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Novel cultivation technologies demand the adaptation of existing analytical concepts. Metabolic flux analysis (MFA) requires stable-isotope labeling of biomass-bound protein as the primary information source. Obtaining the required protein in cultivation set-ups where biomass is inaccessible due to low cell densities and cell immobilization is difficult to date. We developed a non-disruptive analytical concept for 13 C-based metabolic flux analysis based on secreted protein as an information carrier for isotope mapping in the protein-bound amino acids. This "metabolic flux probe" (MFP) concept was investigated in different cultivation set-ups with a recombinant, protein-secreting yeast strain. The obtained results grant insight into intracellular protein turnover dynamics. Experiments under metabolic but isotopically nonstationary conditions in continuous glucose-limited chemostats at high dilution rates demonstrated faster incorporation of isotope information from labeled glucose into the recombinant reporter protein than in biomass-bound protein. Our results suggest that the reporter protein was polymerized from intracellular amino acid pools with higher turnover rates than biomass-bound protein. The latter aspect might be vital for 13 C-flux analyses under isotopically nonstationary conditions for analyzing fast metabolic dynamics.

13 C-based metabolic flux analysis ( 13 C-MFA) is an essential tool for estimating intracellular metabolic flux levels in metabolic engineering and biology. In 13 C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for 13 C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of 13 C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository (https://github.com/fumiomatsuda/mfapy)., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Author(s).)

Metabolic flux analysis based on 13 C-derived constraints has proved to be a powerful method for quantitative physiological characterisation of one of the most extensively used microbial cell factory platforms, Pichia pastoris (syn. Komagataella spp.). Nonetheless, the reduced number of carbon atoms and the symmetry of the glycerol molecule has hampered the comprehensive determination of metabolic fluxes when used as the labelled C-source. Moreover, metabolic models typically used for 13 C-based flux balance analysis may be incomplete or misrepresent the actual metabolic network. To circumvent these limitations, we reduced the genome-scale metabolic model iMT1026-v3.0 into a core model and used it for the iterative fitting of metabolic fluxes to the measured mass isotope distribution of proteinogenic amino acids obtained after fractional 13 C labelling of cells with [1,3- 13 C]-glycerol. This workflow allows reliable estimates to be obtained for in vivo fluxes in P. pastoris cells growing on glycerol as sole carbon source, as well as revising previous assumptions concerning its metabolic operation, such as alternative metabolic branches, calculation of energetic parameters and proposed specific cofactor utilisation., (Copyright © 2019 Elsevier B.V. All rights reserved.)

A novel analytical approach based on liquid chromatography coupled to quadrupole time of flight mass spectrometry, employing data-dependent triggering for analysis of isotopologue and tandem mass isotopomer fractions of metabolites of the primary carbon metabolism was developed. The implemented QTOFMS method employs automated MS/MS triggering of higher abundant, biologically relevant isotopologues for generating positional information of the respective metabolite. Using this advanced isotopologue selective fragmentation approach enables the generation of significant tandem mass isotopomer data within a short cycle time without compromising sensitivity. Due to a lack of suitable reference material certified for isotopologue ratios, a Pichia pastoris cell extract with a defined C distribution as well as a cell extract from a C-based metabolic flux experiment were employed for proof of concept. Moreover, a method inter-comparison with an already established GC-CI-(Q)TOFMS approach was conducted. Both methods showed good agreement on isotopologue and tandem mass isotopomer distributions for the two different cell extracts. [ABSTRACT FROM AUTHOR]

For the study of different levels of (intra)cellular regulation and condition-dependent insight into metabolic activities, fluxomics experiments based on stable isotope tracer experiments using 13 C have become a well-established approach. The experimentally obtained non-naturally distributed 13 C labeling patterns of metabolite pools can be measured by mass spectrometric detection with front-end separation and can be consequently incorporated into biochemical network models. Here, despite a tedious derivatization step, gas chromatographic separation of polar metabolites is favorable because of the wide coverage range and high isomer separation efficiency. However, the typically employed electron ionization energy of 70 eV leads to significant fragmentation and consequently only low-abundant ions with an intact carbon backbone. Since these ions are considered a prerequisite for the analysis of the non-naturally distributed labeling patterns and further integration into modeling strategies, a softer ionization technique is needed. In the present work, a novel low energy electron ionization source is optimized for the analysis of primary metabolites and compared with a chemical ionization approach in terms of trueness, precision, and sensitivity.

In the field of metabolic engineering 13 C-based metabolic flux analysis experiments have proven successful in indicating points of action. As every step of this approach is affected by an inherent error, the aim of the present work is the comprehensive evaluation of factors contributing to the uncertainty of nonnaturally distributed C-isotopologue abundances as well as to the absolute flux value calculation. For this purpose, a previously published data set, analyzed in the course of a 13 C labeling experiment studying glycolysis and the pentose phosphate pathway in a yeast cell factory, was used. Here, for isotopologue pattern analysis of these highly polar metabolites that occur in multiple isomeric forms, a gas chromatographic separation approach with preceding derivatization was used. This rendered a natural isotope interference correction step essential. Uncertainty estimation of the resulting C-isotopologue distribution was performed according to the EURACHEM guidelines with Monte Carlo simulation. It revealed a significant increase for low-abundance isotopologue fractions after application of the necessary correction step. For absolute flux value estimation, isotopologue fractions of various sugar phosphates, together with the assessed uncertainties, were used in a metabolic model describing the upper part of the central carbon metabolism. The findings pinpointed the influence of small isotopologue fractions as sources of error and highlight the need for improved model curation. Graphical abstract ᅟ.

