Yarrowia lipolytica has great potential due to its oleaginous characteristics and unique ability to utilize hydrophobic substrates. However, Y. lipolytica should be further engineered to make it more competitive: the current approaches are mostly intuitive and cumbersome, thus limiting its industrial application.Recently, there have been several attempts to produce long-chain dicarboxylic acids (DCAs) in various microbial hosts. Of these, Y. lipolytica. At the outset, we reconstructed genome-scale metabolic model (GSMM) of Y. lipolytica (iYLI647) by substantially expanding the previous models. Subsequently, the model was validated using three sets of published culture experiment data. It was finally exploited to identify genetic engineering targets for overexpression, knockout, and cofactor modification by applying several in silico strain design methods, which potentially give rise to high yield production of the industrially relevant long-chain DCAs, e.g., dodecanedioic acid (DDDA). The resultant targets include (1) malate dehydrogenase and malic enzyme genes and (2) glutamate dehydrogenase gene, in silico overexpression of which generated additional NADPH required for fatty acid synthesis, leading to the increased DDDA fluxes by 48% and 22% higher, respectively, compared to wild-type. We further investigated the effect of supplying branched-chain amino acids on the acetyl-CoA turn-over rate which is key metabolite for fatty acid synthesis, suggesting their significance for production of DDDA in Y. lipolytica.In this study, we proposed model-guided metabolic engineering strategies for enhanced production of DCAs in In silico model-based strain design strategies allowed us to identify several metabolic engineering targets for overproducing DCAs in lipid accumulating yeast, Y. lipolytica. Thus, the current study can provide a methodological framework that is applicable to other oleaginous yeasts for value-added biochemical production.The online version of this article (10.1186/s12918-018-0542-5) contains supplementary material, which is available to authorized users. Candida sp. [Yarrowia lipolytica [Pseudomonas aeruginosa [Cyptococcus neoformans [Escherichia coli [Y. lipolytica has attracted great attention as a cell factory to manufacture DCAs [Long-chain dicarboxylic acids (DCAs) are widely used in the manufacturing of polyamides and polyesters as the monomeric intermediates . The mosdida sp. , 4, Yarrpolytica , Pseudomruginosa , Cyptocooformans , and Eschia coli . Of thesure DCAs  since thure DCAs –13. In ωure DCAs .Y. lipolytica as oleaginous yeast has huge potential to become a model organism to produce fatty-acid derived products including DCAs. In order to make it industrially competitive, its productivity should be further enhanced, which can be achieved by modification of relevant target genes. However, metabolic engineering of Y. lipolytica has been mainly focused on over-producing lipids, e.g., triacylglycerols (TAGs) with only a handful of studies for the increased DCAs production [in silico genome-scale metabolic model (GSMM) have been developed and successfully applied to several industrial hosts including E. coli [S. cerevisiae [in silico methods using GSMM of Y. lipolytica which was newly reconstructed by substantially expanding the previous models, thus allowing us to identify various genetic engineering targets for overexpression, knockout, and cofactor modification towards DCA overproduction.Indeed, oduction . In addioduction . In this E. coli , 18 and revisiae , 20. SimY. lipolytica for designing DCA overproducing strains. Initially, four existing Y. lipolytica GSMMs  were col iND750  by addinY. lipolytica appeared to not degrade leucine and other BCAA into acetyl-CoA, but there are reports implicating the biosynthetic capacity of leucine as an effector of lipogenic capacity in oleaginous organisms [With the aim of building an objective-oriented model for simulating DCAs production, we first added 9 biosynthetic reactions which are sequentially catalyzed by hydroxylase, oxidase and dehydrogenase enzymes within the ω-oxidation pathway, thus capturing the oxidation of fatty acids to DCAs. The 27 DCAs degradation reactions were also included to represent β-oxidation pathway together with 6 dicarboxylic acids transport and an exchange reaction pertaining to DDDA secretion. Based on the scaffold model, rganisms ; a putatY. lipolytica. Additionally, we corrected the elemental balance and directionality of 45 reactions. For example, the directionality of THRA representing the threonine aldolase mediated breakdown of threonine to glycine and acetaldehyde, was changed to opposite direction favoring acetaldehyde formation based on the direction reported in S. cerevisiae [in silicoY. lipolytica model (iYLI647) consists of 1347 reactions and 1152 metabolites encoded by 647 genes  file  was also constrained according to the experimental data wherever provided. For the biomass maximization in iMK735, biomass equation corresponding to 5% lipid was used. The maximum specific growth rate was determined for all models simulated under same constraints and the results were compared  also provide mitochondrial acetyl-CoA. The most interesting finding from tSOT is GLUDy, which apart from regeneration of NADPH; also plays key role in maintaining the balance of carbon and nitrogen. There is considerable evidence of the presence of GLUDy shunt in plants, which returns the carbon in amino acids biosynthesis back into reactions of carbon metabolism and TCA cycle [Considering that fatty acid biosynthetic pathway is very tightly regulated, transcriptomic data can provide some useful insight into the ON/OFF state of the reactions in a particular condition. To identify the overexpression targets for increasing the DCAs production, we implemented tSOT by resorting to time-course transcriptomic profile of the ry phase . A nitrory phase . AlthougCA cycle  which isCA cycle , making Y. lipolytica which apparently takes the flux away from DCAs production which requires free fatty acid [GDLS algorithm was used to search for growth-coupled solutions for DCA production, identifying up to 5 reaction deletion candidates. Basically, GDLS propose the pathway design which couples the product formation with the cell growth, making its production necessary to reach optimal growth. The strain design strategy deciphered by GDLS combined the simultaneous knockout of DESAT16 (Stearoyl-CoA desaturase) and MI1PS (Myo-inositol-1-phosphate synthase). DESAT16 is the enzyme that catalyzes the conversion of saturated fatty acid to monounsaturated fatty acids. The overexpression of this enzyme is shown to increase lipid accumulation in tty acid , deletinY. lipolytica which lacks acyl-CoA oxidases can more efficiently convert n-alkanes and fatty acids or their derivatives to their corresponding DCAs [Furthermore, formed DCAs can get degraded via β-oxidation pathway. Therefore blocking it by deletion of acyl-CoA oxidase encoded by POX1–6 genes can further enhance the titer of DCAs. ing DCAs .Y. lipolytica involves several unique reactions and is commonly controlled by the supply of precursors and cofactors. Earlier part of this study has focused on overexpression and downregulation of some key enzymes regulating the ω-oxidation pathway. However, cofactors are very important to achieve improvement in productivity. NADPH, as a reducing equivalent, usually plays an important role in coupling catabolism with anabolism and energy generation during metabolism. Several