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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. 13 C-MFA ( 13 C metabolic flux analysis) offers a means to analyze cellular metabolic mechanisms and pinpoint critical nodes for improving product synthesis. Here, we employed 13 C-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 13 C-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 13 C-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., (© 2024. The Author(s).)

Balancing the ATP: NADPH demand from plant metabolism with supply from photosynthesis is essential for preventing photodamage and operating efficiently, so understanding its drivers is important for integrating metabolism with the light reactions of photosynthesis and for bioengineering efforts that may radically change this demand. It is often assumed that the C3 cycle and photorespiration consume the largest amount of ATP and reductant in illuminated leaves and as a result mostly determine the ATP: NADPH demand. However, the quantitative extent to which other energy consuming metabolic processes contribute in large ways to overall ATP: NADPH demand remains unknown. Here, we used the metabolic flux networks of numerous recently published isotopically non-stationary metabolic flux analyses (INST-MFA) to evaluate flux through the C3 cycle, photorespiration, the oxidative pentose phosphate pathway, the tricarboxylic acid cycle, and starch/sucrose synthesis and characterize broad trends in the demand of energy across different pathways and compartments as well as in the overall ATP:NADPH demand. These data sets include a variety of species including Arabidopsis thaliana, Nicotiana tabacum, and Camelina sativa as well as varying environmental factors including high/low light, day length, and photorespiratory levels. Examining these datasets in aggregate reveals that ultimately the bulk of the energy flux occurred in the C3 cycle and photorespiration, however, the energy demand from these pathways did not determine the ATP: NADPH demand alone. Instead, a notable contribution was revealed from starch and sucrose synthesis which might counterbalance photorespiratory demand and result in fewer adjustments in mechanisms which balance the ATP deficit., (© 2024. The Author(s).)

Metabolic flux analysis (MFA) is a valuable tool for quantifying cellular phenotypes and to guide plant metabolic engineering. By introducing stable isotopic tracers and employing mathematical models, MFA can quantify the rates of metabolic reactions through biochemical pathways. Recent applications of isotopically nonstationary MFA (INST-MFA) to plants have elucidated nonintuitive metabolism in leaves under optimal and stress conditions, described coupled fluxes for fast-growing algae, and produced a synergistic multi-organ flux map that is a first in MFA for any biological system. These insights could not be elucidated through other approaches and show the potential of INST-MFA to correct an oversimplified understanding of plant metabolism., (© 2024 The Authors. New Phytologist © 2024 New Phytologist Foundation. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Hyperpolarized carbon-13 labeled compounds are increasingly being used in medical MR imaging (MRI) and MR imaging (MRI) and spectroscopy (MRS) research, due to its ability to monitor tissue and cell metabolism in real-time. Although radiological biomarkers are increasingly being considered as clinical indicators, biopsies are still considered the gold standard for a large variety of indications. Bioreactor systems can play an important role in biopsy examinations because of their ability to provide a physiochemical environment that is conducive for therapeutic response monitoring ex vivo. We demonstrate here a proof-of-concept bioreactor and microcoil receive array setup that allows for ex vivo preservation and metabolic NMR spectroscopy on up to three biopsy samples simultaneously, creating an easy-to-use and robust way to simultaneously run multisample carbon-13 hyperpolarization experiments. Experiments using hyperpolarized [1- 13 C]pyruvate on ML-1 leukemic cells in the bioreactor setup were performed and the kinetic pyruvate-to-lactate rate constants ( k PL ) extracted. The coefficient of variation of the experimentally found k PL s for five repeated experiments was C V = 35 % . With this statistical power, treatment effects of 30%-40% change in lactate production could be easily differentiable with only a few hyperpolarization dissolutions on this setup. Furthermore, longitudinal experiments showed preservation of ML-1 cells in the bioreactor setup for at least 6 h. Rat brain tissue slices were also seen to be preserved within the bioreactor for at least 1 h. This validation serves as the basis for further optimization and upscaling of the setup, which undoubtedly has huge potential in high-throughput studies with various biomarkers and tissue types., (© 2024 John Wiley & Sons Ltd.)

One of the critical steps to characterize metabolic alterations in multifactorial diseases, as well as their heterogeneity across different patients, is the identification of reactions that exhibit significantly different usage (or flux) between cohorts. However, since metabolic fluxes cannot be determined directly, researchers typically use constraint-based metabolic network models, customized on post-genomics datasets. The use of random sampling within the feasible region of metabolic networks is becoming more prevalent for comparing these networks. While many algorithms have been proposed and compared for efficiently and uniformly sampling the feasible region of metabolic networks, their impact on the risk of making false discoveries when comparing different samples has not been investigated yet, and no sampling strategy has been so far specifically designed to mitigate the problem. To be able to precisely assess the False Discovery Rate (FDR), in this work we compared different samples obtained from the very same metabolic model. We compared the FDR obtained for different model scales, sample sizes, parameters of the sampling algorithm, and strategies to filter out non-significant variations. To be able to compare the largely used hit-and-run strategy with the much less investigated corner-based strategy, we first assessed the intrinsic capability of current corner-based algorithms and of a newly proposed one to visit all vertices of a constraint-based region. We show that false discoveries can occur at high rates even for large samples of small-scale networks. However, we demonstrate that a statistical test based on the empirical null distribution of Kullback-Leibler divergence can effectively correct for false discoveries. We also show that our proposed corner-based algorithm is more efficient than state-of-the-art alternatives and much less prone to false discoveries than hit-and-run strategies. We report that the differences in the marginal distributions obtained with the two strategies are related to but not fully explained by differences in sample standard deviation, as previously thought. Overall, our study provides insights into the impact of sampling strategies on FDR in metabolic network analysis and offers new guidelines for more robust and reproducible analyses., Competing Interests: Declaration of competing interest 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., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