(13)C-based metabolic flux analysis is an excellent technique to resolve fluxes in the central carbon metabolism but costs can be significant when using specialized tracers. This work presents a framework for cost-effective design of (13)C-tracer experiments, illustrated on two different networks. Linear and non-linear optimal input mixtures are computed for networks for Streptomyces lividans and a carcinoma cell line. If only glucose tracers are considered as labeled substrate for a carcinoma cell line or S. lividans, the best parameter estimation accuracy is obtained by mixtures containing high amounts of 1,2-(13)C2 glucose combined with uniformly labeled glucose. Experimental designs are evaluated based on a linear (D-criterion) and non-linear approach (S-criterion). Both approaches generate almost the same input mixture, however, the linear approach is favored due to its low computational effort. The high amount of 1,2-(13)C2 glucose in the optimal designs coincides with a high experimental cost, which is further enhanced when labeling is introduced in glutamine and aspartate tracers. Multi-objective optimization gives the possibility to assess experimental quality and cost at the same time and can reveal excellent compromise experiments. For example, the combination of 100% 1,2-(13)C2 glucose with 100% position one labeled glutamine and the combination of 100% 1,2-(13)C2 glucose with 100% uniformly labeled glutamine perform equally well for the carcinoma cell line, but the first mixture offers a decrease in cost of $ 120 per ml-scale cell culture experiment. We demonstrated the validity of a multi-objective linear approach to perform optimal experimental designs for the non-linear problem of (13)C-metabolic flux analysis. Tools and a workflow are provided to perform multi-objective design. The effortless calculation of the D-criterion can be exploited to perform high-throughput screening of possible (13)C-tracers, while the illustrated benefit of multi-objective design should stimulate its application within the field of (13)C-based metabolic flux analysis., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Several bacterial species and filamentous fungi utilize the phosphoketolase pathway (PHK) for glucose dissimilation as an alternative to the Embden-Meyerhof-Parnas pathway. In Aspergillus nidulans, the utilization of this metabolic pathway leads to increased carbon flow towards acetate and acetyl CoA. In the first step of the PHK, the pentose phosphate pathway intermediate xylulose-5-phosphate is converted into acetylphosphate and glyceraldehyde-3-phosphate through the action of xylulose-5-phosphate phosphoketolase, and successively acetylphosphate is converted into acetate by the action of acetate kinase. In the present work, we describe a metabolic engineering strategy used to express the fungal genes of the phosphoketolase pathway in Saccharomyces cerevisiae and the effects of the expression of this recombinant route in yeast. The phenotype of the engineered yeast strain MP003 was studied during batch and chemostat cultivations, showing a reduced biomass yield and an increased acetate yield during batch cultures. To establish whether the observed effects in the recombinant strain MP003 were due directly or indirectly to the expression of the phosphoketolase pathway, we resolved the intracellular flux distribution based on C labeling during chemostat cultivations. From flux analysis it is possible to conclude that yeast is able to use the recombinant pathway. Our work indicates that the utilization of the phosphoketolase pathway does not interfere with glucose assimilation through the Embden-Meyerhof-Parnas pathway and that the expression of this route can contribute to increase the acetyl CoA supply, therefore holding potential for future metabolic engineering strategies having acetyl CoA as precursor for the biosynthesis of industrially relevant compounds. [ABSTRACT FROM AUTHOR]

Abstract: Ethanoligenens harbinense is a promising hydrogen producing microorganism due to its high inherent hydrogen production rate. Even though the effect of media optimization and inhibitory metabolites has been studied in order to improve the hydrogen productivity of these cultures, the identification of the underlying causes of the observed changes in productivity has not been targeted to date. In this work we present a genome based metabolic flux analysis (MFA) framework, for the comprehensive study of E. harbinense in culture, and the effect of inhibitory metabolites and media composition on its metabolic state. A metabolic model was constructed for E. harbinense based on its annotated genome sequence and proteomic evidence. This model was employed to perform MFA and obtain the intracellular flux distribution under different culture conditions. These results allow us to identify key elements in the metabolism that can be associated to the observed production phenotypes, and that can be potential targets for metabolic engineering in order to enhanced hydrogen production in E. harbinense. [Copyright &y& Elsevier]

The measurement of site-specific (13)C enrichments in complex mixtures of (13)C-labeled metabolites is a powerful tool for metabolic flux analysis. One of the main methods to measure such enrichments is homonuclear (1)H 2D NMR. However, the major limitation of this technique is the acquisition time, which can amount to a few hours. This drawback was recently overcome by the design of fast COSY experiments for measuring specific (13)C-enrichments, based on single-scan 2D NMR. However, these experiments are still limited by overlaps because of(1)H-(13)C splittings, thus limiting the metabolic information accessible for complex biological mixtures. To circumvent this limitation, we propose to tilt the (1)H-(13)C coupling into a third dimension via fast-hybrid 3D NMR methods combining the speed of ultrafast 2D NMR with the high resolution of conventional methods. Two strategies are described that allow the acquisition of a complete 3D J-resolved-COSY spectrum in 12 min (for concentrations as low as 10 mM). The analytical potentialities of both methods are evaluated on a series of (13)C-enriched glucose samples and on a biomass hydrolyzate obtained from Escherichia coli cells. Once optimized, the two complementary experiments lead to a trueness and a precision of a few percent and an excellent linearity. The advantages and drawbacks of these approaches are discussed and their potentialities are highlighted.

In a (13)C experiment for metabolic flux analysis ((13)C MFA), we examined isotope discrimination by measuring the labeling of glucose, amino acids, and hexose monophosphates via mass spectrometry. When Escherichia coli grew in a mix of 20% fully labeled and 80% naturally labeled glucose medium, the cell metabolism favored light isotopes and the measured isotopic ratios (δ(13)C) were in the range of -35 to -92. Glucose transporters might play an important role in such isotopic fractionation. Flux analysis showed that both isotopic discrimination and isotopic impurities in labeled substrates could affect the solution of (13)C MFA., (Copyright © 2011 Elsevier Inc. All rights reserved.)

