C. reinhardtii, the central metabolic fluxes of growth on acetate have not yet been determined. In this study, 13C-metabolic flux analysis (13C-MFA) was used to determine and quantify the metabolic pathways of primary metabolism in C. reinhardtii cells grown under heterotrophic conditions with acetate as the sole carbon source. Isotopic labeling patterns of compartment specific biomass derived metabolites were used to calculate the fluxes. It was found that acetate is ligated with coenzyme A in the three subcellular compartments  included in the model. Two citrate synthases were found to potentially be involved in acetyl-coA metabolism; one localized in the mitochondria and the other acting outside the mitochondria. Labeling patterns demonstrate that Acetyl-coA synthesized in the plastid is directly incorporated in synthesis of fatty acids. Despite having a complete TCA cycle in the mitochondria, it was also found that a majority of the malate flux is shuttled to the cytosol and plastid where it is converted to oxaloacetate providing reducing equivalents to these compartments. When compared to predictions by flux balance analysis, fluxes measured with 13C-MFA were found to be suboptimal with respect to biomass yield; C. reinhardtii sacrifices biomass yield to produce ATP and reducing equivalents.Despite the wealth of knowledge available for  Rising levels of carbon dioxide in the atmosphere have increased the need to develop more sustainable sources of feedstocks traditionally supplied by petroleum. Although there are many options for renewable energy, certain sectors (such as transportation) still require liquid fuels. One of the more promising sources of liquid transportation fuel is algae derived biodiesel , 2, whicSynechocystis sp. PCC 6803  acetic acid: 0.3 [13C2] labeled acetic acid. Cells were cultivated at 25°C, 250 rpm and sparged with air at a rate of 1 vvm. Cells were grown in pH-stat mode at pH 7.5, which was controlled using 20% NH4OH and 20% acetic acid in the same isotopic ratio as the original media. Therefore, the concentration of acetate in the reactor was maintained at a constant concentration and isotopic 13C ratio throughout the growth period. Acetate uptake was monitored by tracking acetate addition to the reactor. Cells were sampled in mid-exponential phase growth, as indicated in Cells were sampled and processed in a modified procedure from that reported in Lee and Fiehn . Cells w4OH was then added to the supernatant and dried in a centrivap and kept at -80°C until analysis by GC/MS .Analysis of labeling in starch and cell wall hydrolysates was analyzed as described by Allen et al. . The pelCell pellets resulting from the methanol extraction step was resuspended in 1 ml 6 M HCl and heated at 120°C for 24 hours under vacuum to hydrolyze the proteins to amino acids. The solution was then dried in a centrivap and kept at -80°C until analysis by GC/MS (to measure amino acid composition and concentrations).Samples for organic acids, amino acids and sugars were derivatized for GC/MS analysis. First, dried samples were resuspended in 100 μl methoxyamine HCl in pyridine (20 mg/ml) and placed in a heating block at 40°C for 90 minutes. Immediately following this, 100 μl of either trimethylsilane (TMS) or t-butyldimethylsilyl (TBDMS) was added; samples were vortexed and then, placed in a heating block at 40°C for 30 minutes. For the analysis of sugars and organic acids, the derivatization agent used was TMS; for the analysis of amino acids, TBDMS was used. The TMS derivatized sugars produced characteristic fragment ions that contain information on positional labeling of the carbon atoms .Dried methanol extracts were resuspended in 600 μl hexane to which 500 μl butylamide was added. The reaction was allowed to proceed for one hour at 40°C, after which the reaction was quenched by drop-wise addition of 4M HCl. The samples were then vortexed, the organic layer was removed and the aqueous layer was extracted again with 500 μl hexane. After vortexing, the organic layer was removed and combined with the first extract and dried. The dried butylamide derivitaives were then resuspended in 300 μl hexane and analyzed with GC/MS.GC-MS analysis was carried out on an Agilent 7890A gas chromatograph coupled to an Agilent 5975C inert MSD quadrupole mass spectrometer. The GC was equipped with a HP-5MS column  with helium as the carrier gas. Interface and ion source temperatures were both 230°C. Amino acids were analyzed with a split-less 1μl injection. Oven temperature was initially held at 135°C for 3 minutes followed by a ramp of 2.5°C/minute up to a temperature of 280°C for 2 minutes. Sugars in cell wall hydrolysate and starch samples were analyzed with a 1μl injection 10:1 split ratio. The oven temperature was initially held at 150°C for 1 minute followed by a ramp of 5°C/minute up to a temperature of 175°C followed by a ramp of 3°C /minute up to a temperature of 240°C where it was held for 2 minutes followed by a final ramp of 10°C /minute up to 250°C. Fatty acids were analyzed with a split-less 1μl injection. Oven temperature was initially held at 80°C for 3 minutes followed by a 30°C/min ramp up to 200°C where it was held for 2 minutes followed by another ramp of 6°C/min up to 305°C. Labeling patterns of metabolites, amino acids, fatty acids and sugars used in the model are given in the supplementary material, C. reinhardtii [The previously reported stoichiometric model for nhardtii  was usednhardtii . The objIn general, the process is to identify the set of reactions and corresponding fluxes that are best supported by the 13C labeling data. The evaluation of alternative network topologies was performed first followed by parameter optimization see . Flux evcal was the calculated MDV and MDVexp was the experimental value of a particular species. The optimization was performed in MATLAB R2009b  using fmincon that implements optimization for constrained non-linear functions. Further, since the results from non-linear optimization are dependent on initial flux estimates, different estimates were tested for obtaining the best solution. The flux model variance and 95% confidence intervals were determined with the method of Antoniewicz et al. [The IDVs were used to calculate the MDVs, and the calculated MDVs were used for least square optimization. The objective function was minimization of the sum of the square of the difference between the experimentally observed and simulated MDVs see .Obj=∑i=z et al. . Since wSince this was a non-weighted least squares optimization, the goodness of fit was evaluated by plot of the residual vs. MDV  for the original model and R2 is the residual for the new model to be tested, . The degrees of freedom for the original model and the new model are represented by n1 and n2 respectively. The number of experimental observations, which are the MDVs in our case, is represented by p. It should be noted that the F statistic value accounts for increase in degrees of freedom, and thus precludes improvement in fits as a result of overfitting. Once the F value is calculated, it is compared against critical F value, at a specified confidence level (90–99%), for testing the null hypothesis. If the calculated F value is greater than the critical value at a given confidence, it implies that the model is statistically different and fits the data better at the desired confidence level.Here, R2 or HCO3- are lumped and modeled as CO2. Intracellular CO2 produced by metabolic reactions was considered to be freely transported across compartments, and thus assumed to be at isotopic equilibrium inside the cell. The exchange of extracellular and intracellular CO2 from air was also included in the model. The CO2 transport was modeled as a reversible reaction and was characterized using an exchange coefficient; which is a parameter that gives a degree of reversibility for the transport. We used the method of Weichert and de Graaf for exchange coefficients [In this study, dissolved COficients .C. reinhardtii [13C-metabolic flux analysis (13C-MFA)  see . Reactiohondrial , and CIShondrial . Howeverhondrial . Moreovehondrial  and citrractions . TherefoC. reinhardtii. PCK1b is predicted by TargetP to be targeted to the cytosol [C. reinhardtii, of which PPD2 is most likely chloroplastic based on homology to Flaveria pringlei and TargetP predictions [13C tracing and hence are represented by one reaction in the model. Due to strong evidence for the presence of these reactions in the plastid, plastidic gluconeogenesis was also included in the model. Other reactions included in the simplified network include: glycolysis and pentose phosphate pathways in both the cytoplasm and plastid, TCA cycle and glyoxylate shunt in mitochondria, transport of hexoses, pentoses, trioses and select TCA cycle intermediates across compartments [2 fixation by RuBisCO was not included because the culture was grown heterotrophically (without light on acetate) and therefore RuBisCO would not be active [Gluconeogenesis is necessary for growth on acetate; therefore, we also closely examined the localization of enzymes in this pathway. Two splice variants of phosphoenolpyruvate carboxykinase, PCK1a and PCK1b, have been reported in  cytosol  and the  cytosol . Enzymes cytosol . This padictions , 49. In e active .The ability to accurately model isotopic labeling patterns depends on the selected network topology and the fluxes through these networks. Compartment-specific labeling data was obtained based on the method developed by Allen et al. . DiffereThe importance of cytosolic gluconeogenesis was evaluated by comparing the network topologies where it was included or excluded. Case A and Case E differ in inclusion of cytosolic gluconeogenesis or its exclusion, respectively  and bothIn order to test whether plastidic gluconeogenesis should be included in the final network topology, we compared residuals between Case B and C . InclusiFinally, the presence of a non-mitochondrial citrate synthase was also evaluated. The inclusion of a non-mitochondrial citrate synthase improved the statistical fit of the data in both Case B and D when compared to Case A and E with 94% and 99% confidence respectively. This provides strong evidence that a non-mitochondrial citrate synthase is indeed present and should be included in the final network topology.Based on statistical analysis of possible network topologies derived from the Chlamydomonas genome, the optimal network topology which best describes heterotrophic growth was found to be Case B, and thus all subsequent simulations were based on this network topology.2 found in the sparging gas used in the reactor. During heterotrophic growth conditions, a net transport of CO2 out of the cell is expected due to respiration, however, CO2 can readily diffuse into the cell from the external media or can be actively transported into the cell [2 exchange. As an example, Case B will be described. The base case simulation excluded the G parameter and CO2 exchange, a second simulation included CO2 exchange, which reduced the residual . At this time point, the ratio of total biomass to the initial was approximately 9.5 which results in the % original biomass estimated at 10.5%. CO2 dilution from air had an exchange coefficient of 0.24 which corresponds to a dilution flux of 24% of the net outward CO2 flux.Preliminary metabolic flux analysis simulations had high residual values in comparing the simulated against the experimental labeling patterns . In batcthe cell . For eacresidual  by 60%. C. reinhardtii is presented in 2 exchange. 