13C-Metabolic Flux Analysis (13C-MFA) and Flux Balance Analysis (FBA) are widely used to investigate the operation of biochemical networks in both biological and biotechnological research. Both methods use metabolic reaction network models of metabolism operating at steady state so that reaction rates (fluxes) and the levels of metabolic intermediates are constrained to be invariant. They provide estimated (MFA) or predicted (FBA) values of the fluxes through the network in vivo, which cannot be measured directly. These fluxes can shed light on basic biology and have been successfully used to inform metabolic engineering strategies. Several approaches have been taken to test the reliability of estimates and predictions from constraint-based methods and to compare alternative model architectures. Despite advances in other areas of the statistical evaluation of metabolic models, such as the quantification of flux estimate uncertainty, validation and model selection methods have been underappreciated and underexplored. We review the history and state-of-the-art in constraint-based metabolic model validation and model selection. Applications and limitations of the χ 2 -test of goodness-of-fit, the most widely used quantitative validation and selection approach in 13C-MFA, are discussed, and complementary and alternative forms of validation and selection are proposed. A combined model validation and selection framework for 13C-MFA incorporating metabolite pool size information that leverages new developments in the field is presented and advocated for. Finally, we discuss how adopting robust validation and selection procedures can enhance confidence in constraint-based modeling as a whole and ultimately facilitate more widespread use of FBA in biotechnology., (© 2023 The Authors. Biotechnology Progress published by Wiley Periodicals LLC on behalf of American Institute of Chemical Engineers.)

Central carbon and energy metabolism are the most concerned metabolic pathways in 13 C-Metabolic flux analysis ( 13 C-MFA). However, some α-keto acids, ribonucleoside triphosphate (NTPs) and deoxyribonucleoside triphosphate (dNTPs) involved in central carbon and energy metabolism pathways were unstable or reactive, leading to inaccurate metabolic flux analysis. To achieve accurate 13 C-MFA of central carbon and energy metabolism, we proposed a dual strategy for the detection of 101 metabolites in glucose metabolism pathways. N-Methylphenylethylamine (MPEA) was utilized for derivatization of 4 carboxyl (α-keto acids) and 8 phosphate metabolites (NTPs and dNTPs). After derivatization, the MPEA derivatives were investigated to be stable for 4 weeks under 4 °C and detected with high intensity in ∼10 4  cells. On the other hand, we analyzed an additional 89 metabolites in central carbon and energy metabolic pathways were directly analyzed by liquid chromatography tandem mass spectrometry (LC-MRM-MS). The limit of detection (LODs) of our method were as low as 0.05 ng/mL and the linear range was at least two orders of magnitude with determination coefficient (R 2 ) > 0.9701. The relative standard divisions (RSDs) of intra- and inter-day of 95% metabolites were below 20%. In addition, the isotope list of 82 detected metabolites in central carbon and energy metabolism were generated according to isotopologues and isotopomers for each metabolite resulting from 13 C incorporation. Accurate assessment of mass isotopomer distributions (MIDs) of intracellular 13 C-labeled metabolites was achieved in [U- 13 C]-glucose cultured HepG2 cells by our dual strategy. Finally, we performed MID analysis of 101 metabolites in central carbon and energy metabolism. Overall, this dual method is reproducible and robust for application on 13 C-MFA and has a great potential for studying clinical isotope labeled samples., Competing Interests: Declaration of competing interest 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., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Competing Interests: Conflict of interest statement. None declared.

Background: Production of 3-hydroxypropionic acid (3-HP) through the malonyl-CoA pathway has yielded promising results in Pichia pastoris (Komagataella phaffii), demonstrating the potential of this cell factory to produce this platform chemical and other acetyl-CoA-derived products using glycerol as a carbon source. However, further metabolic engineering of the original P. pastoris 3-HP-producing strains resulted in unexpected outcomes, e.g., significantly lower product yield and/or growth rate. To gain an understanding on the metabolic constraints underlying these observations, the fluxome (metabolic flux phenotype) of ten 3-HP-producing P. pastoris strains has been characterized using a high throughput 13 C-metabolic flux analysis platform. Such platform enabled the operation of an optimised workflow to obtain comprehensive maps of the carbon flux distribution in the central carbon metabolism in a parallel-automated manner, thereby accelerating the time-consuming strain characterization step in the design-build-test-learn cycle for metabolic engineering of P. pastoris., Results: We generated detailed maps of the carbon fluxes in the central carbon metabolism of the 3-HP producing strain series, revealing the metabolic consequences of different metabolic engineering strategies aimed at improving NADPH regeneration, enhancing conversion of pyruvate into cytosolic acetyl-CoA, or eliminating by-product (arabitol) formation. Results indicate that the expression of the POS5 NADH kinase leads to a reduction in the fluxes of the pentose phosphate pathway reactions, whereas an increase in the pentose phosphate pathway fluxes was observed when the cytosolic acetyl-CoA synthesis pathway was overexpressed. Results also show that the tight control of the glycolytic flux hampers cell growth due to limited acetyl-CoA biosynthesis. When the cytosolic acetyl-CoA synthesis pathway was overexpressed, the cell growth increased, but the product yield decreased due to higher growth-associated ATP costs. Finally, the six most relevant strains were also cultured at pH 3.5 to assess the effect of a lower pH on their fluxome. Notably, similar metabolic fluxes were observed at pH 3.5 compared to the reference condition at pH 5., Conclusions: This study shows that existing fluoxomics workflows for high-throughput analyses of metabolic phenotypes can be adapted to investigate P. pastoris, providing valuable information on the impact of genetic manipulations on the metabolic phenotype of this yeast. Specifically, our results highlight the metabolic robustness of P. pastoris's central carbon metabolism when genetic modifications are made to increase the availability of NADPH and cytosolic acetyl-CoA. Such knowledge can guide further metabolic engineering of these strains. Moreover, insights into the metabolic adaptation of P. pastoris to an acidic pH have also been obtained, showing the capability of the fluoxomics workflow to assess the metabolic impact of environmental changes., (© 2023. The Author(s).)

