Escherichia coli was used for overproducing proline-4-hydroxylase (P4H), a dioxygenase catalyzing the hydroxylation of free l-proline into trans-4-hydroxy-l-proline with a-ketoglutarate (a-KG) as cosubstrate. In this whole-cell biocatalyst, central carbon metabolism provides the required cosubstrate a-KG, coupling P4H biocatalytic performance directly to carbon metabolism and metabolic activity. By applying both experimental and computational biology tools, such as metabolic engineering and 13C-metabolic flux analysis (13C-MFA), we investigated and quantitatively described the physiological, metabolic, and bioenergetic response of the whole-cell biocatalyst to the targeted bioconversion and identified possible metabolic bottlenecks for further rational pathway engineering.Understanding the metabolism of the microbial host is essential for the development and optimization of whole-cell based biocatalytic processes, as it dictates production efficiency. This is especially true for redox biocatalysis where metabolically active cells are employed because of the cofactor/cosubstrate regenerative capacity endogenous in the host. Recombinant E. coli strain was constructed by deleting the putA gene encoding proline dehydrogenase. Whole-cell biotransformations with this mutant strain led not only to quantitative proline hydroxylation but also to a doubling of the specific trans-4-l-hydroxyproline (hyp) formation rate, compared to the wild type. Analysis of carbon flux through central metabolism of the mutant strain revealed that the increased a-KG demand for P4H activity did not enhance the a-KG generating flux, indicating a tightly regulated TCA cycle operation under the conditions studied. In the wild type strain, P4H synthesis and catalysis caused a reduction in biomass yield. Interestingly, the ΔputA strain additionally compensated the associated ATP and NADH loss by reducing maintenance energy demands at comparably low glucose uptake rates, instead of increasing the TCA activity.A proline degradation-deficient putA knockout in recombinant E. coli BL21(DE3)(pLysS) was found to be promising for productive P4H catalysis not only in terms of biotransformation yield, but also regarding the rates for biotransformation and proline uptake and the yield of hyp on the energy source. The results indicate that, upon a putA knockout, the coupling of the TCA-cycle to proline hydroxylation via the cosubstrate a-KG becomes a key factor constraining and a target to further improve the efficiency of a-KG-dependent biotransformations.The The online version of this article (doi:10.1186/s12934-015-0298-1) contains supplementary material, which is available to authorized users. These enzymes catalyze a remarkable range of reactions, such as hydroxylation, desaturation, and epoxidation  fragment ions doubled from 1.8, 1.8, and 2.2% to 3.8, 3.4, and 4.0%, respectively  [a-KG towards succinate and malate, may have led to malic enzyme activation, pyruvate surplus, and finally a higher acetate excretion rate.The largest differences among strains were observed in the reactions mediated by the malic enzymes and the phosphoenolpyruvate carboxykinase, the TCA cycle, and the fluxes related to proline metabolism and hydroxylation. As the Δ/or MaeB  has prev glucose . Such a  glucose . Accordio acids) , 21, 22.p4h1 expression and the resulting proline hydroxylation lead to a doubling of the proline synthesis rate in both strains. Additionally, recombinant P4H production was associated with a metabolic burden as reflected by higher relative TCA fluxes in both wt_p4h1of and ΔputA_p4h1of indicating increased biosynthetic and energy demands. When proline was added to the medium, the same effect was observed for wt_p4h1of, but not for ΔputA_p4h1of which retained similar relative TCA cycle fluxes as ΔputA_pET. Another difference between wt_p4h1of and ΔputA_p4h1of is the anaplerotic net flux between phosphoenolpyruvate and oxaloacetate. Upon proline hydroxylation, this flux decreased by 30% in wt_p4h1of, implying an anaplerotic role of proline metabolism, whereas it remained similar in ΔputA_p4h1of, as can be expected from the inability of this strain to metabolize proline. However, in wt_pET such an anaplerotic role of proline metabolism was not observed. Instead, the flux through the malic enzymes was activated as described above. For wt_p4h1of, the missing evidence for an activated malic enzyme flux can be explained by a decreased rate of proline metabolism caused by proline hydroxylation and the lower proline uptake rate.In the absence of proline, α-KG to succinate was decreased by 60% in ΔputA_p4h1of compared to only 10% in wt_p4h1of, which can be ascribed to α-KG withdrawal for hyp synthesis. Strikingly, even though putA deletion led to increased proline uptake and hydroxylation rates, it did not induce a “driven-by-demand” increase of the α-KG generating TCA flux, pointing towards a possible limitation of P4H catalysis by the cosubstrate α-KG. Moreover, the intracellular α-KG concentrations determined for both strains , NAD(P)H, and FADH2 were determined  [Even though stoichiometric modelling provides a precious basis to elucidate how cellular metabolism functions and responds to perturbations, it does not directly offer a deeper insight in the way bacterial cells balance catabolic energy generation with anabolic demands, including redox pools. Thus, based on the metabolic fluxes derived from  → 2ATP) , and (2) → 2ATP) .Table 3EputA_p4h1of which displayed a significantly lower rate in the presence of proline  was generated and physiologically analyzed to uncover the interference of proline hydroxylation with host cell physiology and metabolism. The deletion of the putA gene has already been reported to be a successful engineering strategy for efficient trans-4-hydroxy-l-proline production [E. coli BL21 upon recombinant P4H catalysis is provided. The results show that the mutant strain unable to catabolize proline displayed not only a higher yield of hyp on proline, but also a higher specific hyp formation rate and a higher hyp yield on glucose as compared to the wildtype. Metabolic flux analysis revealed that, although the hyp formation rate increased when using the mutant strain, the TCA flux supplying a-KG (isocitrate to a-KG) did not increase upon accelerated P4H catalysis, leading to improved glucose utilization efficiency and pointing to a possible limitation in a-KG. Furthermore, a redox and energy balance study based on the 13C-MFA allowed a quantitative assessment of how the cells coordinate catabolism with anabolism during product synthesis. It was shown that P4H catalyzed proline hydroxylation in the mutant strain decreases TCA-mediated energy formation by 25% compared to the wildtype. Obviously, the engineered strain compensated this ATP loss by reducing its maintenance energy demand, e.g., by improving the P/O ratio for more efficient glucose utilization, emphasizing the role of maintenance and respiratory chain operation in dynamic cellular adaptation strategies.In the current study, a oduction . Here, a13C (99%) and U-13C labeled glucose (99%) was purchased either from Sigma-Aldrich  or Cambridge Isotope Laboratories ; l-proline was kindly provided by Evonik Rexim SAS ; all other chemicals were purchased from Sigma-Aldrich  or Carl-Roth  and were of the highest purity available.1-putA gene deletion, the Quick & Easy E.coli Gene Deletion Kit based on Red®/ET® Recombination  was used. Recombinant E. coli strains were routinely precultured in LB complex medium [−1 Na2HPO4·2H2O, 3.0 g L−1 KH2PO4, 0.5 g L−1 NaCl, 1.0 g L−1 NH4Cl, and 2 mL L−1 1 M MgSO4, which contained 10 mg L−1 thiamine, 5 mg L−1 biotin, 1 mL L−1 USFe trace element solution [−1 glucose, 5 mM proline (when specified), 34 mg L−1 chloramphenicol, and 50 mg L−1 kanamycin. Such M9 medium was used for main cultures using non-labeled glucose to investigate the physiology and 13C-labeled glucose to perform tracer experiments. For the induction of p4h1of expression, 1 mM isopropyl-β-D-thiogalactopyranoside (IPTG) was added to both the M9 preculture and the main culture at inoculation. All strains were incubated in baffled Erlenmeyer flasks in horizontal shakers at 30°C and 250 rpm.The strains and plasmids used in this study are listed in Additional file x medium  followedsolution , 5 g L−1600), using a Libra S11 spectrophotometer . The correlation factors between OD600 and cell dry weight (CDW) concentration under the conditions investigated were determined as described elsewhere [® HPLC System  equipped with a Trentec 308R-Gel.H ion exclusion column  at 40°C with 5 mM H2SO4 as mobile phase at a flow rate of 1 mL min−1. Analytes were detected either by a UV (λ = 210 nm) (VWR Hitachi L-2420) or refractive index (RI) detector (VWR Hitachi L-2490). Proline and hyp were quantified using a LaChrom Elite® HPLC System  equipped with a diode array detector (VWR Hitachi L-2450) at 200 nm and an Intersil ODS-3 column . The separation was performed at 22°C and 1 mL min−1 flow rate applying a linear gradient profile based on 20 mM KH2PO4 buffer (pH 2.3) containing 1% (v/v) acetonitrile as eluent A and acetonitrile containing 10% (v/v) water as eluent B as follows: 85% eluent A for 2 min, 85% to 70% eluent A within 4 min, 70% eluent A for 8 min, and 70–85% eluent A within 0.5 min. Before analysis, the analytes were derivatized using benzoyl chloride. Briefly, supernatant was vortex-mixed with 100 mM benzoyl chloride in acetonitrile and 0.5 M K2HPO4 (pH 11.7) in a 1:2.5:9 (v/v) ratio followed by incubation for 10 min at room temperature and 20 min at 50°C with stirring. Before analysis, derivatized samples were diluted in a 4:1 ratio using