P.cicadae.we optimized medium components for the production of ergosterol peroxide (EP) by Paecilomyces cicadae based on a mono-factor experiment, a uniform design, and a non-linear regression analysis. The maximum EP yield achieved was 256 μg/L, which was increased by 5 folds compared with that before the optimization. Structured Monod model, Andrews model, Contois model, and Aibe model were developed to describe the effects of viscosity inhibition, substrate, and production on biomass growth. The results showed that the Monod model could predict biomass growth, and the effects of viscosity and substrate on the EP concentration were significantly higher compared with the effect of production. The addition of water and glycerol could decrease the viscosity inhibition and glycerol inhibition, and further increase the EP yield. The newly developed structured model was demonstrated for batch growth of  EP mainly comes from the fruiting bodies of medicinal fungi, including Paecilomycescicadae8, Inonotusobliquus9, Sarcodonaspratus7, Ganodermalucidum, and Hericiumerinaceus10, as well as some yeasts10. Paecilomycescicadae is a fungal strain belonging to the Paecilomyces genus of ascomycete fungi. In the Paecilomyces, 400 species have been described11. P.cicadae has been used in Chinese herbal medicinal prescriptions to treat many types of diseases, including kidney disease, for thousands of years13. P.cicadae has recently received increasing attention due to its reno-protective activity16. In our previous study, EP can be isolated and purified from the mycelia of P.cicadae17. EP derived from mycelia can be used to treat renal cell carcinoma in vitro through multiple mechanisms, including suppressing cell growth, colonization, migration, and invasion, inducing cell cycle arrest, attenuating β-catenin pathways, and triggering apoptosis17.Ergosterol peroxide  possesses a wide spectrum of biological properties, such as anti-oxidant23. As a widely used experimental design of measurement uniformity, the uniform design method is first proposed by mathematicians of Wang and Fang in 1978. The basic idea of the uniform design is to arrange the test points at a uniform distribution within the scope. The uniform design is suitable for the test with multiple factors and multiple levels, and it can effectively reduce the cost of the experiment to improve the research efficiency25.Although many types of food and medical fungi can synthesize EP, large-scale production of EP has not been reported. The bottleneck restricting the mass production of EP can be attributed to the extremely low yield of EP. The strategies to increase the yield of fermentation products include screening and breeding of strains and optimization of culture conditions and medical components. The yields of metabolite and biomass are strongly affected by medium composition and cultivation conditions. The medium composition includes carbon sources, nitrogen sources, and trace elements. The cultivation conditions include aeration volume, agitation speed, pH, incubation time, and temperature. Therefore, to increase microbial production with less economic cost, these factors need to be simultaneously optimized. The surface response method, orthogonal design, and uniform design are effective methods to optimize multivariates29. The unstructured model (logistic model) applied to several fermentation processes of filamentous fungi may be very useful as data fitters31. A great limitation of the unstructured model is that these models are mainly composed of empirical equations, which cannot reveal the mechanisms hidden behind the observed phenomena. The modeling of fungal growth through its biomass is especially interesting for the control of fermentation processes. Therefore, it is necessary to develop more suitable models to provide a better understanding of the mechanisms, which regulate the fermentation process. Although many structured models in the literature fit the microbial growth data well, such as the Monod model, Andrews model (inhibition model by substrate), Contois model (inhibition model by viscosity), and Aibe model (inhibition model by production), these models have not been reported to fit the fermentation process of filamentous fungi.Modeling of fermentation kinetics is widely used to control, predict, optimize, and design fermentation processing. All kinetic models of fermentation can be divided into unstructured and structured models. Both types of models are studied to explain the kinetics of biomass and fermentation reactionsP.cicadae to increase the EP yield. The optimization strategy included: (1) the primary nutritional requirement for EP synthesis was achieved by classical mono-factor design; (2) the interaction between nutritional components was achieved by uniform design and statistical analysis; and (3) the interaction between nutritional components was evaluated based on four structured kinetic models.In the present study, we aimed to optimize the nutritional constituents for 32. The effect of carbon sources on EP synthesis is rarely reported in the literature. In the present study, glucose, sucrose, maltose, fructose, mannitol, and glycerol were used as carbon sources to study the effects of various types of carbon sources on the EP synthesis in the fermentation process of P.cicadae. Figure The carbon source is a major component of the cytoskeleton in the process of fungal growth, and it provides the energy for biomass growth and metabolite synthesis. Saccharide is the main carbon source for the synthesis of biomass and product in the fungal growth process. Most fungi can grow and synthesize the metabolite in the medium using sucrose, glucose, or maltose as carbon sources34. As a carbon source, glycerol or mannitol can avoid the inhibition of carbon catabolism. The effect of membrane fatty acids on permeability has been reported35. Few works have investigated the influence of the membrane fatty acids of R.arrhizus cell on permeability. Different components of the culture medium can change membrane composition and lead to the change of cell membrane permeability. The ratio of saturated and unsaturated fatty acids directly determines the permeability of the membrane. Glycerol has been proven to increase saturated fatty acids of cell membrane36. Fatty acid saturation determines the overall stability of the membrane to some extent, while an increase in fatty acid saturation can cause a decrease in membrane fluidity and thus its permeability37, and it blocks the secretion of ergosterol, the precursor of EP, and promotes the synthesis of EP. Therefore, glycerol was chosen as the carbon source in the subsequent experiments.Carbon source is the main source of nutrients and energy in the process of mycelial growth and metabolite synthesis. Therefore, the type and concentration of carbon source directly determine the specific growth rate, the consumption rate of carbon source, and the synthesis rate of metabolites. In the process of filamentous fungal fermentation, many studies have investigated the relationship between the yield of secondary metabolite and carbon sources. Generally, most organisms prefer to utilize glucose as a carbon source. However, glucose is not conducive to the synthesis of metabolites. The phenomenon can be attributed to the repression of carbon catabolites. Glucose, for example, can inhibit antibiotic synthesis by blocking or inhibiting key enzymes in the antibiotic biosynthetic pathway38. Many nitrogen sources are involved in the synthesis of nitrogen-containing metabolites, such as amino-acid, nucleotides, and vitamins. These amino acids and nucleotides are the precursors of some secondary metabolites39. Therefore, nitrogen sources must meet the needs of mycelial growth and the synthesis of some metabolites. Nitrogen sources can be divided into organic and inorganic nitrogen sources. Peptone, soybean meal, corn syrup, beef extract, and yeast powder are the major organic nitrogen sources, and KNO3, NaNO3, CO(NH2)2, (NH4)2SO4, and NH4OH are the primary inorganic nitrogen sources. Peptone, yeast powder, KNO3, CO(NH2)2, (NH4)2SO4, and NH4OH were used to study the effect of nitrogen source on the EP synthesis by P.cicadae, and the experimental results are shown in Fig. Many fungi can utilize a wide variety of nitrogen sources. The effects of different nitrogen sources on mycelial growth and product synthesis are very differentNO3, CONH2, (NH4)23, CO(NH2)2, (NH4)2SO4, and NH4OH were added, respectively, the mycelia grew slowly. Moreover, after the fermentation, the dried mycelium was dark, and its EP concentration was less than 26.60 μg/L. The results showed that inorganic nitrogen in the fermentation of P.cicadae was not good for EP production.Figure 41. One of the differences between yeast powder and peptone is that yeast powder contains higher levels of biotin. The biotin can increase the cell membrane density, reduce the cell membrane permeability, block the secretion of EP precursor ergosterol, and promote the synthesis of EP. This also led to the highest yield of EP when yeast powder was added. However, excessive biotin prevents the transport of the substrate and metabolites between the extracellular and intracellular. To control the content of biotin in the medium, the yeast powder and peptone were chosen as the nitrogen source in the uniform design experiment.The pathway of nitrogen metabolism is closely related to the pathway of secondary metabolite biosynthesis. Nitrogen regulation in fungi has been extensively reviewedP.cicadae, including KH2PO4, MgSO4, MnSO4, CuSO4, ZnSO4, and FeSO4. Figure 2PO4, MgSO4, and ZnSO4  were added compared with the control group . Especially, the increase of EP yield was the most significant when ZnSO4 was added. ZnSO4 is beneficial to the fermentation of Cordycepsjiangxiensis to produce intracellular polysaccharides and mycelia42, which is consistent with our results. The EP yield was significantly low when MnSO4, CuSO4, and FeSO4 were added. Therefore, we chose KH2PO4, MgSO4, and ZnSO4 as the inorganic salts.As the coenzymes of many types of enzymes, inorganic salts play a vital role in the growth and metabolism of microorganisms, the balance of osmotic pressure, and the translocation of nutrients and metabolites between the intracellular and extracellular cells. Therefore, we investigated the effects of six inorganic salts on the EP yield in the broth of 2PO4, MgSO4, and ZnSO4 were screened as the independent variables, and U10(106) was applied to investigate the effects of six factors on the EP yield. Table 10(106). The sampling time was