Metabolic scaling, the inverse correlation of metabolic rates to body mass, has been appreciated for more than 80 years. Studies of metabolic scaling have largely been restricted to mathematical modeling of caloric intake and oxygen consumption, and mostly rely on computational modeling. The possibility that other metabolic processes scale with body size has not been comprehensively studied. To address this gap in knowledge, we employed a systems approach including transcriptomics, proteomics, and measurement of in vitro and in vivo metabolic fluxes. Gene expression in livers of five species spanning a 30,000-fold range in mass revealed differential expression according to body mass of genes related to cytosolic and mitochondrial metabolic processes, and to detoxication of oxidative damage. To determine whether flux through key metabolic pathways is ordered inversely to body size, we applied stable isotope tracer methodology to study multiple cellular compartments, tissues, and species. Comparing C57BL/6 J mice with Sprague-Dawley rats, we demonstrate that while ordering of metabolic fluxes is not observed in in vitro cell-autonomous settings, it is present in liver slices and in vivo. Together, these data reveal that metabolic scaling extends beyond oxygen consumption to other aspects of metabolism, and is regulated at the level of gene and protein expression, enzyme activity, and substrate supply., Competing Interests: NA, BL, DJ, RD, DO, WZ, ZL, MP, RP No competing interests declared, (© 2023, Akingbesote et al.)

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., Competing Interests: Declaration of competing interest The authors declare no financial or commercial conflict of interest., (Copyright © 2023 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

In nature, photosynthetic organisms are exposed to fluctuating light, and their physiological systems must adapt to this fluctuation. To maintain homeostasis, these organisms have a light fluctuation photoprotective mechanism, which functions in both photosystems and metabolism. Although the photoprotective mechanisms functioning in the photosystem have been studied, it is unclear how metabolism responds to light fluctuations within a few seconds. In the present study, we investigated the metabolic response of Synechocystis sp. PCC 6803 to light fluctuations using 13 C-metabolic flux analysis. The light intensity and duty ratio were adjusted such that the total number of photons or the light intensity during the low-light phase was equal. Light fluctuations affected cell growth and photosynthetic activity under the experimental conditions. However, metabolic flux distributions and cofactor production rates were not affected by the light fluctuations. Furthermore, the estimated ATP and NADPH production rates in the photosystems suggest that NADPH-consuming electron dissipation occurs under fluctuating light conditions. Although we focused on the water-water cycle as the electron dissipation path, no growth effect was observed in an flv3-disrupted strain under fluctuating light, suggesting that another path contributes to electron dissipation under these conditions., (© 2023 American Institute of Chemical Engineers.)

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13 C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process., Competing Interests: Declaration of competing interest The authors declare no commercial or financial conflict of interest., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Stable isotope 13 C labeling is an important tool to analyze cellular metabolic flux. The 13 C distribution in intracellular metabolites can be detected via mass spectrometry and used as a constraint in intracellular metabolic flux calculations. Then, metabolic flux analysis algorithms can be employed to obtain the flux distribution in the corresponding metabolic reaction network. However, in addition to carbon, other elements such as oxygen in the nature also have natural stable isotopes (e.g., 17 O, 18 O). This makes the isotopic information of elements other than the 13 C marker interspersed in the isotopic distribution measured by the mass spectrometry, especially that of the molecules containing many other elements, which leads to large errors. Therefore, it is essential to correct the mass spectrometry data before performing metabolic flux calculations. In this paper, we proposed a method for construction of correction matrix based on Python language for correcting the measurement errors due to natural isotope distribution. The method employed a basic power method for constructing the correction matrix with simple structure and easy coding implementation, which can be directly applied to data pre-processing in 13 C metabolic flux analysis. The correction method was then applied to the intracellular metabolic flux analysis of 13 C-labeled Aspergillus niger . The results showed that the proposed method was accurate and effective, which can serve as a reliable data correction method for accurate microbial intracellular metabolic flux analysis.

Biochemical characterization of metabolism provides molecular insights for understanding biology in health and disease. Over the past decades, metabolic perturbations have been implicated in cancer, neurodegeneration, and diabetes, among others. Isotope tracing is a technique that allows tracking of labeled atoms within metabolites through biochemical reactions. This technique has become an integral component of the contemporary metabolic research. Isotope tracing measures substrate contribution to downstream metabolites and indicates its utilization in cellular metabolic networks. In addition, isotopic labeling data are necessary for quantitative metabolic flux analysis. Here, we review recent work utilizing metabolic tracing to study health and disease, and highlight its application to interrogate subcellular, intercellular, and in vivo metabolism. We further discuss the current challenges and opportunities to expand the utility of isotope tracing to new research areas., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Summary: The number of instationary 13C-metabolic flux (INST-MFA) studies grows every year, making it more important than ever to ensure the clarity, standardization and reproducibility of each study. We proposed CeCaFLUX, the first user-friendly web server that derives metabolic flux distribution from instationary 13C-labeled data. Flux optimization and statistical analysis are achieved through an evolutionary optimization in a parallel manner. It can visualize the flux optimizing process in real-time and the ultimate flux outcome. It will also function as a database to enhance the consistency and to facilitate sharing of flux studies., Availability and Implementation: CeCaFLUX is freely available at https://www.cecaflux.net, the source code can be downloaded at https://github.com/zhzhd82/CeCaFLUX., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2022. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Complete understanding of a biological system requires quantitation of metabolic fluxes that reflect its dynamic state. Various analytical chemistry tools, enzyme-based probes, and microscopy enable flux measurement. However, any method alone falls short of comprehensive flux quantitation. Here we show that integrating these techniques results in a systems-level quantitative map of absolute metabolic fluxes that constitute an indispensable dimension of characterizing phenotypes. Stable isotopes, mass spectrometry, and NMR spectroscopy reveal relative pathway fluxes. Biochemical probes reveal the physical rate of environmental changes. FRET-based and SRS-based microscopy reveal targeted metabolite and chemical bond formation. These techniques are complementary and can be computationally integrated to reveal actionable information on metabolism. Integrative metabolic flux analysis using various quantitative techniques advances biotechnology and medicine., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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., Competing Interests: The authors have declared that no competing interests exist.