eluent A and 50% phosphoric acid in a 7.5:1 (v/v) ratio. Alternatively, a spectrophotometric method described elsewhere [Bacterial cell growth was monitored by measuring the optical density at 600 nm  were precultured aerobically in 5 mL M9 minimal medium supplemented with 5 g L−1 of glucose at 30°C and 250 rpm. In the mid-exponential growth phase, cells were harvested by centrifugation at 4,000g and 4°C for 5 min, washed once with M9 medium to remove unlabeled glucose, and used to inoculate M9 medium containing 5 g L−1 of a 4:1 mixture of 1-13C-labeled and U-13C labeled glucose to an OD600 of 0.05 or lower to minimize introduction of non-labeled carbon from precultivation. For labeling analysis of proteinogenic amino acids, cells were harvested in the mid-exponential growth phase (at an OD600 of 2–2.5) by centrifugation for 10 min at 13,000g and 4°C, and washed twice by resuspension in 1 mL 0.9% (w/v) NaCl. The washed pellet was resuspended in 150 μL of 6 M HCl and hydrolyzed for 6 h at 105°C in a well-sealed vial to prevent evaporation. After drying overnight at 85°C, the hydrolyzate was resuspended in 30 µL acetonitrile, and amino acid silylation was initiated by the addition of 30 µl N-methyl-N-tert-butyldimethylsilyl-trifluoracetamide (MBDSTFA) followed by incubation at 85°C for 60 min. The quantification of mass isotopomer distributions (MIDs) of 12 amino acids  from biomass hydrolyzates was performed as previously described [escribed  using a E. coli was adapted from literature [α-ketoglutarate (α-KG). The stoichiometric model contained 58 reactions and 38 metabolites in case of growth on glucose only, and 65 reactions and 40 metabolites in case proline was added to the medium. The model was constrained with (1) 4 extracellular fluxes (glucose and proline uptake and acetate and hyp formation), (2) the anabolic precursor requirements for biomass formation, as previously described [13C-isotopomer labeling pattern of proteinogenic amino acids. Given that the glyoxylate bypass is considered to be inactive in E. coli grown on glucose and active when grown on acetate [The metabolic network of terature , 44 and  acetate , the pat acetate  and all  acetate . Stoichi2 were estimated from the catabolic reactions using the optimal set of fluxes obtained from 13C-MFA. Growth-related NADPH and ATP consumption rates were estimated using a lumped stoichiometric equation for biomass formation derived from the FiatFlux software [2, respectively [Rates for in vivo energy production in the form of NTP and redox cofactors NAD(P)H and FADHsoftware , in whicsoftware  (see Addectively . Maximal


Aims. To study the changes of the metabolic profile during the pathogenesis in monocrotaline (MCT) induced pulmonary arterial hypertension (PAH). Methods. Forty male Sprague-Dawley (SD) rats were randomly divided into 5 groups . PAH rats were induced by a single dose intraperitoneal injection of 60 mg/kg MCT, while 8 rats given intraperitoneal injection of 1 ml normal saline and scarified in the same day (W0) served as control. Mean pulmonary arterial pressure (mPAP) was measured through catherization. The degree of right ventricular hypertrophy and pulmonary hyperplasia were determined at the end of first to fourth weeks; nuclear magnetic resonance (NMR) spectra of sera were then acquired for the analysis of metabolites. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to discriminate different metabolic profiles. Results. The prominent changes of metabolic profiles were seen during these four weeks. Twenty specific metabolites were identified, which were mainly involved in lipid metabolism, glycolysis, energy metabolism, ketogenesis, and methionine metabolism. Profiles of correlation between these metabolites in each stage changed markedly, especially in the fourth week. Highly activated methionine and betaine metabolism pathways were selected by the pathway enrichment analysis. Conclusions. Metabolic dysfunction is involved in the development and progression of PAH. Pulmonary arterial hypertension (PAH), a chronic and fatal disease with a poor prognosis, is characterized by elevated pulmonary arterial pressure which could lead to right side heart failure –3. The p1H NMR-based metabonomic analysis was used to determine serum metabolic profiles in patients with hypertension and to assess relationships between serum lipoprotein particle compositions and blood pressure  with water suppression. A fixed total spin-spin relaxation delay of 120 ms was used to attenuate broad NMR signals of slowly tumbling molecules with short T2 relaxation times and to retain signals of low molecular weight compounds. The spectral width was 12 KHz with an acquisition time per scan of 1.63 s, and a total of 256 transients were collected into 32 K data points for each spectrum ) in the OPLS-DA model [The resulting bucketed data matrices were imported into SIMCA-PDA model , 18, 20.t-test was used to examine the difference between two groups. Pearson's correlation between metabolites was analyzed in different stages, respectively. The SPSS18.0 software was used to perform statistical analysis , and P < 0.05 was considered statistically significant. The pathway enrichment analysis was performed by a free and web-based tool MetaboAnalyst , which was based on the concentrations of the characteristic metabolites.For relative quantification, the intensities of each metabolite were calculated by using the relative integrals of each NMR spectrum and were represented as mean ± standard deviation. Distributions of the metabolite values were tested. The Student's P < 0.05). At the end of week 2, WT%, mPAP, and WA% increased remarkably, and these changes kept at higher level till the fourth week; all of the observed parameters were shown in Figures At the end of the first week, WT% but not mPAP, RVHI, and WA% in rats was significantly altered compared to the W0 rats  were selected, respectively, by OPLS-DA loadings and their VIP values. The VIP values and the direction of variation (either increased or decreased) were listed in After the multivariate analysis, we also analyzed statistic differences of the relative concentrations of these selected metabolites among groups, as seen in Characteristic metabolites and their concentrations were imported to the web-based tool MetaboAnalyst, to exploit the most disturbed metabolic pathways via Pathway enrichment analysis . BetaineIn this study, it was shown that serum metabolite levels varied and closely related to the pathophysiological changes in different stages of PAH. Changes of metabolic profile of glycolysis, lipid metabolism, and methionine were the most significant determinants in different stages of PAH. Correlation between these metabolites and other pathological indexes from different stages of PAH was quite strong, especially in the fourth week, in which glycolysis, lipid, and methionine metabolism pathways were found to be highly activated.Although there were some studies focusing on the metabolic dysfunction in PAH –25, justGenerally speaking, we found that most of the characteristic metabolites changed significantly in the first week after MCT treatment but tended back to control level in the following weeks. In the first week, the possible explanation for metabolic shifting might be the fact that MCT injection provokes an acute stress response. This could be reflected by reduced level of N-acetyl-L-cysteine, which was believed to play a key role in preventing and suppressing oxidative stress and inhibiting the apoptotic pathways , 27.In the early stage of PAH (weeks 2-3), there was an increase in glucose level; however, there was a decrease in both lactate and pyruvate levels, indicating that both mitochondrial glucose oxidation and glycolytic metabolism were downregulated during this stage.Furthermore, we found that enhanced lipid metabolism was one of the main biochemical characteristics which may be used to indicate an early onset of PAH. Our data suggested that the disorder of metabolism that occurred in the development of pulmonary hypertension shifted from glucose metabolism to fatty acid usage dominant on the whole. Based on our data, it is believed that a well-known mechanism, Randle cycle, defined as the glucose oxidation switching the energy production from carbohydrates to fatty acid , was invIn agreement with previous works , we founInterestingly, we also found an increase in the level of choline, betaine, methionine, and glycine. Furthermore, betaine and methionine pathway was identified as vital pathway in PAH. Importantly, imbalance of metabolism in choline, betaine, and methionine is involved in the pathways of cells proliferation and energy metabolism –37. SimiAlthough the statistical correlations and the pathway enrichment analysis did not provide cause-effect relationship directly, accumulating data suggested a possible underlying mechanism. Choline is known as a precursor for formation of the neurotransmitter acetylcholine which can be oxidized to form betaine . It is well-known that betaine is a methyl donor in the formation of methionine, a critical step in the formation of methyltetrahydrofolate catalyzed by methylene tetrahydrofolate dehydrogenase (MTHFD) . MethionGiven that PAH is a fetal disease with very poor survival prognosis, the research of its specific metabolic mechanism may shed light on potential therapeutic strategies for PAH , 33. Int1H NMR-based metabonomics to scan metabolic shifts in the sera of PAH rats during progression of vascular remodeling. The preliminary results provided valuable knowledge on the biochemistry during PAH process and highlighted the betaine and methionine pathway in the onset of PAH, which could be helpful for further research of PAH pathogenetic mechanism and treatment.At this stage, we used


