Some studies have revealed that pigs with low meat production show more intense myogenesis at the early stage of embryonic muscle development than pigs with high meat production. Here, by gas chromatography–mass spectrometry GC–MS based metabolomics, we concluded that the nucleotide metabolism and energy metabolism of the longissimus lumborum (LL) of the Lantang (LT) and the Landrace (LR) pig at embryonic day 35 (E35). Metabolites showed clear separation between LT and LR, with 40 metabolites having higher abundances in LT and 14 metabolites having lower abundances in LT compared with LR. In addition, these metabolic changes were mainly associated with nucleotide metabolism and energy metabolism, such as purine metabolism, pyrimidine metabolism, the pentose phosphate pathway, and the TCA cycle. More interestingly, the contents of DNA, RNA, and ATP per unit mass of LL tissues were higher in LT, indicating rapid synthesis of nucleic acids and ATP, to meet both the material and energy requirements of rapid cell proliferation and differentiation. Furthermore, enzyme activity associated with the TCA cycle and pentose phosphate pathway, including α-ketoglutaric dehydrogenase (KGDH), malate dehydrogenase (MDH), pyruvate dehydrogenase (PDH), succinate dehydrogenase (SDH), and glucose-6-phosphate dehydrogenase (G6PDH), were higher in LT. Based on these results, we conclude that there are significant differences in nucleotide metabolism and energy metabolism of LL between LT and LR, and we speculate that the enhanced nucleic acid metabolism and energy metabolism in LT can meet the material and energy requirements of rapid cell proliferation and differentiation, making myogenesis more intense in LT compared to LR which might be the metabolic mechanism underlying the distinct skeletal muscle development in the two breeds. Elucidation of the complex regulation of porcine muscle development is key to increasing pork output and improving pork quality. However, the molecular mechanisms involved in early porcine embryonic muscle development in different pig breeds remain largely unknown. Here, GC–MS based metabolomics and metabolomic profiling was used to examine the  The pig is an important domesticated meat animal and it is also a useful model for numerous human muscular diseases, therefore, a more thorough understanding of porcine skeletal muscle development is informative ,2. MyogeLantang (LT) is an indigenous lard-type pig breed grown in southern China, which is characterized by delicious meat, high intramuscular fat content, and early sexual maturity. Landrace (LR) is a commercial meat-type pig breed with high lean meat percentage, fast muscle growth and high body weight . BecauseMetabolomics has emerged as a powerful tool for exploring metabolic processes, identifying crucial metabolic biomarkers, and revealing metabolic mechanisms during cell growth, development, and senescence ,25. FurtLL of LT and LR in E35 embryos. Our study aimed to uncover the metabolic mechanisms associated with differences in early embryonic muscle development between LT and LR breeds, which could contribute to the improvement of pork output as well as the treatment and prevention of muscle diseases in humans.In the present study, to gain further insight into the mechanism underlying the greater myogenic capacity in LT than in LR at the early embryonic stage, GC–MS based metabolomics was used to analyze metabolic profiles in LL tissues of LT and LR at E35. For each sow’s embryo, the LL tissues were dissected. LL tissues from the same sow were cut up and mixed as the experimental samples. These samples were immediately snap-frozen in liquid nitrogen and stored until further use. For GC–MS detection, six biological replicates were used in the LT group and the LR group, each from a sow; for measurement of ATP, NADPH, and enzyme activity, at least three biological replicates were used in the LT group and LR group, each from one sow.Six purebred LT sows with the same genetic background were inseminated with semen from purebred LT boars, and six purebred LR sows with the same genetic background were artificially inseminated with semen from purebred LR boars. All pigs were allowed ad libitum access to feed and water and were housed under identical conditions. All sows were slaughtered at 35 days after insemination, and all embryos of each sow were collected. There was no visual difference between the embryos or g. After centrifugation, supernatants were transferred to a new Eppendorf tube and immediately concentrated in a rotary vacuum centrifuge device. For metabolite derivatization, the residue was dissolved in 100 µL methoxyamine pyridine solution (20 mg per mL) and incubated at 37 °C for 120 min in an incubator shaker. Lastly, each mixture sample was treated with 100 µL MSTFA reagent (containing 1% TMCS) and incubated at 37 °C for 30 min. All experiments were repeated using six biological replicates.For metabolite extraction, we referred to a reported study . In brieGC–MS detection was performed with a variation on the two-stage technique . By spliAfter mass spectra were acquired, deconvolution and calibration were conducted by AMDIS (Agilent OpenLAB CDS ChemiStation C.01.01). Peaks with a signal-to-noise ratio (S/N) < 30 were excluded to avoid false positives . The retα = 0.05) with SPSS 23.0 . Z-value based on the LR was calculated to identify the differential abundance of metabolites of LT compared to LR. The Z-score plot spanned from −10.94 to 24,536.17 in LT. On the Metabo Analyst online website [Z-score analysis, each metabolite was scaled according to a reference distribution, and a calculation was conducted with the mean and standard deviation of reference sets as a control. Using the distance matrix, a hierarchical cluster analysis (HCA) was performed. Then, the relationships among the samples were tested by PCA and OPLS-DA analyses. Comparative metabolic pathway analysis between LT and LR groups was performed using the Metabo Analyst online website  (accessed on 20 February 2021). A hypergeometric test was used to calculate the −log(p) value for reflecting the impact of each metabolic pathway, and pathways with p < 0.05 were retained. Finally, data were represented by histogram and scatter plots using Prism v5.01 . Comparative metabolic pathway analysis between LT and LR was performed using iPath2.0 [Data transformations and manipulations were performed using Excel, and included two core steps. The first was to convert the abundance of each metabolite from original data of GC–MS detection into an Excel table for subsequent analysis. Second, in the process of data standardization, the data matrix in Excel was calculated, which included the Log transformation, scaling, centraling, and other transformation involved in PCA and OPLS-DA analysis. Differential metabolites were obtained by comparing the two groups using the Mann–Whitney U test , pyruvate dehydrogenase (PDH) reaction mix , succinate dehydrogenase (SDH) reaction mix , and malate dehydrogenase (MDH) reaction mix  to a final volume of 200 µL in a 96-well plate. After incubation at 37 °C for 60 min, each plate was measured at 566 nm for absorbance. The samples were fully ground in liquid nitrogen, and a 100 mg sample homogenized with 1 mL PBS (pH 7.4) was broken down by sonication (at a 200 W power) for 10 min on ice. The samples were then centrifuged at 12,000 rpm for 10 min to remove insoluble material. The protein concentration of the supernatants was measured by BCA kit , then an equivalent amount of proteins in the supernatants was vacuum freeze dried. The vacuum freeze dried powder containing 100 μg proteins were redissolved and transferred to a ketoglutarate dehydrogenase (KGDH) reaction mix . Finally, the luminescence value was plugged into the standard curve to determine the ATP content.ATP content was quantified using an ATP Assay Kit . In brief, samples were lysed followed by sonication , then centrifuged at 12,000× g for 10 min at 4 °C and 50 μL supernatants were used to measure NADPH. After being heated in a water bath at 60 °C for 30 min, each supernatant sample was added into 100 μL G6PDH working liquid in a 96-well plate and incubated at 37 °C for 10 min. Following this, color reaction was performed for 30 min. Finally, each plate was measured at 450 nm for the absorbance. NADPH levels were determined using a NADP+/NADPH Detection Kit . Tissue samples of 10 to 30 mg were homogenized on ice with 400 μL NADP+/NADPH-extracting solution, then centrifuged at 12,000× G6PDH activity was detected using a G6PDH activity assay kit  according to the manufacturer’s instructions. The reduction of NADP was measured spectrophotometrically at 450 nm.t-test, and the results are presented as mean ± SEM. Asterisks (*) indicate levels of statistical significance. * p < 0.05; ** p < 0.01; *** p < 0.001.Measurement of ATP, NADPH, and enzyme activity was repeated at least three times independently, and representative results are shown. SPSS 23.0  and Prism v5.01  were utilized for statistical analyses. Differences between groups were assessed for significance with the To characterize the metabolic