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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]

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]

Metabolic fluxes are the manifestations of the co-operating actions in a complex network of genes, transcripts, proteins, and metabolites. As a final quantitative endpoint of all cellular interactions, the intracellular fluxes are of immense interest in fundamental as well as applied research. Unlike the quantities of interest in most omics levels, in vivo fluxes are, however, not directly measureable. In the last decade, C-based metabolic flux analysis emerged as the state-of-the-art technique to infer steady-state fluxes by data from labeling experiments and the use of mathematical models. A very promising new area in systems metabolic engineering research is non-stationary C-metabolic flux analysis at metabolic steady-state conditions. Several studies have demonstrated an information surplus contained in transient labeling data compared to those taken at the isotopic equilibrium, as it is classically done. Enabled by recent, fairly multi-disciplinary progress, the new method opens several attractive options to (1) generate new insights, e.g., in cellular storage metabolism or the dilution of tracer by endogenous pools and (2) shift limits, inherent in the classical approach, towards enhanced applicability with respect to cultivation conditions and biological systems. We review the new developments in metabolome-based non-stationary C flux analysis and outline future prospects for accurate in vivo flux measurement. [ABSTRACT FROM AUTHOR]

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 13C-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 13C-flux analyses under isotopically nonstationary conditions for analyzing fast metabolic dynamics. [ABSTRACT FROM AUTHOR]

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]

Stable isotope based metabolic flux analysis is currently the unique methodology that allows the experimental study of the integrated responses of metabolic networks. This method primarily relies on isotope labeling and modeling, which could be a challenge in both experimental and computational biology. In particular, the algorithm implementation for isotope simulation is a critical step, limiting extensive usage of this powerful approach. Here, we introduce EMUlator a Python-based isotope simulator which is developed on Elementary Metabolite Unit (EMU) algorithm, an efficient and powerful algorithm for isotope modeling. We propose a novel adjacency matrix method to implement EMU modeling and exemplify it stepwise. This method is intuitively straightforward and can be conveniently mastered for various customized purposes. We apply this arithmetic pipeline to understand the phosphoketolase flux in the metabolic network of an industrial microbe Clostridium acetobutylicum. The resulting design enables a high-throughput and non-invasive approach for estimating phosphoketolase flux in vivo. Our computational insights allow the systematic design and prediction of isotope-based metabolic models and yield a comprehensive understanding of their limitations and potentials. [ABSTRACT FROM AUTHOR]

High concentrations of propionate and its metabolites are found in several diseases that are often associated with the development of cardiac dysfunction, such as obesity, diabetes, propionic acidemia, and methylmalonic acidemia. In the present work, we employed a stable isotope-based metabolic flux approach to understand propionate-mediated perturbation of cardiac energy metabolism. Propionate led to accumulation of propionyl-CoA (increased by ~101-fold) and methylmalonyl-CoA (increased by 36-fold). This accumulation caused significant mitochondrial CoA trapping and inhibited fatty acid oxidation. The reduced energy contribution from fatty acid oxidation was associated with increased glucose oxidation. The enhanced anaplerosis of propionate and CoA trapping altered the pool sizes of tricarboxylic acid cycle (TCA) metabolites. In addition to being an anaplerotic substrate, the accumulation of proprionate-derived malate increased the recycling of malate to pyruvate and acetyl-CoA, which can enter the TCA for energy production. Supplementation of 3 mM l-carnitine did not relieve CoA trapping and did not reverse the propionate-mediated fuel switch. This is due to new findings that the heart appears to lack the specific enzyme catalyzing the conversion of short-chain (C3 and C4) dicarboxylyl-CoAs to dicarboxylylcarnitines. The discovery of this work warrants further investigation on the relevance of dicarboxylylcarnitines, especially C3 and C4 dicarboxylylcarnitines, in cardiac conditions such as heart failure. [ABSTRACT FROM AUTHOR]

