Flavonoids are a structurally diverse class of natural products that have been found to have a range of beneficial activities in humans. However, the clinical utilisation of these molecules has been limited due to their low solubility, chemical stability, bioavailability and extensive intestinal metabolism in vivo. Recently, the view has been formed that site-specific modification of flavonoids by methylation and/or glycosylation, processes that occur in plants endogenously, can be used to improve and adapt their biophysical and pharmacokinetic properties. The traditional source of flavonoids and their modified forms is from plants and is limited due to the low amounts present in biomass, intrinsic to the nature of secondary metabolite biosynthesis. Access to greater amounts of flavonoids, and understanding of the impact of modifications, requires a rethink in terms of production, more specifically towards the adoption of plant biosynthetic pathways into ex planta synthesis approaches. Advances in synthetic biology and metabolic engineering, aided by protein engineering and machine learning methods, offer attractive and exciting avenues for ex planta flavonoid synthesis. This review seeks to explore the applications of synthetic biology towards the ex planta biosynthesis of flavonoids, and how the natural plant methylation and glycosylation pathways can be harnessed to produce modified flavonoids with more favourable biophysical and pharmacokinetic properties for clinical use. It is envisaged that the development of viable alternative production systems for the synthesis of flavonoids and their methylated and glycosylated forms will help facilitate their greater clinical application. Flavonoids are one the most abundant and broadly distributed families of biologically active plant natural products (PNP). Chemically, flavonoids have a C6-C3-C6 skeleton, with two phenyl aromatic rings (A and B) along with a heterocyclic ring (C-ring). Based on the substitution of the basic skeleton and B-ring attachment, flavonoids have been split into several subclasses, for example, flavanones, flavones, flavonols, flavan-3-ols, isoflavones, and isoflavanones  1]. Ove. Ove1]. The role of isoflavones as chemopreventive compounds has been well-established. A number of epidemiological studies, along with retrospective meta-analysis and prospective observational studies, have established that flavonoids possess anticancer activities ,3,4,5. ASeveral flavonoids have been analysed for their anticancer activities, both in vitro and in vivo, including daidzein, quercetin, silymarin, luteolin, kaempferol, and apigenin . These cInterest in flavonoids production is growing, which is reflected by their 16.5% projected CAGR, increasing the current market from USD 1.9 billion in 2019 to USD 3.5 billion by 2025 . AlthougMany pharmacological functions are associated with flavonoids; however, a number of problems are holding back their development as approved drugs for clinical use and, to some extent, further research studies . Some ofThe absorption and metabolism of flavonoids have been extensively studied in the last two decades. Generally, the PK profile  of flavonoids is not optimal and varies considerably across different classes . Flavono2 as well as excreted in the feces. On the other hand, the absorbed quercetin was also excreted out of the body within 48 h [Intestinal metabolism also affects the absorption of flavonoids via chemical reactions occurring in epithelial cells of the small intestine and/or mediated by the intestinal microbiota . Flavonohin 48 h . Quercethin 48 h . Furtherhin 48 h ,28,29,30Halia bara was optimum at a wavelength of 310 umolm−2s−1 and any variation in this wavelength results in reduced biosynthesis and bioactivity of flavonoids [Surprisingly, the PK profile of flavonoids is also compromised in planta due to environmental factors like light, temperature, oxygen exposure, pH and ultraviolet radiation. Light exposure, specifically UV light, can alter the biosynthesis of flavonoids in host plants. For example, the antioxidant activity of total flavonoids isolated from plant avonoids . As enviFlavonoids are present in very minute quantities (micro/milligram per kg of plant biomass) in plant hosts. Therefore, continuous extraction from plant sources, on the one hand, strains an already vulnerable agriculture sector and, on the other hand, might result in price hikes due to unstable supply and demand issues . FlavonoChemical stability, PK issues and low availability are significant obstacles to the clinical development of flavonoids as effective chemopreventive therapy, because the required in vivo level is almost impossible to achieve, even with high oral doses ,35. HoweThe chemical structure of flavonoids generally, and different substitutions of the basic skeleton specifically, define chemical stability and bioavailability. It is now widely accepted that the substitution of basic flavonoid skeleton has a strong influence on the absorption, distribution and metabolism of flavonoids ,37.S-adenosyl-L-methionine (SAM) through a biomolecular nuclear substitution (SN2) reaction [Methylation, the addition of a methyl group to a substrate, controls several important functions of cells, from gene regulation through epigenetics to maintaining cellular energy status . Dependireaction ,39. Catereaction .The role of methylation in improving the metabolic stability of flavonoids has been documented . O-methyBCRP-transduced human-leukemia K562 cells [Methylated derivatives of flavonoids usually show higher bioactivity, and the site as well as extent of methylation play an important role. B-ring methylation appears to have greater anti-cancer potential, where a study of over 30 different flavonoids showed that methylation of 4-C of the B-ring is linked with higher potency . For exa62 cells . This ph62 cells . Similar62 cells . The aut62 cells ,50. ThesGlycosylation affects physicochemical properties, immunogenicity, and PK/PD characteristics of chemical compounds ,52. The O-glucoside) were highly stable in their solutions under a range of storage conditions  [Flavonoid glycosides are generally soluble in water and alcohol; however, a few flavonoids, such as rutin and hesperidin, are sparingly soluble. Conversely, aglycans (non-glycosylated parent flavonoid) usually dissolve in non-polar solvents . As wellsolvent) . ImproveO-glucoside and the possible reason for this might be their better water-solubility and higher absorption [Anti-BCRP activity has also been observed for a few glycosylated flavonoids, for example, apigenin-7-glucoside and luteolin-4′-sorption . In anotsorption . Thus, gsorption ,58. On tO-glucoside having metabolites which are five times more available compared to the metabolites of quercetin-3-O-rutinoside [On the other hand, in some cases, glycosylated flavonoids tend to show more variable bioavailability than their aglycans ,60. Thistinoside . Similartinoside . TherefoGlycosylation reactions of flavonoids are carried out by enzymes like glycosyltransferases (GT) and glycosylhydrolases (GH), comprising GH13 and GH70 families . At presDue to the immense potential of flavonoid, chemical synthesis methods have been explored since the beginning of 20th century, aided by the rapid development of new protocols. In fact, the first synthetic flavonoid glycoside, anthocyanin, was synthesized in 1926 by Robertson and Robinson, which is a 3-O-Glycoside of anthocyanidins . Since tEscherichia coli and Saccharomyces cerevisiae [E. coli and S. cerevisiae was a huge breakthrough, paving the way to the use of microbial systems as an alternative synthetic platform for the synthesis of plant natural products [Flavonoid biosynthesis pathways, like other plant secondary metabolites, are very complex and operate under a tight multi-level regulation. The biosynthesis of particular flavonoids depends not only upon pathway-specific enzymes but also on their interaction with other, competing partners; therefore, the genetic engineering of native hosts is difficult ,67. Realrevisiae . The sucproducts . With reIn the following, recent developments in synthetic biology and metabolic engineering approaches are discussed, which will directly or indirectly help us to synthesize flavonoids (and derivatives) with an improved PK profile.Modern metabolic engineering approaches focus on the (re)construction of metabolic pathways in suitable microbial hosts . This caS. cerevisiae [E. coli [S. cerevisiae [Yarrowia lipolytica [Numerous impressive examples of microbial engineering to produce flavonoids in microbes have been reported in recent years . The de revisiae , and E. [E. coli  strains [E. coli , synthesrevisiae  and the polytica  are a fepolytica . ExpandiSynthetic biology efforts are mostly associated with living organisms; however, the rise in cell free systems as a new platform for synthetic biology has been seen in recent decades. Due to its inherent nature, cell-free metabolic engineering (CFME) provides an open reaction environment, can aid metabolic engineering in various ways, and has been adopted by the research community to probe metabolic pathways . Cell-frCell-free metabolic engineering is developing into a powerful approach to produce complex natural product biomolecules, and has successfully been used for the synthesis of flavonoids. Recently, Ying Zhang et al. demonstrated the in vitro biosynthesis of naringenin using a cell-free system, where they were able to produce 11.22 mg/L of product in a three-hour incubation . The repLeishmania cell-free lysate, is a significant progress towards the economic feasibility of CFME [Cell-free systems offer multiple advantages, and can sometimes can be used along with cell-based systems to synthesize complex molecules , optimiz of CFME .The limited availability and challenges in the synthesis of structurally homogenous glycosylated natural products has restricted our understanding of the glycosylation process as well as its applications in biotechnology. Unlike DNA and protein biosynthesis, glycosylation is not a template-driven process; instead, it is carried by a series of glycosylation reactions catalysed by specific glycosyltransferase (GT) enzymes, localized at different subcellular locations . The glyStreptomyces antibiotics and YjiC from many Bacillus species are most commonly used for cell-free glycosylation of small molecules, and these enzymes can accept a diverse set of NDP-sugars as glycosyl donors and have promiscuous substrate specificity [Bacillus licheniformis [Many enzyme cascades and biochemical pathways have been established in the cell-free format for the synthesis of natural products . As mentcificity ,99,100. niformis ,101. CelE. coli CFPS with self-assembled monolayers for matrix-assisted desorption/ionization (SAMDI) mass spectrometer and was used to investigate the enzyme’s substrate specificity using 3480 unique peptides and 13,903 unique reaction conditions, finally revealing the optimal glycosylation sequence [A novel mass spectrometry based high-throughput screening (MS-HTS) technique has been developed for the characterization of enzymes produced through cell-free protein synthesis (CFPS) lysate . The plasequence . Recentlsequence . This syThe selection of appropriate enzymes is an important step for the manipulation or construction of a metabolic pathway in native or heterologous host. The field of enzyme engineering has also made significant progress and enhanced the substrate scope, selectivity and activity and, quite interestingly, enzymes with non-natural activities have also been added to the tool box .E. coli strain and the synthesis of unnatural terpenoids was achieved [Enzyme engineering can help synthetic biology and metabolic engineering in two ways. By making the preset enzymes more efficient, enzyme engineering can help in the microbial biosynthesis of flavonoids and, through creating de novo enzymes, it can help in the synthesis of novel flavonoids. The development of artificial metalloenzymes (ArMs), specifically, P450 class enzymes, is a notable example. Recently, an ArM that contains an iridium porphyrin complex has been assembled in the terpene-producing achieved . The useEnzyme compartmentalization, to mimic nature’s systems of colocalization of enzymes in space, is an intensive area of research in protein engineering as it has multiple advantages ,107,108.In vitro prototyping and the rapid optimization of biosynthetic enzymes (iPROBE) is another interesting avenue that can help in the synthesis of novel flavonoid derivatives. There is no need for large-scale DNA assembly or the metabolic engineering of living cells, so many enzymes and enzyme combinations and cofactor requirements and optimal conditions can be tested in a short period of time . It can An exciting avenue for synthetic biology and metabolic engineering is the implementation of machine learning algorithms to help understand the metabolic and regulatory networks of host organisms to optimise existing pathways and develop new synthetic routes ,115.For modeling gene regulatory networks (GRN), the convolutional neural network (CNN) is a popular machine learning method due to the availability of relevant datasets, reliable prediction and excellent performance in learning unique features linked with biological sequences . RecentlThe reconstruction of a metabolic pathway in a heterologous host is always a difficult task, and is therefore aided by machine learning approaches ,122. RecFlavonoids are an important natural repository for drug discovery, but ready access is limited from the low yields in plants, and their use has been further constrained by stability, bioavailability, and pharmacokinetics in vivo. Synthetic biology and metabolic engineering offer solutions to both the low availability of flavonoids in plants, and their challenging in vivo pharmacokinetic properties. Using these methods, the synthesis of flavonoids and their methylated and/or glycosylated forms can be transferred into ex planta production, either using microorganisms or using cell-free biosynthesis. Additionally, the application of protein engineering techniques and machine-learning algorithms can also provide greater insights into ex planta flavonoid production and yield. A number of flavonoids were successfully produced using microbial hosts in recent years, demonstrating the utility of the approach.Key to the future of flavonoids in the clinical setting is the understanding and optimisation of their properties for improved efficacy, stability, bioavailability and pharmacokinetics. Recent advances in genome sequencing, coupled with bioinformatics, have provided ready access to data for the identification of endogenous plant methylation and glycosylation enzyme genes, which can then be integrated into ex planta flavonoid biosynthesis pathways. It is this integration of different flavonoid modification enzymes that it is envisaged will open the landscape for the optimisation of flavonoid biophysical and pharmacokinetic properties.The possible applications of synthetic biology are exciting and almost limitless, but to achieve this, greater insight into how microbial and cell-free systems function is required. Together with this, efforts to use the cheaper and more sustainable substrates such as agricultural leftovers and waste streams will significantly reduce the overall cost as well as potentially increase the productivity. The successful synthesis of flavonoids in a sufficient quantity for research and commercial space will prove groundbreaking for the development of flavonoids as effective drugs.


