A considerable challenge in the development of bioprocesses for producing chemicals and fuels has been the high cost of feedstocks relative to oil prices, making it difficult for these processes to compete with their conventional petrochemical counterparts. Hence, in the absence of high oil prices in the near future, there has been a shift in the industry to produce higher value compounds such as fragrances for cosmetics. Yet, there is still a need to address climate change and develop biotechnological approaches for producing large market, lower value chemicals and fuels.Escherichia coli (E. coli) to consume EG and examine glycolate production as a case study for chemical production. Using a combination of modeling and experimental studies, we identify oxygen concentration as an important metabolic valve in the assimilation and use of EG as a substrate. Two oxygen-based strategies are thus developed and tested in fed-batch bioreactors. Ultimately, the best glycolate production strategy employed a target respiratory quotient leading to the highest observed fermentation performance. With this strategy, a glycolate titer of 10.4 g/L was reached after 112 h of production time in a fed-batch bioreactor. Correspondingly, a yield of 0.8 g/g from EG and productivity of 0.1 g/L h were measured during the production stage. Our modeling and experimental results clearly suggest that oxygen concentration is an important factor in the assimilation and use of EG as a substrate. Finally, our use of metabolic modeling also sheds light on the intracellular distribution through central metabolism, implicating flux to 2-phosphoglycerate as the primary route for EG assimilation.In this work, we study ethylene glycol (EG), a novel feedstock that we believe has promise to address this challenge. We engineer Overall, our work suggests that EG could provide a renewable starting material for commercial biosynthesis of fuels and chemicals that may achieve economic parity with petrochemical feedstocks while sequestering carbon dioxide. However, it is possible to select for a strain that does, and to our knowledge, only one study has ever reported EG utilization by E. coli. [E. coli by overexpressing two genes: fucO (encoding propanediol oxidoreductase) and aldA . This synthetic pathway is similar to the second natural EG utilization pathway previously introduced: EG is sequentially oxidized to glyoxylate thereby providing a gluconeogenic carbon substrate for growth. More specifically, the promiscuous activity of propanediol oxidoreductase converts EG to glycolaldehyde, which is subsequently converted to glycolate by glycolaldehyde dehydrogenase. The native glycolate oxidase then transforms glycolate to glyoxylate to support cell growth and maintenance.Wildtype E. coli. . That stE. coli. , 25. In E. coli. –21. Henc2 reduction (eCO2R), and it has already been successfully employed within biological systems [Motivated by the prospect of utilizing EG as a renewable and alternative feedstock, we sought to compare EG with more conventional feedstocks for the production of select chemicals of industrial significance. In particular, formate, glucose, and xylose were selected as the comparative feedstocks, while succinate, ethanol, glycolate and 2,3-butanediol, were selected as the products of interest. Formate was selected as it is another well-studied, non-sugar feedstock that can be produced via electrochemical CO systems –30. Mean systems . The fou systems , 32–34.E. coli as a biocatalyst capable of growth and glycolate production, using EG. This case study attempts to validate our orthogonal approach for chemical production, relating the network topology and two-stage fermentation.In this study, we began by comparatively evaluating EG as a feedstock by measuring the orthogonality of each substrate-product combination, using select bioconversion pathways. Consistent with our previous evaluation , EG demoE. coli have focused on using glucose and/or xylose as a substrate, and typically implement genetic strategies that couple production to growth [E. coli [E. coli and is predicted to reach molar yields of 2.5 and 3, on xylose and glucose, respectively [E. coli, it has not yet reached the model-predicted yields in vivo. In the identified studies, the highest glycolate titer in E. coli is reported to be 65.5 g/L with a corresponding yield of 0.765 g/g, using glucose as the substrate [E. coli, the highest reported glycolate titer is 110.5 g/L with a corresponding yield of 94.4%, using EG as the substrate. This feat was achieved by Hua and colleagues in Gluconobacter oxydans using an integrated production, separation and purification technology [E. coli [Glycolate is an alpha-hydroxy acid used in the synthesis of a variety of different plastics and polymers, cosmetics and industrial detergents , 35, 36.o growth , 37–41. o growth , 40, 41,[E. coli , 37, 38.ectively . However[E. coli –45.E. coli growing on EG , for which mutants have reportedly shown promiscuous activity with EG [aldA). From here, glycolate can be accumulated as the desired product, or further converted to support cell growth. In the latter case, glycolate oxidase converts glycolate to glyoxylate to support cell growth via native metabolic pathways. This pathway fits the ideal network architecture of a branched pathway, for which a metabolic valve can be designed and implemented for dynamic control of production and biomass fluxes. More specifically, in the EG-glycolate pathway shown, the glyoxylate oxidase reaction naturally provides a suitable metabolic valve controllable by oxygen levels. Under fully aerobic conditions, glycolate is converted to glyoxylate and channeled to the central metabolism for growth via glycerate metabolism. Alternatively, under oxygen limiting conditions, this reaction can be limited, and the cell can accumulate glycolate.As previously reported in Table  with EG . This obE. coli MG1655. In this case study, a combination of computational and experimental evaluations were performed to characterize this production pathway, and to explore its implementation within a two-stage fermentation system.Ultimately, as evidenced by the orthogonality scores and the pathway topologies for glycolate production, EG is more orthogonal than the traditional substrates investigated, and hence suitable for validating the concept of pathway design based on orthogonality. Thus, we performed a case study for the production of glycolate from EG in E. coli core model was modified with the addition of the EG assimilation pathway previously shown in Fig. To gain insight into the expected metabolic behaviour, the intracellular fluxes of the production system were first investigated using flux balance analysis (FBA) and flux variability analysis (FVA). For this analysis, the −1 (mass yield of 0.49 gDW/gEG), no glycolate production and no by-product production other than CO2 . Approximately 91% of the glyoxylate flux is channeled towards 2-phosphoglycerate (2PG) and enters lower glycolysis. The remaining glyoxylate is used to generate malate via malate synthase. Of the total carbon (EG) entering the cell, 22% is channeled towards acetyl-CoA and 3.3% enters the TCA cycle. Conversely, about 18% of the total carbon is channeled by gluconeogenic pathways towards upper glycolysis and the pentose phosphate pathways, when accounting for stoichiometry.Under aerobic conditions Fig. a, FBA pr−1 and a glycolate export flux of 2.2 mmol/gDW·h. These values correspond to a biomass mass yield of 0.29 and a glycolate mass yield of 0.56. The only by-product observed under micro-aerobic conditions is CO2. The FVA values reported for both aerobic and micro-aerobic conditions provide a range of flux values that may be possible. In the case of the tartronate semialdehyde reductase (TRSARr) reaction, the large lower bound flux predicted by FVA indicates that alternate optima exist for this system, for the set of constraints used. Overall, the FBA results suggest that micro-aerobic conditions are required for the secretion of glycolate and that oxygen level could be used as an effective control mechanism for switching from growth to production. In the sections that follow, we attempt to experimentally validate the predicted link between oxygen levels and glycolate production and to use modeling to better understand this relationship. Applying these insights, we then develop and test two strategies to optimize glycolate production in E. coli, using EG as the feed material.Under micro-aerobic conditions, modeling suggests that glycolate production will occur Fig. b. More sE. coli through the expression of a suitable 1,2-propanediol oxidoreductase mutant (fucO), and glycolaldehyde dehydrogenase . Previous studies have shown that Fe2+-dependent propanediol oxidoreductases (encoded by fucO) can be inactivated by metal-catalyzed oxidation (MCO) and are therefore sensitive to oxygen [fucO mutants reported as being more oxygen-stable. Variant 1 (strain LMSE11) contained fucO with mutations I7L and L8V, as reported by earlier mutagenesis studies [fucO with a single L8M mutation, as it was also suggested to play a role in alleviating metal catalyzed oxidation (MCO) toxicity in propanediol assimilation by E. coli [. Both variants had the same ribosome binding site and trc promoter upstream of the start codon. As shown in Fig. E. coli iAF1260 model (previously described). Thus, the experimental results suggest that the two genes introduced  are sufficient for supporting the conversion of EG to biomass, when combined with E. coli’s natural biosynthetic pathways. However, the actual yield is less than 60% of the model-predicted yield, thereby suggesting that the pathway may not be operating optimally (i.e. oxygen sensitivity) or