Background: Constraint-based models allow the calculation of the metabolic flux states that can be exhibited by cells, standing out as a powerful analytical tool, but they do not determine which of these are likely to be existing under given circumstances. Typical methods to perform these predictions are (a) flux balance analysis, which is based on the assumption that cell behaviour is optimal, and (b) metabolic flux analysis, which combines the model with experimental measurements., Results: Herein we discuss a possibilistic framework to perform metabolic flux estimations using a constraint-based model and a set of measurements. The methodology is able to handle inconsistencies, by considering sensors errors and model imprecision, to provide rich and reliable flux estimations. The methodology can be cast as linear programming problems, able to handle thousands of variables with efficiency, so it is suitable to deal with large-scale networks. Moreover, the possibilistic estimation does not attempt necessarily to predict the actual fluxes with precision, but rather to exploit the available data--even if those are scarce--to distinguish possible from impossible flux states in a gradual way., Conclusion: We introduce a possibilistic framework for the estimation of metabolic fluxes, which is shown to be flexible, reliable, usable in scenarios lacking data and computationally efficient.

Stable isotope, and in particular (13)C-based flux analysis, is the exclusive approach to experimentally quantify the integrated responses of metabolic networks. Here we describe a protocol that is based on growing microbes on (13)C-labeled glucose and subsequent gas chromatography mass spectrometric detection of (13)C-patterns in protein-bound amino acids. Relying on publicly available software packages, we then describe two complementary mathematical approaches to estimate either local ratios of converging fluxes or absolute fluxes through different pathways. As amino acids in cell protein are abundant and stable, this protocol requires a minimum of equipment and analytical expertise. Most other flux methods are variants of the principles presented here. A true alternative is the analytically more demanding dynamic flux analysis that relies on (13)C-pattern in free intracellular metabolites. The presented protocols take 5-10 d, have been used extensively in the past decade and are exemplified here for the central metabolism of Escherichia coli.

Corynebacterium glutamicum is intensively used for the industrial large-scale (fed-) batch production of amino acids, especially glutamate and lysine. However, metabolic flux analyses based on 13C-labeling experiments of this organism have hitherto been restricted to small-scale batch conditions and carbon-limited chemostat cultures, and are therefore of questionable relevance for industrial fermentations. To lever flux analysis to the industrial level, a novel Sensor Reactor approach was developed (El Massaoudi et al., Metab. Eng., submitted), in which a 300-L production reactor and a 1-L Sensor Reactor are run in parallel master/slave modus, thus enabling 13C-based metabolic flux analysis to generate a series of flux maps that document large-scale fermentation courses in detail. We describe the successful combination of this technology with nuclear magnetic resonance (NMR) analysis, metabolite balancing methods and a mathematical description of 13C-isotope labelings resulting in a powerful tool for quantitative pathway analysis during a batch fermentation. As a first application, 13C-based metabolic flux analysis was performed on exponentially growing, lysine-producing C. glutamicum MH20-22B during three phases of a pilot-scale batch fermentation. By studying the growth, (co-) substrate consumption and (by-) product formation, the similarity of the fermentations in production and Sensor Reactor was verified. Applying a generally applicable mathematical model, which included metabolite and carbon labeling balances for the analysis of proteinogenic amino acid 13C-isotopomer labeling data, the in vivo metabolic flux distribution was investigated during subsequent phases of exponential growth. It was shown for the first time that the in vivo reverse C(4)-decarboxylation flux at the anaplerotic node in C. glutamicum significantly decreased (70%) in parallel with threefold increased lysine formation during the investigated subsequent phases of exponential growth.

Abstract Background The Gram-positive Streptomyces lividans TK24 is an attractive host for heterologous protein production because of its high capability to secrete proteins—which favors correct folding and facilitates downstream processing—as well as its acceptance of methylated DNA and its low endogeneous protease activity. However, current inconsistencies in protein yields urge for a deeper understanding of the burden of heterologous protein production on the cell. In the current study, transcriptomics and $$^{13}\hbox {C}$$ 13C -based fluxomics were exploited to uncover gene expression and metabolic flux changes associated with heterologous protein production. The Rhodothermus marinus thermostable cellulase A (CelA)—previously shown to be successfully overexpressed in S. lividans—was taken as an example protein. Results RNA-seq and $$^{13}\hbox {C}$$ 13C -based metabolic flux analysis were performed on a CelA-producing and an empty-plasmid strain under the same conditions. Differential gene expression, followed by cluster analysis based on co-expression and co-localization, identified transcriptomic responses related to secretion-induced stress and DNA damage. Furthermore, the OsdR regulon (previously associated with hypoxia, oxidative stress, intercellular signaling, and morphological development) was consistently upregulated in the CelA-producing strain and exhibited co-expression with isoenzymes from the pentose phosphate pathway linked to secondary metabolism. Increased expression of these isoenzymes matches to increased fluxes in the pentose phosphate pathway. Additionally, flux maps of the central carbon metabolism show increased flux through the tricarboxylic acid cycle in the CelA-producing strain. Redirection of fluxes in the CelA-producing strain leads to higher production of NADPH, which can only partly be attributed to increased secretion. Conclusions Transcriptomic and fluxomic changes uncover potential new leads for targeted strain improvement strategies which may ease the secretion stress and metabolic burden associated with heterologous protein synthesis and secretion, and may help create a more consistently performing S. lividans strain. Yet, links to secondary metabolism and redox balancing should be further investigated to fully understand the S. lividans metabolome under heterologous protein production.

