Clostridium acetobutylicum and Wolinella succinogenes that interact via interspecies hydrogen transfer and applied different environmental conditions for which we expected the metabolic-exchange rates to change. We used stoichiometric models of the metabolism of the two microorganisms that represents our current physiological understanding and found that this understanding - the model - is sufficient to infer the identity and magnitude of the metabolic-exchange fluxes and it suggested unexpected interactions. Where the model could not fit all experimental data, it indicates specific requirement for further physiological studies. We show that the nitrogen source influences the rate of interspecies hydrogen transfer in the co-culture. Additionally, the model can predict the intracellular fluxes and optimal metabolic exchange rates, which can point to engineering strategies. This study therefore offers a realistic illustration of the strengths and weaknesses of model-based integration of heterogenous data that makes inference of metabolic-exchange fluxes possible from community-level experimental data.An important challenge in microbial ecology is to infer metabolic-exchange fluxes between growing microbial species from community-level data, concerning species abundances and metabolite concentrations. Here we apply a model-based approach to integrate such experimental data and thereby infer metabolic-exchange fluxes. We designed a synthetic anaerobic co-culture of  Microbial communities carry out important processes for the planet’s ecosystem, animal health and industrial purposes. Engineering such communities is not straightforward; they are generally composed of many interacting microorganisms, and they are highly dynamic. Developing methods that relate the systemic properties of communities to the underlying metabolic processes and interactions of the community members is a major challenge in microbial ecology. Those interactions drive community behaviour, including its (in-)stability upon environmental perturbations . MetabolCurrent approaches focus mostly on correlation and co-occurrence of species members for inference of community-interaction partners , 6. ThesUnfortunately, quantifying metabolic exchange fluxes—metabolic interactions—directly from experimental data is rarely possible. Generally, only net fluxes are inferred from dynamic metabolite levels measured at the community level that result from contributions of many species. To determine the individual contributions of those species, we suggested that their metabolic capacities, expressed in terms of quantitative models, should be integrated with experimental data . Here weet al. on a methanogenic co-culture  the substrate concentration and Km the substrate affinity. At each time step in dFBA the biomass production rate for each organism is independently optimised based on the specific substrate uptake rate. Implementing such dynamic uptake reaction enables the model to respond to changes in external concentrations.We used stoichiometric metabolic models of s (dFBA) , which eC. acetobutylicum and for W. succinogenes a coarse-grained model, which lumps metabolic segments into single reactions. We used a coarse-grained model as we expected that we could model our results without the need for full genome-scale reconstruction. We did do the reconstruction to verify that this was indeed the case . Having a coarse-grained model facilitates computation and interpretation, especially if these methods are applied to larger and more complex microbial ecosystems.For the simulations we use a genome-scale metabolic model for the metabolism of C. acetobutylicum has the advantage that it captures changes in its metabolism, as we observed that final acetate and butyrate concentrations, produced by C. acetobutylicum changes when the growth medium was varied. Another advantage is that it describes the metabolic flexibility associated with H2 production by C. acetobutylicum and leads to oxidization of ferredoxin. Ferredoxin is a conserved moiety whose total concentration does not change (only its redox state), and when it is oxidized by another reaction, it will result in decreased H2 production. Since C. acetobutylicum contains several reactions that can oxidize or reduce ferredoxin, the H2 production flux is flexible, influencing the community interactions that we aim to quantify. For instance, NO2- uptake decreases the amount of H2 production, because both reactions require ferredoxin, which results in potential interesting dynamics between the two species, because W. succinogenes provides NO2-, but requires H2 which is produced by C. acetobutylicum. Several genome-scale metabolic models of C. acetobutylicum are available [Conversely, the detailed model of vailable , 33. We vailable , becauseW. succinogenes, containing eight reactions  (https://sourceforge.net/projects/cbmpy/files/publications/data/2017_Hanemaaijer/). Those 41 reactions are involved in either malate cycling, proline and nucleotide biosynthesis, and carry very small fluxes relative to the fluxes in C. acetobutylicum’s core-metabolism. Two reactions that did carry a high flux are involved in ferredoxin cycling, but had no impact on the H2 production flux. There was also no variability identified in other uptake or production fluxes. These results suggest that in our system, the calculated flux distribution within C. acetobutylicum can be robustly predicted from community-level experimental data alone.We therefore performed a Flux Variability Analysis (FVA) , to checBesides providing a method for inferring experimental fluxes from experimental data, the metabolic model can also be used to predict the optimal behaviour of the community when not constrained with experimental data. Optimisation informs us about optimal metabolic coupling between the microorganisms when growth of both species are optimised.4+ + NO3-’ and ‘NO3-’ condition when we released the constraints on the butyrate/acetate production ratio and the NH4+/NO2- consumption ratio of C. acetobutylicum , growth rate and biomass abundances corresponding to the optimal state in the ‘NHutylicum .C. acetobutylicum would have a changed metabolism relative to the experimental data. For instance, acetate rather than butyrate should be produced when C. acetobutylicum grows optimally. This behaviour was seen both in the ‘NH4+ + NO3-’ and the ‘NO3-’ condition. The explanation is that more ATP can be generated from acetate production than from butyrate production. An optimal C. acetobutylicum would not consume NO2- when growing at the ‘NH4+ + NO3-’ condition, because NO2- reduction by nitrite reductase decreases growth of C. acetobutylicum according to the metabolic model. As explained, the amount of H2 produced is inversely related with the amount of NO2- taken up. As a consequence, 159% more H2 was produced in the optimal scenario relative to the experiments in the ‘NH4+ + NO3-’ condition. A smaller increase in H2 production (+23%) was shown in the ‘NO3-’ condition, suggesting that the interactions between the two species were not optimal in the experiments at those two conditions. However, the experimental growth-rate of C. acetobutylicum was close to its maximal, computed value. For the ‘NH4+ + NO3-’ the experimental growth rate was 87% of the optimal value, whereas for the ‘NO3-’ this was 94%. These results suggest that the experimental behaviour of the community with imposed bidirectional interactions is closer to the optimal metabolic behaviour than a community with unidirectional interactions.The model predicted that an optimal In this study we showed that the combination of a stoichiometric metabolic model with measured community-level concentrations and biomass abundances allows for the inference of metabolic interactions in a microbial community. This approach is complementary with current approaches that are based on correlation or co-occurrence networks, which predicts the community-interaction structure for large complex ecosystems , 41, 42.Stoichiometric models of metabolism make inference of metabolic exchange fluxes possible because they relate the uptake and production of environmental metabolites, to intracellular metabolic fluxes, organism abundance and growth rate. Such models are therefore integrating heterogeneous data types from which we make inferences about community behaviour. In addition to experimental data of species abundances and metabolite concentrations, molecular data can be used in the construction of stoichiometric metabolic models –45. It i2 exchange in our co-culture. Recently, Embree et al. [Metabolic models have been used to predict the behaviour of industrially relevant co-cultures and the type of interactions required to predict this behaviour , 14. We e et al.  also infW. succinogenes sufficed for the inference of metabolic interactions, as we recently suggested [We have shown that our approach works for a co-culture and a next step would be the inference of metabolic interactions in a complex ecosystem. A complex community will have more interactions between the members of the community. In addition to beneficial relationships, competition can also play an important role in microbial communities . The advuggested . More deEven though metabolic interactions can be inferred from experimental data, not all data and systems are equally amenable to a modeling approach. Firstly, a community with growing micro-organisms is advantageous. Growing cells have an active metabolism and increase their biomass in time, while interacting with other species. While non-growing species may also have an active metabolism, they do not increase in biomass, causing them to have lower fluxes, which makes it difficult to assign flux-values to such organisms. Secondly, controlled environments allow for more targeted perturbations of the community. Manipulation of the environment results in different dominating processes in the ecosystem . In addiTo conclude, in our eyes metabolic models are an indispensable tool for the inference of the metabolic interactions, as illustrated with our co-culture study. We have shown that the nitrogen-source quantitatively influences the interspecies hydrogen transfer, which was not feasible with experimental data alone. Moreover, the metabolic models can be used for more purposes than inference of metabolic interactions. They can also be used to infer the intracellular fluxes of species, which informs us about the metabolic adaptations of microorganisms, during different conditions. The models can also be used to investigate whether species in a community grow optimally or whether they are limited by external factors. This is particularly interesting for communities used for industrial processes, such as the anaerobic digestion process, where high biogas yields are desired. Finally, metabolic models could indicate strategies to steer the community in a desired direction, guiding community engineering approaches. Thus metabolic models have great potential –are simply required– to study complex, microbial ecosystems.S1 Material and Methods(PDF)Click here for additional data file.S1 Table+ and H2O, are element-balanced in the metabolic model.All metabolites, except for H(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S1 FigThe metabolite profile plots contain error bars, but the other subplots not. The biomass ratios are based on the gene-copy data and the carbon balance consisted of the measured metabolites that contained carbon. The remaining missing carbon is assumed to be incorporated into biomass.(EPS)Click here for additional data file.S2 Figμ (h-1). All lines are lineair what suggests that the specific uptake and production rates of both species are scaled with the growth. The N2 condition is not plotted, because the simulated growth rate was constant over the whole simulation.The extracted specific uptake and production rates (mmol/(gDW⋅h)) are plotted against the simulated growth rate (EPS)Click here for additional data file.S3 FigA the calculated flux-values for C. acetobutylicum are normalized for the glucose uptake. B shows the calculated flux-values for W. succinogenes and are normalized for the H2 uptake. Note that H2 production by C. acetobutylicum is not equal to the specific H2 uptake rate of W. succinogenes as the biomass abundances should be taken into account, which were not equal during the experiment.In (EPS)Click here for additional data file.S1 FileThe reactions and the corresponding reversibility and bounds are listed in ‘reactions’. The metabolites-id and the corresponding names are listed in ‘metabolites’. All reactions and their corresponding substrates and products are listed in ‘network_react’, where in ‘network_metab’ all reactions per metabolite is listed. All metabolites and their corresponding MIRIAM information is listed in ‘miriam.’(XLS)Click here for additional data file.