metabolic engineering approaches have been implemented to manipulate the cofactors level to increase the product yield in other microorganisms [Streptococcus mutants gapN gene which encodes a GAPDH to increase the L-lysine production [Kluyveromyces lactis [Similar to other biosynthetic pathways, the DCAs biosynthesis via ω-oxidation pathway in rganisms , 45 Incroduction ; enginees lactis ; overexps lactis , are a fY. lipolytica. This excessive cofactor demand is mainly satisfied through the pentose phosphate pathway (PPP) reactions. But, the increased usage of the PPP through overexpression is suboptimal as one mole of carbon is lost as CO2 for every two moles of NADPH produced [Fatty acid biosynthesis and ω-oxidation pathway are NADPH demanding oxidative pathway in produced . To circTo achieve a high-level production of DCA in yeast, we propose to enhance the supply of NADPH to the ω-oxidation pathway. Hence, a multi-step metabolic engineering strategy can be devised to simultaneously modify the glycolysis step, optimize the NADPH supply and to enhance the activity of some NADPH producing enzymes.Y. lipolytica that lipid accumulation can occur de novo without exogenous supply of hydrophobic substrate, laying the grounds for a possibility to be used for de novo DCAs production as well, from primary carbon sources, i.e., glucose or glycerol [S. cerevisiae [Y. lipolytica [Y. lipolytica, we sought to explore the effect of BCAA supplementation on acetyl-CoA pool, which can be produced via degradation pathway of BCAA. In order to theoretically analyze the flux distribution and flux-sum changes that occur when BCAA is supplemented, we maximized the DDDA production using glucose as the primary carbon source supplemented with 10 C-mmol/gDCW-hr of leucine, isoleucine, or valine. To elucidate the difference in pathway utilization and metabolite turn-over in different amino acids during DDDA production, we prepared the heat map of flux distribution and flux-sum as shown in Fig. Y. lipolytica. This is the first report implicating a prominent role of valine degradation as an alternate route of acetyl-CoA biosynthesis in Y. lipolytica, experimental validation of which can give insight to intricate correlation between fatty acid biosynthesis and amino acid degradation.In most recombinant DCAs production studies, the hydrophobic substrates such as alkane and fatty acid methyl ester (FAME) were used for the biotransformation . As suchglycerol . In addirevisiae  and Y. lpolytica . Taking Y. lipolytica, iYLI647, was reconstructed using iMK735 as a scaffold and the relevant information from other available Y. lipolytica models. iYLI647 consists of 1347 reactions and 647 genes. The biomass equations were carefully formulated with various experimental information of Y. lipolytica, which is perhaps the reason for accurate model prediction. The potential of de novo DCAs production in Y. lipolytica combined with a model–driven strain design for metabolic engineering and media optimization strategies were then evaluated using the reconstructed iYLI647. The flux towards DDDA production was increased following the overexpression and deletion of few reactions and the model-based strain design gives us a good starting point to explore the metabolic capabilities of Y. lipolytica to produce fatty acid derived products. Moreover, the workflow and procedure presented in this analysis can be utilized as a platform to perform similar analyses with different organisms.The GSMM of iNL895, iYL619, iMK735, and iYali4, to compare and select the best suited as scaffold model. Following our qualitative and quantitative comparison, we chose iMK735 as a scaffold model and proceeded to manual curation to expand the model coverage and characteristics. To do this, using literature data, we verified the presence of reactions and relevance in Y. lipolytica metabolism. Then, we added ω-oxidation pathway to convert fatty acids to DCAs, and also the subsequent degrading β-oxidation pathway. Following the literature evidence and its established importance in fatty acid metabolism, we also included BCAA degradation pathways. Furthermore, we checked the mass balance of reactions and made appropriate changes to make stoichiometrically balanced reactions. In addition, we derived the biomass equation in carbon and nitrogen limited conditions using updated and relevant literature sources. Using refined biomass equation, we simulated the model to identify the loops, and missing link reactions using GapFinder [We chose all four separately developed publicly available metabolic network reconstructions, apFinder . Loops wY. lipolytica was simulated under varying environmental conditions using constraints-based flux analysis. The biomass reaction was maximized to simulate the growth under various culture conditions as described elsewhere [Sij refers to the stoichiometric coefficient of metabolite i involved in reaction j, vj denotes to the flux or specific rate of metabolic reaction j, j, respectively; and Z1 corresponds to the cellular objective as a linear function of all the metabolic reactions where the relative weights are determined by the coefficient cj. In this study, the constraints-based flux analysis problems were solved using COBRA toolbox [The cellular metabolism of lsewhere –56. The  toolbox .in silico strain design approaches have been employed to identify metabolic engineering targets for overproducing DCA from glucose.Four GDLS strain design algorithm  was implfj, is defined as the absolute value of reaction flux, vj [To identify the upregulation and downregulation gene/reaction targets which lead to the enhanced production of desired compound, we first needed to quantify the flux activity of all reactions in the wild-type strain. Flux activity, flux, vj . This caSubject to:αj and βj are upper and lower bounds of fluxes, respectively. The fj+ and fj− are the positive two variables into which the flux, vj, is decomposed. It is observed that fj+ or fj− is equal to zero. This condition was introduced by new binary variables Ij+ and Ij− which are when multiplied with a large integer which should be larger than the flux of experimentally measured value, M, will be always lesser than fj+ and fj−, respectively. Additionally, the constraint fj+ or fj− are equal to zero.Where, Once the reference flux activities are established, i.e. wild-type, maximum and minimum values, we then solve the below mentioned MILP problem to analyze the effects of perturbing a particular flux activity on cellular growth.Subject to:ORkatt and kint are gradually varied between 0 and 1 in steps of 0.1 to analyze the effect of reaction upregulation between minimal and wild-type values, and reaction downregulation between the wild-type and maximal values, respectively.Where, constraints (C1) and (C2) are applicable for upregulation and downregulation problems, respectively. Parameters Finally, the objective value obtained from the solution of Eq.  is used Subject to:Bj,k is the maximum biomass obtainable while solving problem  database using accession number GSE29046 [Since optimality assumption based FBA algorithms ignores the regulatory consideration while strain designing, we implemented transcriptomics-based strain optimization tool (tSOT)  to identGSE29046 .Since the ω-oxidation pathway is an oxidative process and requires cofactor optimization to maximize theoretical yield, we used CMA to identify the cofactor specificity engineering target which can increase the yield of DDDA. CMA