Metabolic flux analysis (MFA) involves model-based estimation of metabolic reaction rates (i.e., fluxes) and, in some cases, metabolite content (i.e., pool sizes) from experimental measurements. Applying MFA to biological data helps determine the fate of substrates and the activity of specific pathways within metabolic networks. However, reliably estimating fluxes by using simplified "core" models to predict the dynamics of larger metabolic networks remains a challenge. One point of uncertainty relates to the advantages and potential pitfalls of including pool size measurements as experimental inputs for isotopically nonstationary MFA (INST-MFA). Here, we directly assessed the role of pool sizes using various core models and simulated datasets. To investigate the effects of pool size measurements on INST-MFA, we assessed the accuracy and precision of flux estimates obtained using different subsets of data (e.g., with or without pool size measurements) and simple network models that either matched or differed from the true network. The inclusion of pool size measurements provided incremental improvements to the precision of the flux estimates. However, adding pool size measurements increased the sensitivity of the flux solution to unmodeled reactions outside the core network. These results were confirmed using a large Escherichia coli model that is representative of realistic metabolic networks examined in MFA studies. Our findings indicate that accurate flux estimates can be obtained in the absence of pool size measurements, even when using core models that lack full network coverage. Addition of pool size measurements to INST-MFA datasets may reveal the activity of non-core reactions that influence the labeling dynamics and therefore necessitate network expansion in order to reconcile all available data to the model. Our findings also emphasize the key role that goodness-of-fit testing plays in assessing the quality of model fits obtained with INST-MFA., (© 2022 Wiley-VCH GmbH.)

The metabolic plasticity of tobacco leaves has been demonstrated via the generation of transgenic plants that can accumulate over 30% dry weight as triacylglycerols. In investigating the changes in carbon partitioning in these high lipid-producing (HLP) leaves, foliar lipids accumulated stepwise over development. Interestingly, non-transient starch was observed to accumulate with plant age in WT but not HLP leaves, with a drop in foliar starch concurrent with an increase in lipid content. The metabolic carbon tradeoff between starch and lipid was studied using 13 CO 2 -labeling experiments and isotopically nonstationary metabolic flux analysis, not previously applied to the mature leaves of a crop. Fatty acid synthesis was investigated through assessment of acyl-acyl carrier proteins using a recently derived quantification method that was extended to accommodate isotopic labeling. Analysis of labeling patterns and flux modeling indicated the continued production of unlabeled starch, sucrose cycling, and a significant contribution of NADP-malic enzyme to plastidic pyruvate production for the production of lipids in HLP leaves, with the latter verified by enzyme activity assays. The results suggest an inherent capacity for a developmentally regulated carbon sink in tobacco leaves and may in part explain the uniquely successful leaf lipid engineering efforts in this crop., (Published by Elsevier Inc.)

Hepatocytes play an important role in maintaining homeostasis in living organisms by carrying out various metabolic functions. The urea cycle, one of the metabolic pathways taking place in hepatocytes, is an important metabolic pathway that converts toxic ammonia to nontoxic urea. Performing quantitative assessments of individual metabolite levels using a mass spectrometer is useful for assessing the metabolic state of the urea cycle in hepatocytes. In addition, metabolic flux analysis using stable isotopes and a mass spectrometer is a new technique for measuring the metabolic state. It enables conducting specific, objective, and quantitative measurement of the activated state of the target metabolic pathway regardless of external disturbing factors. This section describes the technical background and methodology of performing metabolic flux analysis of the urea cycle by mass spectrometry., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Metabolic flux analysis (MFA) combines experimental measurements and computational modeling to determine biochemical reaction rates in live biological systems. Advancements in analytical instrumentation, such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), have facilitated chemical separation and quantification of isotopically enriched metabolites. However, no software packages have been previously described that can integrate isotopomer measurements from both MS and NMR analytical platforms and have the flexibility to estimate metabolic fluxes from either isotopic steady-state or dynamic labeling experiments. By applying physiologically relevant cardiac and hepatic metabolic models to assess NMR isotopomer measurements, we herein test and validate new modeling capabilities of our enhanced flux analysis software tool, INCA 2.0. We demonstrate that INCA 2.0 can simulate and regress steady-state 13 C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different 13 C acetate tracers, we show that MFA based on dynamic 13 C NMR measurements can more precisely resolve cardiac fluxes compared to isotopically steady-state flux analysis. Finally, we show that estimation of hepatic fluxes using combined 13 C NMR and MS datasets improves the precision of estimated fluxes by up to 50%. Overall, our results illustrate how the recently added NMR data modeling capabilities of INCA 2.0 can enable entirely new experimental designs that lead to improved flux resolution and can be applied to a wide range of biological systems and measurement time courses., (Copyright © 2021 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Platelet metabolism is linked to platelet hyper- and hypoactivity in numerous human diseases. Developing a detailed understanding of the link between metabolic shifts and platelet activation state is integral to improving human health. Here, we show the first application of isotopically nonstationary 13 C metabolic flux analysis to quantitatively measure carbon fluxes in both resting and thrombin activated platelets. Metabolic flux analysis results show that resting platelets primarily metabolize glucose to lactate via glycolysis, while acetate is oxidized to fuel the tricarboxylic acid cycle. Upon activation with thrombin, a potent platelet agonist, platelets increase their uptake of glucose 3-fold. This results in an absolute increase in flux throughout central metabolism, but when compared to resting platelets they redistribute carbon dramatically. Activated platelets decrease relative flux to the oxidative pentose phosphate pathway and TCA cycle from glucose and increase relative flux to lactate. These results provide the first report of reaction-level carbon fluxes in platelets and allow us to distinguish metabolic fluxes with much higher resolution than previous studies., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Leucine, isoleucine and valine, known as branched chain amino acids (BCAAs), have been reported to be degraded by different cancer cells, and their biodegradation pathways have been suggested as anticancer targets. However, the mechanisms by which the degradation of BCAAs could support the growth of cancer cells remains unclear. In this work, 13 C experiments have been carried out in order to elucidate the metabolic role of BCAA degradation in two breast cancer cell lines (MCF-7 and BCC). The results revealed that up to 36% of the energy production via respiration by MCF-7 cells was supported by the degradation of BCAAs. Also, 67% of the mevalonate (the precursor of cholesterol) synthesized by the cells was coming from the degradation of leucine. The results were lower for BCC cells (14 and 30%, respectively). The non-tumorigenic epythelial cell line MCF-10A was used as a control, showing that 10% of the mitochondrial acetyl-CoA comes from the degradation of BCAAs and no mevalonate production. Metabolic flux analysis around the mevalonate node, also revealed that significant amounts of acetoacetate are being produced from BCAA derived carbon, which could be at the source of lipid synthesis. From these results we can conclude that the degradation of BCAAs is an important energy and carbon source for the proliferation of some cancer cells and its therapeutic targeting could be an interesting option., Competing Interests: Declaration of competing interest The authors declare no financial and non-financial competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Metabolic flux analysis (MFA) is a powerful tool for studying microbial cell physiology. Isotope-based MFA is the accepted standard for studying metabolic fluxes under steady-state conditions. However, its application under dynamic extracellular conditions is limited due to lack of proper techniques, such as rapid sampling and quenching, high cost and laborious execution. Here, we propose a new strategy to tackle this through incorporating dynamic metabolite abundance data into genome-scale metabolic models (GEM). First, a dummy extracellular pool concept is proposed for each dynamically changing metabolite, which represents a "sink" or "source", with corresponding dummy reactions coded into the GEM model. The dynamic model (expressed as differential equations) is then transformed into a quasi-steady-state model (expressed as linear equations), which can be easily solved by constraining the GEM model with the dynamic metabolite quantification data. For this, common linear-programming optimization algorithms were utilized to estimate the dynamic fluxes. Dynamic high-accuracy metabolite abundance data were obtained through the Isotope Dilution Mass Spectrometry (IDMS) method and high-speed sampling-quenching, and it was demonstrated that the newly proposed strategy could be successfully applied to obtain intracellular dynamic fluxes of Aspergillus niger under regimes of single and periodic extracellular glucose pulses. The applicability of the new method was also tested on dynamic fluxes estimation in a glucose pulse-response study of Saccharomyces cerevisiae. The proposed method provides a powerful tool to investigate cell physiology under dynamic conditions, especially relevant for bioprocess scale-up to industrial-scale bioreactors., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Glycerol is an attractive raw material for the production of useful chemicals using microbial cells. We previously identified metabolic engineering targets for the improvement of glycerol assimilation ability in Saccharomyces cerevisiae based on adaptive laboratory evolution (ALE) and transcriptome analysis of the evolved cells. We also successfully improved glycerol assimilation ability by the disruption of the RIM15 gene encoding a Greatwall protein kinase together with overexpression of the STL1 gene encoding the glycerol/H + symporter. To understand glycerol assimilation metabolism in the evolved glycerol-assimilating strains and STL1-overexpressing RIM15 disruptant, we performed metabolic flux analysis using 13 C-labeled glycerol. Significant differences in metabolic flux distributions between the strains obtained from the culture after 35 and 85 generations in ALE were not found, indicating that metabolic flux changes might occur in the early phase of ALE (i.e., before 35 generations at least). Similarly, metabolic flux distribution was not significantly changed by RIM15 gene disruption. However, fluxes for the lower part of glycolysis and the TCA cycle were larger and, as a result, flux for the pentose phosphate pathway was smaller in the STL1-overexpressing RIM15 disruptant than in the strain obtained from the culture after 85 generations in ALE. It could be effective to increase flux for the pentose phosphate pathway to improve the glycerol assimilation ability in S. cerevisiae.