Salt tolerance in the γ-proteobacterium Halomonas elongata is linked to its ability to produce the compatible solute ectoine. The metabolism of ectoine production is of great interest since it can shed light on the biochemical basis of halotolerance as well as pave the way for the improvement of the biotechnological production of such compatible solute. Ectoine belongs to the biosynthetic family of aspartate-derived amino-acids. Aspartate is formed from oxaloacetate, thereby connecting ectoine production to the anaplerotic reactions that refill carbon into the tricarboxylic acid cycle (TCA cycle). This places a high demand on these reactions and creates the need to regulate them not only in response to growth but also in response to extracellular salt concentration. In this work, we combine modeling and experiments to analyze how these different needs shape the anaplerotic reactions in H. elongata. First, the stoichiometric and thermodynamic factors that condition the flux distributions are analyzed, then the optimal patterns of operation for oxaloacetate production are calculated. Finally, the phenotype of two deletion mutants lacking potentially relevant anaplerotic enzymes: phosphoenolpyruvate carboxylase (Ppc) and oxaloacetate decarboxylase (Oad) are experimentally characterized. The results show that the anaplerotic reactions in H. elongata are indeed subject to evolutionary pressures that differ from those faced by other gram-negative bacteria. Ectoine producing halophiles must meet a higher metabolic demand for oxaloacetate and the reliance of many marine bacteria on the Entner-Doudoroff pathway compromises the anaplerotic efficiency of Ppc, which is usually one of the main enzymes fulfilling this role. The anaplerotic flux in H. elongata is contributed not only by Ppc but also by Oad, an enzyme that has not yet been shown to play this role in vivo. Ppc is necessary for H. elongata to grow normally at low salt concentrations but it is not required to achieve near maximal growth rates as long as there is a steep sodium gradient. On the other hand, the lack of Oad presents serious difficulties to grow at high salt concentrations. This points to a shared role of these two enzymes in guaranteeing the supply of oxaloacetate for biosynthetic reactions.

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA. • This review discusses the medical application of metabolic flux analysis (MFA). • Constraint-based reconstruction and analysis (COBRA) involves metabolic simulation. • Isotope-based metabolic flux analysis (iMFA) allows more accurate flux estimation. • COBRA and iMFA have been applied to various medical problems, including cancers. • MFA bridges metabolic engineering and biomedical science as a synergistic interface. [ABSTRACT FROM AUTHOR]

Developing methylotrophic cell factories that can efficiently catalyze organic one-carbon (C1) feedstocks derived from electrocatalytic reduction of carbon dioxide into bio-based chemicals and biofuels is of strategic significance for building a carbon-neutral, sustainable economic and industrial system. With the rapid advancement of RNA sequencing technology and mass spectrometer analysis, researchers have used these quantitative microbiology methods extensively, especially isotope-based metabolic flux analysis, to study the metabolic processes initiating from C1 feedstocks in natural C1-utilizing bacteria and synthetic C1 bacteria. This paper reviews the use of advanced quantitative analysis in recent years to understand the metabolic network and basic principles in the metabolism of natural C1-utilizing bacteria grown on methane, methanol, or formate. The acquired knowledge serves as a guide to rewire the central methylotrophic metabolism of natural C1-utilizing bacteria to improve the carbon conversion efficiency, and to engineer non-C1-utilizing bacteria into synthetic strains that can use C1 feedstocks as the sole carbon and energy source. These progresses ultimately enhance the design and construction of highly efficient C1-based cell factories to synthesize diverse high value-added products. The integration of quantitative biology and synthetic biology will advance the iterative cycle of understand--design--build--testing--learning to enhance C1-based biomanufacturing in the future. [ABSTRACT FROM AUTHOR]

Background: Research about tumor “metabolic flexibility”—the ability of cells to toggle between preferred nutrients depending on the metabolic context—has largely focused on obesity-associated cancers. However, increasing evidence for a key role for nutrient competition in the tumor microenvironment, as well as for substrate regulation of immune function, suggests that substrate metabolism deserves reconsideration in immunogenic tumors that are not strongly associated with obesity. Methods: We compare two murine models: immunologically cold YUMM1.7 and immunologically-hot YUMMER1.7. We utilize stable isotope and radioisotope tracer-based metabolic flux studies as well as gas and liquid chromatography-based metabolomics analyses to comprehensively probe substrate preference in YUMM1.7 and YUMMER1.7 cells, with a subset of studies on the impact of available metabolites across a panel of five additional melanoma cell lines. We analyze bulk RNA-seq data and identify increased expression of amino acid and glucose metabolism genes in YUMMER1.7. Finally, we analyze melanoma patient RNA-seq data to identify potential prognostic predictors rooted in metabolism. Results: We demonstrate using stable isotope tracer-based metabolic flux studies as well as gas and liquid chromatography-based metabolomics that immunologically-hot melanoma utilizes more glutamine than immunologically-cold melanoma in vivo and in vitro. Analyses of human melanoma RNA-seq data demonstrate that glutamine transporter and other anaplerotic gene expression positively correlates with lymphocyte infiltration and function. Conclusions: Here, we highlight the importance of understanding metabolism in non-obesity-associated cancers, such as melanoma. This work advances the understanding of the correlation between metabolism and immunogenicity in the tumor microenvironment and provides evidence supporting metabolic gene expression as potential prognostic factors of melanoma progression and may inform investigations of adjunctive metabolic therapy in melanoma. Trial registration: Deidentified data from The Cancer Genome Atlas were analyzed. [ABSTRACT FROM AUTHOR]

The modeling of rates of biochemical reactions—fluxes—in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint‐based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to‐date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C‐metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands‐on computer laboratory component of a four‐day metabolic modeling workshop and participant survey results showed improvements in learners' self‐assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level. [ABSTRACT FROM AUTHOR]