that the modeling may need additional constraints. For example, the substrate uptake rate in shake-flasks was determined to be 5 mmol/gDW·h, compared to the 10 mmol/gDW·h assumed previously for modeling. When the modeling is performed instead for an EG uptake flux of 5 mmol/gDW·h, the predicted biomass yield decreases to 0.43 gDW/gEG. Similarly, EG transport was modelled as free diffusion, however if in reality proton symport is required, it may further limit the achievable yield. Oxygen-limiting conditions could also explain a lower than optimal yield, however this is unlikely since analysis of the fermentation media by high-performance liquid chromatography (HPLC) showed the absence of intermediate metabolites  and fermentation products, such as acetate or lactate. The experimental growth rate was calculated to be 0.18 h−1, corresponding to a 3.85 h doubling time. Since LMSE11 showed higher utilization rates, this variant was used in subsequent experiments.As previously described and predicted computationally, EG assimilation and conversion to glycolate can be introduced in o oxygen . Thus, w studies . Variant E. coli . Both vaE. coli, we next explored the use of EG as an orthogonal substrate for the production of glycolate within a two-stage fermentation system. As previously described, the branched topology of the EG-glycolate pathway lends itself to the design of a metabolic valve for the separation of cell growth and chemical production. In particular, glycolate oxidase was identified as a potential metabolic valve, for which oxygen level was predicted to be a control mechanism. A higher oxygen level is expected to support biomass growth, while lower oxygen levels are expected to trigger glycolate accumulation. Thus, two reductions in oxygen (air flow rate) were tested to evaluate the effect of oxygen on glycolate production.Having established EG utilization by an engineered strain of 2 production. These values were then converted to the mass of EG required and compared to the actual mass of EG consumed, leading to the values reported above. Further refinements to the FBA model for E. coli growth on EG may close the carbon balance with improved accuracy.For this evaluation, LMSE11 (variant 1) was grown in bioreactors with minimal media, supplemented with 2 g/L of yeast extract. The bioreactors were inoculated at an initial OD of ~ 0.4 (approximately 0.23 gDW/L), with inoculum prepared as described in the methods. Expression of the EG utilization genes was induced with 1 mM IPTG. During the growth stage, the impeller agitation was set at 1000 rpm, and the reactors were sparged with air to maintain an aeration rate of 300 mL/min (1 v/vm). At 20 h, the aeration was reduced to 150 mL/min (0.5 v/vm) or 50 mL/min (0.16 v/vm), to simulate high and low aeration rates, and the impeller agitation was dropped to 500 rpm  being oxygen-sensitive, it was observed that the higher secondary aeration rate (150 mL/min) led to higher glycolate titers  and the oxygen-dependent regeneration of NAD+ required by the pathway. Based on these results, we turned to metabolic modeling to computationally evaluate the effect of oxygen and thus determine a strategy for improved glycolate production in vivo.Counter-intuitively, despite propanediol oxidoreductase , biomass production (biomass flux), the cell’s respiratory quotient , and the substrate specific productivity (SSP) were modelled as a function of the oxygen uptake rate (OUR). The formation of by-products was also followed over the same OUR range. The normalized results are shown in Fig. PS·μ). Based on Fig. Since the specific oxygen uptake rate is a function of air intake, this analysis allowed us to implicitly correlate the glycolate yield to the flowrate of air into the reactor. Furthermore, as the RQ and glycolate yield can be correlated via their relationship to the OUR, it should also be possible to optimize glycolate production in real-time by measuring and controlling the RQ throughout a fermentation experiment. Controlling the RQ was thus identified as a possible strategy to improve glycolate production. The selected RQ must balance the glycolate yield, substrate specific productivity and cell growth, to optimize glycolate production. The substrate specific productivity (SSP) is a measure of the moles of product obtained per mole of substrate per time (molP/molS h), and is calculated as the product of the product yield and cell growth rate . For both strategies, bioreactor experiments were performed as previously described, but with modified aeration rates and mixing speeds. The first strategy was tested with the goal of reducing the biomass production phase and increasing the glycolate production phase. To achieve this, the aeration rate during the growth phase was increased to 600 mL/min (2 v/vm) to prevent glycolate accumulation and divert flux towards cell growth. In the second phase, the aeration rate was dropped to 100 mL/min. Results for this strategy are shown in Fig. Cell growth continued for approximately 70 h, after which the reactor appeared to reach stationary phase. Production of glycolate began at approximately 70 h and continued until the fermentation was terminated at 140 h (70 h of production). Final glycolate titers reached 6.8 g/L, with an initial production phase biomass concentration of approximately 4 gDW/L, corresponding to an average productivity 0.1 g/L h or approximately 0.32 mmol/gDW·h. The initial yield of glycolate was 0.92 g/g after the first sample was taken, however, the cumulative yield decreased during the course of the production stage with the final overall production yield being 0.75 g/g, or 61% of theoretical (see Table fucO). Even using the more oxygen-stable mutant (strain LMSE11), the increased dissolved oxygen levels and faster oxygen mass transfer rates likely caused oxygen toxicity in the cells due to the inactivation of propanediol oxidoreductase by MCO. This likely placed a high metabolic burden on the cell stemming from high protein demand without a sufficient means to utilize EG as a carbon source. Considering all of the bioreactor experiments up to this point, it seems that higher aeration rates lead to higher glycolate titers and yield, yet also retard cell growth when too high.In implementing this first strategy, we produced significantly more product at a higher yield; however the cells took much longer to reach an appropriate concentration for the production phase. At 600 mL/min (2 v/vm) during the growth phase, it took almost 70 h to reach a concentration of 4 gDW/L, while comparatively the previously tested 300 mL/min (1 v/vm) produced the same cell concentration within 30 h. We hypothesize that it took longer to reach a higher OD due to the oxygen sensitivity of propanediol oxidoreductase  [fucO) in the presence of excess aeration and could be addressed by using O2-tolerant Zn2+-dependent variants.Our results demonstrate the applicability of 100 g/L) , this co100 g/L) . FurtherfucO mutants predicted to have improved O2-tolerance were tested. Ultimately, the I7L/L8V mutant (LMSE11) showed the greatest EG assimilation and growth rate/yield, and was thus used for subsequent experiments. It should be noted, however, that the growth yield with this strain was still lower than the value predicted by modeling: 0.28 g DW/g EG compared to 0.43 g DW/g EG predicted under aerobic conditions at the measured substrate uptake rate. This difference suggests that the enzyme is still not operating as efficiently as possible and that additional improvements may be achievable through enzyme engineering. In particular, directed evolution or rational design approaches could be used to improve the enzyme activity and oxygen tolerance of fucO [Based on previous reports from literature, two  of fucO , 57. Suc+-dependent, we believe that the reduced oxygen results in increased NADH pools, leading to a decrease in the rates of reaction catalyzed by fucO and aldA (which each require NAD+ as a cofactor). This change in the rates had a net effect of lowering the flux of EG into the cell. This finding necessitates a further study of cellular physiology under EG utilization in order to understand the trade-off in yield and productivity as a function of the dissolved oxygen feeding in bioreactors. For example, in the first set of bioreactor experiments we found that using a higher aeration rate led to a higher overall glycolate titer (4.1 g/L at 150 mL/min compared to 2.5 g/L at 50 mL/min). Comparatively, by maintaining a target respiratory quotient during the second set of bioreactor experiments, an even higher product titer was achieved at the lower aeration rate (10.4 g/L at 50 mL/min). Hence, optimization of aeration in the bioreactor would substantially improve economic performance, not only in terms of product formation but also in terms of the absolute cost of aeration.An important observation made was a reduction in the substrate uptake rate during oxygen limiting conditions. When oxygen is limiting, reduced metabolites and electron carriers can accumulate . Since tIn this study, computational modeling allowed us to predict and better understand the metabolic behaviour for EG utilization. Through flux balance analysis (FBA) it was demonstrated that EG is assimilated into native metabolism through glyoxylate oxidase, and that this enzyme could serve as an effective metabolic valve for the control of cell growth and production. FBA predicted that glycolate was unlikely to be produced under aerobic conditions, however under oxygen-limiting conditions glycolate was likely to accumulate. This agreed well with bioreactor studies, which showed that glycolate accumulation could be induced through a reduction in the aeration. Modeling also predicted