95% confidence intervals for each flux are given in the supplementary material  is converted to AcCoA by acetyl-coA synthetase in the cytoplasm and condensed with oxaloacetate (OAA) into citrate by citrate synthase before being transported across the mitochondrial membrane for entry into TCA cycle. The second pathway of acetate assimilation, the transport of acetate directly into the mitochondrion, accounts for roughly one-third of the total acetate uptake (35.6%). In the mitochondrion, acetate is converted to AcCoA and proceeds through the TCA cycle, providing reducing equivalents and biomass precursors. About 80% of the isocitrate flux in the mitochondrion is diverted through isocitrate dehydrogenase while 20% is sent through the glyoxylate shunt (isocitrate lyase), which indicates that a large portion of the flux is diverted towards NAD(P)H generation. The flux through mitochondrial malate dehydrogenase was 28% of the incoming flux into malate, indicating a minor contribution towards the TCA cycle. This is a direct effect of citrate being the major input into the TCA cycle. The majority, c.a. 72%, of the TCA cycle flux through malate is exported to the cytosol and converted to oxaloacetate. As shown in Based on the results of our All of the cytosolic gluconeogenesis flux is directed toward the production of cytosolic trioses; there is no flux through cytosolic pyruvate kinase and pyruvate dehydrogenase. Most of the flux through the trioses in the cytosol is directed toward the plastid. Overall, the fluxes in the plastid and cytoplasm derived from cytosolic gluconeogenesis account for roughly 11.5% of the acetate uptake flux. There is no evidence of oxidative pentose phosphate pathway flux in either the cytosol or plastid.13C-MFA were compared to predicted fluxes from flux balance analysis (FBA) for two different objective functions, maximize biomass and maximize ATP. There are two major differences between MFA and FBA; first, FBA includes balances on energy and reducing equivalents, and second, FBA uses linear programming to determine the optimal solution for the underdetermined system. In comparison, MFA uses experimental measurements to quantify in vivo fluxes. The FBA model originally reported by Boyle and Morgan [in vivo fluxes calculated with 13C-MFA H. We found that the split towards the glyoxylate shunt was only 20% of the incoming flux from isocitrate. By comparison, Samejima and Myers have reported 26% acetate assimilation to biomass in Chlorella pyrenoidosa in heterotrophic growth on acetate [E. coli and S. cerevisiae grown on acetate have a split towards glyoxylate of 47% and 27% of the total incoming isocitrate flux respectively [The glyoxlate shunt plays a key role in the utilization of acetate as it conserves the 2 carbons of acetate, and the flux through the shunt is indicative of acetate assimilated into biomass. In contrast, the flux through isocitrate dehydrogenase results in loss of CO acetate . Flux anectively , 56. Theectively , 58. Thuectively  and for ectively .13C-MFA to determine the best fit to our data. This analysis resulted in improved fit with the inclusion of a cytosolic citrate synthase, indicating that there is indeed activity of non-mitochondrial citrate synthase in heterotrophically grown Chlamydomonas, which has been reported previously [13C-MFA. This problem could be addressed by obtaining more detailed compartment specific isotopic labeling data; however, the premier method used in plant cells, non-aqueous fractionation [We evaluated several different network topologies based on the presence of specific genes in the Chlamydomonas genome using eviously . It is dionation , 62, hasArabidopis [C. reinhardtii [In our MFA calculations, no flux through the oxidative pentose phosphate pathway (oxPPP) was found in the plastid. Low plastidic oxPPP was also observed in heterotrophic growth on glucose for Arabidopsis suspension culture . Many biabidopis , where anhardtii . It shounhardtii . In thisS. cerevisiae grown on acetate [C. reinhardtii, there is no direct competition between fatty acid synthesis and starch synthesis. A similar result was reported by Li et al. for triacylglycerol (TAG) production in C. reinhardtii; when grown heterotrophically, there was no significant difference in the total amount of TAG between a starchless mutant and its parent strain [C. protothecoides for growth on glucose [13C labeling data alone. Thus, we could not definitively distinguish whether plastidic gluconeogenesis in addition to the cytosolic gluconeogenesis is actually playing a physiological role in heterotrophic growth.The overall fluxes in the plastid were relatively low and were mainly for providing precursors for biomass. The main carbon source for plastidic biomass synthesis, aside from lipids, was triose phosphates imported from the cytosol. Interestingly, we found that plastidic fatty acid synthesis occurs via direct assimilation of acetate with no apparent contribution from plastidic pyruvate. The direct assimilation of acetate to lipids was also observed in  acetate . Thus, ut strain . Labelli glucose , 37. We 13C-MFA were compared with fluxes predicted using FBA with either maximal growth or for maximal ATP production as objective functions. Although fluxes are readily estimated for a variety of objective functions, they do not necessarily reflect what is actually occurring in the cell, if the cell metabolism is suboptimal. Previous studies of E. coli grown on acetate or succinate have shown that FBA can predict intracellular fluxes quite well with an objective function of maximizing biomass [E. coli grown under these conditions operates at near optimal conditions. While the two objective functions we evaluated are in no way an exhaustive search of the solution space, they are two of the most widely used in trying to capture wild type growth. In this study, FBA is not able to accurately capture intracellular fluxes  plays an important role in acetate assimilation based on the reduced residuals when included in the network topology. However, we are unable to definitively determine the relative contribution of the cytosolic form over the mitochondrial form. These fluxes could potentially be resolved with more detailed compartment specific isotope labeling data, or improved methods for fast and specific subcellular fractionation in algae. The remaining acetate is assimilated directly into fatty acids in the plastid and is determined mainly by the demand for lipids in the biomass composition. Fluxes obtained from our 13C-MFA experiment were compared to predictions made by FBA. We found that neither objective function of maximum biomass or maximum ATP could quantitatively predict intracellular fluxes; in fact, C. reinhardtii grew suboptimally, sacrificing carbon for energy production. Metabolic flux analysis using isotopically labeled substrates can be further used for studying different growth modes, such as autotrophic or mixotrophic growth with consideration of dynamic labeling patterns [C. reinhardtii accumulating lipids when starved for nitrogen [Using patterns , 68, whipatterns . An intenitrogen –72, sulfnitrogen  or tracenitrogen .S1 Figo) (panel A). Stationary phase is typically not reached until approximately 130 hours. pH in the bioreactor during the course of the experiment is shown in panel B.Cells were sampled in mid-exponential phase as is evident in the plot of ln(OD/OD(DOCX)Click here for additional data file.S2 FigStatistical evaluation of the residuals for Case B: (A) Probability plot for residuals approximated by a normal distribution of μ = -.0774 and σ = 1.85, indicated that the residuals were normally distributed. (B) Residuals versus Mass distribution vectors (MDV) indicated no correlation.(DOCX)Click here for additional data file.S3 FigFor a given initial estimate for fluxes, the set of isotopomer balances were solved to obtain isotopomer distribution vectors (IDV). The isotopomer distribution vectors (IDV) were used for calculation of the Mass distribution vectors (MDV), and the calculated MDVs were used for least square optimization. The objective function was minimization of the sum of the squares of the difference between the experimentally observed and simulated MDVs. The iterative process is repeated till a minimum is found to obtain a set of fluxes that can explain the labeling patterns, 43, 44.(DOCX)Click here for additional data file.S1 TableFlux values in mol/10 mol of acetate uptaken.(DOCX)Click here for additional data file.S2 Table(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.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S8 TableOA and MAL are lumped, as are PEP and 3PG.(DOCX)Click here for additional data file.S9 TableFlux values in mol/10 mol of acetate.(DOCX)Click here for additional data file.S10 Table(DOCX)Click here for additional data file.