Probiotics produce small molecules that may serve as alternatives to conventional antibiotics by suppressing growth of antimicrobial resistant (AMR) pathogens. The objective of this study was to identify and examine antimicrobials produced and secreted by probiotics using 'omics' profiling with computer-based metabolic flux analyses. The cell-free supernatant of Gram-positive Lacticaseibacillus rhamnosus GG (LGG) and Gram-negative Escherichia coli Nissle (ECN) probiotics inhibited growth of AMR Salmonella Typhimurium, Escherichia coli, and Klebsiella oxytoca ranging between 28.85 − 41.20% (LGG) and 11.48 − 29.45% (ECN). A dose dependent analysis of probiotic supernatants showed LGG was 6.27% to 20.55% more effective at reducing AMR pathogen growth when compared to ECN. Principal component analysis showed clear separation of ECN and LGG cell free supernatant metabolomes. Among 667 metabolites in the supernatant, 304 were differentially abundant between LGG and ECN probiotics. Proteomics identified 87 proteins, whereby 67 (ECN) and 14 (LGG) showed differential expression as enzymes related to carbohydrate and energy metabolic pathways. The whole genomes and metabolomes were next used for in-silico metabolic network analysis. The model predicted the production of 166 metabolites by LGG and ECN probiotics across amino acid, carbohydrate/energy, and nucleotide metabolism with antimicrobial functions. The predictive accuracy of the metabolic flux analysis highlights the novel utility for profiling probiotic supplements as dietary-based antimicrobial alternatives in the control of AMR pathogen growth. [ABSTRACT FROM AUTHOR]

Saccharomyces cerevisiae shows a Crabtree effect that produces ethanol in a high glucose concentration even under fully aerobic condition. For efficient production of cake yeast or compressed yeast for baking, ethanol by-production is not desired since glucose limited chemostat or fed-batch cultivations are performed to suppress the Crabtree effect. In this study, the 13 C-based metabolic flux analysis ( 13 C-MFA) was performed for the S288C derived S. cerevisiae strain to characterize a metabolic state under the reduced Crabtree effect. S. cerevisiae cells were cultured at a low dilution rate (0.1 h −1 ) under the glucose-limited chemostat condition. The estimated metabolic flux distribution showed that the acetyl-CoA in mitochondria was mainly produced from pyruvate by pyruvate dehydrogenase (PDH) reaction and that the level of the metabolic flux through the pentose phosphate pathway was much higher than that of the Embden–Meyerhof–Parnas pathway, which contributes to high biomass yield at low dilution rate by supplying NADPH required for cell growth. [ABSTRACT FROM AUTHOR]

Chromochloris zofingiensis is a potential source of natural astaxanthin; however, its rapid growth and astaxanthin enrichment cannot be achieved simultaneously. This study established autotrophic, mixotrophic, and heterotrophic preculture patterns to assess their ameliorative effect on the C. zofingiensis heterotrophic growth state. In comparison, mixotrophic preculture (MP) exhibited the best improving effect on heterotrophic biomass concentration of C. zofingiensis (up to 121.5 g L−1) in a 20 L fermenter, reaching the global leading level. The astaxanthin productivity achieved 111 mg L−1 day−1, 7.4-fold higher than the best record. The transcriptome and 13C tracer-based metabolic flux analysis were used for mechanism inquiry. The results revealed that MP promoted carotenoid and lipid synthesis, and supported synthesis preference of low unsaturated fatty acids represented by C18:1 and C16:0. The MP group maintained the best astaxanthin productivity via mastering the balance between increasing glucose metabolism and inhibition of carotenoid synthesis. The MP strategy optimized the physiological state of C. zofingiensis and realized its heterotrophic high-density growth for an excellent astaxanthin yield on a pilot scale. This strategy exhibits great application potential in the microalgae-related industry. Key points: • Preculture strategies changed carbon flux and gene expression in C. zofingiensis • C. zofingiensis realized a high-density culture with MP and fed-batch culture (FBC) • Astaxanthin productivity achieved 0.111 g L−1day−1with MP and FBC [ABSTRACT FROM AUTHOR]

Background: Elucidating cellular metabolism led to many breakthroughs in biotechnology, synthetic biology, and health sciences. To date, deriving metabolic fluxes by 13C tracer experiments is the most prominent approach for studying metabolic fluxes quantitatively, often with high accuracy and precision. However, the technique has a high demand for experimental resources. Alternatively, flux balance analysis (FBA) has been employed to estimate metabolic fluxes without labeling experiments. It is less informative but can benefit from the low costs and low experimental efforts and gain flux estimates in experimentally difficult conditions. Methods to integrate relevant experimental data have been emerged to improve FBA flux estimations. Data from transcription profiling is often selected since it is easy to generate at the genome scale, typically embedded by a discretization of differential and non-differential expressed genes coding for the respective enzymes. Result: We established the novel method Linear Programming based Gene Expression Model (LPM-GEM). LPM-GEM linearly embeds gene expression into FBA constraints. We implemented three strategies to reduce thermodynamically infeasible loops, which is a necessary prerequisite for such an omics-based model building. As a case study, we built a model of B. subtilis grown in eight different carbon sources. We obtained good flux predictions based on the respective transcription profiles when validating with 13C tracer based metabolic flux data of the same conditions. We could well predict the specific carbon sources. When testing the model on another, unseen dataset that was not used during training, good prediction performance was also observed. Furthermore, LPM-GEM outperformed a well-established model building methods. Conclusion: Employing LPM-GEM integrates gene expression data efficiently. The method supports gene expression-based FBA models and can be applied as an alternative to estimate metabolic fluxes when tracer experiments are inappropriate. [ABSTRACT FROM AUTHOR]

