INCA: a computational platform for isotopically non-stationary metabolic flux analysis

Loss of hepatic phosphoenolpyruvate carboxykinase 1 dysregulates metabolic responses to acute exercise but enhances adaptations to exercise training in mice

Acute exercise increases liver gluconeogenesis to supply glucose to working muscles. Concurrently, elevated liver lipid breakdown fuels the high energetic cost of gluconeogenesis. This functional coupling between liver gluconeogenesis and lipid oxidation has been proposed to underlie the ability of regular exercise to enhance liver mitochondrial oxidative metabolism and decrease liver steatosis in individuals with nonalcoholic fatty liver disease. Herein we tested whether repeated bouts of increased hepatic gluconeogenesis are necessary for exercise training to lower liver lipids. Experiments used diet-induced obese mice lacking hepatic phosphoenolpyruvate carboxykinase 1 (KO) to inhibit gluconeogenesis and wild-type (WT) littermates. 2H/13C metabolic flux analysis quantified glucose and mitochondrial oxidative fluxes in untrained mice at rest and during acute exercise. Circulating and tissue metabolite levels were determined during sedentary conditions, acute exercise, and refeeding postexercise. Mice also underwent 6 wk of treadmill running protocols to define hepatic and extrahepatic adaptations to exercise training. Untrained KO mice were unable to maintain euglycemia during acute exercise resulting from an inability to increase gluconeogenesis. Liver triacylglycerides were elevated after acute exercise and circulating β-hydroxybutyrate was higher during postexercise refeeding in untrained KO mice. In contrast, exercise training prevented liver triacylglyceride accumulation in KO mice. This was accompanied by pronounced increases in indices of skeletal muscle mitochondrial oxidative metabolism in KO mice. Together, these results show that hepatic gluconeogenesis is dispensable for exercise training to reduce liver lipids. This may be due to responses in ketone body metabolism and/or metabolic adaptations in skeletal muscle to exercise.NEW & NOTEWORTHY Exercise training reduces hepatic steatosis partly through enhanced hepatic terminal oxidation. During acute exercise, hepatic gluconeogenesis is elevated to match the heightened rate of muscle glucose uptake and maintain glucose homeostasis. It has been postulated that the hepatic energetic stress induced by elevating gluconeogenesis during acute exercise is a key stimulus underlying the beneficial metabolic responses to exercise training. This study shows that hepatic gluconeogenesis is not necessary for exercise training to lower liver lipids.

Millet-inspired systems metabolic engineering of NUE in crops

The use of nitrogen (N) fertilizers in agriculture has a great ability to increase crop productivity. However, their excessive use has detrimental effects on the environment. Therefore, it is necessary to develop crop varieties with improved nitrogen use efficiency (NUE) that require less N but have substantial yields. Orphan crops such as millets are cultivated in limited regions and are well adapted to lower input conditions. Therefore, they serve as a rich source of beneficial traits that can be transferred into major crops to improve their NUE. This review highlights the tremendous potential of systems biology to unravel the enzymes and pathways involved in the N metabolism of millets, which can open new possibilities to generate transgenic crops with improved NUE.

Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production

In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. ¹³C-metabolic flux analysis (¹³C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.

Conversion of CO2 into organic acids by engineered autotrophic yeast

The increase of CO2 emissions due to human activity is one of the preeminent reasons for the present climate crisis. In addition, considering the increasing demand for renewable resources, the upcycling of CO2 as a feedstock gains an extensive importance to establish CO2-neutral or CO2-negative industrial processes independent of agricultural resources. Here we assess whether synthetic autotrophic Komagataella phaffii (Pichia pastoris) can be used as a platform for value-added chemicals using CO2 as a feedstock by integrating the heterologous genes for lactic and itaconic acid synthesis. 13C labeling experiments proved that the resulting strains are able to produce organic acids via the assimilation of CO2 as a sole carbon source. Further engineering attempts to prevent the lactic acid consumption increased the titers to 600 mg L-1, while balancing the expression of key genes and modifying screening conditions led to 2 g L-1 itaconic acid. Bioreactor cultivations suggest that a fine-tuning on CO2 uptake and oxygen demand of the cells is essential to reach a higher productivity. We believe that through further metabolic and process engineering, the resulting engineered strain can become a promising host for the production of value-added bulk chemicals by microbial assimilation of CO2, to support sustainability of industrial bioprocesses.

In situ diversity of metabolism and carbon use efficiency among soil bacteria

The central carbon (C) metabolic network harvests energy to power the cell and feed biosynthesis for growth. In pure cultures, bacteria use some but not all of the network's major pathways, such as glycolysis and pentose phosphate and Entner-Doudoroff pathways. However, how these pathways are used in microorganisms in intact soil communities is unknown. Here, we analyzed the incorporation of ¹³C from glucose isotopomers into phospholipid fatty acids. We showed that groups of Gram-positive and Gram-negative bacteria in an intact agricultural soil used different pathways to metabolize glucose. They also differed in C use efficiency (CUE), the efficiency with which a substrate is used for biosynthesis. Our results provide experimental evidence for diversity among microbes in the organization of their central carbon metabolic network and CUE under in situ conditions. These results have important implications for our understanding of how community composition affects soil C cycling and organic matter formation.