profiling of skeletal muscle in early embryos of local Chinese pigs and foreign lean pigs, we applied an untargeted metabolomic analysis to determine the metabolites in the LDM of LT and LR at E35. Representative total ion current chromatograms from the LT and LR samples are listed in p < 0.05). The identified metabolites are shown in Using the Mann–Whitney U test, we found 54 differential metabolites  A. ImportTo explore the most crucial metabolites differentiating LT from LR, orthogonal partial least square discriminant analysis (OPLS-DA) was conducted to recognize the sample pattern. Results showed that LT and LR groups were distributed in two separate quarters  by Component p  Figure A. DiscriLL tissues was measured, including PDH, KGDH, SDH, and MDH. Activity of the four enzymes were elevated in LT compared with LR , the content of DNA and RNA in unit weight LL of LT was higher than that of LR, which further proved that LT had boosted nucleotide metabolism.Comparative metabolic pathway analysis between the LT group and the LR group was carried out in iPath. The resulting global overview map provided a better insight into the different metabolic states, where the red line represent increased pathways in LT and the blue line represent increased pathways in LR. We identified that most of the presented pathways were elevated in LT, especially nucleotide metabolism and energy metabolism A. To valIncreasing evidence shows that there is a genetic difference in the regulation of muscle development, which leads to distinct early embryonic porcine myogenesis processes between pig breeds ,9. Here,LL tissues of LT and LR. Among the differential metabolites, in terms of purine metabolism, the increase in guanine, guanosine, and glutamine reflected a higher accumulation for purine nucleotides synthesis in LT. Likewise, in terms of pyrimidine metabolism, a higher abundance of uridine, uridine-5′-monophosphate, beta-alanine, and glutamine was found in LT. Purine metabolites and pyrimidine metabolites, serving as building blocks for DNA and RNA, are integral to the promotion of cell survival and proliferation [LL were higher in LT pigs.The importance of nutrient utilization and metabolism in the process of muscle cell differentiation has been well described ,45. Postferation . ConsistGlutamine can be used as a substrate for nucleotide synthesis , as well as NADPH and many other biosynthetic pathways involved in the maintenance of cellular integrity and function . TherefoLL, which contributed to the rapid proliferation and differentiation of myogenic cells. Glucose-6-phosphate dehydrogenase (G6PDH) is a rate-limiting enzyme in the PPP; it catalyzes the dehydrogenation of 6-phosphoglucose to form 6-phosphogluconate-δ-lactone [The pentose phosphate pathway (PPP) which produces ribose-5-phosphate for de novo nucleotide biosynthesis also generates NADPH, which is crucial for supporting cell proliferation via the reductive biosynthesis of macromolecules such as lipids ,54. In a-lactone . The incLL tissue was significantly greater than in LR. These findings revealed that enhanced TCA cycles lead to the rapid generation of ATP, which could meet the energy requirements of rapid cell proliferation and differentiation in LT.The TCA cycle plays a central role in metabolism by completely oxidizing acetyl-CoA, a key product of carbohydrate, fatty acid, and amino acid catabolism, into carbon dioxide, producing ATP and meeting most of the cell’s energy requirements . MetabolThe above shows that different myogenic abilities of LT and LR pigs in early embryogenesis are related to cellular metabolic status. We found that the metabolic profiles of embryonic skeletal muscle of LT was more conducive to the rapid formation of myofiber. Although metabolic regulation of skeletal muscle development has been widely reported, it has not been further verified whether the differences in metabolic profiles between LT and LR at E35 are the cause or consequence of the differences in skeletal muscle development. It is also unclear whether the differences affect muscle mass at a later stage. This is the deficiency of this study. In addition, the mechanism underlying the metabolome differences is unclear; this may be owing to several factors. As the alterations were observed in embryos whose parents were raised under uniform conditions, different levels of metabolism may be linked with a genetic or epigenetic origin. For example, the occurrence of severe obesity is related to differing methylation modification signatures near the gene promoter of several metabolic enzymes, resulting in altered gene expression ,62. It hLL tissues of LT and LR. Furthermore, comparative metabolic pathway analysis and enzyme activity detection revealed that nucleotide metabolism and energy metabolism, including purine metabolism, pyrimidine metabolism, PPP, and the TCA cycle, were increased in LT, permitting rapid generation of nucleic acids and ATP, to meet the material and energy requirements of rapid cell proliferation and differentiation. By OPLS-DA and pathway enrichment analysis, four crucial biomarkers were identified that may have important effects on porcine muscle development. To our knowledge, this is the first report that reveals the relationship between functional metabolomics and differences in embryonic muscle development among pig breeds. It will provide a new insight for elucidating the regulatory mechanism concerning porcine embryonic muscle development.In conclusion, GC–MS-based metabolomics was adopted to understand the mechanisms by which LT had a more intense myogenic capacity than LR at the early embryonic stage. Results showed that abudances of individual metabolites were clearly different in the


Preeclampsia is one of the most common severe pregnancy complications in obstetrics, which is considered a placental source disease. However, the mechanisms underlying preeclampsia remain largely unknown. In this study, UPLC-MS/MS-based metabolomic and lipidomic analysis was used to explore the characteristic placental metabolites in preeclampsia. The results revealed that there were significant changes in metabolites between preeclampsia and normotensive placentas. Weighted correlation network analysis (WGCNA) identified the correlation network module of metabolites highly related to preeclampsia and the clinical traits reflecting disease severity. The metabolic perturbations were primarily associated with glycerophospholipid and glutathione metabolism, which might influent membrane structures of organisms and mitochondria function. Using linear models, three metabolites had an area under receiver operating characteristic curves (AUROC) ≥ 0.80 and three lipids had an AUROC ≥ 0.90. Therefore, metabolomics and lipidomics may offer a novel insight for a better understanding of preeclampsia and provide a useful molecular mechanism underlying preeclampsia. Preeclampsia is one of the most common severe pregnancy complications in obstetrics, with new-onset hypertension and other organs dysfunction and accompanied by new-onset proteinuria or not . The incIt is considered that preeclampsia is a placental disease. Placentas from women with preeclampsia display an increased frequency of infarcts of villous, villous-free placental lakes, inflammation, fibrin deposition, syncytial knots, and abnormal cytotrophoblast proliferation . It is cIn contrast to genomics, transcriptome and proteomics, metabolomics, a systematic approach that studies the number and variety of endogenous metabolites caused by the pathophysiological changes of external stimuli and mutations of the organism itself, can more directly and accurately reflect the state of the organism . MetabolStudying the metabolic perturbations of the placenta in preeclampsia, and how it is affected by disease characteristics, may give insight into which pathological processes are specific for these disorders. In this study, ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS) was applied to investigate metabolomics and lipidomics profiling of placentas from preeclampsia and normotensive pregnant women. Therefore, metabolomics and lipidomics may offer a novel insight for a better understanding of preeclampsia and provide a useful molecular mechanism underlying preeclampsia.This case-control study and placenta collection were approved by the Medical Ethics Committee of The Third Affiliated Hospital of Guangzhou Medical University with Medical Research No. 2018022.Preeclampsia was defined as new-onset hypertension after 20 weeks of gestation with systolic blood pressure ≥ 140 mm Hg or diastolic blood pressure ≥ 90 mm Hg on two occasions at least 4 h apart after, and with proteinuria ≥ 300 mg per 24 h urine or dipstick reading of 2+ (used only if quantitative methods not available), or other organ dysfunction in the absence of proteinuria . PreeclaThis study included 12 preeclampsia pregnant women and 14 normotensive pregnant women who delivered by cesarean section in the Department of Obstetrics and Gynecology, Third Affiliated Hospital of Guangzhou Medical University. We excluded pregnant women complicated with diabetes mellitus, metabolic