Gastric cancer (GC) is one of the most malignant tumors with a poor prognosis. Alterations in metabolic pathways are inextricably linked to GC progression. However, the underlying molecular mechanisms remain elusive. We performed NMR-based metabolomic analysis of sera derived from a rat model of gastric carcinogenesis, revealed significantly altered metabolic pathways correlated with the progression of gastric carcinogenesis. Rats were histologically classified into four pathological groups  and the normal control group (CON). The metabolic profiles of the five groups were clearly distinguished from each other. Furthermore, significant inter-metabolite correlations were extracted and used to reconstruct perturbed metabolic networks associated with the four pathological stages compared with the normal stage. Then, significantly altered metabolic pathways were identified by pathway analysis. Our results showed that oxidative stress-related metabolic pathways, choline phosphorylation and fatty acid degradation were continually disturbed during gastric carcinogenesis. Moreover, amino acid metabolism was perturbed dramatically in gastric dysplasia and GC. The GC stage showed more changed metabolite levels and more altered metabolic pathways. Two activated pathways  substantially contributed to the metabolic alterations in GC. These results lay the basis for addressing the molecular mechanisms underlying gastric carcinogenesis and extend our understanding of GC progression. H. pylori infection and environmental factors such as diet  with water suppression. A fixed total spin-spin relaxation delay of 80ms were used to attenuate broad NMR signals of slowly tumbling molecules with short T2 relaxation times, and retain signals of low molecular weight compounds. The spectral width was 12 KHz with an acquisition time per scan of 2.7 s, and a total of 256 transients were collected into 64K data points for each spectrum. For the purpose of resonance assignments, 2D NMR 1H-1H TOCSY spectra were recorded on selected samples. The acquisition parameters were referred to the literatures [The serum samples (300 μL) were thawed on the ice, and mixed with 210 μL of deuterated phosphate buffer . After centrifugation at 12000 g at 4°C for 10 min to remove the precipitates, 500 μL of the supernatants was transferred into a 5 mm NMR tube and analyzed by NMR spectroscopy . 1D 1H Ceratures , 59.3 resonance of lactate at 1.33 for serum spectra. Each 1H NMR spectrum was segmented into regions of 0.001 ppm and integrated using the MestReNova Version 6.1 . For the metabolites with highly overlapping peaks, we selected the non-overlapping peaks to accurately calculate the spectral integrals for these metabolites. The spectral region of serum was δ 9.00-0.00, while the data of region δ 5.7-4.6 was set to zero to eliminate distorted baseline from imperfect water saturation. The icoshift algorithm was performed to remove misalignment of NMR signals in MATLAB  [The FIDs were multiplied by an exponential function corresponding to a 0.3 Hz line-broadening factor before Fourier transformation. The obtained NMR spectra were manually phased and baseline-corrected by using the Bruker TOPSPIN 2.1 package  and referenced to the CHks, USA) .Normalization was applied to the total sum of integrated data from each sample , making the data directly comparable with each other , 62. RelFurthermore, the SRV arithmetic was utilized to analyze the NMR spectra , 63. SRV2X(cum) and Q2(cum) were used to evaluate the quality of the PCA model. R2X(cum) denotes the fraction of the sum of the squares of the explanation of the integral values in the model, while Q2(cum) represents the cross-validated explained variation [As the following OR-STOCSY analysis was uniquely conducted on the data of the SRV clusters to identify intra/inter-metabolite correlations, PCA was thus performed on the SRV data too for keeping the consistency of data analysis. Using the software SIMCA-P+12.0 , we performed PCA to reveal trends, highlight outliers, and show clusters among the observations. The parameters Rariation . Based o2 and Q2) of the original model with that of several models based on data where the order of the Y-observations is randomly permuted, while the X-matrix is kept intact. If the original model is valid, all the Q2-values to the left are lower than the original points to the right, and the regression line of the Q2-points intersects the vertical axis below zero [Moreover, we performed PLS-DA on the SRV data to check grouping trends. The supervised approach used class membership information to attempt at the maximum segregation among the groups of the rats. Furthermore, RPT was utilized to determine the reliability of the sample classification , and asslow zero . Both PLwww.mathworks.com/help/stats/discriminant-analysis.html). The linear classifiers were used to verify the accuracy of classification [OPLS-DA is a derivative PLS-DA removing uncorrelated variables in the within-class with the orthogonal filter , 64. Mosfication . From thfication . The tabThe R-STOCSY analysis was developed by a combination of SRV with the STOCSY analysis . STOCSY Where, NS is the number of spectra in the data set, X represents the autoscaled SRV cluster matrix of NS spectra × NV SRV clusters, C is the autocorrelation matrix (NV× NV).Compared to STOCSY, R-STOCSY removes random correlations from the noise that otherwise deteriorate the quality of the STOCSY spectrum . TherefoAs one of the preprocessing methods, the orthogonal filter is extensively used to remove undesirable systemic variation within the data set , 65. Thewww.genome.jp/kegg/) has been extensively utilized as one of the main data resources to reconstruct metabolic networks [www.genome.jp/kegg/xml/) [The metabolism is the set of life-sustaining chemical transformations within the cells of living organisms which include thousands of reactions between metabolites and enzymes. As a comprehensive knowledge repository, the Kyoto Encyclopedia of Genes and Genomes  . The infFrom the OR-STOCSY analyses of GS, LGD, HGD, GC rats vs. CON rats, we identified significant inter-metabolite correlations linked to the four pathological stages with a correlation threshold of 0.9. Through calculating the shortest path lengths among the corrected metabolites , we extrwww.metaboanalyst.ca/) [A perturbed metabolic network is consist of several metabolic pathways linked by metabolites, enzymes and metabolites-enzyme relations. Expectedly, metabolic alterations occurring in important nodes of the metabolic network would potentially trigger significant impacts on the pathway than those occurring in marginal or relatively isolated nodes . The arcyst.ca/)  to calcuyst.ca/) .