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.

Metabolic reprogramming is a hallmark of cancer and increasing evidence suggests that reprogrammed cell metabolism supports tumor initiation, progression, metastasis and drug resistance. Understanding metabolic dysregulation may provide therapeutic targets and facilitate drug research and development for cancer therapy. Metabolomics enables the high-throughput characterization of a large scale of small molecule metabolites in cells, tissues and biofluids, while metabolic flux analysis (MFA) tracks dynamic metabolic activities using stable isotope tracer methods. Recent advances in metabolomics and MFA technologies make them powerful tools for metabolic profiling and characterizing metabolic activities in health and disease, especially in cancer research. In this review, we introduce recent advances in metabolomics and MFA analytical technologies, and provide the first comprehensive summary of the most commonly used isotope tracing methods. In addition, we highlight how metabolomics and MFA are applied in cancer pharmacology studies particularly for discovering targetable metabolic vulnerabilities, understanding the mechanisms of drug action and drug resistance, exploring potential strategies with dietary intervention, identifying cancer biomarkers, as well as enabling precision treatment with pharmacometabolomics., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Chinese hamster ovary (CHO) cells are the leading platform for the production of biopharmaceuticals with human-like glycosylation. The standard practice for cell line generation relies on trial and error approaches such as adaptive evolution and high-throughput screening, which typically take several months. Metabolic modeling could aid in designing better producer cell lines and thus shorten development times. The genome-scale metabolic model (GSMM) of CHO can accurately predict growth rates. However, in order to predict rational engineering strategies it also needs to accurately predict intracellular fluxes. In this work we evaluated the agreement between the fluxes predicted by parsimonious flux balance analysis (pFBA) using the CHO GSMM and a wide range of 13C metabolic flux data from literature. While glycolytic fluxes were predicted relatively well, the fluxes of tricarboxylic acid (TCA) cycle were vastly underestimated due to too low energy demand. Inclusion of computationally estimated maintenance energy significantly improved the overall accuracy of intracellular flux predictions. Maintenance energy was therefore determined experimentally by running continuous cultures at different growth rates and evaluating their respective energy consumption. The experimentally and computationally determined maintenance energy were in good agreement. Additionally, we compared alternative objective functions (minimization of uptake rates of seven nonessential metabolites) to the biomass objective. While the predictions of the uptake rates were quite inaccurate for most objectives, the predictions of the intracellular fluxes were comparable to the biomass objective function., Competing Interests: The authors have declared that no competing interests exist.

¹³C metabolic flux analysis (¹³C-MFA) enables the precise quantification of intracellular metabolic reaction rates by analyzing the distribution of mass isotopomers of proteinogenic amino acids or intracellular metabolites through ¹³C labeling experiments. ¹³C-MFA has received much attention as it can help systematically understand cellular metabolic characteristics, guide metabolic engineering design and gain mechanistic insights into pathophysiology. This article reviews the advances of ¹³C-MFA in the past 30 years and discusses its potential and future perspective, with a focus on its application in industrial biotechnology and biomedicine.