was implemented as described in our previous work . MathemaSij, except the reactions which involve either NAD(H) or NADP(H). These reactions are swapped for cofactors in the k and which is fixed at 1 for all simulations in this work. The bi-level MINLP problem was reformulated as a single-level MINLP problem using the primal dual transformation as implemented earlier [Where,  earlier . The MILi can be formulated as Φi = 0.5 ∑ |Sijvj|. Each term in this summation series gives us the absolute rate of consumption/generation of metabolite i due to reaction j and thus by halving the sum of these terms, we can obtain the overall turnover rate for metabolite i.In the constraints-based flux analysis, there is no accumulation of intermediate metabolites due to the steady-state condition. However, the turnover rate, which is also equivalent to the total consumption or production rate, of the intermediates can be nonzero, which is defined as their flux-sum . Since tAdditional file 1:iYLI647 model in comparison with iMK735 scaffold model. (XLSX 22 kb)Changed, added and deleted reactions in Additional file 2:iYLI647. (XML 2312 kb)SMBL file of Additional file 3:Y. lipolytica in C- and N- limited conditions and GAM and NGAM calculations. (DOCX 52 kb)Biomass composition of


Clostridium butyricum. However, despite the increasing role of Clostridium butyricum as a bio-production platform, its metabolism remains poorly modeled.The increase in glycerol obtained as a byproduct of biodiesel has encouraged the production of new industrial products, such as 1,3-propanediol (PDO), using biotechnological transformation via bacteria like iCbu641, the first genome-scale metabolic (GSM) model of a PDO producer Clostridium strain, which included 641 genes, 365 enzymes, 891 reactions, and 701 metabolites. We found an enzyme expression prediction of nearly 84% after comparison of proteomic data with flux distribution estimation using flux balance analysis (FBA). The remaining 16% corresponded to enzymes directionally coupled to growth, according to flux coupling findings (FCF). The fermentation data validation also revealed different phenotype states that depended on culture media conditions; for example, Clostridium maximizes its biomass yield per enzyme usage under glycerol limitation. By contrast, under glycerol excess conditions, Clostridium grows sub-optimally, maximizing biomass yield while minimizing both enzyme usage and ATP production. We further evaluated perturbations in the GSM model through enzyme deletions and variations in biomass composition. The GSM predictions showed no significant increase in PDO production, suggesting a robustness to perturbations in the GSM model. We used the experimental results to predict that co-fermentation was a better alternative than iCbu641 perturbations for improving PDO yields.We reconstructed Clostridium strain to propose new scenarios for PDO production, such as dynamic simulations, thereby reducing the time and costs associated with experimentation.The agreement between the predicted and experimental values allows the use of the GSM model constructed for the PDO-producing The online version of this article (doi:10.1186/s12918-017-0434-0) contains supplementary material, which is available to authorized users. Clostridium butyricum or Klebsiella spp. [Clostridium species are the more attractive alternative because they are safer and achieve higher yields than Klebsiella [Clostridium butyricum could therefore shed light on a better ways to promote glycerol transformation to PDO in this organism.The rising biodiesel industry has resulted in a major overproduction of glycerol as a byproduct, which now threatens the economic viability of this industry , 2. Thislla spp. , 7. Closebsiella . Howeverebsiella –11. Therebsiella . A more Clostridium butyricum have generally focused on its central metabolism, which is composed of oxidative and reductive branches [Clostridium strain, indicating the functioning of 77 reactions and 69 metabolites. The model, in addition to the oxidative and reductive branches, also included simplified synthesis reactions for amino acids, macromolecules, and biomass. However, at present, metabolic models based on genome annotation information, also known as genome-scale metabolic (GSM) models [Clostridium butyricum.Metabolism studies of glycerol by the anaerobic bacterium branches . The oxibranches , 9, 15. branches  reported) models , 18, areClostridium sp. IBUN 158B cultured in glycerol [Clostridium butyricum 5521 cultured in glycerol. Nevertheless, despite this experimental information and the computational tools available, the prediction of PDO production by Clostridium based on its metabolic behavior is still limited.A proteomics study of the native Colombian strain glycerol  has provglycerol  also useOne computational tool commonly employed for metabolic modeling is flux balance analysis (FBA). FBA allows the use of a steady state assumption of defined culture conditions to predict the phenotype of one microorganism based on its GSM model –25. HoweIn general, predictions using GSM models assume biomass yield maximization as the objective function, based on the assumption that cells have evolved to select the most efficient pathways that achieve the best yields . NeverthClostridium strain. The biological model selected was the Colombian strain Clostridium sp. IBUN 13A, a strain isolated by our Bioprocesses and Bioprospecting Group. This strain is a natural PDO producer and has been employed over the last 20 years in several studies aimed at understanding PDO production, including the annotation of its genome [For these reasons, the initial purpose of the present research was to construct the first GSM model of a PDO producer s genome –42. AddiClostridium sp. IBUN 13A strain was constructed based on RAST annotation [Clostridium butyricum cultured in glycerol [C. beijerinckii GSM [C. acetobutylicum [C. thermocellum [C. ljungdahlii [C. cellulolyticum [1.624O0.456N0.216P0.033S0.0047.A draft metabolic model for the notation . The dranotation  analysisglycerol , 19, 44 glycerol , 45–52. utylicum . This exmocellum , C. ljunolyticum  include iCbu641 GSM model included 641 genes, 891 reactions, and 701 metabolites. Table Clostridium strains showed 11 unique enzymes, including as PDO dehydrogenase (EC.1.1.1.202) and glycerol dehydratase (EC.4.2.1.30). These two enzymes function in lipid metabolism but are associated with the reductive branch of glycerol metabolism [After curation, elemental balancing, and loop deletion, the constructed tabolism .Table 1MiCbu641 metabolic network is the first GSM model curated for a PDO producing Clostridium strain [iCbu641 model also includes all the enzymes associated with glycolysis and the pentose phosphate pathway and most of the enzymes involved in the TCA cycle. The enzymes from the TCA cycle that were not included are malate dehydrogenase (EC.1.1.5.4), succinate-CoA ligase (EC.6.2.1.4 - EC.6.2.1.5) and fumarate reductase (EC.1.3.5.4), which were not detected in the genome. Therefore, additional experimentation is required to verify the presence or absence of genes encoding these three enzymes in the genome of Clostridium sp. IBUN 13A. The model is able to synthesize de novo all the precursors involved in the biomass reaction .The m strain , 18, 49 iCbu641 GSM model and FBA were employed to predict the flux distribution of Clostridium butyricum cultured in glycerol. FBA was solved using linear programming (LP) with biomass maximization as an