Escherichia coli NZN111 was constructed by insertional inactivation of lactate dehydrogenase (LDH) and pyruvate formate lyase (PFL) encoded by the genes ldhA and pflB, respectively. However, this double-deletion mutant is incapable of anaerobically growing on glucose in rich or minimal medium even with acetate supplementation. A widespread hold view is that the inactivation of NADH-dependent LDH limits the regeneration of NAD+ and consequently disables proper growth under anaerobic conditions.Succinic acid is a valuable product due to its wide-ranging utilities. To improve succinate production and reduce by-products formation, E. coli was reconstructed and employed to perform all simulations in silico according to the reconstruction of engineered strain E. coli NZN111. Non-optimized artificial centering hit-and-run (ACHR) method and metabolite flux-sum analysis were utilized to evaluate metabolic characteristics of strains. Thus, metabolic characteristics of the strains wild-type E. coli, ldhA mutant, pflB mutant, and NZN111 under anaerobic conditions were successfully unraveled.In this study, genome-scale metabolic core model of + in NZN111 mainly resulted from the inactivation of PFL rather than the inactivation of LDH. In addition, the two alternative anaerobic fermentation pathways, lactate and ethanol production pathways, were blocked owing to the disruption of ldhA and pflB, resulting in insufficient NAD+ regeneration to oxidize or metabolize glucose for cell growth. Furthermore, we speculated reaction NADH16, the conversion of ubiquinone-8 (q8) to ubiquinol-8 (q8h2), as a potential amplification target for anaerobically improving cell growth and succinate production in NZN111. We found a viewpoint contrary to the widespread realization that an NADH/NADThe online version of this article (doi:10.1186/s40643-016-0125-5) contains supplementary material, which is available to authorized users. Saccharomyces cerevisiae, Escherichia coli, and Mannheimia succiniciproducens, are employed to produce succinate. Under anaerobic conditions, wild-type E. coli brings about mixed-acid fermentation but produces only minor quantities of succinate. Therefore, to overcome this limitation, E. coli NZN111 was constructed by insertional disruption of the ldhA and pflB genes, which encode the lactate dehydrogenase (LDH) and pyruvate formate lyase (PFL) enzymes, respectively, to improve succinate production and reduce by-product formation  in NZN111 improved growth and succinate production  has also been shown to exhibit cell growth on glucose and improved anaerobic succinate production with suppressed normal glucose repression  in NZN111 resulted in poor growth under anaerobic conditions. However, it is not clear whether the insufficient regeneration of NAD+ from NADH is a result of LDH or PFL inactivation in NZN111. It is well recognized that E. coli possesses a robust regulatory catabolic system, which allows it to cope with a wide range of environmental or genetic perturbations  or fully fermentatively. However, under anaerobic conditions and in the absence of an alternate oxidizing agent, the regeneration of NAD+ is achieved by using NADH to reduce metabolic intermediates. For example, pyruvate can be reduced to lactate or ethanol along with the regeneration of NAD+ from NADH  have been utilized to better understand the genotype–phenotype relationship in microbial metabolism  method . Cells of the wild-type E. coli BW25113 and NZN111 strains growing microaerobically in M9 medium containing 10 g/L glucose were harvested in the mid-exponential phase. Total RNA was isolated using Qiagen’s RNeasy kit following the manufacturer’s protocol. Expression data of 136 genes existed in Model except for gene s0001 were selected for preprocessing using log2  between the two E. coli strains.Gene expression data of wild-type Furthermore, reaction expression profile can be mathematically characterized by gene expression data based on logical relations of gene reaction described in the model as follows: E. coli were calculated to better indicate the differential expression reactions.Thus, the ratios of logarithmic reaction expression data for strain NZN111 to that of wild-type Escherichia coli NZN111 was constructed to anaerobically produce succinate by the disruption of ldhA and pflB genes. Accordingly, based on the metabolic core model of E. coli, we constructed four constraint-based models: Model, Model_del_ldh, Model_del_pfl, and Model_nzn for wild-type E. coli, ldhA mutant, pflB mutant, and NZN111, respectively (see “−6), indicating the correctness of these sampling results. On the basis of these sampling results, we calculated the average flux and standard deviation of each reaction and plotted a histogram for each reaction  resulted in growth defects on glucose under anaerobic conditions. , which encode a transmembrane NADH:ubiquinone oxidoreductase (NDH-I) responsible for coupling redox chemistry to proton-motive force generation  effectively decreased NADH/NAD+ ratio in the cell and improved cell growth and succinate production under anaerobic conditions.Additionally, we found that the accumulated NADH enforced the conversion of ubiquinone-8 (q8) to ubiquinol-8 q8h2) by strengthening reaction NADH16, while q8h2 facilitated the conversion of fumarate to succinate Fig. . This ob by strenE. coli NZN111 under anaerobic conditions, Model_nzn stepwise setting distinct upper and lower bounds of succinate exchange reaction  were performed to sample the solution space using ACHR method. Meanwhile, histograms were plotted and the normalized average flux distributions were evaluated as described above ], a matrix of Pearson’s correlations between reactions was calculated and is shown in Fig. i.e., inactivation of pyruvate metabolism  due to the disruption of ldhA and pflB genes suppressing the biosynthesis of acetyl-coA facilitated reaction NADH16 to convert q8 to q8h2. This further confirms that strengthening reaction NADH16 may be an effective strategy for decreasing accumulation of NADH and increasing succinate production in strain NZN111.Additionally, based on the 2000 fluxes for each reaction obtained using the ACHR method in +, and with reaction ATPS4r due to the supply of ATP. Moreover, researchers reported that overexpressing malate dehydrogenase (MDH) encoded by the gene mdh improved cell growth and succinate production in E. coli NZN111  algorithm was utilized to sample the solution space, and metabolite flux-sum analysis was utilized to calculate global turnovers of several cofactors. The use of such methods allowed us to successfully unravel the metabolic networks of cell growth and succinate production under anaerobic conditions. In contrast to the widespread consensus that the imbalance of NADH/NADAdditionally, on the basis of the analysis of metabolic characteristics and correlations, we speculated that strengthening reaction NADH16 may be an effective target for improving cell growth and succinate production in strain NZN111 under anaerobic conditions.In summary, using an unbiased non-optimized ACHR method allows for the sampling of solution space of constraint-based models, which can shape the global network properties and provide insights into the physiological metabolism of microorganisms under specific conditions.


Synechococcus UTEX 2973, a strain closely related to Synechococcus PCC 7942, was recently identified as having the fastest measured growth rate among cyanobacteria. To facilitate the development of 2973 as a model organism we developed in this study the genome-scale metabolic model iSyu683. Experimental data were used to define CO2 uptake rates as well as the biomass compositions for each strain. The inclusion of constraints based on experimental measurements of CO2 uptake resulted in a ratio of the growth rates of Synechococcus 2973 to Synechococcus 7942 of 2.03, which nearly recapitulates the in vivo growth rate ratio of 2.13. This identified the difference in carbon uptake rate as the main factor contributing to the divergent growth rates. Additionally four SNPs were identified as possible contributors to modified kinetic parameters of metabolic enzymes and candidates for further study. Comparisons against more established cyanobacterial strains identified a number of differences between the strains along with a correlation between the number of cytochrome c oxidase operons and heterotrophic or diazotrophic capabilities.The photosynthetic capabilities of cyanobacteria make them interesting candidates for industrial bioproduction. One obstacle to large-scale implementation of cyanobacteria is their limited growth rates as compared to industrial mainstays.  Cyanobacteria are a morphologically and geographically diverse group of photosynthetic prokaryotes that have adapted to a wide variety of environmental niches including fresh, sea, and wastewater125791115E. coli and Saccharomyces cerevisiae. The model cyanobacterium Synechocystis PCC 6803 has a maximum doubling time of 7–10 hours under optimal conditionsSynechococcus UTEX 2973 has been shown to have the lowest doubling time among cyanobacteria at 1.9 hoursSynechococcus PCC 7942, with a total of 55 single nucleotide polymorphisms and insertion-deletions, including a 188.6 kb inversion and a deletion of six open reading frames that are present in 7942Synechococcus 2973 an attractive candidate for bioproduction. Developing Synechococcus 2973 as a platform requires a more thorough understanding of its metabolic network and capabilities.Despite the inherent advantage of not requiring a reduced carbon substrate, cyanobacteria are still limited by growth rates lower than industrial mainstays such as de novo model development with the risk of losing details specific to certain organisms associated with completely automated developmentet al. uses an available curated model and reviewed gene annotations to expedite the development of a GSM modelSynechocystis PCC 680324Cyanothece ATCC 51142Synechococcus PCC 7002Genome-scale metabolic (GSM) models provide a blueprint for tracing all possible routes for utilizing carbon substrates in the context of the overall metabolism. They contain the set of metabolic reactions catalyzed within the organism, the gene-protein-reaction (GPR) relationships, and reaction directionalities21Once a GSM model is created, flux balance analysis (FBA) can be used to estimate the maximum theoretical biomass yield by maximizing the flux through the biomass equationSynechococcus 7942 and Synechococcus 2973 using a previously defined workflowiSyn731 GSM model of Synechocystis PCC 6803 was chosen as the reference model following the identification of similar percent identity between previously modeled cyanobacteria24Synechococcus strains . These two