Clostridium acetobutylicum is one of the most important butanol producing strains. However, environmental stress in the fermentation process usually leads to a lower yield, seriously hampering its industrialization. In order to systematically investigate the key intracellular metabolites that influence the strain growth and butanol production, and find out the critical regulation nodes, an integrated analysis approach has been carried out in this study.Based on the gas chromatography-mass spectrometry technology, the partial least square discriminant analysis and the pathway analysis, 40 metabolic pathways linked with 43 key metabolic nodes were identified. In-depth analysis showed that lots of amino acids metabolism promoted cell growth but exerted slight influence on butanol production, while sugar metabolism was favorable for cell growth but unfavorable for butanol synthesis. Besides, both lysine and succinic acid metabolism generated a complex effect on the whole metabolic network. Dicarboxylate metabolism exerted an indispensable role on cell growth and butanol production. Subsequently, rational feeding strategies were proposed to verify these conclusions and facilitate the butanol biosynthesis. Feeding amino acids, especially glycine and serine, could obviously improve cell growth while yeast extract, citric acid and ethylene glycol could significantly enhance both growth and butanol production.The feeding experiment confirmed that metabolic profiling combined with pathway analysis provided an accurate, reasonable and practical approach to explore the cellular metabolic activity and supplied a basis for improving butanol production. These strategies can also be extended for the production of other important bio-chemical compounds.The online version of this article (doi:10.1186/s12934-016-0436-4) contains supplementary material, which is available to authorized users. Clostridium acetobutylicum, Clostridium beijerinckii and similar strains, has been brought to light again . Th. Th20]. The key pathways and their corresponding metabolites would be summarized as follows.l-threonine, pyruvic acid, glycine and l-glutamic acid accounted for the major abundance of the pathway  and gluconolactone/glucose (GL/Glc) maintained at a relatively high level at 24 h, then decreased, and finally increased at 80 h  is the first-step product of pentose phosphate (PP) pathway with NADP18H34O2) and stearic acid (C18H36O2), initially declined at the early phase but then increased at the rapid butanol synthesis phase , while the intracellular d-glucose and d-arabitol initiated their decline from the late stationary phase (60 h)  Fig. . All theentation , 26.l-serine, l-aspartic acid) were depressed at the beginning of fermentation (48 h)  exhibited a downtrend from 24 to 80 h, indicating a shortage of protein synthesis. L-aspartate and glutamate metabolism  had the highest impact value (0.31) on the metabolic network than any other pathway in Additional file Type E was mainly involved in amino acid metabolism which was beneficial for cell growth but without significant effect on butanol production VIPb < 1). Amino acids associated with methionine and cysteine metabolism ( h) Fig. . Moreove. Amino a value 0.1 on the l-lysine (type G) displayed the coincident behavior viewed from the degradation pathway. The ratio of pipecolic acid/l-lysine was 0.099, 0.848, 0.247 and 2.276, performing an uptrend of catabolism of l-lysine, especially at the end of fermentation  cycle, were exogenously fed into the fermentation medium to verify the previous pathway analysis and alleviate the production bottlenecks. In addition, histidine and valine were considered since they were involved in PP pathway and valine metabolic pathway, separately. Yeast extract was also added into the medium as a complex nitrogen source. Sugar metabolism was not considered to be further strengthened by feeding glucose because several studies had been carried out on fed-batch/continuous cultivation and genetic engineering previously –29. Theyl-glycine and l-serine generated the highest cell biomass with the 13.9 and 14.3 % improvement, respectively, compared with the control. However, l-histidine addition failed to enhance the cell growth or butanol production, which was probably due to the inhibition of PP pathway as the end product. In addition, no amino acid could significantly increase butanol production in our study and all the increments were less than 5 %. Unexpectedly, the addition of l-lysine improved biomass by 12.3 %, which was inconsistent with the above analysis that all the lysine-related metabolic pathways had a negative effect on cell growth and butanol production. This phenomenon would be discussed in the Discuss section.As shown in Fig. In addition, low concentration (<2 g/L) yeast extract exerted a positive effect on both cell growth and butanol production. However, when yeast extract was supplemented at the range of 2–5 g/L, cell growth continuously increased while butanol production changed slightly. Moreover, higher than 10 g/L of yeast extract inhibited both cell growth and butanol production. The highest butanol production of 10.98 ± 0.15 g/L was obtained at 2 g/L yeast extract. Therefore, it is necessary to control the yeast extract concentration in the feeding experiment.600 by 21.0 % and butanol production by 14.6 %, and 5 mL/L ethylene glycol generated 21.4 and 10.9 % increase of OD600 and butanol production, respectively, compared with the control. The highest butanol production was 11.20 ± 0.25 g/L for ethylene glycol and 11.5 ± 0.15 g/L for citric acid. Therefore, these results indicated that these intermediates were effectively utilized by feeding glyoxylate shunt (or TCA cycle) to enhance butanol titers. However, the feeding of oxalic acid, glycolic acid and glyoxylic acid failed to improve the butanol production. It was quite out of our expectation but it would not rule out the possibility of their chemical chelation with Fe2+ and Mn2+ in the medium, seriously depressing a series of enzymes.Based on the analysis of dicarboxylate metabolism, as well as their relationships with butanol synthesis, various dicarboxylates were supplemented to improve intracellular dicarboxylates levels, which might promote more flux towards key precursors available for biomass synthesis and butanol biosynthesis. It’s notable that supplementation of ethylene glycol and citric acid resulted in an improvement on both cell growth and butanol production. In detail, 4 g/L citric acid increased the ODAlthough succinic acid was a member of butyric acid synthesis pathway in KEGG database, supplementation of this compound displayed an inhibition effect on cell growth and butanol production Fig. , indicatC. acetobutylicum to the inside and outside disturbance in a more comprehensive manner, thus enabled us to gain a more in-depth insight into complex mechanisms. In fact, the relationship between butanol synthesis and extracellular acetic acid, butyric acid, pH and residual sugar has been deeply studied previously, and many cues from acidogensis to solventogensis have been found [13C isotopomer labelling of C. acetobutylicum has been employed to elucidate the metabolic characterization associated with butanol production [Streptomyces tsukubaensis [Rhizopus oryzae [Schizochytrium sp. [In this study, metabolomics was applied to measure and evaluate the dynamic multiparametric metabolic responses of en found –32. Howeoduction –35, suppubaensis , Rhizopus oryzae , Schizocrium sp.  and so oFrom the viewpoint of precursors to products, intracellular metabolite abundance represents a potential, or an indispensable power driving the metabolic reaction to the target product. Redundancy or shortage of crucial metabolites could change the profiles of cell metabolism since a certain metabolite often participates in several metabolic pathways. In metabolic profiling, metabolites are also the nodes of several related metabolic pathways, just as shown in Fig. With regard to the sugar metabolism, glucose was used as the sole carbon source in our study, but maltose, trehalose, sorbose, sucrose and other 20 kinds of sugar and their derivatives were also detected by GC-MS, indicating that the catabolism rate of glucose was restricted under our experiment condition , leading to the accumulation of other sugars. As the core metabolism, sugar conversion and catabolism provide ATP and NAD(P)H for both cell growth and shift signal from acidogensis to solventogensis . In our 2H2O4) and its reductive product ethylene glycol (C2H6O2). Up to now, both glyoxylate and dicarboxylate metabolism have been poorly investigated in C. acetobutylicum. As shown in Fig. According to the metabolic profiling analysis, we should take more attention to the accumulation of oxalic acid . Superabundant nitrogen source could promote cell growth, but would not result in more butanol in these media since nourishing media might be not beneficial for the initiation of butanol synthesis, such as the conditions of Clostridium growth medium, bromcresol purple dextrose peptone medium and the RCM medium, in which organic acids were the main products. Therefore, most of amino acids could significantly improve the cell density rather than butanol production.Another significant aspect to be considered was the accumulation of amino acids. As shown in Fig. l-glycine and l-glutamic acid were abundant, in accordance with the previous study [C. acetobutylicum under the butanol and organic acids stress.For single amino acid, both glycine and glutamate were correlated to the