syndrome, twin pregnancy, complicated with hepatitis B virus infection, and syphilis infection. The detailed patient characteristics are presented in Sample preparation included metabolite and lipid extraction was shown in t-test and the Mann–Whitney U test were used to compare continuous variables with normal and non-normal distributions between the two groups, respectively. Categorical variables were compared using the Chi-squared test or Fisher’s exact test, as appropriate. Area under receiver operating characteristic curves (AUROC) of metabolites and lipids was calculated using linear models.For clinical information, statistical analyses were conducted using SPSS v25.0 for Windows. Student’s p-values of less than 0.05 were identified to be differentially expressed. The metabolic pathway analysis of these differentially expressed metabolites was performed by MetaboAnalyst website  was applied to filter and classification of irrelevant noise to improve the analytical ability and validity of the model. Metabolites with variable influence on projection values (VIP) of greater than 1.0 and  website .A weighted metabolite co-expression network was built with R package “WGCNA” . Briefly2Y = 0.845 and Q2 = 0.351 in metabolomics analysis and R2Y = 0.848 and Q2 = 0.421 in lipidomics analysis, indicating that the models were moderately fit. 69 metabolites (p < 0.05). In this study, we identified 1,235 annotated metabolites and 1,545 annotated lipid species. The proportions of all kinds of metabolites were shown in abolites  and 61 labolites  were fouTo elucidate the relationships between metabolomics profile and clinical characteristics of pregnant women, we next constructed the correlation networks among all identified metabolites using WGCNA. Six modules were identified based on average hierarchical clustering and dynamic tree clipping  in the mp = 7.7e-48). Metabolite set enrichment analysis of turquoise module was performed using the Small Molecule Pathway Database (p < 0.05) using the KEGG database | 2.35, 2.06, 2.25, and 0.94  between the preeclampsia and control group . Lipid sand 0.94 . The difand 0.94 . As showD-glucose 1-phosphate and 3-phospho-D-glycerate  was formed with the differentially expressed metabolites and lipids . The glulycerate  between p < 0.05, FC > 1.5 or < 0.67, with top five of VIPs) was performed (The receive operating characteristic (ROC) analysis of five potential metabolic and five potential lipid biomarkers , producing glutathione disulfide (GSSG). The resulting GSSG is then reduced by glutathione reductase (GSR) and NADPH, regenerating GSH and NADP+  . Mitochond NADP+ . In the nd NADP+ . Interesclampsia .To date, there have been only two reports of global metabolite profiling of preeclampsia placentas were performed. L-glutamine enhances ammonium locally and leads to mitochondrial dysfunction. It is known that glutamine is converted to glutamate in mitochondria by glutaminase (GLS) and glutamate is converted to α-ketoglutaric acid, which then participates in the TCA cycle. The TCA cycle can be fueled mainly by three substrates, fatty acids, pyruvate, and glutamine/glutamate (D-glucose 1-phosphate and 3-phospho-D-glycerate between preeclampsia and normotensive placentas, both of which participated in the glycolysis pathway. We assumed that reduction of oxidative phosphorylation capacity in placentas of preeclampsia partly led to a dampened mitochondrial pyruvate and glutamate uptake. Wang et al., reported that the number of mitochondria in preeclampsia placenta is far greater and these mitochondria have greater susceptibility to lipid peroxidation (Mitochondria, particularly from trophoblastic cells, are responsible for the production of energy, which is extremely important for normal placentation. In preeclampsia placenta, mitochondrial fission/fusion appeared to be impaired . More rolutamate . In thisxidation . Hypoxicxidation .In this study, the differential expressed metabolites and lipids in preeclampsia placentas were both enriched in the glycerophospholipids metabolism pathway. Glycerophospholipids are the major structural component of cell membranes  and are Based on the screening criteria for biomarkers reported by in vivo or in vitro.A limitation of our study is that the sample size was small, which lead to some of the differential metabolites being undetected. Therefore, large-scale validation or prospective studies are needed. Although some significant metabolites were identified, there were still many metabolites unidentified due to the limited number of reliable spectral reference databases for metabolite identification. This is a cross-sectional study that cannot draw a causal inference about the association between differential metabolites and preeclampsia. The differential metabolites found in this study and potential mechanism are needed to be further validated In summary, we used UHPLC to characterize the placental metabolomics and lipidomics profiling for women complicated with preeclampsia. There were metabolic and lipidomic disturbances in both early-onset and late-onset preeclampsia placenta. The metabolic perturbations were primarily associated with glycerophospholipid and glutathione metabolism. The former might influent membrane structures of organisms, and then cell behaviors. The latter might inversely influent glutamate metabolism, and then mitochondria function. Our research highlighted some important mechanisms involved in the pathophysiological changes of preeclampsia.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by The Third Affiliated Hospital of Guangzhou Medical University. The patients/participants provided their written informed consent to participate in this study.LZ and SB: conceptualization, samples collection, analysis, and writing. YLi, LH, YLia, MH, BH, JL, WD, and SG: samples collection, editing, and analysis. JC, LD, DC, and ZW: supervision, project administration, and funding acquisition. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Escherichia coli was analysed by perturbation studies and metabolic control analysis. The insertion of a non-natural shikimate transporter into the genome of an Escherichia coli L-tryptophan production strain enabled targeted perturbation within the product pathway with shikimate during parallelised short-term perturbation experiments with cells withdrawn from a 15 L fed-batch production process. Expression of the shikimate/H+-symporter gene (shiA) from Corynebacterium glutamicum did not alter process performance within the estimation error. Metabolic analyses and subsequent extensive data evaluation were performed based on the data of the parallel analysis reactors and the production process. Extracellular rates and intracellular metabolite concentrations displayed evident deflections in cell metabolism and particularly in chorismate biosynthesis due to the perturbations with shikimate. Intracellular flux distributions were estimated using a thermodynamics-based flux analysis method, which integrates thermodynamic constraints and intracellular metabolite concentrations to restrain the solution space. Feasible flux distributions, Gibbs reaction energies and concentration ranges were computed simultaneously for the genome-wide metabolic model, with minimum bias in relation to the direction of metabolic reactions. Metabolic control analysis was applied to estimate elasticities and flux control coefficients, predicting controlling sites for L-tryptophan biosynthesis. The addition of shikimate led to enhanced deviations in chorismate biosynthesis, revealing a so far not observed control of 3-dehydroquinate synthase on L-tryptophan formation. The relative expression of the identified target genes was analysed with RT-qPCR. Transcriptome analysis revealed disparities in gene expression and the localisation of target genes to further improve the microbial L-tryptophan producer by metabolic engineering.L-tryptophan production from glycerol with The online version contains supplementary material available at 10.1007/s00449-021-02630-7. Metabolic engineering has been widely used to reroute fluxes towards the desired products to overcome production limitations of biotechnological processes due to constraints and regulations in cell metabolism. However, rational design is impeded by the complexity of biosynthesis pathways, and in vitro analyses of single enzymes cannot display processes in vivo realistically. Perturbation experiments have opened up the possibility of characterising metabolic pathways in vivo during microbial production processes . Termed Stationary metabolic analyses are one kind of metabolic analyses and they are based on the deflection of a reference state by either change in substrate or substrate availability –4. As a To enable targeted genetic modifications of production strains, metabolic control analysis (MCA) is applied. The mathematical method allows the identification of rate-limiting steps within the biosynthesis pathway . PrerequFurthermore, the implementation of MCA requires information on intracellular reaction rates. Fluxome data, derived from perturbation experiments, is used to compute intracellular flux distributions via constraint-based approaches like Flux Balance Analysis or Flux Variability Analysis . MethodsCompared to NET-analysis, TFA is beneficial since the flux solution space is not biased concerning reaction directionalities . A PythoE. coli from glycerol [Aromatic amino acids are an example of a group of biochemicals where production capacities and economic efficiency by fermentation remain low, whereas market demand steadily increases , 16. Henglycerol . As L-trglycerol , 16, theglycerol .Escherichia coli (E. coli) and Corynebacterium glutamicum. However, biosynthetic L-tryptophan production is energetically expensive and to prevent cells from wasting energy, it is controlled by strict regulation on multiple levels, including feedback inhibition, attenuation and repression [−1 with glucose as carbon source were reported with rationally engineered E. coli strains [−1 were reached in combination with adapted pH and dissolved oxygen control strategies in the fed-batch process [For humans and livestock, L-tryptophan is an essential amino acid and they are dependent on consuming it with nutrition . Thus, opression –22. To opression , 22–26.  