selected as the control variable, which was set at 28, 56, 84, and 112 h. Table According to the result of the mono-factor experimentation, glycerol, yeast powder, peptone, KHFi is the sampling time (T), glycerol concentration, yeast extract concentration, peptone concentration, ZnSO4 concentration, MgSO4 concentration, or KH2PO4 concentration. αi and βi represent the coefficients of factor Fi and Fi2, respectively. C represents the fitting constant. Table p-value (0.000) of the model with 95% confidence in Table To more accurately analyze the relationship between each factor and EP yield, the following formula was used to fit the data in Table entclass1pt{minimaentclass1pt{minimaThe coefficients of each item in Table 3, F6, and F7 greater than 0 indicated that positive correlations existed between YEP and F3, between YEP and F6, and between YEP and F7, respectively. Namely, the higher concentrations of yeast extract, MgSO4, and KH2PO4 in the tested range of concentration, the higher YEP. The coefficients of F4 and F5 less than 0 indicated that negative correlations existed between YEP and F4, and between YEP and F5, respectively. Namely, the lower concentrations of peptone and ZnSO4 in the tested range of concentration, the higher YEP.The coefficients of FF12 and F22 less than 0 exhibited that there was an inverted-U-shaped relationship between YEP and F1, and between YEP and F2, and YEP had the maximum value. The following two equations were obtained by taking the derivation of YEP to F1 and F2.The coefficients of YEP’ = 0, F1 = 8.617/0.118 = 73 h, F2 = 50.605/12.468 = 4.06. When the fermentation time was 73 h and glycerol concentration was 4%, the YEP yield reached the maximum value.Suppose that 2PO4, 2.4 MgSO4, and 0.05 ZnSO4. The optimum fermentation time was 73 h. To verify the optimization efficiency, an experiment was conducted with the basal fermentation medium and optimum fermentation medium, and the fermentation time was 80 h. Under the above-mentioned conditions, the maximum theoretical yield of EP is 203.92 μg/L at 80 h. Actually, the measured EP yield with the optimum medium was 256 µg/L and significantly higher compared with the basal medium (43 µg/L) and the maximum theoretical yield.To sum up, the optimum fermentation medium of EP contained (g/L): 40 glycerol, 6.5 yeast extract, 2 peptone, 2.4 KHP.cicadae fermentation. The biomass was increased after 12 h, and then it continued to increase rapidly till the end of the growth phase. The EP production was rapidly increased from 12 to 76 h. The maximum EP yield was obtained at 76 h (261.47 μg/L). After 76 h, the EP yield was rapidly decreased , F-values (> 240), and p-value (0.000) exhibited that these four models simulated better the kinetic experimental data, described the time course profile of EP productivity, and had the high explanation rate and credibility.The experimental data of glycerol concentrations in Fig. 44, the model gives very little valuable information. The combination of these four models can evaluate the reasonableness of any parameter changes in the process of fermentation.Monod and logistic models are adopted as the most applicable models for cell growth. However, in addition to the limitation of nitrogen and phosphorus sources, there is an oxygen limitation in aerobic fermentation. The Monod model is generally constrained due to the presence of oxygen limitation in the aerobic fermentation process. In contrast, the logistic equation can well reflect the relationship between the cell growth rate and the biomass. In fact, in the aerobic fermentation process, the oxygen concentration is steeply decreased in the initial stage of fermentation, and then the oxygen concentration remains constant till the strain begins to die. Therefore, before the strain begins to die, the effect of oxygen concentration on cell growth can be neglected. Although the logistic model can simulate the cell growth of fungip-value of the four models was 0.000, the F-value of the Aibe model was lower compared with the other models. Based on the F-value, we deduced that the effect of the product on the EP concentration was less compared with the viscosity and substrate. Here, it should be pointed that the product was not only EP but also other metabolites that might inhibit the synthesis of EP. To decrease the effects of viscosity and substrate on EP synthesis in the fermentation process and to increase the EP yield, fed-batch glycerol and water supplementation were used to increase the EP yield. Furthermore, water supplements could reduce the inhibitory action of production on biomass growth.Table To verify our deduction, we designed three groups of experiments, and each experiment was carried out in triplicate. The EP yield of group 3 (glycerol supplementation) was 327 ± 4.93 μg/L, which was significantly higher compared with group 2 . The EP concentration of group 2 and group 3 was markedly higher compared with group 1 . The result further proved that the combination of four structured models could guide the optimization of the fermentation process.The EP standard was purchased from Yunnan Xuli Biological Technical Co., Ltd. Methanol of the chromatographic grade was obtained from Thermo Fisher Technology Co., Ltd. Other reagents were of analytical grade or reagent grade and provided by the Sinopharm Chemical Reagent Co., Ltd., Shanghai, China.P.cicadae strain was purchased by the China Microbiological Culture Collection Center (NO. bio-33088), maintained on the new potato dextrose agar (PDA) slant, stored at 4 °C, and passaged once every 3 months.2PO4, and 1 MgSO4. The basal fermentation medium was composed of (g/L) 200 fresh potato, 40 glucose, 4 peptone, 1 KH2PO4, and 2 MgSO4. All media were sterilized at 121 °C for 30 min in an autoclave.The seed medium was a PDA medium composed of (g/L) 20 glucose, 4 yeast powder, 3 peptone, 1 KHg for 15 min, 4 °C) to precipitate the biomass. The biomass was dried to constant weight at 80 °C for EP analysis.Erlenmeyer flasks (250 mL) containing 100 mL seed medium were inoculated and incubated in a rotary shaker at 25 °C and 120 rpm for 72 h to prepare the inoculums. A 10% (v/v) inoculum was aseptically inoculated to 100 mL fermentation medium. The fermentation was carried out in 250-mL Erlenmeyer flasks in a rotary shaker at 25 °C and 120 rpm for 48 h. The cultured samples were centrifuged 2SO4] were implemented to basal fermentation medium at a concentration of 3 g/L, The medium without nitrogen source was used as the control. The tested inorganic salts included (g/L) KH2PO4 (1), MgSO4 (1), (mg /L) MnSO4 (20), CuSO4 (20), ZnSO4 (20), and FeSO4 (20). The cultures were incubated in a rotary shaker at 25 °C and 120 rpm for 48 h. After 48 h, the flasks were harvested to analyze the dry cell weight and EP yield.Growth and EP production were studied with glucose, sucrose, maltose, fructose mannitol, or glycerol as a carbon source at a concentration of 20 g/L . Different nitrogen sources [peptone, urea, yeast extract, NH4, MgSO4, and KH2PO4. Sampling time was used as the control variable. Each independent variable was assessed at 10 different levels, resulting in a U10(10)6 , substrate inhibition model (Andrews model), and product inhibition model (Aibe model) are a partial link between cell growth and viscosity of fermentation broth, substrate concentration, or product concentration, respectively. These models were used in our work.Two types of models (such as structured and unstructured models) were applied to describe a microbial process. Compared with the unstructured model, a structured model reveals more information on physiological characterization, composition, and regulatory adaptations to the environment of the microorganisms46.The mycelium ball is thought to be a tiny chemical reactor. In the reactor, the substrate is converted into mycelia by a complex network of enzyme-catalyzed reactions. In this process, there is no mass transfer resistance inside the mycelium ball.In the complex network of enzyme-catalyzed reactions, glycerol is the only limiting substrate. In all metabolic pathways converting glycerol into mycelia and metabolite, only one pathway is the limiting pathway, in which the velocity is the slowest. In all reactions of the limiting pathway, only one key step reaction controls the whole velocity of the limiting pathway. Namely, the biomass growth velocity depends on the enzyme reaction of the key step and the effect of the glycerol concentration on the limiting reaction rate.The enzyme concentration of the limiting reaction is proportional to the concentration of the mycelium ball, and the concentration of the mycelium ball is proportional to the consumption concentration of the limiting substrate. The growth velocity of the mycelium ball is proportional to the consumption velocity of the limiting substrate, and the concentration of the product is proportional to the consumption concentration of the limiting substrate.Kinetic models of microbial growth were carried out based on the Monod model, Andrews model, Contois model, and Aibe model. To describe the effect of glycerol concentration on biomass growth, the following considerations were assumedP.cicadae.Based on these assumptions, the Monod model, Andrews model (inhibition model by substrate), Contois model (inhibition model by viscosity), and Aibe model (inhibition model by production) can be used to fit the biomass data in the progress of Monod model, Contois model, Andrews model, and Aibe model were expressed by the following equations, respectively:μ and μmax are the specific growth rate and the maximum specific growth rate (h−1), respectively; Cs is the substrate concentration, namely glycerol concentration; ks is the saturation constant , X is the biomass concentration, ki and kp are the inhibition constants of substrate and inhibition product (mM), respectively, and Cp is the concentration of the inhibition product.In these models, μ can be expressed by the following equationAccording to the definition of specific growth rate, Yb and Yp are the biomass and product yield coefficients on the substrate (glycerol), respectively.According to the above-mentioned assumptions, there are the following equations:After substituting Eqs. –8) in E in E8) iThese equations were applied to fit the data of kinetic experiments.Experimental data from batch fermentation in a 20-L mechanically stirred fermentor were utilized to simulate the kinetic parameters by the developed model Eqs. –12). Th. Th12). The verification tests were carried in a 20-L mechanically stirred fermentor. The three experiments were designed