glutathione metabolism as described in Results section. It should be noted that glutathione plays an important role in the anti-oxidative process  and the us study , ensurinThe last notable feature was the fatty acid biosynthesis, which plays an important role in cell membrane system. The cell membrane fluidity can be adjusted by regulating the length and unsaturation degree to resist the butanol stress from acidogenesis to solventogensis, which has been deeply reported in the previous works , 47. In l-histidine inhibited both cell growth and butanol production, and ethylene glycol and citric acid promoted both cell growth and butanol production, suggesting a more sophisticated metabolic regulation for C. acetobutylicum.From the PLS model and pathway analysis, two types of metabolic pathway based on over representation analysis and pathway topology analysis were not considered. One case was that no metabolites could simultaneously satisfy the condition: VIPb > 1 & VIPg > 1 & Coeffb > 0 & Coeffg > 0. It meant that there was no metabolite that could strengthen both cell growth and butanol production under our fermentation condition. Another phenomenon was that only erythrose met the condition: VIPb > 1 & VIPg > 1 & Coeffb < 0 & Coeffg < 0. Unfortunately, erythrose cannot be divided into any pathways, which was out of our expectation. Thus, there was no metabolite that was excess for both cell growth and butanol in our study. However, our experiments have verified that feeding of succinic acid and Based on in-depth analysis of key metabolites and pathways, rational feeding strategies of these potential metabolites were proposed to direct the improvement of cell growth and butanol production. From a practical point of view, these strategies can also be extended to strengthen biosynthesis of other important microbial natural products and process optimization. Besides, it is necessary to combine in silico genome-scale model with metabolomics and genetic engineering, which will reveal more complicated metabolic mechanisms and form a more powerful tool for the titer improvement in the future.Clostridium acetobutylicum ATCC 824 obtained from the American type center culture collection  was stored with 20–30 % glycerin-water solution at −80 °C.l-cysteine hydrochloride, 3 g/L NaCl, 3 g/L NaAc, 3 mg/L resazurin sodium, pH 7.0.RCM was used for seed culture: 3 g/L yeast extract, 10 g/L beef extract, 10 g/L peptone, 1.0 g/L soluble starch, 5 g/L glucose, 0.5 g/L 2HPO4, 2.2 g/L ammonium acetate, 0.5 g/L KH2PO4, 0.2 g/L MgSO4·7H2O, 0.01 g/L FeSO4·7H2O, 0.01 g/L NaCl, 0.01 g/L MnSO4·H2O, 1 mg/L amino benzoic acid, 1 mg/L vitamin B1 and 0.001 mg/L D-biotin, pH 6.4 ~ 6.6. The media were sterilized at 115 °C for 20 min.P2 culture was used for fermentation: 50 g/L glucose, 0.5 g/L KSpore suspension was heat-shocked at 85 °C for 15 min, then quickly put into cold water (<4 °C) for 15 min before inoculation.2:H2:CO2 = 85:10:5, purity >99.9 %). Seed medium was maintained in the anaerobic chamber for more than 12 h to eliminate the remaining oxygen. Then 1 mL spore suspension was inoculated into 50 mL seed medium and cultivated at 37 °C for another 24 h.YQX-II anaerobic chamber  was used for anaerobic cultivation by purging with mixed gas  was bubbled into fermentation culture for 60 min and then inoculated with 5 % seed media. Subsequently, the sterile mixed gas rate was set at 0.01 LPM. The initial pH was adjusted to 6.8 by 6 M ammonia and monitored by pH meter. The temperature and stirring rate were maintained at 37 °C and 100 rpm, respectively.Anaerobic fermentation operation was carried out in a 3.5 L fermentation tank , with a 1.8 L working volume. Before inoculation, 0.1 L per minute (LPM) sterile mixed gas  after an appropriate dilution. The concentration of acetic acid, butyric acid and butanol were detected by gas chromatography (GC)  equipped with BR-S Wax capillary column  and FID detector. Fermentation pH was measured by pH electrode . Residual glucose concentration in the fermentation broth was detected by biosensor analyzer SBA-40C . Each experiment was detected by at least triple biological replicates.Biomass was monitored by measuring the optical density. Generally, 1 mL of fermentation broth was sampled to determine ODSample preparation was conducted by a modified two-step protocol: quenching-extraction and derivation under the atmospheric condition , 48.In the first step, all the operation should be kept under a low temperature condition. 10 mL fermentation broth was collected and immediately quenched by 20 mL, −20 °C 60 % methanol solution to terminate cell physiological metabolism, followed by centrifugation at 3000 rpm, 4 °C for 3 min. Precipitate was washed by phosphate buffered solution (PBS) for three times at 4 °C and re-suspended with 1.5 mL of −40 °C 50 % (v/v) methanol solution, then thawed in an ice bath for 5 min. The mixture was blended with a vortex mixer for 20 s, and put into the liquid nitrogen to be re-freezed for 5 min and thawed for 1 min at −20 °C. Five times freezing-thawing operation was carried out and the supernatant was collected and blended with 50 µL succinic acid-2,2,3,3-d4  as internal standard, frozen drying for 8 h at −45 °C, then stored at −80 °C.N-methyl-N-(trimethylsilyl) trifluoroacetamide was added and mixed, later kept at 40 °C for 30 min.Derivatization was used for lowering the polarity of the compounds and gasification temperature to expand the spectrum of GC-MS . Here, wData acquisition by GC-MS was performed by the system including A7683B automatic sampler, GC analyzer 6890 N and 5975C MSD mass spectrometer  equipped with Agilent HP-5MS capillary column. GC parameters: sample quantity 1 µL; split ratio 1:1; injection port temperature 280 °C; interface temperature 280 °C; helium gas velocity, constant pressure of 91 kpa, 1.0 mL/min. Temperature program: 70 °C for 2 min; 5 °C/min up to 290 °C, holding for 3 min. Mass spectrometer parameters: electron impact (EI+) ion source; 250 °C; electron bombardment energy 70 eV; ion current 40 µA; scan range of mass: 50–650 m/z.http://MZmine.sourceforge.net/) for MS spectrum deconvolution, denoising, retention time aligning, peak area integration and compound identification combined with NIST 2010 mass spectrum database (http://webbook.nist.gov/chemistry/). All the identified compounds should match the KEGG compounds after substituting the trimethylsilane (-TMS) type by hydrogen atom.MS data was processed with the Agilent MSD Productivity ChemStation (Agilent Technologies Inc. USA), and MZ-2.10  to investigate the relationship between intracellular metabolites and extracellular phenotypes (µ and q).


Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health.Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA). Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further,  Continuous production of sperm throughout reproductive life is a fundamental feature of mammalian spermatogenesis. Understanding the complex process of spermatogenesis has important implications for male infertility and contraception. Presently, 70 million i.e., 15%) married couples worldwide are infertile and half of these cases are attributed to male factors [5% marrieThe initial development of spermatogonia to spermatozoa is termed as the first wave of spermatogenesis. It commences prior to puberty and is followed by subsequent rounds of spermatogenesis. Retinoic acid (RA), a derivative of vitamin A, is required for several processes during spermatogenesis, including the transition from undifferentiated to differentiating spermatogonia –4. This It remains unclear which enzymes and reactions in which cell types synthesize or degrade RA, thereby generating the RA pulse. Evidence suggests that RA from Sertoli cells—the somatic supporting cells—is critical for the first wave of spermatogenesis but not for subsequent waves , 7. HoweOther metabolic pathways may be crucial in regulating spermatogonial differentiation through connection with vitamin A metabolism. Lipid metabolism affects the availability of retinol, a precursor of RA, as retinol is primarily stored in the form of retinyl esters –11. VitaThese knowledge gaps are likely due to the enormous effort required to perform germ cell-specific gene knockouts (KOs) and measure metabolic activity within the germ cell. Individual gene KOs in the vitamin A pathway often yield no phenotype , 9, 14. in silico perturbations of enzymes and reactions and predict system-wide outcomes on a scale that would be impossible by in vivo approaches [in silico platform, we predict enzymes and reactions that are imperative for controlling RA levels within the germ cell. Further, we identify active pathways and energy producing pathways in the germ cell. This study provides a comprehensive understanding of how global metabolism regulates RA availability and germ cell differentiation.Because germ cell metabolism is highly complex, a thorough understanding cannot be achieved by only studying individual genes in animal models. A more profound understanding requires computational models to integrate genome-scale and individual studies of enzymes, metabolites, and reactions into networks of interacting components. A metabolic network model can reveal key players by performing proaches –18. We dproaches . With thWe compiled a list of metabolites and genes known to be involved in vitamin A metabolism . ReactioWe constructed a genome-scale mouse metabolic network from human Recon2  by folloA germ cell-specific network was constructed by extracting pathways potentially active in germ cells from the mouse genome-scale network. We utilized time-course germ cell-specific gene expression during the