objective function. When compared with the experimental data, FBA predicted a biomass overestimation and no PDO production  and PDO yield (YPDO/S) errors of 300% and 100%, respectively [Clostridium butyricum does not grow optimally in glycerol. This can be explained mathematically, because the objective function and all constraints  used were linear. This means that the optimum found is indeed a vertex of the feasible solution space [The constructed ectively . Therefoon space , where nClostridium sp. IBUN 13A is related to dha operon expression and the enzyme activity of both PDO dehydrogenase (EC.1.1.1.202) and glycerol dehydratase (EC.4.2.1.30), which have been experimentally detected in the presence of glycerol as main carbon source [C. butyricum to produce formic acid and hydrogen (H2) [Clostridium butyricum is an anaerobic bacterium, so according to the redox balance, the substrate must act simultaneously as an acceptor and donor of electrons [2 formation, which would require no substrate as an electron acceptor to produce reduced compounds such as PDO. In other words, in the vertex predicted by LP optimization, the substrate is used mainly as an electron donor due to the formation of formic acid and H2, and no PDO is produced.Contrary to FBA predictions, PDO production in n source , 53, 54.gen (H2) . Clostrilectrons . However2 were overproduced instead of reduced products like butanol or ethanol, which have been detected experimentally (data not shown) [Clostridium butyricum, which is capable of producing formic acid or H2. Nevertheless, LP optimization is commonly employed in solventogenic Clostridium strains, although all of them are able to produce at least hydrogen [2 secretion in LP optimization; however, no linear trend was observed, especially in H2 secretion [Similar results were observed using glucose as a substrate, where formic acid and Ht shown) . Consequhydrogen , 50, 51.ecretion .Clostridium butyricum cultured in glycerol as a mechanism to control acetyl-CoA/CoA and ATP/ADP ratios [X/S and YPDO/S errors of nearly 4.5%, and 1.5%, respectively, when compared with the experimental values obtained under limiting conditions of glycerol (<15 g/L) [2. It also suggests that Clostridium butyricum indeed minimizes enzyme usage and prefers short pathways (PDO production) to maximize its growth under limiting nutrient conditions.A new objective function of maximizing biomass yield per flux unit Equation  was therP ratios . The new<15 g/L) . TherefoIdentical phenotypic predictions were also observed using both LP and NLP optimizations when the production of hydrogen and formic acid were blocked as additional constraints (data not shown). This validates, on the one hand, the effect of these products in the lack of prediction using LP optimization. On the other hand, NLP optimization indeed predicts global optima, as suggested Schuetz et al. .X/S under glycerol-excess conditions (without reaching inhibition conditions) [Clostridium butyricum grows under sub-optimal conditions when the substrate is present in excess, as is also observed in E. coli [growth < 0). Growth Gibbs free energy can be expressed as a function of biomass yield and the Gibbs free energies for catabolism and anabolism, as shown Eq. However, the new objective function overestimated Yditions) , suggest E. coli . This bew) that minimizes both the enzyme usage and ATP production of the network , where the average experimental errors were minimal [X/S and YPDO/S errors were 5.3% and 2.5%, respectively, as shown Fig. Clostridium butyricum cultured anaerobically in glycerol; additional experimental data would validate its usage.The sub-optimal behavior was accounted for in the objective function of Eq. Equation . The ATPp to 98% , 39. On  minimal . The ave2 and less PDO than is observed under the excess condition, where ferredoxin reductase produces reducing equivalents. Consequently, Zeng quantified ferredoxin reductase directionality by calculating the ratio between hydrogen and reduced ferredoxin formed . The remaining 24 non-predicted enzymes correspond only to directionally coupled growth and most of them (21 enzymes) are involved in carbohydrate metabolism and the synthesis of nitrogenous compounds (amino acids and nucleotides). The absence of a prediction for these enzymes could be due the presence of alternate pathways and isozymes, as is the case for asparagine synthase (EC.6.3.5.4), isocitrate dehydrogenase (NADP) (EC.1.1.1.42), and glycerol-3-phosphate dehydrogenase (EC.1.1.1.94), where the alternative enzymes are asparagine synthetase (EC.6.3.1.1), isocitrate dehydrogenase (NAD) (EC.1.1.1.41), and glycerol kinase (EC.2.7.1.30), respectively. Another possible cause is the inability of FBA to predict regulation mechanisms , as is tiCbu641 was further tested by comparing the experimental and simulated data using other substrates at optimal conditions, Eq. Clostridium butyricum W5 cultured in glucose [The robustness of  glucose , as showClostridium butyricum TM-9A using different carbohydrates. Our comparison of the experimental yields with the predicted yields obtained through FBA is shown in Table X/S agree with the experimentally obtained values, showing smaller yields for pentoses and the highest yield for the trisaccharide raffinose, while the yields using monosaccharides were lower than those obtained using disaccharides. However, some differences are observed between the experimental and predicted values. First, ribose and xylose had considerably higher experimental than predicted yields. Second, the experimental yield for cellobiose was much lower than the predicted yield. Finally, although the simulations predicted the same yields for arabinose and ribose, their experimental yields differed. The first case could be a result of an incomplete curation of iCbu641 related to pentose consumption; therefore, more experimental information is needed. The second and third cases could be due to miscalculation of the experimental yields of arabinose and cellobiose, since these substrates were not consumed completely, as can be inferred from their pH reports [Clostridium was not well adapted to these substrates and may have needed to undergo more generations to reach its optimal growth [H2/S) showed experimental values that were mostly within their respective predicted feasible ranges. The only unpredicted YH2/S corresponded to arabinose, which supports the necessity of complementing iCbu641 for consumption of pentoses. Consequently, iCbu641 has the capacity to be employed to predict Clostridium butyricum growth using different carbohydrates as substrates.Junghare et al.  also eva reports . The lasl growth . The hydC. butyricum strain CWBI 1009 cultured in glucose [We also assumed a qualitative correlation between the enzyme flux and mRNA expression and compared FBA predictions and experimental transcriptomics results for the  glucose . Of the  glucose .  