in agreement with the kinetic experiments. Water and glycerol were not supplied in the group 1 experiment, and 1 L water was supplied at 40 h and 60 h in the process of fermentation in the group 2 experiment. In the group 3 experiment, 900 g glycerol was added in three batches, 300 g was added to the medium before sterilization, and another 600 g glycerol  was added at 40 h and 60 h, respectively. The fermentation conditions were the same as the kinetic conditions and fermentation time 80 h.P.cicadae was dried to constant weight and powdered in a grinding mill. Next, 0.10 g biomass powder was loaded into a 5-mL centrifuge tube and mixed with 3 mL MeOH. The tube was sonically extracted at 25 °C for 30 min. The conditions of ultrasonic treatment were set as follows: frequency 40 Hz and power 50 W. The tube was centrifuged at 1.3 × 104 rpm for 2 min. The supernatant was subjected to LC–MS/MS analysis. The extraction was performed in triplicate.Briefly, the biomass of Briefly, 10 mg standard EP was dissolved in 200 mL methanol to prepare a standard EP stock solution. Before use, the stock solution was diluted with methanol to prepare the working solutions of various concentrations.An HPLC–MS/MS  system equipped with an Acquity UPLC BEH C18 column  was used in the present determination. A gradient elution procedure was applied to the elution of EP. The mobile solvent A and solvent B used in the gradient elution procedure were 0.1% formic acid–methanol and 0.1% formic acid–water, respectively. The elution procedure was programmed as follows: 10% A to 95% A (0–2.5 min), 95% A to 95% A (2.5–10 min), 95% A to 10% A (10–10.5 min), and 10% A to 10% A (10.5–12.5 min). Other conditions were set as follows: flow rate 0.2 mL/min, column temperature 40 °C, injection volume 5 μL, and sample temperature 20 °C. The quantization was operated in the multi-response monitoring (MRM) mode with a positive ion mode to monitor the precursor-production ion pair transitions of m/z 429.6–393.6 for EP.48. The p-value, F-value and R2 derived from Statistical Computing software were used to apply multi-variable non-linearregression analysis. In the study, after a series of modeling, according tot p-value, F-value and R2, the optimum medium and reaction process were found.Non-linear multiple regression analyses have been utilized in the optimization of medium components and the parameters of reaction process to find the optimum medium and to determine the optimum reaction process, which often seem to provide higher accuracy49. Thus, the one way Anova was applied into the study as a filter method to select the relevant features.One way analysis of variance (Anova) is a method to analyze the results of univariate analysis and to test whether test factors have a significant effect on test results. One-way ANOVA has proven its effectiveness in solving the problem of high dimensionality in the feature spaceAll data were expressed as means ± standard errors (in the mono-factor at time and the Uniform-design experimentation) or as means (in the kinetic model experiment). All the multi-ple non-linear regression equations were conducted using SPSS 17.0 software  and Microsoft Excel , the analysis of variance (ANOVA) was performed by Dunnett's test, and p < 0.05 was considered as statistically significant.P.cicadae was determined by a combination of a mono-factor experiment, a uniform design method, and a non-linear regression. This was the first report on EP production by P.cicadae fermentation. The optimum fermentation time was 80 h. The maximum flask culture yield of EP yield (256 μg/L) after optimization was increased by nearly five-fold compared with that before the optimization, which was also higher than the maximum theoretical EP yield (203.92 μg/L). The combinational use of four structured models indicated that glycerol and water could further increase the yield of EP in the fermentation process of P.cicadae.The optimal medium for maximum production of EP by


Escherichia coli – global control of the central metabolism and local control of the acetate pathway – but neither accounts for all observations. Here, we develop a kinetic model of E. coli metabolism that quantitatively accounts for observed behaviours and successfully predicts the response of E. coli to new perturbations. We reconcile these theories and clarify the origin, control, and regulation of the acetate flux. We also find that, in turns, acetate regulates glucose metabolism by coordinating the expression of glycolytic and TCA genes. Acetate should not be considered a wasteful end-product since it is also a co-substrate and a global regulator of glucose metabolism in E. coli. This has broad implications for our understanding of overflow metabolism.Overflow metabolism refers to the production of seemingly wasteful by-products by cells during growth on glucose even when oxygen is abundant. Two theories have been proposed to explain acetate overflow in  Overflow metabolism refers to the production of seemingly wasteful by-products by cells during growth on glycolytic substrates such as glucose, even when oxygen is abundant. Overflow metabolism has been reported for most (micro)organisms: yeasts produce ethanol  plus different concentrations of 12C-acetate , as detailed in E. coli simultaneously produces acetate from glucose and consumes it from the environment . This initial model (model 1) did not fit the data satisfactorily . To determine whether this inhibition actually occurs in vivo at the transcriptional level, we monitored gene expression in E. coli grown on glucose (15 mM) plus acetate at different concentrations .A key hypothesis required for the model to fit the experimental data is that acetate inhibits the flux capacity of both the glycolytic pathway and the TCA cycle in −1)  .ptsGHI, crr), without inducing the expression of alternative systems of glucose internalisation and phosphorylation  that could have compensated for reduced PTS activity and the corresponding inhibition of glucose uptake   was reduced by 15–40% at 100 mM acetate. At this concentration, acetate also inhibits the expression of virtually all TCA cycle genes  by 30–67%. In terms of acetate metabolism, the expression of pta and ackA, which code for enzymes in the Pta-AckA pathway, remained remarkably stable at all acetate concentrations. Expression of pyruvate oxidase (poxB) was increased by a factor of 8 in the presence of acetate, though this promiscuous enzyme contributes to acetate metabolism mainly in the stationary phase and apparently not under our conditions (acs) – which converts acetate to acetyl-CoA with a high affinity – decreased with increasing acetate concentration, indicating that under glucose excess, the presence of acetate does not activate acetate recycling through the Pta-AckA-Acs cycle  . In contnditions . Expresscs cycle .E. coli to produce acetyl-CoA from glucose and its capacity to utilise acetyl-CoA as a source of energy and of anabolic precursors for growth. Acetate does not appear to influence the flux capacity of the acetate pathway itself, which is also consistent with the proposed model.These data confirm our hypothesis that acetate gradually modulates both the flux capacity of E. coli to new perturbations and comparing the results with experimental data , and we predicted the steady-state glucose and acetate fluxes at different dilution rates (from 0.1 to 0.5 hr−1). Since the present model cannot account for the activation of acetyl-CoA synthetase – involved in acetate assimilation – under glucose limitation   . The gro 100 mM) . The aceE. coli to sudden variations in the acetate concentration, which is not possible using existing models. In the exponential growth phase on glucose, we simulated the time courses of the changes in the concentrations of glucose and acetate after a pulse of acetate (30 mM) or of water (control experiment), as described in Third, we tested the dynamic properties of the model by predicting the response of E. coli over a broad range of glucose and acetate concentrations. Importantly, the model was not trained on these data , so this test is a strong validation of its predictive power.Overall, the predictions of the model are in agreement with experimental findings. The model accurately predicts the steady-state and dynamic relationships between glucose uptake, growth, and acetate fluxes in E. coli to the same perturbations with alternative models . The simulations in the absence of inhibition are qualitatively different  on each flux (J)  .Metabolic control analysis revealed that rather than being controlled entirely from within the acetate pathway, acetate flux is controlled to a similar degree by the acetate pathway (with a flux control coefficient of 0.76), glycolysis (0.88), and the TCA cycle (−0.64). The balance between acetyl-CoA production and demand , therefore has a strong effect on acetate production. As expected, this control is positive for glycolysis (since it produces acetyl-CoA) and negative for the TCA cycle (since it consumes acetyl-CoA). Still, acetate flux is controlled to a large extent from within the acetate pathway, mainly by AckA (0.69), with a small contribution from acetate transport (0.07). Overall, acetate flux control is distributed around the acetyl-CoA node.Since control of acetate flux is distributed around the acetyl-CoA node, we tested how flux changes around this node might affect its control properties. Given that the concentration of acetate is a major determinant of acetate flux , we perfE. coli metabolism on the acetate flux . However, more information is needed to understand the mechanisms involved in flux responses to perturbations of an external parameter such as the acetate concentration. Indeed, while a metabolic reaction may exert some control on a given flux, this does not necessarily mean that it is involved in the observed flux response. For instance, AckA exerts some trations , hence iE. coli to changes in acetate concentration, we used the concept of response coefficients (aceJ) on an external effector (the concentration of acetate). The partitioned response relationship  to predict the steady-state and dynamic responses of E. coli to a broad range of perturbations. In this coarse-grained model, the simplified formalism used to consider acetate inhibition represents the integrated regulatory response of E. coli metabolism to acetate, regardless of the underlying molecular mechanisms. Regulation may indeed take place through a variety of mechanisms acting at the (post-)transcriptional, (post-)translational, and metabolic levels. These regulatory