Isotope-Assisted Metabolic Flux Analysis: A Powerful Technique to Gain New Insights into the Human Metabolome in Health and Disease

Cell metabolism represents the coordinated changes in genes, proteins, and metabolites that occur in health and disease. The metabolic fluxome, which includes both intracellular and extracellular metabolic reaction rates (fluxes), therefore provides a powerful, integrated description of cellular phenotype. However, intracellular fluxes cannot be directly measured. Instead, flux quantification requires sophisticated mathematical and computational analysis of data from isotope labeling experiments. In this review, we describe isotope-assisted metabolic flux analysis (iMFA), a rigorous computational approach to fluxome quantification that integrates metabolic network models and experimental data to generate quantitative metabolic flux maps. We highlight practical considerations for implementing iMFA in mammalian models, as well as iMFA applications in in vitro and in vivo studies of physiology and disease. Finally, we identify promising new frontiers in iMFA which may enable us to fully unlock the potential of iMFA in biomedical research.

AMPK activation is sufficient to increase skeletal muscle glucose uptake and glycogen synthesis but is not required for contraction-mediated increases in glucose metabolism

The AMP-activated protein kinase (AMPK) is a cellular sensor of energetics and when activated in skeletal muscle during contraction can impart changes in skeletal muscle metabolism. Therapeutics that selectively activate AMPK have been developed to lower glucose levels through increased glucose disposal rates as an approach to abrogate the hyperglycemic state of diabetes; however, the metabolic fate of glucose following AMPK activation remains unclear. We have used a combination of in vivo evaluation of glucose homeostasis and ex vivo skeletal muscle incubation to systematically evaluate metabolism following pharmacological activation of AMPK with PF-739, comparing this with AMPK activation through sustained intermittent electrical stimulation of contraction. These methods to activate AMPK result in increased glucose uptake but divergent metabolism of glucose: pharmacological activation results in increased glycogen accumulation while contraction-induced glucose uptake results in increased lactate formation and glucose oxidation. These results provide additional evidence to support a role for AMPK in control of skeletal muscle metabolism and additional insight into the potential for AMPK stimulation with small molecule direct activators.

A synthetic C2 auxotroph of Pseudomonas putida for evolutionary engineering of alternative sugar catabolic routes

Acetyl-coenzyme A (AcCoA) is a metabolic hub in virtually all living cells, serving as both a key precursor of essential biomass components and a metabolic sink for catabolic pathways for a large variety of substrates. Owing to this dual role, tight growth-production coupling schemes can be implemented around the AcCoA node. Building on this concept, a synthetic C2 auxotrophy was implemented in the platform bacterium Pseudomonas putida through an in silico-informed engineering approach. A growth-coupling strategy, driven by AcCoA demand, allowed for direct selection of an alternative sugar assimilation route-the phosphoketolase (PKT) shunt from bifidobacteria. Adaptive laboratory evolution forced the synthetic P. putida auxotroph to rewire its metabolic network to restore C2 prototrophy via the PKT shunt. Large-scale structural chromosome rearrangements were identified as possible mechanisms for adjusting the network-wide proteome profile, resulting in improved PKT-dependent growth phenotypes. ¹³C-based metabolic flux analysis revealed an even split between the native Entner-Doudoroff pathway and the synthetic PKT bypass for glucose processing, leading to enhanced carbon conservation. These results demonstrate that the P. putida metabolism can be radically rewired to incorporate a synthetic C2 metabolism, creating novel network connectivities and highlighting the importance of unconventional engineering strategies to support efficient microbial production.

1-deoxysphingolipid synthesis compromises anchorage-independent growth and plasma membrane endocytosis in cancer cells

Serine palmitoyltransferase (SPT) predominantly incorporates serine and fatty acyl-CoAs into diverse sphingolipids that serve as structural components of membranes and signaling molecules within or amongst cells. However, SPT also uses alanine as a substrate in the contexts of low serine availability, alanine accumulation, or disease-causing mutations in hereditary sensory neuropathy type I (HSAN1), resulting in the synthesis and accumulation of 1-deoxysphingolipids. These species promote cytotoxicity in neurons and impact diverse cellular phenotypes, including suppression of anchorage-independent cancer cell growth. While altered serine and alanine levels can promote 1-deoxysphingolipid synthesis, they impact numerous other metabolic pathways important for cancer cells. Here we combined isotope tracing, quantitative metabolomics, and functional studies to better understand the mechanistic drivers of 1-deoxysphingolipid toxicity in cancer cells. We determined that both alanine treatment and SPTLC1^C133W expression induce 1-deoxy(dihydro)ceramide synthesis and accumulation but fail to broadly impact intermediary metabolism, abundances of other lipids, or growth of adherent cells. However, we found spheroid culture and soft agar colony formation were compromised when endogenous 1-deoxysphingolipid synthesis was induced via SPTLC1^C133W expression. Consistent with these impacts on anchorage-independent cell growth, we observed that 1-deoxysphingolipid synthesis reduced plasma membrane endocytosis. These results highlight a potential role for SPT promiscuity in linking altered amino acid metabolism to plasma membrane endocytosis.

13C metabolic flux analysis: Classification and characterization from the perspective of mathematical modeling and application in physiological research of neural cell

¹³C metabolic flux analysis (¹³C-MFA) has emerged as a forceful tool for quantifying in vivo metabolic pathway activity of different biological systems. This technology plays an important role in understanding intracellular metabolism and revealing patho-physiology mechanism. Recently, it has evolved into a method family with great diversity in experiments, analytics, and mathematics. In this review, we classify and characterize the various branch of ¹³C-MFA from a unified perspective of mathematical modeling. By linking different parts in the model to each step of its workflow, the specific technologies of ¹³C-MFA are put into discussion, including the isotope labeling model (ILM), isotope pattern measuring technique, optimization algorithm and statistical method. Its application in physiological research in neural cell has also been reviewed.