During photosynthesis, organisms respond to their energy demand and ensure the supply of energy and redox equivalents that sustain metabolism. Hence, the photosynthetic apparatus can, and in fact should, be treated as an integrated supply-demand system. Any imbalance in the energy produced and consumed can lead to adverse reactions, such as the production of reactive oxygen species (ROS). Reaction centres of both photosystems are known sites of ROS production. Here, we investigate in particular the central role of Photosystem I (PSI) in this tightly regulated system. Using a computational approach we have expanded a previously published mechanistic model of C3 photosynthesis by including ROS producing and scavenging reactions around PSI. These include two water to water reactions mediated by Plastid terminal oxidase (PTOX) and Mehler and the ascorbate-glutathione (ASC-GSH) cycle, as a main non-enzymatic antioxidant. We have used this model to predict flux distributions through alternative electron pathways under various environmental stress conditions by systematically varying light intensity and enzymatic activity of key reactions. In particular, we studied the link between ROS formation and activation of pathways around PSI as potential scavenging mechanisms. This work shines light on the role of alternative electron pathways in photosynthetic acclimation and investigates the effect of environmental perturbations on PSI activity in the context of metabolic productivity. Thereby a proton gradient is formed, which is used to drive the synthesis of ATP by the ATP synthase. The second part of the photosynthetic process is the Calvin-Benson-Bassham (CBB) cycle, regulated by the thioredoxinsystem   Asada,  and affe) Asada, .To fine-adjust the formation of ATP and NADPH in the PETC, alternative electron transport pathways evolved  cycles during photosynthesis  that clearly showed a high impact of the Sedoheptulose-bisphosphate enzyme (SBPase) on the ROS scavenging mechanism, CBB and the PETC. Finally, the role of the SBPase was further elucidated. With this scientific work, we formalised a connection between the CBB cycle, PETC, and ASC-GSH cycle. We showed the interconnection between these parts of photosynthesis and also shed light on the control each part has over others via mathematical modelling. We therefore expanded our understanding of the complex interplay between different acclimatory processes in photosynthesis and created a computational framework to stimulate future scientific efforts in this direction.2O2) by the superoxide dismutase (SOD). This implementation required changing the description of the PSI mechanism from the original model  is twice the simulated rate through PSII. Likewise, the rate of CEF is twice the rate mediated by FNR.−1 and inside the stroma in mM. To convert the concentrations of ATP, NADPH and H2O2 produced in the lumen to the unit of the stroma, where these metabolites are consumed/scavenged, we employ a conversion factor where 1 mmol (mol Chl)−1 corresponds to 3.2·10−5 M in the stroma  concentrations .We first investigate the steady-state behaviour of the model under various light intensities . In the trations . Neverth2O2 production rates are not affected, also the ratio of NADPH required for scavenging ROS is unaltered in the MDA reductase knock-down.A key enzyme in the ASC-GSH cycle is the MDA reductase, which reduces MDA back to ASC using NADPH as an electron donor . Interes2O2 levels. The reduced photosynthetic capacity of PGR5 mutant plants has been demonstrated experimentally  and low (PPFD 100 μmol m−2s−2) light conditions. A selection of flux and concentration control coefficients are depicted in The above investigations illustrate that electron flow around PSI apparently affects not only the PETC itself but also downstream metabolism, in particular carbon fixation. In order to understand which processes carry the strongest control in this complex supply-demand system, we performed MCA and systematically determined flux and concentration control coefficients for high (PPFD 1000 μmol m6f complex) predominantly increases the Mehler reaction. This behaviour changes dramatically under low light. Here, CBB enzymes exert almost no flux control, but electron transport and carbon fixation rates are mostly controlled by the activities of the photosystems. Increasing PSII leads to more reduced redox pools and lower ATP levels, whereas increasing PSI leads to more oxidised redox pools and higher ATP levels. Both photosystems have a positive control on CBB cycle intermediates RuBP and PGA, while only PSI positively affects the bisphosphates FBP and SBP. Altogether these analyses confirm the previous observation  under Germany's Excellence Strategy - EXC-2048/1 - project ID 390686111 (AM and OE), the Deutsche Forschungsgemeinschaft Research Grant MA 8103/1-1 (AM), the Deutsche Forschungsgemeinschaft (DFG), project ID 391465903/GRK 2466 (TN), and the EU's Horizon 2020 research and innovation programme under the Grant Agreement 862087 (MA).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.