One commonly accepted theory is that a species with a high substrate affinity and low growth rate (k-strategist) will win the competition against a second species with a lower substrate affinity and higher growth rate (r-strategist) if both species are subjected to low substrate concentrations. In this study two nitrite oxidizing bacteria (NOB), Nitrospira defluvii (k-strategist) and Nitrobacter vulgaris (r-strategist), were cultivated in a continuous reactor systems. The minimal hydraulic retention time (HRT) required for maintaining the slower growing Nitrospira was first determined. A reactor containing Nitrobacter was set to the same HRT and Nitrospira was injected to evaluate the effect of the dilution rate on the competition between both species. By following the microbial population dynamics with qPCR analysis, it was shown that not only the substrate affinity drives the competition between k- and r-strategists but also the dilution rate. Experimental data and numerical simulations both revealed that the washout of Nitrobacter was significantly delayed at dilution rates close to the μmax of Nitrospira. The competition could be even reverted towards Nitrobacter (r-strategist) despite of low nitrite concentrations and dilution rates lower than the μmax of Nitrospira.The conditions present in both  Nitrospira (k-strategist) and Nitrobacter (r-strategist). Nitrospira possess a low maximum specific growth rate, but is well-adapted to low nitrite concentrations, whereas Nitrobacter is known to be a relatively fast-growing NOB with low affinities to nitrite [Nitrospira spp. and Nitrobacter spp. both use nitrite as substrate for growth, only the most adapted species will win the competition for nitrite and will hence outcompete the other one [Nitrobacter spp., since it was found in wwtp all over the world [Nitrobacter spp. were present in wwtps, while the dominant bacterium was Nitrospira spp. [Nitrospira and not Nitrobacter is the major NOB. However, in natural environments such as in soils or biofilms k and r strategists are found both at the same time despite of low bulk substrate concentrations [Nitrobacter and Nitrospira, the washout of Nitrobacter was delayed [a priori that the winner would always be the species with the highest affinity for the limiting substrate. Nevertheless, in 1980, Hansen and Hubell established a theory, in which the competition of two species was described at a known limiting substrate concentration and at a certain dilution rate. Their study suggested that the dilution rate can revert the competition towards the r-strategist even at low substrate concentrations [In microbiology the competition of two species for one substrate is driven by their substrate affinity and their doubling time , 2. An e nitrite , 4. Sincther one , 6. NOB ther one  and playhe world . Nonetheira spp. , 9, 10. trations . Their otrations . While ttrations , 14. A s delayed . In theitrations .Nitrobacter and Nitrospira was evaluated at low substrate concentrations by means of dedicated experiments and numerical simulations. The aim was to show that not only the substrate concentration and hence the nitrite affinity constant will drive the competition between two species, but also the dilution rate, leading to a delayed washout of Nitrobacter and in some cases even to the unexpected dominance of Nitrobacter at low nitrite concentrations.In this study, the effect of the dilution rate (retention time) on the competition of Nitrobacter vulgaris AB1 and Nitrospira defluvii A17 and the strains were provided from Hamburg University, Germany. Both bacteria were cultivated in batch flasks in a mineral salt freshwater medium for oligotrophic growth [2-N l-1 day-1, the pH was kept at pH 7, the temperature was controlled at 28°C a temperature controlled water jacket and DO at 7 mgO2 l-1. Baffles were used to assure good liquid gas transfer. Contamination was tested on a regular basis using Tryptone Soy agar CM0131 (Thermo Scientific). Nitrate and nitrite were measured with a spectrophotometer.The strains used in this experiment were Nitrospira defluvii in the system was evaluated in the highly enriched Nitrospira defluvii reactor. The dilution rate was increased in a step-wise manner from 0.55 to 0.7 day-1 and the nitrite concentration was determined on a daily basis to follow nitrite accumulation over time. An increase of nitrite concentrations to values equal to the influent nitrite concentrations was evaluated as washout; the corresponding dilution rate was identified as the growth rate of Nitrospira defluvii.In order to determine the effect of the washout rate on interspecies competition, the minimal retention time needed to keep Nitrobacter reactor was set to 0.58 days-1, corresponding with a value just below the minimal HRT needed to avoid the washout of Nitrospira as determined in a separate test  and a light microscope (BX41—Olympus).The dilution rate of the ate test . Both re-1) and were kept in a warm water bath at 95°C for 15 minutes, followed by a cooling step on crushed ice for 10 minutes. The samples were centrifuged for 30 seconds at 13000 rpm and the pellet re-suspended in 180 μL deionized water.Samples were obtained from the effluent (600ml) and spun down at 4000 rpm for 20 minutes (Thermo Scientific Megafuge), the supernatant was discarded and the gained cell pellet was stored at -80°C until further usage. DNA extraction was based on the method of Niemann . The sam® Green Supermix . A “StepOne—Real Time PCR System” by Applied Biosystems was used to run the qPCR assays. All primers were optimized with a gradient qPCR. The resulting conditions, primer concentrations are listed in ref)T before the inoculation of the second culture (Ctarget)T in order to have a zero value and to check if the reactors were contaminated. In order to compare the ΔCT values between samples the following equation ΔCT = CT(ref) − CT(target) was used [Nitrobacter culture) and 0 (no Nitrobacter present).In addition to the alkaline lysis, a phenol extraction and ethanol precipitation was completed to remove possible contaminants from the extracted DNA . A phenowas used . For theSi) on the limiting substrate (S), at the given intrinsic rate of increase (ri), as quantified by the smallest value for J, k will win the competition against the other species. The intrinsic rate of increase (ri) of strain i is defined as the difference between the growth rate (μi) of the strain and the given dilution rate (D) [ri = (μi − D) with Nitrobacter and Nitrospira. The critical parameter J corresponding with the experimental conditions, was calculated and a sensitivity analysis was conducted to assess the effect of the dilution rate. For the nitrite affinity constant values of 1.5 and 0.21 mgNO2-N l-1 as well as maximum specific growth rates of 1.6 and 1 day-1 were used for Nitrobacter, whereas the values for Nitrospira were kept constant (0.12 mgNO2-N l-1 and 0.67 day-1), respectively  : ri = (μ2KO2+SO2 . Dilutioectively .Nitrobacter and Nitrospira was implemented in the Aquasim software [Nitrospira (Nsp) is represented as k-strategist, with a low growth rate but a high affinity for nitrite, whereas Nitrobacter (Nb) is represented as r-strategist, with a high growth rate and low affinity for nitrite. The affinity constants and growth rates were set according to reported literature values  and an r-strategist (Nitrobacter). Since the knowledge of maximum growth rate of Nitrospira defluvii was highly important for this experiment, its value was estimated by determining the minimal hydraulic retention time before washout occurred, by gradually increasing the flow rate  than that of Nitrospira (2*109 cells mL-1) due to a higher yield coefficient of the latter one [Nitrobacter was inoculated with Nitrospira based on cell concentration with a ratio 0.75:0.25 and this ratio was also used as starting condition in the model. In order to compare the simulation results with molecular data from the qPCR assay, both results were expressed in a ratio of Nb: (Nsp+Nb). A ratio of 1 meant that a pure culture of Nitrobacter was present and a ratio of 0 that Nitrospira dominated the system. Accordingly, at a ratio of 0.5 both bacteria were present in equal amounts.Two separate reactors were run and as expected the number of cells of tter one , 31. The-1) applied in the experiment perfectly corresponded with the simulations conducted at a similar dilution rate  could be explained by inaccuracies in the pump frequency. The validated model was subsequently applied to study the effect of the dilution rate on the competition at given nitrite affinity constants and growth rates  small changes had a big impact on the time needed for complete washout of Nitrobacter. The time needed for washout of Nitrobacter increased from 100 hours to over 1000 hours for dilution rates from 0.58 to 0.63 d-1  delivered comparable results since both measuring points overlap . The simth rates . At low 0.63 d-1 . For hyd67 day-1 . Pure cuNitrobacter was coupled to the nitrite concentrations in the reactor .Simulations on the concentration in the reactor showed that the delayed washout of  reactor . Howeverwth rate . At the robacter . The ranNitrobacter  would have led to a washout only when the dilution rate exceeded the maximal growth rate  and hence very close to the μmax of Nitrospira (0.67 day-1). The value of the affinity constant had a more severe effect on the competition (Nitrobacter (Nitrospira occurred at significantly lower dilution rates (0.3 day-1) than the μmax of Nitrospira (0.67 day-1)  , zone IINitrobacter at higher dilution rates matched the simulation results very well, hence providing a reliable starting point for further simulation studies evaluating for various operational strategies, which would have been very time consuming to be tested experimentally. In this study, the chosen dilution rate was not close enough to the μmax of Nitrospira in order to provoke its washout by competition, which is nevertheless possible as shown in Figs Nitrospira at a dilution rate close to its μmax would have been easily interpreted as washout due to high dilution rate (e.g. due to inaccuracies of the pumps). Our simulation results show that the sludge retention time (SRT) has a big impact on the substrate concentrations, hence severely influencing the competition between Nitrospira and Nitrobacter. Under these circumstances it was shown here that the bacterium with the highest substrate affinity (Nitrospira) does not necessarily win the competition at low substrate concentrations as commonly perceived in literature [Nitrobacter) and can even be reverted from one to another species as shown in the simulation studies  can be controlled independently from the hydraulic retention time . The J criterion can also be applied in systems in which the SRT is not equal to the HRT but still the same for all bacteria , 34. NatNitrobacter is capable of mixotrophic growth, hence using other substrates than nitrite [Earlier modelling studies, investigating the growth of bacteria on multiple substrates, have shown that the coexistence of several species at steady state is possible due to niche differentiation by substrate inhibition, even when all the organisms strongly prefer one substrate . In addi nitrite  increasi nitrite .Nitrobacter) winning the competition at low substrate concentrations. In biotechnological processes the bacterial retention time is often uncoupled from dilution rate by using biofilm systems, which retain high biomass concentrations. However, in these biofilm systems suspended biomass is present as well and the selection for different nitrifying populations based on biofilms and/or suspended biomass and the impact on ecophysiological population dynamics is poorly understood. Therefore one must better comprehend what conditions bacteria experience when growing in suspension versus in a biofilm. Recent studies insinuate that suspended biomass plays different functional roles in biofilm systems, and that suspended biomass and biofilms can coexists in a single system [Despite of the complexity of substrate competition between different species, the outcome of this can study give an explanation for situations in which a r-strategist is dominating a system at low substrate concentrations. In this study, it was shown that the dilution rate within an ecosystem creates a selection pressure favouring the most adapted species under given environmental conditions, hence out-competing other less adapted species , 40, whie system –44. Since system , 46, ande system  and a vaIn conclusion, it was demonstrated that not only the substrate concentrations but also the dilution rate in natural habitats and engineered systems drives interspecies competition and must be taken into consideration, while investigating the competition between k and r strategists.S1 Table(DOCX)Click here for additional data file.S2 Tablei) of a component i is related to the process rates (ρj) through Ri = ∑jAijρj.The conversion rate (R(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.