reduction was also predicted for the mutant strain. By contrast, the experimental reduction of hydrogen yield in the mutant was not predicted; however, this is not conclusive since the FVA ranges of the wild type and mutant strains did agree with their experimental values.Finally, the chnology  was emplPDO/S using iCbu641. The first strategy was to use ROOM to predict single and double mutants through in silico enzyme deletion. The 145 enzymes associated with reactions directionally coupled to growth at culture conditions, as reported by Comba et al. [PDO/S by up to 1% in relation to the wild type strain. One of the best mutant predicted had simultaneous deletion of lactate dehydrogenases (EC.1.1.1.27 and EC.1.1.1.28), which increased YPDO/S only to nearly 1.2% , lactate dehydrogenase (ΔldhA-508 s), and 3-hydroxybutyryl-CoA dehydrogenase (Δhbd-414 s), corresponding the lack of production of hydrogen, lactic acid, and butyric acid, respectively. Montoya [Single BUN 158B  to impro Montoya  reportedknockout is an inadequate strategy for improving PDO yields. Similar results were obtained using Optknock, with up to 3 deletions [Optknock maximum PDO yield value was 17.4% higher than the predicted value under glycerol limitation, but it was only 0.4% higher than the predicted value under glycerol excess, which reinforces that blocking reactions are useless. This can be understood by considering that PDO is a primary metabolite and its production is associated with growth [The experimental values for PDO yields agreed with the predicted range, validating the ROOM simulations. Moreover, the FVA of the wild type strain indicated a PDO maximum flux that was 11.6% higher than the flux predicted by FBA; therefore, none of the mutants would show an increase in the PDO yield greater than this value without affecting biomass yield. This validates the single and double mutant predictions and suggests that mutant elaboration by eletions , 74, wheh growth , 75.X/S and YPDO/S correlation values, indicating low correlation with the precursors  but a higher correlation with macromolecules, especially proteins . This could suggest that a low protein content in the cell could improve YX/S and reduce the YPDO/S, due their negative and positive correlations, respectively. However, the relative standard deviations obtained for YX/S and YPDO/S were 3.2% and 0.45%, respectively, meaning that the model is sufficiently stable to perturbations in biomass composition. These predictions suggest that changes in culture media aimed at modifying biomass composition and enhancing PDO yields would be unnecessary. However, modifications in the culture media could reduce PDO yield, as Dabrock et al. found using iron in excess [The second strategy evaluated was perturbation in the biomass composition by simultaneous random variation of stoichiometric coefficients of 44 precursors and 8 macromolecules. This strategy was evaluated since modifications in the culture media can affect biomass composition, such as accumulation of lipids during nitrogen starvation or protein accumulation with excess of nitrogen in the culture medium . Normal n excess .Fig. 3PePDO/S values than obtained using glycerol as single substrate [PDO/S at different glucose and glycerol uptake fluxes, as shown Fig. PDO/S = 0) in the absence of a glycerol uptake flux. The results also show the complete transformation of glycerol to PDO (YPDO/S = 1), using ratios of at least 0.375 between uptake fluxes of glucose and glycerol. Therefore, these predictions permit the proposal that co-fermentation is the best alternative for improving biomass and PDO yields. However, a priori prediction of the molar ratio between these substrates that could allow these flux ratios is difficult.The third strategy evaluated was to use two substrates simultaneously: glucose and glycerol. Glucose is used as carbon source, while glycerol is used to maintain redox balance, therefore generating higher Yubstrate . Figure iCbu641, the first curated genome-scale metabolic model for a PDO producer Clostridium strain. During iCbu641 validation, we solved flux balance analysis using LP optimization; however, according to the experimental data, the model predicted errors of nearly 300% for biomass yield and failed to predict PDO production. Therefore, NLP optimization was employed in FBA simulations, and the new objective function maximized biomass yield per flux unit [Clostridium butyricum growth and PDO production under limiting and excess glycerol conditions. Additional validations were developed using proteomics and transcriptomics data, as well data from knockout mutants, which allowed verification of the accuracy of predicting perturbations of iCbu641. All validations were completed using experimental data from different Clostridium butyricum strains and suggested that iCbu641 is an agnostic GSM model at the state steady, but the differences may be observed during dynamic predictions.We generated lux unit . The valiCbu641 allows the proposal of this GSM model for predicting dynamic cultures (i.e. batch and fed-batch fermentations) capable of increasing PDO production, thereby minimizing the need for direct experimental efforts.Subsequently, perturbations in the metabolic network and biomass composition were proposed to increase the PDO yield predictions. However, these perturbations predicted no significant increments. We also evaluated glucose-glycerol co-fermentation as a strategy to improve PDO yields. We found that a ratio of glucose and glycerol uptake fluxes greater than or equal to 0.375 would allow the complete conversion of glycerol to PDO; however, experimental analysis is needed to find the molar ratio that allows the achievement of this flux ratio. Finally, predictions of PDO production in state steady cultures using Clostridium sp. IBUN 13A, isolated and stored by the Bioprocesses and Bioprospecting Research Group from the Institute of Biotechnology of the Universidad Nacional de Colombia. This draft genome was previously sequenced and annotated and is available in GenBank with the accession no NZ_JZWG00000000.1 [Clostridium strains C. acetobutylicum [C. thermocellum ATCC 27405 [C. beijerinckii NCIMB 8052 [C. ljungdahlii ATCC 55383 [C. cellulolyticum H10 [The draft genome was obtained for the Colombian-native strain 000000.1 . The str000000.1 ; automat000000.1 . The GSMutylicum –49, C. tCC 27405 , C. beijIMB 8052 , C. ljunicum H10  were useicum H10  was usedClostridium acetobutylicum ATCC 824 by Lee et al. and are 40 mmol·ATP·g−1 and 5 mmol·ATP·g−1·h−1, respectively [−1 h−1 as initial and maximum values, respectively, and −0.0879·g·h·mmol−1 as the accumulation rate, as shown in Eq. The values for growth-associated maintenance (GAM) and non-growth–associated maintenance (NGAM) were reported for ectively . An alloectively  and Papaectively . The kinxi involved in N reactions is described in Eq. Sij is the stoichiometric coefficient of metabolite i in reaction j, and vj is the flux value in which this reaction occurs [M metabolites; however, since N > M, the prediction of fluxes vj can be achieved using FBA, which maximizes or minimizes an objective function Z , both according to the glycerol uptake flux (vGlycerol). The robustness analyses were made using different objective functions for both glycerol limited and glycerol excess conditions. The objective function employed under glycerol limitation was biomass maximization per enzyme usage. Under glycerol excess, the biomass was maximized, while both enzyme usage and ATP production were minimized, where ATP production corresponds only to thermodynamically feasible reactions able to produce ATP. According to KEGG nomenclature, these reactions are R00156, R00158, R00200, R00315, R00332, R00512, R00570, R00722, R01512, R01547, R01665, R01688, R01724, R02090, R02093, R02094, R02098, R02326, R02331, R03005, R03035, R03530, and R03920. The predicted values were compared with the experimental values reported for the Clostridium butyricum DSM 5431 strain cultured in glycerol limited and glycerol excess conditions [The experimental validation used two