Danio rerio) using 1H nuclear magnetic resonance (1H-NMR) spectroscopy. However, there is no significant information available regarding the characterization of organic molecules, and metabolites with SWCNT exposure. Noninvasive biofluid methods have improved our understanding of SWCNT metabolism in zebrafish in recent years. Here, we used targeted metabolomics to quantify a set of metabolites within biological systems. SWCNT at various concentrations was given to zebrafish, and the metabolites were extracted using two immiscible solvent systems, methanol and chloroform. Metabolomics profiling was used in association with univariate and multivariate data analysis to determine metabolomic phenotyping. The metabolites, malate, oxalacetate, phenylaniline, taurine, sn-glycero-3-phosphate, glycine, N-acetyl mate, lactate, ATP, AMP, valine, pyruvate, ADP, serine, niacinamide are significantly impacted. The metabolism of amino acids, energy and nucleotides are influenced by SWCNT which might indicate a disturbance in metabolic reaction networks. In conclusion, using high-throughput analytical methods, we provide a perspective of metabolic impacts and the underlying associated metabolic pathways.The present study aims to investigate the metabolic effects of single-walled carbon nanotubes (SWCNT) on zebrafish ( In the past few decades, the carbon nanotubes (CNT) have gained a great attention in biomedical applications that are observed as pipe-shaped atomic layers of carbon particles. It is made up of layer-by-layer orientation of graphite sheets . Single-The hydrophobic nature of CNT, as well as poor biocompatibility in solvent media, is its drawbacks. Furthermore, surface changes in materials, such as chemical alterations in CNT, may improve solubility in an aqueous environment . When wo50), the path of inhalations, and solvent materials played a lead role in toxicological responses (Tigriopus japonicas), frogs , rat, and mice to profile the toxicological responses and folic acid (FA) coated CNT are achieved higher anti-proliferative effects. The cytotoxicity is found to be associated with DOX and FA in MCF-7 cells . CNT sizesponses . Recentlresponses. In in vy levels .2−) in dose dependent manner  is used as a model organism. Zebrafish is a tiny tropical freshwater fish. Zebrafish are an important vertebrate model organism that is frequently used in toxicological research due to their ease of maintenance in laboratory conditions. , chloroform (CHCl3), sulfuric acid (H2SO4) and nitric acid (HNO3) were obtained from Carlo Reactifs . SWCNT , and sodium salt of 3-(trimethylsilyl) propionic-2,2,3,3-d4 acid (TSP-d4) were purchased from Sigma-Aldrich, United States. According to datasheet, carbon content (≥95%), color, black; appearance, the powder was confirmed by Thermogravimetric (TGA) investigation. Fish food was obtained from Tera . Deuterium water (D2O: 99.8% purity) was obtained from Cambridge Isotope Laboratories, United States. Food was purchased from Tera .Methanol  were obtained from a local dealer. The age ranges from 5 to 6 months. Zebrafish were maintained for more than two weeks in glass tanks, which contained a fresh 60 L dechlorinated water. Fish were fed 1.0% body weight twice daily with commercial food. All animals were standardized at 26.0 ± 1°C and maintained on 14:10 h light/dark for a reproductive cycle. All examinations were carried out with various dosages of SWCNT  for 72 h. Each experimental exposure contains eight fish  which contains equally in a treated tank. These concentrations were determined based on the previous publications (Zebrafish (ications . The feeThe metabolites were extracted according to previous reports . Briefly2-edited CPMG pulse sequence (RD-90°-[τ-180°-τ]n) was applied to the visualization of small molecules/metabolites. With relation to solvent peaks (TSP at 0 ppm), 1H-NMR chemical shifts were described in ppm (δ). The chemical shifts from −2 to 14 ppm were covered and the multiplicity stated as s, singlet; d, doublet; t, triplet; q, quartet; and m, multiplet. Heavy water acts as field frequency locking. The acquisition time, 3.0 s; relaxation delay, 1.0 s; width, 9,615.6 Hz; and 128 scans were acquired from each spectrum. Total time per sample was taken for 10 min.For temperature equilibration, the samples were kept for 10 min inside the NMR probe. NMR spectra were recorded on a 600 MHz spectrometer using one NMR probe. A water-suppressed t1H- spectral data.All Fourier transform spectra were manually corrected for phase and baseline distortions. TSP calibration at 0 ppm was processed. Mnova software  was used to visualize, process the 1D NMR data. To avoid statistical error, the water domain (δ 4.7–4.9 ppm) was removed. δ 0–10 ppm was uniformly divided as buckets that have 0.001 ppm. Further, binned data were exported to MetaboAnalyst software (v 5.0) for multivariate statistical analysis. Initially, to eradicate outlier data, principal component analysis (PCA) was analyzed (data not shown). Using supervised techniques, particle least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were applied to NMR data. From score data, each point represents a https://www.metaboanalyst.ca/) was used to analyze all of the samples. The newly aligned and filtered metabolite lists were then processed. The metabolic expression, metabolic networking, and biological interpretation were determined after a logarithmic transformation. GraphPad Prism eight software was used to perform statistical analysis. Numerical values were delivered as the mean ± SD. Two-way analysis of variance (ANOVA) has been used to examine multiple subjects. The significant changes of metabolites were recognized by p < 0.05.The data normalization (to avoid concentration differences among metabolites) and validation for score plot analysis was accomplished using the Pareto scaling algorithm, which measured each variable using the square root of its standard deviations. MetaboAnalyst 5.0 program  profiled and quantified. Mnova software was used to organize the spectral data. Before and after normalization with SWCNT exposed metabolites, Pareto Scaling-based Kernel density plots and Box plots analysis are shown in The chemical structure of SWCNT is shown in There was a strong distinction between the four classes , suggesting that zebrafish has a unique metabolic profile. The possible metabolites  are highlighted in the volcano map. A univariate OPLS-DA score plot of SWCNT-applied samples shows that they are widely separated from each other and starving fish  (controlPLS-DA scores were used to select the candidate quantified metabolites with a variable importance of projection (VIP) score >1 . FifteenR2 (0.8: 80%) and Q2 (0.4: 40%) revealed satisfactory goodness of fit and goodness prediction, respectively (2 indicates that a four-component model is the best (marked with a red star). R2 and Q2 values were indicating near 1 that was considered as robust score plots. The evidence of R2 and Q2 values from score plots indicate that the metabolic disconcertion was found in fish by SWCNT exposures.As shown in ectively . Q2 indiAdenosine phosphates are involved in energy hydrolysis in cells (ATP to ADP to AMP), and these phosphates serve as a caption of energy yield. AMP, ADP, and ATP are increased in low to high concentrations. These changes also perplexed the ratio of total adenosine phosphate groups, which could disrupt the zebrafish's equilibrium between energy production and ATP consumption. The AMP-activated protein kinase (AMPK) pathway is activated by metabolic changes in adenosine phosphate . In cellAnaerobic conditions can develop in zebrafish as a result of adenosine phosphate degradation. The metabolic disturbance caused by SWCNT exposures, which involves increased ADP, ATP, and decreased glucose, disrupts ATP intake in cellular environments, which serves as cellular energy. When the zebrafish was given large doses of SWCNT, the metabolic chemical reaction took place primarily via the oxygen-dependent pathway. ATP, ADP's primary metabolite, is formed during purine metabolism. As a result, these findings indicate that SWCNT disrupts purine metabolism.As shown in Metabolites of NAD-utilizing reactions such as oxaloacetate are found to be significantly increased. The NAD involved oxidation in TCA cycle intermediates such as acetyl CoA, alpha-ketoglutarate, and succinyl-CoA can perplex the ATP energy production and metabolic pathway process. Pyruvate, oxaloacetate, NADPH, and NAD + have significantly increased which may lead to an intra and extracellular oxidative chemical reaction. Furthermore, upregulated NADPH/NAD + ratio has been perplexed and lead to ROS development which could affect the oxidative chemical reaction in lipid metabolisms, glycolysis, and galactose metabolism. As shown in According to the latest updates, carbon nanoparticles and carbon nanotubes (CNTs) uptake causes oxidative stress. The internalization of nanoparticles and their aggregation in cells have been explored . The accAs shown in pareto-scaling, normalization, and trans-formation, the metabolomic dataset was deferred. Kernel density estimation, which used to estimate the probability density functions. This analysis may yield the practical applications of probability distributions.This is the first study to use a zebrafish model to evaluate SWCNT's effects on metabolites and determine if those metabolites are upregulated or downregulated in their metabolism. The density plots that before normalization (left side), after normalization (right side) are delivered based on all the metabolites datasets. These box plots defined the distribution of each variable or metabolite concentrations . According to R2 and Q2 values are reported, which decreases as the score plot becomes robust . We first analysed the metabolites and associated metabolic pathways in zebrafish metabolisms with SWCNT exposures. The detection of top fifteen metabolites such as malate, oxalacetate, phenylaniline, taurine, sn-glycero-3-phosphate, glycine, N-acetyl mate, lactate, AMP, ADP, ATP, valine, pyruvate, serine, and niacinamide has been significantly impacted by SWCNT exposures. Here, an asparagine, glycine, histidine, malate, and oxalacetate are increased from control zebrafish. The TCA metabolites, malate, fumarate and oxalacetate contribute to the production of energy  exhibited clear separation between the groups. The f energy .1H-edited NMR-detected fish metabolites.The amino acid of histidine is required for protein biogenesis. These metabolites were then selected as prognostic biomarkers through differential expression, survival, and aquatic vertebrate model analysis. Many studies have delivered new therapeutic metabolites for disease prevention, and toxicity with limited success in aquatic zebrafish model . We tookWhen analyse each metabolite, we found a pattern of changes in the metabolic intricate of zebrafish. The most remarkable alteration found in our analysis was reduction in the abundance of metabolites that contribute directly or indirectly to metabolic reprograming in zebrafish . InteresRemarkably, we found that SWCNT induced major metabolic pathways in zebrafish metabolism and are quantified by MESA investigations. The creatine and creatine phosphate are modified that may fail to serve as energy in cell. Here, purine metabolism might be disconnected from energy metabolisms such as Krebs cycle and glycolysis . The enrAccording to these data, we quantified a set of metabolic pathways, glycolysis/gluconeogenesis, primary bile acid biosynthesis, phenylalanine metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, taurine and hypotaurine metabolism, ether lipid metabolism, citrate cycle might play important role in 10 mg/L of SWCNT exposure in zebrafish. The cysteine and methionine metabolism, ether lipid metabolism, pantothenate and CoA biosynthesis, taurine and hypotaurine metabolism, primary bile acid biosynthesis, glycine, serine and threonine metabolism are modified in 50 mg/L of SWCNT treatment in zebrafish. Finaly, we extended the quantification of metabolic pathway in 100 mg/L of SWCNT treatment. Purine metabolism, glycolysis/gluconeogenesis, taurine and hypotaurine metabolism, citrate cycle and primary bile acid biosynthesis are impacted.1H-NMR technique, SWCNT exposure caused significant phenotypic changes in metabolites. 1H-NMR offers significant benefits to the metabolomics field via the detection of metabolites integral to critical cellular metabolism and cell signaling. Malate, oxalacetate, phenylaniline, taurine, sn-glycero-3-phosphate, glycine, N-acetyl mate, lactate, ATP, AMP, valine, pyruvate, ADP, serine, niacinamide are significantly impacted by SWCNT. We believe that these metabolome alterations and metabolic pathways in zebrafish, such as energy, amino acid, and nucleotide metabolisms, may contribute in the development of future treatments that target diseases, especially as SWCNT exposure becomes more important as a pharmacological target. However, more investigations are necessary to better understand the SWCNT exposure and underlying mechanisms.This study convincingly demonstrated the impacts of metabolites caused by SWCNT exposure in zebrafish for the first time at the omics scale. According to our findings based on