The variable compartmental origin of traced metabolites is a feature that allows application of the model to yeasts with uncertain genomic and transcriptional backgrounds. The presented test case includes the baker's yeast Saccharomyces cerevisiae and the methylotrophic yeast Hansenula polymorpha. Highly similar flux solutions were computed using either a model with undefined pathway localization or a model with constraints based on curated (S. cerevisiae) or computationally predicted (H. polymorpha) localization information, while false solutions were found with incorrect localization constraints. These results indicate a potentially adverse effect of universally assuming Saccharomyces-like constraints on amino acid biosynthesis for non-conventional yeasts and verify the validity of neglecting compartmentation constraints using a small-scale metabolic model. The model was specifically designed to investigate the intracellular metabolism of wild-type yeasts under various growth conditions but is also expected to be useful for computing fluxes of other eukaryotic cells.Recent advances in the availability and applicability of genetic tools for non-conventional yeasts have raised high hopes regarding the industrial applications of such yeasts; however, quantitative physiological data on these yeasts, including intracellular flux distributions, are scarce and have rarely aided in the development of novel yeast applications. The compartmentation of eukaryotic cells adds to model complexity. Model constraints are ideally based on biochemical evidence, which is rarely available for non-conventional yeast and eukaryotic cells. A small-scale model for  •Compartmentation influences computed intracellular fluxes.•Improper localization constraints potentially produce false flux solutions.•Minimal compartmentation constraints result in high-quality flux computations. The investigation of intracellular reaction rates has become accessible to a wide range of scientists, rather than exclusively experts, and has been applied to expand detailed knowledge on cellular physiology  as well ased MFA  or non-sased MFA . Further balances . An MDV describes the relative abundance of mass isotopomers differing in the number of included 13C-isotopes of a single metabolite or a fragment thereof and are determined by mass spectrometric analyses. The equation system is solved, i.e., the fluxes are computed, by least squares parameter estimation, in which an initial random flux distribution is iteratively adapted to minimize the sum of the squared residuals (SSR) between experimentally and simulated MDVs. A χ2-cut-off defines the maximum SSR value for a statistically acceptable flux solution. The solution with the smallest SSR in a sufficient number of minimizations is then assumed to be the optimal flux solution. Flux confidence intervals as a result of a sensitivity analysis indicate the resolvability and thus the robustness of the flux solutions  and H. polymorpha (Pichia angusta) CLIB 421  were used in all experiments. Growth experiments were conducted in 1.3 L shake flasks filled with 50 mL Verduyn medium containing per liter, 5 g (NH4)2SO4, 3 g KH2PO4, 0.5 g MgSO4·7H2O, 4.5 mg ZnSO4·7H2O, 0.3 mg CoCl2·6H2O, 1.0 mg MnCl2·4H2O, 0.3 mg CuSO4·5H2O, 4.5 mg CaCl2·2H2O, 3.0 mg FeSO4·7H2O, 0.4 mg NaMoO4·2H2O, 1.0 mg H3BO3, 0.1 g KI, 15 mg EDTA, 0.05 mg biotin, 1.0 mg calcium pantothenate, 1.0 mg nicotinic acid, 25 mg inositol, 1.0 mg pyridoxine, 0.2 mg p-aminobenzoic acid, and 1.0 mg thiamine  and an air-tight sample port, and were shaken at 130 rpm with an amplitude of 30 mm. 13C-tracer experiments were performed with a mixture of 80% 1-13C-glucose and 20% U-13C-glucose , reported to have a high potential for resolving the network of central carbon metabolism at a reasonable prize per experiment (600) of 0.05 from precultures grown in the same minimal medium, but supplemented with naturally labeled glucose after cells were harvested and washed with 0.9% NaCl solution. Precultures were grown in 100 mL shake flasks at a shaking frequency of 200 rpm at 30 °C and 40 °C for S. cerevisiae and H. polymorpha, respectively. Samples of the main culture were taken by connecting a syringe to the sample port, opening the sealing clamp, and flushing the sampling line with air before creating a vacuum with the syringe and allowing approximately 1 mL to flow into the sample tube. The sampling line was flushed with air before the sealing clamp was closed to minimize disturbances of the closed headspace and corresponding gas analysis. After the cultures reached stationary phase, the headspace analysis was continued for several hours to verify the tightness of the system by stable gas analysis signals.thiamine . The medperiment . The mai2.2600 was measured, and the samples were centrifuged at 13,000 rpm for 5 min in a tabletop centrifuge. The biomass pellet was separated from the supernatant, and both fractions were stored at −20 °C until further analysis. The supernatant was used to determine the concentrations of glucose and the excreted metabolites ethanol, glycerol, and acetate. The metabolites were separated on an Aminex HPX-87H column  at 60 °C with a flow rate of 0.8 mL/min of 5 mM H2SO4. Glucose, ethanol, and glycerol were detected on a Shodex RI-101 detector, and acetate was detected at a wavelength of 210 nm in a variable wavelength detector of an UltiMate 3000 HPLC system .Immediately after sampling, the OD600 and a calibration curve, which was recorded for both yeasts under corresponding experimental conditions. The correlation curves were generated by weighing the biomass from duplicates of 10 mL culture samples with five different OD600 values ranging from 0.5 to 6. The samples were cooled on ice before centrifugation in glass tubes at 3500 rpm for 20 min in a Heraeus Megafuge 16 R . The supernatant was discarded, and biomass pellets were washed with water and centrifuged again. Finally, the supernatant was discarded again and biomass pellets were dried at 60 °C for 48 h before weighing.The cell dry weight (CDW) of samples was calculated from the measured OD2 and BCP-CO2 sensors connected to a BACCom12 communication box using the FermVis software . The signals were obtained in units vol% (H) and transformed into molar concentrations as described by R is the real molar volume, and Vm is the volume of one mole of ideal gas.The off-gas was measured using BCP-O0, s0, and p0 are the biomass, substrate, and product concentrations, respectively, at the beginning of the experiment; xt is the biomass at time point t, and YSX and YPX are the substrate and product yields on biomass, respectively. The equations were solved with SigmaPlot Version 12.5 . For experiments in which the formation of glycerol, acetate or ethanol was not observed, the corresponding equations were excluded from the fit.Exponential growth rate (µ), uptake rates, and production rates (r) were calculated by a simultaneous nonlinear fit of the time-dependent concentration (c) changes using Eqs. 2 production rate was not used as an input constraint for 13C-MFA because the CO2 sensor was calibrated for unlabeled carbon and was therefore insensitive to 13C-labeled CO2. Specifically, due to the calibration of the sensors, only 6% of 13CO2 was detected. A pre-simulation correction of this rate was not feasible because the labeling of CO2 is not necessarily equal to the total labeling of substrate carbon, but rather depends on the actual flux distribution. Once a flux distribution was found, the simulated CO2 labeling was used to correct the original CO2 rate, and this rate was used as a quality indicator for the obtained flux solution. The corrected CO2 production rates  were calculated from the measured rates (rCO2) and the simulated fraction of unlabeled and labeled CO2  derived from the best flux solution using Eq. The calculated CO2.3tert-butyldimethylsilyl-trifluoroacetamide , followed by incubation at 85 °C for 1 h to allow derivatization. The derivatized samples were analyzed on a gas chromatography-mass spectrometry (GC-MS) single quadrupole system consisting of a TRACE™ GC Ultra and an ISQ single quadrupole MS with electron impact ionization  equipped with a TraceGOLD TG-5SilMS fused silica column . The GC-MS was operated as described by Biomass that was sampled during the mid-exponential growth phase was used to determine the MDVs of proteinogenic amino acids. Approximately 0.3 mg of biomass was resuspended in 150 µL of a 6 M HCl solution, then hydrolyzed at 105 °C for 6 h and dried overnight. The dried hydrolysate was resuspended in 30 µL of acetonitrile and 30 µL of N-methyl-N-The raw GC-MS data were transformed into MDVs and corrected for the influence of unlabeled biomass from the inoculum and the natural abundance of heavy isotopes using iMS2Flux , as desc2.4OpenFLUX was used to compute the intracellular fluxes . The sofS. cerevisiae ATCC 18824  was included with an undefined ratio. In this manner, the model could compensate for possible small deviations from the intended ratio of the two isotopomers, which may occur due to weighing errors or inaccurate declaration of 13C-enrichment. As OpenFLUX requires a flux basis for starting the parameter estimation, the biomass-building drain of glycine was fixed in every model. The complete models, including MDVs, measurement errors, and flux ranges of extracellular reaction rates defined in the preSolverScript