first wave of spermatogenesis . SpecifiTo extract potentially active pathways in the germ cell, we first classified genes in the mouse genome-scale network as “highly expressed” or “lowly expressed” using the expression data described above. A gene is considered “highly expressed” in the germ cell if its expression value is above the median value of all genes across all time points. Conversely, a gene is considered “lowly expressed” if its expression value is below the median value. A metabolic pathway is considered active if it is significantly enriched for reactions that are associated with “highly expressed” genes . Reactions without gene associations were excluded prior to the P-value calculation.th pathway because of its important role in spermatogonial differentiation [Utilizing this procedure, 10 out of 92 metabolic pathways (excluding transport and exchange reactions) were identified as active in the germ cell for at least one time point: cholesterol metabolism, TCA, fatty acid oxidation, fatty acid synthesis, folate metabolism, glycolysis and gluconeogenesis, keratan sulfate degradation, keratan sulfate synthesis, N-glycan synthesis, and nucleotide interconversion. Vitamin A metabolism was added as the 11ntiation . Then exntiation  to visuaUsing the germ cell-specific network, we simulated network flux  at eight time points during the first wave of spermatogenesis. As not all enzymes are expressed at a given time, we used the germ cell-specific gene expression  to definOur result shows that total RA production exhibits two peaks: an initial peak from 4 to 24 hours and a second peak on day eight post-RA injection . The iniCyp26 was detected in other cell types but not germ cells in the testis [RA degradation also peaks on day one and eight post-RA injection . Our rese testis , 32. RA e testis , 10, 33.e testis .Stra6 is minimal throughout the first eight days of spermatogenesis. This is consistent with experimental evidence that Stra6 is not expressed in germ cells and global Stra6 KO shows no spermatogenic defect [Lrat, suggesting that retinol storage does not occur in germ cells. This is supported by experimental data that Lrat is not expressed in germ cells [We further examined reactions that control the levels of retinol and retinal, two RA precursors . We findc defect , 35. In rm cells , 32. FurUsing the tractable computational model, we can perturb enzymes and reactions individually or collectively. These virtual experiments allow us to overcome genetic redundancy to predict germ cell-specific enzymes and reactions that are most important for controlling RA levels. Two statistical tests were used to assess the difference between wild type (WT) (previous simulations in the subsection of “Dynamic flux of the vitamin A metabolic pathway”) and perturbation simulations. The Spearman correlation coefficient measures differences in global network flux values between WT and perturbation simulations. The Kolmogorov-Smirnov (K-S) statistic measures differences in available RA within the germ cell between WT and perturbation simulations. Perturbations are considered to cause spermatogenic defects if both network flux and available RA are different from those of WT simulation for at least one time point.in silico enzyme KOs and treatments for which the effect on spermatogenesis has been experimentally demonstrated. We collected a total of 17 experimental perturbations on retinoid metabolism, including global KOs, germ cell KOs, Sertoli cell KOs, and compound treatments [Crabp1+Crabp2 global KO) were predicted correctly.To identify thresholds of the two statistical tests used to predict spermatogenic defects, we conducted eatments –43. AmonLipe, would result in a spermatogenic defect at hour 4 post-RA injection  in the testis to elucidate how enzymes/reactions in other cells affect RA availability in germ cells.In summary, our FBA model constitutes a powerful tool for manipulating genes and reactions to determine their contribution to vitamin A metabolism in the germ cell. These Sm × r, where m is the number of metabolites and n is the number of reactions. Matrix entries are metabolite coefficients in each reaction. Constraints are placed upon each reaction: Vmin ≤ V ≤ Vmax, defining the range of allowable flux values. Steady state analysis is carried out by setting mass balance equations equal to zero: Sm × rV = 0.FBA calculates the flow of metabolites through a network under the assumption that each metabolite has equal production and consumption. All reactions in the network are represented in a stoichiometric matrix, Maximum and minimum constraints were placed on each reaction in the network. Germ cell-specific gene expression was used to define the maximum flux. Briefly, 1 dpp mice were fed WIN 18,446 for 9 consecutive days. At 9 dpp, mice were injected with RA to initiate spermatogenesis. Testes were collected at 0, 4, 12 hours, 1, 2, 4, 6, 8 days post-RA injection for expression profiling. Ribosome associated mRNAs within germ cells were pulled down to evaluate global gene expression (GSE54408) . MicroarMinimum constraints were determined based on reaction reversibility; irreversible reactions were given a value of zero while reversible reactions were assigned the negative value of the maximum constraint. For intracellular binding reactions of retinyl-ester, retinol, retinal, and RA, the minimum constraint was the maximum constraint at the time point subtracted by the highest maximum constraint across the eight time points.Monte Carlo Markov chain sampling was used to acquire the distribution for all possible flux states. This was performed using a hit-and-run algorithm—gpSampler in the Cobra Toolbox . At eachRdh10 and Aldh1a1,2,3 were mimicked by setting the minimum constraint of RA exchange reaction to zero, thus eliminating RA from the extracellular region. Conversely, Sertoli KO of Cyp26b1 was mimicked by setting the maximum constraint of RA exchange reaction to -900, thus forcing RA to be present in the extracellular region. WIN 18,446 treatment was reproduced by setting maximum constraints to zero for all reactions catalyzed by aldehyde dehydrogenases, in addition to setting the minimum constraint of RA exchange reaction to zero. To replicate vitamin A deficiency, minimum constraints were set to zero for exchange reactions of any form of vitamin A . After the network was manipulated for each perturbation, candidate states were re-sampled and compared with normal candidate flux states by calculating the Spearman correlation coefficient and the K-S statistic.To simulate germ cell-specific enzyme deletion, the expression of the enzyme was set to zero, and the maximum constraints were re-calculated for reactions catalyzed by this enzyme. To simulate germ cell-specific reaction deletion, the maximum and minimum constraints were both set to zero. Enzyme deletion in the Sertoli cell was simulated by altering the extracellular availability of metabolites through exchange reactions. Specifically, Sertoli KOs of S1 FigMice were treated with WIN 18,446 for nine days starting from 1 dpp. At 9 dpp, mice were given an injection of RA to initiate spermatogonial differentiation.(EPS)Click here for additional data file.S2 FigMedian flux values from the model are plotted across eight days after initiation of spermatogenesis via an RA injection.(EPS)Click here for additional data file.S3 FigA. Difference in median flux values between WT and Lipe KO for reactions in the vitamin A pathway. Reactions with difference less than 50 are not shown. B. Sampling flux distribution for the reaction of available RA.(EPS)Click here for additional data file.S4 FigA. Difference in median flux values between WT and this reaction KO for reactions in the vitamin A pathway. Reactions with difference less than 50 are not shown. B. Sampling flux distribution for the reaction of available RA.(EPS)Click here for additional data file.S5 FigA. Difference in median flux values between WT and this reaction KO for reactions in the vitamin A pathway. Reactions with difference less than 50 are not shown. B. Sampling flux distribution for the reaction of available RA.(EPS)Click here for additional data file.S6 FigA. Difference in median flux values between WT and this reaction KO for reactions in the vitamin A pathway. Reactions with difference less than 50 are not shown. B. Sampling flux distribution for the reaction of available RA.(EPS)Click here for additional data file.S7 FigA. Difference in median flux values between WT and single reaction KO for reactions in the vitamin A pathway. Reactions with difference less than 50 are not shown. B. Sampling flux distribution for the reaction of available RA.Three single reaction deletions yielded the same result: RA(e) exchange, RA(e) to ALB-RA(e), and ALB-RA(e) to RA. (EPS)Click here for additional data file.S1 File(XML)Click here for additional data file.S2 Filehttp://www.cytoscape.org/download.php. Visualization is provided for the full network as well as ten metabolic pathways: cholesterol metabolism, TCA, fatty acid oxidation, fatty acid synthesis, folate metabolism, glycolysis and gluconeogenesis, keratan sulfate degradation, keratan sulfate synthesis, N-glycan synthesis, and nucleotide interconversion. Pink diamonds represent reactions, gray circles depict metabolites, and green squares represent enzymes. Reversible reactions are linked to substrate or product metabolites with red edges whereas irreversible reactions are linked to metabolites with blue edges. Enzymes are linked to reactions with yellow edges.Cytoscape software can be downloaded from (ZIP)Click here for additional data file.S1 Table(DOC)Click here for additional data file.