strains , and inc process . Aside f process . The res process –30. Ther process , 31. For process , 30, 32.E. coli cells, glycerol is taken up either via passive diffusion or through protein-assisted facilitated diffusion [−1 L-tryptophan [−1 were obtained by further strain optimisation [In iffusion , 34. Thiiffusion . Despiteiffusion –38. An Lyptophan . L-tryptmisation . Detailemisation . Indol-gmisation .The accuracy of these results is strongly dependent upon the degree of deflection, which is induced by the applied perturbation substrates. The variety of substrates for metabolic analysis is limited to a narrow selection of carbon sources, which can be taken up naturally by the cells under examination. Most perturbation experiments are performed with glucose; other standard perturbation substrates are glycerol, acetate, succinate, pyruvate or ethanol , 39–46. E. coli metabolism is depicted in Fig. E. coli carries a shiA gene, encoding a shikimate/ H+-symporter, it shows relatively poor ability to importshikimic acid [ShiA gene from E. coli has been characterised and cloned by Whipp [shiA on a multicopy plasmid, enabled the growth of E. coli with shikimate. Kubota [shiA gene in the Gram-positive C. glutamimcum used in this study to express a shikimate transporter to a sufficiently high extent so that an enhanced deflection of fluxes and metabolome within the pathway of aromatic amino acids during parallelised metabolic analysis can be achieved. MCA is performed to reveal the control inside the cells, during L-tryptophan production. As new approach, the recombinant shikimate transporter strain was engineered, which enabled the usage of shikimic acid as additional perturbation substrate during metabolic analysis, to reach a highly targeted perturbation within the pathway of aromatic amino acid biosynthesis. To improve the validity of flux analyses and MCA, we combined the extensive dataset derived from metabolic analysis with a recently published thermodynamics-based flux analysis by Salvy [Shikimic acid is an intermediate metabolite in the common aromatic amino acid pathway. Due to its central position in chorismate biosynthesis and proximity to the L-tryptophan biosynthesis pathway, it is a promising substrate for perturbation during the metabolic analysis of L-tryptophan production. Its position in mic acid . ShiA geby Whipp , and int. Kubota  first idby Salvy .Fig. 1ScshiACg strain was constructed using the CRISPR-Cas-mediated method as described earlier by Jiang et al.[sgMal was developed by inverse PCR with the “sg-RNA -mal” and “sg-reverse” primers  flanked plasmid .Table 1SE. coli NT1259 shiA pFaroFBLKan in preculture, the production process, and metabolic analysis. Heat-resistant components (g L−1): 3.00 KH2PO4, 12.00 K2HPO4, 5.00 (NH4)2SO4, 0.10 NaCl, were dissolved in water and autoclaved, heat-labile ingredients (g L−1): 0.30 MgSO4·H2O, 0.015 CaCl2·2 H2O, 0.1125 FeSO4·7 H2O, 1.50 sodium citrate, 0.0075 thiamine and 0.05 kanamycin were sterile-filtrated separately and added after cooling. A stock solution of glycerol (1000 g L−1) was also autoclaved, and 7 g L−1 were added to the medium for inoculum preparation, 4 g L−1 to the medium for the production process. 1 mL L−1 of a trace-element solution [−1) 11.2 MnSO4·H2O, 10.0 AlCl3·6 H2O, 7.3 CoCl2·6 H2O, 2.0 ZnSO4·7 H2O, 2.0 Na2MoO4·2 H2O, 1.0 CuCl2·2 H2O, 0.5 H3BO3, was added to the production medium.A defined minimal medium, adapted from Albermann , was usesolution , containE. coli NT1259 shiA pFaroFBLKan were picked from an agar plate to inoculate two 100 mL shake flasks with 20 mL minimal medium each to generate a starter culture for the fed-batch cultivation on a 15 L scale. After 40 h of incubation at 37 °C and 100 min−1 in an orbital shaker , the cell suspension was transferred into ten 500 mL shake flasks with 100 mL minimal medium each. A volume of 1.0 L of cell suspension for the inoculation of the production process was obtained after 20 h incubation at 37 °C and 250 min−1 orbital shaking. The emerging cell suspension was transferred into a sterile bottle and pumped aseptically into the stirred-tank reactor.Two individual colonies of 4OH or 42% H3PO4 and maintained at a pH of 7.0. The dissolved oxygen concentration was kept above 30% air saturation by adjusting stirrer speed (200 min−1–1000 min−1), aeration rate (5 L min−1–40 L min−1) and pressure (up to 1.3 bar). Carbon dioxide production and oxygen uptake were monitored via an exhaust gas analyser .A modified fed-batch process was used to produce L-tryptophan , 37. Cul−1) 120.0 glycerol, 60.0 (NH4)2SO4, and 0.1 kanamycin, as well as 4.5 L feeding solution B with (g L−1) 400.0 glycerol, 25.0 (NH4)2SO4, and 0.1 kanamycin. The setpoint for exponential feeding was at a growth rate of µset = 0.1 h−1. Following the exponential feeding phases, 0.3 mM Isopropyl-β-D-thiogalactopyranosid (IPTG), as well as the initial amounts of MgSO4·H2O, CaCl2·2 H2O, FeSO4·7 H2O and thiamine, were aseptically injected into the reactor via syringes through a septum; a volumetrically constant feeding phase was initiated. Feed solution C (5.0 L) containing (g L−1) 800.0 glycerol, 8.0 (NH4)2SO4, 8.0 (NH4)2HPO4 and 0.1 kanamycin was applied with 0.2 gglycerol gCDW−1, based on the biomass concentration at the end of the exponential feeding phase.After the initial batch phase, two consecutive feeding solutions were supplied exponentially into the reactor: 1.0 L feeding solution A with , the supernatant was discarded, and cells were resuspended in 400 mL fresh minimal medium (37 °C) without carbon source. The cell suspension was distributed equally to the four parallel bioreactors, which contained 400 mL fresh minimal medium and 0.3 mM IPTG and 0.1% antifoam.−1, stirrer speed 1200 min−1), and a three-stage feeding profile was applied with separate individual feeding solutions. All the feed supply rates were increased 9.0 and 15.0 min after inoculation. The first reactor was supplied with a feeding solution containing 100 g L–1 glycerol with 22 mL h−1, 44 mL h−1 and 66 mL h−1. In the feeding solution for the second reactor, glucose (80 g L–1) was used as a carbon source and was supplied with 24 mL h−1, 45 mL h−1 and 66 mL h−1. The feeding solution for the third reactor contained 100 g L–1glycerol and an additional 12 g L–1 shikimate; the supply rates were applied with 22 mL h−1, 44 mL h−1 and 66 mL h−1. Feeding solution 4 consisted of 80 g L−1 glucose and 9 g L–1 shikimate, the feeding rates for this reactor were 24 mL h−1, 45 mL h−1 and 66 mL h−1.The parallel short-term perturbation studies lasted 21 min  and their empty weight was measured. Then, 2 mL of cell suspension was transferred to pre-weighed sample tubes and centrifuged , the supernatant was discarded, and the reaction tube was dried and weighed again.To determine extracellular metabolites by high-performance liquid chromatography (HPLC), samples were centrifuged , and the supernatant was filtered (0.2 µm).18 150 × 4.6 mm, 5 µm; Phenomenex, Torrance, CA, USA), and the analytes were separated chromatographically at 40 °C for 43 min at a 1 mL min−1 flow rate. A gradient from solvent A ) and solvent B (MeOH/acetonitrile/water) (v/v/v)) was applied as follows: 0–3 min 100% solvent A, 8.5 min 75% solvent A, 30 min 60% solvent A, 30.02 min 0% solvent A, 32 min 0% solvent A, 32.02 20% solvent A, 34 min 20% solvent A, 38 min 100% solvent A, 43 min 100% solvent A.Amino acids were derivatised before analysis by an HPLC system  with a fluorescence detector . Derivatisation was performed by the autosampler at 8 °C with o-phtaldialdehyde (OPA), mercaptopropionic acid (MCP) and iodoacetic acid (IAA), all in a 40 mM bicine buffer (pH 10.2). First, 10 µL of the sample was transferred (in 658 µL 0.3 mM MCP) to a separate destination vial. Then, 20 µL of IAA (3.5 mM) was added and mixed, followed by the addition of 70 µL OPA (11 mM in bicine/MeOH/MCP (928:7.1:0.1) (v/v/v)) and subsequent mixing. 