Mycobacterium tuberculosis (Mtb) to successfully sustain a tuberculosis infection. However, the metabolic plasticity of this pathogen and the complexity of the metabolic networks present a major obstacle in identifying those nodes most amenable to therapeutic interventions. It is therefore critical that we define the metabolic phenotypes of Mtb in different conditions. We applied metabolic flux analysis using stable isotopes and lipid fingerprinting to investigate the metabolic network of Mtb growing slowly in our steady‐state chemostat system. We demonstrate that Mtb efficiently co‐metabolises either cholesterol or glycerol, in combination with two‐carbon generating substrates without any compartmentalisation of metabolism. We discovered that partitioning of flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle is the critical metabolic nodes which underlie the nutritional flexibility of Mtb. These findings provide novel insights into the metabolic architecture that affords adaptability of bacteria to divergent carbon substrates and expand our fundamental knowledge about the methyl citrate cycle and the glyoxylate shunt.The co‐catabolism of multiple host‐derived carbon substrates is required by  Mycobacterium tuberculosis growing slowly in a steady state chemostat system.Quantitative metabolic analysis using stable isotopes, lipid fingerprinting, and mathematical modelling are applied to investigate the metabolic network of  Mycobacterium tuberculosis (Mtb) is the causative agent of a global tuberculosis (TB) pandemic which has now reached staggering levels making Mtb once again a leading cause of death globally  cycle, pentose phosphate pathway (PPP) and Embden–Meyerhof–Parnas (EMP) pathway, as well as enzymes providing a metabolic link between glycolysis and the TCA cycle  at different growth rates in carbon limited conditions , which is an essential gene for the conversion of glycerol to glycerol 3‐phosphate   =  69 h). We adopted this dilution rate as we have previously demonstrated that the transcriptional response at this growth rate has many similarities to the transcriptional response characteristic of the adaptation of Mtb to the macrophage environment, and others have shown a similar profile from Mtb isolated from sputum . Samples were taken every volume change for a total of four volume changes to ensure that an isotopic stationary state was attained. Labelling of proteinogenic amino acids and intracellular metabolites was measured using gas chromatography–mass spectrometry (GC‐MS) or liquid chromatography–mass spectrometry (LC‐MS) as previously described , pyruvate (PYR) and the pyruvate‐derived amino acids , valine (VAL) and leucine (LEU)) had ≥ 50% unlabelled carbon indicating that the carbon backbone of these metabolites was predominantly derived from unlabelled cholesterol. This is expected as cholesterol enters central carbon metabolism as succinate and pyruvate. Canonical 13C isotopologue analysis of proteinogenic amino acids. As expected, the profiles of all metabolites derived from CHL‐ACE were different to that derived from GLY‐OLA grown Mtb  and pyruvate carboxylase (PCA) with high confidence so the summed flux is shown between pyruvate and malate/oxaloacetate , indicating that the other 13C label has been lost as CO2 via the TCA cycle consistent with the metabolic flux prediction that Mtb lacks PPDK activity , in either condition. There was flux towards pyruvate mediated by pyruvate kinase (PYK = EMP R6) and this was higher on GLY/OAA than with CHL/ACE Fig . We prevurce Fig  This expvity Fig .et al,We observed only a very low gluconeogenic flux Fig  during get al,The MCC in mycobacteria is required for the utilisation of the propionyl‐CoA derived from sterols, branched chain amino and fatty acids that are acquired from the host Fig . Here, w13C acetate into SL‐1 or an increased chain length due to the incorporation of carbon from unlabelled cholesterol, as predicted by our flux profiles, we also measured the lipid fingerprint of control samples derived from the same chemostat cultures but prior to attaining an isotopic steady state. In accordance with our metabolic flux predictions, we identified an increase in chain length in SL‐1 in these unlabelled control samples which confirmed our expectations that lipids derived from CHL‐ACE‐grown Mtb had significant differences in the composition of the methylmalonyl‐CoA‐derived SL‐1s, specifically an increase in chain length and abundance when compared to GLY‐OLA grown Mtb  significant amounts of unlabelled glutamate which Mtb can use as both a nitrogen and a carbon source  are unable to grow in the presence of cholesterol because they cannot metabolise propionate due to the role of this enzyme in the MCC  complements for the absence of the MCC pathway 13C MFA, conditions in which the glyoxylate shunt would not be expected to be active  was grown in a 2 litre bioreactor (Adaptive Biosystem Voyager) under aerobic conditions and at pH 6.6 as previously described . Culture samples were withdrawn from the chemostat to monitor cellular dry weight, viable counts, optical density and nutrient utilisation, and CO2 and O2 levels were measured in the exhaust gas  100% U‐[13C2] acetate and unlabelled cholesterol, or (ii) 30% U‐[13C3] glycerol and unlabelled Tween 80 were used as carbon substrates in Roisin’s minimal media. To confirm isotopic steady state, cultures were withdrawn at regular intervals and the amount of 13C label incorporation was measured using gas chromatography–mass spectrometry (GC‐MS) analysis for every volume change.Once metabolic steady state was reached, 13C2] cholesterol as the sole carbon source until mid‐log phase (OD600 = 0.6–0.8) before being harvested to analyse isotopologue incorporation into intracellular metabolites. To evaluate the incorporation of 2H into the amino acids of Mtb, deuterated cholesterol (0.0625 g/l) was used as the single carbon source in Roisin’s media. Deuterated cholesterol was prepared using a recombinant Pichia pastoris strain CBS7435 Δhis4Δku70 Δerg5::pPpGAP‐ZeocinTM‐[DHCR7] Δerg6::pGAP‐G418[DHCR24] (54). Cultures were adapted to growth in deuterated minimal in the Deuteration Laboratory (D‐Lab) within the Life Sciences of the Institut Laue‐Langevin  + ion. To obtain further information relating to the 13C incorporation at individual positions within individual amino acids, isotopologue abundances were also determined for fragments formed by cleavage of the C1–C2 bond in some samples. Within a subset of these, isotopologue abundances for the fragment formed by cleavage of the C2–C3 bond in serine were also determined allowing comprehensive characterisation of the isotopic incorporation into serine. Mass spectra of the derivatised amino acids were corrected for the natural abundance of all stable isotopes  fitted with a DB‐5ms capillary column  and deactivated quartz wool packed FocusLiner (SGE) coupled to a Pegasus III time‐of‐flight (TOF) mass spectrometer (Leco) equipped with a splitless injector. The injector temperature was initially held at 70°C for 2 min followed by heating to 350°C at a rate of 17°C/min. This temperature was held for 1.5 min. The He Carrier flow was held constant at 1.4 ml/min. The injection volume was 5 µl, and inlet temperature was 250 °C, interface temperature at 310 °C and source temperature at 245°C. The system was operated with a mass range of 40–800 For the detection of intracellular metabolites, cultures were quenched in a solution of 60% methanol and 0.9% NaCl at −20°C. The metabolites were extracted in a pre‐chilled (−20°C) solution of methanol:chloroform (1:1), and cells were lysed twice by bead beating in the FastPrep at 6.5 for 20 s with careful cooling in between. Soluble extracts were filtered twice and then stored at −80°C until analysis by LC‐MS.13C isotopomer profiling of organic acids and sugar phosphates were separated using a synergy hydro C18 reversed phase column on an Agilent 1200 chromatography system . Free amino acids were separated using a Luna SCX cation exchange column on a JASCO HPLC system. In both cases, the HPLC outlets were coupled to an API 4000  mass spectrometer equipped with a TurboIon spray source. The separation methods, optimal MS parameter settings as well as extraction and integration of ion chromatograms as described  matrix in chloroform/methanol (CHCl3/MeOH) 90:10 v/v. The mycobacterial sample and matrix were mixed directly on the target by pipetting and allowed to dry under a stream of air. MALDI‐TOF MS analysis was performed on a 4800 Proteomics Analyzer (Applied Biosystems) using the reflectron mode. Samples were analysed by operating at 20 kV in the negative ion mode using an extraction delay time set at 20 ns.et al,13C2] acetate for CHL‐ACE and 30% [U‐13C3] glycerol for GLY‐OLA. Flux values for net and exchange rates were derived from 86 independent flux parameters that are estimated using 108 labelling measurements for CHL‐ACE and 120 for GLY‐OLA, respectively (supplementary data file S1). Substrate consumption rates for cholesterol, acetate, glycerol and Tween 80 and overall CO2 net production rate (Table Metabolic modelling was conducted using INCA, version 1.7 Young, . An exteet al,Flux estimations were performed using INCA Young,  using noKB designed and conducted experiments, analysed data and wrote the manuscript. TM analysed data and wrote the manuscript. NDH conducted the experiments. JLW and MHB analysed data. GLM conducted experiments and analysed data. AB wrote the manuscript. MM, MH, GS, HP and VTF synthesised the deuterated hydrogen and designed experiments. SN analysed data. CWG analysed data. JM obtained funding and wrote manuscript. DJVB designed the experiment, obtained funding, analysed data and wrote manuscript.The authors declare that they have no conflict of interest.Expanded View Figures PDFClick here for additional data file.Table EV1Click here for additional data file.Table EV2Click here for additional data file.Table EV3Click here for additional data file.Review Process FileClick here for additional data file.