Carbon source availability drives nutrient utilization in CD8+ T cells

How environmental nutrient availability impacts T cell metabolism and function remains poorly understood. Here, we report that the presence of physiologic carbon sources (PCSs) in cell culture medium broadly impacts glucose utilization by CD8⁺ T cells, independent of transcriptional changes in metabolic reprogramming. The presence of PCSs reduced glucose contribution to the TCA cycle and increased effector function of CD8⁺ T cells, with lactate directly fueling the TCA cycle. In fact, CD8⁺ T cells responding to Listeria infection preferentially consumed lactate over glucose as a TCA cycle substrate in vitro, with lactate enhancing T cell bioenergetic and biosynthetic capacity. Inhibiting lactate-dependent metabolism in CD8⁺ T cells by silencing lactate dehydrogenase A (Ldha) impaired both T cell metabolic homeostasis and proliferative expansion in vivo. Together, our data indicate that carbon source availability shapes T cell glucose metabolism and identifies lactate as a bioenergetic and biosynthetic fuel for CD8⁺ effector T cells.

Metabolic flux analysis: a comprehensive review on sample preparation, analytical techniques, data analysis, computational modelling, and main application areas

Metabolic flux analysis (MFA) quantitatively describes cellular fluxes to understand metabolic phenotypes and functional behaviour after environmental and/or genetic perturbations. In the last decade, the application of stable isotopes became extremely important to determine and integrate in vivo measurements of metabolic reactions in systems biology. 13C-MFA is one of the most informative methods used to study central metabolism of biological systems. This review aims to outline the current experimental procedure adopted in 13C-MFA, starting from the preparation of cell cultures and labelled tracers to the quenching and extraction of metabolites and their subsequent analysis performed with very powerful software. Here, the limitations and advantages of nuclear magnetic resonance spectroscopy and mass spectrometry techniques used in carbon labelled experiments are elucidated by reviewing the most recent published papers. Furthermore, we summarise the most successful approaches used for computational modelling in flux analysis and the main application areas with a particular focus in metabolic engineering.

Glucose feeds the tricarboxylic acid cycle via excreted ethanol in fermenting yeast

Ethanol and lactate are typical waste products of glucose fermentation. In mammals, glucose is catabolized by glycolysis into circulating lactate, which is broadly used throughout the body as a carbohydrate fuel. Individual cells can both uptake and excrete lactate, uncoupling glycolysis from glucose oxidation. Here we show that similar uncoupling occurs in budding yeast batch cultures of Saccharomyces cerevisiae and Issatchenkia orientalis. Even in fermenting S. cerevisiae that is net releasing ethanol, media ¹³C-ethanol rapidly enters and is oxidized to acetaldehyde and acetyl-CoA. This is evident in exogenous ethanol being a major source of both cytosolic and mitochondrial acetyl units. ²H-tracing reveals that ethanol is also a major source of both NADH and NADPH high-energy electrons, and this role is augmented under oxidative stress conditions. Thus, uncoupling of glycolysis from the oxidation of glucose-derived carbon via rapidly reversible reactions is a conserved feature of eukaryotic metabolism.

Acetate reprograms gut microbiota during alcohol consumption

Liver damage due to chronic alcohol use is among the most prevalent liver diseases. Alcohol consumption frequency is a strong factor of microbiota variance. Here we use isotope labeled [1-13C] ethanol, metagenomics, and metatranscriptomics in ethanol-feeding and intragastric mouse models to investigate the metabolic impacts of alcohol consumption on the gut microbiota. First, we show that although stable isotope labeled [1-13C] ethanol contributes to fatty acid pools in the liver, plasma, and cecum contents of mice, there is no evidence of ethanol metabolism by gut microbiota ex vivo under anaerobic conditions. Next, we observe through metatranscriptomics that the gut microbiota responds to ethanol-feeding by activating acetate dissimilation, not by metabolizing ethanol directly. We demonstrate that blood acetate concentrations are elevated during ethanol consumption. Finally, by increasing systemic acetate levels with glyceryl triacetate supplementation, we do not observe any impact on liver disease, but do induce similar gut microbiota alterations as chronic ethanol-feeding in mice. Our results show that ethanol is not directly metabolized by the gut microbiota, and changes in the gut microbiota linked to ethanol are a side effect of elevated acetate levels. De-trending for these acetate effects may be critical for understanding gut microbiota changes that cause alcohol-related liver disease.

Synergistic Effect of β-Lapachone and Aminooxyacetic Acid on Central Metabolism in Breast Cancer

The compound β-lapachone, a naturally derived naphthoquinone, has been utilized as a potent medicinal nutrient to improve health. Over the last twelve years, numerous reports have demonstrated distinct associations of β-lapachone and NAD(P)H: quinone oxidoreductase 1 (NQO1) protein in the amelioration of various diseases. Comprehensive research of NQO1 bioactivity has clearly confirmed the tumoricidal effects of β-lapachone action through NAD+-keresis, in which severe DNA damage from reactive oxygen species (ROS) production triggers a poly-ADP-ribose polymerase-I (PARP1) hyperactivation cascade, culminating in NAD+/ATP depletion. Here, we report a novel combination strategy with aminooxyacetic acid (AOA), an aspartate aminotransferase inhibitor that blocks the malate-aspartate shuttle (MAS) and synergistically enhances the efficacy of β-lapachone metabolic perturbation in NQO1+ breast cancer. We evaluated metabolic turnover in MDA-MB-231 NQO1+, MDA-MB-231 NQO1-, MDA-MB-468, and T47D cancer cells by measuring the isotopic labeling of metabolites from a [U-13C]glucose tracer. We show that β-lapachone treatment significantly hampers lactate secretion by ~85% in NQO1+ cells. Our data demonstrate that combinatorial treatment decreases citrate, glutamate, and succinate enrichment by ~14%, ~50%, and ~65%, respectively. Differences in citrate, glutamate, and succinate fractional enrichments indicate synergistic effects on central metabolism based on the coefficient of drug interaction. Metabolic modeling suggests that increased glutamine anaplerosis is protective in the case of MAS inhibition.