networks are tightly intertwined and act concertedly to ensure a coordinated response of E. coli to perturbations on the seconds to minutes time scale . Acetate regulates E. coli metabolism over a range of concentrations spanning three orders of magnitude, and its regulatory action involves a combination of direct and indirect mechanisms. In the laboratory, E. coli is typically grown on glucose at low acetate concentrations, which enhances its production. In the intestine however, E. coli’s environmental niche, the concentration of acetate is high (between 30 and 100 mM)  in the intestine. This will require a quantitative characterisation of the available nutrients and of their dynamics in this particularly complex environment.These results call for a reconsideration of the role of acetate, and more generally of overflow metabolism, in  100 mM)  and wellE. coli are similarly capable of co-consuming glucose with its metabolic by-products, even when glucose is abundant. Saccharomyces cerevisiae can co-consume ethanol and glucose organisms.Microorganisms other than  glucose , and mam glucose . These sE. coli K-12 MG1655 was grown in M9 medium . Growth was monitored by measuring the optical density (OD) at 600 nm using a Genesys 6 spectrophotometer , and a conversion factor of 0.37 gDW/L/OD unit (9 medium  compleme/OD unit  was used600nm = 1), 4 mL of each culture was centrifuged for 90 s at 14,000 rpm, the supernatant was discarded, and the pellets were immediately frozen in liquid nitrogen. Total RNA was extracted using a Qiagen RNAeasy MiniKit and quantified using a Nanodrop spectrophotometer. Double-stranded complementary DNA (cDNA) synthesis and array processing were performed by One-Color Microarray-Based gene Expression Analysis (Agilent Technologies). The images were analysed with the software DEVA (v1.2.1). All array procedures were performed using the GeT-Biopuces platform (http://get.genotoul.fr). Four independent biological replicates were analysed for each condition. For each data set, the log2 intensities obtained in the presence of acetate were divided by the log2 intensities obtained in the absence of acetate. These ratios were then normalised by the log2 median intensity. Genes whose expression level differed by a factor of 2 or more between the two conditions were used for further analysis. Gene ontology analyses were performed using Ecocyc (https://ecocyc.org), p-values were estimated using Fisher exact test with Bonferroni correction. The transcriptomics data can be downloaded from the ArrayExpress database (http://www.ebi.ac.uk/arrayexpress) under accession number E-MTAB-9086. Theoretical expression data at a given growth rate were obtained by extrapolating the data from Cells were grown in flasks in M9 minimal media with 15 mM glucose and 0, 10, 50, or 100 mM acetate. In mid-exponential growth phase , as detailed previously (2O containing TSPd4 (3-(trimethylsilyl)-1-propanesulfonic acid-tetra deuterated, used as internal standard) at a concentration of 10 mM. A sequence using presaturation (ZGPR) was used for water signal suppression, with a 30° pulse and a relaxation delay between scans of 10 s to ensure full signal recovery. A total of 32 scans were accumulated  after 4 dummy scans. From each spectrum, we quantified glucose and unlabelled and labelled acetate. Spectra were processed using TopSpin v3.2 (Bruker).Extracellular concentrations of labelled and unlabelled glucose and acetate were quantified during growth by 1D eviously . Brieflyhttps://github.com/MetaSys-LISBP/acetate_regulation; swh:1:rev:c8bcf8fad3459269df30dba7e52c81e62ca181d0. The calibrated kinetic model has also been deposited in the Biomodels database (https://www.ebi.ac.uk/biomodels) . The scripts used to perform the simulations, to analyse the models and to generate the figures are provided in the Supplementary Information (https://github.com/MetaSys-LISBP/acetate_regulation to ensure reproducibility and reusability.All models are provided in SBML and COPASI formats in the Supplementary Information  and at homodels)  with theomodels)  with theormation  and at h−3 (L/gDW) DW) for amounts of metabolites and biomass, respectively. All reactions are listed in The model contains six reactions, six species, and two compartments  and intracellular  species:Terms within square brackets are required only to simulate chemostat experiments  inhibition of the glycolytic and/or TCA cycle pathways by acetate (terms within square brackets). This regulatory term represents the immediate, integrated response of Finally, these models were extended with isotopic equations for parameter estimation, as detailed in Concentrations of cofactors were taken from a published kinetic model of the Pta-AckA pathway (E. coli K-12 MG1655 grown on 13C-glucose (15 mM) plus 12C-acetate . These data included time-course concentrations of biomass, glucose, and acetate and 13C-labelling of acetate. The parameters p were estimated by minimising the objective function f defined as the weighted sum of squared errors:ix is the experimental value of data point i, with an experimental standard deviation iσ, and iy(p) is the corresponding simulated value. The objective function f was minimised with the particle swarm optimisation algorithm . The experimental and fitted data are shown in The values of 14 of the 24 parameters were taken directly from the literature . Parametχ2 statistical tests to assess the goodness of fit of each model and determine whether they described the data with sufficient accuracy. The minimised variance-weighted sum of squared residuals (SSR) is a stochastic variable with a χ2 distribution. The acceptable threshold for SSR values is χ², where d represents the number of degrees of freedom and is equal to the number of fitted measurements n minus the number of estimated independent parameters p. The parameter α was set to 0.95 to define a 95% confidence threshold and models with SSRs above this threshold were rejected since they cannot accurately reproduce the experimental data.We used not used to train or fit the models, were obtained for E. coli K-12 MG1655 and its close derivative strain BW25113 grown on different concentrations of glucose and/or acetate, as detailed in the Results section and in E. coli to the corresponding perturbations were predicted using each model  were collected from the literature to evaluate the predictive power of each model. These data, which were ch model . The resch model  to identJ in response to a fractional change in the rate of the reaction E  on the extracellular concentration of acetate  to outside (high concentrations) the acetate pathways.This paper provides an analysis of the metabolic feedback mechanisms regulating overflow metabolism during glycolysis in Decision letter after peer review:Escherichia coli" for consideration by eLife. Your article has been reviewed by three peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Naama Barkai as the Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "Control and regulation of acetate overflow in The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.eLife". Please let us know if you would like to pursue this option. As the editors have judged that your manuscript is of interest, but as described below that additional work is required before it can be published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv  along with this decision letter and a formal designation that the manuscript is "in revision at Essential revisions:1) Modeling choices, model robustness and the question of alternative models need to be more thoroughly addressed.2) Interpretation of experimental results need to be strengthened, in particular with regards to the time scales of transcription and regulation.3) All comments made by reviewers need to be thoroughly and constructively addressed.Reviewer #2 (Recommendations for the authors (required)):E. coli and the dependence of its physiology on acetate present in the environment. This is an interesting topic relevant to a broad spectrum of questions, including cancer metabolism. The approach is original, and the specific hypothesis that acetate influences transcription and the metabolic flows in previously unknown ways is well-posed and intriguing. I must admit though that I am not totally convinced that the results provide conclusive evidence about their hypothesis, ruling out other possibilities. Clear comparisons with alternative models, parameter sensitivity analyses, and a clearer expression of possible caveats would alleviate this concern, and help readers clearly understand the possible limitations of the approach employed. The work should be of interest to a fairly broad audience of biochemists, microbiologists and systems biologists.The manuscript delves into a major classical question in the study of metabolism, namely how organisms balance the decision of respiring or fermenting carbon sources, and what role fermentation by-products play in this process. Specifically, the authors focus on Subsection “Testing the model”: the authors mention an excellent agreement between model and experiment in Figure 3. I think the agreement is not bad, but I would not call it excellent. I am not sure a p-value is absolutely necessary, but it would be good to at least have an idea of how much variability the experimental data may have beyond the shown error bars, and whether the agreement is really unexpected, also in light of the parameter fitting presented in Figure 1.It would be useful to see Figure 3 in absence of the acetate inhibition effects. Would the results look only quantitatively different or also qualitatively different? Can the authors comment on the intuitive meaning of these curves. Is any of these curves dramatically affected by the new model, relative to alternative standard models?As opposed to what is mentioned in the Introduction, stoichiometric models could in principle help address the questions posed by the authors. This would not be the case for regular FBA models, but would be true for dynamics FBA models, in which the extracellular concentration of substrates is explicitly accounted for.The “Metabolomics experiments” subsection in the Materials and methods is very brief, and does not get into the detail of flux measurements reported, which are crucial for the model and its interpretation.E. coli to simultaneously secrete acetate due to fermentation and take it up from the environment, but it would be good for the authors to comment on this.I am curious about whether acetate inflow and outflow are separately measurable (and measured) in the system, or whether