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ2-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development. Author summary: Measuring metabolic reaction fluxes in living cells is difficult, yet important. The gold standard is to label extracellular metabolites with 13C, to use mass spectrometry to find out where the 13C-atoms ends up, and finally use mathematical modelling to calculate how quickly each reaction must have flowed, for the 13C-atoms to end up like that. This measurement thus relies on usage of the right mathematical model, which must be selected among various candidate models. In this manuscript, we present a new way to do this model selection step, utilizing validation data. Using an adopted approach to calculate the uncertainty of model predictions, we identify new validation experiments, which are neither too similar, nor too dissimilar, compared to the previous training data. The model candidate that is best at predicting this new validation data is the one chosen. Tests on simulated data where the true model is known, shows that the validation-based method is robust when the magnitude of the error in the measurement uncertainty is unknown, something that conventional methods are not. This improvement is important since true uncertainties can be difficult to estimate for these data. Finally, we demonstrate how the new method can be used on real data, to identify fluxes and important reactions. [ABSTRACT FROM AUTHOR]

S -Adenosyl- l -methionine (SAM) is a major biological methyl group donor, and is used as a nutritional supplement and prescription drug. Yeast is used for the industrial production of SAM owing to its high intracellular SAM concentrations. To determine the regulation mechanisms responsible for such high SAM production, 13 C-metabolic flux analysis ( 13 C-MFA) was conducted to compare the flux distributions in the central metabolism between Kyokai no. 6 (high SAM-producing) and S288C (control) strains. 13 C-MFA showed that the levels of tricarboxylic acid (TCA) cycle flux in SAM-overproducing strain were considerably increased compared to those in the S228C strain. Analysis of ATP balance also showed that a larger amount of excess ATP was produced in the Kyokai 6 strain because of increased oxidative phosphorylation. These results suggest that high SAM production in Kyokai 6 strains could be attributed to enhanced ATP regeneration with high TCA cycle fluxes and respiration activity. Thus, maintaining high respiration efficiency during cultivation is important for improving SAM production. [ABSTRACT FROM AUTHOR]

Abstract Background Research about tumor “metabolic flexibility”—the ability of cells to toggle between preferred nutrients depending on the metabolic context—has largely focused on obesity-associated cancers. However, increasing evidence for a key role for nutrient competition in the tumor microenvironment, as well as for substrate regulation of immune function, suggests that substrate metabolism deserves reconsideration in immunogenic tumors that are not strongly associated with obesity. Methods We compare two murine models: immunologically cold YUMM1.7 and immunologically-hot YUMMER1.7. We utilize stable isotope and radioisotope tracer-based metabolic flux studies as well as gas and liquid chromatography-based metabolomics analyses to comprehensively probe substrate preference in YUMM1.7 and YUMMER1.7 cells, with a subset of studies on the impact of available metabolites across a panel of five additional melanoma cell lines. We analyze bulk RNA-seq data and identify increased expression of amino acid and glucose metabolism genes in YUMMER1.7. Finally, we analyze melanoma patient RNA-seq data to identify potential prognostic predictors rooted in metabolism. Results We demonstrate using stable isotope tracer-based metabolic flux studies as well as gas and liquid chromatography-based metabolomics that immunologically-hot melanoma utilizes more glutamine than immunologically-cold melanoma in vivo and in vitro. Analyses of human melanoma RNA-seq data demonstrate that glutamine transporter and other anaplerotic gene expression positively correlates with lymphocyte infiltration and function. Conclusions Here, we highlight the importance of understanding metabolism in non-obesity-associated cancers, such as melanoma. This work advances the understanding of the correlation between metabolism and immunogenicity in the tumor microenvironment and provides evidence supporting metabolic gene expression as potential prognostic factors of melanoma progression and may inform investigations of adjunctive metabolic therapy in melanoma. Trial registration Deidentified data from The Cancer Genome Atlas were analyzed.

Background: The Gram-positive Streptomyces lividans TK24 is an attractive host for heterologous protein production because of its high capability to secrete proteins—which favors correct folding and facilitates downstream processing—as well as its acceptance of methylated DNA and its low endogeneous protease activity. However, current inconsistencies in protein yields urge for a deeper understanding of the burden of heterologous protein production on the cell. In the current study, transcriptomics and 13 C \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{13}\hbox {C}$$\end{document} -based fluxomics were exploited to uncover gene expression and metabolic flux changes associated with heterologous protein production. The Rhodothermus marinus thermostable cellulase A (CelA)—previously shown to be successfully overexpressed in S. lividans—was taken as an example protein. Results: RNA-seq and 13 C \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{13}\hbox {C}$$\end{document} -based metabolic flux analysis were performed on a CelA-producing and an empty-plasmid strain under the same conditions. Differential gene expression, followed by cluster analysis based on co-expression and co-localization, identified transcriptomic responses related to secretion-induced stress and DNA damage. Furthermore, the OsdR regulon (previously associated with hypoxia, oxidative stress, intercellular signaling, and morphological development) was consistently upregulated in the CelA-producing strain and exhibited co-expression with isoenzymes from the pentose phosphate pathway linked to secondary metabolism. Increased expression of these isoenzymes matches to increased fluxes in the pentose phosphate pathway. Additionally, flux maps of the central carbon metabolism show increased flux through the tricarboxylic acid cycle in the CelA-producing strain. Redirection of fluxes in the CelA-producing strain leads to higher production of NADPH, which can only partly be attributed to increased secretion. Conclusions: Transcriptomic and fluxomic changes uncover potential new leads for targeted strain improvement strategies which may ease the secretion stress and metabolic burden associated with heterologous protein synthesis and secretion, and may help create a more consistently performing S. lividans strain. Yet, links to secondary metabolism and redox balancing should be further investigated to fully understand the S. lividans metabolome under heterologous protein production. [ABSTRACT FROM AUTHOR]