20 µL of the derivatised sample dilution was injected into a Gemini column (C−1 isocratic flow (5 mM H2SO4).Quantification of alcohols and organic acids was performed with an HPLC system  with a refractive index detector at 950 nm. A 20 µL volume of sample was used for injection; separation was conducted on an Aminex HPLX-87H column  at 65 °C with 0.7 mL minGlucose and ammonia were quantified enzymatically with assays from Boehringer Mannheim/R-Biopharm .13C cell extract as internal standard  was added. The U-13C cell extract was prepared as described by [shiA pFaroFBLKan. After extraction, the samples were cooled and centrifuged , and the supernatant was stored at − 80 °C until analysed by mass spectrometric analysis . For analysis, a UHPLC-MS/MS method adapted from Buescher [High-resolution sampling for the quantification of intracellular metabolites was performed using a rapid sampling device with inner tubes adapted from . The samribed by  with NT1Buescher  was usedBuescher . SolventftsZ gene was used as endogenous calibrator for the experiment. The run was performed in a qTower system . Data were analysed using the qPCRsoft 3.4 software application .Total RNA for the RT-qPCR was prepared using a Quick RNA mini kit . The RNA quality was analysed by running RNA-agarose gel electrophoresis. The cDNA was created using EpiScript Reverse Transcriptase  with a 6 N random primer. The real-time quantitative PCR (RT-qPCR) was carried out using Blue S-Green qPCR kit  according to the supplier’s recommendations, using the ∆∆Ct comparative method. The primers used for this analysis are listed in Table iJO1366 [Thermodynamics-based flux variability analysis (TFA) was performed with a python package implemented by Salvy , which iiJO1366 . Genome-iJO1366  or addediJO1366 . The comiJO1366 ) with 10vj upon the change in metabolite concentration level Xi and are defined as ,,2), wherFor reactions near to the thermodynamic equilibrium, For reactions operating far from equilibrium, an active regulation is assumed, and therefore effectors approach  Eq. 3),,\documenCN, reduced stoichiometric matrix NR Link-Matrix L0 and independent metabolites Xind Indexing with 0 refers to the reference state, described by the fluxes, enzymes and metabolite concentrations of cells from the L-tryptophan production process at the time of metabolic analysis.The elasticities obtained were summarised in matrix approach , which iwithFlux control coefficients represent the effects of a 1% increase of enzymatic activity on a specific metabolic flux in the model. In this work, a reduced metabolic model (50 reactions and 57 metabolites) including glycolysis, glycerol metabolism, tricarboxylic acid (TCA) cycle, pentose-phosphate-pathway (PPP), L-serine biosynthesis and biosynthesis of aromatic amino acids, was used. Uncertainties of estimated flux distributions, Gibbs free energies of reaction and metabolite concentrations were taken into consideration through Monte Carlo sampling  .E. coli cells with glycerol as a carbon source, focusing on fluxes within the biosynthesis of aromatic amino acids. In a first step, the successful integration and expression of the shiA-transporter gene was verified by RT-qPCR  and the shikimate uptake by L-tryptophan producing cells without shikimate-transporter  in parallel cultivations in shake-flasks. The integration of the shiA-gene from Corynebacterium glutamicum enabled the uptake of shikimate with a measured rate of 0.26 mmol gCDW−1 h−1 in shake-flask, whereas the concentration of shikimate remained constant during the cultivation of the cells without transporter . The underlying reference fed-batch process for the cultivation of the shikimate-transporter strain was established before [E. coli L-tryptophan producer [E. coli NT1259 shiACg pF112aroFBLKan in this work. Process performance on a 15 L scale of E. coli NT1259 shiACg pF112aroFBLKan is summarised in Fig. −1 after 42.7 h (µ = 0.07 h−1) . The L-tryptophan concentration steadily increased during exponential feeding, leading to a product concentration of 8.8 g L−1 L-tryptophan at the end of the first process phase. At a process time of 45.1 h, exponential feeding was switched to constant feeding with a glycerol feeding rate of 0.2 ggly gCDW−1 h−1 and 0.3 mM IPTG were added for the induction of gene expressions. L-tryptophan concentration further increased to 14.6 g L−1 until 66.8 h. The highest cell-specific production rate of 23 mgL-trp gCDW−1 h−1 was determined at a process time of 56.6 h. After 66.8 h no further product formation was observed. Due to dilution by feeding and base addition, the product concentration declined to 10.6 g L−1 until the end of the process.This work is intended to study the metabolic control of L-tryptophan production in d before . This feproducer , used inproducer , 37 was −1 of the initial glycerol was consumed. No glycerol accumulation was observed until 52.7 h. Afterwards, glycerol concentration in the medium started to rise so that a maximum concentration of 73.0 g L−1 was observed at the end of the fed-batch process (data not shown). Throughout the process, ammonia was present in concentrations between 2–4 g L−1. After a process time of 65 h, ammonia accumulated to 7.1 g L−1 at the end of the process (data not shown) due to reduced uptake by the cells. By-product formation of L-phenylalanine and L-tyrosine started after induction, and the aromatic amino acids accumulated to 0.4 g L−1 L-phenylalanine and 0.2 g L−1 L-tyrosine. Acetate production was initiated 49.5 h after inoculation; the maximum acetate concentration of 10.4 g L−1 was reached at the end of the process. Additionally, 1.0 g L−1 lactate and 0.8 g L−1 succinate was present at the end of the process (data not shown).During the batch phase, 4 g LE. coli NT1259 shiA pF112aroFBLKan with E. coli NT1259 pF112aroFBLKan [E. coli NT1259 pF112aroFBLKan was 14.3 g L−1, the maximum production rate after induction was also 23 mgL-trp gCDW−1 h−1 and the maximum cell dry weight concentration (31.8 g L−1) was slightly higher compared to E. coli NT1259 shiA pF112aroFBLKan (26.1 g L−1). It can thus be deduced that the insertion and expression of the gene encoding an additional shikimate transporter does not influence the process performance significantly.In comparing the process performance data of roFBLKan , it emer−1.L-tryptophan production of the fed-batch process shown above was further examined with parallelised short-term perturbation experiments, which were conducted locally separated from the reference process. 3.6 L of cell suspension was sampled 4 h after induction during the constant feeding phase at a process time of 48.8 h. High cell-specific L-tryptophan production rates were observed at this timepoint, and the cell dry weight concentration was 22.4 g LThe metabolic analysis is based on the perturbation of the cells’ metabolism, caused by a change in substrates and substrate supply rates in short-term studies. To achieve an intense deflection within the L-tryptophan biosynthesis pathway, shikimate was used as an additional effector substrate, supplementing the main carbon sources glycerol and glucose applied for the perturbation experiments. Rapid sampling enabled the quantification of the cells’ reactions to the varying substrate supplies by evaluating the extra- and intracellular metabolome. The proteome is assumed to be unchanged for the total analysis time of 21 min . Data frSamples for quantifying extracellular metabolites were taken at the beginning and end of each substrate supply level. Figure CDW−1 h−1 in the first feeding stage to 4.3 mmol gCDW−1 h−1 at the highest feed rate. This led to three corresponding differential gradations in oxygen uptake and carbon dioxide production rates. Product formation was not increased with elevated substrate uptake rates. During the first feeding stage, a high L-tryptophan production rate of 0.32 mmol gCDW−1 h−1 was reached, whereas the lowest L-tryptophan production rate of 0.17 mmol gCDW−1 h−1 was measured during the last feeding period. During the second and third substrate supply level, acetate started to accumulate at an increasing rate (data not shown). Substrate uptake and respiration rates from the reference process during metabolic analysis lay between the lowest and intermediate perturbation steady-state rates. From this result, it can be deduced that the deflection in the metabolism of L-tryptophan producing cells from the reference process was successful in both directions.In all analysis reactors, the three-step feeding profile was clearly reflected by the substrate uptake and respiration rates. With glycerol as perturbation substrate, the substrate uptake was increased from 2.0 mmol gCDW−1 h−1 and 0.12 mmol shikimate gCDW−1 h−1. The uptake rates increased in two equal steps to 3.8 mmol glycerol gCDW−1 h−1 and 0.24 mmol shikimate gCDW−1 h−1 during the last supply level. Respiration rates were staggered, corresponding to the substrate uptake rates. Oxygen uptake and carbon dioxide production were slightly lower than the analysis reactor with glycerol as the sole carbon source. Constant L-tryptophan production rates of around 0.2 mmol gCDW−1 h−1 