B. subtilis, a “generally recognized as safe” organism that is highly tolerant to fermentation products, is an ideal platform microorganism to engineer the pathways for the production of valuable bio-based chemicals, but it has never been engineered to improve 2,3-BD production from glycerol. In this study, we aimed to enhance 2,3-BD production from glycerol in B. subtilis through in silico analysis. Genome-scale metabolic model (GSM) simulations was used to design and develop the metabolic pathways of B. subtilis. Flux balance analysis (FBA) simulation was used to evaluate the effects of step-by-step gene knockouts to improve 2,3-BD production from glycerol in B. subtilis.Glycerol is a desirable alternative substrate for 2,3-butanediol  production for sustainable development in biotechnological industries and non-food competitive feedstock. B. subtilis was bioengineered to enhance 2,3-BD production from glycerol using FBA in a published GSM model of B. subtilis, iYO844. Four genes, ackA, pta, lctE, and mmgA, were knocked out step by step, and the effects thereof on 2,3-BD production were evaluated. While knockout of ackA and pta had no effect on 2,3-BD production, lctE knockout led to a substantial increase in 2,3-BD production. Moreover, 2,3-BD production was improved by mmgA knockout, which had never been investigated. In addition, comparisons between in silico simulations and fermentation profiles of all B. subtilis strains are presented in this study.The strategy developed in this study, using in silico FBA combined with experimental validation, can be used to optimize metabolic pathways for enhanced 2,3-BD production from glycerol. It is expected to provide a novel platform for the bioengineering of strains to enhance the bioconversion of glycerol into other highly valuable chemical products.The online version contains supplementary material available at 10.1186/s12934-021-01688-y. Biodiesel is a liquid biofuel derived from natural fats through a chemical reaction process (esterification or transesterification). Biodiesel has advantages over fossil diesel in terms of health and environmental concerns, such as lower sulfur and carbon dioxide emissions, which may contribute to a reduction in global warming. In 2019, 24% of the global carbon dioxide emissions from fossil fuel consumption were related to the transport section, and approximately 75% of the total carbon dioxide emissions from this section was from commercial vehicles [Biodiesel production generates large amounts of crude glycerol as a by-product; every ton of biodiesel produced generates approximately 100 kg of crude glycerol. Despite the increasing purification capacity and the development of new applications, biodiesel production has been associated with increased glycerol generation over the past few decades. Hejna et al.  reportedCurrently, sugar, starch, and molasses are the major raw materials for commercial biological products, such as proteins and biochemical monomers. Recent studies have focused on the use of renewable and non-food competitive materials as alternative resources; glycerol is a promising substrate for use as a renewable and low-cost substrate for biological production platforms.-2,3-Butanediol  is a valuable bio-based chemical compound that has a wide range of potential applications, such as in the manufacturing of printing inks, antifreeze agents, foodstuffs, chemicals, and pharmaceuticals . In addiBacillus subtilis, a “generally recognized as safe” organism, is an ideal platform organism for engineering bacterial pathways for the biological production of various useful metabolites, such as lactate, acetate, acetoin, and 2,3-BD [B. subtilis is a superior host for bioproduction as compared to Escherichia coli because it is more tolerant to fermentation products, such as butanol and ethyl acetate [E. coli and other gram-negative bacteria [Among industrial microbes, d 2,3-BD , 10. B.  acetate , and lacbacteria .B. subtilis. Thus, in silico simulation with FBA is available for efficient metabolic pathway design of B. subtilis for glycerol assimilation and 2,3-BD production in a complex metabolic system. In preliminary experiments, we found that the B. subtilis model described by Oh et al. [Bioinformatics tools have become increasingly attractive. Various techniques have been used to simulate and analyze biochemical systems. In silico genome-scale metabolic models (GSMs), which can be used to evaluate all metabolic reactions in a cell, are formulated on the basis of genome annotation data and experimental data. GSMs computationally describe gene-protein-reaction associations for a whole set of metabolic genes in an organism and can be used to simulate metabolic fluxes . GSMs cah et al. , iYO844 B. subtilis has focused mostly on improving the production of 2,3-BD from sugars such as glucose [Klebsiella oxytoca [Klebsiella pneumoniae [Bacillus amyloliquefaciens [B. subtilis to improve 2,3-BD production from glycerol represents an alternative for applications in biorefineries. The iBsu1147 metabolic network model of B. subtilis has been used to generate an in silico metabolic engineering design of reactions to increase the production of 2,3-BD from glucose [B. subtilis and evaluate the effects of step-by-step gene knockouts by validating the in silico design with experimental data.Previous bioengineering research on  glucose –19, not  oxytoca , Klebsieeumoniae , and Bacefaciens . Bioengi glucose ; howeverB. subtilis using in silico analysis to identify gene knockout targets in order to improve the metabolic pathway. A prototype of the metabolic pathways of B. subtilis for 2,3-BD production from glycerol was designed using the iYO844 model, considering growth in M9 medium. Changes in the bio-fermentation profiles caused by four gene knockouts are presented, and fermentation products were periodically collected and analyzed. Furthermore, the in silico analysis results were compared with experimental fermentation profiles.The aim of this study was to enhance 2,3-BD production from glycerol in B. subtilis model iYO844 [. Briefly, the environmental conditions in the iYO844 model were modified to the use of M9 medium with glycerol and an oxygen consumption rate of 10 mmol/gCDW/h, and biomass flux was the objective function. The regulation of glycerol uptake and metabolism in the model was optimized using the glycerol kinase (glpK) pathway . Fluxes of other volatile fatty acids (VFAs) were not observed.We modified and optimized the l iYO844 , which iway see “. The priackA) and phosphate acetyltransferase (encoded by pta) reactions, were set to zero   increased from 0 to 1.86 mmol/gCDW/h, and the fluxes of other fermentation products were not affected. The results from this simulation showed that inactive ACKr and PTAr reactions did not promote or contribute to 2,3-BD production.To test the inhibitory effect of acetic acid production, the metabolic fluxes of the ACKr and PTAr reactions, which refer to the acetate kinase  deletion , whereas the LDH_L reaction flux became zero. The flux of acetoacetate production decreased to 1.5 mmol/gCDW/h. Interestingly, 2,3-BD production became a major by-product (1.43 mmol/gCDW/h), whereas the production of other VFAs remained zero. The results of this simulation clearly showed that suspension of the LDH_L reaction can enhance 2,3-BD production from glycerol in B. subtilis.Next, we inactivated the LDH_L reaction to evaluate the effect of ion Fig. C. The remmgA, was a suitable target for inactivation , and the metabolic fluxes of the OCOAT1 and ACACT1 reactions became zero. As expected, the PDH flux was reduced by about half, and 2,3-BD production increased to 2.54 mmol/gCDW/h. Importantly, 2,3-BD was the only major product at this stage, whereas other products, such as ethanol, diacetyl, and propionic acid, were not detected. In the next phase of our study, the order of the simulations was applied to step-by-step gene modification in B. subtilis to validate the hypothesis from these simulations.From the simulation results of the three inactivated reactions, we inferred that the major fermentation products were acetoacetate and 2,3-BD. Since the target of this study was 2,3-BD production alone, we had to eliminate the metabolic flux of acetoacetate production. The conversion of pyruvate into acetoacetate involves three reactions catalyzed by pyruvate dehydrogenase (PDH), acetyl-CoA C-acetyltransferase ACACT1), and 3-oxoacid CoA-transferase (OCOAT1). The PDH reaction is an essential metabolic reaction that cannot be disabled, and the OCOAT1 reaction involves multiple genes, including BSU38990 and BSU38980. Therefore, the reaction catalyzed by ACACT1, encoded by , and 3-oB. subtilis GSM (iBsu1147) and flux variability analysis simulations to identify target reactions of genes to overexpress or underexpress to allow 2,3-BD production from glucose. The micro-aerobic condition was applied in the simulation (0.0005 mmol/gCDW/h), referring to the constrained oxygen condition at 0.5% saturated dissolution. Candidate reactions were predicted by using the flux variability analysis. The simulation results suggested that the first four genes to underexpress were pyc (encoding pyruvate carboxylase), ytiB or yvdA (encoding putative carbonic anhydrase), ndk (encoding nucleoside diphosphate kinase), and gmk (encoding guanylate kinase). These four genes are entirely different from the target genes and not related to those of the simulation for gene deletion in this study. Also, the biomass flux was zero when FBA simulation was conducted with knockout of these four genes in the iYO844 model. The difference in the prediction results may be due to the use of different models, environmental conditions, and prediction methods.Hao et al.  used a BB. subtilis step-by-step knockout strains  were performed to validate the predictions based on the GSM simulation. The growth, glycerol utilization, and fermentation characteristics of each strain are shown in Fig. –1, and the glycerol consumption rate was 7.26 mmol/gCDW/h. The fermentation profile of W168  as mentioned in the previous section, we found that acetic acid can also be produced by N-acetylglucosamine-6-phosphate deacetylase  and cysteine synthase , which correspond to metabolic reaction of B. subtilis 168 in the Kyoto Encyclopedia of Genes and Genomes . Moreover, there is an indirect pathway of B. subtilis 168 in which pyruvate is converted into acetic acid via pyruvate oxidase  and acylphosphatase .Figure u et al.  reported–1), but the glycerol consumption rate was higher (8.7 mmol/gCDW/h). In addition, some VFAs were produced at slightly lower final concentrations than by W168, which may be explained by the decrease in glycerol consumption after the exponential phase. These results indicated that knockout of ackA and pta did not improve 2,3-BD production  reaction might compete with the -butanediol dehydrogenase  reaction for NADH, which is mainly produced by glycerol-3-phosphate dehydrogenase  and glyceraldehyde-3-phosphate dehydrogenase . Hence, the deletion of lctE eliminates the NADH competitive reaction, which results in an increase in 2,3-BD production from glycerol. In addition, lctE deletion has been reported to increase the production of 2,3-BD in other platforms, including B. subtilis [K. oxytoca [Enterobacter cloacae [K. pneumoniae [lctE deletion can be regarded as key in improving 2,3-BD production in many microorganisms.When considering the metabolic pathway of glycerol dissimulation to 2,3-BD, we found that the L-lactate dehydrogenase , is involved in the conversion of acetyl-CoA into acetoacetyl-CoA, which is a precursor of acetoacetate production for pyruvate and butanoate metabolism. Thus, mmgA deletion leads to the suppression of the metabolic flux from pyruvate to acetyl-CoA, which may contribute to an increase in the fluxes associated with 2,3-BD production. We conducted a micro-aerobic fermentation experiment to ensure the promotive effect of mmgA knockout on 2,3-BD production from glycerol. Under the same conditions, W168∆3 produced 1.93 ± 0.10 g/L 2,3-BD, whereas W168∆4 produced 2.13 ± 0.05 g/L after 96 h of incubation. This result confirmed that mmgA knockout contributed to the increase in 2,3-BD production.Remarkably, B. subtilis and other microorganisms [The theoretical yield of the conversion of glycerol into 2,3-BD is 0.5 mol/mol glycerol . The 2,3rganisms , 28, 29.