Mitochondrial Ca2+ plays a positive role in regulating pyruvate dehydrogenase, as well as the TCA cycle enzymes isocitrate dehydrogenase and α-ketoglutarate dehydrogenase. This regulation boosts the production of reducing equivalents that fuel the electron transport chain, ultimately driving ATP production. The Mitochondrial Calcium Uniporter (MCU) is the highly selective channel responsible for mitochondrial Ca2+ uptake when local Ca2+ levels reach the threshold for channel activation. In a recent study, LaMoia et al. used an innovative [13C 5 ]glutamine-based metabolic flux analysis method (Q-flux) to measure in vivo hepatic metabolic fluxes in liver-specific MCU-/- mice. Surprisingly, they observed increased flux through isocitrate dehydrogenase and α-ketoglutarate dehydrogenase. Metabolic pathways are continuously reorganized in response to intrinsic cellular signals, as well as hormonal and nutritional inputs. Integrating metabolic flux analysis into complex systems can provide deeper insights into how metabolic adaptations occur under different conditions. [ABSTRACT FROM AUTHOR]

Histone acetylation is an important epigenetic modification that regulates various biological processes and cell homeostasis. Acetyl-CoA, a hub molecule of metabolism, is the substrate for histone acetylation, thus linking metabolism with epigenetic regulation. However, still relatively little is known about the dynamics of histone acetylation and its dependence on metabolic processes, due to the lack of integrated methods that can capture site-specific histone acetylation and deacetylation reactions together with the dynamics of acetyl-CoA synthesis. In this study, we present a novel proteo-metabo-flux approach that combines mass spectrometry-based metabolic flux analysis of acetyl-CoA and histone acetylation with computational modelling. We developed a mathematical model to describe metabolic label incorporation into acetyl-CoA and histone acetylation based on experimentally measured relative abundances. We demonstrate that our approach is able to determine acetyl-CoA synthesis dynamics and site-specific histone acetylation and deacetylation reaction rate constants, and that consideration of the metabolically labelled acetyl-CoA fraction is essential for accurate determination of histone acetylation dynamics. Furthermore, we show that without correction, changes in metabolic fluxes would be misinterpreted as changes in histone acetylation dynamics, whereas our proteo-metabo-flux approach allows to distinguish between the two processes. Our proteo-metabo-flux approach expands the repertoire of metabolic flux analysis and cross-omics and represents a valuable approach to study the regulatory interplay between metabolism and epigenetic regulation by histone acetylation. [Display omitted] • Dynamic metabolic flux analysis of acetyl-CoA and histone acetylation. • Simultaneous determination of acetyl-CoA synthesis rates and histone acetylation dynamics. • Integrated approach improves mechanistic understanding of metabolomics and epigenomics. [ABSTRACT FROM AUTHOR]

Highsucrose diet (HSD) was reported as a causative factor for multiorgan injuries. The underlying mechanisms and therapeutic strategies remain largely uncharted. In the present study, by using a metabolomics approach, we identified the soluble epoxide hydrolase (sEH) as a therapeutic target for HSD-mediated gut barrier dysfunction. Specifically, 16-week feeding on an HSD caused gut barrier dysfunction, such as colon inflammation and tight junction impairment in a murine model. A metabolomics analysis of mouse colon tissue showed a decrease in the 5(6)-epoxyeicosatrienoic acid [5(6)-EET] level and an increase in soluble epoxide hydrolase, which is related to HSD-mediated injuries to the gut barrier. The mice treated with a chemical inhibitor of sEH and the mice with genetic intervention by intestinal-specific knockout of the sEH gene significantly attenuated HSD-caused intestinal injuries by reducing HSD-mediated colon inflammation and improving the impaired tight junction caused by an HSD. Further, in vitro studies showed that treatment with 5(6)-EET, but not its hydrolytic product 5,6-dihydroxyeicosatrienoic acid (5,6-DiHET), significantly ablated high sucrose-caused intestinal epithelial inflammation and impaired tight junction. Additionally, 5(6)-EET is anti-inflammatory and improves gut epithelial tight junction while 5,6-DiHET cannot do so. This study presents an underlying mechanism of and a therapeutic strategy for the gut barrier dysfunction caused by an HSD. [ABSTRACT FROM AUTHOR]

Identification of therapeutic targets can directly elucidate the mechanism and effect of drug therapy, which is a central step in drug development. The disconnect between protein targets and phenotypes under complex mechanisms hampers comprehensive target understanding. Metabolomics, as a systems biology tool that captures phenotypic changes induced by exogenous compounds, has emerged as a valuable approach for target identification. A comprehensive overview was provided in this review to illustrate the principles and advantages of metabolomics, delving into the application of metabolomics in target identification. This review outlines various metabolomics‐based methods, such as dose–response metabolomics, stable isotope‐resolved metabolomics, and multiomics, which identify key enzymes and metabolic pathways affected by exogenous substances through dose‐dependent metabolite–drug interactions. Emerging techniques, including single‐cell metabolomics, artificial intelligence, and mass spectrometry imaging, are also explored for their potential to enhance target discovery. The review emphasizes metabolomics' critical role in advancing our understanding of disease mechanisms and accelerating targeted drug development, while acknowledging current challenges in the field. [ABSTRACT FROM AUTHOR]