files, are included in the supplement.The model was constrained with glucose and oxygen uptake, byproduct secretion rates, and precursor drains into biomass. The glucose uptake rate was normalized to 1000, while all other constraining rates were scaled accordingly. These rates were defined in a separate MATLAB file (‘preSolverScript.m’) that contained a matrix with the lower and upper boundaries of the respective rates. In the presented model, one uptake reaction for each of the glucose isotopomers . Java 7  was used to convert the model in text file format into the respective MATLAB files. The resulting flux distributions were visualized on metabolic pathway maps created with Omix® .2.5Saccharomyces Genome Database (www.yeastgenome.org)  . Hits from the BLASTP analysis and annotation information from the JGI database were compared. Resulting BLASTP hits were computationally analyzed with TARGETP 1.1 (TP) (The ome.org)  was used1.1 (TP)  and WoLF1.1 (TP) . Further33.12 exchange reaction according to 13C label by naturally labeled CO2 contained in the headspace of the shake flask at the beginning of the experiment. A similar compensation was applied for methyltetrahydrofuran (MTHF), allowing label adjustment. A simplified respiratory chain reaction was used to allow reoxidation of NADH and balancing of O2. The model was compartmented into cytosol and mitochondrion and featured transport reactions between the compartments for isocitrate, malate, acetyl-CoA (ACCOA), pyruvate (PYR), and oxaloacetate (OAA). The amino acids serine (SER), glycine (GLY), and threonine (THR) were exceptions in the model, as they contributed measured MDVs and were featured as mass-balanced intermediates of the modeled network. These exceptions were made because of the complex interactions among the three amino acids and with the rest of the network. Furthermore, the model definition allowed to simulate the origin of each amino acid independently and uncoupled from other amino acids stemming from the same precursor, e.g., alanine and leucine, which share the common precursor pyruvate.For metabolic flux analysis, a model was developed that described the central carbon metabolism, including glycolysis, the pentose phosphate pathway (PPP), and the tricarboxylic acid (TCA) cycle, with approximately 50 reactions and linked biomass production to twelve intermediates of this central network. A COThe model reactions for valine (VAL), leucine (LEU), and isoleucine (ILE) biosynthesis shown in To independently simulate the labeling patterns of leucine and valine, which share the common precursor 2-oxoisovalerate (ISV), the synthesis pathways of these two amino acids had to be uncoupled. To this end, leucine synthesis was defined by two reactions, pyruvate condensation (r128) and acetylation (r129), to account for a possible exchange of the intermediate 2-oxoisovalerate between compartments. In contrast, valine synthesis was aggregated into one reaction, thereby skipping the intermediate 2-oxoisovalerate  and, hen3.2S. cerevisiae was obtained from the Saccharomyces Genome Database. Eleven genes that contribute to nine anabolic pathways or pathway segments that link amino acids to precursors with separate cytosolic and mitochondrial pools were identified. The localization in the non-conventional yeast H. polymorpha was not reported and was inferred with the computational localization analysis tool WoLF PSORT and TargetP based on the protein sequences of the eleven identified genes. The comparison with protein annotations from the JGI database confirmed the BLASTP results and yielded no further protein entities suitable for analysis. The computational prediction of the most likely compartmental pathway localization was used to generate a pathway localization pattern for H. polymorpha. A summary of genes and proteins with the results of the computational analysis of compartmental localization is provided in Curated information on the localization of amino acid biosynthesis for S. cerevisiae model was constrained to the mitochondria, while lysine biosynthesis was constrained to the cytosol. For lysine biosynthesis, this concerned only the carbon atoms stemming from acetyl-CoA, as the second precursor α-ketoglutarate (OGA) was exclusively located in the mitochondria. The uncertainties in the localization of leucine and lysine biosynthesis were neglected, as it is has been shown by H. polymorpha BLASTP hits were consistently mitochondrial for valine and alanine biosynthesis and consistently cytosolic for lysine biosynthesis. In the cases of leucine and isoleucine biosynthesis, inconsistent results were neglected, as such inconsistencies were neglected for S. cerevisiae as well, and localizations were assumed to be mitochondrial. The localization of aspartate, threonine, and methionine biosynthesis was left variable based on parallel cytosolic and mitochondrial prediction results. This resulted in an overall pathway map of H. polymorpha that was identical to that of S. cerevisiae. The final compartmentation constraints are indicated in the corresponding models in the supplement.Based on the curated localization information provided in 3.313C-MFA is applicable to cultures grown in a (pseudo-)metabolic and isotopic steady state. This was ensured here by growing S. cerevisiae and H. polymorpha in glucose minimal media in batch shake flask experiments and sampling for proteinogenic amino acid labeling analysis during the mid-exponential phase, approximately five generations after inoculation. Physiological data were monitored throughout the entire growth period ; specifically, the acetolactate synthase involved in the biosynthesis of valine and leucine was defined as uniquely mitochondrial because of the unambiguous enzyme localization prediction results. The acquired physiological data  or without these additional constraints (Sf). Additionally, one solution was generated using a minimal set of localization constraints .To further investigate the differences between the differently constrained models, the labeling patterns of compartmental pools of the amino acid precursors pyruvate, oxaloacetate, and acetyl-CoA were calculated by a forward isotopomer simulation in OpenFLUX (task 8) based on the computed flux distributions . The resThe differences between Sf and Sd were ascribed to a vast switch in the simulated compartmental origin of amino acids produced from pyruvate as a precursor . With the unconstrained model, these amino acids were simulated to be synthesized, to a great extent, from cytosolic pyruvate, in contrast to the curated mitochondrial origin. This discrepancy can be attributed to the additional freedom of the unconstrained model, Sf, but does not result in deviations between flux solutions Sf and Sd beyond the confidence of the properly constrained solution, Sd.The complete flux solutions for each model, the results of the simulated labeling of acetyl-CoA, pyruvate, and oxaloacetate, and the sensitivity analyses are included in the supplement .Fig. 5Si3.5H. polymorpha  was simulated. A high PPP activity of ca. 50% was previously reported for this H. polymorpha strain during growth at 30 °C (The flux solutions of lymorpha  differedat 30 °C . One posat 30 °C . High PPat 30 °C .Fig. 6SiS. cerevisiae dataset, the majority of differences between the H. polymorpha flux solutions Sf and Sd were found downstream of the pyruvate kinase reaction. In contrast to the unconstrained solution, Sf, the constrained solution, Sd, showed pyruvate decarboxylase activity and import of the cytosolic acetyl-CoA produced via this pathway into the mitochondria. With the free model, minimal activity of the malic enzyme and the glyoxylate shunt was computed, while no activity was simulated with the constrained model. The significance of all differences between Sf and Sd was evaluated by a sensitivity analysis of the fluxes, which revealed that the confidence intervals of all fluxes computed for Sf included the optimal solution of Sd, with the respiratory chain reactions being the sole exceptions (S. cerevisiae flux solutions, the majority of biomass precursors were simulated to originate from the mitochondrial pools. This simulation result is questionable and is certainly a subject to be addressed for further improvement of the constraints, but it was not a determinant of flux ratios in the present study. In contrast to the S. cerevisiae dataset, the Sdmin solution of H. polymorpha was not explicitly more similar to Sd compared with Sf; rather, the Sdmin solution showed flux values that were often in between the values of Sf and Sd, which in some instances were closer to Sf and in others closer to Sd.As in the case of the ceptions . There wmin. The absent exchange flux of acetyl-CoA between the mitochondria and cytosol prevented an equilibration of these two pools. However, as cytosolic acetyl-CoA was not used as an amino acid precursor in the flux solution, this difference had no further impact on the flux estimates. The simulated extensive exchange of acetyl-CoA across the mitochondrial membrane in Sd was reflected in equivalent labeling patterns of the respective parallel pools (S. cerevisiae flux solutions, the absent exchange of oxaloacetate resulted in significant differences in the compartmental labeling of oxaloacetate for all three solutions. The activity of the malic enzyme was reflected in a slightly different pyruvate labeling