Perfluorooctanoic acid (PFOA) is well known to break glucose homeostasis. However, the effects of PFOA on glucose metabolism are difficult to be evaluated because related metabolites may be synthesized from other nutritional substrates. Here, the relative contribution of glucose to metabolites (e.g., pyruvate and citrate) in the PFOA-treated human liver cells (HepG2) was determined using the 13 C isotope-based metabolic flux analysis (MFA), i.e., pathway activities. The relative percentage of [U- 13 C 6 ] glucose-derived pyruvate in cells exposed to PFOA was not significantly different from that in the controls, indicating that the metabolic pattern of glycolysis was not substantially changed by PFOA. The pathway activity of [U- 13 C 6 ] glucose-driven tricarboxylic acid (TCA) cycle was dramatically inhibited by PFOA. Consequently, mitochondrial respiratory function was phenotypically impaired by PFOA, as observed from the decreasing basal oxygen consumption rate (OCR), ATP-linked OCR and spare respiratory capacity. This study suggests that PFOA may cause the abnormal glucose metabolism via altering the metabolic pattern of TCA cycle instead of glycolysis. The MFA is strongly recommended as a promising and robust tool to address the toxicity mechanisms of contaminants associated with glucose metabolism., Competing Interests: Declaration of competing interest 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., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Metabolic flux analysis (MFA) aims at revealing the metabolic reaction rates in a complex biochemical network. To do so, MFA uses the input of stable isotope labeling patterns of the intracellular metabolites. Elementary metabolic unit (EMU) is the computational framework to simulate the metabolite labeling patterns in a network, which was originally designed for simulating mass isotopomer distributions (MIDs) at the MS1 level. Recently, the EMU framework is expanded to simulate tandem mass spectrometry data. Tandem mass spectrometry has emerged as a new experimental approach to provide information on the positional isotope labeling of metabolites and therefore greatly improves the precision of MFA. In this review, we will discuss the new EMU framework that can accommodate the tandem mass isotopomer distributions (TMIDs) data. We will also analyze the improvement on the MFA precision by using TMID. Our analysis shows that combining the MIDs of the parent and daughter ions and the TMID for the MFA is more powerful than using TMID alone.

Hyperpolarized [1- 13 C] pyruvate can be used to examine the metabolic state of cancer cells, highlighting a key metabolic characteristic of cancer: the upregulated metabolic flux to lactate, even in the presence of oxygen (Warburg effect). Thus, the rate constant of 13 C exchange of pyruvate to lactate, k PL , can serve as a metabolic biomarker of cancer presence, aggressiveness and therapy response. Established in vitro hyperpolarized experiments dissolve the probe for each cell sample independently, an inefficient process that consumes excessive time and resources. Expanding on our previous development of a microcoil with greatly increased detection sensitivity (10 3 -fold) compared with traditional in vitro methods, we present a novel microcoil equipped with a 10-μL vertical reservoir and an experimental protocol utilizing deuterated dissolution buffer to measure metabolic flux in multiple mass-limited cell suspension samples using a single dissolution. This method increases efficiency and potentially reduces the methodological variability associated with hyperpolarized experiments. This technique was used to measure pyruvate-to-lactate flux in melanoma cells to assess BRAF-inhibition treatment response. There was a significant reduction of k PL in BRAF V600E cells following 24 and 48 hours of treatment with 2 μM vemurafenib (P ≤ .05). This agrees with significant changes observed in the pool sizes of extracellular lactate (P ≤ .05) and glucose (P ≤ .001) following 6 and 48 hours of treatment, respectively, and a significant reduction in cell proliferation following 72 hours of treatment (P ≤ .01). BRAF inhibition had no significant effect on the metabolic flux of BRAF WT cells. These data demonstrate a 6-8-fold increase in efficiency for the measurement of k PL in cell suspension samples compared with traditional hyperpolarized in vitro methods., (© 2020 John Wiley & Sons, Ltd.)

Photosynthetic organisms produce ATP and NADPH using light as an energy source and further utilize these cofactors during metabolism. Photosynthesis involves linear and cyclic electron flows; as the cyclic electron flow produces ATP more effectively than the linear electron flow without NADPH, the cell efficiently adjusts ATP and NADPH production using the two different pathways. Nevertheless, direct measurement of ATP and NADPH production during photosynthesis has been difficult. In the present study, the photosynthetic ATP and NADPH production rates of Synechocystis sp. PCC 6803 under three different single peak wavelength lights (blue: 470 nm, R630: 630 nm, and R680: 680 nm) were evaluated based on 13 C-metabolic flux analysis ( 13 C-MFA) by considering the mass balance of ATP and NADPH between photosynthesis and metabolism. The ratios of ATP/NADPH production via photosynthesis were estimated as 3.13, 1.70, and 2.10 under blue, R630, and R680 light conditions, respectively. Moreover, the linear and cyclic electron flow ratios were estimated to be 1.1-2.2, 0.2-0.5, and 0.5-1.0 under blue, R630, and R680 light conditions, respectively. The predicted linear and cyclic electron flow ratios were consistent with the excitation ratio between photosystems I and II, as observed in the steady-state fluorescence spectra., (Copyright © 2020 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.)