Background: Myceliophthora thermophila has been engineered as a significant cell factory for malic acid production, yet strategies to further enhance production remain unclear and lack rational guidance. 13C-MFA (13C metabolic flux analysis) offers a means to analyze cellular metabolic mechanisms and pinpoint critical nodes for improving product synthesis. Here, we employed 13C-MFA to investigate the metabolic flux distribution of a high-malic acid-producing strain of M. thermophila and attempted to decipher the crucial bottlenecks in the metabolic pathways. Results: Compared with the wild-type strain, the high-Malic acid-producing strain M. thermophila JG207 exhibited greater glucose uptake and carbon dioxide evolution rates but lower oxygen uptake rates and biomass yields. Consistent with these phenotypes, the 13C-MFA results showed that JG207 displayed elevated flux through the EMP pathway and downstream TCA cycle, along with reduced oxidative phosphorylation flux, thereby providing more precursors and NADH for malic acid synthesis. Furthermore, based on the 13C-MFA results, we conducted oxygen-limited culture and nicotinamide nucleotide transhydrogenase (NNT) gene knockout experiments to increase the cytoplasmic NADH level, both of which were shown to be beneficial for malic acid accumulation. Conclusions: This work elucidates and validates the key node for achieving high malic acid production in M. thermophila. We propose effective fermentation strategies and genetic modifications for enhancing malic acid production. These findings offer valuable guidance for the rational design of future cell factories aimed at improving malic acid yields. [ABSTRACT FROM AUTHOR]

Simple Summary: Glioblastoma (GBM) uses the Leloir pathway to catabolize D-Galactose (Gal) for tumor growth. Selective targeting of the Leloir pathway with Gal-based antimetabolites has potential for the treatment of GBM. Here, we tested the effect of a Gal-based antimetabolite, 4-deoxy-4-fluorogalactose (4DFG) on the viability and metabolism of GBM cells in culture. 4DFG is a good Glut3/Glut14 substrate and acts as a potent glioma chemotherapeutic. GBM cell cultures were used to examine toxicity and alterations in glycan composition. 4DFG is moderately potent against GBM cells in vitro (IC50: 125–300 µM). Glycosylation in GBM was disrupted by 4DFG. The effect of 4DFG on D-glucose (Glc) metabolism in GBM cells was assessed by using 13C NMR-based tracer studies. 13C-NMR-based metabolic flux analysis revealed that both glycolytic and mitochondrial metabolic fluxes of [U-13C]Glc were significantly decreased in the presence of 4DFG in GBM cells. Survival analysis in an intracranial mouse model during treatment with 4DFG (6 × 25 mg/kg of 4DFG, intravenously) showed improved outcomes by three-fold (p < 0.01). 4DFG is metabolized by GBM in vitro and in vivo, and is lethal to GBM tumors, but well tolerated in mice. A functional Gal-scavenging pathway in GBM allows Gal-based antimetabolites to act as chemotherapeutics. Background: Glioblastoma (GBM) uses Glut3 and/or Glut14 and the Leloir pathway to catabolize D-Galactose (Gal). UDP-4-deoxy-4-fluorogalactose (UDP-4DFG) is a potent inhibitor of the two key enzymes, UDP-galactose-4-epimerase (GALE) and UDP-Glucose 6-dehydrogenase (UGDH), involved in Gal metabolism and in glycan synthesis. The Gal antimetabolite 4-deoxy-4-fluorogalactose (4DFG) is a good substrate for Glut3/Glut14 and acts as a potent glioma chemotherapeutic. Methods: Primary GBM cell cultures were used to examine toxicity and alterations in glycan composition via lectin binding in fixed cells and by Western blots. Toxicity/efficacy in vivo data was performed in mouse flank and intracranial models. The effect of 4DFG on D-glucose (Glc) metabolism in GBM cells was assessed by using 13C NMR-based tracer studies. Results: 4DFG is moderately potent against GBM cells (IC50: 125–300 µM). GBM glycosylation is disrupted by 4DFG. Survival analysis in an intracranial mouse model showed that treatment with 4DFG (6 × 25 mg/kg of 4DFG, intravenously) improved outcomes by three-fold (p < 0.01). Metabolic flux analysis revealed that both glycolytic and mitochondrial metabolic fluxes of [U-13C]Glc were significantly decreased in the presence of 4DFG in GBM cells. Conclusion: A functional Gal-scavenging pathway in GBM allows Gal-based antimetabolites to act as chemotherapeutics. 4DFG is metabolized by GBM in vitro and in vivo, is lethal to GBM tumors, and is well tolerated in mice. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC) comprises the majority, approximately 70–80%, of renal cancer cases and often remains asymptomatic until incidentally detected during unrelated abdominal imaging or at advanced stages. Currently, standardized screening tests for renal cancer are lacking, which presents challenges in disease management and improving patient outcomes. This study aimed to identify ccRCC-specific volatile organic compounds (VOCs) in the urine of ccRCC-positive patients and develop a urinary VOC-based diagnostic model. Methods: This study involved 233 pretreatment ccRCC patients and 43 healthy individuals. VOC analysis utilized stir-bar sorptive extraction coupled with thermal desorption gas chromatography/mass spectrometry (SBSE-TD-GC/MS). A ccRCC diagnostic model was established via logistic regression, trained on 163 ccRCC cases versus 31 controls, and validated with 70 ccRCC cases versus 12 controls, resulting in a ccRCC diagnostic model involving 24 VOC markers. Results: The findings demonstrated promising diagnostic efficacy, with an Area Under the Curve (AUC) of 0.94, 86% sensitivity, and 92% specificity. Conclusions: This study highlights the feasibility of using urine as a reliable biospecimen for identifying VOC biomarkers in ccRCC. While further validation in larger cohorts is necessary, this study's capability to differentiate between ccRCC and control groups, despite sample size limitations, holds significant promise. [ABSTRACT FROM AUTHOR]