the reports are for the net flow. I am not sure if multiple acetate transporters exist, and whether it is in principle possible for No parameter sensitivity analysis was performed. I would recommend that the authors either add it, or comment on why it would not be useful.The authors' conclusions are presented in an absolute way, that leaves no room for alternative explanations. Partly due to the issues mentioned above, after reading the paper I felt highly intrigued by the results, but not 100% convinced that their explanation is the only possible. I would suggest that the authors (a) take steps to present in a more critical way the results in light of other models that were not explicitly tested, and (b) acknowledge explicitly possible limitation of this study, and avenues to further corroborate the proposed mechanisms.Introduction, second paragraph: it becomes clear from the context, but the authors should clearly mention that they refer to extracellular concentration of acetate.Reviewer #3 (Recommendations for the authors (required)):The manuscript presents an extension of previous work of the group . The authors present an interesting dynamical model of overflow metabolism, producing new insights into a long-standing question in bacterial physiology. The model is confronted with new data that appear to confirm specific model predictions. The major novelty of the model is the addition of a direct control by acetate of the activities of glycolysis (or glucose uptake) and the TCA cycle. Very interesting transcriptomics data appear to confirm these regulatory relationships. However, transcriptional regulation and the direct regulations suggested by the model act at different time-scales. In conclusion, the work is very well carried out and interesting. The connection between the transcriptomics data and the model need to be clarified.1) The first major argument in favor of control of glycolysis and the TCA cycle by acetate is the quality of fit to bioreactor data. It does not seem surprising to me that the quality of fit  is improved by adding additional regulatory interactions. Even though the figure shows a significant improvement of the fits, it would be nice to see the alternative fits  in supplementary information.2) A strong claim of the manuscript is that a kinetic model is needed to account for experimental observations. The fits in Figure 1 use the dynamical model at steady state. How do the predictions compare to the alternative models ?3) The transcriptomics data are very interesting. However, these data were acquired at steady state, reflecting a slow adaptation (order of magnitude of the generation time) of physiology. The regulatory interactions (square brackets in the equations in the subsection “Model construction”) are instantaneous effects of the concentration of acetate. I fail to see how the transcriptomics data can argue in favor (or disfavor) of the kinetic model.4) The perturbation experiments  are probably the best test of a dynamical model. The model correctly predicts the observed dynamics, which are clearly on time-scale of minutes, precluding transcriptional regulation as a cause. The authors could suggest possible molecular mechanisms.5) The remaining results of the manuscript address model properties and are very well carried out. Under the hypothesis that the model is correct, these analyses yield interesting insights into the physiology of overflow metabolism.E. coli. The experiments are very well carried out and pertinent new data are presented.In conclusion: the manuscript describes a very interesting extension to current models of overflow metabolism in My major concern is that the transcriptomics data seem disconnected from the kinetic model. Long-term adaptation can be achieved by modifications in gene expression. However, this does not correspond to the mechanism described by the model equations. The manuscript would be much stronger if the authors could show at least one of the molecular mechanisms by which acetate controls metabolic pathways. I realize that this would be "another" manuscript and cannot be reasonably covered by major revisions. The authors could at least discuss possible molecular mechanisms of this regulation. Essential revisions:1) Modeling choices, model robustness and the question of alternative models need to be more thoroughly addressed.In the revised manuscript, we provide additional details to clarify our modelling choices, in particular regarding (i) the formalism used to model the regulation of metabolism by acetate and (ii) the definition of extracellular acetate as the controlling factor. We have also extensively tested the robustness of the model by performing global sensitivity analyses for each step, from model construction to the analysis of regulation, which confirm that the model and its predictions are robust to parameter uncertainty. Finally, simulations performed with alternative models highlight the biological importance of the identified regulatory interaction on the properties of the system. We appreciate these constructive suggestions, which significantly improve the manuscript.2) Interpretation of experimental results need to be strengthened, in particular with regards to the time scales of transcription and regulation.We have strengthened the interpretation of the experimental results, in particular with regards to the time scales of the different regulatory mechanisms.3) All comments made by reviewers need to be thoroughly and constructively addressed.All of the reviewers’ comments are thoroughly addressed below.Reviewer #2 (Recommendations for the authors (required)):E. coli and the dependence of its physiology on acetate present in the environment. This is an interesting topic relevant to a broad spectrum of questions, including cancer metabolism. The approach is original, and the specific hypothesis that acetate influences transcription and the metabolic flows in previously unknown ways is well-posed and intriguing. I must admit though that I am not totally convinced that the results provide conclusive evidence about their hypothesis, ruling out other possibilities. Clear comparisons with alternative models, parameter sensitivity analyses, and a clearer expression of possible caveats would alleviate this concern, and help readers clearly understand the possible limitations of the approach employed. The work should be of interest to a fairly broad audience of biochemists, microbiologists and systems biologists.The manuscript delves into a major classical question in the study of metabolism, namely how organisms balance the decision of respiring or fermenting carbon sources, and what role fermentation by-products play in this process. Specifically, the authors focus on We thank reviewer 2 for their insightful comments and constructive suggestions.The revised manuscript contains detailed comparisons with alternative models (i.e. models without inhibition of the glycolytic and/or TCA cycle pathways by acetate), which highlight the importance of the identified regulatory interaction for the functioning of the system and its control properties. We have also performed a global sensitivity analysis, which confirms our conclusions are robust to parameter uncertainty, and we discuss the limitations of our approach.E. coli metabolism to acetate, without any a priori knowledge of the exact mechanism. Experimental results confirm that acetate does indeed regulate gene expression in E. coli, but this does not rule out other possibilities. In fact, as stated in the original manuscript, we believe that other mechanisms work in concert, and that acetate may “also regulate E. coli metabolism at the post-translational level  and at the metabolic level ”. These regulatory mechanisms are likely also involved in the response of E. coli to acetate, and this should be explored in future work. We have made this point clearer in the revised manuscript.We would like to stress that the simplified formalism used to consider acetate inhibition represents the integrated regulatory response of Subsection “Testing the model”: the authors mention an excellent agreement between model and experiment in Figure 3. I think the agreement is not bad, but I would not call it excellent. I am not sure a p-value is absolutely necessary, but it would be good to at least have an idea of how much variability the experimental data may have beyond the shown error bars, and whether the agreement is really unexpected, also in light of the parameter fitting presented in Figure 1.We agree with this recommendation. In the revised manuscript, we have removed the term “excellent”, and have carried out global sensitivity analyses to determine 95% confidence intervals around the model predictions. We now evaluate these predictions based on a quantitative metric, namely the variance-weighted sum of squared residuals between predicted and experimental data. This metric is useful to compare the proposed model with alternatives. Results indicate that the model with inhibitions, which is already the only model that satisfactorily fits the data shown in Figure 1, is also the one that most accurately predicts the data used for validation . We present and discuss these new results in the revised manuscript (in the Results and Discussion sections).It would be useful to see Figure 3 in absence of the acetate inhibition effects. Would the results look only quantitatively different or also qualitatively different? Can the authors comment on the intuitive meaning of these curves. Is any of these curves dramatically affected by the new model, relative to alternative standard models?We have followed this suggestion and have predicted the responses to the perturbations shown in Figure 3 with the alternative models, i.e. in the absence of acetate inhibition of the glycolytic and/or TCA pathways . The new simulation results are quantitatively and qualitatively different, which confirms that the curves are drastically affected by the absence of inhibition, further supporting the regulatory role of acetate. We have also carried out metabolic control analyses for the alternative models , which also indicate that the control properties of the system are qualitatively affected by the absence of inhibition. These new results are discussed in the revised manuscript.As opposed to what is mentioned in the Introduction, stoichiometric models could in principle help address the questions posed by the authors. This would not be the case for regular FBA models, but would be true for dynamics FBA models, in which the extracellular concentration of substrates is