CeCaFLUX: the first web server for standardized and visual instationary 13C metabolic flux analysis

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.

CDK12 promotes tumorigenesis but induces vulnerability to therapies inhibiting folate one-carbon metabolism in breast cancer

Cyclin-dependent kinase 12 (CDK12) overexpression is implicated in breast cancer, but whether it has a primary or only a cooperative tumorigenic role is unclear. Here, we show that transgenic CDK12 overexpression in the mouse mammary gland per se is sufficient to drive the emergence of multiple and multifocal tumors, while, in cooperation with known oncogenes, it promotes earlier tumor onset and metastasis. Integrative transcriptomic, metabolomic and functional data reveal that hyperactivation of the serine-glycine-one-carbon network is a metabolic hallmark inherent to CDK12-induced tumorigenesis. Consistently, in retrospective patient cohort studies and in patient-derived xenografts, CDK12-overexpressing breast tumors show positive response to methotrexate-based chemotherapy targeting CDK12-induced metabolic alterations, while being intrinsically refractory to other types of chemotherapy. In a retrospective analysis of hormone receptor-negative and lymph node-positive breast cancer patients randomized in an adjuvant phase III trial to 1-year low-dose metronomic methotrexate-based chemotherapy or no maintenance chemotherapy, a high CDK12 status predicts a dramatic reduction in distant metastasis rate in the chemotherapy-treated vs. not-treated arm. Thus, by coupling tumor progression with metabolic reprogramming, CDK12 creates an actionable vulnerability for breast cancer therapy and might represent a suitable companion biomarker for targeted antimetabolite therapies in human breast cancers.

Finding biomarkers for targeted therapy is a promising approach to treat cancer. Here, the authors show that in breast cancer preclinical models and patients, CDK12 promotes tumourigenesis but induces selective vulnerability to therapies that target folate one-carbon metabolism.

Hot isopropanol quenching procedure for automated microtiter plate scale 13C-labeling experiments

Background

Currently, the generation of genetic diversity for microbial cell factories outpaces the screening of strain variants with omics-based phenotyping methods. Especially isotopic labeling experiments, which constitute techniques aimed at elucidating cellular phenotypes and supporting rational strain design by growing microorganisms on substrates enriched with heavy isotopes, suffer from comparably low throughput and the high cost of labeled substrates.

Results

We present a miniaturized, parallelized, and automated approach to ¹³C-isotopic labeling experiments by establishing and validating a hot isopropanol quenching method on a robotic platform coupled with a microbioreactor cultivation system. This allows for the first time to conduct automated labeling experiments at a microtiter plate scale in up to 48 parallel batches. A further innovation enabled by the automated quenching method is the analysis of free amino acids instead of proteinogenic ones on said microliter scale. Capitalizing on the latter point and as a proof of concept, we present an isotopically instationary labeling experiment in Corynebacterium glutamicum ATCC 13032, generating dynamic labeling data of free amino acids in the process.

Conclusions

Our results show that a robotic liquid handler is sufficiently fast to generate informative isotopically transient labeling data. Furthermore, the amount of biomass obtained from a sub-milliliter cultivation in a microbioreactor is adequate for the detection of labeling patterns of free amino acids. Combining the innovations presented in this study, isotopically stationary and instationary automated labeling experiments can be conducted, thus fulfilling the prerequisites for ¹³C-metabolic flux analyses in high-throughput.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12934-022-01806-4.

Mutants lacking global regulators, fis and arcA, in Escherichia coli enhanced growth fitness under acetate metabolism by pathway reprogramming

Global regulatory transcription factors play a significant role in controlling microbial metabolism under genetic and environmental perturbations. A system-level effect of carbon sources such as acetate on microbial metabolism under disrupted global regulators has not been well established. Acetate is one of the major substrates available in various nutrient niches such as the mammalian gut and a keto diet. A substantial amount of acetate gets secreted in aerobic metabolism. Therefore, investigating the study on acetate metabolism is highly significant. It is known that the global regulators fis and arcA regulate acetate uptake genes in E. coli under glucose conditions. This study deciphered the growth and flux distribution of E. coli transcription regulatory knockouts Δfis, ΔarcA and double deletion mutant, ΔarcAΔfis under acetate using ¹³C-metabolic flux analysis (MFA), which has not been investigated before. We observed that the mutants exhibited an expeditious growth rate (~ 1.2-1.6-fold) with a proportionate increase in acetate uptake rates compared to the wild type. ¹³C-MFA displayed the distinct metabolic reprogramming of intracellular fluxes via the TCA cycle, anaplerotic pathway and gluconeogenesis, which conferred an advantage of a faster growth rate with better carbon usage in all the mutants. This resulted in higher metabolic fluxes through the TCA cycle (~ 18-90%), lower gluconeogenesis (~ 15-35%) and higher CO₂ and ATP production with the proportional increase in growth rate. The study reveals a novel insight by stating the sub-optimality of the wild-type strain grown under acetate substrate aerobically. These mutant strains efficiently oxidize acetate, thus acting as potential candidates for the biosynthesis of isoprenoids, biofuels, vitamins and various pharmaceutical products.Key Points• Mutants exhibited a better balance between energy and precursor synthesis than WT.• Leveraged in the unravelling of regulatory control under various nutrient shifts.• Metabolic readjustment resulted in optimal biomass requirement and faster growth.