a reduction in EG uptake rate, under oxygen-limiting conditions, which was equally seen experimentally.During the experiments, we also observed small amounts of acetate and trace amounts of ethanol in the fermentation media during the micro-aerobic glycolate production phase. The presence of acetate and ethanol in the fermentation medium, typical products of anaerobic growth, suggests that EG may be a suitable feedstock for the production of other anaerobic products. Comparatively, ethanol production, depending on the ratio of the EG uptake flux and the OUR Fig. b. These Finally, by extending the observations from FBA, we were able to correlate the glycolate production with the respiratory quotient. Thus, by measuring the respiratory quotient in real-time, using data from the process mass spectrometer, we were able to use this correlation to improve and control glycolate production during the course of the fermentation. This computational-based strategy, which led to the highest glycolate titers and production rate at a relatively low aeration rate, could be employed in other production systems to achieve similar improvements in production. Ultimately, we believe that our results successfully demonstrate the design and optimization of bioproduction pathways using computational tools and their metrics , particularly for the design of pathways for unconventional, non-sugar feedstocks.E. coli using EG as a substrate. We describe, for the first time, the successful production of glycolate from EG using the substrate as a feedstock for growth and for production. We also used metabolic modeling to identify the oxygenation conditions that optimize the production of glycolate and validated the strategy experimentally, thereby illustrating the value of metabolic models in bioprocess optimization. Further, we also showed that ethylene glycol utilization pathways are highly orthogonal to cellular metabolism making it an important feedstock for accelerated metabolic engineering using dynamic control. We also illustrated the value of orthogonal pathways for dynamic control of metabolism using oxygen as a control valve to switch to and optimize glycolate production. We believe this can have important implications in the future for integrating biorefineries into industries where carbon dioxide can be captured and converted from point sources.The results described in this study establish a framework for future production of chemicals in fucO mutant, informed based on previously published studies (discussed further in text). The best growing strain was then selected for further testing in bioreactors  as per manufacturer’s instructions  for all strain construction and fermentation pre-cultures. Pre-cultures were grown in LB media in 10 mL test tube cultures overnight and transferred to fresh 250 mL shake-flaks containing 50 mL LB, 1 mM IPTG and 10 g/L EG. After 24 h, these cells were harvested by centrifugation, re-suspended in 2 mL of residual supernatant and used as inoculum for bioreactor or minimal media shake-flasks for characterization at 37 °C.4Cl, 3.0 g/L KH2PO4, 6.8 g/L Na2HPO4, 0.50 g/L NaCl. Supplements of yeast extract at 2 g/L were added to minimal media. EG was used as the carbon source at a concentration of ~ 10 g/L. IPTG was used at a concentration of 1 mM to induce expression of the EG assimilation pathway. A trace metal solution was prepared according to the following composition prepared in 0.1 M HCl per litre and added to the media at a concentration of 1/1000: 1.6 g FeCl3, 0.2 g CoCl2⋅6 H2O, 0.1 g CuCl2, 0.2 g ZnCl2⋅4H2O, 0.2 g NaMoO4, 0.05 g H3BO3. 1 M MgSO4 and 1 M CaCl2 was also added to the media at a concentration of 1/500 and 1/10,000, respectively. For all cultures, carbenicillin was added as appropriate at 100 µg/mL. All characterization experiments were conducted with 50 mL media in 250 mL shake flasks, continuously agitated at 230 rpm and at 37 °C. Culturing techniques employed in the bioreactors are described below.When characterizing strains see Fig. c, cells E. coli strain LMSE11 in minimal media, supplemented with yeast extract at 2 g/L. The bioreactors contained 1 mM IPTG to maintain induced expression of the EG pathway genes. All bioreactor cultivations were carried out in fed-batch. Systematic changes in aeration rate and impeller speed were applied between cultivations, as detailed below. Flowrate was controlled using a Books Instruments mass flow controller (GF Series) and gas was analyzed using Thermo Scientific™ Sentinel dB mass spectrometer for online gas measurement.Applikon MiniBio500 500 mL fermentation vessels with a 300 mL working volume were used for cultivating strains in bioreactors. The fermentation vessels were equipped with condensers to prevent changes in volume due to aeration. Dissolved oxygen and pH probes were used in accordance with the manufacturers operating guidelines. pH was maintained at 7 with the addition of 3 N KOH. Growth conditions were maintained at 37 °C. Bioreactors were inoculated with pre-culture (previously described) at OD ~ 0.4 (approx. 0.23 gDW/L). In total, four bioreactor cultivations were conducted using Cultivations 1 and 2: Cells were grown at an impeller speed of 1000 rpm and sparged with air to maintain oxygen at 300 mL/min (1 v/vm). At 20 h, the aeration was reduced to 150 mL/min (0.5 v/vm) or 50 mL/min (0.16 v/vm) to simulate high and low secondary aeration rates, and the impeller speed was dropped to 500 rpm. See Fig. Cultivation 3: Cells were grown at an impeller speed of 1000 rpm and sparged with air to maintain oxygen at 600 mL/min (2 v/vm) during the growth phase. In the production phase, the impeller speed was reduced to 500 rpm while the aeration rate was dropped to 100 mL/min (0.33 v/vm). See Fig. Cultivation 4: During the growth phase, cells were sparged with air to maintain oxygen at 50 mL/min 0.16 v/vm) throughout the cultivation and the impeller speed was controlled to maintain a minimum oxygen level of 20%, up to a maximum impeller speed of 1000 rpm. In the production phase, the impeller speed was reduced as necessary to achieve a respiratory quotient (RQ) of ~ 0.4. See Fig. 6 v/vm th2SO4 as the eluent and a flowrate of 0.4 mL/min at 50 °C. Organic acids were detected at 210 nm. Cell densities of the cultures were determined by measuring optical density at 600 nm (GENESYS 20 Visible Spectrophotometer). Cell density samples were diluted as necessary, to fall within the linear range. A differential refractive index detector  was used for analyte detection and quantification. Yields were calculated between two time points, whereas the cumulative yield was calculated between the initial and final measurements.Analysis of fermentation production was measured via high performance liquid chromatography (HPLC). We used a Bio-rad HPX-87H organic acids column with 5 mM HfucO and aldA were cloned from E. coli MG1655 genomic DNA and assembled using Gibson Assembly [aldA. AAGTTAAGAGGCAAGA was the RBS sequence used in front of fucO. The trc promoter was used to drive expression. Wild-type strains of E. coli MG1655 were obtained from the Coli Genetic Stock Centre .Genes Assembly  into a pE. coli core model, as indicated below. The ATP maintenance reaction was left unchanged for both models, corresponding to a value of 8.39 mmol/gDW·h. Both the core and genome scale models were modified by the addition of missing reactions in the EG assimilation pathway, including the transport and exchange of EG by free diffusion (no proton translocation). The default glycolate transport and exchange reactions were used for the genome scale model and equivalent reactions were added for the core model. Formate exchange was knocked out for all analyses in which oxygen was present.Flux balance analysis (FBA) was performed using both MATLAB R2015a installed with COBRA 2.0 toolbox and the COBRApy toolbox with Python 3.7.3, using the GLPK linear solver (GNU Project). All modeling was performed using either the genome scale model iAF1260 or the The orthogonality evaluation was performed using the core model, as previously described . All otht), while all those contributing to biomass production compose the biomass subspace (Sx). To determine the orthogonality of the given chemical product and metabolic network, the elementary flux modes (EFMs) must first be determined and assigned to the product and biomass subspaces. All EFMs with non-zero chemical flux and zero biomass flux are assigned to the product subspace, while those with non-zero biomass flux and zero chemical flux are assigned to the biomass subspace. The degree of similarity between these two subspaces is defined as the average similarity (AS) coefficient, and is determined by the number of shared reactions, normalized by the size of the supporting biomass mode  conditions on glycolate production, as predicted by modeling at different substrate uptake rates (SUR) and non-growth associated ATP maintenance (NGA ATPM) values. (A) SUR of 5 mmol/gDW·h and default NGA ATPM value (8.39 mmol/gDW·h.). (B) SUR of 0.7 mmol/gDW·h and NGA ATPM value of 4.8 mmol/gDW·h. (C) SUR of 0.7 mmol/gDW·h and NGA ATPM value of 3.2 mmol/gDW·h. (D) SUR of 0.7 mmol/gDW·h and NGA ATPM value of 1.6 mmol/gDW·h. In each case, flux balance analysis (FBA) was used to predict the glycolate yield (mol glycolate/mol EG), cell growth rate (gDW/gDW·h), respiratory quotient  and the substrate specific productivity  over a range of oxygen uptake rates . All values other than the SSP are shown on the primary vertical axis. An RQ value of 0.4 (shown by the dotted black line) was selected for the RQ-based strategy employed in the final bioreactor experiment. The molar yield (mol glycolate/mol EG) predicted at this RQ value is indicated for each case.