were determined within the estimation error. Thus, an increase in substrate supply rate did not result in varying product formation rates. In contrast, the by-products acetate and succinate accumulated with increasing rates during the second and third supply level (data not shown).In the analysis reactor with glycerol and supplementary shikimate as carbon sources, the lowest uptake rate of glycerol was 1.9 mmol gCDW−1 h−1 at the lowest supply level to 1.4 mmol gCDW−1 h−1 at the highest. The response of the E. coli cells to glucose supply became apparent in three stages of respiration rates, whereas the differences were less between the second and third supply levels than between the first and second. The product formation rate was 0.31 mmol L-tryptophan gCDW−1 h−1 during the first feeding stage and decreased to 0.16 mmol L-tryptophan gCDW−1 h−1 during the second and rose again to 0.23 mmol L-tryptophan gCDW−1 h−1 during the last. Small amounts of acetate, ethanol and succinate were formed during the last feeding stage (data not shown).With glucose as the carbon source, the substrate uptake rate was increased from 0.7 mmol gCDW−1 h−1 during the first feeding period to 1.5 mmol gCDW−1 h−1 during the last. The shikimate uptake rate rose from 0.11 mmol gCDW−1 h−1 during the lowest perturbation steady state to 0.2 mmol gCDW−1 h−1 during the highest. Respiration rates were similar to the analysis with glucose as the sole carbon source, although total values were slightly higher. L-tryptophan formation rate was constant at 0.27 mmol gCDW−1 h−1 during the first and second supply level and decreased to a very low rate of 0.09 mmol gCDW−1 h−1 during the last. During the last feeding stage, acetate and formate were formed, ethanol production occurred throughout the analysis time with increasing rates (data not shown). The dissolved oxygen concentration decreased to 10% air saturation during the last feeding stage when glycerol was used as the perturbation substrate.In the analysis reactor where glucose and shikimate were feeded in, the glucose uptake rate increased from 0.9 mmol gRapid sampling for metabolome quantification took place at the end of each substrate supply phase, simultaneously from the four analysis reactors and the reference fed-batch production process during metabolic analysis. Aside from extracellular fluxome data, the examination of intracellular metabolite concentrations provides insight into the metabolic state of the cells and enables the quantification of the cells’ reaction to the perturbation. In total, 45 intracellular metabolites of relevance for the production of L-tryptophan were quantified from glycolysis, glycerol metabolism, TCA cycle, PPP, L-serine biosynthesis and synthesis of aromatic amino acids. A representative variety of metabolites is depicted in Fig. FBP participates as a central metabolite in glycolysis and gluconeogenesis and is formed either from fructose-6-phosphate in glycolytic direction or from DHAP and glyceraldehyde 3-phosphate in gluconeogenetic direction. For FBP, rising intracellular concentrations were measured with all carbon sources. This effect is more pronounced, when glycerol is used as main carbon source instead of glucose and is further enhanced when shikimate is used as additional substrate. The intracellular concentrations of FBP varied from 0.45 mM to 3.23 mM. The intracellular FBP concentration of the cells of the sample from the reference process was 1.7 mM and fits well between steady-state 2 and 3 of the glycerol perturbation experiment.DHAP is formed at the connection point of glycolysis and gluconeogenesis and rising intracellular concentrations are an immediate consequence of the increasing glycerol and glucose supply during metabolic analysis. The increase is significantly more distinct when glycerol was used as carbon source. Intracellular DHAP concentrations ranged from 0.04 mM to 0.70 mM. The measured concentration in the reference state sample was comparable to the concentrations reached with glycerol as perturbation substrate.6PG and R5P were chosen as representative metabolites from PPP. The 6PG is located in the oxidative part of the PPP and R5P acts as immediate precursor metabolite for 5-phospho-alpha-D-ribose 1-diphosphate (PRPP), an important precursor in L-tryptophan biosynthesis. Intracellular 6PG concentrations were almost constant at around 0.4 mM in the perturbation experiments with glycerol and glucose as carbon sources. An increase in concentrations was measured in both cases when shikimate was used as secondary perturbation substrate. The highest intracellular concentration of 1.01 mM 6PG was reached during the third steady-state with glycerol and shikimate as perturbation substrates. The intracellular 6PG concentration in the sample from the reference process was slightly higher than in the perturbation experiment with glycerol as sole carbon source. However, it was lower than the concentration measured during the first steady state with glycerol and additional shikimate. Changes observed in intracellular R5P concentrations were less significant. Rising intracellular concentrations from 0.22 mM to 0.33 mM were measured for the two analyses with glycerol as carbon source and constant values of around 0.20 mM R5P were obtained for both analyses with glucose as carbon source. The intracellular concentration measured in the cells of the sample from the reference state marginally exceeded the intracellular concentration of steady-state 3 with glycerol as perturbation substrate.As part of the chorismate biosynthesis, 3DHS is formed from 3-dehydroquinate and is the direct precursor of shikimate. Whereas the intracellular concentrations stay below 2.00 mM when glucose is used as carbon source, intracellular 3DHS concentrations rose to 24.04 mM in the third steady state when glycerol was used as sole carbon source and even higher to 51.89 mM when shikimate was added to glycerol. This observation has been described earlier . The intA good accordance was obtained between the intracellular 3DHS concentration of the reference process and the metabolic analyses experiment with glycerol as sole carbon source.ANTH is the first metabolite downstream from chorismate in the direction of L-tryptophan biosynthesis and ANTH is combined with PRPP by the anthranilate synthase, whereby N-(5-phosphoribosyl)-anthranilate (PRAN) is formed Fig. . WhereasE. coli, induced by the supply of different substrates and changes in substrate supply rates. Shikimate addition triggered strong deviations, especially in the biosynthesis pathway of chorismate and in L-tryptophan biosynthesis, which become visible in changes in intracellular metabolite concentrations. However, the influence of extracellular shikimate supply is not constrained to the biosynthesis of aromatic amino acids; it is also detectable in intracellular metabolite concentrations in the PPP and the glycolysis.These datasets reveal clear deflections throughout the central carbon metabolism of E. coli model used in this work was iJO1366, containing 2,583 reactions and 1805 metabolites [Extracellular fluxome and intracellular metabolite concentrations provide a large data volume, which allows quantifying the cells’ metabolic state and capacity during the different metabolic steady states achieved in the perturbation experiments. In general, measured extracellular rates enable the theoretic estimation of intracellular flux distributions within a genome-wide metabolic model with methods like flux balance and flux variability analysis. These computations were improved by taking thermodynamic constraints of the examined metabolic network into account, using thermodynamics-based flux analysis (TFA) , 14. Thuabolites . MaximisCDW−1 h−1 to 5 mmol gCDW−1 h−1. The lower heatmap comprises reactions of the PPP, chorismate biosynthesis (CHOR), by-product formation of L-phenylalanine and L-tyrosine (L-phe/L-tyr), as well as L-tryptophan biosynthesis (L-trp) with low rates from − 0.4 mmol gCDW−1 h−1 to 0.4 mmol gCDW−1 h−1.Figure Activities of PTS and glycerol degradation (glyk and gly3pdh) are predetermined by the primary carbon source, which was used for perturbation. Fluxes through PTS increase with higher glucose supply rates and are inactive when glycerol is used as the primary carbon source. With increasing glycerol uptake rates, fluxes in aerobic glycerol metabolism are enhanced. In the upper part of glycolysis, reactions between glucose-6-phosphate and DHAP are inverted into the gluconeogenetic direction, when glycerol is used as carbon source. Glycolytic fluxes downstream from glyceraldehyde 3-phosphate are estimated to be higher when glycerol is used as the primary substrate than with the steady states where glucose was consumed. Pyruvate kinase was only active at glycerol consumption. In cases where glucose was used as the primary substrate, phosphoenolpyruvate conversion is executed by PTS when glucose is taken up and at the same time converted into glucose-6-phosphate.CDW−1 h−1 to 0.91 mmol gCDW−1 h−1, and their estimated activity is mainly influenced by measured product biosynthesis, biomass formation and L-serine usage