robustness analyses, as reported by Price et al. : the fornditions .iCbu641 and the experimental proteome of the strain Clostridium butyricum DSM 10702 cultured in glycerol [. The enzymes in the model were classified as blocked or directionally, partially or fully coupled to growth using FCF, according to the methodology reported by Burgard et al. [The prediction capability of enzyme expression was also evaluated by comparing the enzymes present in both metabolic model glycerol . The enzd et al. . BlockedClostridium butyricum strains in substrates other than glycerol, such as glucose [C. butyricum CWBI 1009, was also used for validation; this organism had been cultured in glucose using batch fermentation with uncontrolled pH. The mRNA detected in the transcriptome coded a total of 913 enzymes (532 unique and 381 redundant), where values at the exponential growth phase were used in a similar way to those from the proteomic data.Validation was also obtained using data from experimental cultures of  glucose  and othe glucose . The tra glucose  for straknockout of enzymes directionally coupled to growth. In total, 145 single mutants were evaluated. Double deletion was also studied by simultaneously blocking two enzymes, excluding enzymes previously detected as essential in the single deletion; this led to evaluation of almost 7900 double mutants.Regulatory on/off minimization (ROOM)  was empliCbu641. A total number of 10,000 simulations were made, where 965 of them were excluded because at least one of the concentrations was negative. Coefficients of correlation were calculated for biomass and PDO yields using the remaining 9035 biomass precursor combinations.We also made perturbations in the biomass composition expressed by the variation of stoichiometric coefficients of 8 macromolecules, 7 fatty acids, 20 amino acids, 8 nucleotides, 3 polar lipids, and 6 cofactors. FBA was performed using different compositions randomly generated using normal distributions with standard deviations equivalent to 30% the values used in GSM model FBA, FVA, and ROOM were computer simulated using GAMS  software V.24.2.2 r44857 for Linux. Linear and Nonlinear Programming (LP and NLP) were developed with solver CONOPT v3.15 N, and Mixed Integer Programming (MIP) was developed with solver CPLEX 12.6.0.0. Data were analyzed using Microsoft Excel® 2010.Additional file 1:iCbu641Genome Scale Metabolic Model. (XLSX 203 kb)Excel format of Additional file 2:iCbu641Genome Scale Metabolic Model. (XML 2250 kb)SBML format of Additional file 3:Proteomic and transcriptomic comparison results. (XLSX 30 kb)Additional file 4:Results of modeling scenarios with PDO yield increment. (XLSX 5507 kb)


TPC) for metabolic rate—that metabolic rate is a unimodal function of temperature often with maximal values in the biologically relevant temperature range and that activation energies are temperature dependent. We show that the temperature dependence of metabolic rate in ectotherms is well described by an enzyme‐assisted Arrhenius (EAAR) model that accounts for the temperature‐dependent contribution of enzymes to decreasing the activation energy required for reactions to occur. The model is mechanistically derived using the thermodynamic rules that govern protein stability. We contrast our model with other unimodal functions that also can be used to describe the temperature dependence of metabolic rate to show how the EAAR model provides an important advance over previous work. We fit the EAAR model to metabolic rate data for a variety of taxa to demonstrate the model's utility in describing metabolic rate TPCs while revealing significant differences in thermodynamic properties across species and acclimation temperatures. Our model advances our ability to understand the metabolic and ecological consequences of increases in the mean and variance of temperature associated with global climate change. In addition, the model suggests avenues by which organisms can acclimate and adapt to changing thermal environments. Furthermore, the parameters in the EAAR model generate links between organismal level performance and underlying molecular processes that can be tested for in future work.A mechanistic understanding of the response of metabolic rate to temperature is essential for understanding thermal ecology and metabolic adaptation. Although the Arrhenius equation has been used to describe the effects of temperature on reaction rates and metabolic traits, it does not adequately describe two aspects of the thermal performance curve ( The product kT is the average kinetic energy of the reactants, such that as temperature increases, the energy of the reactants increases, reducing the value of the exponent and raising e−Ea/kT toward 1.The Arrhenius factor varies between 0 and 1 Figure , giving Topt)  on temperature (T). Most of these models were originally developed to describe enzyme‐catalyzed reactions, rather than metabolic rate per se, but model 4 was developed to describe population growth rate and model 3 originally dealt with development rate. Nonetheless, they all have potential as descriptions of the temperature dependence of metabolic rate. All of the models begin with a monotonically increasing function of temperature, either the Eyring or the Arrhenius equation, the difference being that the Eyring model explicitly includes temperature in the constant. All of the models invoke a reduction in enzyme performance at low and/or high temperature due to the decreased probability of enzymes being in an active state. A key difference between the EAAR model and models 1‐5 is that in the EAAR model, enzymes increase reaction rates over some baseline rate, while in models 1‐5, reduced enzyme performance lowers the reaction rate from a maximal rate .A range of models have been developed to describe the dependence of a reaction rate  enzymes are inactive at high temperature, (2) enzymes are inactive at low temperature, (3) active state is given by a three‐state transition process, (4) active state is given by a protein stability process, (5) substrate supply is unlimited, (6) the proportion of enzymes that are in an active state is at equilibrium, (7) enzymes denature through time, (8) the activation energy of the catalyzed reaction is equal to the free energy of the catalyzing enzymes, (9) heat capacity is negative, (10) activation energy corresponds to maximal enzyme activity level, (11) there is no activation energy in the absence of enzymes, (12) the activation energy of the reaction is lowered as a function of the free energy of the catalyzing enzymes.H, enthalpy change of folding the enzymes, relative to a reference or melting temperature, subscripted A for active state and L for lower temperature inactive state; ΔS, entropy change of an enzyme with temperature, relative to the melting temperature, subscripted A for active state and L for lower temperature inactive state; ΔCp, the difference in heat capacity between the folded and unfolded state of the enzymes, relative to the melting temperature; Tm, melting temperature. The Schoolfield model includes three summary parameters: ρ(25°C), rate at 25°C assuming no inactivation; T1/2, the temperature at which the enzyme is half active and half inactive, subscripted L for low‐temperature inactive and H for high‐temperature inactive. The equilibrium model includes four parameters: Teq, the temperature at which the concentration of active and inactive enzymes is equal; ΔHeq, the change in enthalpy associated with the