Zymomonas mobilis ZM4 has recently been used for a variety of biotechnological purposes. To rationally enhance its metabolic performance, a reliable genome-scale metabolic network model (GEM) of this organism is required. To this end, we reconstructed a genome-scale metabolic model (iHN446) for Z. mobilis, which involves 446 genes, 859 reactions, and 894 metabolites. We started by first reconciling the existing GEMs previously constructed for Z. mobilis to obtain a draft network. Next, recent gene annotations, up-to-date literature, physiological data and biochemical databases were used to upgrade the network. Afterward, the draft network went through a curative and iterative process of gap-filling by computational tools and manual refinement. The final model was evaluated using experimental data and literature information. We next applied this model as a platform for analyzing the links between transcriptome-flux and transcriptome-metabolome. We found that experimental observations were in agreement with the predicted results from our final GEM. Taken together, this comprehensive model (iHN446) can be utilized for studying metabolism in Z. mobilis and finding rational targets for metabolic engineering applications. Zymomonas mobilis, as an ethanologenic Gram-negative bacteria, possesses desirable characteristics because of its unique usage of the Entner-Doudoroff (ED) pathway. Compared to other ethanologenic microorganisms, Z. mobilis shows a higher glucose uptake rate and also higher yields of ethanol production. These characteristics are considered as the prerequisites for an industrially effective ethanol production process2.Bioethanol production has received increasing attention as a sustainable replacement for fossil fuels. Z. mobilis’ metabolism consists of endogenous metabolic pathways that produce other industrially notable products including levan, sorbitol, glycerol, gluconic, succinic, lactic and acetic acids3. Levan is an important biopolymer has versatile applications in pharmaceutical, food and cosmetic industries3. Levan is a homo-exopolysaccharide which is composed of d-fructose monomers linked by β (2 → 6) bonds. Levansucrase (EC 2.4.1.10) is involved in the polymerization of fructose units, using sucrose as substrate4. All these characteristics suggest that Z. mobilis has significant potential for the production of an extended range of valuable biochemical compounds for industrial biotechnology applications4In addition to ethanol, Z. mobilis have been reported via systems biology and computational modeling, evolutionary engineering, genome editing, omics technology  and synthetic biology5. In addition, diverse metabolic engineering approaches have been used to redesign the new metabolic pathways in Z. mobilis such as deletion of competing pathways, insertion of new genes to increase substrate ranges, improve tolerance to extreme process conditions, ethanol toxicity and lignocellulosic hydrolysate inhibitors6.Recently, manipulation and improvement of Z. mobilis ZM4 was published and its annotation upgraded in 20098. Recently, complete chromosome along with four native plasmids were sequenced in ZM4. These plasmids were in the size range of 32 to 39 kb and containing about 150 predicted ORFs9. In recent decades, in silico genome-scale metabolic models (GEMs) have introduced novel avenues for systems-level redirection of metabolic fluxes, and consequently, rational strain improvement11. Z. mobilis is generally considered as a suitable platform for genetic engineering because of its GRAS , genome size, and efficient fermentation performance during a low biomass production rate2. Thus, purposeful metabolic engineering modifications can potentially be done in this organism.In 2005, the primary genome sequence of Z. mobilis ZM4. Several studies have previously reported core metabolic model12 or GEMs14 for Z. mobilis ZM4. Furthermore, a GEM for a related strain, Z. mobilis ZM1 was reported15. However, due to the existing limitations and inconsistencies of those models, we decided to reconstruct an updated, reconciled and comprehensive Z. mobilis ZM4 GEM. None of the previously-published Z. mobilis GEMs were made in formats that are ready for simulation testing. We tried to check and find the required reactions to make a working model because none of these GEMs were able to produce biomass. The process of biomass correction, which was done for both GEMs (ZmoMBEL60 and iZM363), was in fact non-negligible. The previous GEMs use different metabolite names and different metabolic subnetworks. In addition, in some cases, multiple names of metabolites and reaction abbreviations were included. Both of the previously-published GEMs were constructed in the same year. Interestingly, their metabolic coverage, biochemical evidence and GPRs are not consistent and differ in some cases. The core metabolic network was improved mainly based on literature evidence, but new enzyme-related together with new reactions and GPRs were not necessarily included.In the present study, our goal is to develop an updated GEM for Z. mobilis ZM4, it is necessary to update the GEM of this organism by retrieving the relevant data from literature and publicly available databases. We did this update with additional manual curation  involved in the previous Z. mobilis models were collected. As a key bottleneck in metabolic models reconstruction, incompatibilities in the content of metabolic models such as multiple metabolites names, reaction abbreviations and stoichiometric errors were fixed. The redundancies were then verified and components with multiple/inconsistent IDs were detected and removed. Consequently, in our model, a consistent and unique ID (compatible with the KEGG database) was assigned to each protein, reaction and metabolite. obtained and manually checked for association with enzyme and/or reactions in KEGG.The three previous 16 and BRENDA17 databases. Furthermore, the list of all genes present in the Z. mobilis ZM4 genome was obtained and manually checked for association with enzyme and/or reactions in KEGG. Next, new genes that were not present in previous GEMs were identified and added to the model in order to improve gene-protein-reaction association correctness. In some cases, for confirming the functions of genes, sequence similarity with Escherichia coli was used. Non-gene-associated reactions, including spontaneous reactions and literature-based metabolic activities were also manually curated and added to the model. All instances of mass imbalance and stoichiometric errors were then found and fixed. The reversibility types of reactions were assumed to be the same as those of the template models. In cases of discrepancies among the templates, the reversibility types of reactions were determined based on BioCyc18 and KEGG. Exchange reactions were retrieved from the relevant literature and TransportDB19.In the next step, for each enzyme, all associated genes as well as all possible reactions were determined using KEGG20. We identified such metabolites in the draft model. Then, in an iterative procedure, we tried to find a minimal set of biologically relevant reactions that were able to fix the gaps using fastGapFill21. Additionally, GapFind22 algorithm was used to find further dead-end metabolites and blocked reactions. Some reactions were proposed by these reaction-addition algorithms. We tried to identify those that are biologically reasonable by using genomic and biological evidences to avoid overfitting model. We also evaluated the accuracy of identifying filled gaps manually when possible, by finding missing reactions when compared to the GEMs in BiGG23 database. In some cases, direction of reactions in model was changed based on biological knowledge. Some spontaneous reactions that were previously confirmed experimentally in E. coli were also added to our network. Finally, as there are several orphan reactions in each GEM, we analyzed blocked reactions of the E. coli model24. So, more biological discoveries should be done to fill these gaps.No-production and/or no-consumption metabolites often represent knowledge gaps in metabolic networks10. Unless stated otherwise, in the present work, maximization of biomass production rate is considered as the objective function. All analyses were performed using COBRA Toolbox v2.025. GLPK was applied as the linear programming solver to perform FBA.In a metabolic network model, there are prevalent interdependencies among reaction fluxes due to stoichiometry, reversibility and capacity constraints of metabolism. Flux balance analysis (FBA) is used for optimizing a pre-defined objective function in the specified metabolic constraints. Fundamentals of FBA have been explained previouslyin silico simulations based on carbon source usage, the lower bound of the carbon source uptake rate was considered as −10 mmol gDCW−1 h−1. Unless stated otherwise, for all reactions, the upper bound was assumed to be +1000 mmol gDCW-1. The lower bounds of the irreversible and reversible exchange reactions were set 0 and −1000 mmol gDCW−1 h−1, respectively.For all −1 h−1 was considered for oxygen exchange reaction, respectively. ATP hydrolysis and synthesis flux values were set to −10 and 50 mmol gDCW−1 h−1 respectively, based on Motamedian et al. results15. The lower bound of amino acid exchange reactions was set to 0.1 mmol gDCW−1 h−1 when the medium was consisting of yeast extract. Non-growth-associated (NGAM) and growth-associated maintenance (GAM) were same as Widiastuti et al. results14.For simulation of the aerobic and anaerobic conditions, −1000 and 0 mmol gDCWZ. mobilis ZM4 grown on different culture media in aerobic and anaerobic conditions was done to examine whether our model correctly predicts the growth rate patterns. Moreover, a comprehensive literature search was performed to find studies of genetic engineering and gene knockout on Z. mobilis. Furthermore, gene essentiality analysis was done to check the accuracy of the model prediction. In order to further evaluate the accuracy of our model, the profile of differentially expressed genes in aerobic vs. anaerobic conditions was investigated. Then, the transcriptionally-changed enzymes were used to qualitatively validate the oxygen-related changes in the predicted fluxes26. Additionally, Biolog GN2 MicroPlate™ was used in order to profile growth phenotypes of Z. mobilis ZM4 on diverse carbon sources. According to the standard protocol, a colony of the bacterium was inoculated on 5% Sheep Blood that is supplemented to a Biolog Universal Growth medium for an overnight at 37 °C. The bacteria were then removed using a sterile swab and suspended in GN/GP Inoculating Fluid to the definite turbidity. Then, 150 µl aliquot of cell suspension was inoculated into each of the 96 carbon source-containing wells. In positive cases, i.e., where Z. mobilis can grow, by making a redox potential, a purple color will appear because of reduced tetrazolium dyes. Additionally, a previously reported list of biochemical results was used to validate the predictions27.We validated our model through four major approaches. Batch fermentation of Z. mobilis ZM4 ATCC 31821 was cultured in RM medium  at 30 °C26. For ethanol production, fermentation was carried out in either of two different media, namely, standard RM and modified RM 