The field of metabolic engineering is primarily concerned with improving the biological production of value-added chemicals, fuels and pharmaceuticals through the design, construction and optimization of metabolic pathways, redirection of intracellular fluxes, and refinement of cellular properties relevant for industrial bioprocess implementation. Metabolic network models and metabolic fluxes are central concepts in metabolic engineering, as was emphasized in the first paper published in this journal, "Metabolic fluxes and metabolic engineering" (Metabolic Engineering, 1: 1-11, 1999). In the past two decades, a wide range of computational, analytical and experimental approaches have been developed to interrogate the capabilities of biological systems through analysis of metabolic network models using techniques such as flux balance analysis (FBA), and quantify metabolic fluxes using constrained-based modeling approaches such as metabolic flux analysis (MFA) and more advanced experimental techniques based on the use of stable-isotope tracers, i.e. 13 C-metabolic flux analysis ( 13 C-MFA). In this review, we describe the basic principles of metabolic flux analysis, discuss current best practices in flux quantification, highlight potential pitfalls and alternative approaches in the application of these tools, and give a broad overview of pragmatic applications of flux analysis in metabolic engineering practice., (Copyright © 2020 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Microbial engineering forces flux redistribution to accommodate higher production rates, straining the cellular supply chain and leading to growth deficiency. Thus, there is a selective pressure to alleviate metabolic burden and revert towards the innate flux distribution ('flux memory') via mutations. Suboptimal fermentation exacerbates this phenomenon as increased number of generations prolong the selection window for the underlying flux memory to generate faster growing non-producers. New strategies to mitigate host genetic instability include laboratory evolution, high-resolution genome resequencing combined with phenotype screening, mismatch repair protein engineering, and advanced synthetic biology approaches (e.g. oscillators and biosensor regulators). Moreover, 13 C-metabolic flux analysis can quantify flux suboptimality driven by metabolic burdens and cultivation stresses. Elucidation of correlations between metabolic suboptimality and host mutation rates/spectra may lead to early stage risk assessments of culture-population's regime shift during process scale-up as well as strategies to boost bioproductions., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Elementary flux mode (EFM) analysis is a well-studied method in constraint-based modeling of metabolic networks. In EFM analysis, a network is decomposed into minimal functional pathways based on the assumption of balanced metabolic fluxes. In this paper, a system architecture is proposed that approximately models the functionality of metabolic networks. The AND/OR graph model is used to represent the metabolic network and each processing element in the system emulates the functionality of a metabolite. The system is implemented on a graphics processing unit (GPU) as the hardware platform using CUDA environment. The proposed architecture takes advantage of the inherent parallelism in the network structure in terms of both pathway and metabolite traversal. The function of each element is defined such that it can find flux-balanced pathways. Pathways in both small and large metabolic networks are applied to the proposed architecture and the results are discussed., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Motivation: Elementary flux modes (EFMs) are a key tool for analyzing genome-scale metabolic networks, and several methods have been proposed to compute them. Among them, those based on solving linear programming (LP) problems are known to be very efficient if the main interest lies in computing large enough sets of EFMs., Results: Here, we propose a new method called EFM-Ta that boosts the efficiency rate by analyzing the information provided by the LP solver. We base our method on a further study of the final tableau of the simplex method. By performing additional elementary steps and avoiding trivial solutions consisting of two cycles, we obtain many more EFMs for each LP problem posed, improving the efficiency rate of previously proposed methods by more than one order of magnitude., Availability and Implementation: Software is freely available at https://github.com/biogacop/Boost_LP_EFM., Contact: fguil@um.es., Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author(s) 2020. Published by Oxford University Press.)

Isotope-based assessment of metabolic flux is achieved through a judicious balance of measurements and assumptions. Recent publications debate the validity of key assumptions used to model stable isotope labeling of liver metabolism in vivo. Here, we examine the controversy surrounding estimates of liver citric acid cycle and gluconeogenesis fluxes using a flexible modeling platform that enables rigorous testing of standard assumptions. Fasted C57BL/6J mice are infused with [ 13 C 3 ]lactate or [ 13 C 3 ]propionate isotopes, and hepatic fluxes are regressed using models with gradually increasing complexity and relaxed assumptions. We confirm that liver pyruvate cycling fluxes are incongruent between different 13 C tracers in models with conventional assumptions. When models are expanded to include more labeling measurements and fewer constraining assumptions, however, liver pyruvate cycling is significant, and inconsistencies in hepatic flux estimates using [ 13 C 3 ]lactate and [ 13 C 3 ]propionate isotopes emanate, in part, from peripheral tracer recycling and incomplete isotope equilibration within the citric acid cycle., Competing Interests: Declaration of Interests J.D.Y. is a co-founder, shareholder, and company director of Metalytics. C.M.H. is an employee of Metalytics., (Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.)

Fumarate hydratase (FH) is an enzyme in the tricarboxylic acid (TCA) cycle, biallelic loss-of-function mutations of which are associated with hereditary leiomyomatosis and renal cell cancer. However, how FH defect modulates intracellular metabolic fluxes in human cells has remained unclear. This study aimed to reveal metabolic flux alterations induced by reduced FH activity. We applied 13 C metabolic flux analysis ( 13 C-MFA) to an established cell line with diminished FH activity (FH dim ) and parental HEK293 cells. FH dim cells showed reduced pyruvate import flux into mitochondria and subsequent TCA cycle fluxes. Interestingly, the diminished FH activity decreased FH flux only by about 20%, suggesting a very low need for FH to maintain the oxidative TCA cycle. Cellular ATP production from the TCA cycle was dominantly suppressed compared with that from glycolysis in FH dim cells. Consistently, FH dim cells exhibited higher glucose dependence for ATP production and higher resistance to an ATP synthase inhibitor. In summary, using FH dim cells we demonstrated that FH defect led to suppressed pyruvate import into mitochondria, followed by downregulated TCA cycle activity and altered ATP production pathway balance from the TCA cycle to glycolysis. We confirmed that 13 C-MFA can provide direct and quantitative information on metabolic alterations induced by FH defect.

Stable-isotope metabolic flux analysis is an important approach to unravel the metabolic network and its regulation in organisms. It has become a key analytical technology for biotechnological applications. During recent years non-model microorganisms have received increasing attention because they possess unique metabolic capabilities and can serve as a host for production of biofuels and biochemicals. Stable-isotope metabolic flux analysis has been widely used in these microorganisms for exploring novel pathways, elucidating the operation of central metabolic networks, and revealing the metabolic changes that result from genetic manipulations. Here, we review recent applications of stable-isotope metabolic flux analysis in characterizing non-model microbial hosts, guiding the development of rational engineering strategies for enhancement of biochemical production and extension of substrate range, and understanding of industrial production processes., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