Background: Constraint-based metabolic flux analysis of knockout strategies is an efficient method to simulate the production of useful metabolites in microbes. Owing to the recent development of technologies for artificial DNA synthesis, it may become important in the near future to mathematically design minimum metabolic networks to simulate metabolite production. Results: We have developed a computational method where parsimonious metabolic flux distribution is computed for designated constraints on growth and production rates which are represented by grids. When the growth rate of this obtained parsimonious metabolic network is maximized, higher production rates compared to those noted using existing methods are observed for many target metabolites. The set of reactions used in this parsimonious flux distribution consists of reactions included in the original genome scale model iAF1260. The computational experiments show that the grid size affects the obtained production rates. Under the conditions that the growth rate is maximized and the minimum cases of flux variability analysis are considered, the developed method produced more than 90% of metabolites, while the existing methods produced less than 50%. Mathematical explanations using examples are provided to demonstrate potential reasons for the ability of the proposed algorithm to identify design strategies that the existing methods could not identify. Conclusion: We developed an efficient method for computing the design of minimum metabolic networks by using constraint-based flux balance analysis to simulate the production of useful metabolites. The source code is freely available, and is implemented in MATLAB and COBRA toolbox. [ABSTRACT FROM AUTHOR]

Fungicides are an indispensable tool in plant disease control. Various modes of action (MOAs) have been identified in different fungicides to suppress plant pathogens. The combined use of fungicides with distinct MOAs has been recommended to prevent the development of pathogen resistance. In studying MOAs, metabolomics has been proven to be a robust and high-throughput method. Because metabolites are unique and distinct depending on the biological activities of an organism, MOAs can be identified and classified by establishing metabolic fingerprinting and metabolic profiles. Similarly, if fungicide resistance is developed in a pathogen, the metabolome will change, which can be identified. In this review, we have discussed the principles and advanced applications of metabolomics in the study of MOAs and resistance mechanisms of fungicides, and the potential of metabolic data in understanding the interaction between fungicides and pathogens. Challenges are also discussed in the application of metabolomics, improvement of the study on the mechanism of fungicides in their functions against pathogens and advancing the development of novel fungicides. [ABSTRACT FROM AUTHOR]

Recombinant protein production in Komagataella phaffi (K. phaffi), a widely utilized host organism, can be optimized by enhancing the metabolic flux in the central carbon metabolism pathways. The methanol utilization pathway (MUT) during methanol-based growth plays a crucial role in providing precursors and energy for cell growth and development. This study investigated the impact of boosting the methanol dissimilation pathway, a branch of MUT that plays a vital role in detoxifying formaldehyde and providing energy in the form of NADH, in K. phaffi. This was achieved by integrating two orthologous genes from Hansenula polymorpha into the K. phaffi genome: formaldehyde dehydrogenase (HpFLD) and formate dehydrogenase (HpFMDH). The HpFLD and HpFMDH genes were isolated from the Hansenula polymorpha genome and inserted under the regulation of the pAOX1 promoter in the genome of recombinant K. phaffi that already contained a single copy of model protein genes (eGFP or EGII). The expression levels of these model proteins were assessed through protein activity assays and gene expression analysis. The findings revealed that while both orthologous genes positively influenced model protein production, HpFMDH exhibited a more pronounced upregulation in expression compared to HpFLD. Co-expression of both orthologous genes demonstrated synergistic effects, resulting in approximately a twofold increase in the levels of the model proteins detected. This study provides valuable insights into enhancing the production capacity of recombinant proteins in K. phaffi. [ABSTRACT FROM AUTHOR]