Thermobifida fusca is a cellulolytic bacterium with potential to be used as a platform organism for sustainable industrial production of biofuels, pharmaceutical ingredients and other bioprocesses due to its capability of potential to convert plant biomass to value-added chemicals. To best develop T. fusca as a bioprocess organism, it is important to understand its native cellular processes. In the current study, we characterize the metabolic network of T. fusca through reconstruction of a genome-scale metabolic model and proteomics data. The overall goal of this study was to use multiple metabolic models generated by different methods and comparison to experimental data to gain a high-confidence understanding of the T. fusca metabolic network.Thermobifida fusca sp. XY developed using three different approaches . The model closest to in vivo growth was the proteomics-derived model that consists of 975 reactions involving 1382 metabolites and account for 316 EC numbers (296 genes). The model was optimized for biomass production with the optimal flux of 0.48 doublings per hour when grown on cellobiose with a substrate uptake rate of 0.25 mmole/h. In vivo activity of the DXP pathway for terpenoid biosynthesis was also confirmed using real-time PCR.We report the generation of three versions of a metabolic model of iTfu296 provides a platform to understand and explore the metabolic capabilities of the actinomycete T. fusca for the potential use in bioprocess industries for the production of biofuel and pharmaceutical ingredients. By comparing different model reconstruction methods, the use of high-throughput proteomics data as a starting point proved to be the most accurate to in vivo growth. Thermobifida fusca, and utilize three different methods to gain a better understanding of its metabolic network and identify preferred methodologies for network characterization.With ongoing research in genomics, metagenomics, and bioprospecting, the breadth of novel and interesting biochemistry continues to grow. One of the current challenges associated with the large amount of data and resources available is to conduct detailed analyses to curate information to translate raw data into knowledge that provides functional insight. One computational method that facilitates metabolic analysis and dovetails well with genomic and biochemical information is genome-scale constraint-based modeling. While constraint-based models have benefits of being easily scalable and providing gene-protein-reaction level specificity, there are a number of limitations. One of the most fundamental problems revolves around the fact that metabolic networks are underdetermined and thus, there exist alternative flux states with different pathway usage that produce indistinguishable cellular phenotypes. This is an underlying problem with constraint-based models that impacts multiple facets of these models including the initial reconstruction (association of specific reactions with annotated genes) to producing simulation predictions . In this study, we consider the metabolically under-characterized actinobacterium, Thermobifida fusca  is known for its high temperature and pH stability as well as highly expressed cellulolytic system. The cellulolytic system is comprised of three endocellulases , two exocellulases (Cel6B and Cel48A) and a processive cellulase (Cel9A) . The. TheTher enzymes ,5]. Hen. HenTherT. fusca may present an interesting opportunity for consolidated bioprocessing - CBP  in 2005 sets up a milestone towards understanding this industrially applicable microbe ,, Bi, BiT. fu. Af. AfT. fulgorithm . The moslgorithm , OptForclgorithm , EMiLio lgorithm  are baseZ is the flux through objective function (biomass production and product optimization), S: stoichiometry of the reactions represented as matrix, v is reaction flux vector, ai and bi are the constraints placed on the flux vi of the reaction i. In. InT. fuThermobifida fusca. The three models varied based upon what was used as the starting point for generating the initial reaction list for the model . In all cases, after initial generation of a reaction list, all reactions were associated with KEGG IDs to standardize comparisons between models.Three different draft metabolic models were constructed for Tfu_v1: The taxonomy number of T. fusca was used to generate a draft model from Model SEED (Tfu_v1) . Th. ThTfu_vTfu_v2: The second version of a T. fusca model (Tfu_v2) was created using an in-house semi-automated reconstruction method as defined in our past publications . Th. ThTfu_vTfu_v3: The third version of the model generated in this study (Tfu_v3) is based on proteomic experimental data generated by our group in collaboration with the Manitoba Centre for Proteomics & Systems Biology, the University of Manitoba. The detailed protein characterization of T. fusca based on this data is beyond the scope of this paper and will be explained in future publications along with the other experimental data. This method of generating a model varied from Tfu_v1 and Tfu_v2 as the initial reaction list was populated from reactions associated with experimentally detected proteins, not informatics-based annotation. The Tfu_v3 model contains 975 reactions, 1382 metabolites, and 316 EC numbers (296 unique genes annotated in T. fusca). FBA simulations with an input constraint of a cellobiose uptake rate of 0.25 mmoles/gDW/h gave a predicted growth rate of 0.49 doublings/h . A comparison of the model contents (by reaction) is summarized in Figure Due to the critical role of central metabolism to heterotrophic organisms, an analysis was conducted using all three model versions to study central metabolism. To study the individual pathways in carbohydrate metabolism and amino acid metabolism, each metabolic pathway was annotated using EC numbers recorded in KEGG. The summary table is shown in Table of T. fusca are based on the Tfu_v3 model. The Tfu_v3 model is designated as iTfu296 (represents 296 annotated genes).Based upon the initial model contents and growth rate simulation results, the the Tfu_v3 appears to most closely predict experimental results. Thus, further detailed computational analyses T. fusca accounts for 216 amino acid reactions and 182 carbohydrate-related reactions as illustrated in Figure The proteomics-based model of In carbohydrate metabolism, the tricarboxylic acid cycle (TCA cycle) was complete and active. In the current study, we will be focusing on the seven compounds of TCA cycle that play crucial roles in carbon exchange between various pathways in the central metabolism. These intermediates include: pyruvate (KEGG compound C00022), acetoacetyl CoA (C00332), acetylyl CoA (C00024), alpha-ketoglutarate C00026), succinyl CoA (C00091), fumarate (C00122) and oxaloacetate (C00036). Studying the reaction use and fluxes involving these compounds will help us understand the probable activity in amino acid biosynthesis and degradation pathways. To summarize these nodes in the current proteomics-based version of our model, 60 unique reactions (associated with carbohydrates and amino acid metabolism) were found with some significant flux (i.e. flux was > 0.01 units). Among these, 23 unique reactions correspond to amino acid metabolism. This indicates the activity around the most of the amino acid – carbohydrate connection nodes as shown in Figure , succinyIt was observed that in the carbohydrate-amino acid network connection the majority of reactions (11 out of 23) branch in and out of alpha – ketoglutarate. The acetyl CoA node was more centralized by connecting the amino acids and fatty acid pathways with 8 significantly high flux reactions. The pathway specific details are also illustrated in the Table in vivo flux states. In this study, we used two methods to combine metabolic models and experimental data to understand and characterize the metabolic network of T. fusca. For the first approach, an MILP algorithm . In addition, most of the amino acid pathways were fully or partially incomplete in Tfu_v2. Thus, the Tfu_v2 model had artificially high fluxes through transport reactions to uptake external nutrients to satisfy simulation requirements for optimal biomass production. The Tfu_v3 model showed most of the pathways significantly complete except cysteine and methionine metabolism. Besides cysteine and methionine metabolism, phenylalanine metabolism was sparsely populated, but active flux through the reaction involved in the interconversion of phenylalanine to phenylpyruvate suggested making a present call for the entire phenylalanine pathway. These functional differences indicate some of the potential danger associated with over-reliance on genome annotation .in vivo biochemical system of T. fusca. While the Tfu_v3 model is the most accurate of the three models constructed in this study, there are numerous areas of metabolism that are not well-characterized. For comparison, even the most update model of E. coli has an account of only for 30% of the gene products in the model  and andTfu_vTfu_v2: yaml. The *.yaml autobuilt drafts were created by our group in 2008 using the then available organism specific annotations and reaction database from KEGG. ,, IM, IMT. fuase KEGG  and UniPase KEGG  was usedIn-house python scripts were used to run the FBA simulations using the linear programming algorithm as shown in the introduction.T. fusca growth conditions [This draft model aims at the growth optimization and the best estimates used in metabolic modeling scenario are biomass equation, which was designed by slightly manipulating the biomass equations from closely related species and available published information about nditions . The boxThe draft model consists of the list of reactions however there are patches in the network that obstruct continuous flow of flux through the pathway. These links are filled in by using the reaction databank and suggesting the list of reactions required to complete the network. FBA-GAP is used to suggest the connection nodes/reactions that are missing . The useThe mixed integer linear programming algorithm (MILP) published in 2008 by Shlomi et al.  was usediTfu296 or (Tfu_v3) was used to enlist the already existing secondary metabolites production pathway based reactions (characterized by EC numbers) and metabolites (characterized by KEGG compound IDs). In addition, besides studying the complete secondary metabolite network a focused study on terpenoids backbone biosynthesis (TBB) pathway using mevalonate and non-mevalonate pathway was performed using KEGG (map00900). The reactions database of these pathways was created using the information from KEGG as shown in the Table Metabolic model T. fusca strain YX grown on Cellobiose media to till the early log phase with dry cell weight of 2.005 mg/mL was used for isolation of RNA using the Qiagen RNA Protect and Qiagen RNAeasy kit. The total RNA was sent to Nucleic Acid Research Facility  for RT-PCR. Tfu_2950 was selected as housekeeping gene to measure the relative expression levels.The authors declare that they have no competing interests.NV constructed, analyzed and compared the three versions of model. NV and JPB ran the model simulation using MetModel the scripts. NV, VS, DS, OK and JAW worked on proteomics experiments to generate, pre-process and analyze proteomics data. NV integrated the experimental dataset with the model and completed model validation with the programming support from JPB. YD worked on growth curve experimental analysis, biomass equation and first draft of the version one of model. SSF conceived of the study. All authors read and approved the final manuscript.Three_Versions_of_Model.xlxs. xlxs file with three spreadsheets illustrative the reaction flux in three version of the model.Click here for fileBiomass Equation Compound Identifiers.xlxs. xlxs file with the list of reactants and products on the biomass equation and their description from Kegg database.Click here for file