explicitly accounted for.We agree with reviewer 2 that dynamic FBA models can be used to estimate and predict dynamic flux reorganizations. However, dynamic FBA models are not purely stoichiometric since they include a kinetic description of the uptake pathways, and thus can equally be considered (partly) kinetic models.E. coli to perturbations, but not to obtain a quantitative, mechanistic understanding of overflow metabolism. Indeed, to the best of our knowledge, dynamic FBA models lack regulation of intracellular pathways (such as the inhibition of the TCA cycle and of glycolysis identified in this work and implemented in our model) and do not account for phenomena such as enzyme saturation. Since these models cannot be used to compute elasticity coefficients for the intracellular pathways, they cannot be used either to quantify the flux control exerted by each pathway, or to determine the regulatory routes activated in response to changes of acetate concentration.One of the main objectives of this work was to better understand the control and regulation of acetate metabolism. We could have used dynamic FBA models to predict the response of The “Metabolomics experiments” subsection in the Materials and methods is very brief, and does not get into the detail of flux measurements reported, which are crucial for the model and its interpretation.E. coli K-12 MG1655 and its close derivative strain BW25113 grown on glucose with or without acetate ”. The methods used to obtain these flux measurements are described in detail in the corresponding publications. We have made this point clearer in the revised manuscript:The set of 152 experimental data used for model calibration consist only of time-course exometabolome concentrations and labellings, no flux data . All the flux measurements reported in this study were obtained from the literature. As detailed in the Materials and methods section, to validate the model we used “A total of 170 experimental data (extracellular fluxes and concentrations) obtained from the literature for E. coli to the corresponding perturbations were predicted using each model .”“A total of 170 experimental data (extracellular fluxes and concentrations) were collected from the literature to evaluate the predictive power of each model. […] The steady-state and dynamic responses of We have also added the following details in the “Metabolomics experiments” subsection of the Materials and methods section:2O containing TSPd4 (3-(trimethylsilyl)-1-propanesulfonic acid-tetra deuterated, used as internal standard) at a concentration of 10 mM. […] Spectra were processed using TopSpin 3.2 (Bruker).”“Briefly, 150 µL of filtered broth  were mixed with 50 µL of DE. coli to simultaneously secrete acetate due to fermentation and take it up from the environment, but it would be good for the authors to comment on this.I am curious about whether acetate inflow and outflow are separately measurable (and measured) in the system, or whether the reports are for the net flow. I am not sure if multiple acetate transporters exist, and whether it is in principle possible for 13C-labeling experiments performed in this work can indeed be exploited to determine acetate production and consumption fluxes separately. In fact, in a previous study , we integrated these experiments into an isotopic (stoichiometric) model to demonstrate, as suggested by reviewer 2, that E. coli simultaneously produces acetate from glucose and takes it up from the environment. We have added a couple of sentences about these results in the revised manuscript:The dynamic E. coli simultaneously produces acetate from glucose and consumes it from the environment . They provide information on the forward and reverse fluxes of acetate between the cell and its environment , and thereby improve the calibration of the model.”“These experiments were designed to demonstrate that No parameter sensitivity analysis was performed. I would recommend that the authors either add it, or comment on why it would not be useful.We agree, and are grateful for this suggestion. In the revised manuscript, we present the results of a global sensitivity analysis (Monte Carlo approach) to determine 95 % confidence intervals on (i) the fit of the experimental data , (ii) the estimated parameters (Table 3), (iii) the predicted responses to perturbations , and (iv) the flux control and regulation coefficients . These procedures are detailed in the revised manuscript. The results confirm that our conclusions are robust to parameter uncertainty.The authors' conclusions are presented in an absolute way, that leaves no room for alternative explanations. Partly due to the issues mentioned above, after reading the paper I felt highly intrigued by the results, but not 100% convinced that their explanation is the only possible. I would suggest that the authors (a) take steps to present in a more critical way the results in light of other models that were not explicitly tested, and (b) acknowledge explicitly possible limitation of this study, and avenues to further corroborate the proposed mechanisms.In the revised manuscript, we provide a more critical presentation of the results in light of other models that are now explicitly tested, we discuss the limitations of the approach, and we discuss alternative hypotheses and avenues to further investigate the regulatory role of acetate.Introduction, second paragraph: it becomes clear from the context, but the authors should clearly mention that they refer to extracellular concentration of acetate.We now refer explicitly to the extracellular concentration of acetate.Reviewer #3 (Recommendations for the authors (required)):The manuscript presents an extension of previous work of the group . The authors present an interesting dynamical model of overflow metabolism, producing new insights into a long-standing question in bacterial physiology. The model is confronted with new data that appear to confirm specific model predictions. The major novelty of the model is the addition of a direct control by acetate of the activities of glycolysis (or glucose uptake) and the TCA cycle. Very interesting transcriptomics data appear to confirm these regulatory relationships. However, transcriptional regulation and the direct regulations suggested by the model act at different time-scales. In conclusion, the work is very well carried out and interesting. The connection between the transcriptomics data and the model need to be clarified.E. coli to acetate. Transcriptomics data confirm that acetate regulates E. coli metabolism at least partly at the transcriptional level, and this does not exclude the possibility that other mechanisms may be involved in this integrated response. New analyses with the alternative models confirm that the proposed regulatory interactions are required not only to fit the data shown in Figure 1, but also to accurately predict the response of E. coli to perturbations . Finally, we also demonstrate that unexpected control properties emerge from regulation by acetate . The results of a global sensitivity analysis (suggested by reviewer 2) also strengthen these conclusions.We thank reviewer 3 for their positive feedback and for raising important points that we address and discuss in the revised manuscript. We have clarified the fact that regulation as encoded in the model does not aim to specifically represent transcriptional regulation  but rather the integrated response of 1) The first major argument in favor of control of glycolysis and the TCA cycle by acetate is the quality of fit to bioreactor data. It does not seem surprising to me that the quality of fit  is improved by adding additional regulatory interactions. Even though the figure shows a significant improvement of the fits, it would be nice to see the alternative fits  in supplementary information.We have followed this suggestion and now provide alternative fits  in Figure 1—figure supplements 1–3. These new figures indeed help to visualize the extent to which the alternative models do not reproduce the data, as confirmed by the goodness-of-fit statistical analysis .2) A strong claim of the manuscript is that a kinetic model is needed to account for experimental observations. The fits in Figure 1 use the dynamical model at steady state. How do the predictions compare to the alternative models ?The fits in Figure 1 are not based on the dynamic model at steady-state. The simulated and experimental data are the time-course dynamics of biomass, glucose and acetate concentrations and of acetate labelling. We now provide the predictions of alternative models  in supplementary figures. The predictions are drastically affected by the absence of inhibition, confirming that regulation by acetate is necessary to explain the observed behaviours.3) The transcriptomics data are very interesting. However, these data were acquired at steady state, reflecting a slow adaptation (order of magnitude of the generation time) of physiology. The regulatory interactions (square brackets in the equations in the subsection “Model construction”) are instantaneous effects of the concentration of acetate. I fail to see how the transcriptomics data can argue in favor (or disfavor) of the kinetic model.As noted by reviewer 3, the inhibition of glycolysis and of the TCA cycle by acetate is considered immediate.Saccharomyces cerevisiae cells exhibit a major transcriptional reprogramming within the first 5 minutes after a glucose pulse. The authors showed that most TCA genes “were immediately downregulated” after the pulse. A similar time-scale has been reported for mRNA dynamics in E. coli, with a median mRNA half-live of just 3.1 min during exponential growth on glucose . This fast mRNA dynamics explains how the transcriptome of E. coli cells respond in less than 5 minutes to external perturbations, such as glucose exhaustion  and changing oxygen levels . As shown in the latter study, “significant differential expression of several genes within 30 seconds indicates immediate cell adaptation to the changed environment”. These findings indicate that the metabolic and transcriptional responses of E. coli and other microorganisms are tightly intertwined rather than occurring sequentially on different, independent time scales.However, transcriptional regulation acts much faster than assumed by reviewer 3 and is not of the “order of magnitude of the generation time”. For instance, Kresnowati et al. (doi: 10.1038/msb4100083) found that glucose-limited E. coli to acetate. With this objective in mind, the model indeed provides new insights on global rules by which acetate governs E. coli metabolism. Transcriptomics experiments confirm that acetate control regulates the flux capacity of the glycolytic and TCA pathways at the transcriptional level, but we do not rule out other hypotheses. As discussed in the manuscript, we also believe that transcriptional regulation is not the only mechanism by which acetate regulates E. coli metabolism and physiology. We stated in the initial manuscript that acetate may “also regulate E. coli metabolism at the post-translational level  and at the metabolic level ”. These mechanisms act on a similar time scale – seconds to minutes – and are also represented by the feedback interaction included in the model. We have made this point clearer in the revised manuscript. Further work will be necessary to determine whether additional mechanisms are involved in the response of E. coli metabolism to acetate. We thank reviewer 3 for raising this important point, which we have clarified in the revised manuscript by adding the following sentences in the Discussion:It seems important here to clarify that when constructing the model, the aim was not to specifically represent transcriptional regulation but rather the integrated response of E. coli metabolism to acetate, regardless of the underlying molecular mechanisms. […] These models should help to computationally evaluate the biological relevance of detailed regulatory mechanisms, as illustrated here.”