in other parts of the metabolism.Fluxes within L-serine biosynthesis vary from 0.21 mmol gThe metabolic reactions point in the same direction for all substrate combinations in the TCA cycle that were used for metabolic analysis. The computed activities within the TCA cycle rise with the increase of substrate uptake for each of the datasets. Good accordance was obtained between the reference state and the perturbation experiment with glycerol for reactions of glycolysis, glycerol metabolism, L-serine biosynthesis and the TCA cycle due to the high similarity in the measured (and therefore predetermined) uptake and production rates.Flux distributions within the PPP are afflicted with uncertainties due to redundancies and reversibility of several reactions. However, through restriction with measured rates and intracellular metabolite concentrations, varying flux directions are determined only for the reactions of transketolase (tkt1 and tkt2), ribulose 5-phosphate 3-epimerase (rpe), and transaldolase . Activities are estimated for the oxidative and the non-oxidative pathway of the PPP. Fluxes through PRPP synthetase (prpps) are mainly predetermined by the measured and prescribed L-tryptophan production rate.Likewise, the flux distributions in the aromatic amino acid biosynthesis are defined through the measured L-tryptophan-, L-phenylalanine and L-tyrosine production rates, which served as constraints for the estimations.CDW−1 h−1. In L-tryptophan biosynthesis, deviant fluxes were measured for the different steady states. However, an increase in substrate uptake rates did not lead to enhanced fluxes within L-tryptophan biosynthesis. Hypothetically assumed, that all the supplemented shikimate could be channelled to chorismate on top of the already synthesised chorismate from erythrose-4-phosphate and phosphoenolpyruvate, the product formation is estimated to be increased by 108% at the maximum. Comparing the results of the metabolic analysis with glycerol and of cells from the reference process, a high comparability between the two steady states was also found for the reactions of the PPP and the biosynthesis of aromatic amino acids.In chorismate biosynthesis, strong deviations appear due to the supplementary feeding of shikimate in two reactors. Fluxes in L-phenylalanine and L-tyrosine production remain below 0.04 mmol gThe estimated intracellular flux distributions reveal clear deflections throughout the metabolism originating from changes in substrate supply. Shikimate addition led to significant deviations, especially in chorismate biosynthesis. In all, 13 steady states were characterised by thermodynamics-based flux analysis, which was performed using the experimentally acquired intracellular and extracellular metabolome data.−1 [With pyTFA thermodynamic feasibility studies are directly coupled to flux variability analysis in the same computation approach. As a result, flux distributions are received, as well as thermodynamic profiles of the analysed steady states. The displacement from the thermodynamic equilibrium of reactions is defined by the Gibbs energies of reactions, which is crucial information for MCA. Reactions operating close to thermodynamic equilibrium can be distinguished by Gibbs energies close to zero, whereas reactions that operate far from equilibrium have Gibbs energies with an offset from zero of at least −10 kJ mol−1 . The fea−1 . Gibbs r−1 to − 5 kJ mol−1 were calculated for glycerol-3-phosphate dehydrogenase (gly3pdh). Canelas [−1 and − 14 kJ mol−1 for this enzyme. Glycerol kinase (glyk), fructose-1,6-bisphosphatase (fbp), pyruvate kinase (pyk) and pyruvate dehydrogenase (pdh) were classified as operating far from equilibrium, according to their highly negative Gibbs reaction energies. In the TCA cycle, only succinyl-CoA synthetase (sucoas) showed an invertible direction of the reaction. For the enzymes aconitase A and B (acont), fumarase (fum) and malate dehydrogenase (mdh), Gibbs reactions energies were estimated to be close to zero. Gibbs reaction energies close to zero were received in good accordance with the literature [Gibbs energies close to zero were obtained in glycolysis and glycerol metabolism for the reactions glucose-6-phosphate isomerase (pgi), fructose-bisphosphate aldolase (fba), triose-phosphate isomerase (tpi), glyceraldehyde-3-phosphate dehydrogenase (gapd), phosphoglycerate kinase (pgk), phosphoglycerate mutase (pgm) and enolase (eno), similar results were published by Kümmel et al. . Highly  Canelas  estimateterature , 65 in trG’ close to zero are not likely to be subject to active regulation as their responsiveness to minimal changes in their reactants is very sensitive [−1 were obtained for other reactions of the L-serine biosynthesis. Highly negative values for ΔrG’ were calculated for several reactions in the aromatic amino acid biosynthesis, indicating a strong metabolic regulation of these enzymes. Exceptional negative values were estimated for 3-deoxy -7-phosphoheptulonate synthase (dahpts), 3-dehydroquinate synthase (dhqs), chorismate synthase (chors), chorismate mutase (chorm), prephenate dehydratase (ppndh), anthranilate synthase (ans), anthranilate phosphoribosyltransferase (anprt), indole-3-glycerol-phosphate synthase (igps) and tryptophan synthase (trps2). Likewise, Henry [Reactions with Δensitive . Phosphoensitive . Gibbs ee, Henry  suggesterG’ < −10 kJ mol−1. Control coefficients were estimated via a linearisation approach by Visser and Heijnen [The data acquired during metabolic analysis with shikimate as additional perturbation substrate reveals detailed insights into the cells’ metabolism during L-tryptophan production from glycerol and provides important information for the modelling of intracellular flux distributions and for thermodynamic analysis of the metabolic network. Nonetheless, due to the complexity of the considered pathways, identifying targets for the genetic strain optimisation is challenging. Local and global coefficients, which mathematically represent metabolic connections, offer the possibility of quantifying the changes in steady-state fluxes and metabolite concentrations in response to perturbations and this enables the quantitative description of metabolic control . In this Heijnen , and theConsequently, values between −1.0 and 1.0 were obtained. Activating effects are represented by positive coefficients, inhibiting effects by negative values. In Fig. E. coli.Both enzymes of glycerol degradation (glyk and gly3pdh) have a negative control on fluxes of glycolysis, gluconeogenesis and glycerol metabolism, except for pgi. A positive control regarding fluxes of the PPP was estimated. This control appears due to the competing redistribution of carbon fluxes between PPP and upper glycolysis. Both chorismate and L-tryptophan biosynthesis are negatively controlled by glyk and gly3pdh, indicating that a higher substrate supply does not lead to enhanced fluxes in these pathways. This can be attributed to the measured decrease in L-tryptophan production rates, despite increasing substrate uptake rates. Possibly high DHAP concentrations trigger the activation of methylglyoxal-pathway when glycerol is used as substrate . This miIn the L-serine biosynthesis pathway, only phosphoserine phosphatase (psp_L) has controlling effects. Positive FCCs were received for the dephosphorylating enzyme towards all the reactions that are directly or indirectly connected with L-tryptophan biosynthesis, and highly negative FCCs were estimated for fluxes from chorismate to L-phenylalanine. The highest control was estimated for tryptophan synthase (trps2). Accordingly, an L-serine supply enhancement would lead to increased L-tryptophan production but decreased by-product synthesis. This contrary effect is linked to the L-serine consumption in L-tryptophan biosynthesis, where indole and L-serine are converted to L-tryptophan. L-serine is not used for L-phenylalanine biosynthesis, and through improving L-serine provision, fluxes starting from chorismate are shifted in the direction of L-tryptophan production while by-product formation is decreased. This result agrees with Tröndle .The common PPP, including the reactions glucose 6-phosphate dehydrogenase (g6pdh), phosphogluconate dehydrogenase (gnd), rpe, rpi, tkt1, tkt2 and tala does not exercise control on any flux in the considered metabolic network, which can be explained by their thermodynamic state that is characteristically close to equilibrium. In a continuation of the classic PPP, prpps catalyses the reaction, where PRPP is generated from R5P. A strong positive control of prpps for all fluxes involved in L-tryptophan biosynthesis was found, since PRPP acts as a direct precursor metabolite. In correspondence to this, L-phenylalanine biosynthesis from chorismate is negatively influenced by prpps. Here, the results also coincide with Tröndle .E. coli. Control of dhqs on L-tryptophan biosynthesis reaches down to trps3, while L-serine deficiency attenuates the effect on trps2.Among the enzymes participating in chorismate biosynthesis, only dhqs has control over reactions in the metabolic network, according to the