2SO4 1 g/L)14. For levan production, the culture medium (SRM) contained sucrose 100 g/L, Yeast Extract 10 g/L, and KH2PO4 2.0 g/L. Cells were grown for preculture preparation in 50 mL medium in a 250 mL Erlenmeyer flask. Shaking and incubation were done at 150 rpm and at 30 °C, respectively. Afterward, anaerobic condition for ethanol production was achieved by culturing in sealed bottles filled with 50 ml of culture medium. For aerobic ethanol or levan production, 20 mL medium was used in a 100 mL Erlenmeyer flask. Samples were withdrawn throughout fermentation at different intervals for further analysis. All experiments were repeated at least three times with three replicates under each of the conditions.28. Gas chromatography was applied to determine the ethanol content of each distilled medium. Levan extraction was performed by absolute ethanol and dialysis (MWCO 14000 Da). Quantitative analysis of produced levan was done by phenol-sulfuric acid method and estimated as fructose units29.During fermentation, growth was checked spectrophotometrically by measuring optical density at a wavelength of 600 nm (Rayleigh UV-1601). Glucose content of each sample was quantified by using an enzymatic kit that is based on glucose oxidase activityZ. mobilis ZM4 GEMs were unified and reconciled. Furthermore, in order to construct our comprehensive GEM, several new genes, enzymes, reactions and metabolites were added to the previous models based on database searches and literature evidence.Figure Z. mobilis ZM4 for analyzing sequence similarity by using BLAST and NCBI, (iii) previously-published Z. mobilis GEMs for gathering all reactions and genes, and other literature-based evidence that were reported earlier. Two models of Z. mobilis were genome-scale, while the other one is limited to central metabolism, in which the focus was mostly on biochemical and enzymatic evidence. Consequently, we gathered all these pieces of information for gaining a comprehensive draft model from previous researches. Furthermore, in some cases that dead-end metabolites or blocked reactions were found, E. coli24 and B. subtilis30 GEMs were used, as high quality bacterial templates, to ensure the presence of spontaneous reactions. Then we checked the presence of these reactions in Z. mobilis, as well.Different data sources were used for model compilation, including (i) BiGG, BioCyc, KEGG, BRENDA, and TransportDB databases, (ii) genomic sequence of i.e., non-gene-associated) reactions to check their reliability, followed by reaction gap filling. Altogether, 188 reactions were added from the KEGG database to refine our model. In this procedure, a considerable number of gaps were filled, and several blocked reactions were unblocked.Since the two GEMs had different identifiers for metabolites, we searched for equivalent reactions and removed redundancies. Furthermore, whenever necessary, we fixed the discrepancies in stoichiometries, reversibility type of reactions and gene-protein-reaction associations. We then analyzed the orphan 9. However most of these genes have non-metabolic functions. Their function was characterized in the field of phage structure proteins, toxin-antitoxin proteins, membrane-associated transporters and transcriptional regulators, as well as several hypothetical proteins. It should be noted that certain plasmid-related genes with metabolic functions had the same enzymatic activity as those genes which were present in our GEM. Finally, we updated our GEM and added some reactions based on these plasmid sequence revisions. The final GEM, which will be referred to as iHN446, includes 446 genes, 859 reactions, and 894 metabolites. The iHN446 model, in XLS format, which includes the complete list of reactions and metabolites, can be found in File S1. We also provide the model in SBML format (File S2).A recent report has presented comprehensive data on the sequences and annotations of plasmid-related genes in iHN446. For genetic engineering purposes, the gene-to-protein associations are of central importance. Our results suggest that the reactions included in iHN446 are reasonably reliable, as most of the reactions in the model are associated with their corresponding metabolic genes , the gene coverage of iEM439 should not be compared with the gene coverage of other GEMs.Total gene coverage of model is 22.3% in iHN446, the gene-associated reactions account for 80% of all non-transport reactions. In comparison, ZmoMBEL601, iZM363 and iEM439, 87%, 68% and 84% of the non-exchange reactions are gene-associated, respectively.In iZM363 and ZmoMBEL60, respectively. In addition, iZM363 has 63 more unique enzymes and 30 more unique genes in comparison to ZmoMBEL60.As expected, our reconciled model includes a considerable number of unique metabolites, genes and reactions . The number of unique  genes, enzymes and reactions are depicted in Fig. iHN446 is shared by the previous models, we excluded genes, enzymes or reactions from our model for which we did not find adequate evidence confirming their existence. Additionally, in certain cases, changes in the biochemical data or genomic annotations might have resulted in the inconsistencies . Consequently, simulation results are not performed in the same condition as experimental ones.VI.+, AMP and NMN are considered to be present in the model. Such a dependency to the uptake of NMN (and other metabolites) does not occur in reality.When we tried to build a biomass-producing model, ZmoMBEL60 was dependent on NMN entrance to the cell for biomass production in simulation tests. Besides, some exchange reactions for some metabolites like, NADVII.iZM363 model, phospholipid and cardiolipin were not able to be produced, and consequently, no biomass production was obtained. Therefore, we decided to allow these two biomass constituents to enter the network by adding their exchange reactions and checking the biomass production. In the culture condition proposed in Widiastuti et al. article, biomass production was simulated. However, neither in the original condition nor in this new biomass formulation, a non-zero growth rate was seen. We tried in an iterative process to find essential metabolite(s) for enabling biomass production. In this case we found that isoleucine, glycine and S-adenosyl-l-methionine supplementation is vital for biomass production.In VIII.et al. included a number of biochemical reactions with experimental evidence, mainly on NAD(P)H balance, which is necessary for stoichiometric balance. Also, a number of important changes were made in respiratory chain and catabolic genes in the central carbon metabolism, which were not incorporated in previous reconstructions. Although this model was improved mainly based on literature evidence, new enzyme-related changes, together with new reactions and GPRs were not necessarily included. Basically, this model cannot be considered as a GEM with comprehensive list of genes, reactions and metabolites. The improvements suggested by Pentjuss et al. are included in our GEM.In the core metabolic network, Pentjuss Taken together, we propose that our model is upgraded in various parts in comparison to previous models of Z. mobilis. The Biolog analysis was used to qualitatively study the utilization of several different carbon sources. Additionally, consumption of glucose, fructose and sucrose by Z. mobilis are further analyzed quantitatively. In each case, growth rate, together with ethanol and/or levan production were measured in aerobic and anaerobic conditions.In case of carbon sources, we used both qualitative and quantitative studies to determine the metabolic capabilities of Z. mobilis ZM4. Among the carbon sources available on the Biolog MicroPlate™, we observed carbon source utilization only in two cases, namely fructose and glucose. Although sucrose utilization has been reported in Z. mobilis, our Biolog phenotypic result was negative, which is consistent with the previously reported Z. mobilis growth results27. Since Z. mobilis is known to be able to utilize sucrose for growth and levan production31, we carefully studied sucrose-dependent growth. We used broth culture and plates supplied by sucrose as carbon source. In both of the solid and liquid cultures, the growth of Z. mobilis was observed.A high-throughput 96-well Biolog phenotypic test was performed to profile carbon usage of Z. mobilis GEMs, we used FBA. As it is presented in Fig. Z. mobilis. In iZM363 model, sorbitol, gluconic acid, glycerol, inosine, uridine, thymidine and phenylalanine were incorrectly predicted to be consumed by Z. mobilis. Also, iHN446 mistakenly predicts the gluconic acid utilization as the carbon source. Therefore, a comparison of modeling results with experimental data confirms the precision of our model.In order to computationally simulate the carbon source utilization capabilities and comparing the results obtained with previous et al. for model validation27. Simulations suggest that iHN446 is able to successfully predict l-asparagine, l-aspartic acid, l-glutamine and ammonia consumption as the nitrogen source. ZmoMBEL60 model was able to predict the consumption of ammonia as the nitrogen source. Although, since yeast extract (including amino acids) is not included in the in silico growth medium, simulation on different amino acids as nitrogen source was not possible. In this case, we included transport reactions of nitrogen sources to model. Simulation proposed that ZmoMBEL60 is able to predict l-asparagine and l-aspartic acid, correctly. In contrast, l-glutamine was mistakenly consumed by Z. mobilis. In addition, in iZM363 model, isoleucine and glycine should be added to the growth medium to enable biomass synthesis. Therefore, these amino acids are essential and are consumed, and hence, they can provide the required nitrogen for Z. mobilis. Consequently, it is not possible to simulate the necessity of any nitrogen sources.In case of nitrogen and sulfur sources, we used the previously-published data by Bochner l-cysteine and l-methionine utilization were correctly predicted to be used by iHN446 as the sulfur source. ZmoMBEL60 model, in contrast, could only predict the sulfate consumption. In iZM363 model, S-adenosyl-l-methionine supplementation was necessary for growth, and therefore, one could not simulate the usage of sulfur sources. Taken together, simulation results of the iHN446 model showed an improved consistency with the experimental results, compared to the two previously-published GEMs  were determined experimentally. Then, these data were compared with in silico simulation results obtained by FBA using the biomass production rate as the objective function. In RM and modified RM media, we found the model predictions to be in agreement with the experimental data in all conditions. By performing FBA in the conditions of culture media, the growth rate was determined as 0.136 h−1 and 0.5 h−1, respectively, which is comparable to the experimentally measured growth rate (0.154 ± 0.03 in RM and 0.39 ± 0.04 in modified RM). The ethanol content was 6.28 ± 0.8 g/L and 7.38 ± 0.51 in RM medium in aerobic and anaerobic conditions, respectively. Additionally, in the modified RM medium, ethanol quantities were 7.23 ± 0.36 g/L in aerobic and 8.6 ± 0.48 g/L in anaerobic fermentation. Furthermore, we ran FVA and analyzed the maximum and minimum