in Sf. The ratios of simulated compartmental origins of amino acids in Sf were reversed in comparison to Sd for all amino acids stemming from pyruvate; however, the rather poor resolvability of malic enzyme activity (The labeling patterns of acetyl-CoA, pyruvate, and oxaloacetate differed only marginally between the three solutions, with the exception of cytosolic acetyl-CoA computed for the solution Sdel pools . In contactivity , which iS. cerevisiae dataset. To further strengthen this indication, four flux solutions were simulated, each with a different set of randomly assigned constraints regarding the compartmental origin of amino acids. Due to a lack of experimental data from a strain with definitive differences in the compartmentation of amino acid biosynthesis from the assumed compartmentation in S. cerevisiae, these simulations were meant to represent scenarios of improper use of compartmentation assumptions. These simulations resulted in a large number of fluxes outside of the flux confidence intervals of Sd or Sf (The differences between labeling patterns of mitochondrial and cytosolic acetyl-CoA, pyruvate, and, especially, oxaloacetate in all three solutions showed the potential for a higher influence of compartmentation constraints on the flux solutions compared with that seen in the Sd or Sf .r3 exceeding the χ2-cut-off values (r3 would be rated as statistically acceptable, although it resulted in significant inaccuracy of the flux estimates.In contrast to the differences between Sf and Sd, the deviations of the flux values found in these additional solutions were often more significant. Most remarkably, there were fluxes with confidence intervals that did not overlap at all with those of Sf or Sd. Likewise, the SSRs of these additional solutions were significantly increased compared with those of Sf or Sd, with all but Sdf values . A solutThese analyses indicate that improper use of constraining compartmentation assumptions can yield inaccurate results that may not always be revealed as false solutions by statistical measures.The complete flux solutions for each model, the results of simulated labeling of acetyl-CoA, pyruvate, and oxaloacetate, and the sensitivity analyses are included in the supplement.4The presented analysis highlights the potential disadvantage of extrapolating established constraints, which were verified for cognate organisms or under different experimental conditions without reconfirmation for the application case. The comparison of flux solutions computed with and without compartmental localization constraints revealed that the retraction of these constraints did not significantly alter the simulated flux distributions, as the computed flux solutions showed extensive consistency. In contrast, an alarmingly adverse effect of randomly chosen constraints was shown, which represents the potentially equivalent negative consequence extrapolated curated or computationally predicted constraints would have when they are improperly assumed. These results do not allow a recommendation of total dismissal of compartmentation constraints but strongly support the demand for a more rigorous evaluation of such constraints and a rejection of constraints where there are no good reasons to extrapolate assumptions from other organisms or only vague computational indications.


Ascomycin is a 23-membered polyketide macrolide with high immunosuppressant and antifungal activity. As the lower production in bio-fermentation, global metabolic analysis is required to further explore its biosynthetic network and determine the key limiting steps for rationally engineering. To achieve this goal, an engineering approach guided by a metabolic network model was implemented to better understand ascomycin biosynthesis and improve its production.Streptomyces species was first investigated by comparing the metabolic enzymes of Streptomyces coelicolor A3(2) with those of 31 Streptomyces strains, the results showed that more than 72% of the examined proteins had high sequence similarity with counterparts in every surveyed strain. And it was found that metabolic reactions are more highly conserved than the enzymes themselves because of its lower diversity of metabolic functions than that of genes. The main source of the observed metabolic differences was from the diversity of secondary metabolism. According to the high conservation of primary metabolic reactions in Streptomyces species, the metabolic network model of Streptomyces hygroscopicus var. ascomyceticus was constructed based on the latest reported metabolic model of S. coelicolor A3(2) and validated experimentally. By coupling with flux balance analysis and using minimization of metabolic adjustment algorithm, potential targets for ascomycin overproduction were predicted. Since several of the preferred targets were highly associated with ethylmalonyl-CoA biosynthesis, two target genes hcd (encoding 3-hydroxybutyryl-CoA dehydrogenase) and ccr (encoding crotonyl-CoA carboxylase/reductase) were selected for overexpression in S. hygroscopicus var. ascomyceticus FS35. Both the mutants HA-Hcd and HA-Ccr showed higher ascomycin titer, which was consistent with the model predictions. Furthermore, the combined effects of the two genes were evaluated and the strain HA-Hcd-Ccr with hcd and ccr overexpression exhibited the highest ascomycin production (up to 438.95 mg/L), 1.43-folds improvement than that of the parent strain FS35 (305.56 mg/L).The metabolic conservation of S. hygroscopicus var. ascomyceticus showed that the general metabolic network model of Streptomyces species could be used to analyze the intracellular metabolism and predict the potential key limiting steps for target metabolites overproduction. The corresponding overexpression strains of the two identified genes (hcd and ccr) using the constructed model all displayed higher ascomycin titer. The strategy for yield improvement developed here could also be extended to the improvement of other secondary metabolites in Streptomyces species.The successful constructing and experimental validation of the metabolic model of The online version of this article (doi:10.1186/s12934-017-0787-5) contains supplementary material, which is available to authorized users. Ascomycin FK520) is a 23-membered polyketide macrolide antibiotic with immunosuppressant activity, which has similar characterization to FK506 (tacrolimus), exhibiting various therapeutic applications, including antifungal , immunos0 is a 23Recently, considerable efforts have been made to enhance the ascomycin titer of the production strains, including random mutagenesis and selection , fermentStreptomyces coelicolor A3(2), the model organism of the Streptomyces genus, is the first one to be completely sequenced species of the genus Streptomyces [S. coelicolor A3(2) was first constructed in 2005 (iIB711) [S. coelicolor A3(2) with that of Streptomyces avermitilis [S. avermitilis and some of the non-conserved ORFs might have synteny-conserved isoenzymes with S. avermitilis. The results illustrated that metabolic genes were highly conservative in the above two Streptomyces strains. In addition, it has been demonstrated that the size of panmetabolism  in Escherichia coli was limited to the set of reactions already known and approached a constant [Streptomyces species, the size of panmetabolism might not approach a plateau because they contain large number of genes in their secondary metabolism. Nevertheless, the primary metabolic genes in Streptomyces species are more conserved, which has been illustrated by comparing genome and pangenome analysis [Streptomyces strain would not significantly expand the size of the set of known primary metabolic reactions in Streptomyces species. That is, the construction of a general metabolic network model for Streptomyces strains only requires the addition of limited diversity to the current primary metabolic network of the model strain S. coelicolor A3(2).ptomyces . The GSM(iIB711) , and was(iIB711)  and 2014rmitilis . It was constant . This phconstant , 22. Howanalysis , 24. ThuStreptomyces species has so far not been fully analyzed, and the primary reactions in model iMK1208 of S. coelicolor A3(2) have not been investigated to establish if these reactions are conserved and could be applied to construct a general metabolic network model for Streptomyces strains. To resolve these issues, the metabolic enzymes in the updated model iMK1208 of S. coelicolor A3(2) were compared with those of 31 Streptomyces strains. And high conservation of the primary metabolic enzymes had been found in the all selected Streptomyces strains. Then, a metabolic network model of S. hygroscopicus var. ascomyceticus for ascomycin biosynthesis was attempted to construct by using the universal primary metabolic reactions from model iMK1208 and adding the reactions associated with the synthesis of ascomycin in S. hygroscopicus var. ascomyceticus. Using the thus constructed metabolic network model, some potential targets for ascomycin overproduction were predicted with the aid of FBA [hcd and ccr) located in the ethylmalonyl-CoA pathway showed high promoting effects on ascomycin overproduction. After gene overexpression manipulation, all the engineered strains had shown higher ascomycin production than control, and the highest ascomycin titer reached to 438.95 mg/L in the strain HA-Hcd-Ccr. These findings not only