The relationship between cerebral ischemia and metabolic disorders is poorly understood, which is partly due to the lack of comparative fusing data for larger complete systems and to the complexity of metabolic cascade reactions. Based on the fusing maps of comprehensive serum metabolome, fatty acid and amino acid profiling, we identified 35 potential metabolic biomarkers for ischemic stroke. Our analyses revealed 8 significantly altered pathways by MetPA  and 15 significantly rewired modules in a complex ischemic network using the Markov clustering (MCL) method; all of these pathways became more homologous as the number of overlapping nodes was increased. We then detected 24 extensive pathways based on the total modular nodes from the network analysis, 12 of which were new discovery pathways. We provided a new perspective from the viewpoint of abnormal metabolites for the overall study of ischemic stroke as well as a new method to simplify the network analysis by selecting the more closely connected edges and nodes to build a module map of stroke. Cerebral ischemia is one of the most devastating neurological conditions, with an approximate mortality of 5.5 million persons annually and a loss of 44 million disability-adjusted life years worldwide21H-NMR metabolomics approach1819Metabolomics is a systemic global quantitative assessment for the comprehensive analysis of changes in endogenous metabolites in a biological matrix, which can be directly coupled to biological phenotypic responses to a disease1113rd day; we then placed 2 ml of this blood into an EDTA tube, fully mixed the sample, centrifuged the sample at 3,000 rpm for 10 min, and extracted 500 μL plasma. The remaining 3 mL blood were stored at 4 °C for 60 min and then centrifuged at 3,000 rpm for 10 min, and 1000 μL of the supernatant was collected as the serum sample. All of the samples were stored in a −80 °C freezer for further analysis.Six-week-old male Sprague-Dawley (SD) rats were purchased from the China Academy of Military Medical Science   were mixed with acetonitrile (400 μL) by vortexing for 30 s, stored at 4 °C for 60 min, and centrifuged at 12,000 rpm for 10 min. Then, 500 μL of the supernatant was collected for freeze drying. The lyophilized sample was re-dissolved in 200 μL acetonitrile/water , centrifuged at 12,000 rpm for 5 min, and then filtered through a syringe filter (0.22 μm) for UHPLC MS analysis.Serum metabolomics was performed with UHPLC Q-TOF/MS. Chromatography was conducted using a Zorbax Eclipse plus C18 column . The binary mobile phase was composed of phase A (water with 0.2% methanoic acid) and Phase B (acetonitrile). The gradient for the serum sample was 0 ~ 25 min, 0 ~ 100% B. The proportion of phase B returned to 5% in 1 min, and the column was allowed to re-equilibrate for 5 min before the next injection. The flow rate was 0.5 mL/min, and 1 μL was injected into the column. The column temperature was maintained at 40 °C.MS analysis was conducted using a QTOF 6520  operating in positive (ESI +) electrospray ionization mode. Nitrogen was used as the dry gas, the cone gas flow was maintained at 10.0 L/min, and the desolvation temperature was set at 325 °C. The atomization gas pressure was set at 50 psig, the capillary voltage was set at 4000 V, the cone voltage was set at 65 V, the OCT 1 RF Vpp was set at 750 V, and the fragmentor voltage was set at 175 V. The TOF data were collected from 100–1000 m/z. The UHPLC Q-TOF/MS analysis platform provides the retention time, the precise molecular mass, and MS/MS data for the structural identification of biomarkers. The precise molecular mass was determined within measurement errors (<5 ppm) by Q-TOF/MS.First, we optimized the extraction method of plasma samples. We attempted two pretreatment methods, including nitrogen blow drying and freeze drying, and found that the freeze drying method was more advantageous to obtain a product closer in nature to the sample analyte. The selected solvent was simultaneously optimized; we ultimately chose the 1:1 mixed solution of acetonitrile/methanol to quantitatively analyze the samples to ensure that as more as possible metabolites were dissolved. We tried different chromatographic columns to optimize the separation conditions in experiments, eventually selecting the Zorbax Eclipse plus C18 chromatographic column  under positive patterns for use.First, we removed the kit from storage (Rat Elisa Kit (48T×1), Batch number: Lot#: 20130419, BlueGene Biotech Company, Shanghai, China) and let it sit in a 20−25 °C environment for 30 min. Then, on the enzyme-labeled plate, we added 50 μL standard solution into each of the blank micro-wells, according to the standard order. We then added 50 μL of standard solution into the blank micro-well or 50 μL distilled water into the blank contrast well. Subsequently, we added 100 μL enzyme marker solution to each well (excluding the blank contrast well). After sealing the enzyme mark plate with glue, we incubated the plate at 37 °C for 60 min. During the experiment, we washed the enzyme marker plates 3−5 times to ensure that the wells were filled with adequate water pressure. Finally, we added 50 μL stop solution into each well to terminate the reaction.Instrument: ELx800 ELIASA, BioTek Co., USA.http://metpa.metabolomics.ca), and database sources, including the KEGG, the Human Metabolome database, and METLIN, were used to identify the related metabolic pathways. The possible biological roles were evaluated by enrichment analysis using MetaboAnalyst. IPA used the high-quality KEGG metabolic pathways as the backend knowledge base. The impact-value threshold calculated from pathway topology analysis was set as 0.10, and those above the threshold were screened out as potentially significant pathways.The construction, interaction and pathway analysis of potential biomarkers were performed with MetPA  see . CompareThe total ion current chromatogram (TIC) of plasma samples derived from the sham and ischemia groups showed significant differences in metabolite abundance , which m2 test, the separation score (sep-t) with its t-test (t-statistic of sep-t) were computed for discriminating between the ischemic and sham groups  for 12 free fatty acids (FFAs) ; satisfam groups . Three am groups . After sm groups . The PLSm groups . The thrm groups , indicatSerum samples collected from the ischemic rats and sham controls contained approximately the same amino acids . The maiMetabolic pathway analysis with MetPA revealed that 35 metabolites , 2 and 3A disease network model can express the complex relationship between drugs, target and disease; nodes represent entities such as genes, proteins, small molecules, drugs, and diseases, and edges represent the interaction between nodes. We further reconstructed ischemia-related metabolic networks based on all of the relevant metabolites identified by Metscape. The whole metabolic network included 189 nodes (167 main compounds) and 237 edges (208 main reactions), involving 23 pathways in the same database .The evidence shows that the network of biological systems has the module structure, indicating that some molecules in the network perform some types of biological function. We then divided the constructed metabolic network into 15 different modules by MCL when the maximum values of modularity and entropy were both achieved , S6. TheOur study demonstrated that ischemic stroke is a network phenomenon. The ischemia-related metabolic networks constructed here may be beneficial for disease classification and diagnosis and drug target candidate identification. The metabolic network was characterized by a high intrinsic potential. Our study successfully established 15 cerebral infarction modules. Module division will help to rebuild the decreased cerebral state in the course of the disease to reduce the complexity of or to shrink the disease network without the loss of information.The existing connections in the 8 most closely connected modules out of 15 modules in the center of the modular map were visualized by Cytoscape . We founBased on the pathway structure, the above 8 modules had some overlapping nodes with a certain pathway from the same database. We evaluated the similarity between the pathway and the module by the vectorial angle methodFirst, we described the number of convergent and divergent pathways corresponded to each module. We then re-entered the nodes of each module into a web-based metabolomics tool (MetPA) for significant pathway analysis and visualization. As a result, 5 convergent pathways were identified in the glycine module , and 8 cThen, we analyzed multiple pathway-related convergent and divergent modules. With the exception of 3 modules  other modules could converge or diverge into multiple pathways. For example, the glycine and glutamate modules converged into the ArP metabolism pathway, while the serine module diverged into 3 pathways, Met and GST metabolism and A-tRNA biosynthesis. To further explore the role of these pathways in ischemia, we verified them using a data mining method. In addition, 3 of 8 pathways extracted from MetPA and 9 of 16 pathways identified in this study  could be verified in the literature . The mod(Metscape) ((Metscape) was activated in the ischemic process, manifesting as an increase in pyruvate (P = 0.0024) by ELISA  . The resby ELISA  and reduby ELISA ; the levBrain ischemia is a process of delayed neuronal cell death, not an instantaneous eventet al.It has been shown that creatine supplementation has some positive effects on the central nervous system283135444648494853Complex diseases or complex physiological processes have been described as network phenomena for quite some time5960Metabolic pathways are a series of chemical reactions occurring within a cell; each pathway is modified by a series of chemical reactions and is mainly related to a certain biological function. A module is the minimum functional unit in a pharmacological profile. Disease can be viewed as a breakdown of the robustness of normal physiological systems and the re-establishment of robust and potentially progressive disease64The significance of this research includes the following: (1) we creatively integrated the non-targeted and targeted metabolomic results to detect all of the potential biomarkers that may be useful for the early diagnosis of ischemic stroke; (2) the network map provided a new perspective from the viewpoint of abnormal metabolites for the overall study of ischemic stroke and offered a new method for further studies of the stroke-related network; (3) in addition to constructing a complicated network map, we also provided a method (modular pattern) to simplify the map by selecting the more closely connected edges and nodes to build a module map for stroke; and (4) we discovered 12 new pathways based on modularity of the metabolic network and found that most of the new pathways were related to stroke, indicating that more modules of the network may be somewhat related to ischemia. Further studies will be required to shed more light on the actual contribution of these metabolic pathways and modules that are related to cerebral ischemia.In brief, our study found glutamic acid, serine, asparagine, glycine, taurine, tyrosine, valine, ornithine, gamma-linolenic acid, arachidonic acid, palmitic acid, creatine, uric acid, phytosphingosine, L-carnitine, glycerophosphocholine, lysoPE (18:0/0:0) and lysoPC (18:1(9z)) may be potential biomarkers for ischemic stroke. These metabolite biomarker can be used for early stage disease risk identification, early diagnosis, pathological mechanism research and drug target screening. Additionally, the result indirectly confirmed that the module structure in the network has self-similarity and overlapping ability. The module can be viewed as a sub-network with a similar function that includes multiple pathways. Different levels of network comparison may uncover novel functions and disease-specific changes, thereby aiding drug design.How to cite this article: Zhu, Y. et al. System-wide assembly of pathways and modules hierarchically reveal metabolic mechanism of cerebral ischemia. Sci. Rep.5, 17068; doi: 10.1038/srep17068 (2015).