strains are represented via a composite model given that the minimal genomic differences between the two strains do not result in any changes to the set of reactions in each strain. The model is combined with additional experimental measurements; specifically carbon dioxide uptake rates and biomass composition, to further interrogate what factors contribute to these differences in growth rates. When the two strains were evaluated, the same reaction network was used with carbon dioxide uptake rates and a biomass reaction specific to each strain. Additional model refinement was performed by comparing the model against available gene essentiality data2 uptake as constraints on the model, the in silico ratio of the growth rate of Synechococcus 2973 to Synechococcus 7942 of 2.03 is close to the in vivo ratio of 2.13, identifying differing carbon uptake rates as the main contributing factor to the fast-growth phenotype. Specific SNPs and their possible effects on metabolism and the fast-growth growth phenotype are discussed. Genomic comparisons between the Synechococcus strains and several subsets of organisms identify genes and functionalities exclusive to the two strains.In this paper we describe the development of a composite GSM model for both Synechococcus PCC 7942 and Synechococcus UTEX 2973 (hereafter referred to as Synechococcus 7942 and Synechococcus 2973). The composite model contains 1,178 reactions and 1,028 metabolites. GPRs were developed for both strains, with the Synechococcus 2973 GPRs containing 683 genes and the Synechococcus 7942 GPRs containing 687 genes. This difference in gene number is due to four pairs of Synechococcus 7942 genes that each map to a single Synechococcus 2973 gene. The composite model contains 150 reactions not present in iSyn731Synechocystis model used in the model generation workflow. The energetic parameters from iSyn731 (i.e. non-growth and growth associated maintenance energy costs) were also used in the Synechococcus model. 145 reactions were not transferred from iSyn731 to the composite model. 93 of these reactions have a GPR and did not have the necessary homologs to transfer.This work resulted in the creation of a composite GSM model for two closely related cyanobacteria strains, iSyu683 have in vivo essential/nonessential verdicts from the transposon library by Rubin et al.In silico predictions of these gene knockouts correctly predicted 232 of 268 non-essential genes and 234 of 395 essential genes .607 of the 687 genes in al genes . All refet al., in review) and used in the formulation of strain specific biomass equations . The most pronounced difference between the strains is in the glycogen levels, with Synechococcus 7942 generating 8.3 times the amount produced by Synechococcus 2973 during the exponential growth phase . This supports the observations made by Yu et al. on the number of electron-dense bodies between the two strainsSynechococcus 2973 biomass by weight composed of amino acids, as compared to 40.9% for Synechococcus 7942. Both strains have similar weight percentages of lipids, although measurements for C18:0 and C18:1 fatty acid chains are an order of magnitude higher in Synechococcus 7942. The weight percentage of chlorophyll a in the two strains is also quite similar, with 2973 having only 1.05 times the amount of the pigment found in 7942. These measured components sum to 755.46 and 743.89 mg/gDW for Synechococcus 7942 and Synechococcus 2973, respectively. Therefore, the unmeasured biomass components (e.g. nucleic acids) account for similar fraction of biomass.Measurements for each strain were obtained for amino acids, lipids, glycogen, and chlorophyll  present in Synechococcus 7942 that were not found in Synechococcus 2973. The six ORFs not found in Synechococcus 2973 are five hypothetical proteins and a chromophore lyase, none of which are present within the Synechococcus 7942 GPR. Of the 21 genes with SNPs, 11 are found within the Synechococcus 7942 GPR  categoriescus 7942 . AnotherSynechococcus 2973: Synechococcus 7942 is 2.132 taken up. The required moles of carbon for one theoretical mole of biomass were calculated by multiplying biomass stoichiometric coefficients by the number of carbon atoms in each precursor. Synechococcus 2973 requires 0.502 g carbon per g biomass as compared to 0.497 for Synechococcus 7942. When supplied equal amounts of CO2 the ratio of biomass yields per mol of carbon of Synechococcus 2973 to Synechococcus 7942 is 0.98 implying that Synechococcus 2973 requires slightly more carbon per gDW of biomass. Experimental measurements have shown that under their optimal conditions, Synechococcus 2973 takes up CO2 at a rate 2.06 times that of 7942  was used to calculate the flux ranges for both strains at max biomass with minimum required photons. When the flux ranges are standardized by the amount of CO2 taken up the reactions with non-overlapping ranges are dictated by the biomass composition, with 2973 having a higher flux through the amino acid biosynthetic pathways and 7942 having a higher flux through the biosynthesis pathways for glycogen as well as C18:0 and 18:1 lipids , competes with the carboxylation of RuBP and generates 2-phosphoglycolate (2PG). Eisenhut et al. previously showed that 2PG metabolism is essential for organisms such as cyanobacteria that perform oxygenic photosynthesisin silico modelsSynechococcus 2973 could be due to the production of glyoxylate, a precursor to glycine, serine, and cysteine, from the photorespiration pathway and the increased requirements for amino acids in Synechococcus 2973.Under their optimal illumination conditions at 38 C, the growth rate ratio of  of 7942 . Using t1 lipids . Other n2 flux, both models can achieve growth rates higher than experimentally reported, with Synechococcus 2973 achieving a growth rate of 0.4569 hr−1 (doubling time: 1.5 hours) as compared to its experimentally reported optimal rate of 0.3014 hr−1 (doubling time: 2.3 hours), and Synechococcus 7942 with a growth rate of 0.2256 hr−1 (doubling time: 3.1 hours) as compared to the experimental value of 0.1415 hr−1 (doubling time: 4.9 hours). When the flux through the biomass reaction was fixed to the experimentally reported values, the model identified the main destinations of excess carbon as TCA cycle intermediates or metabolites that are several reactions removed from the TCA cycle . In total 33.4% of the carbon taken up is exported for 2973 and 37.2% is exported for 7942. This increased fraction of carbon allocated to biomass in 2973 could in part contribute to the divergent growth rates.With the supplied COSynechococcus 2973’s potential as a production strain, it is important to compare its capabilities with that of model cyanobacteria, with pairwise genome-wide BLAST comparisons performed between Synechococcus 7942 and Synechocystis PCC 6803, Synechococcus PCC 7002, Cyanothece ATCC 51142, Anabaena PCC 7120, and Anabaena variabilis. A set of 696 genes in 7942 does not have satisfactory hits in the other five genomes . 47 of these genes are in the 7942 GPR relationships and are essential for 21 reactions that can carry flux at max biomass production. These reactions are spread across a variety of pathways including amino acid and deoxyribonucleotide biosynthesis. As these genes comprise only 6.8% of the genes within the Synechococcus 7942 GPR relationships, a majority of the metabolic genes have homologs in other cyanobacterial strains. Therefore insights gained into the metabolism of these strains can more readily inform work in the metabolism of the fast-growing strain. Expanding past metabolism, gene ontology (GO) terms provide a means to contextualize the remaining genes. Of these 696 genes, 304 have annotations including GO terms in the Uniprot database. This fraction of genes with GO terms (43.7%) is much lower than that of the entire 7942 genome (70.7%), following a trend seen in the percent of reviewed annotations  and percent of genes annotated as uncharacterized . Two GO terms have at least ten genes in the subset and greater than 50% of the genes in the organism with that GO term within the subset. There are 14 genes within this subset annotated with the term GO:0000155 – phosphorelay sensor kinase activity, and 10 genes annotated with the term GO:0043565 – sequence specific DNA binding. This suggests that most of the differences between these strains are related to the response to external stimuli and transcription regulation. These differences are consistent with the comparison of a number of thioredoxins involved in light dependent protein regulationSynechococcus 7942 in contrast to four and eight thioredoxin genes in Synechocystis 6803 and Anabaena 7120Given et al. showed that the deletion of a three gene cytochrome c oxidase (coxBAC) operon from Anabaena variabilis ATCC 29413 resulted in a strain capable of unhindered photoautotrophic growth but which was no longer capable of chemoheterotrophic growthA. variabilis 29413’s five operons and seven other cyanobacteria with information on their ability to grow heterotrophically. An independent annotation search of each organism was also performed, which identified the same operon sets for each organism as were identified through the BLAST search. coxBAC operons including an operon required for growth on sugarAnother key difference between cyanobacterial strains is the classification of some organisms as obligate photoautotrophs. Schmetterer Synechococcus 7942 and Synechococcus 2973 with GPR relationships that were tested and refined using gene essentiality data for Synechococcus 7942. This model was used to compare the two strains and identify possible reasons for the diverging fast-growth phenotype. The GSM model allowed for the identification of the pathways where flux was allocated to differing levels between the two strains along with the identification of four SNPs as possible contributors to modified kinetic parameters of metabolic enzymes and candidates for further study. Several differences between the two strains were identified including the fraction of carbon allocated to biomass and the weight fractions of different biomass precursors, with the most prominent being the difference in carbon uptake rates, which contributed to the different in silico growth rates. Finally, a genomic comparison between Synechoccocus 7942 and model organisms highlighted a number of differences including regulatory genes without identified homologs and a variation in the number of copies of the cytochrome c oxidase operon as related to heterotrophic and diazotrophic capabilities.In this paper we presented a GSM model for both et al.iSyn731 model of Synechocystis PCC 6803Synechococcus 7002 and Synechocystis 680324Synechococcus 7942, resulting in the selection of our