“In this coarse-grained model, the simplified formalism used to consider acetate inhibition represents the integrated regulatory response of 4) The perturbation experiments  are probably the best test of a dynamical model. The model correctly predicts the observed dynamics, which are clearly on time-scale of minutes, precluding transcriptional regulation as a cause. The authors could suggest possible molecular mechanisms.E. coli metabolism via other mechanisms operating at the post-translational level or at the metabolic level. We have added a paragraph discussing in detail the time-scales of regulation and other possible regulatory mechanisms in the revised manuscript (see our answer to comment #3).As noted by reviewer 3, the perturbation experiments are indeed the best test of a dynamic model. The dynamics observed in response to an acetate pulse  occur on a time-scale of 8 minutes, which cannot be explained by transcriptional regulation alone. In fact, as discussed in the manuscript, we also think it likely that acetate regulates 5) The remaining results of the manuscript address model properties and are very well carried out. Under the hypothesis that the model is correct, these analyses yield interesting insights into the physiology of overflow metabolism.We thank reviewer 3 for this positive feedback on the analyses performed with the model.E. coli. The experiments are very well carried out and pertinent new data are presented.In conclusion: the manuscript describes a very interesting extension to current models of overflow metabolism in My major concern is that the transcriptomics data seem disconnected from the kinetic model. Long-term adaptation can be achieved by modifications in gene expression. However, this does not correspond to the mechanism described by the model equations. The manuscript would be much stronger if the authors could show at least one of the molecular mechanisms by which acetate controls metabolic pathways. I realize that this would be "another" manuscript and cannot be reasonably covered by major revisions. The authors could at least discuss possible molecular mechanisms of this regulation.E. coli cells. Importantly, we show that this inhibition involves at least the transcriptomics response, which is in fact fast and tightly intertwined with the metabolic response (see above). We do not rule out other possible explanations and are working on identifying additional mechanisms involved in this response, which are indeed beyond the scope of this manuscript. In the revised manuscript, we discuss other molecular mechanisms possibly involved in the previously unknown regulatory role of acetate, and how the present model can help to address this question.We thank reviewer 3 for their constructive suggestions. Using the model, we determined that acetate inhibits the glycolytic and TCA fluxes, and identified its global impact on


Since their initial discovery in 1976, mesenchymal stem cells (MSCs) have been gathering interest as a possible tool to further the development and enhancement of various therapeutics within regenerative medicine. However, our current understanding of both metabolic function and existing differences within the varying cell lineages  is severely lacking making it more difficult to fully realize the therapeutic potential of MSCs. Here, we reconstruct the MSC metabolic network to understand the activity of various metabolic pathways and compare their usage under different conditions and use these models to perform experimental design. We present three new genome-scale metabolic models (GEMs) each representing a different MSC lineage  that are biologically feasible and have distinctive cell lineage characteristics that can be used to explore metabolic function and increase our understanding of these phenotypes. We present the most distinctive differences between these lineages when it comes to enriched metabolic subsystems and propose a possible osteogenic enhancer. Taken together, we hope these mechanistic models will aid in the understanding and therapeutic potential of MSCs. In vitro MSCs are a heterogeneous population of cells that can be expanded and differentiated to chondrocyte, adipocyte and osteocyte lineages  originate in the mesenchymal germ layer of the embryo and can be isolated from various adult tissues, including bone marrow, adipose tissue and skeletal tissue to name a few. lineages . MSCs halineages . The diflineages  and chanlineages .in vitro expansion and initial in vitro osteogenic differentiation of MSCs needed for therapeutic techniques to succeed.This study focuses on MSCs during expansion, early osteogenesis and early adipogenesis because of the potential utility of the cells as immune-modulators  and the Over the past decade as the rate of biological data generation has increased, data analysis and interpretation has become a bottleneck of biological discovery , necessiin silico laboratory that helps to design experiments for the cell culture laboratory with a higher probability of success.Genome scale metabolic models have been applied to various biological problems including drug resistance and biomarker identification and have been explored using these approaches . PreviouHere, we describe three parallel GEMs of MSC metabolism: during expansion, iMSC-E-1972; osteogenic differentiation, iMSC-O-1900; and adipogenic differentiation, iMSC-A-2036. We have benchmarked these models against generic human metabolic functions, the existing model of MSC metabolism, iMSC1255, and known metabolic differences between lineages. We then used these benchmarked models to propose ideas for optimizing osteogenic differentiation of MSCs that will hopefully improve our understanding of the mechanisms underlying these processes.Transcriptomic data were obtained from ArrayExpress . Data seThe selected data sets were processed using MATLAB . First, the data set IDs were converted to Entrez IDs using either DAVID  or the c2 flask in basal medium in an incubator at 5% CO2, 37°C and 95% humidity. For experiments cells were seeded in 75cm2 flasks at 6000cells/cm2. Cells were either grown in basal growth medium or osteogenic differentiation medium. 5000 IU/ml of heparin , 1% Penicillin/Streptomycin , and 10% Platelet lysate  into DMEM/F12 + Glutamax growth medium . This mixture will hereby be referred to as the basal growth-medium. The platelet lysate (PIPL) was centrifuged at 5000rpm/4975g for ten minutes before the supernatant being added to the medium to remove platelet debris and coagulation. The differentiation medium also with the addition of dexamethasone , BMP-2 , β-glycerophosphate , and ascorbic acid . Cells were used between passage 2 and 6 and care was taken to ensure that at least 5 of the same donors were used for each lineage. Medium was changed on average every 48 h.Mesenchymal stem cells were obtained from 5 to 6 different donors from LONZA . Cells were stored in liquid nitrogen, thawed and seeded in 175cmBaseline and spent medium samples were assayed for glucose and lactate concentration using an ABL90 blood gas analyzer . In addition, medium samples were analyzed by high pressure liquid chromatography mass spectrometry as described below.Cell number was determined by trypan blue exclusion and counting in a hemocytometer and an approximate doubling time calculated, this was confirmed with estimates from the literature . An estiUPLC-MS Analyses were performed with an UPLC system  coupled in line with a quadrupole-time of flight hybrid mass spectrometer  as described in Model construction was performed using the Constraint-based reconstruction and analysis (COBRA) Toolbox version 3 in MATLAB, 2017b . The GEM Recon3  was usedRxn_fluxes_for_O_model, Rxn_fluxes_for_E_model, and Rxn_fluxes_for_A_model). Finally, these models were pruned to give fully functional condition specific models. The models were then checked for the inclusion of core reactions and biological feasibility. We validated the three new reconstructions against the original Recon3 model using Memote , in combination with flux variability analysis (FVA)  and fluxThree comparable models of MSC metabolism representing expansion, osteogenesis and adipogenesis were built using publicly available transcriptomic data and parameterized with newly generated metabolomic data. It was shown that these models could recapitulate known metabolic differences among the three cell subtypes and that has allowed the proposal of novel interventions to optimize osteogenesis that will guide future investigations.In order to parameterize the three models of MSC differentiation extracellular metabolomic data (UPLC-MS) was generated for each cell lineage. The estimates of uptake and secretion rate of the measured metabolites were applied as constraints to the metabolic model, enabling them to be analyzed in the context of previously published data on metabolic network structure as summarized in Recon 3 (add in relevant reference), and MSC transcripts from cells at the relevant stage and lineage of differentiation (add in references for the array express data sets).A total of 59 unique metabolites were detected across the three cell culture lineages. Of these