II), which is understood to have had high concentrations in the Archean oceans. These observations reveal that reaction sequences that constitute central carbon metabolism could have been constrained by the iron‐rich oceanic environment of the early Archean. The origin of metabolism could thus date back to the prebiotic world.The reaction sequences of central metabolism, glycolysis and the pentose phosphate pathway provide essential precursors for nucleic acids, amino acids and lipids. However, their evolutionary origins are not yet understood. Here, we provide evidence that their structure could have been fundamentally shaped by the general chemical environments in earth's earliest oceans. We reconstructed potential scenarios for oceans of the prebiotic Archean based on the composition of early sediments. We report that the resultant reaction milieu catalyses the interconversion of metabolites that in modern organisms constitute glycolysis and the pentose phosphate pathway. The 29 observed reactions include the formation and/or interconversion of glucose, pyruvate, the nucleic acid precursor ribose‐5‐phosphate and the amino acid precursor erythrose‐4‐phosphate, antedating reactions sequences similar to that used by the metabolic pathways. Moreover, the Archean ocean mimetic increased the stability of the phosphorylated intermediates and accelerated the rate of intermediate reactions and pyruvate production. The catalytic capacity of the reconstructed ocean milieu was attributable to its metal content. The reactions were particularly sensitive to ferrous iron Fe( Glycolysis and the pentose phosphate pathway overlap and share most reactions with the Entner‐Doudoroff pathway and the Calvin cycle. The Entner‐Doudoroff pathway is common in bacteria and is specific for the metabolite 2‐keto‐3‐deoxyphosphogluconate, whereas the Calvin cycle is common in photosynthetic organisms. While the reactions that constitute these pathways are enzyme‐catalysed in modern organisms, in some cases, non‐enzymatic pendants have been observed. For instance, Reynolds We used liquid chromatography‐selective reaction monitoring (LC‐SRM) to systematically elucidate non‐enzymatically catalysed interconversions of metabolites that constitute glycolysis and the pentose phosphate pathway. The LC‐SRM technique facilitates such an investigation, as its data‐independent acquisition nature allows not only conclusions about the presence, but also about the absence of a given molecule , and as it yields absolute quantities, enables calculation of reaction rates. Most importantly, however, the high sensitivity of the targeted method facilitates full coverage of all intermediates of a given pathway at a reasonable throughput, so that reaction conditions can be systematically tested.et al, et al, et al, et al, We first determined the spontaneous reactivity upon heat exposure of the metabolic intermediates glucose 6‐phosphate (G6P), fructose 6‐phosphate (F6P), fructose 1,6‐bisphosphate (F16BP), dihydroxyacetone phosphate (DHAP), glyceraldehyde 3‐phosphate (G3P), 3‐phosphoglycerate (3PG), phosphoenolpyruvate (PEP), 6‐phosphogluconate (6PG), ribulose 5‐phosphate (Ru5P), ribose 5‐phosphate (R5P), xylulose 5‐phosphate (X5P) and sedoheptulose 7‐phosphate (S7P) . 6PG was stable at 70°C in water and did not interconvert in any glycolytic or other pentose phosphate metabolites. However, upon adding the Archean ocean ionic constituents Fe, Ni, Co, Mo and phosphate, this metabolite reacted into intermediates of the pentose phosphate pathway and yielded pyruvate Fig A.et al, We next tested the contribution of the individual mimetic constituents to the reactivity. The reactivity of 6‐phosphogluconate (6PG) or fructose 6‐phosphate (F6P), specifically inter‐converted by the Archean ocean mimetic, was mostly sensitive to the presence of iron. The other metal constituents , as well as phosphate, contributed to the catalysis of a subset of the interconversion reactions only Fig B. Indeed2 concentration below 8 ppm, and monitoring the iron redox state (To simulate an Archean ocean in the presence of ferrous iron [Fe(II)], the following series of experiments were conducted under anoxic conditions by controlling the Oox state . Whereasox state B and C. To address the stability of the glycolytic and pentose phosphate pathway intermediates, we analysed reactivity in a time‐dependent manner. A time series upon heat exposure is exemplified for glyceraldehyde 3‐phosphate (G3P), the least stable glycolytic intermediate. At 70°C and in water, this metabolite degrades within minutes ‐rich Archean ocean mimetic. The reaction rates were assessed by fitting the detected concentration changes to least‐squared linear, nonlinear formation or exponential functions, depending on which best described the observed formation dynamics . The detIn order to get a global picture of the reactivity in the system, we therefore combined all intermediate metabolites at a concentration of 7.5 μM and determined the temperature‐dependent rate of pyruvate formation. At temperatures of 40°C and below, no pyruvate was formed, confirming the absence of enzymatic contamination in the combined reaction mimetic. Above 50°C, pyruvate formation was detected and increased in a temperature‐dependent manner. The fastest pyruvate formation rate was detected at 90°C, where typical metabolic enzymes are not functional. Comparing the three conditions, pyruvate formation rate was lowest in water, increased in the presence of the ocean components and achieved its highest rate in the ferrous iron‐rich Archean ocean simulation  Interconversion reactions were observed at a temperature that is plausible for the early earth, including ocean environments close to heat sources such as volcanic activity (70–90°C), but which substantially exceeds the heat stability of typical metabolic enzymes; (ii) the interconversion reactions were not observed at temperatures of below 40°C, at which both, common and heat‐stable (thermophilic) metabolic enzymes would be functional see Fig B. (iii) et al, et al, et al, et al, et al, One of the difficulties in describing the origin of metabolism is the fact that the metabolic network is largely composed of intermediates that are not characterized by long‐time stability, at least when considering geological environments and timescales. As shown here and previously, this in particular applies to sugar phosphate molecules . Buffer A and B consisted of 10% and 50% acetonitrile, respectively, and both contain 750 mg/l octylammonium acetate. Separation was achieved by isocratic flow (flow rate 0.6 ml/min) at 5% B for 3.5 min, then ramping to 70% B within 2.5 min, followed by washing with 80% B for 0.5 min and re‐equilibration at 5% B for 0.5 min, resulting in a total cycle time of 7.5 min. Metabolites were identified by matching retention time and fragmentation pattern with the commercially obtained standards as indicated in Quantification of sugar phosphates was conducted with a method modified from our earlier procedures (Wamelink http://www.R-project.org). The following fitting models address the different observed reaction modes of product accumulation : (i) least squares linear model for continuously accumulating metabolites; (ii) nonlinear growth model with the R module SSgompertz for metabolites reaching a steady state; (iii) maximum initial growth rate within the linear range using the least squares model for products with dynamic formation/degradation behaviour; (iv) exponential decay model for first‐order‐like degradation of educts. The best fitting model was selected based on R2 values and was verified by graphical examination of the fitted curves. Qualitative and quantitative description of the observed reactions was extracted from the respective fitting model . The automated peak integration was reviewed manually for all metabolite measurements. Peak areas were, when needed, corrected by referring to repeatedly measured quality control samples. As pentose sugar phosphates trigger a low signal within the G3P transition 169.0→97.0, G3P concentrations were corrected by subtracting the maximum observed contribution of these signals. Each of the replicates contained time‐series data for the 182 out of the 210 theoretically possible reactions, which were individually analysed with R (R Core Team, ng model  and S2. MAK conducted the experiments, data analysis and illustration, and MAK, AVT and MR designed the study and experiments. MR wrote the first draft of the paper, and all authors contributed to writing and finalizing the manuscript.The authors declare that they have no conflict of interest.Supplementary Table S1Click here for additional data file.Supplementary Table S2Click here for additional data file.Supplementary Table S3Click here for additional data file.Supplementary Table S4Click here for additional data file.Supplementary Table S5Click here for additional data file.Supplementary Table S6Click here for additional data file.Review Process FileClick here for additional data file.