Metabolite concentrations, fluxes, and free energies constitute the basis for understanding and controlling metabolism. Mass spectrometry and stable isotopes are integral tools in quantifying these metabolic features. For absolute metabolite concentration and flux measurement, 13 C internal standards and tracers have been the gold standard. In contrast, no established methods exist for comprehensive thermodynamic quantitation under physiological environments. Recently, using high-resolution mass spectrometry and multi-isotope tracing, flux quantitation has been increasingly adopted in broader metabolism. The improved flux quantitation led to determination of Gibbs free energy of reaction (ΔG) in central carbon metabolism using a relationship between reaction reversibility and thermodynamic driving force. Here we highlight recent advances in multi-isotope tracing for metabolic flux and free energy analysis., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Fatty liver involves ectopic lipid accumulation and dysregulated hepatic oxidative metabolism, which can progress to a state of elevated inflammation and fibrosis referred to as nonalcoholic steatohepatitis (NASH). The factors that control progression from simple steatosis to NASH are not fully known. Here, we tested the hypothesis that dietary vitamin E (VitE) supplementation would prevent NASH progression and associated metabolic alterations induced by a Western diet (WD). Hyperphagic melanocortin-4 receptor-deficient (MC4R -/- ) mice were fed chow, chow+VitE, WD, or WD+VitE starting at 8 or 20 weeks of age. All groups exhibited extensive hepatic steatosis by the end of the study (28 weeks of age). WD feeding exacerbated liver disease severity without inducing proportional changes in liver triglycerides. Eight weeks of WD accelerated liver pyruvate cycling, and 20 weeks of WD extensively upregulated liver glucose and oxidative metabolism assessed by 2 H/ 13 C flux analysis. VitE supplementation failed to reduce the histological features of NASH. Rather, WD+VitE increased the abundance and saturation of liver ceramides and accelerated metabolic flux dysregulation compared with 8 weeks of WD alone. In summary, VitE did not limit NASH pathogenesis in genetically obese mice, but instead increased some indicators of metabolic dysfunction., (Copyright © 2020 Hasenour et al.)

Mathematical modeling of plant metabolism enables the plant science community to understand the organization of plant metabolism, obtain quantitative insights into metabolic functions, and derive engineering strategies for manipulation of metabolism. Among the various modeling approaches, metabolic pathway analysis can dissect the basic functional modes of subsections of core metabolism, such as photorespiration, and reveal how classical definitions of metabolic pathways have overlapping functionality. In the many studies using constraint-based modeling in plants, numerous computational tools are currently available to analyze large-scale and genome-scale metabolic networks. For 13 C-metabolic flux analysis, principles of isotopic steady state have been used to study heterotrophic plant tissues, while nonstationary isotope labeling approaches are amenable to the study of photoautotrophic and secondary metabolism. Enzyme kinetic models explore pathways in mechanistic detail, and we discuss different approaches to determine or estimate kinetic parameters. In this review, we describe recent advances and challenges in modeling plant metabolism.

The metabolic responses of bacteria to dynamic extracellular conditions drives not only the behavior of single species, but also entire communities of microbes. Over the last decade, genome-scale metabolic network reconstructions have assisted in our appreciation of important metabolic determinants of bacterial physiology. These network models have been a powerful force in understanding the metabolic capacity that species may utilize in order to succeed in an environment. Increasingly, an understanding of context-specific metabolism is critical for elucidating metabolic drivers of larger phenotypes and disease. However, previous approaches to use network models in concert with omics data to better characterize experimental systems have met challenges due to assumptions necessary by the various integration platforms or due to large input data requirements. With these challenges in mind, we developed RIPTiDe (Reaction Inclusion by Parsimony and Transcript Distribution) which uses both transcriptomic abundances and parsimony of overall flux to identify the most cost-effective usage of metabolism that also best reflects the cell's investments into transcription. Additionally, in biological samples where it is difficult to quantify specific growth conditions, it becomes critical to develop methods that require lower amounts of user intervention in order to generate accurate metabolic predictions. Utilizing a metabolic network reconstruction for the model organism Escherichia coli str. K-12 substr. MG1655 (iJO1366), we found that RIPTiDe correctly identifies context-specific metabolic pathway activity without supervision or knowledge of specific media conditions. We also assessed the application of RIPTiDe to in vivo metatranscriptomic data where E. coli was present at high abundances, and found that our approach also effectively predicts metabolic behaviors of host-associated bacteria. In the setting of human health, understanding metabolic changes within bacteria in environments where growth substrate availability is difficult to quantify can have large downstream impacts on our ability to elucidate molecular drivers of disease-associated dysbiosis across the microbiota. Our results indicate that RIPTiDe may have potential to provide understanding of context-specific metabolism of bacteria within complex communities., Competing Interests: The authors have declared that no competing interests exist.

Resveratrol, a phytoalexin produced by plants, has several beneficial effects in humans. It can be produced using Escherichia coli by introducing only three heterologous genes: TAL , 4CL , and STS . However, the resveratrol synthesis pathway requires two precursors, tyrosine and acetyl-CoA, which are produced by two branched central metabolic pathways. Therefore, overexpression of these genes in E. coli results in the production of only trace amounts of resveratrol. In this study, we attempted to produce resveratrol via coculture of two engineered strains in which the two metabolic pathways are activated. The first strain was engineered to produce p -coumaric acid using tyrosine as a precursor, which can be synthesized by the pentose phosphate pathway. The second strain produced resveratrol by combining p -coumaric acid from the first strain and malonyl-CoA synthesized from acetyl-CoA, which is produced by the glycolytic pathway. In total, 55.7 mg/L of resveratrol was produced from 20 g/L of glucose via coculture of these two strains in glucose minimal medium without any supplements. The metabolic fluxes in each of the strains producing resveratrol were successfully investigated by 13 C metabolic flux analysis. The results showed that the balance between the citric acid cycle and the malonyl-CoA supply node was important for resveratrol production.

Cyanobacteria acclimate and adapt to changing light conditions by controlling the energy transfer between photosystem I (PSI) and II (PSII) and pigment composition. Photosynthesis is driven by balancing the excitation between PSI and PSII. To predict the detailed electron transfer flux of cyanobacteria, we refined the photosynthesis-related reactions in our previously reconstructed genome-scale model. Two photosynthetic bacteria, Arthrospira and Synechocystis, were used as models. They were grown under various spectral light conditions and flux balance analysis (FBA) was performed using photon uptake fluxes into PSI and PSII, which were converted from each light spectrum by considering the photoacclimation of pigments and the distribution ratio of phycobilisome to PSI and PSII. In Arthrospira, the FBA was verified with experimental data using six types of light-emitting diodes (White, Blue, Green, Yellow, Red1, and Red2). FBA predicted the cell growth of Synechocystis for the LEDs, excepting Red2. In an FBA simulation, cells used respiratory terminal oxidases and two NADH dehydrogenases (NDH-1 and NDH-2) to balance the PSI and PSII excitations depending on the light conditions. FBA simulation with our refined model functionally implicated NDH-1 and NDH-2 as a component of cyclic electron transport in the varied light environments.