MCA results. The Gibbs reaction energy of dhqs was estimated to be highly negative, indicating an active regulation of its enzymatic capacity and suggesting a controlling potential of the reaction. Even though dhqs was previously identified as a limitation in shikimate biosynthesis , 69, it Enzymes of the biosynthesis of the aromatic by-products have high positive control over themselves and slightly negative control towards fluxes producing L-tryptophan, due to the competitive consumption of chorismate of both branches. Since only very low L-phenylalanine and L-tyrosine formation rates and concentrations were measured, the controlling effect of these enzymes on L-tryptophan formation is relatively small. Presumably, a further downregulation of by-product formation will have little impact on productivity towards L-tryptophan.Enzymes participating in L-tryptophan production starting from chorismate have controlling effects on the central metabolism, but these enzymes mainly impact L-tryptophan formation itself. For anthranilate synthase (ans) a positive control was received for the PPP and chorismate synthesis on itself and the following flux of anprt, whereas a slightly negative control was estimated for fluxes of igps and trps3. The resulting positive control of ans on igps complies well with earlier findings of non-competitive feed-forward inhibition of igps by ANTH . The farAs is apparent from the results of MCA discussed above, metabolic control does not occur at one single point in the metabolism, but there is a distribution of FCCs over the whole considered network. Several enzymes are contributing to the overall control, and limitations arise at multiple sites in the metabolism. Kacser and Burns  firstly prs, serA, serB, aroF, aroB, trpC and trpB. MCA revealed a strong positive control of prpps on the entire metabolic network. The corresponding gene prs was expressed significantly higher before induction. Relative expression was at its minimum at the time point immediately after IPTG addition. Comparably low expressions were measured in both analysis reactors when glycerol was used for perturbation. In the analysis reactors where glucose was supplied as perturbation substrate, the gene expressions were more than twice as high as in the reactors with glycerol. In those cases where shikimate was added as secondary perturbation substrate, expression of the prs gene was slightly higher than without addition. Towards the end of the process, the relative expression increased so that it reached again the level measured at a process time of 28 h. Low expression of the prs gene during metabolic analysis correlate with the results from MCA. Possibly, a downregulation of prs during the production phase may lead to constraints in enzymatic capacity of prpps, which in turn limit PRPP supply for L-tryptophan biosynthesis.The results of MCA provided information on possible starting points for genetic strain optimisation. To further investigate the potential limitations in enzymatic capacity, a relative expression of selected genes of interest was analysed by RT-qPCR. Figure serB gene, was estimated to be a limiting step for L-tryptophan formation in the considered metabolic model. In contrast to serB gene, which is not overexpressed in the E. coli strain used, serA gene is already integrated chromosomally in extra copies. More precisely, the gene inserted, encodes for a feed-back-resistant phosphoglycerate-3-phosphate dehydrogenase (serAFBR) to circumvent inhibition by L-serine. A comparison of the relative expressions of serA and serB shows that whereas the relative expression of serA steadily increases, the serB expression dropped after induction and stagnated afterwards. The relative expression of serA during metabolic analysis was similarly high compared to the reference state. However, the relative expression was significantly increased in the analysis reactor with glycerol and shikimate as perturbation substrates. Relative expression of serB during metabolic analysis was slightly lower compared to the reference state. Unlike the expression of serA, the expression of serB dropped after induction and did not increase significantly until the end of the process. Possibly, the unbalanced expression of genes in L-serine biosynthesis pathway may lead to disparities between reaction fluxes and consequently, limitations occur. An increase in serB expression might compensate for these imbalances.High metabolic control on L-tryptophan production was also determined in L-serine supply. The enzyme psp_L, encoded by the aroB was identified as a limiting step for L-tryptophan production by MCA, even though the gene is overexpressed together with aroF and aroL on the plasmid in the E. coli strain used. Figure aroF and aroB comparison. An abrupt rise in the relative expression of aroF from 6.6 before induction to 556.8 immediately after IPTG addition was measured. During the continuing process, a constant relative expression of aroF was observed. In samples taken from the analysis reactors during metabolic analysis, the relative expression of aroF was lower than the reference state. Relative expression of aroB below 1 was observed until a process time of 45 h. After induction, the expression steadily increased until the end of the process, where a relative expression of 51.7 was reached. Similar relative expressions were measured in the sample from the reference process state and the analysis reactors. At the timepoint of metabolic analysis, the expression of aroF is 20-fold higher than the expression of aroB in the sample from the reference state. In the analysis reactors, the relative expression disparity varies from 13 to 26-fold between aroF and aroB. Although the relative expression of aroF reaches its maximum level immediately after induction, the upregulation of aroB lags behind and is weakened compared to aroF. The delayed rise in the relative expression of aroB might be the reason for emerging limitations within chorismate biosynthesis. The differences in the expressions are probably a consequence of the arrangement of the genes in the expression cassette on the plasmid, where aroF directly follows up the promotor.In chorismate biosynthesis pathway, trpB and trpC, respectively. The relative expressions are depicted in Fig. trpB is higher than that of trpC in both the cells from the production process and the cells from the analysis reactors. Due to the competing expression of many inducible genes after IPTG addition, the metabolic machinery of E. coli operates at full capacity, and this might be the reason for the downregulation of both genes after induction.Further limitations were estimated in L-tryptophan biosynthesis itself. The strongest control was found for trps2, and igps was also identified to slightly control L-tryptophan formation. The corresponding genes for these limiting enzymes are The application of thermodynamics-based flux analysis  enabled aroB and aroF genes, which are most likely responsible for the limitations occurring in chorismate biosynthesis. Therefore, the insertion of additional copies of aroB gene, in combination with the resolution of other limitations  might lead to improved productivity.Enhanced perturbation in chorismate biosynthesis induced by an external shikimate supply, clearly showed that dhqs holds positive control over reactions of chorismate- and L-tryptophan biosynthesis, reaching down to trps3. A previously unknown controlling enzyme was identified from the intensified deflections emerging from shikimic acid. Transcriptome analysis of the corresponding genes showed disparities in gene expression of the prs, trpC and trpB genes encoding the enzymes prpps, igps and trps2, which were estimated by MCA to limit L-tryptophan production, were downregulated after induction with IPTG. It seems that the expression of the inducible genes is boosted by induction with IPTG at the expense of the transcription of other genes. Consequently, overexpression of the prs, trpB and trpC limiting genes or the use of alternative strong promoters, will probably help to tackle the bottlenecks and reroute the carbon flow from by-product formation to L-tryptophan biosynthesis. Due to the close connection to energy metabolism via ATP dephosphorylation and PRPP’s contribution in salvage metabolism [Surprisingly, the expressions of the tabolism , geneticserA and serB genes, which might be the reason for unbalanced fluxes and consequential internal constraints in L-serine biosynthesis. Limitations could be avoided by overexpressing psp_L or by reducing competing reactions. The negative control of glycerol degradation (enzymes glyk and gly3pdh) on chorismate and L-tryptophan production might be attributed to the toxic intracellular accumulation of DHAP. Further research is necessary to analyse these limitations in detail and devise possible solutions.In L-serine biosynthesis, psp_L was estimated to control L-tryptophan formation during metabolic analysis. Results of the expression analysis revealed differing expression levels of the Supplementary file1 (PDF 64 KB)Below is the link to the electronic supplementary material.