Cyanobacteria are the only prokaryotes capable of performing oxygenic photosynthesis. Many cyanobacterial strains can live in different trophic modes, ranging from photoautotrophic and heterotrophic to mixotrophic growth. However, the regulatory mechanisms allowing a flexible switch between these lifestyles are poorly understood. As anabolic fixation of CO2 in the Calvin-Benson-Bassham (CBB) cycle and catabolic sugar-degradation pathways share intermediates and enzymatic capacity, a tight regulatory network is required to enable simultaneous opposed metabolic fluxes. The Entner-Doudoroff (ED) pathway was recently predicted as one glycolytic route, which cooperates with other pathways in glycogen breakdown. Despite low carbon flux through the ED pathway, metabolite analyses of mutants deficient in the ED pathway revealed a distinct phenotype pointing at a strong regulatory impact of this route. The small Cp12 protein downregulates the CBB cycle in darkness by inhibiting phosphoribulokinase and glyceraldehyde 3-phosphate dehydrogenase. New results of metabolomic and redox level analyses on strains with Cp12 variants extend the known role of Cp12 regulation towards the acclimation to external glucose supply under diurnal conditions as well as to fluctuations in CO2 levels in the light. Moreover, carbon and nitrogen metabolism are closely linked to maintain an essential C/N homeostasis. The small protein PirC was shown to be an important regulator of phosphoglycerate mutase, which identified this enzyme as central branching point for carbon allocation from CBB cycle towards lower glycolysis. Altered metabolite levels in the mutant DpirC during nitrogen starvation experiments confirm this regulatory mechanism. The elucidation of novel mechanisms regulating carbon allocation at crucial metabolic branching points could identify ways for targeted redirection of carbon flow towards desired compounds, and thus help to further establish cyanobacteria as green cell factories for biotechnological applications with concurrent utilization of sunlight and CO2. [ABSTRACT FROM AUTHOR]

L-tryptophan is an essential amino acid widely used in the pharmaceutical and feed industries. Enhancing its production in microorganisms necessitates activating and inactivating specific genes to direct more resources toward its synthesis. In this study, we developed a classification model based on Qualitative Perturbation Analysis and Machine Learning (QPAML). The model uses pFBA to obtain optimal reactions for tryptophan production and FSEOF to introduce perturbations on fluxes of the optima reactions while registering all changes over the iML1515a Genome-Scale Metabolic Network model. The altered reaction fluxes and their relationship with tryptophan and biomass production are translated to qualitative variables classified with GBDT. In the end, groups of enzymatic reactions are predicted to be deleted, overexpressed, or attenuated for tryptophan and 30 other metabolites in E. coli with a 92.34% F1-Score. The QPAML model can integrate diverse data types, promising improved predictions and the discovery of complex patterns in microbial metabolic engineering. It has broad potential applications and offers valuable insights for optimizing microbial production in biotechnology. [ABSTRACT FROM AUTHOR]

Stoichiometric genome-scale metabolic models (generally abbreviated GSM, GSMM, or GEM) have had many applications in exploring phenotypes and guiding metabolic engineering interventions. Nevertheless, these models and predictions thereof can become limited as they do not directly account for protein cost, enzyme kinetics, and cell surface or volume proteome limitations. Lack of such mechanistic detail could lead to overly optimistic predictions and engineered strains. Initial efforts to correct these deficiencies were by the application of precursor tools for GSMs, such as flux balance analysis with molecular crowding. In the past decade, several frameworks have been introduced to incorporate proteome-related limitations using a genome-scale stoichiometric model as the reconstruction basis, which herein are called resource allocation models (RAMs). This review provides a broad overview of representative or commonly used existing RAM frameworks. This review discusses increasingly complex models, beginning with stoichiometric models to precursor to RAM frameworks to existing RAM frameworks. RAM frameworks are broadly divided into two categories: coarse-grained and fine-grained, with different strengths and challenges. Discussion includes pinpointing their utility, data needs, highlighting framework strengths and limitations, and appropriateness to various research endeavors, largely through contrasting their mathematical frameworks. Finally, promising future applications of RAMs are discussed. [ABSTRACT FROM AUTHOR]

Objectives: 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. Methods: 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. Results: 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. Conclusions: 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.

Adenosine-5'-triphosphate (ATP), the primary energy currency in cellular processes, drives metabolic activities and biosynthesis. Despite its importance, understanding intracellular ATP dynamics' impact on bioproduction and exploiting it for enhanced bioproduction remains largely unexplored. Here, we harness an ATP biosensor to dissect ATP dynamics across different growth phases and carbon sources in multiple microbial strains. We find transient ATP accumulations during the transition from exponential to stationary growth phases in various conditions, coinciding with fatty acid (FA) and polyhydroxyalkanoate (PHA) production in Escherichia coli and Pseudomonas putida, respectively. We identify carbon sources (acetate for E. coli, oleate for P. putida) that elevate steady-state ATP levels and boost FA and PHA production. Moreover, we employ ATP dynamics as a diagnostic tool to assess metabolic burden, revealing bottlenecks that limit limonene bioproduction. Our results not only elucidate the relationship between ATP dynamics and bioproduction but also showcase its value in enhancing bioproduction in various microbial species. ATP dynamics influence bioproduction yet are largely unexplored in this context. Here, authors unravel ATP dynamics across various conditions, identify carbon sources which boost ATP levels and bioproduction, and uncover metabolic bottlenecks, shedding light on how ATP dynamics can be used to enhance bioproduction. [ABSTRACT FROM AUTHOR]