suggested a general metabolic network model could be construed for Streptomyces species, but also provided an effective method for the titer improvement of other products in different Streptomyces species.Nevertheless, the metabolic conservation of d of FBA  and MOMAd of FBA . Among aStreptomyces strains with completely sequenced genomes (deposited in the NCBI by September 2015) were used in the conservation analysis. All the available protein coding sequences of the 31 Streptomyces strains were retrieved from the National Center for Biotechnology Information (NCBI), and plasmids were omitted from the analysis. The accession numbers of all 31 strains are listed in Additional file S. coelicolor A3(2) in metabolic model iMK1208 were used as the reference, and the sequence similarities on protein level were calculated from a pair-wise comparison of homologous sequences between the protein sequences in the model iMK1208 and the protein coding sequences of the 31 Streptomyces strains using BLAST v2.2.30+. The top hit for each protein sequence  was selected as the optimal comparison result. The protein sequences were considered as conserved at the threshold of 40% identity, 50% of query protein length aligned and e value < 1e − 05.A total of 31 S. hygroscopicus var. ascomyceticus FS35 was constructed based on the model iMK1208 of S. coelicolor A3(2). Several secondary metabolism subsystems actinorhodin, undecylprodigiosin, calcium dependent antibiotic, and germicidin biosynthesis were removed from the model iMK1208. Some specific secondary metabolites biosynthesis reactions in other subsystems such as membrane transport, cofactor and prosthetic group biosynthesis were also removed from the model. The reactions related with ascomycin specific precursors biosynthesis and overall biosynthesis reactions were added to the new model.The initial model of S. coelicolor [S. hygroscopicus var. ascomyceticus [S. lividans [S. coelicolor [As a kind of constraints-based metabolic model, the necessary constraints were used to obtain the optimal flux solution space. For example, 6-phosphogluconate dehydrogenase was shown to be NADP-dependent and no detectable activity with NAD , the uppelicolor , S. hygryceticus  and S. llividans , the lowelicolor . MenaquifPH value  was performed to calculate the flux distributions throughout the metabolic network by defining a maximized-biomass equation as the objective function using COBRAToolbox-2.0  in MATLAalue Eq. , definedS. hygroscopicus var. ascomyceticus FS35 was isolated after femtosecond laser irradiation [S. hygroscopicus var. ascomyceticus mutants used in this study were derived from FS35. Escherichia coli JM109 was used to propagate all plasmids. E. coli ET12567/pUZ8002 was used as the nonmethylating plasmid donor strain for intergeneric conjugation with FS35. All E. coli strains were grown in Luria–Bertani (LB) medium at 37 °C. The integrative E. coli-Streptomyces shuttle vector pIB139 containing the ermE* promoter (PermE*) was used for gene overexpression in S. hygroscopicus var. ascomyceticus FS35. S. hygroscopicus var. ascomyceticus strains was grown at pH 7.0 and 28 °C, MS medium was used for the preparation of Streptomyces spores suspensions, seed cultures were prepared as described previously [2SO4, 1.0 g/L K2HPO4·3H2O, 2.0 g/L CaCO3, 1.0 g/L MgSO4·7H2O, 0.001 g/L FeSO4·7H2O, 0.001 g/L MnCl2·4H2O, 0.001 g/L ZnSO4·7H2O, MOPS 5.0 g/L, pH 7.0. The production medium contained 20 g/L soluble starch, 40 g/L dextrin, 5.0 g/L peptone, 5.0 g/L yeast powder, 6.0 g/L corn steep liquor, 12 mL/L soybean oil, 1.0 g/L MgSO4·7H2O, 0.5 g/L MnSO4·1H2O, 1.5 g/L (NH4)2SO4, 0.5 g/L K2HPO4·3H2O, and 2.0 g/L CaCO3. Streptomyces coelicolor A3(2) were cultured in YEME at 28 °C for the isolation of genome. For conjugation experiments, S. hygroscopicus var. ascomyceticus strains were performed according to the Streptomyces standard protocols [S. hygroscopicus var. ascomyceticus were selected on 2CMY contained starch 10 g/L, casein tryptone 2.0 g/L, NaCl 1.0 g/L, (NH4)2SO4 2.0 g/L, K2HPO4 1.0 g/L, CaCO3 2.0 g/L, FeSO4·7H2O 0.001 g/L, MgCl2·6H2O 0.001 g/L, ZnSO4·7H2O 0.001 g/L, agar 20 g/L, pH 7.0. Agar plates by overlaying with an appropriate antibiotic or combination of antibiotics.All strains and plasmids used in this study are summarized in Table adiation . All S. eviously . SynthetE. coli with Streptomyces were performed according to standard protocols [hcd and ccr gene overexpression. The plasmids pIB/ccr and pIB/hcd were constructed as follows. The coding sequence of hcd and ccr were amplified from the genomic DNA of S. coelicolor A3(2) by PCR using the primer pairs hcdF1/hcdR1 and ccrF1/ccrR1 respectively. The hcd and ccr PCR products were digested using the restriction enzymes NdeI and XbaI, and the resulting fragments produced by digestion were cloned into the pIB139 vector between NdeI and XbaI respectively to obtain pIB139R and pIB139D, respectively. The constructed plasmids pIB/hcd and pIB/ccr were both confirmed by DNA sequencing. The conformed plasmids pIB/hcd and pIB/ccr were introduced into the parent strain S. hygroscopicus var. ascomyceticus FS35 to obtain the hcd and ccr overexpression strains HA-Hcd and HA-Ccr, respectively. To construct the vector pIB/hcd/ccr, the primer pairs HcdF2 and HcdR2 were used for amplifying the gene hcd and the primer pairs CcrF2 and CcrR2 were used for amplifying the gene ccr. The hcd PCR product was digested using NdeI and PstI, and then the fragment was cloned into the pUC18 vector to form pUC/hcd. The resulted plasmid pUC/hcd was cut using PstI and XbaI, and linked with the ccr PCR product, which was digested with the same restriction enzymes, to form the plasmid pUC/hcd/ccr. The plasmid pUC/ccr/hcd was excised by NdeI/XbaI and the fragment including gene hcd and ccr was cloned into pIB139 to generate the plasmid pIB/hcd/ccr. The resulting plasmid was introduced into the parental strain S. hygroscopicus var. ascomyceticus FS35 via conjugal transfer, generating the overexpression strain HA-Hcd-Ccr. The parental strain S. hygroscopicus var. ascomyceticus FS35 with pIB139 (FS-PIB) was used as the negative control. All the positive mutants were confirmed by PCR amplification and DNA sequence analysis.General DNA manipulation and intergeneric conjugation of rotocols . All therotocols , pIB139,g for 10 min at 4 °C. Cell pellets were washed 3 times by 100 mM Tris·HCl (pH 7.0) containing 5 mM MnSO4, 20 mM KCl, 2 mM DTT and 0.1 mM EDTA, and then resuspended in the same buffer [g for 20 min at 4 °C was stored at − 80 °C for assays of enzyme activity and total protein concentration. The concentration of total protein was detected by the Bradford method [The culture samples for enzyme activity analysis in vitro were harvested at the end of exponential phase (72 h) by centrifugation at 8000×e buffer . Cell sud method . One uni3-Hydroxybutyryl-CoA dehydrogenase  activity was assayed by monitoring the decrease in NADH concentration at 340 nm using acetoacetyl-CoA as the substrate, the reaction mixture contained 100 mM Tris–HCl buffer (pH 7.5), 100 μM acetoacetyl-CoA, 150 μM NADH, and extract. The reaction was started by the addition of acetoacetyl-CoA , 47. ActS. hygroscopicus var. ascomyceticus FS35 and its derivatives were carried out as described previously [g for 10 min, washed once with 0.1 M HCl and twice with distilled water separately, and dried at 80 °C to constant weight. The concentration of ascomycin was quantitated by high-performance liquid chromatography (HPLC) analysis as described previously [Fermentations of eviously . Biomasseviously . The reseviously .S. coelicolor A3(2), which is an important model strain for Streptomyces species, were used as the standard for metabolic conservation analysis of 31 Streptomyces strains. Except for the exchange and teichoic acid biosynthesis subsystems, the 1208 proteins in the other 43 metabolic subsystems of the model iMK1208 were preformed blastp homology analysis against 31 Streptomyces strains  and the stain S31-sxi had lowest homology (72%) with the all 1208 metabolic enzymes. Specially, 24 strains had higher homology more than 80%, and only 2 strains had homology least than 75% with the analyzed proteins in S. coelicolor A3(2). The conservation of metabolic enzymes from different metabolic subsystems in Streptomyces species was also investigated by cluster analysis. The conservation of different subsystems showed a similar distribution in all strains  and S. hygroscopicus limoneus KCTC 1717 (S16-shyl) as examples. Homology analysis of 1166 primary metabolic enzymes from S. coelicolor A3(2) was preformed against S14-shy and S16-shyl. Both strains showed high numbers of enzymes homologous with those of the primary metabolic enzymes in S. coelicolor A3(2) . However, among these 146 proteins, there were 95 proteins with at least one isozyme present in S14-shy. Moreover, among the remaining 51 non-homologous proteins, 15 had similar protein annotation information with proteins in S14-shy. Taking all these factors into consideration, there were only 36 proteins without homologs among all 1166 primary metabolic related proteins from S14-shy, which represents a mere 3% of all proteins in the primary metabolism subsystems of the metabolic network model of S. coelicolor A3(2). Similar results were also obtained for the strain S16-shyl , and the details are listed in the Additional file −4 mM/g DCW/h respectively. Based on the above constraints, the maximal specific growth rate calculated by the model was 0.0653/h, which was very close to the experimental result of 0.0616/h  and d-fructose 6-phosphate. In transaldolase-overexpressing strain, the carbon flux to E4P was enhanced which resulted in an increased supply of shikimate acid [fPH = 1.906) was catalyzed by crotonyl-CoA carboxylase/reductase (Ccr). In this reaction, crotonyl-CoA could be transformed into ethylmalonyl-CoA, an important precursor in ascomycin biosynthesis [After model verification, the potential overexpression targets for ascomycin overproduction were predicted by coupling the model with FBA and MOMA algorithms. In order to improve the accuracy, targets located in the inorganic ion transport and metabolism, exchange and nucleotide salvage pathway subsystems were excluded in advance. The targets related to ATP maintenance requirements were also been excluded. Using their respective H values , 29 poteonyl-CoA , 50. Theate acid . The tarate acid –53. The ynthesis . Overexpynthesis –58.Fig. hcd and ccr genes were individually overexpressed and co-overexpressed in S. hygroscopicus var. ascomyceticus respectively . In the ccr-overexpressing strain HA-Ccr, the crotonyl-CoA carboxylase/reductase activity was 0.28 U/mg protein, an increase of 2.5 times over the control (0.08 U/mg protein). The enzymes activity in each mutant strain was higher than parental strain, which confirmed that both hcd and ccr genes were overexpressed. In addition, in the strain HA-Ccr, the 3-hydroxybutyryl-CoA dehydrogenase activity was 0.27 U/mg protein, a similar level with that of the control (0.25 U/mg protein). By contrast, in the hcd-overexpressed strain HA-Hcd, the crotonyl-CoA carboxylase/reductase activity was 0.13 U/mg protein, which was 1.62-fold higher than that of the control (0.08 U/mg protein). In the strain HA-Hcd, the ascomycin production reached up to 375.32 mg/L at 168 h, increased 1.23 times compared with that of the parent strain FS35 (305.56 mg/L) . The crotonyl-CoA carboxylase/reductase activity was 0.35 U/mg protein, and thus was increased 3.37 times compared with that of the parent strain FS35 (0.08 U/mg protein). Enzyme activity analysis confirmed that the activity of 3-hydroxybutyryl-CoA dehydrogenase and crotonyl-CoA carboxylase/reductase activity in the strain HA-Hcd-Ccr was significantly improved compared with the control strain FS35. The final biomass concentration of the double-overexpression strain (16.92 g/L at 168 h) exhibited a slightly decrease compared with the parent strain FS35 (18.02 g/L at 168 h)  involved in the ethylmalonyl-CoA pathway were selected for the experimental overexpression. HACD1 and CCCR were catalyzed by 3-hydroxybutyryl-CoA dehydrogenase (Hcd) and crotonyl-CoA carboxylase/reductase (Ccr), respectively  has been constructed and updated for 2 times and successfully used in the guide of metabolic engineering targets identity [Streptomyces species [Streptomyces species, the conservation of these metabolic reactions existed in the reported GSMM had not been investigated completely. Previous studies showed that primary metabolism might be highly conserved between Streptomyces species [S. coelicolor A3(2) are conserved in other Streptomyces species, a more comprehensive, deeper metabolic gene analysis of Streptomyces species was performed in this study.In previous reports, GSMM was confirmed as an effective method for better understanding of intracellular metabolic behavior and identifying the key limiting steps for target metabolites biosynthesis. In identity –20. It h species , 59. How species , 24. To Streptomyces species was investigated by comparing the metabolic enzymes in the latest reported model of S. coelicolor A3(2) with those of 31 completely sequenced Streptomyces strains. The results showed that the metabolic enzymes were highly conserved within the 31 Streptomyces strains, especially for primary metabolism. This was consistent with the previous comparative genome and pangenome analyses of Streptomyces species [Streptomyces species, and metabolic reactions showed higher conservation than the data obtained through protein homology analysis. Therefore, majority of the primary metabolic reactions in the model of S. coelicolor A3(2) are likely universal across Streptomyces species. In addition, due to the conservation of metabolic genes and our limited knowledge of gene functions and biochemical characteristics, the set of primary metabolic reactions in Streptomyces strains is expected to converge to a constant value with the addition of analyzed genomes, as was demonstrated for E. coli strains [Streptomyces species, it is reasonable to reference the primary metabolic reactions in the model of S. coelicolor A3(2) to construct general metabolic network models for other Streptomyces strains.The metabolic conservation of  species , 23, 24. strains , 22. ConS. hygroscopicus var. ascomyceticus was firstly attempted to be constructed and applied to identify the genetic engineering targets for improving ascomycin production. The constructed metabolic model of S. hygroscopicus var. ascomyceticus facilitated the understanding of the ascomycin biosynthesis network. Using this model in conjunction with the FBA and MOMA algorithms, we identified 29 potential gene overexpression targets according to a ranking of their respective fPH values. Among all the predicted targets, several targets with high fPH values  were found in the ethylmalonyl-CoA pathway, which had also been confirmed to be an limiting pathway for other products synthesis in some Streptomyces species [S. hygroscopicus var. ascomyceticus FS35 could utilize acetate and ethylmalonic acid as a sole carbon source (data not shown), indicating the presence of the ethylmalonyl-CoA pathway in this strain. However, the ethylmalonyl-CoA pathway was incomplete in the most reported model of Streptomyces species. According to the conservative analysis of primary metabolism, the related metabolic reactions had been supplied into the general metabolic model by adding additional constraints and the missing reactions in the pathway  biosynthesis , HACD1 could converts acetoacetyl-CoA to 3-hydroxybutyryl-CoA through 3-hydroxybutyryl-CoA dehydrogenase encoded by sis Fig. . PHB hadyceticus . CCCR ismonensin , tacrolimonensin , and salmonensin . Furtherhcd and ccr were overexpressed and co-overexpressed in S. hygroscopicus var. ascomyceticus. The experimental results suggested that all the engineered strains had a higher ascomycin production than control, and the highest ascomycin production could reach to 438.95 mg/L. The genetic manipulation about the target genes further verified the feasibility the strategy about model construction. However, the increase of production titer was different from the predicted improvements. This may be due to some limitations of the constraint-based metabolic network simulation, such as the lack of detailed knowledge on metabolic regulation, as well as thermodynamic and kinetic information. The strategy for metabolic model construction in Streptomyces species will make the construction process of metabolic network easier and make it possible for the strain without genomic information.The two target genes S. hygroscopicus var. ascomyceticus, the ethylmalonyl-CoA pathway is also found in many Streptomyces species, and the corresponding enzymes can be found in 30 of the 31 investigated Streptomyces strains  as model, a general metabolic model of Streptomyces species was attempted to be constructed based on the conservative analysis of the primary metabolic reactions of 31 Streptomyces strains. Using this general metabolic model, the GSMM of S. hygroscopicus var. ascomyceticus was reconstructed and applied to identify the potential targets for ascomycin overproduction. According to the simulated results, two targets hcd and ccr from the ethylmalonyl-CoA pathway were selected for overexpression, the experimental results indicated ascomycin production showed higher improvement in both of single- and double-overexpression strains. These findings could not only indicate that improving the supply of ethylmalonyl-CoA could improve ascomycin yield effectively, but also suggest that a general metabolic model could be existed and applied to guide for the yield improvement of other products in Streptomyces species.In this study, using the latest reported model of Additional file 1. Conservation analysis of the proteins in the model iMK1208 against 31 Streptomyces strains.Additional file 2. Metabolic network model of S. hygroscopicus var. ascomyceticus FS35 and potential targets identified using this model.Additional file 3: Table S1. Primers used in this section. Figure S1. Heat map illustrating the conservation of metabolic enzymes in different metabolic subsystems among 31 Streptomyces strains. Figure S2. Homology analysis of proteins related to the primary metabolism of S. coelicolor A3(2) and S16-shyl. Figure S3. Production profiles of the stain S. hygroscopicus var. ascomyceticus FS35 in batch fermentation. Table S2. Genetic targets chosen for experimental implementation. Table S3. The encoded enzymes involved in the ethylmalonyl-CoA pathways of the 31 Streptomyces strains.Additional file 4. The updated model including the complete ethylmalonyl-CoA pathway, and the potential targets identified using this model.