Isotope-assisted metabolic flux analysis as an equality-constrained nonlinear program for improved scalability and robustness

Stable isotope-assisted metabolic flux analysis (MFA) is a powerful method to estimate carbon flow and partitioning in metabolic networks. At its core, MFA is a parameter estimation problem wherein the fluxes and metabolite pool sizes are model parameters that are estimated, via optimization, to account for measurements of steady-state or isotopically-nonstationary isotope labeling patterns. As MFA problems advance in scale, they require efficient computational methods for fast and robust convergence. The structure of the MFA problem enables it to be cast as an equality-constrained nonlinear program (NLP), where the equality constraints are constructed from the MFA model equations, and the objective function is defined as the sum of squared residuals (SSR) between the model predictions and a set of labeling measurements. This NLP can be solved by using an algebraic modeling language (AML) that offers state-of-the-art optimization solvers for robust parameter estimation and superior scalability to large networks. When implemented in this manner, the optimization is performed with no distinction between state variables and model parameters. During each iteration of such an optimization, the system state is updated instead of being calculated explicitly from scratch, and this occurs concurrently with improvement in the model parameter estimates. This optimization approach starkly contrasts with traditional “shooting” methods where the state variables and model parameters are kept distinct and the system state is computed afresh during each iteration of a stepwise optimization. Our NLP formulation uses the MFA modeling framework of Wiechert et al. [1], which is amenable to incorporation of the model equations into an NLP. The NLP constraints consist of balances on either elementary metabolite units (EMUs) or cumomers. In this formulation, both the steady-state and isotopically-nonstationary MFA (inst-MFA) problems may be solved as an NLP. For the inst-MFA case, the ordinary differential equation (ODE) system describing the labeling dynamics is transcribed into a system of algebraic constraints for the NLP using collocation. This large-scale NLP may be solved efficiently using an NLP solver implemented on an AML. In our implementation, we used the reduced gradient solver CONOPT, implemented in the General Algebraic Modeling System (GAMS). The NLP framework is particularly advantageous for inst-MFA, scaling well to large networks with many free parameters, and having more robust convergence properties compared to the shooting methods that compute the system state and sensitivities at each iteration. Additionally, this NLP approach supports the use of tandem-MS data for both steady-state and inst-MFA when the cumomer framework is used. We assembled a software, eiFlux, written in Python and GAMS that uses the NLP approach and supports both steady-state and inst-MFA. We demonstrate the effectiveness of the NLP formulation on several examples, including a genome-scale inst-MFA model, to highlight the scalability and robustness of this approach. In addition to typical inst-MFA applications, we expect that this framework and our associated software, eiFlux, will be particularly useful for applying inst-MFA to complex MFA models, such as those developed for eukaryotes (e.g. algae) and co-cultures with multiple cell types.

Isotope-assisted metabolic flux analysis (MFA) is a computationally intensive parameter estimation problem. Isotopically nonstationary MFA (inst-MFA) represents the most computationally burdensome MFA application. We present the formulation of the steady-state and inst-MFA problems as equality-constrained nonlinear programs (NLPs), solved by a state-of-the-art solver implemented in an algebraic modeling language. We show that this formulation leads to robust convergence properties compared to traditional approaches, particularly for inst-MFA. We developed a software, eiFlux that uses the NLP formulation to perform both steady-state and inst-MFA. We demonstrate the application of eiFlux on several examples, including a genome-scale inst-MFA model, and show that it has robust optimal convergence even when started from a very poor initial guess for the parameters. eiFlux is implemented using the Python programming language and the General Algebraic Modeling System (GAMS), using the CONOPT solver. eiFlux is available upon request, pending institutional approval, and is free for academic use.

Attenuation of glutamine synthetase selection marker improves product titer and reduces glutamine overflow in Chinese hamster ovary cells

The glutamine synthetase (GS) expression system is commonly used to ensure stable transgene integration and amplification in CHO host lines. Transfected cell populations are typically grown in the presence of the GS inhibitor, methionine sulfoximine (MSX), to further select for increased transgene copy number. However, high levels of GS activity produce excess glutamine. We hypothesized that attenuating the GS promoter while keeping the strong IgG promoter on the GS-IgG expression vector would result in a more efficient cellular metabolic phenotype. Herein, we characterized CHO cell lines expressing GS from either an attenuated promoter or an SV40 promoter and selected with/without MSX. CHO cells with the attenuated GS promoter had higher IgG specific productivity and lower glutamine production compared to cells with SV40-driven GS expression. Selection with MSX increased both specific productivity and glutamine production, regardless of GS promoter strength. ¹³ C metabolic flux analysis (MFA) was performed to further assess metabolic differences between these cell lines. Interestingly, central carbon metabolism was unaltered by the attenuated GS promoter while the fate of glutamate and glutamine varied depending on promoter strength and selection conditions. This study highlights the ability to optimize the GS expression system to improve IgG production and reduce wasteful glutamine overflow, without significantly altering central metabolism. Additionally, a detailed supplementary analysis of two "lactate runaway" reactors provides insight into the poorly understood phenomenon of excess lactate production by some CHO cell cultures. This article is protected by copyright. All rights reserved.