flux rates of reactions in which ammonium is involved. As expected, in modified RM with ammonium supplementation, these reaction fluxes changed to zero or decreased significantly. It should be noticed that there are 20 reactions in the iHN446 model that produce or consume ammonium. So, our model could reveal the effect of ammonium addition. Based on FVA results under two different culture media, evaluation of fluxes of amino acid production metabolism revealed that in modified RM amino acid metabolism is considerably less active. These observations confirmed that the addition of ammonium to medium reduces the dependence of Z. mobilis to amino acids because ammonium could go through different pathways. In conclusion, the addition of ammonium and flux changes agrees with the reported experimental results32.The growth patterns of in silico and experimental growth rate were 0.136 h−1 and 0.168 ± 0.03 h−1, respectively. Measurement of levan production showed 0.6 ± 0.07 g/L of product formation under the experimental condition. Furthermore, we used FVA to assess the range of levansucrase flux. FVA results confirmed that this pathway was functional, as the maximum and the minimum fluxes of the reaction were 5 mmol gDW−1.h−1. Moreover, pathways of sucrose and fructose metabolisms were active when sucrose was added as a carbon source to the medium. Minimum and maximum flux of d-sorbitol dehydrogenase were −15.25 and 20 mmol gDW−1.h−1, respectively as fructose is needed for sorbitol production. Also, glucose-fructose oxidoreductase, which is responsible for sorbitol production, showed the minimum and the maximum fluxes of 0 and 20 mmol gDW−1.h−1, respectively. According to experimental evidence, sorbitol and levan are the products of sucrose utilization33. Therefore, our model could show that these pathways are practical in the presence of sucrose and fructose as carbon sources.In the simulation with sucrose as the carbon source, the predicted results confirmed our experimental outcomes. The calculated Z. mobilis lacks a complete pentose utilization pathway. Therefore, many efforts have been taken to introduce the necessary genes to the metabolism of this microorganism1. As shown in Table 34. We simulated the growth of Z. mobilis on minimal medium, and additionally, permitted xylose to go into the system by setting the lower bound of its exchange reaction to −10 mmol gDW−1 h−1. Investigation of the modeling predictions for growth rate in this case by running FBA showed 0.136 mmol gDW−1.h−1. The in silico ethanol production simulations were done using FBA. In the case of glucose and xylose consumption, 29.42 and 21.92 mmol gDW−1.h−1 ethanol was produced, respectively. In the mixed culture of glucose and xylose, 49.42 mmol gDW−1.h−1 ethanol production was obtained. The results showed that ethanol production would be improved in mixed culture as more carbon sources are available for the microorganism. Based on growth and ethanol production, the modeling outcome is compatible with experimental results.Glucose and xylose are the main derivatives of lignocellulosic biomass digestion. Therefore, the conversion of these carbon sources to ethanol is economically important. Z. mobilis, arabinose utilization genes were inserted into this bacterium. Similar to the case of xylose, arabinose is another five-carbon sugar that is found in lignocellulosic biomass. Arabinose isomerase, ribulose-5-phosphate-4-epimerase, ribulokinase and transaldolase were added to the model35. The lower bound was set to −10 mmol gDW−1 h−1 in arabinose exchange reaction. By performing FBA, the model predicted 0.136 mmol gDW−1 h−1 for the growth rate and 24.42 mmol gDW−1 h−1 ethanol production. Therefore, the simulations reflected the experimental observations adequately.Along with the previous attempts for expanding substrate usage ability in Z. mobilis36. By engineering this pathway, flux from ethanol production is redirected to alanine production. In order to analyze the splitting of carbon sources to these two end products, we used FBA. Our modeling observation is compatible with experimental results and showed less ethanol, 28.45 mmol gDW−1 h−1 (compared to 29.42) when alanine is produced.Alanine dehydrogenase gene has been previously employed to demonstrate successive improvements in broadening the product formation in Z. mobilis37. As presented in Table −1 h−1 and −1000 mmol gDCW−1 h−1, respectively. The β-carotene production reaction flux was predicted to be 1.016 mmol gDW-1.h-1. Therefore, our results of FBA demonstrated that the aforementioned pathway is active when β-carotene synthesizing genes were provided.β-carotene is popular mainly because of its vast applications in the production of pharmaceuticals and as a food additive. Biosynthesis of β-carotene is achieved by heterologous expression of β-carotene synthesizing genes in an appropriate host like Z. mobilis38. Here we added butanediol dehydrogenase gene to Z. mobilis metabolic network to model the engineered strain. In order to study the modeling predictions in this condition, we kept the glucose and/or xylose uptake rate at −10 mmol gDW−1 h−1 and allowed butanediol dehydrogenase activity by adding the constraint −1000 mmol gDCW−1 h−1 and 1000 mmol gDCW−1 h−1 to see how it would affect the butanediol production. By using FBA, in the case of glucose or xylose consumption, 14.71 and 10.96 mmol gDCW−1 h−1 was produced, respectively. As butanediol production was 24.71 mmol gDCW−1 h−1, the model could reflect the experimental observations of butanediol production in the mixed culture, successfully,Microbial production of 2,3-butanediol, which is often produced petrochemically, has gained scientists’ attention in recent years. Production of this important chemical may be achieved by introducing related genes to gfo gene. In a previous study, a gfo knockout in Z. mobilis was constructed and the metabolic features of this bacterium were studied33. Here, we report the consequences of the experimental and in silico simulation of these evaluates. In experimental results, the inactivation of gfo had different results on the growth of Z. mobilis at high or low concentration of glucose, fructose or sucrose. However, as shown in the simulation results, the model could not predict the experimental observations correctly. These observations proposed that these effects may be related to regulation mechanisms in Z. mobilis. So, more research is needed in differences in transcription profile in these conditions.The glucose-fructose oxidoreductase is encoded by pdc and ldh are genes that participate in lactate and ethanol production. By omitting these two genes, fluxes are redirected to succinic acid production and resulted in improved concentrations of succinic acid39. By running FVA, when ethanol and lactate were produced, minimum and maximum fluxes of succinic acid production were 0 and 0.33 mmol gDCW−1 h−1, respectively. Here we knocked out the above two genes in silico, so maximum succinic acid production changed to 5.33 mmol gDCW−1 h−1. Consequently, more succinic acid production in knock-out condition was confirmed.Succinic acid can be used in numerous applications like surfactants, detergents and solvents or food and pharmaceutical products. Z. mobilis ZM4 survival. In this case, a gene cluster of cellulose synthase was found to be vital for microorganism40. It should be noticed that, cells within flocs are linked together by a cellulosic extracellular matrix. So, we added a new cellulose-containing biomass reaction to be used for modeling in minimal medium. By simulating this minimal medium in aerobic condition, our model could reflect the essentiality of cellulose synthase, successfully.A recently-published article reports that, floc formation in a defined minimal medium under the aerobic condition is essential for Z. mobilis ZM4, the maximum specific growth rates are comparable under aerobic and anaerobic conditions, while genes related to ethanol (and other byproducts) formation may be differentially regulated, which results in differences in the transcriptomic profiles26. For validating gene activity of different pathways under aerobic vs. anaerobic conditions, in the first step, the “reporter metabolite algorithm” was used41. In this method, by integrating transcriptome data with a GEM, key metabolites that are linked to the most significant transcriptional changes are identified. Glucose 6-phosphate, glycerate, glucose, 2-Phosphoglycerate, l-Phenylalanine, l-Threonine and lactate were found different in the metabolomic profile of Z. mobilis during aerobic and anaerobic fermentation based on reporter metabolite algorithm results  reaction fluxes were compared with the list of up- (or down-) regulated genes based on the transcriptomic data. Comparison of transcriptomes with flux data showed, while some of these changes were consistent with previous transcriptome studies, most of them are not metabolic genes. These observations are the evidence of the role of transcriptionally controlled mechanisms and showed new insights into the physiology of anaerobically growing Z. mobilis ZM4.We compared flux changes between aerobic and anaerobic conditions by using a random sampling of the metabolic flux spaceOur results demonstrated that pathways most significantly impacted by aeration shift were glycolysis, amino acid, purine and pyrimidine metabolism. Between reactions pyruvate decarboxylase and alcohol dehydrogenase had significantly lower flux in aerobic condition. This finding is in accordance with experimental evidence. We also evaluate our results with up or down-regulated genes that were obtained from transcriptomic data. Additionally, we assessed the accordance between results achieved from reporter metabolite algorithm with sampling results. In the case of up-regulated genes, 36% of the sampling positive results were confirmed by the transcriptomic data, while the down-regulated genes were 73% consistent with sampling results. While our results showed an acceptable level of correctness , but there is still room for improvement. (Table Z. mobilis ZM4, iHN446, is presented. The model is more comprehensive in scope than previous models and updated based on the latest gene annotations, databases and literature. The model validation was achieved by comparing model predictions to experimentally gained results or from the literature. iHN446 showed desirable simulated performance when predicting specific growth rate on the carbon sources and the effects of gene deletion or gene insertion on cell growth and/or product formation. We also investigated the predictive power of the model based on transcription data in aerobic and anaerobic conditions by comparing changes in the flux of each reaction vs. transcription data. This advanced and comprehensive genome-scale metabolic model, make a potential platform for a better understanding of Z. mobilis metabolic features at a systems-level and is a valuable tool to develop engineered Z. mobilis strains for several biotechnological applications. Finally, this model can be a framework for the integration of transcriptomic data to provide more accurate metabolic engineering predictions.In this work, an upgraded high-quality metabolic model of Supplementary Dataset 1.Supplementary Dataset 2.Supplementary Table S3.Supplementary Table S4.