equilibrium; ΔGcat, activation energy for the reaction; and ΔGinact, activation energy for enzyme inactivation. The EAAR model introduces Eb, the baseline activation energy, the change in activation energy associated with the change in enthalpy of the catalysts (ECpΔ), and the change in activation energy associated with the change in heat capacity of the catalysts (EHΔ). The physical constants are k, Boltzmann's constant, R, the gas constant, and h, Planck's constant.The common parameters in these models are ΔA model that describes and predicts metabolic rate TPCs based on more realistic biological mechanisms is therefore urgently needed Schulte, . Here, w2Ea in the Arrhenius equation, as it is used to describe biological rates, will be set by enzymes. However, it is more precise to say that the realized Ea represents the difference between the kinetic requirements of a reaction as it would occur outside of an organism  and the enzymatic contribution (Ec) of the organism to the reaction , then, is the kinetic hurdle that remains after enzymes have done their job (Eb − Ec).The observed activation energy  and rewrite Equation Biochemical reactions within organisms require sufficient kinetic activation and the catalytic contribution of enzymes Segel, . Protein Feller, . As indiSubstituting Equation H is by definition zero at the melting point and increases below the melting point, meaning that the colder it gets, the more stable the enzyme is and the more effectively it can contribute to a reaction  and the change in activation energy associated with the change in heat capacity of the catalysts (EHΔ).The thermodynamic parameters in Equation n Figure c. ΔCp sTopt), where the top of the unimodal TPC is reached:Taking the derivative of Equation Topt through acclimation or adaptation potentially involves changes in the activation energy of the reaction as well as the thermodynamic properties of enzymes, EHΔ, ECpΔ, and Tm.This expression shows that modifying Cp—the difference in heat capacity between the folded and unfolded state of enzymes—directly affects the spread or breadth of the TPC, so one clear route to becoming a thermal generalist is to lower enzymatic ΔCp. The model predicts that lowering ΔCp not only broadens the curve, but also elevates the curve, suggesting that we might not predict a specialist—generalist trade‐off for all TPCs. Enzymes with higher ΔH are predicted to elevate the TPC due to increases in the enzyme contribution to Eb and to shift Topt to lower temperatures, while increases in Tm will lower the curve and move the Topt to the right. Any potential genetic correlations among parameters, however, could constrain the options for acclimation or evolution of the curves with changing thermal environments  acclimated to two different temperatures  using a fit of a quadratic function to the right side of the data. This step is essential because the model is defined with respect to Tm, and otherwise attempting to fit Tm in the overall fitting process can provide poor estimates of both Tm and the remaining parameters. In step (2), we fit the EAAR model to the rest of the data with the Tm set at the value identified in step (1). For each modeled data set, we also calculated the Topt using Equation Tm, and Topt.As an initial assessment of the model's ability to describe real data, we fit the model to metabolic rate TPCs for three species of amphipod , although there is some variation among taxa . The EAAR model uses the thermodynamics of protein stability to describe how enzymes increase and then decrease in effectively lowering the activation energy as temperatures rise, and the model describes well the dependence of metabolic rate on temperature for diverse organisms  for metabolic rates represents an average activation energy for the rate‐limiting enzyme‐catalyzed biochemical reactions that govern metabolism . Protein stability may be modified by extrinsic changes in pH, thermoprotectant osmolytes, protein concentration, and, in the case of membrane‐localized proteins, the membrane architecture. Additionally, a variety of stressors including temperatures near and above thermal optima activate the heat‐shock protein response  via regulation of flux in response to temperature may be a critical component of thermal responses of metabolic rate , the sharing of similar thermal dependencies of the many enzymes that underlie metabolic rate within an organism, or whether it is the average of many enzymes which share control of metabolism, each of which may differ somewhat in their thermal optima , but other parameters, such as substrate levels (A0), EHΔ, and ECpΔ were unchanged. One possibility for such changes is that acclimation of mitochondrial membranes to temperature may be a factor determining the thermal stability of the membrane‐embedded protein oxidative phosphorylation complexes, which may play a critical role in how thermal acclimation shapes TPCs for aerobic metabolic rate (Dahlhoff & Somero, In addition, the model makes useful connections between whole‐organism rates and underlying mechanisms by building on fundamental, thermodynamic aspects of all protein stability curves Feller, . Thus, oThe molecular evolution underlying divergence in intrinsic protein stability across the range of temperatures inhabited by life appears to be shaped by a common set of thermodynamic rules that govern protein folding Feller, . NonetheTm, where by definition the enzymes are no longer contributing to the reaction and the organism is near death. Above this point, however, the reactant kinetic effect of temperature is still increasing, which suggests that the remaining reactants present in an organism that is pushed above this temperature should react at a faster rate, even if the organism has died, at least until the reactants decline in availability. Surprisingly, this outcome has been observed in what is known as thermolimit respirometry, where the metabolic rate of an organism is measured as the temperature is ramped up, and an increase in the rate of metabolism is observed after the organism dies (Lighton, The right column of Figure Lighton, . AlthougLighton, .In summary, the EAAR model captures the empirical patterns and incorporates the minimum necessary processes shaping the temperature dependence of metabolic rate in a relatively simple and useful manner. It resolves long‐term problems with the Arrhenius equation and other reaction models and provides a way to move forward with temperature in metabolic ecology. In particular, the EAAR model will facilitate more comparative analyses to elucidate mechanisms involved in acclimation and adaptation to different thermal environments. Furthermore, the many biological processes that depend on metabolic rate can be understood as extensions of the EAAR process. For example, the EAAR model should apply equally well to photosynthesis, which also shows a unimodal response to temperature (Padfield, Yvon‐Durocher, Buckling, Jennings, & Yvon‐Durocher, The authors declare no conflict of interest.The data used in this article are previously published and available in the original sources. Our digitization of the data will be made available on Data Dryad upon acceptance of the manuscript.All authors were involved in the conception of the paper and contributed to the collection of data. JPD and JPG developed the model. JPD and TML analyzed the data. All authors contributed critically to the drafts and figures and gave final approval for publication. Click here for additional data file.