Reimport of carbon from cytosolic and vacuolar sugar pools into the Calvin-Benson cycle explains photosynthesis labeling anomalies

SignificancePhotosynthesis metabolites are quickly labeled when ¹³CO₂ is fed to leaves, but the time course of labeling reveals additional contributing processes involved in the metabolic dynamics of photosynthesis. The existence of three such processes is demonstrated, and a metabolic flux model is developed to explore and characterize them. The model is consistent with a slow return of carbon from cytosolic and vacuolar sugars into the Calvin-Benson cycle through the oxidative pentose phosphate pathway. Our results provide insight into how carbon assimilation is integrated into the metabolic network of photosynthetic cells with implications for global carbon fluxes.

Switching to the cyclic pentose phosphate pathway powers the oxidative burst in activated neutrophils

Neutrophils are cells at the frontline of innate immunity that can quickly activate effector functions to eliminate pathogens upon stimulation. However, little is known about the metabolic adaptations that power these functions. Here we show rapid metabolic alterations in neutrophils upon activation, particularly drastic reconfiguration around the pentose phosphate pathway, which is specifically and quantitatively coupled to an oxidative burst. During this oxidative burst, neutrophils switch from glycolysis-dominant metabolism to a unique metabolic mode termed 'pentose cycle', where all glucose-6-phosphate is diverted into oxidative pentose phosphate pathway and net flux through upper glycolysis is reversed to allow substantial recycling of pentose phosphates. This reconfiguration maximizes NADPH yield to fuel superoxide production via NADPH oxidase. Disruptions of pentose cycle greatly suppress oxidative burst, the release of neutrophil extracellular traps and pathogen killing by neutrophils. Together, these results demonstrate the remarkable metabolic flexibility of neutrophils, which is essential for their functions as the first responders in innate immunity.

Glucose-6-phosphatase catalytic subunit 2 negatively regulates glucose oxidation and insulin secretion in pancreatic β-cells

Elevated fasting blood glucose (FBG) is associated with increased risks of developing type 2 diabetes (T2D) and cardiovascular-associated mortality. G6PC2 is predominantly expressed in islets, encodes a glucose-6-phosphatase catalytic subunit that converts glucose-6-phosphate (G6P) to glucose, and has been linked with variations in FBG in genome-wide association studies. Deletion of G6pc2 in mice has been shown to lower FBG without affecting fasting plasma insulin levels in vivo. At 5 mM glucose, pancreatic islets from G6pc2 knockout (KO) mice exhibit no glucose cycling, increased glycolytic flux, and enhanced glucose-stimulated insulin secretion (GSIS). However, the broader effects of G6pc2 KO on β-cell metabolism and redox regulation are unknown. Here we used CRISPR/Cas9 gene editing and metabolic flux analysis in βTC3 cells, a murine pancreatic β-cell line, to examine the role of G6pc2 in regulating glycolytic and mitochondrial fluxes. We found that deletion of G6pc2 led to ∼60% increases in glycolytic and citric acid cycle (CAC) fluxes at both 5 and 11 mM glucose concentrations. Furthermore, intracellular insulin content and GSIS were enhanced by approximately two-fold, along with increased cytosolic redox potential and reductive carboxylation flux. Normalization of fluxes relative to net glucose uptake revealed upregulation in two NADPH-producing pathways in the CAC. These results demonstrate that G6pc2 regulates GSIS by modulating not only glycolysis but also, independently, citric acid cycle activity in β-cells. Overall, our findings implicate G6PC2 as a potential therapeutic target for enhancing insulin secretion and lowering FBG, which could benefit individuals with prediabetes, T2D, and obesity.

Detecting de novo Hepatic Ketogenesis Using Hyperpolarized [2-13C] Pyruvate

The role of ketones in metabolic health has progressed over the past two decades, moving from what was perceived as a simple byproduct of fatty acid oxidation to a central player in a multiplicity of disease states. Previous work with hyperpolarized (HP) 13C has shown that ketone production can be detected when using precursors that labeled acetyl-CoA at the C1 position, often in tissues that are not normally recognized as ketogenic. Here, we assay metabolism of HP [2-13C]pyruvate in the perfused mouse liver, a classic metabolic testbed where nutritional conditions can be precisely controlled. Livers perfused with long-chain fatty acids or the medium-chain fatty acid octanoate showed no evidence of ketogenesis in the 13C spectrum. In contrast, addition of dichloroacetate, a potent inhibitor of pyruvate dehydrogenase kinase, resulted in significant production of both acetoacetate and 3-hydroxybutyrate from the pyruvate precursor. This result indicates that ketones are readily produced from carbohydrates, but only in the case where pyruvate dehydrogenase activity is upregulated.

MetAMDB: Metabolic Atom Mapping Database

MetAMDB is an open-source metabolic atom mapping database, providing atom mappings for around 43,000 metabolic reactions. Each atom mapping can be inspected and downloaded either as an RXN file or as a graphic in SVG format. In addition, MetAMDB offers the possibility of automatically creating atom mapping models based on user-specified metabolic networks. These models can be of any size (small to genome-scale) and can subsequently be used in standard 13C metabolic flux analysis software.

Pool size measurements improve precision of flux estimates but increase sensitivity to unmodeled reactions outside the core network in isotopically nonstationary metabolic flux analysis (INST-MFA)

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 E. 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. This article is protected by copyright. All rights reserved.