–1) and glycerol consumption rate  derived from the experimental data of the B. subtilis strains were applied in the in silico FBA simulation to determine the oxygen consumption rate , AHSERL3 (cysteine synthase), and ALDD2x , were zero. Moreover, other reactions involved in acetic production, such as those catalyzed by pyruvate oxidase  and acylphosphatase , are not represented in this model. While we found that many reactions are missing the iYO844, this model provided the most reliable result of biomass production from glycerol, as mentioned above. Therefore, further development and improvement of the B. subtilis model are necessary to allow for more accurate in silico simulations in future. To date, substantial research has focused on reconstructing GSMs of B. subtilis as a representative model for other gram-positive bacteria [In the FBA, the highest acetic production rate was obtained for the W168 strain, which corresponded to the experimental result of acetic production of W168 during the exponential phase. However, in the FBA, after inactivation of the ACKr and PTAr reactions, the acetic production rate became 0.09 mmol/gCDW/h, which was not in line with the experimental findings after bacteria .–1), whereas the glycerol consumption rates were higher (8.7 mmol/gCDW/h). However, the final concentrations of some of the major VFAs were slightly lower for W168∆2 than for W168, which may have been due to the decrease in glycerol consumption after the exponential phase. For ackA and pta knockout in W168∆2, the FBA results predicted that the fluxes of 2,3-BD production were zero. Indeed, knockout of these two genes did not affect 2,3-BD production  was knocked out in previous studies in order to enhance 2,3-BD production in B. subtilis [B. subtilis W168 in M9 medium containing glucose or glycerol did not indicate ethanol to be a significant product. Therefore, knockout of acoA was not required. This demonstrates that the use of in silico simulation can save time, energy, labor, and costs when compared to trial-and-error experiments.We found that subtilis , 24. HowB. subtilis strains was substantially higher than that of the wild-type strain. The promotive effect of mmgA knockout on 2,3-BD production from glycerol, which was predicted based on the FBA simulation results, was demonstrated for the first time in this study.Although the FBA results were not exactly the same as the experimental results, the guidance of gene knockout based on in silico simulations was very accurate, and the trends in the simulation results were consistent with the experimental results. 2,3-BD production from glycerol by the modified B. subtilis was applied to design and develop the metabolic pathways. FBA results provided precise target reactions to guide gene knockouts to improve the metabolic pathway for 2,3-BD production from glycerol. Furthermore, the use of in silico simulation of FBA could provide a novel platform solution for the bioengineering of B. subtilis to enhance 2,3-BD production from glycerol, which was the guidance of mmgA knockout. Additionally, the prediction results can save time and costs from unnecessary gene knockouts. The methodology of this study can be applied to increase other target fermentation products through the use of FBA in GSMs of other microorganisms. We are currently focusing on the development of GSMs and prediction strategies to provide better simulation results in future studies. The experimental knowledge and updated information derived from these experiments can be used to develop accurate and efficient models and predictions.Glycerol is a renewable and low-cost substrate that has potential as a raw material for industrial biological production processes. 2,3-BD is a valuable bio-based chemical compound for a wide range of potential applications and was selected as the target product in this study. The iYO844 genome-scale metabolic model of B. subtilis wild-type 168 (W168), was obtained from the Microbe Division at the RIKEN BioResource Research Center, Japan. We used the CRISPR-Cas9 system for gene knockout in B. subtilis [Escherichia coli NovaBlue .The strains and plasmids used in this study are listed in Table subtilis . PlasmidackA gene knockout (pJOE_ackA), plasmid pJOE8999 was cut at the BsaI restriction site and purified by gel purification . The primers used in this study are listed in Table BsaI sites (P_ackA01 and P_ackA02 primers) was introduced into pJOE8999 by polymerase chain reaction (PCR) using KOD FX Neo DNA polymerase . The PCR product was digested with DpnI restriction enzyme to remove the methylated DNA templates and gel-purified. Plasmid pJOE8999 containing a 20-nucleotide sgRNA for ackA deletion was cut using the SfiI restriction enzyme and purified. Meanwhile, two homology arms for ackA deletion were PCR-amplified from B. subtilis W168 chromosomal DNA using the P_ackA03 and P_ackA04, and P_ackA05 and P_ackA06 primers. After PCR product purification, these two PCR fragments were combined by overlap extension PCR using the P_ackA03 and P_ackA06 primers. The overlap extension PCR fragment was integrated into the SfiI site of the vector using NEBuilder HiFi DNA Assembly Master Mix , and transformed into E. coli NovaBlue for plasmid construction and replication. Transformants were selected on 25 μg/mL kanamycin in Luria–Bertani (LB) medium. pJOE_pta, pJOE_lctE, and pJOE_mmgA were constructed using the same protocol as that used for pJOE_ackA plasmid construction.For plasmid construction for B. subtilis W168 by natural transformation [cas9 under the control of PmamP. The plate was incubated at 30 °C for 2 days, and colonies were selected and transferred to LB agar without antibiotics and incubated at 50 °C overnight. The replication of pJOE8999 derivatives was temperature-sensitive, so the plasmid was removed during this process. The colonies were streaked on LB agar and incubated at 42 °C to obtain single colonies. The colonies were checked for target gene deletion by PCR using the above primers; for example, P_ackA07 and P_ackA08 primers were used to check for ackA deletion. Loss of the plasmid was evaluated by plating single colonies onto LB agar with and without kanamycin. Finally, target gene deletion was confirmed using DNA sequencing.Plasmids pJOE_ackA, pJOE_pta, pJOE_lctE, and pJOE_mmgA were sequentially introduced into ormation  for multLB liquid medium contains (per L) 10.0 g tryptone, 5.0 g yeast extract, and 10.0 g sodium chloride, and for plates, 1.5% bacto-agar is added for solidification. LB medium was used for the maintenance and preservation of all strains, and for plasmid construction. All strains were preserved at − 80 °C and revived by inoculation on LB agar plates.B. subtilis strains. Colonies of B. subtilis strains were cultured in 5 mL of M9 medium at 37 °C under rotation at 180 rpm overnight. Then, 1% (v/v) of the culture medium was activated by culture in 5 mL of M9 medium at 37 °C and 180 rpm for 18 h. Subsequently, 0.5% (v/v) liquid culture was inoculated into a 300-mL flask with 150 mL of M9 medium at 37 °C and 180 rpm for 72 h. Bacterial growth was periodically observed by measuring the optical density of the liquid culture at 600 nm (OD600). At the same time, liquid samples were collected and analyzed for residual glycerol and fermentation products, including acetic acid, lactic acid, pyruvic acid, succinic acid, diacetyl, acetoin, 2,3-BD, and ethanol. To confirm the effect of mmgA knockout on 2,3-BD production, micro-aerobic fermentation was performed using 120 mL of M9 medium at 37 °C and 100 rpm for 96 h.M9 medium  containiB. subtilis strains was periodically observed by measuring the OD600 using a UVmini-1240 spectrophotometer . The cell dry weight (CDW) was converted from the OD600 value; 1 OD600 is equal to 0.325 of g CDW/L [2SO4 at a flow rate of 0.5 mL/min.The growth of  g CDW/L . The conB. subtilis strain 168 in this study referred to the iYO844 model constructed by Oh et al. [http://bigg.ucsd.edu/). The last version of the iYO844 model contained 990 metabolites, 1250 metabolic reactions, and 844 genes. For simulations to determine the metabolic flux distributions, FBA was performed with the commercial GLPK (GNU Linear Programming Kit) package, which supplies a solver for large-scale linear programming (LP) and mixed integer programming (MIP), and the COBRA toolkit [B. subtilis was applied by setting a lower bound to allow unlimited uptake on the exchange reactions of the necessary minerals. The ALCD19_D and ALCD19_L reactions, which involve alcohol dehydrogenases, were deleted from the model because of the determined roles of YhdN in methylglyoxal resistance [The GSM of  toolkit  using MAsistance .B. subtilis strains under limited oxygen conditions, the glycerol and oxygen consumption rates were set at 10 mmol/gCWD/h (the maximum uptake rate), and the biomass objective function was set to maximize the target product for the growth simulation. Gene knockout simulations were performed by setting the minimum and maximum fluxes of the related reactions of the target genes to zero. The available reactions for gene knockout did not include exchange reactions, transporter reactions, or essential gene reactions. Details of the gene targets for knockout are listed in Table B. subtilis wild-type strain 168, which was originally used in the iYO844 model. The modified strains correspond with the gene knockout strains of W168: W168∆2 (deletion of the ACKr and PTAr reactions), W168∆3 , and W168∆4 .To evaluate the growth and metabolic pathways of glycerol utilization as the sole substrate in the simulation of −1) and glycerol consumption rate  were evaluated from experimental data during the logarithmic phase using Eqs. The specific cell growth rate μ, h− and glycAfter deriving the experimental results of the specific cell growth and glycerol consumption rates, FBA simulations of each strain were performed again to compare the in silico results with the experimental results. The biomass flux was used as an objective function. Glycerol consumption rates were fixed according to the experimental results of each strain, and oxygen consumption rates were varied to obtain the specific cell growth rates based on the experimental results. The production of the target VFAs was compared between the experimental results and FBA simulation results.Additional file 1. FBA analysis results from the experimental data.