Saccharomyces cerevisiae growing in the presence of two carbon sources. Even though diauxic shifts emerge naturally from the model when two resources are qualitatively very different, the model predicts that the existence of such shifts is not a prerequisite for species coexistence in competitive communities.Microbes are capable of physiologically adapting to diverse environmental conditions by differentially varying the rates at which they uptake different nutrients. In particular, microbes can switch hierarchically between different energy sources, consuming first those that ensure the highest growth rate. Experimentally, this can result in biphasic growth curves called “diauxic shifts” that typically arise when microbes are grown in media containing several nutrients. Despite these observations are well known in microbiology and molecular biology, the mathematical models generally used to describe the population dynamics of microbial communities do not account for dynamic metabolic adaptation, thus implicitly assuming that microbes cannot switch dynamically from one resource to another. Here, we introduce dynamic metabolic adaptation in the framework of consumer-resource models, which are commonly used to describe competitive microbial communities, allowing each species to temporally change its preferred energy source to maximize its own relative fitness. We show that dynamic metabolic adaptation enables the community to self-organize, allowing several species to coexist even in the presence of few resources, and to respond optimally to a time-dependent environment, thus showing that dynamic metabolic adaptation could be an important mechanism for maintaining high levels of diversity even in environments with few energy sources. We show that introducing dynamic metabolic strategies in consumer-resource models is necessary for reproducing experimental growth curves of the baker’s yeast  Saccharomyces cerevisiae on two different resources and we show that dynamic metabolic strategies allow the community to self-organize and to ensure the coexistence of multiple species competing for few resources.Since a celebrated experiment by Monod in 1949, we know that microbes can adapt their metabolic strategies in response to their environment, thus uptaking different nutrients at different rates depending on their relative quality and availability. It is currently unclear whether this physiological plasticity of microbes contributes to maintaining the high degree of diversity found in natural microbial communities, even in the presence of very few resources. By introducing dynamic and optimal metabolic strategies in consumer-resource models, we show that we can correctly reproduce the experimental growth of  Biodiversity is one of the most fascinating aspects of nature: from the microscopic to the continental scale, complex communities composed of tens to thousands of species compete for resources and yet coexist. In particular, the survival of a species depends on the availability of resources in the environment, which is not static and can be altered by the presence of other species in the community. Furthermore, biodiversity is crucial for the functioning and maintenance of whole ecosystems, directly impacting their productivity, stability and many other properties . It is tEscherichia coli and Bacillus subtilis grown in a culture medium containing two different sugars exhibit a biphasic growth curve, which he called “diauxie”. Instead of metabolizing these two nutrients simultaneously, bacteria consumed them sequentially using the most favorable one first  and once it had been depleted, following a lag phase, they resumed growth using the other sugar. Since then, diauxic growth has been the subject of thorough empirical study   m speciem species and p resources, if the total maximum resource uptake rates δσ , does not depend on the species’ identities. In mathematical terms, if m > p species can coexist. This requirement is compatible with the experimental observations that led to the formulation of the metabolic theory of ecology [δσ) depend only on the characteristic mass of a species. It is indeed possible to show analytically that our model can strictly violate the CEP if the CTR σ = dδσ, where d > 0 regulates the speed of adaptation.Considering now our model in the general case of cies see . We focu ecology  , if δσ = δ∀σ  and if si, belongs to the convex hull of the metabolic strategies Eσ) will lead to the extinction of at least m − p species, i.e. the system will obey the CEP; in this sense the system allows coexistence only when fine-tuned, a situation that is unlikely to be true for all natural communities. However, if we now use  is possible only if j, then there will be extinctions and in the end the CEP will hold  -dimensional simplex where our system can be represented. In other words, we find that if the degradation rate μj of resource j becomes too large, then again all the j-th components of the metabolic strategies will become null . On the other hand, if we introduce the resource degradation rates in MacArthur’s consumer-resource model with fixed metabolic strategies, extinctions will occur and the CEP will hold  see , and if i-th components of the metabolic strategies vanish . Increasing the value of d leads to metabolic strategies that adapt more rapidly, and as a consequence species’ growth rates will be optimized for longer periods of time. Thus, in a community in which the CTR is the same for all species, stationary population densities will be higher for larger values of d. When d tends to zero, instead, we recover the case of fixed metabolic strategies and thus the CEP will determine the fate of the community. As shown in d. On the other hand, if the system is subject to variable environmental conditions like the ones discussed previously  becomes less and less variable across different species. We suggest that this requirement is compatible with the experimental measurements of the scaling of vital and metabolic rates with body size that led to the metabolic theory of ecology , accordiy traits –57  + 2% (w/v) glucose. 1 mL of the overnight culture was spun down and resuspended in CSM + 0.5% (w/v) galactose to a concentration of 1.6 ⋅ 105 cell/mL. Eight wells of a 96-well plate were inoculated with 150 μL of the resuspended culture and incubated with constant shaking at 30°C in a plate reader. The 96-well plate was sealed with a sealing membrane that allowed gas exchange. The temperature on the top of the 96-well plate was kept at 31°C to avoid condensation on the membrane. Optical density (OD) measurements were taken every 10 min, for a total duration of about 70 h. To build the calibration curve used to convert OD to cell density, 1.4 mL of the same overnight culture were spun down and resuspended in 1 mL of CSM + 0.5% (w/v) galactose. The density of this suspension was measured using a Coulter counter and serial dilutions of this suspension were inoculated in a 96-well plate covered with the same sealing membrane used for the growth curve measurement. The OD of the wells containing the serial dilution of the suspension was measured after equilibration to 30°C using the same plate reader used to measure the growth curves, and these measurements were used to build the calibration curve converting OD to cell density.The HK459 of  and is iS. cerevisiae is grown on galactose as the primary carbon source, the sugar is partially respired and partially fermented. Yeast cells excrete ethanol as a byproduct of fermentation, which can then be used as a carbon resource. For this reason, in order to describe such system we use our adaptive consumer-resource model with m = 1, p = 2 and we slightly modify the equations for the temporal dynamics of ethanol concentration to take into account the fact that this resource is not initially present in the system but is produced by the yeast. In particular, the equations we used in order to describe the system are  is the relative likelihood of the two models, and as such measures the probability that the model with fixed metabolic strategies minimizes the information loss (i.e. it is a better fit to the data than the one with adaptive metabolic strategies). In our case, by comparing the fits shown in AIC = −938, so the probability that the results could be better explained using the model with fixed metabolic strategies is infinitesimal, even if the curve can nevertheless reproduce a diauxic behavior. The situation is of course even more extreme if we compare the fits shown in AIC = −1327. We show in Figure C in Notice that since the model has several parameters  there can be several different choices that lead to apparently equivalent fits. We therefore used a Markov Chain Monte Carlo algorithm  to fit t to show  that expS1 Text(PDF)Click here for additional data file.S1 Data(XLS)Click here for additional data file.