Mis-annotation aside, it is unclear whetherin silico reconstructions of metabolic structure fromcomponent parts accurately captures the higher levels of network organizationand flux distribution. For this approach to provide accurate predictions, onemust assume that the conservation of metabolic components leads to conservationof metabolic network architecture and function. This assumption has not beenrigorously tested. Here we describe the implications of a recent study : e01840-15), which demonstrated that conservation of metabolic componentswas not sufficient to predict network structure and function.For decades, experimental work has laid the foundation for our understanding ofthe linear and branched pathways that are integrated to form the metabolicnetworks on which life is built. Genetic and biochemical approaches applied inmodel organisms generate empirical data that correlate genes, gene products andtheir biological activities. In the post-genomic era, these results have servedas the basis for the genome annotation that is routinely used to infer themetabolic capabilities of an organism and mathematically model the presumedmetabolic network structure. At large, genome annotation and metabolic networkreconstructions have demystified genomic content of non-culturablemicroorganisms and allowed researchers to explore the breadth of metabolisms Salmonellaenterica has served as a model system to probe metabolic integration anddefine network architecture in vivo. When this system is constrained,it is highly sensitive to slight changes in the metabolic network, and subtle changes incarbon flux can be monitored using growth analyses. A large part of our work has focusedon defining metabolic redundancy and recruited pathways that can produce the firstshared purine/thiamine synthesis intermediate, phosphoribosylamine (PRA). PRA issynthesized by phosphoribosylpyrophosphate amidotransferase (PurF)  and in apurF mutant, six alternative mechanisms for synthesizing PRAsufficient for thiamine synthesis have been defined experimentally. As PurF is feedbackinhibited by purines, bypassing PurF with alternative or recruited pathways woulddecouple thiamine synthesis from regulation by purines. Such a scenario is significantwhen the natural habitat of S. enterica is expected to have substantiallevels of purines due to lysed cells and degraded DNA. However, the extent to whichthese alternative pathways contribute to thiamine synthesis in wild type S.enterica has remained elusive. The following fundamental questions aroseduring the course of the aforementioned studies: i) are metabolic network structuresdissected by mutational analysis biologically relevant? ii) if metabolisms containidentical components, is the network structure and function predetermined? In otherwords, does the conservation of metabolic components demand the conservation of thenetwork? To begin to address these questions we took a comparative approach and queriedthe metabolic network surrounding PRA synthesis in Escherichia coli, aclose relative of S. enterica with conserved metabolic pathways andregulatory paradigms. Importantly, all metabolic components relevant for PRA synthesisin S. enterica are present and are > 95 % identical in E.coli.Biosynthesis of the coenzyme thiamine pyrophosphate (TPP) in S. enterica and E. coli were recently reportedin mBio : e01840-15) and are summarized and expanded here. As expected,deletion of purF in either E. coli or S.enterica results in a requirement for exogenous purines for growth.However, the purF mutants of E. coli and S.enterica differ in their ability to synthesize thiamine. Both organismsmaintain thiamine synthesis due to non-enzymatic formation of PRA fromribose-5-phosphate (oxidative pentose phosphate pathway) and ammonia. When thenon-enzymatic mechanism is eliminated, E. coli retains PRA synthesis,while growth of S. enterica becomes dependent on exogenous thiamine.Further experimentation showed that E. coli utilizes an unusual pathwaystitched together from isoleucine and tryptophan biosynthetic enzymes. We learned thatin E. coli, threonine is converted to 2-aminocrotonate (2-AC) byserine/threonine dehydratase , which is then condensedwith phosphoribosylpyrophosphate by anthranilate synthase component II  to form PRA . Importantly, results of labeling studies([15N]-threonine) showed that, in E. coli, IvA/TrpD-dependent synthesiscontributes significantly (~ 35 %) to the thiamine pool in a wild type strain.Therefore, the pathway described for canonical thiamine synthesis contributes littlemore than half of the cell’s total thiamine under some growth conditions. This findingwas unexpected since it was not apparent from the knowledge of metabolic componentspresent. This work demonstrated that S. enterica and E.coli do not use the same set of pathways to synthesize thiamine, andhighlighted the fact that there is something fundamentally different about the networkdeterminants of the two organisms. We can then conclude that even in closely relatedorganisms, conserved metabolic enzymes do not guarantee conserved structure and functionof the metabolic network.Our initial results from a study to address the conservation of metabolic structurebetween S.enterica as a pathway activated by a suppressor mutation(ridA) in a purF strain. ridAencodes the reactive intermediate deaminase, RidA, a universally conserved metabolicstress protein. RidA protects critical PLP enzymes  by quenching the reactive enamine 2-aminoacrylate (2-AA)generated by other PLP enzymes such as IlvA (when serine is the substrate). Thethreonine-derived product of IlvA, 2-AC, that is utilized by the IvA/TrpD-dependent PRAbiosynthetic pathway is also a substrate of RidA . The fact that this pathwayis active in wild type E. coli, but must be activated by aridA lesion in S. enterica, suggests that 2-ACdoes not accumulate in S. enterica because of the presence of RidA.Then the question is, why does 2-AC accumulate in E. coli, which alsohas a functional RidA? In other experiments we have shown that diverse phenotypes causedby 2-AA damage in a S. enterica ridA strain are not observed in anE. coli ridA strain (unpublished data). Together our observationssuggest that wild type E. coli makes more 2-AC and less 2-AA thanS. enterica, implicating IlvA activity as the key difference. Wehypothesize that a metabolite balance  is responsible for the multiple phenotypic differences  between the two organisms. Calculations of kinetic parameters suggestthis is a feasible explanation. Advantageously, we have the genetic tools to manipulateboth E. coli and S. enterica. Future efforts willattempt to “switch networks”, that is, convert the E. coli network intothe S. enterica network (with respect to PRA synthesis) and viceversa.The IvA/TrpD-dependent pathway for PRA synthesis was first identified in A natural extension of metabolic studies is the implementation of mathematical models tocapture the inherent complexity of biological systems. One of the current challenges inthis effort is to move beyond a focus on high-flux metabolic pathways and incorporatethe subtle intricacies of a metabolic network into a series of mathematical models withincreasing validity. These intricacies are often responsible for the plasticity that ischaracteristic of metabolic networks, and they are critical for the responsiveness anetwork has to perturbations by external and internal signals. To accurately predict thephenotypes we observe, a productive modeling approach has to be rigorously applied to asmall-scale network. Toward this end, in collaboration with Eberhard Voit and colleagues(Georgia Tech), we are developing the metabolic node that includes the purine, histidineand TPP biosynthetic pathways into a single system that can be experimentally andtheoretically queried. This node is composed of low and high flux pathways and extensivepoints of metabolic crosstalk. We envision developing the purine/histidine/TPP node ofmetabolism as a model system to integrate mathematical modeling, quantitative metabolitemeasurements and experimental approaches that will uncover fundamental properties of themetabolic network. The integration of experimental and theoretical approaches plannedmay provide dynamic models to address critical questions at the interface of thephysical and biological sciences. We expect to pinpoint network behaviors with a levelof detail that is not yet possible for a metabolic system at a whole-cell level.E. coli was originally characterized in mutant analysis ofS. enterica, a strategy that is sometimes dismissed as lackingphysiological relevance. The biochemical and genetic characterization of this pathwayidentified hallmark behaviors that were easily detected in E. coli andled to the quick identification of the relevant pathway. The resultant metabolicinsights, that transcend the organism of discovery, underscores that the role of geneticanalysis of mutants is to better understand wild type cell behavior and uncovermetabolic paradigms. The most dramatic point made by this work is the recognition thatmetabolisms that contain the same components do not necessarily configure them the sameway. This study emphasized the value in going beyond the cataloging of component partsas we strive to understand network structure, and the critical role that experimentalapproaches still have in deciphering cellular metabolism.The IlvA/TrpD-dependent PRA pathway that contributes to TPP biosynthesis in wild type


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.