Given that approaches for compartment-specific metabolomics exist, the benefits they offer compared to conventional non-compartmented 13C flux studies remain to be determined. Using compartment-specific labeling information of IgG1-producing Chinese hamster ovary cells, this study investigated differences of flux patterns exploiting and ignoring metabolic labeling data of cytosol and mitochondria. Although cellular analysis provided good estimates for the majority of intracellular fluxes, half of the mitochondrial transporters, and NADH and ATP balances, severe differences were found for some reactions. Accurate flux estimations of almost all iso-enzymes heavily depended on the sub-cellular labeling information. Furthermore, key discrepancies were found for the mitochondrial carriers vAGC1 (Aspartate/Glutamate antiporter), vDIC , and vOGC . Special emphasis is given to the flux of cytosolic malic enzyme (vME): it could not be estimated without the compartment-specific malate labeling information. Interesting enough, cytosolic malic enzyme is an important metabolic engineering target for improving cell-specific IgG1 productivity. Hence, compartment-specific 13C labeling analysis serves as prerequisite for related metabolic engineering studies.The online version contains supplementary material available at 10.1007/s00449-021-02628-1. First, applications dealt with prokaryotic cells -d-glucose as a carbon tracer with an average isotopic ratio of 25% [U-12C6]- and 75% [U-13C6]-d-glucose. Experiments were performed as biological duplicates. In addition to carbon labeling experiments, bioreactor cultivations with [U-12C6]-d-glucose were performed using the same conditions for metabolome profiling.Bioreactor cultivations were performed in a two-fold parallel CellFerm Pro bioreactor system  equipped with pitched blade impellers and a process control system. Bioreactor cultivations were started with a VCD of about 0.4 × 10d-glucose and ʟ-lactate were monitored offline with LaboTRACE, an amperometric biosensor system . Extracellular antibody (IgG1) concentrations were measured using ELISA as reported previously  were defined: q for cellular uptake and secretion rates, k as inter-compartment transport, and v as compartment-specific reaction. The balance of metabolite i participating in reaction j localized externally, in cytosol, or in mitochondria was described by Eqs. ci denotes the concentration of metabolite i [mol L−1], cx denotes VCD [cell L−1], t denotes time [h], and Qi,feed denotes the feed-rate of metabolite i [pmol L−1 h−1].The two-compartment CHO-cell model comprises the stoichiometric matrix q for metabolite i by time-series analysis of extracellular concentrations ci. Therefore, the metabolic steady-state was defined as mirrored in the constraint 13C flux analysis. Both stationary and non-stationary labeling patterns were analyzed, originating from the metabolic steady-state condition.The process model describing the batch cultivation is given in Eq. S considering the following constraints: (i) pool sizes of cytosolic and mitochondrial metabolites were in a steady-state and (ii) the entire system was (over)-determined because of the ample 13C labeling information. Fluxes were estimated according to:M is the measurement matrix containing the stoichiometric coefficients of qmeas (measured rates [pmol cell−1 h−1]) and p contains the estimated fluxes using mass-isotopomer data [pmol cell−1 h−1]).MFA was performed using the metabolic network 13C tracers into intracellular metabolite carbon skeletons -d-glucose after 2.5 days, revealing no phenotypic changes, i.e., no alterations of cellular metabolism.During the exponential growth phase, cells grew with 0.025 ± 0.001 h13C MFA was performed using compartment-specific metabolome data reflecting subcellular pools of cytosol and mitochondria together with isotopomer profiles of the said compartments. Flux estimations were performed at least 100 times with randomized input values and rational boundary values for each parameter . Finally, the chi-square tests achieved 228.87, which served the statistical constraint of 232.92 on a 95% significance level.−1 h−1 of hexokinase) and extremely low PPP fluxes (0.008 ± 0.001 pmol cell−1 h−1 of G6P dehydrogenase) were found. The latter accounted for 6.68% of the d-glucose consumed. These observations are in agreement with the findings of Ahn & Antoniewicz [13C MFA in adherent CHO-K1 cells. In addition, approximately 15% (0.016 ± 0.002 pmol cell−1 h−1) of intracellular G6P was continuously in exchange with endogenous glycogen.High glycolytic  mitochondrial citrate carrier  served as a citrate exporter to provide cytosolic acetyl-CoA for the de novo lipid biosynthesis pathway; (ii) the malate-aspartate shuttle comprising 2-oxoglutarate carrier  and aspartate-glutamate carrier (aspartate/glutamate antiporter), which is often described as an indirect NADH shuttle because imported malate is oxidized to oxaloacetate, releasing NADH, fulfilled a different function; malate was net exported from mitochondria to fuel cytosolic ME.Glycolytic carbon fueled into mitochondria via two transport mechanisms; 77% entered via the mitochondrial pyruvate carrier (MPC1/2) and 23% via a putative cyt) was identified as the primary provider serving NADPH needs. Compartment-specific 13C MFA estimated that about 86% of the NADPH requirement was fulfilled by MEcyt (0.09 ± 0.01 pmol cell−1 h−1).NADPH is a key electron donator for anabolic pathways and is essential for monoclonal antibody biosynthesis. Ahn & Antoniewicz, Templeton et al. , 7 sugge13C MFA was performed to investigate the importance of distinct sub-cellular information to elucidate proper in vivo subcellular flux patterns. Analyzing the merged data .An additional data Eq.  via 13C This study was performed using the same model consisting of 42 intracellular biochemical reactions. Figure 13C. This is also true for citrate synthase vCS, although identifiability was poor. Similar results were observed for cytosolic-based reactions: pyruvate carboxylase (vpc) and PEP carboxykinase (vpepck)  Fig. C. These vmdh), ME (vme), aspartate amino-transferases (vast), and alanine amino-transferases  are localized in cytosol and mitochondria, respectively. Of the eight iso-enzymes analyzed, seven conversion rates were significantly different. The only exception is the mitochondrial malate dehydrogenase  which revealed statistical similarity although fluxes even reversed. On contrary, the cytosolic malate dehydrogenase  also disclosed flux reversion but with a sound statistical identifiability.Special emphasis is laid on the so-called iso-enzymatic reactions of Fig. vast), namely alanine amino-transferases  and aspartate amino transferases (vast). The analysis of whole-cell data resulted in flux overestimation compared to compartment-specific analysis. Notably, the substrate aspartate occurred in cytosol and mitochondria and is a key player of the aspartate-malate shuttle. Moreover, alanine was involved in the co-transport of glycolytic carbon into mitochondria with the MPC1/2. In this case, proper localization and labeling information of the compound is key to estimate fluxes correctly.Non-compartmented data were not able to properly reflect real fluxes of the amino-transferases (vme) as displayed in Fig. 13C flux estimations using non-compartmented data identified significantly lower (about 30%) cytosolic vme,cyt than the non-compartmented data. Regarding mitochondria, the opposite was found. The finding for vme using non-compartmented data is consistent with the observations of Ahn & Antoniewicz, Templeton et al. [13C MFA with cellular data. Importantly, cytosolic ME activity via vme,cyt was identified as a key supplier for NADPH needed for IgG production in CHO cells .vAGC1), malate carrier (vDIC), α-ketoglutarate/malate antiporter (vOGC), and the putative alanine carrier (vmAla)  and the corresponding reactions . Shuttle estimations regarding vDIC and vOGC using non-compartment-specific data contradict flux calculations using compartment-specific information estimation. The sub-cellular labeling information of malate is essential to get accurate flux estimates. Interestingly, the flux estimation of putative asparagine carrier (vmAsn) was not biased by the use of whole-cell labeling data only. This may reflect that vmAsn heavily depends on the measured ʟ-asparagine uptake rate (qAsn) irrespective of the existence of additional subcellular information.Comparing shuttle activities using sub-cellular and cellular labeling information reveals significant differences for half of the inter-compartment transporters, namely the aspartate/glutamate antiporter (la) Fig. D. Similaf factors were estimated for each metabolite and compared with the measurements of Junghans et al. [f) were poorly identified with pyruvate showing the smallest difference of 8.59% only. On average, 59.71% difference was found compared to the real labeling fraction. Notably, the best estimates of pyruvate and asparagine also enabled improved flux values for the corresponding biochemical reactions, e.g. vMPC1/2, vpdh for pyruvate, and vasns, vmAsn for asparagine.Using Eq. s et al.   outlines that very similar key messages are obtained analyzing the new data set: Glycolytic fluxes are fairly similar irrespective whether subcellular or cellular 13C MFA with compartment-specific metabolome data compared to exploiting cellular labeling information not distinguishing between cytosol and mitochondria.This study challenges the information gain when performing vpgi, vGAPdh vG6Pdh, and vphdgh) located in a single compartment (here: cytosol) disclose equal values irrespective of the analytical approach selected. Interestingly, this also holds true for vcs, located in mitochondria, primarily due to poor flux identifiability. Furthermore, vpepck and vpc revealed such high flux variances that no distinction could be found whether cellular or subcellular 13C data were used. Apparently, both reactions depend on cytosolic (OAAcyt) and mitochondrial oxaloacetate (OAAmit). They act at the interphase of the two compartments and rely on proper sub-cellular measurement information (τ13C) for correct identification. Distinct OAA measurements were not available in the current study due to challenging analytical access to the compound. Accordingly, flux estimations might be biased by the quality of OAA pool estimations.Figure vmdh,mit) and the pyruvate carrier vMPC1. Both disclose large error bars rendering a discrimination between cellular versus subcellular approaches hardly possible  flux estimation failed to estimate the mitochondrial and cytosolic fluxes of the amino-transferases vAGC1 and vmAla are also reflected by the results of vast and valt. In addition, the poor estimation of the malate carriers vDIC and vOGC depended on vme. In general, fluxes of transporters and bioreactions heavily relied on the labeling dynamics measured in the related metabolites. Regarding vMPC1, the reduced shuttle activity based on non-compartmented data reflects the missing malate exported into cytosol . Interestingly, without information about compartment-specific metabolomics, cytosolic 13C signals obtained from simulations are pretty similar to those of the whole-cell. This is mainly due to the relatively low information gain with respect to the key mitochondrial metabolites malate and aspartate. Compartment-specific labeling information and turnover of the latter are decisive to resolve activities of mitochondrial transporters.To check whether the additional use of labeled glutamine  might haOften, cellular analysis achieved similar flux estimations as subcellular studies by fitting measured cytosolic labeling fractions for the sake of estimating pool sizes properly Table . In othevme. Remarkably, this flux was found to be a promising metabolic engineering target to maximize the formation of heterologous proteins by improved NADPH supply [Among the fluxes with the largest discrepancies is the cytosolic ME H supply . AccordiH supply . WhetherH supply .In general, most of the flux estimations using either non-compartmented or compartmented data led to similar values. Even global cell qualifications, such as rates of total ATP formation and NADH production, were similar. However, two main findings should be considered:cis-aconitate (in mitochondrion) to correct for mitochondrial leakage. In general, fast metabolic inactivation, standardized sample processing and use of internal standards are prerequisites for any compartment-specific metabolomics approach that might be used in future applications.To date, the compartment-specific analytical approach has shown its suitability for multiple metabolomic studies investigating CHO cells under in vivo-like conditions , 24–30. 13C labeling data, the study revealed that non-compartmented data enabled to identify most fluxes involving single compartment metabolites. Besides, half of the mitochondrial shuttle fluxes and global properties, such as ATP and NADH formation, were fairly well estimated without requiring further subcellular labeling information. However, there is a number of sensitive fluxes that could only be identified properly if compartment-specific pool information was used. Among those were mitochondrial shuttles that rely on alanine, aspartate and malate. Furthermore, key metabolic engineering targets, such as the cytosolic ME flux for NADPH formation, were severely underestimated using  cellular data. This may disguise their role as promising metabolic engineering target if non-compartmented pool analysis is performed, only. The finding underlines the necessity to apply subcellular data for flux estimation, not only to quantify cytosolic/mitochondrial shuttle activities but also to identify metabolic engineering targets and obtain valid values for real pool sizes.Investigating the need for using subcellular Supplementary file1 (PDF 137 kb)Supplementary file2 (PDF 158 kb)Supplementary file3 (PDF 169 kb)Supplementary file4 (PDF 1935 kb)Supplementary file5 (PDF 73 kb)Supplementary file6 (DOCX 178 kb)Below is the link to the electronic supplementary material.