A model based solely on mass-balance constraints refines our understanding of the trade-offs, energy requirements, leaf-level fluxes, and plasticity mechanisms in different biochemical types of assimilation. 3 plants. However, detailed quantification of the biochemical activities in the bundle sheath is lacking. This work presents a general stoichiometric model for C3, C2, C2+C4, and C4 assimilation (SMA) in which energetics, metabolite traffic, and the different decarboxylating enzymes  are explicitly included. The SMA can be used to refine experimental data analysis or formulate hypothetical scenarios, and is coded in a freely available Microsoft Excel workbook. The theoretical underpinnings and general model behaviour are analysed with a range of simulations, including (i) an analysis of C3, C2, C2+C4, and C4 in operational conditions; (ii) manipulating photorespiration in a C3 plant; (iii) progressively upregulating a C2 shuttle in C3 photosynthesis; (iv) progressively upregulating a C4 cycle in C2 photosynthesis; and (v) manipulating processes that are hypothesized to respond to transient environmental inputs. Results quantify the functional trade-offs, such as the electron transport needed to meet ATP/NADPH demand, as well as metabolite traffic, inherent to different subtypes. The SMA refines our understanding of the stoichiometry of photosynthesis, which is of paramount importance for basic and applied research.The goal of suppressing photorespiration in crops to maximize assimilation and yield is stimulating considerable interest among researchers looking to bioengineer carbon-concentrating mechanisms into C These then diffuse to the BS where they are decarboxylated. C4 plants have traditionally been grouped into three subtypes (Zea mays) has been shown to operate two BS decarboxylation enzymes (NADP-ME and PEPCK) and two CO2 delivery pathways   . Both th3 to C4 has recently been studied ; (ii) is based only on stoichiometry and therefore does not rely on kinetic measurements; and (iii) includes all main photosynthetic reactions, but is user-friendly for non-specialists. The theoretical underpinnings of the SMA are described in detail and a range of simulations to exemplify the model rationale and behaviour are provided. Numerous topics are covered, including (i) an analysis of C3 and C2 photosynthesis and all subtypes of C2+C4 and C4  in operational conditions; (ii) the energetics involved in manipulating photorespiration in a C3 plant; (iii) the consequences of progressively upregulating a C2 shuttle in a background of C3 photosynthesis; and (iv) the consequences of progressively upregulating a C4 cycle in a background of C2 photosynthesis. Results quantify ATP and NADPH demand, which link dark and light reactions; refine our understanding of the stoichiometry of photosynthesis and the trade-offs inherent to different photosynthetic subtypes; and represent a useful framework for the integration of existing biochemical models.The aim of this work was to develop a stoichiometric model of assimilation (hereafter SMA) which (i) generalizes all pathways of assimilation , and PEP carboxylation rate (VP). The ATP and NADPH requirements are SMA outputs and they are not related to light reactions at this stage. Reactions are typically grouped by the biochemical function of the pathways, of which only the entry point is calculated. The SMA is based only on well-established reaction stoichiometry. The SMA accounts for the interactions between C2 and C4 cycles, including the fluxes associated with amino group rebalancing, and for the NADPH and ATP demand of assimilatory processes. The theory underpinning the SMA can be followed in The SMA was developed on the basis of a stoichiometric model of NADP-ME CA; respiration in the light, RLIGHT; and Rubisco rate of oxygenation relative to carboxylation, rO/C), two define the activity of the CCM , and seven partition key processes between the BS and M . In addition, for NAD-ME subtypes it is possible to constrain malate dehydrogenase (MDH) activity to zero. Inputs can be constrained in different ways depending on the research questions. When the goal is to refine the analysis of a particular metabolic state of the leaf, input quantities may represent realistic biochemistry, otherwise inputs can be freely manipulated to simulate bioengineering or explore hypothetical scenarios. A, RLIGHT, and rO/C can be measured with accuracy , or based on the rate of ATP and NADPH made available by light reactions (and referred to as light-limited). Integrating enzyme-limited formulations in the SMA is straightforward, because the A, rO/C, and VP output by the biochemical model can be directly input into the SMA (see references below). Integrating light-limited formulations is more complicated and will be addressed in a dedicated paper. Physiological values are available for rPEPCK, gained through extensive biochemical work , predicting fC and fO requires that the increased CO2 concentration in the BS be modelled, for instance using the validated models for C4, C3, C2, and C2+C4 photosynthetic subtypes . For each type, two sets of simulations are presented: static scenarios, where the SMA is calculated for one set of inputs, representing realistic operational conditions  cycle operate in the M, while no carbon fixation is occurring in the BS. Although photorespiration and GDC activity are high, no glycine (GLY) is exported in the BS, and there is no net metabolite flux at the M–BS interface. All ATP and NADPH demand is in the M.Outputs for C3 photosynthetic type was simulated by varying rO/C between 0 and 1 while keeping other quantities at 0  diffuses back to the M, exporting half of the GLY amino groups. The excess ammonia produced by GDC in the BS is fixed by glutamine synthetase/glutamine oxoglutarate aminotransferase (GS/GOGAT), transaminated to alanine (ALA), and diffuses back to the M so as to re-balance the amino groups. For the C2 shuttle to operate, an import flux of pyruvate (PYR) equimolar to R is required in the M. The C4 CCM is not operational. In this example, PGA is reduced mainly in the M and a substantial BS↔M triose phosphate exchange occurs. Because the CCM is not operational, there is no net export of NADPH from M to BS and the NADPH demand in BS of 1.1 μmol m−2 s−1 must be met by linear electron flow (LEF); however, cyclic electron flow (CEF) may be preponderant as the ratio of ATPBS to NADPHBS was ~8.Outputs for CfGDC was varied from 0 to 1 in an idealized C3 plant, thus simulating the transition to C2 photosynthesis through intermediate states of GDC compartmentalization, which are generally referred to as proto-Kranz  cycle to the BS results in changing the locality of ATP and NADPH demand  in the M and transaminated to ASP, which diffuses back to the BS and is transaminated back to OAA to supply MDH in the BS. The pair PYR/ALA balances the amino groups resulting from OAA/ASP transamination, and the amino groups resulting from the flux of GLY and SER are directly dependent on the operation of the C2 shuttle. Alternative scenarios may involve NADH resulting from GDC activity sustaining a minimal level of PR in the BS . PR is mainly located in the M, and triose phosphate trafficking is higher than for the C2 photosynthetic type. Because a large part of Rubisco is mainly located with GDC in the BS, and photorespiration is reduced by the activity of the CCM, the effectiveness of the C2 shuttle (as R) is reduced relative to C2 photosynthesis. PR in the BS consumes all NADPH available through MDHM, therefore T ≈ 0; however, there is no residual NADPH deficit, and LEF is not required in the BS.An example of NADP-ME C2+C4 photosynthetic subtype  remained zero . The net effect of the C2 shuttle (as R), which depends on Rubisco oxygenating activity in the M, is zero. The CCM supplies all NADPHBS (6.54 μmol m−2 s−1), no LEF is required in the BS, and there is excess OAA that is not reacted with by MDH and is subsequently transaminated (T).Outputs for a typical NADP-ME subtype are shown in ATP/GA than for the NADP-ME subtype. However, PEPCK activity is generally low, all NADPHBS is supplied by the CCM, and no LEF is required in the BS.4 photosynthetic subtype with no engagement of PEPCK. The CCM exports CO2 at the same rate as the NADP-ME subtype; however, MDH activity in M is zero, all OAA is transaminated, and the NADPH demand in the BS has to be generated in the BS through LEF.rPEPCK = 1). The CCM exports CO2 at the same rate as the other subtypes. Because MDH is present in the M, the CCM would export reducing power to the BS if rPEPCK < 1, but, here, PEPCK activity in the BS diverts OAA produced by PEPC in the M, making OAA unavailable for MDH activity in the M, which is consequently zero. Furthermore, because MDH activity is zero, the CCM does not export reducing power to the BS, and the NADPH must be supplied through LEF in the BS.rPEPCK = 1). The CCM exports CO2 at the same rate as the other subtypes. Here, MDH in M is not operational, and the CCM does not have the potential to export reducing power to the BS.rO/C was set at a typical C4 value of 0.05  required the engagement of LEF in the BS. In the NAD-ME subtype, the demand for LEF in the BS increased linearly for fPR > 0.The fraction of PGA reduced in the BS was manipulated through the input parameter subtypes , B. The rPEPCK. fPR was set at 0.25 to highlight differences between NADP-ME and NAD-ME subtypes, while fPPDK and fCS were kept at zero  and Rubisco  activity, leaf-level Rubisco rates of carboxylation and oxygenation (as rO/C), and PEP engagement (as VP) are known. While previously published models are tailored for a particular species or a discrete photosynthetic type that is fixed a priori, the SMA allows rates to vary continuously between boundaries defined by what is biochemically feasible. The SMA is designed for integration with existing biochemical models. However, these models rely on assumptions that are specific to a particular photosynthetic type and, as such, they were not included in the SMA at this stage. The SMA is based upon logical constraints and well-established reaction stoichiometry, and is, consequently, of general use. Compared with other recent flux-balance models [e.g. Flux-balance analysis models are constraint-based models in which steady state fluxes are predicted in a metabolic network by applying mass-balance constraints based on reaction stoichiometry . Lately 4 plasticity mechanisms, 4 plants can exploit to stabilize their energy and redox state were recently reviewed  and can be followed in Supplementary file 2 ; the exchange of triose phosphate and PGA and the effect of switching decarboxylase (ME versus PEPCK) are covered in simulations 4.2.1 and 4.2.2. In addition to the list of fPR (simulation 4.2.3), and I suggest a possible role for PPDK in the BS, which was investigated by manipulating fPPDK (simulation 4.2.4). The simulations were carried out as a single factor analysis in which only one input was varied at a time , to virtually complete engagement in the atypical C4Alloteropsis semialata . Similarly, in NAD-ME subtypes the gradient spans Panicum species with virtually no PEPCK engagement, Cleome C4 species with intermediate PEPCK activity, and Spartina maritima where PEPCK engagement is virtually complete . Although PEPCK plants have numerous chloroplasts in the BS  contribute to fine-tuning ATP and NADPH demand in the BS in response to illumination of the BS chloroplast. Light availability in the BS is determined by anatomy and changes dynamically according to light intensity and quality. The locality of ATP production is therefore largely independent of metabolic control [2, and PCO cycles in the BS impose a limit, and a threshold of ATP demand in the BS (ATPBSMIN = RuPphospBS + VOBS + R), corresponding to an ATP partitioning ratio of ATPBSMINATPM≈0.3. If ATP supply in the BS is lower than the minimum demand, C4 photosynthesis ceases, leading to stunted phenotypes  in a single tool. A range of examples detailed the energetics and metabolite fluxes involved in the gradual activation of the CSMA is coded in an Excel workbook that is freely available to download. Macros are avoided.JXB online.Supplementary data are available at 3 photosynthesis: Figure S2.File 1. SMA development and equations S1–S34; simulation of static scenarios under physiological operational conditions: Table S1 and Figures S1, S3–S5E; simulation 1.2, a dynamic scenario for CFile 2. Excel workbook coding the SMA.Supplementary_Table_S1_Figures_S1_S5Click here for additional data file.Supplementary_Data_ModelClick here for additional data file.