previously developed iSyn731 model of Synechocystis 6803Synechocystis and the Synechococcus strains to identify which reactions should be transferred to the new GSM model. To identify homolog pairs a bidirectional BLAST search between each organism pair was performed using an e value cutoff of 10−2. A result was determined to be a hit if the alignment length was at least 75% of the length of both the query and target genes, and if the raw score was at least 25% of the self-hit score of the query gene. A homolog pair was identified if both genes had only each other as a hitSynechococcus 7942 and 2973. Given that the organisms are closely related, the score cutoff was increased to 75% of the self-hit score. This reduced the number of occurrences of multiple hits for a gene and identified only the best hit, increasing the number of homolog pairs from 2274 to 2538. These homolog pairs were used to map reviewed annotations from Synechococcus 7942 to Synechococcus 2973. The composite model is available in both table . These measurements were used to determine biomass equation stoichiometric coefficients for lipids, glycogen, amino acids, and chlorophyll. The coefficients for the unmeasured precursors were updated by scaling the coefficients from the iSyn731 biomass equation so that all biomass precursors added up to 1 g per gDW of biomass. This standardization facilitates comparison between the two strains.The biomass equation previously developed for the Synechococcus strains were grown in a MC-1000 multicultivator (Photon Systems Instruments) in standard BG11 medium at 38 C, and supplemented with 3% CO2 to an OD720 of 1.0. Synechococcus 2973 was grown at 500 μE·m−2·s−1 light and 7942 was grown at 300 μE·m−2·s−1 light, the light intensities at which each strain achieved its maximal growth rate2 for 5 minutes. One tube for each strain was placed on its side on a shaker under their respective white LED illumination at 38 C for 1 hour after which time total CO2 was measured. The second tube was measured immediately. Total CO2 was calculated as the sum of the dissolved + gaseous CO2 within a sealed system. Total CO2 of the system was determined after injecting 100uL 10 N HCl into the tube to gas out dissolved CO2, followed by quantification of the headspace CO2 content on a HP 5980 gas chromatograph under the following conditions: porapak N column; temperature, 100 °C; carrier gas, helium at a flow rate of 40 ml min−1; and TCD detector. These uptake rates were given as umol CO2 mg−1 chl hr−1. Using the molecular weight and the biomass stoichiometry (mmol chl gDW−1) of chlorophyll A these rates were converted to the units of CO2 uptake flux within the model (mmol CO2 gDW−1 hr−1). The original measurements and their conversion to fluxes are in Experiemental measurements of the rate of carbon dioxide uptake were taken for both strains. Both et al.FBA was performed as described by Orth in silico and in vivo essentiality, the GPR for each reaction was reviewed upon the removal of each individual gene. If the GPR logic statement was no longer true  then the reaction was not allowed to carry flux for the in silico mutant. If the model could not produce at least 10% of the wild-type biomass the mutation was classified as lethal. Gene knockouts were performed in silico for every gene in the model. The in vivo results were taken from those genes that were identified as essential or non-essential from the transposon library of Synechococcus 7942 published by Rubin et al.in silico and in vivo results for growth or no growth match (i.e. GG and NGNG) or conflict (i.e. GNG and NGG)In order to compare How to cite this article: Mueller, T. J. et al. Identifying the Metabolic Differences of a Fast-Growth Phenotype in Synechococcus UTEX 2973. Sci. Rep.7, 41569; doi: 10.1038/srep41569 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


The method is compared with the method based on a binary distinction between reversible and irreversible reactions. When all reactions are reversible, adding the constraints based on equilibrium constants reduces the number of elementary flux modes (EFMs) by a factor of two. Declaring in advance some reactions as irreversible, based on reliable biochemical expertise, can in general reduce the number of EFMs by a greater factor. But, even in this case, computing tEFMs can rule out some EFMs which are biochemically irrelevant. We applied our method to two published models described with binary distinction: the monosaccharide metabolism and the central carbon metabolism of Chinese hamster ovary cells. The results show that the binary distinction is in good agreement with biochemical observations. Moreover, the suppression of the EFMs that are not consistent with the equilibrium constants appears to be biologically relevant.We present a method for computing thermodynamically feasible elementary flux modes ( EFM) is a key concept in the analysis of metabolic networks from a pathway-oriented perspective. An EFM is defined as a smallest (with respect to reactions set inclusion) sub-network that enables the metabolic system to operate at steady state with all irreversible reactions proceeding in the appropriate direction  . It is known that lactate comes from cytosolic pyruvate while alanine comes from mitochondrial pyruvate (which arises from glutamine). Moreover, after T = 48h the concentration of glutamine decreases and is not enough to be assimilated. For the same reason, the EFM 13 and EFM 16 cannot be thermodynamically feasible. At T = 0, the cells are entering the exponential growth phase. Therefore, the EFM 16 makes no biological sense with ATP consumption for the transformation of extracellular glutamine into extracellular alanine, while cells massively need supporting anabolic activity using intermediate metabolites as precursors as well as the ATP regenerated. EFM 18 cannot be feasible as well as there is no lactate in the medium but it can be feasible thereafter because the concentration of lactate increases. EFM 23 comes down to CO2 production and ATP consumption by ATPase, such as a futile cycle. The thermodynamical infeasibility of this EFM seems pertinent because a major problem of this EFM is that the flux associated to proton leak is amplified compared to biological expected levels, and this flux may thus serve as a fitting parameter reaching improbable level. At time 48h, the cells are in the middle of the exponential growth phase. Therefore, the EFM 15 and EFM 16 are barely possible while cells require precursors and ATP as stated above looking at the EFMs at T = 0. More importantly, these pathways can barely be active since glutamine is decreasing from 72h in the culture medium to low level that does not allow the EFM thermodynamical feasibility. It is worth noting that if we suppress the reaction leak because this reaction normally accounts for less than 20 (percent) of O2 consumption in reality, all the EFMs are thermodynamically feasible at T = 0h.We then verified if the shown in . EFM 8 ilutamine . In the EFMs and so 7,529 tEFMs since they have all to verify tEFMs which consume glucose to produce biomass increases from 2,610 at T = 0h to 2,852 at T = 96h. The number of tEFMs which consume glucose to produce lactate is on average the same from 1,003 at T = 0h to 1,013 at T = 96h. Moreover, for each period of time T, we have tested if the thermodynamics imposes some reactions to be irreversible. We verified if there exists reactions that always operate in the same direction in each set of tEFMs. Actually, this is not the case, i.e. all the reactions remain reversible, which means that the knowledge of the equilibrium constants is not enough to replace  the binary distinction of the expert. And the example of the six non-thermodynamical EFMs above, depending on the context, show that, for biochemical reasons, they are not feasible. And these reasons are not reducible to thermodynamics .Let us now consider all the reactions of the CHO network as reversible. The new network contains 7,529 reversible Xi and Mi. The stoichiometric coefficients for metabolites i in reactions j are denoted by sij and combined into the m × r real stoichiometry matrix S, where m is the number of internal metabolites and r the number of reactions. It is known from thermodynamics that the Gibbs free energy difference must be negative in order that a reaction j can proceed in the forward direction [Gj = 0). One gets:Kj is the equilibrium constant of reaction j.We denote the concentrations of internal and external metabolites to the system by irection :ΔGj=ΔGjj proceeds in the forward direction (ΔGj < 0) we can, using For non-equilibrium states where the reaction This equation is intuitively understandable: if the concentrations of substrates are high, the reaction proceeds in the forward direction, while it would run backwards if the concentrations of products were sufficiently high.j that involve external metabolites, we move their concentrations to the right-hand side of For those reactions Under the plausible assumption that all concentrations are positive, it is allowed to use the logarithm of concentrations (concentrations are assumed to be dimensionless quantities after division by the unit concentration):To do so, we can fix a common unit (e.g. mM) for all concentrations and equilibrium constants and neglect that unit when taking the logarithm. In the equations and inequalities, the unit would cancel anyway. Now we can write vj ≥ 0, i.e. j such that vj > 0, that is, for all reactions in the support supp(v) of the flux distribution v.In this way, we have obtained a linear inequality system. We now deal with the question whether a certain flux distribution n monomolecular reactions. The chain starts with an external metabolite For example, consider a  linear chain of yi for some given Ki and Thus, each internal metabolite is confined to a certain interval in order that the flux is positive. In a large, complex metabolic network, the inequality system EFMs analysis , the syvj to be non-negative (see above) is formally equivalent to assuming all reactions j to be irreversible. Actually, we will assume this is the case, by splitting each reversible reaction into two irreversible reactions of opposite directions. Under this condition, the set of flux distributions is a pointed convex polyhedral cone [EFMs are the extremal rays of Considering all fluxes ral cone :K={v∈IRv, we can use the above argument of adding inequalities. More precisely, if When we check the feasibility of linear inequality system j) of the inequalities Let EFMs with support included in supp(v). This leads to the general result stated as Lemma 1 in [From the right-hand side we obtain:mma 1 in :Lemma 1The linear inequality systemfor j ∈ supp(v), has a