Lactococcus lactis. Here, we present a proteome‐constrained genome‐scale metabolic model of L. lactis (pcLactis) to interpret growth on multiple nutrients. Through integration of proteomics and flux data, in glucose‐limited chemostats, the model predicted glucose and arginine uptake as dominant constraints at low growth rates. Indeed, glucose and arginine catabolism were found upregulated in evolved mutants. At high growth rates, pcLactis correctly predicted the observed shutdown of arginine catabolism because limited proteome availability favored lactate for ATP production. Thus, our model‐based analysis is able to identify and explain the proteome constraints that limit growth rate in nutrient‐rich environments and thus form targets of fitness improvement.Cells adapt to different conditions via gene expression that tunes metabolism for maximal fitness. Constraints on cellular proteome may limit such expression strategies and introduce trade‐offs. Resource allocation under proteome constraints has explained regulatory strategies in bacteria. It is unclear, however, to what extent these constraints can predict evolutionary changes, especially for microorganisms that evolved under nutrient‐rich conditions, i.e., multiple available nitrogen sources, such as  Lactococcus lactis (pcLactis) is presented. The model is used to interpret microbial behaviour in nutrient‐rich conditions and predicts testable targets for fitness improvement.A proteome‐constrained genome‐scale metabolic model of  We compared a glucose‐rich condition and a glucose‐limited condition in which the reference growth rate is 0.5/h. Of the 845 parameters, only 24 led to variations > 1% under the glucose‐rich condition  that was selected during long‐term cultivation in glucose‐limited chemostat conditions at D = 0.5/h and that harbors a point mutation in the global carbon catabolite repression regulator (CcpA)   Fig , confirm001) Fig , while o001) Fig . These mP = 0.030 compared with random distribution of significantly changed proteins over membrane and cytosolic protein fraction, Appendix Table We did not find a considerable change in the total protein membrane fraction  and inhibits glyceraldehyde‐3‐phosphate dehydrogenase (GAPDH), which has NADH as a product   than for arginine catabolism can be explained by the lower protein costs for serine than for arginine , a tool that reconstructs genome‐scale metabolic networks based on previous manually curated ones by homology between genes, and subsequently manual curation. For non‐spontaneous reactions with missing GPRs, we assigned a “dummy” protein as their catalysts to eliminate potential bias toward using them in simulations. Then, we split reactions with isozymes into multiple reactions that each is catalyzed by one isozyme, and reversible reactions into forward and reverse direction. In addition to updating and reformulating metabolic reactions, we formulated reactions for transcription, stable RNA cleavage, mRNA degradation, tRNA modification, rRNA modification, tRNA charging, ribosomal assembly, translation, protein maturation, protein assembly, enzyme formation, and protein degradation. Additionally, we formulated dilution reactions for RNA and enzymes to represent their dilution to daughter cells during cell division. Lastly, we modified the biomass equation of the metabolic model, i.e., removed protein and RNA from the equation as they were represented by dilution reactions, and added an unmodeled protein to account for all the other proteins that are not synthesized by the model. The genome‐scale metabolic model was updated using CBMPy (https://doi.org/10.5281/zenodo.3485023), while pcLactis was constructed using the COBRA toolbox .The binary search solved successive individual linear programs, which maximizes the production of the dummy protein subject to stoichiometric and coupling constraints. Lower and upper bounds of some reactions were also constrained. Notably, amino acid exchange fluxes were constrained by growth rate‐dependent upper bounds based on experimental data (iq/iµ). We imposed a small increase in the uptake rate of each amino acid Δq = 0.01 to investigate the change in growth rate Δµ.We performed the scaled reduced cost analysis (ic/iµ). With pcLactis, we imposed a small increase Δc = 0.01 in the glucose transporter and modeled proteome to investigate the change in growth rate Δµ at different conditions. We also performed sensitivity analysis with the small model for glucose uptake, proteome allocated to the small model and arginine uptake using the small increase Δc = 0.01 to investigate the change in ATP production rate ATPq, i.e., the sensitivity score iS is calculated as: iS = (ΔATPq/Δc)(ic/ATPq).We performed sensitivity analysis for two constraints with pcLactis, i.e., the glucose transporter and modeled proteome, on growth rate. The sensitivity score Lactococcus lactis ssp. cremoris MG1363 is a plasmid cured derivative of strain NCDO712 , while standing in 30°C water baths. The headspace was continuously flushed with 5% COTo start up a chemostat culture, 5 ml CDMpc with 25 mM glucose was inoculated with glycerol stocks of the respective strains, taken from a −80°C. This was directly added to the reactor containing 265 ml fresh 25 mM glucose CDMpc. The reactor was operated in batch mode  for 24 h, allowing the cells to reach the stationary phase. This was verified by observing that the pH remained constant without the addition of 2 M NaOH. After 24 h, the medium pump was switched on. The chemostat was operated for 8 volume changes before samples were taken. Samples were taken from the effluent of the reactor. Retention time in the effluent tube was < 2 min. Sample tubes were kept on ice while sample was being collected. For each strain, four replicate chemostats were cultivated.g for 3 min at 4°C, and the supernatant was filtered through a 0.22 µm polyether sulfone filter  and stored at −20°C until further analysis. Extracellular concentrations of lactate, acetate, formate, ethanol, and glucose were determined by high‐performance liquid chromatography (HPLC) as described previously , 2 ml samples were centrifuged at 27,237 4, 0.381% w/v Na2B4O7·10H2O, 0.0325% w/v sodium azide in deionized water (DI water); and eluent 2 had composition: 45% v/v methanol, 45% v/v acetonitrile in DI water. Samples were prepared by mixing 25 µl sample with 875 µl DI water, 25 µl Borate buffer , and 25 µl 1 mM Norvaline as internal standard. For amino acid derivation, 3 µl phthaldialdehyde (Sigma‐Aldrich), i.e., OPA, was automatically added 3 min before sample injection (5 µl) into the column. Concentrations were determined by comparison of sample peak areas to those of a calibration curve identically run with representative amino acid concentrations.Extracellular concentrations of amino acids were determined by HPLC on a Shimadzu system with: LC‐20AD pumps, DGU‐14A degasser, SIL‐10ADvp autosampler, CTO‐10ASvp oven, SCL‐10Avp system controller, and RF‐10AXL fluorescence detector. Separation occurred on an Agilent Zebra Eclipse plus solvent saver 3.0 × 150 × 3.5 column over a period of 38 min per sample, with an isocratic flow of two eluents both at a flow rate of 0.64 ml/min. Eluent 1 had composition: 0.142% w/v NaHPOg for 3 min at 4°C. The supernatant was discarded, and the pellet was frozen in liquid N2 prior to storage at −20°C until further analysis.Proteome samples were collected in low protein binding tubes, centrifuged at 27,237 For dry weight measurements, 10 ml broth was collected and filtered over a pre‐dried  pre‐weighed 0.2 µm cellulose nitrate filter (Whatman GmbH). The filter was then washed once with 10 ml demineralized water and dried , before weighing again.iq in mmol/gCDW/h) were calculated as: qi=D·Ci,supernatant‐Ci,medium/Xbiomass, where D is the dilution rate (/h), C the concentration of compound i (mM) and Xbiomass the biomass concentration (g/l).Fluxes  at room temperature. The digested peptides were eluted by centrifugation for 30 min, subsequent addition of 100 µl 1 ml/l HCOOH in water, and again centrifugation for 30 min. pH was adjusted to pH 3 using 10 v/v% trifluoroacetic acid.Proteins were isolated using the FASP method as described previously  and frequently observed contaminants. In addition to the standard settings, Trypsin/P with a maximum of two missed cleavages was set as the digestion mode, acrylamide modifications on the cysteines were set as a fixed modification, and methionine oxidation, protein N‐terminal acetylation, and asparagine or glutamine deamidation were set as variable modifications. A false discovery rate of 1% at protein and peptide level was allowed, and the minimum required protein length was set at 7. At least two peptides were required for protein identification of which at least one peptide was required to be unique in the database. Identified proteins were quantified with MaxQuant’s LFQ algorithm  and variation  to make calculations possible. We then performed a 2‐sided two‐sample Study conception: BT, HB, and JN; Modeling, simulations, and data analysis: YC and EvP‐K; Experiments: BvO and SD; Data analysis: DM and SB; Manuscript writing: YC, EvP‐K, and BT; Manuscript editing: All authors.The project is organized by and executed under the auspices of TiFN, a public–private partnership on precompetitive research in food and nutrition. The authors have declared that no competing interests exist in the writing of this publication. Funding for this research was obtained from Friesland Campina, CSK Food Enrichment, the Netherlands Organisation for Scientific Research and the Top‐sector Agri&Food.AppendixClick here for additional data file.Dataset EV1Click here for additional data file.Dataset EV2Click here for additional data file.Dataset EV3Click here for additional data file.Review Process FileClick here for additional data file.