Assessing the impact of substrate-level enzyme regulations limiting ethanol titer in Clostridium thermocellum using a core kinetic model

Clostridium thermocellum is a promising candidate for consolidated bioprocessing because it can directly ferment cellulose to ethanol. Despite significant efforts, achieved yields and titers fall below industrially relevant targets. This implies that there still exist unknown enzymatic, regulatory, and/or possibly thermodynamic bottlenecks that can throttle back metabolic flow. By (i) elucidating internal metabolic fluxes in wild-type C. thermocellum grown on cellobiose via ¹³C-metabolic flux analysis (¹³C-MFA), (ii) parameterizing a core kinetic model, and (iii) subsequently deploying an ensemble-docking workflow for discovering substrate-level regulations, this paper aims to reveal some of these factors and expand our knowledgebase governing C. thermocellum metabolism. Generated ¹³C labeling data were used with ¹³C-MFA to generate a wild-type flux distribution for the metabolic network. Notably, flux elucidation through MFA alluded to serine generation via the mercaptopyruvate pathway. Using the elucidated flux distributions in conjunction with batch fermentation process yield data for various mutant strains, we constructed a kinetic model of C. thermocellum core metabolism (i.e. k-ctherm138). Subsequently, we used the parameterized kinetic model to explore the effect of removing substrate-level regulations on ethanol yield and titer. Upon exploring all possible simultaneous (up to four) regulation removals we identified combinations that lead to many-fold model predicted improvement in ethanol titer. In addition, by coupling a systematic method for identifying putative competitive inhibitory mechanisms using K-FIT kinetic parameterization with the ensemble-docking workflow, we flagged 67 putative substrate-level inhibition mechanisms across central carbon metabolism supported by both kinetic formalism and docking analysis.

Isotopically nonstationary 13C metabolic flux analysis in resting and activated human platelets

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 ¹³C 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.

Metabolic flux analysis of the non-transitory starch tradeoff for lipid production in mature tobacco leaves

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 13CO2-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.

INCA 2.0: A tool for integrated, dynamic modeling of NMR- and MS-based isotopomer measurements and rigorous metabolic flux analysis

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 13C NMR datasets from perfused hearts with an accuracy comparable to other established flux assessment tools. Furthermore, by simulating the infusion of three different 13C acetate tracers, we show that MFA based on dynamic 13C 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 13C 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.

Carbon flux through photosynthesis and central carbon metabolism show distinct patterns between algae, C3 and C4 plants

Photosynthesis-related pathways are regarded as a promising avenue for crop improvement. Whilst empirical studies have shown that photosynthetic efficiency is higher in microalgae than in C3 or C4 crops, the underlying reasons remain unclear. Using a tailor-made microfluidics labelling system to supply 13CO2 at steady state, we investigated in vivo labelling kinetics in intermediates of the Calvin Benson cycle and sugar, starch, organic acid and amino acid synthesis pathways, and in protein and lipids, in Chlamydomonas reinhardtii, Chlorella sorokiniana and Chlorella ohadii, which is the fastest growing green alga on record. We estimated flux patterns in these algae and compared them with published and new data from C3 and C4 plants. Our analyses identify distinct flux patterns supporting faster growth in photosynthetic cells, with some of the algae exhibiting faster ribulose 1,5-bisphosphate regeneration and increased fluxes through the lower glycolysis and anaplerotic pathways towards the tricarboxylic acid cycle, amino acid synthesis and lipid synthesis than in higher plants.

Metabolic Remodeling during Nitrogen Fixation in Zymomonas mobilis

Zymomonas mobilis is an ethanologenic bacterium currently being developed for production of advanced biofuels. Recent studies have shown that Z. mobilis can fix dinitrogen gas (N2) as a sole nitrogen source. During N2 fixation, Z. mobilis exhibits increased biomass-specific rates of ethanol production. In order to better understand the physiology of Z. mobilis during N2 fixation and during changes in ammonium (NH4+) availability, we performed liquid chromatography-mass spectrometry (LC-MS)-based targeted metabolomics and shotgun proteomics under three regimes of nitrogen availability: continuous N2 fixation, gradual NH4+ depletion, and acute NH4+ addition to N2-fixing cells. We report dynamic changes in abundance of proteins and metabolites related to nitrogen fixation, motility, ammonium assimilation, amino acid biosynthesis, nucleotide biosynthesis, isoprenoid biosynthesis, and Entner-Doudoroff (ED) glycolysis, providing insight into the regulatory mechanisms that control these processes in Z. mobilis. Our analysis identified potential physiological mechanisms that may contribute to increased specific ethanol production during N2 fixation, including decreased activity of biosynthetic pathways, increased protein abundance of alcohol dehydrogenase (ADHI), and increased thermodynamic favorability of the ED pathway. Of particular relevance to advanced biofuel production, we found that intermediates in the methylerythritol phosphate (MEP) pathway for isoprenoid biosynthesis were depleted during N2 fixation, coinciding with decreased protein abundance of deoxyxylulose 5-phosphate synthase (DXS), the first enzyme in the pathway. This implies that DXS protein abundance serves as a native control point in regulating MEP pathway activity in Z. mobilis. The results of this study will inform metabolic engineering to further develop Z. mobilis as a platform organism for biofuel production. IMPORTANCE Biofuels and bioproducts have the potential to serve as environmentally sustainable replacements for petroleum-derived fuels and commodity molecules. Advanced fuels such as higher alcohols and isoprenoids are more suitable gasoline replacements than bioethanol. Developing microbial systems to generate advanced biofuels requires metabolic engineering to reroute carbon away from ethanol and other native products and toward desired pathways, such as the MEP pathway for isoprenoid biosynthesis. However, rational engineering of microbial metabolism relies on understanding metabolic control points, in terms of both enzyme activity and thermodynamic favorability. In Z. mobilis, the factors that control glycolytic rates, ethanol production, and isoprenoid production are still not fully understood. In this study, we performed metabolomic, proteomic, and thermodynamic analysis of Z. mobilis during N2 fixation. This analysis identified key changes in metabolite levels, enzyme abundance, and glycolytic thermodynamic favorability that occurred during changes in NH4+ availability, helping to inform future efforts in metabolic engineering.