solution for y if and only if the vectorfulfills the inequality systemwhere ek).v to be thermodynamically feasible. In particular, if the flux distribution considered is an elementary mode e, then the set of EFMs with support included in supp(e) is reduced to e itself  and the necessary condition for thermodynamic feasibility becomes:Thus the linear inequality system EFM, checking its thermodynamic feasibility by inequality e with its coefficients and not only its support as in system v, the complexity of using kmax of EFMs with support included in supp(v) and supposes these EFMs previously computed.We see that, for such an S verifies the condition, so it is for any flux distribution with support included in S. This is obvious either on the form EFMs have to be considered for a smaller support) of the condition. Thus, if a flux distribution is not thermodynamically feasible because it violates the condition, so it is for any flux distribution with a larger support.The thermodynamic condition verifies the nice property to be monotonic with regard to set inclusion of the supports; i.e., if a given flux distribution of support EFMs. In general, the nullspace approach is adopted at the initialization step, which guarantees the satisfiability of all the m stoichiometric equalities and of r − m irreversibility inequalities among the r ones defining m iteration steps, each one integrating a new irreversibility constraint vj ≥ 0 among the remaining ones. At step l the following cone EFMs computed:This property is key for integrating the filtering by the condition inside the iterative process of the Motzkin Double Description (DD) method , 49, on m steps, as well at its EFMs. Let suppl(v) be the support of a vector r − m + l components (those from which we are sure they are nonnegative). At each step l, 1 ≤ l ≤ m, the new cone vj ≥ 0 (say j = r − m + l for a convenient ordering) and its extremal rays are computed from those of el at step l could contribute to build would have its support larger than suppl(el). And thus, would violate the necessary thermodynamical feasibility condition if suppl(el) does so. So, in this case, el can be definitely discarded at step l.Starting from the cone el. This can obviously be done for the condition expressed as consistency of the linear inequality system j ∈ suppl(el), and achieved by a call to an LP program. This is actually what is done in  implemented as an extension of efmtool [m) EFMs with support included in suppl(el), which are not yet known at current step l. This means we would need at step l the EFMs of suppl(el) but we only have the extremal rays of To make the filtering effective along the iteration process, it remains to check if the thermodynamic feasibility condition can be checked at each step on the restricted support of each newly created extremal ray done in , 29) wit witel. T efmtool  by callsthermoEFM, for performing the thermodynamic calculations explained above and a program, EFMdraw, allowing one to visualize all the EFMs. The implementation of both programs is performed with MATLAB 2015a, and they are freely available on our website https://www.lri.fr/~speres/EFM/ and figshare https://figshare.com/. They are both connected to METATOOL [EFMs. Celldesigner [EFMs generated with EFMdraw.We developed a software, METATOOL  to compudesigner  was usedEFM analysis [EFMs, we have here used a Lemma described in a previous work [thermoEFM) in a MATLAB function connected to Metatool [EFMs and then selects those that are consistent with the formula EFMdraw) using Metatool and allowing the graphical representation of all the EFMs of a metabolic network designed in cellDesigner [In this work, we have compared the traditional approach in metabolic pathway analysis in which a binary distinction is made between reversible and irreversible reactions, with an approach in which more detailed thermodynamic information is used. In particular, the values of equilibrium constants and external metabolite concentrations are considered. The latter approach had been suggested even before the advent of “traditional” analysis . To decious work . In the ous work , 28. We Metatool . The metDesigner .EFMs on the basis of the binary declaration is often much lower than that based on the thermodynamic method. We show that in the case of a network composed of an arbitrary number of linear chains sharing one internal “hub” metabolite, the steady-state assumption allows a number of possible EFMs that is quadratic in the number of chains. Using thermodynamics in addition to the steady-state assumption divides this number by two, but it is worth to notice that knowing the direction of a number of reactions linear in the number of chains would produce the same result and even completely fix the set of EFMs. So, as the steady-state assumption already strongly constrains the directionality of reactions, only little additional gain can be expected from using thermodynamics without knowledge of the concentrations of internal metabolites, as demonstrated in Section 2.1. In particular the number of EFMs is only divided by two. Indeed, a biochemical knowledge of the irreversible directions of some reactions is enough to achieve similar or even better results than thermodynamics.The number of EFMs which seem to be biologically irrelevant. Moreover, for the CHO model, we have changed over time the concentrations of external metabolites as reported in [tEFMs. In addition, we found three EFMs that never operate at each period of time; their infeasibility is coherent for each phase of the cell.However, the advantage of our reformulation of the thermodynamic method is that formula orted in . The restEFMs are biochemically irrelevant, although they are thermodynamically feasible. This might be because they would require inhibition of some enzymes and activation of some others, i.e. regulatory mechanisms that are not taken into account. This hypothesis has been verified by several examples, e.g. monosaccharide metabolism (glycolysis and pentose phosphate pathway) [EFMs).The question arises whether authors have sometimes declared too many reactions as irreversible. Or perhaps they were right and included, intuitively, kinetic information in addition to thermodynamics. We here suppose that some of the pathway)  and a mopathway) . Taken aEFMs.The present approach allows the testing of hypotheses on metabolic network structure as well as on the direction of fluxes. Therefore, our work contributes to expanding our knowledge on feasible metabolic networks. For reduced, incomplete networks, such as those covered in this work, results may be useful in indicating where to focus for better describing sub-networks in which only lumped reactions can be described because of lack of knowledge. In conclusion, thermodynamics is key, in the form of knowledge on reaction irreversibility or on reaction equilibrium constants, and finally experimental data on a cell behavior, enabling the reliable determination of feasible S1 File(PDF)Click here for additional data file.


Oryza sativa) is one of the most important grain crops, which serves as food source for nearly half of the world population. The study of rice development process as well as related strategies for production has made significant progress. However, the comprehensive study on development of different rice tissues at both transcriptomic and metabolic flux level across different stages was lacked.Rice  contains supplementary material, which is available to authorized users. Oryza sativa) is one of the most important grain crops in the world, which serves as food source for nearly half of the world population. Rice is also a model organism for research in plant growth and development. The process of rice growth and development starts from seed germination and ends when the formation of the grain begins. This complex process can be divided into three periods: vegetative growth, reproductive growth, grain filling and maturity. These three main phases correspond to the development of different tissues and organs in rice. Since it is important for the agricultural production of rice, the study of rice development process as well as related strategies for production has made significant progress , where negative number means reverse reaction flux. We directly used the flux value to measure the pattern similarity and made hierarchical clustering as shown in Fig. By evaluating the total variation (see Methods) of every reaction across the 8 different stages and tissues, we found some pathways exhibit significant changes, including glycolysis/gluconeogenesis, fatty acid metabolism, nitrogen metabolism, TCA cycle, glyoxylate cycle, cysteine and methionine metabolism, photosynthesis (light phosphorylation), folate metabolism, and fermentation process.The flux distribution of primary metabolism in 8 tissue-specific models were shown in Additional file We determined the reactions achieving upper bound of flux as rate-limiting steps, and then selected the pathways enriched in the rate-limiting reactions. Glycolysis/Gluconeogensis pathway is found to be rate limited in all tissue-specific models, Fatty acid, Porphyrin and Chlorophyll Metabolism are also rate limited in most of the tissues. These observations are in good agreement with the point that the basal metabolism maintains the most basic energy expenditure of the organism’s life activities. In comparison, folate metabolism and TCA cycle are rate-limiting steps in only two or three tissues. Nitrogen metabolism is rate-limiting only in leaves at booting stage, and glyoxylate cycle is rate-limiting step only in endosperm at mature stage. It is consistent with previous studies that ynthesis . The glyynthesis . MoreoveNext, we focused on the metabolic features of some fundamental processes, the flux distribution of Fatty acid metabolism and Amino acid metabolism are shown in Fig. The flux distribution of Nitrogen metabolism and Starch and sucrose metabolism are shown in Fig. Oryza sativa (monocot plant) [The secondary metabolism of plants has very important effect on orchestrating the cellular phenotype in response to abiotic stresses , 28. In t plant) . Abscisit plant) .Fig. 9MeFlux distributions in quinone and folates metabolism are similar in all tissues. Folates (vitamin B9) and phylloquinone (vitamin K1) have potential antioxidant activity within the plant cell . Stamen There is a significant difference between the gene expression level and metabolic flux distribution, which indicate the importance of post-transcriptional control in organisms. And the integration of transcriptome data with metabolic model is important to analyze the metabolic changes. Here we compared the pairs of samples of the same tissues at different stages or the