showcases the capability of isotope-assisted metabolite analysis in complementing genome annotation with a more in-depth understanding of microbial metabolism.C. thermocellum DSM 1313 derived strain Δhpt . Hydrogen gas was measured using a gas chromatograph  equipped with a thermal conductivity detector and a stainless-steel Supelco 60/80 Mol Sieve column (6 ft × 1/8 in) with argon as the carrier gas. Peak areas were compared with a standard curve. Glucose, lactate, formate, acetate, and ethanol were measured by HPLC  with a mobile phase of 4 mM H2SO4 at 0.6 mL/min flow rate using an Aminex HPX-87H column with a Micro Guard Cation H Cartridge. The column temperature was set to 55°C.Cell growth was measured as a function of optical density by spectrophotometry  at OD13C-labeling experiment was performed with different nutrient combinations: (1) 5.56 mM [U-13C6] glucose (20%) + 22.22 mM unlabeled glucose (80%); (2) 20 mM 13C-bicarbonate + 27.78 mM (5 g/L) glucose; (3) 20 mM 13C-formate + 27.78 mM glucose. (4) 20 mM [U-13C5] glutamate + 27.78 mM glucose. These nutrient combinations are supplemented into the CTFUD defined medium, respectively. For cell growth, C. thermocellum strains were inoculated into these media with a starting OD600 of 0.05. When late log phase was reached (OD600 above 0.6), 3 mL of cultures was sampled.We adopted a steady-state labeling strategy. The The sample treatment and GC-MS analysis were done as previously reported (5), with a few modifications. Briefly, 3 mL of sampled cultures was centrifuged at 10,000 × g for 1 min, and the cell pellets were digested with 500 μL of 6 M HCl at 105°C for 12 h. The hydrolysate was dried under nitrogen gas flow at 65°C and dissolved in 50 μL of water-free dimethylformamide. For the GC/MS measurement, the proteinogenic amino acids were derivatized before analysis. The dried hydrolysate, dissolved in N, N-dimethylformamide, was derivatized by 1% tert-butyl-dimethylchlorosilane (TBDMS) at 85°C for 60 min. One microliter of the sample in the organic phase was loaded onto the GC/MS instrument [Agilent GC-6890 gas chromatograph equipped with an Agilent 19091J-413 column (30 m × 0.32 mm × 0.25 μm) directly connected to a MS-5975C mass spectrometer]. Helium was the carrier gas. The oven temperature was initially held at 50°C for 2 min; it was then raised to 150°C at 5°C/min and held at that value for 2 min. Finally, it was raised to 320°C at 7°C/min and held at that final value for 2 min. Other settings included splitless and electron impact ionization at 70 eV. The amino acids, including alanine, aspartate, glutamate, glycine, isoleucine, leucine, methionine, phenylalanine, proline, serine, threonine, tyrosine, and valine, were separated and analyzed. Histidine was not detected due to its low abundance in cell biomass.MDVα, was assigned according to Nanchen et al. , hydrogen (H), and carbon (C) atoms using a correction matrix (Equation 2) as described by Nanchen et al.  between computationally simulated and experimentally determined measurements. INCA, a 13C-flux software based on Matlab platform  can be incorporated into cell biomass, and thus interfere with a quantitative flux analysis. To test medium effect, we used 20% fully 13C-labeled and 80% fully unlabeled glucose as the carbon source and cultivated the cells in CTFUD rich medium containing yeast extract (1 g/L) and CTFUD defined medium without yeast extract, respectively. We used cultures when cells were at late exponential phase of growth (OD600 above 0.6). The labeling steady state is assumed to be reached at this growth stage, we then quantify the fractional labeling of each proteinogenic amino acid (AA) fragments by GC-MS. As shown in Figure C. thermocellum directly and thus diluting the labeling ratio significantly. In contrast, the labeling ratio of AA fragments in CTFUD define medium is much close to theoretical value (20%), confirming that 13C-labeling is not interfered by medium nutrients in the defined medium and the pseudo steady state for labeling has been reached. Overall, our results have shown that tracer experiment using minimal CTFUD medium is a valid system for 13C metabolic flux analysis.The first task for C. thermocellum, the labeling profiles of 13 proteinogenic amino acids were analyzed. We used 13C-bicarbonate labeling data as the blueprint, since 13C-bicarbonate labeling leads to the [1-13C] pyruvate through reversed pyruvate ferredoxin oxidoreductase (PFOR)  suggests that two possible pathways may contribute to the biosynthesis of isoleucine: the threonine pathway and the citramalate pathway  of isoleucine showed that main labeling of isoleucine is exactly located at the carboxylic group of isoleucine . The flux from the EMP pathway can be quantified accordingly and the results are shown in Figure C. thermocellum. However, origin of the rest 38% of serine remains as an intriguing question.The pathway activities in C. thermocellum genome but are identified in other Clostridia species  of pyruvate which is the precursor of alanine  may contribute to the biosynthesis of the remaining labeled serine. With respect to the ED pathway, genes for ED pathway are not annotated in C. thermocellum genome either. Additionally, their biochemical activity was absent from cell-free extracts  and malate shunt, and then to citrate by the stereospecific citrate synthases. The stereotype of citrate synthase can be mirrored by the labeling patterns of downstream glutamate . As shown in Figure 13C-carbon, presumably on the δ-carboxylic group of glutamate. This is consistent with Re-type citrate synthase. We then checked the labeling pattern of glutamate C1-2. Interestingly 28% glutamate C1-2 fragment also has one 13C-carbon, suggesting the first carboxyl group of glutamate was labeled as well. This labeling pattern is consistent with the notion that C. thermocellum also carries an active Si-citrate synthase. To confirm this observation, we investigated the genome of C. thermocellum DSM 1313 and indeed the gene for Si-citrate synthase (CLO1313_RS02945) is present. It has 67% sequence identity with a known Clostridial Si-citrate synthase  shares 73% homology with aconitase, strongly indicating their joint functionality in the TCA cycle by converting acetyl CoA and oxaloacetate to isocitrate. Integrating all the acquired knowledge, our data provides labeling evidence for the first time to support that C. thermocellum carries both Re- and Si-Citrate synthases to initiate the TCA cycle.As a ubiquitous energetic and biosynthetic pathway, the tricarboxylic acid (TCA) cycle however remains elusive for anaerobic bacteria. As the first step of the TCA cycle, biosynthesis of citrate from acetyl-coenzyme A and oxaloacetate is catalyzed in most organisms by a t Figure  in whichProteus mirabilis  vs. the ratio of oxaloacetate derived through anaplerotic reaction by e.g., phosphoenolpyruvate (PEP) carboxylase (1-f) H is not input into the model either, because whether a particular redox reaction involves NADH vs. NADPH needs to be validated experimentally, and any assumptions for NAD(P)H-specific reactions will bring uncertainty to the modeling. Experimental inputs to the model include steady-state labeling data from proteinogenic amino acids, specific growth rates, sugar uptake rates, excretion rates for fermentation products H they produce per glucose metabolized. Specific to C. thermocellum, however, we only identified the EMP pathway as the dominant one. Given various energetic and carbon requirements by the cells, C. thermocellum uses the EMP pathway exclusively, representing a typical metabolic rigidity. It implies that C. thermocellum may choose alternative strategies to modulate anaerobic energy and carbon demand. Indeed, the EMP pathway in C. thermocellum has many atypical features. For instance, the conversion of PEP to pyruvate features the malate shunt, which is believed to catalyze a transhydrogenase reaction converting NADH to NADPH and generate GTP + and influence energy metabolism including ferredoxin:NAD+ oxidoreductase (Rnf) vs. malate shunt, both of which synthesize pyruvate from PEP but without forming distinguishable carbon-carbon bond. In this case, kinetic flux profiling which records labeling trajectories of intermediate as a function of time can provide a complementary solution  we used is different from the kinetic flux profiling which is another promising fluxomics method (Yuan et al., C. thermocellum and exemplifies the ability of isotope tracer experiments and metabolic modeling in understanding microbial metabolism of industrial interests.In conclusion, this work represents the first global investigation of the central carbon metabolism in WX and PM led the research. WX designed the experiments. WX, KC, CW, LM, and TD performed the experiments. WX, JL, KC, and PM wrote the article.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.