The role of our gut microbiota in health and disease is largely attributed to the collective metabolic activities of the inhabitant microbes. A system-level framework of the microbial community structure, mediated through metabolite transport, would provide important insights into the complex microbe-microbe and host-microbe chemical interactions. This framework, if adaptable to both mouse and human systems, would be useful for mechanistic interpretations of the vast amounts of experimental data from gut microbiomes in murine animal models, whether humanized or not. Here, we constructed a literature-curated, interspecies network of the mammalian gut microbiota for mouse and human hosts, called NJC19. This network is an extensive data resource, encompassing 838 microbial species  and 6 host cell types, interacting through 8,224 small-molecule transport and macromolecule degradation events. Moreover, we compiled 912 negative associations between organisms and metabolic compounds that are not transportable or degradable by those organisms. Our network may facilitate experimental and computational endeavors for the mechanistic investigations of host-associated microbial communities. Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12336164 Recent advances in metagenomics have revealed that alterations in the human gut microbiota are implicated in a number of disorders, such as obesity, inflammatory bowel disease, colorectal cancer, and diabetes7. At the center of the gut microbiota functions are the various interactions between microbes and their interplay with the host environment8. Microbes degrade diet-derived and host-derived chemical substances, and release the degradation products to other members of the community. The microbial transport of nutrients and metabolic byproducts gives rise to competition for resources and cooperative relationships via metabolic cross-feeding8. The metabolites secreted by the microbes are absorbed by host tissues, and translate into beneficial or detrimental mediators of host physiology9. As a result, such microbe-microbe and microbe-host interactions form a complex ecological network in the gut environment10.The mammalian intestinal tract is colonized by various microorganisms, called the gut microbiota or microbiome12. This method, by its nature, does not delineate biochemical reactions to the species from which they originate, making it difficult to elucidate interspecies interactions. On the other hand, there exist previous works on the modeling of diverse interspecies interactions explicitly mediated by metabolites that are transported (imported or exported) by individual microbial species14. Yet, these works are based on error-prone, automated identification protocols for transportable metabolites, which are possibly inaccurate to some degrees. There are ongoing computational efforts towards biologically realistic microbial interactions, by using manually curated, constraint-based metabolic models or relatively simple kinetic models16. Nevertheless, most of these models are far from the scale of diversity seen in the gut community, which typically comprises hundreds of different microbial species. Notably, this scale of microbial diversity has been recently captured by constraint-based metabolic models with semi-automatic model reconstructions17, but they still exhibit limited biological accuracies20.In the microbiome research, one common practice for reconstructing metabolite-mediated microbial networks is to combine the entire biochemical reactions inferred from annotated metagenomes10. This network is primarily based on biological knowledge and experimental evidence documented in the literature. The network NJS16 encompasses >4,000 small-molecule transport and macromolecule degradation events of >500 bacterial and archaeal species and 3 human cell types. Although NJS16 is useful to explore the microbial community inside the human gut, mechanistic studies in the microbiome research field have been mainly conducted on animal models, rather than on human subjects, due to the technical and regulatory limitations on human experimentation22. Regarding animal models, physiological, anatomical, and genetic similarities between humans and mice, as well as massively accumulated knowledge of mouse genetics, have facilitated the use of murine models, to elucidate causality and mechanisms of host-microbiota interactions23. In this regard, a phylogenetic extension of NJS16 to murine gut microbes would be useful for the system-level mechanistic exploration of gut microbiota functions using murine animal models.Recently, we have constructed an extensive, literature-curated interspecies metabolic interaction network of the human gut microbiota, NJS16, which represents another system-level framework for gut microbiota analysis10. Specifically, NJC19 greatly expands the diversity of microbial species and host cells to those relevant to the mouse gut environments, and even covers a certain range of eukaryotic microbes that were completely missing in the predecessor NJS16. Therefore, NJC19 serves as a global network template, adaptable to the gut microbiota of either a mouse, human, or humanized mouse. Moreover, not only does NJC19 incorporate metabolite transport and macromolecule degradation events of the microbiota, but it also provides literature-annotated, negative information of which metabolic compounds are not able to be transported or degraded by the organisms. Such negative information would be useful to curate computational microbial models, such as constraint-based metabolic models, which can include false-positive transport reactions from automatic genome annotations.Here, we present a literature-curated, interspecies metabolic interaction network of the microbiota associated with the mouse and human gut, NJC19. To our knowledge, NJC19 represents the largest ever, literature-based network data resource for the mammalian gut microbiota, as a compilation of information from 769 research and review articles and textbooks  was given to species absent in our previous network, NJS1610. In the case of the metagenome sequence data, the number of the detected species was rather excessive for our further processing; therefore, among those species, we only considered the species inhabiting ≥90% of the metagenome samples (with the relative abundance ≥0.001%) in each study. We found that the genera of these selected species account for the vast majority [89.6 ± 4.3% (avg. ± s.d.)] of the total microbial abundances in the metagenome samples. In addition, we manually considered some relevant species, such as Citrobacter rodentium32  and the degradation of macromolecules , reported in literature. For the small-molecule metabolites, we mostly considered primary metabolites, i.e., nutrients and metabolic byproducts associated with microbial growth or reproduction. In addition, literature sources that report the mRNA or protein expression for metabolite-specific enzymes or transporters were considered. When encountering the information of which chemical compounds are not able to be transported or degraded by a given organism, we recorded this negative information as well, as part of our collected data. Despite technically not being a part of the gut microbiota, some host cells directly affect or are affected by microbial metabolism, and thus were considered to be a functional extension of the microbial community. In a similar fashion to our previous network NJS16 for the human case, the specific mouse tissue cells that we considered were the intestinal absorptive cell, the mucin-secreting goblet cell, and the bile acid-secreting hepatocyte. Although the hepatocyte is not part of intestinal tissue, its secreted bile acids are utilized by microbes in the gut. In the case of the mouse intestinal absorptive cell, the information from a manually-curated, genome-scale metabolic model (iSS1393) was adopted34, and removed the incorrectly-placed components and added new links found from literature, according to more specific and accurate information. The revised NJS16 was finally connected to the above mouse gut microbiota interaction network through the common chemical compounds shared by the both networks, to form the mouse and human gut microbiota interaction network, NJC19  file of NJC19 is provided for interactive network visualization35.In parallel, we carefully re-examined the existing components of NJS16Our network NJC19 offers the reference map of the mammalian gut microbiota and chemical compound relationships (from 769 literature sources), which can be adapted for each context of mouse, human, and humanized mouse microbiomes. In NJC19, one set of nodes corresponds to organisms , while the other set corresponds to chemical compounds . An organism and a chemical compound are connected if the organism imports, exports, or degrades the chemical compound. NJC19 comprises 838 microbial species  in the mouse and human gut, 6 mouse and human cell types metabolically interacting with those microbes, and 283 chemical compounds —all interconnected by 8,224 small-molecule transport or macromolecule degradation events. In addition, NJC19 provides information on small molecules and macromolecules that are reportedly not transportable or degradable by certain organisms—described through 912 negative metabolic associations. These negative associations can be particularly useful for the curation of automatically-generated metabolic models, which may include false-positive transport reactions derived from inaccurate genome annotations.k metabolites follows an exponential distribution P(k) ∝ erk−  by k species follows a power-law distribution P(k) ∝ kγ− 39. Therefore, for these two studies, microbial producers of a given metabolite are defined as the microbial genera, with each having the species whose majority (>50%) can produce that metabolite in NJC19. We also defined microbial consumers in a similar way, although they were excluded from Fig. To further assess the validity of our network, we examined the correlations between microbe-metabolite links in the network and measured metabolite levels in the mouse gut and portal vein plasma. Specifically, we examined whether the abundance increase/decrease of microbes associated with a particular metabolite in our network is consistent with the shift of the metabolite level across different experimental conditions. Regarding this analysis, three published mouse studies were found to provide the information of both microbial and metabolite levels in their collected samples: one study is for antibiotics (cefoperazone) treatment and recovery37, and thus the metabolite concentration changes here are not likely to be a mere consequence of the microbial load changes. To test the statistical significance of these correlations, we introduce quantities fij, gij, and fij’ for each pair of mouse groups i and j: fij (gij) denotes a fold change from group i to group j in the measured producer (metabolite) abundance averaged over the replicates in the group i or j. fij’ denotes a group-i-to-j fold change in the average abundance of randomly-assigned producers, while the number of those randomly-assigned producers from all the data sources in Fig. P value as the probability of satisfying fij’ ≥ fij (fij’ ≤ fij) for all -pairs that have fij and gij with both ≥ 1 (≤1). Accordingly, our P value calculation reveals that the producers in NJC19 and the detected metabolic compounds are significantly correlated in Fig. P = 0.02 for Fig. P < 10–4 for Fig. P = 0.06 for Fig. Figure Supplementary Table