The cell surface of Toxoplasma gondii is rich in glycoconjugates which hold diverse and vital functions in the lytic cycle of this obligate intracellular parasite. Additionally, the cyst wall of bradyzoites, that shields the persistent form responsible for chronic infection from the immune system, is heavily glycosylated. Formation of glycoconjugates relies on activated sugar nucleotides, such as uridine diphosphate N-acetylglucosamine (UDP-GlcNAc). The glucosamine-phosphate-N-acetyltransferase (GNA1) generates N-acetylglucosamine-6-phosphate critical to produce UDP-GlcNAc. Here, we demonstrate that downregulation of T. gondii GNA1 results in a severe reduction of UDP-GlcNAc and a concomitant drop in glycosylphosphatidylinositols (GPIs), leading to impairment of the parasite's ability to invade and replicate in the host cell. Surprisingly, attempts to rescue this defect through exogenous GlcNAc supplementation fail to completely restore these vital functions. In depth metabolomic analyses elucidate diverse causes underlying the failed rescue: utilization of GlcNAc is inefficient under glucose-replete conditions and fails to restore UDP-GlcNAc levels in GNA1-depleted parasites. In contrast, GlcNAc-supplementation under glucose-deplete conditions fully restores UDP-GlcNAc levels but fails to rescue the defects associated with GNA1 depletion. Our results underscore the importance of glucosamine-6-phosphate acetylation in governing T. gondii replication and invasion and highlight the potential of the evolutionary divergent GNA1 in Apicomplexa as a target for the development of much-needed new therapeutic strategies. Author summary: Toxoplasma gondii, Plasmodium, and Cryptosporidium spp., pose serious threats to human health. T. gondii, an intracellular and opportunistic pathogen, effectively avoids the host immune defences by forming long-lasting tissue cysts. Finding potent drugs to eliminate these persisting parasites remains a challenge. The glucosamine-phosphate-N-acetyltransferase (GNA1) catalyses a critical key step in the production of activated sugar nucleotides to build glycoconjugates essential for various functions in the cell. In P. falciparum, this enzyme has been identified as a potential target for antimalarial drugs. In this study, we explored the importance of this pathway in T. gondii and discovered that these sugar-containing compounds play a vital role in the parasite's ability to invade and replicate in host cells–crucial processes for its survival and ability to cause disease. Intriguingly, unlike some organisms that can bypass the pathway, T. gondii relies critically on glucosamine-6-phosphate acetylation. This reliance sheds light on the parasite's distinct metabolic properties and highlights the pathway's potential as a target for new therapeutic strategies. [ABSTRACT FROM AUTHOR]

Metabolic reprogramming is a hallmark of cancer, driving the development of therapies targeting cancer metabolism. Stable isotope tracing has emerged as a widely adopted tool for monitoring cancer metabolism both in vitro and in vivo. Advances in instrumentation and the development of new tracers, metabolite databases, and data analysis tools have expanded the scope of cancer metabolism studies across these scales. In this review, we explore the latest advancements in metabolic analysis, spanning from experimental design in stable isotope-labeling metabolomics to sophisticated data analysis techniques. We highlight successful applications in cancer research, particularly focusing on ongoing clinical trials utilizing stable isotope tracing to characterize disease progression, treatment responses, and potential mechanisms of resistance to anticancer therapies. Furthermore, we outline key challenges and discuss potential strategies to address them, aiming to enhance our understanding of the biochemical basis of cancer metabolism. [ABSTRACT FROM AUTHOR]