different tissues at same stage to uncover the difference between transcriptomic level and metabolic level. The significantly up- and down-regulated genes were determined by the fold change of fpkm larger than 2 or smaller than 0.5. While for the significantly up- and down-regulated reaction fluxes, we calculated the difference of absolute of two flux values and divided by the range of flux variability (See Methods). If the ratio is larger than 0.5, the reaction is upregulated; If the ratio is smaller than − 0.5, the reaction is downregulated. Then we conducted functional enrichment analysis to show the significantly up- and down-regulated pathways between three compared pairs at the two different levels in Fig. The second pair is the sheath and leaves both at booting stage. We found the process of photosynthesis and fatty acid biosynthesis are much stronger in leaves than in sheath, as shown in Fig. The third pair is embryo and endosperm both at mature grain stage. Rice is a representative of a monocotyledonous plant, which possesses a tiny embryo and large endosperm used for starch storage. During germination, sucrose has the potential to transport carbohydrate from endosperm to embryo. The endosperm of rice is a major storage tissue for starch and proteins, which is essential for embryo growth and postembryonic development prior to the start of seedling photosynthesis . PreviouTranscriptome can reveal the gene expression level at different development stage, but in reality, the flow of information deriving from genes is networked by many loops with their downstream products, resulting in a complex and dynamic system of transcripts, proteins and metabolites. Therefore metabolic flux analysis can be applied to reveal the mechanism of rice development. By integration of gene expression data with the genome-scale rice metabolic model, we constructed tissue-specific models, and simulated the flux distribution, which can reflect the changes of metabolic features without further experimental measurements. There are different numbers of reactions in the 8 tissue-specific models, majority of them are common, but each has specific reactions. More reactions are specifically required in palea and lemma in both booting and mature grain stage for cell growth and development. This might probably because compared with other organs, the palea and lemma is more resistant to drought stress. Meanwhile, the palea and lemma may be the major sources of carbon for grain-filling during drought, which requires more reactions to support cell growth and development . In addi2 assimilation and photosynthesis as well as other electron transport chains [Oryza sativa (monocot plant) [Primary and secondary metabolism played different roles in rice development, and we revealed their different features at both transcriptomic and metabolic level. The transcriptome data indicated the gene expression of the primary processes could be categorized into four clusters: sheath and leaves, pistil and stamen, endosperm and embryo, lemma and palea of booting and mature grain stages Fig. , which dt chains . And glyt chains . The patt plant) . Abscisit plant) . DiffereWe compared the pairs of samples of the same tissues at different stages or the different tissues at same stage to uncover the difference between transcriptomic level and metabolic level. Three compared pairs showed there are more altered pathways at gene expression level than metabolic level, which indicate the metabolism is more robust to reflect the phenotype. By comparing palea and lemma at maturity stage with booting stage, we found although gene expression of calvin cycle, photosynthesis, terpenoid metabolism are upregulated in mature palea and lemma, these processes have no significant changes in metabolic flux. While genes in photorespiration, phenylalanine, tyrosine and tryptophan metabolism are upregulated, the fluxes are downregulated. Thiamine and phytohormones pathways are enriched in downregulated genes, but no significant changes in flux. Since more reactions including starch metabolism, fermentation, photorespiration, fatty acid biosynthesis, are downregulated in mature grain stage, we concluded that palea and lemma mainly develop in booting stage. During the booting stage, the palea and lemma interlock with each other to enclose the developing floral organs , 38.For leaves and sheath both at booting stage, the process of photosynthesis, photorespiration, and fatty acid biosynthesis are much stronger in leaves than in sheath, which is consistent with the actual situation that leaves are responsible for photosynthesis. For comparison of endosperm with embryo, the starch and sucrose metabolism were upregulated in endosperm. The endosperm of rice is a major storage tissue for starch and proteins, which is essential for embryo growth and postembryonic development prior to the start of seedling photosynthesis . The genOryza sativa tissues in booting and mature grain stage. By further integrating transcriptome data with the rice metabolic model, we uncovered metabolic pattern shift among different tissues and stages, which can be exploited to uncover the mechanism of the complex metabolic behavior of rice and further improve production and quality of rice.This study revealed the systematic transcriptomic profile of eight Oryza sativa L. ssp. japonica., cv. Zhonghua 11, were grown under natural growth conditions at an experimental field. The tissue samples of Zhonghua 11 were collected from leaves, sheath, stamen, pistil, lemma and palea of the booting stage, and from embryo, endosperm, and lemma and palea of the mature grain stage. Tissue samples were separated from the connecting tissues with a scalpel under a dissecting microscope before they were collected for RNA extraction.The rice line of O. sativa tissue samples were extracted following the manufacturer’s instructions. RNA degradation and contamination was monitored on 1% agarose gels. RNA purity was checked using the NanoPhotomete® spectrophotometer . RNA concentration was measured using Qubit® RNA Assay Kit in Qubit® 2.0 Flurometer . The RIN number was checked to determine RNA integrity by RNA Nano 6000 Assay Kit of the Agilent Bioanalyzer 2100 system .Total RNA from the eight A total amount of 3 μg RNA per sample was used as input material for the RNA sample preparations. Sequencing libraries were generated using NEBNext® Ultra™ RNA Library Prep Kit for Illumina®  following manufacturer’s recommendations and index codes were added to attribute sequences to each sample. Briefly, mRNA was purified from total RNA using poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent cations under elevated temperature in NEBNext First Strand Synthesis Reaction Buffer (5X). First strand cDNA was synthesized using random hexamer primer and M-MuLV Reverse Transcriptase (RNase H-). Second strand cDNA synthesis was subsequently performed using DNA Polymerase I and RNase H. Remaining overhangs were converted into blunt ends via exonuclease/polymerase activities. After adenylation of 3′ ends of DNA fragments, NEBNext Adaptor with hairpin loop structure were ligated to prepare for hybridization. In order to select cDNA fragments of preferentially 150~ 200 bp in length, the library fragments were purified with AMPure XP system . Then 3 μl USER Enzyme  was used with size-selected, adaptor-ligated cDNA at 37 °C for 15 min followed by 5 min at 95 °C before PCR. Then PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and Index (X) Primer. At last, PCR products were purified (AMPure XP system) and library quality was assessed on the Agilent Bioanalyzer 2100 system.The clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq SR Cluster Kit v3-cBot-HS (Illumia) according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Hiseq 2500 platform and 125 bp paired-end reads were generated.http://rapdb.dna.affrc.go.jp/download/irgsp1.html). Clean reads were aligned onto the Os-Nipponbare-Reference-IRGSP-1.0 reference genome using TopHat (version 2.0.12) with qdefault parameters [Sequencing reads were filtered according to fastq_quality_filter of fastx_toolkit-0.0.14, a command line tools for Short-Reads FASTA/FASTQ files preprocessing (−q 30). The genome and gene models of Oryza. sativa were downloaded from RAPDB .KEGG and GO annotations were grabbed from reference gene GFF file , including 2165 genes, 2444 reactions and 1999 metabolites, which is classified into 59 major metabolic sub-systems . Model iThe Integrative Metabolic Analysis Tool (iMAT) method was used to integrate transcriptome data with the rice metabolic model to reconstruct tissue-specific models. We defined “highly expressed” and “lowly expressed” genes according to their expression level, corresponding to the highest 15% genes and the lowest 15% genes respectively . Next, aTo analyze the fluctuation of each reaction flux across different tissues in different stages, we used the concept of total variation in statistics Eq. .1\documei is the flux value in the i-th tissue-specific model. The greater the total variation of a reaction is, the fluctuation of this reaction is more remarkable.Where, fFor the comparison of the same tissue in different stages or the different tissues in same stage, we need to determine the significantly up- and down-regulated genes and reaction fluxes. The genes with fold change of fpkm between two tissues larger than 2 or smaller than 0.5 are regarded as significantly up- and down-regulated respectively. While for metabolic level, the fluxes carried by reactions may be negative because of reversibility, therefore the simple metric like fold change is not suitable for up- and down-regulation definition. Here we calculated the difference of absolute of two flux values and divided by the range of flux variability to get the ratio of flux change, as shown in Eq. Where the maxflux and minflux represent the maximal and minimal value of flux variability, derived by the function ‘fluxVariabilityAnalysis’ in COBRA toolbox in MATLAB. The reactions with the ratio larger than 0.5 are determined as upregulated, while those with the ratio smaller than − 0.5 are determined as downregulated. Then we conducted functional enrichment analysis using hypergeometric test in MATLAB, to find the significantly up- and down-regulated pathways between three compared pairs at the two different levels.Additional file 1:The list of primary and secondary metabolic pathways in rice. (DOCX 13 kb)Additional file 2:Input data for iMAT to construct tissue-specific models, including rice model, expression data, and transformation of reaction activity. (TIFF 3788 kb)Additional file 3:Metabolic flux distribution of primary metabolism in different tissues at booting and mature grain stages. (ZIP 3206 kb)