metabolites, 42 were detected in the expansion samples, 50 were detected in the osteogenic samples, and 44 were detected in the adipogenic samples. Interestingly, some metabolites showed opposite trends in the different cell lineages. For example, asparagine, folate, and fumarate were taken up only in expanding cells. Ascorbate, aspartate (measured only in O), glycerol 2 phosphate, glycl groups, palmitate, histidine, pantothenate, proline and threonine (only measurable in O) were taken up only in osteogenic cells. Oxoproline, acetyl carnitine, adenine, adenosine, biotin, phosphocholine, guanine, phenylalanine, riboflavin, and spermine were taken up only in adipogenic cells  and glutamine  under both aerobic and anaerobic conditions. There were slight differences in theoretical ATP production in the literature and existing models but around 31 from aerobic glucose, 2 from anaerobic glucose, 23 from aerobic glutamine and anaerobic glutamine are common figures obtained in other models including those specifically curated for central energy metabolism . HoweverATP production from other important carbon sources was also generally close to theoretical values see  in the tThe uptake and secretion fluxes of amino acids in the proliferation model and the original iMSC1255 values were compared . Both differentiating models had mean predicted values twice as high as the proliferating model. The minimum values for the iMSC-A and –O were around ten times than in iMSC-E. However, the maximum value in iMSC-E was approximately 10% higher than either differentiating model, indicating that differentiating models showed increased mitochondrial function (p ∼ 0 Wilcoxon rank test).Various studies have analyzed metabolism in osteogenic and adipogenic differentiation of cells, sometimes in MSCs and sometimes in other cell types . The newfunction . It has function . This isConversely, it has been shown that suppressing glutaminase activity and the contribution of glutamine to the TCA cycle prevents osteogenesis . iMSC-O-A key feature of adipogenesis is the upregulation of glucose 6 phosphate dehydrogenase activity. G6PDH forms 6-Phosphonoglucono-D-lactone (6PGL) and NADPH. 6PGL contributes to the pentose phosphate pathway and therefore the production of nucleotides and reducing potential via NAPDH. NADPH contributes to the synthesis of fatty acids by supplying a reducing agent. This is a key metabolic coupling between glucose and fatty acid utilization and storage . Overallt-test was performed using 1e-12 as a p-Value cut-off; see Metabolic_difference_rxn_pred in To assess metabolic differences between MSCs undergoing expansion, osteogenic differentiation, and adipogenic differentiation, Markov chain Monte Carlo MCMC,  samplingReactions in the model were grouped by metabolic subsystem enabling an enrichment analysis to be performed on lists of up regulated reactions (predicted to carry more flux) in each cell type compared to each of the others, shown in Metabolic modeling allows the generation of hypotheses about means of optimizing one cell lineage over another. To propose means of improving osteogenesis desirable for regenerative medicine, combined models of either expansion-and-osteogenesis or adipogenesis-and-osteogenesis were created. Two of each of these models were produced, one representing the constraints on reactions from each lineage with other reactions blocked. These models were then subjected to relaxation analyses.Relaxed_rxn_lists, The results of relaxing the model to find a solution intermediate between either the expansion model or the adipogenic model and the osteogenic model produced lists of key reactions to modify to move towards osteogenesis  were reconstructed, from the global human metabolic network (Recon3), publicly available transcriptomic data and metabolomic data generated here. These models largely share reactions , an expected result considering the transcriptomic data seem to have had the strongest effect on inclusion/exclusion of reactions as demonstrated by showing the biggest change in the number of reactions included in each model . These tThis comparison is in some ways limited to broad comments as the enzyme activity assays in the paper presented by The new MSC models all pass key metabolic sanity checks and manage an almost 3-fold increase in metabolic coverage compared to iMSC1255, roughly a proportional increase to that between the base model and Recon1. This similarity suggests retention of similar level of cell specificity compared to iMSC1255 despite the great increase in metabolic coverage. However, the models reported here are able to more correctly capture the production of ATP from various substances and are additionally able to perform more metabolic functions compared to the previously existing MSC model, iMSC1255 . One of the areas of metabolic function that the models presented in this paper are more adept at in comparison to iMSC1255 is fatty acid metabolism. This is illustrated by the example of iMSC1255 lacking beta hydroxylase activity , but theAs fatty acid metabolism is key to adipogenesis, the additional detail of beta hydroxylase inclusion in the new presented models will likely prove important to the utility of their predictions. The increased coverage of fatty acid and lipid metabolism that is seen in them is in large part due to the updated base reconstruction used for the model’s reconstruction . Additionally, when the iMSC1255 model was constrained using the same metabolomic data sets as were used to reconstruct the presented three new MSC models, the new models  were better able to match the results of A good qualitative fit between known metabolic differences between the three lineages studied and the relationships between the predicted fluxes in the three new models was observed. Key metabolic features, such as the production of ATP changing in osteogenesis, are reflected in the differences between the models, indicative of a realistic reconstruction reflecting expected biological behavior. Differences in amino acid metabolism, ornithine and to a lesser extent glutamine and tryptophan, are shown between osteogenesis and expansion in ways that reflect known differences in metabolism during osteogenic differentiation.G6PDH expression is known to be key to producing NADPH for fatty acid synthesis and overall activity of this enzyme is greater in the adipogenic model than the other two cell lineage models, an observation suggesting that these models reflect this aspect of metabolism well. However, comments on the activity of G6PDH in relation to this paper are complicated by the reversibility of this reaction and the presence in the models of multiple forms of this enzyme. In iMSC1255, the flux is only predicted to be in the reverse direction, creating NADP not NADPH. This does not agree with the previous consensus, our models suggest a mix, specifically our adipogenic model favors NADPH production in accordance with the literature . These cOverall, these results demonstrate that this key area of metabolism is well reflected in the models which gives a good ground for building the iMSC-O-1900 model into the later and potentially more metabolically diverse later stages of osteogenesis.The groups of metabolic reactions that are upregulated in each of the lineages reflect the changes in function of cells during expansion and differentiation. The metabolic subsystems upregulated in expansion are varied, reflecting the necessity of the cells for varied reaction activity to be able to produce cell biomass, membranes, and nucleic acids in order to grow. On the other hand, the changes in adipogenesis are more concentrated around fatty acids and fatty acid synthesis. This is to be expected, especially the increased activity of fatty acid synthesis, which is a key feature of adipocytes. The areas of metabolism with the most concentrated changes in osteogenesis is fatty acid oxidation, which reflects increased oxidative metabolism in the differentiating cells – a phenomenon previously reported by multiple studies . All of The results of the relaxation of reactions between adipogensis or expansion and osteogenesis suggest that alterations in the need for malate are changed, with either reduced uptake or a switch to secretion being observed in both cases. Fatty acid and lipids, particularly those involved in signaling  and those involved in cell membranes and glycosylation  are altered as well. These are key metabolites for the rearrangement of cell membranes and trans-membrane glycoproteins as well as cell signaling and therefore seem plausible as metabolic markers of differentiation. One of the reactions highlighted is the need to increase transport of citrate from the extracellular space to the cytosol. It has been shown that growing cells on citrate rich materials encourages osteogenesis , this main vitro osteogenesis. One of predicted solutions, the citrate transporter, concurs with a previously identified target which encourages further possibilities of similar predictions. Through the use of mechanistic models such as are presented here, we provide a blueprint for the application and engineering of regenerative medicine therapies where promising therapeutics like MSCs can be made more efficient and attainable.We present three new genome-scale metabolic models of MSC metabolism, representing expansion, osteogenesis and adipogenesis differentiation. These newly reconstructed models are increased in scope compared to previous models of this cell type both in terms of the coverage of multiple lineages in models produced specifically for two new lineages and with the models due to usage of a new and much improved base human metabolic reconstruction (Recon3). We computed a variety of metabolic phenotypes, demonstrating that the models presented here accurately represent qualitative and quantitative cellular characteristics and important differences between the cell types. Having validated these models, we used them for model-driven experimental design with the goal to optimize https://www.ebi.ac.uk/metabolights/MTBLS2844), accession number MTBLS2844.The data presented in the study are deposited in the MetaboLights repository (The studies involving human participants were reviewed and approved by the National Bioethics committee number VSN19-189. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.TS and SMcG designed the study, analyzed the data, and drafted the manuscript. JY, ÓR, and ÓS designed the study and reviewed the manuscript. All authors gave their final approval of the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.