Stable Isotopes for Tracing Cardiac Metabolism in Diseases

Although metabolic remodeling during cardiovascular diseases has been well-recognized for decades, the recent development of analytical platforms and mathematical tools has driven the emergence of assessing cardiac metabolism using tracers. Metabolism is a critical component of cellular functions and adaptation to stress. The pathogenesis of cardiovascular disease involves metabolic adaptation to maintain cardiac contractile function even in advanced disease stages. Stable-isotope tracer measurements are a powerful tool for measuring flux distributions at the whole organism level and assessing metabolic changes at a systems level in vivo. The goal of this review is to summarize techniques and concepts for in vivo or ex vivo stable isotope labeling in cardiovascular research, to highlight mathematical concepts and their limitations, to describe analytical methods at the tissue and single-cell level, and to discuss opportunities to leverage metabolic models to address important mechanistic questions relevant to all patients with cardiovascular disease.

Metabolic Analysis of the Asparagine and Glutamine Dynamics in an Industrial CHO Fed-Batch Process

Chinese hamster ovary (CHO) cell lines are grown in cultures with varying asparagine and glutamine concentrations, but further study is needed to characterize the interplay between these amino acids. By following ¹³C‐glucose, ¹³C‐glutamine, and ¹³C‐asparagine tracers using metabolic flux analysis (MFA), CHO cell metabolism was characterized in an industrially relevant fed‐batch process under glutamine supplemented and low glutamine conditions during early and late exponential growth. For both conditions MFA revealed glucose as the primary carbon source to the tricarboxylic acid (TCA) cycle followed by glutamine and asparagine as secondary sources. Early exponential phase CHO cells prefer glutamine over asparagine to support the TCA cycle under the glutamine supplemented condition, while asparagine was critical for TCA activity for the low glutamine condition. Overall TCA fluxes were similar for both conditions due to the trade‐offs associated with reliance on glutamine and/or asparagine. However, glutamine supplementation increased fluxes to alanine, lactate and enrichment of glutathione, N‐acetyl‐glucosamine and pyrimidine‐containing‐molecules. The late exponential phase exhibited reduced central carbon metabolism dominated by glucose, while lactate reincorporation and aspartate uptake were preferred over glutamine and asparagine. These ¹³C studies demonstrate that metabolic flux is process time dependent and can be modulated by varying feed composition.

Stable isotope tracing to assess tumor metabolism in vivo

Cancer cells undergo diverse metabolic adaptations to meet the energetic demands imposed by dysregulated growth and proliferation. Assessing metabolism in intact tumors allows the investigator to observe the combined metabolic effects of numerous cancer cell-intrinsic and -extrinsic factors that cannot be fully captured in culture models. We have developed methods to use stable isotope-labeled nutrients (e.g., [13C]glucose) to probe metabolic activity within intact tumors in vivo, in mice and humans. In these methods, the labeled nutrient is introduced to the circulation through an intravenous catheter prior to surgical resection of the tumor and adjacent nonmalignant tissue. Metabolism within these tissues during the infusion transfers the isotope label into metabolic intermediates from pathways supplied by the infused nutrient. Extracting metabolites from surgical specimens and analyzing their isotope labeling patterns provides information about metabolism in the tissue. We provide detailed information about this technique, from introduction of the labeled tracer through data analysis and interpretation, including streamlined approaches to quantify isotope labeling in informative metabolites extracted from tissue samples. We focus on infusions with [13C]glucose and the application of mass spectrometry to assess isotope labeling in intermediates from central metabolic pathways, including glycolysis, the tricarboxylic acid cycle and nonessential amino acid synthesis. We outline practical considerations to apply these methods to human subjects undergoing surgical resections of solid tumors. We also discuss the method's versatility and consider the relative advantages and limitations of alternative approaches to introduce the tracer, harvest the tissue and analyze the data.

Probing Light-Dependent Regulation of the Calvin Cycle Using a Multi-Omics Approach

Photoautotrophic microorganisms are increasingly explored for the conversion of atmospheric carbon dioxide into biomass and valuable products. The Calvin-Benson-Bassham (CBB) cycle is the primary metabolic pathway for net CO₂ fixation within oxygenic photosynthetic organisms. The cyanobacteria, Synechocystis sp. PCC 6803, is a model organism for the study of photosynthesis and a platform for many metabolic engineering efforts. The CBB cycle is regulated by complex mechanisms including enzymatic abundance, intracellular metabolite concentrations, energetic cofactors and post-translational enzymatic modifications that depend on the external conditions such as the intensity and quality of light. However, the extent to which each of these mechanisms play a role under different light intensities remains unclear. In this work, we conducted non-targeted proteomics in tandem with isotopically non-stationary metabolic flux analysis (INST-MFA) at four different light intensities to determine the extent to which fluxes within the CBB cycle are controlled by enzymatic abundance. The correlation between specific enzyme abundances and their corresponding reaction fluxes is examined, revealing several enzymes with uncorrelated enzyme abundance and their corresponding flux, suggesting flux regulation by mechanisms other than enzyme abundance. Additionally, the kinetics of ¹³C labeling of CBB cycle intermediates and estimated inactive pool sizes varied significantly as a function of light intensity suggesting the presence of metabolite channeling, an additional method of flux regulation. These results highlight the importance of the diverse methods of regulation of CBB enzyme activity as a function of light intensity, and highlights the importance of considering these effects in future kinetic models.

The next phase in the development of 13C isotopically non-stationary metabolic flux analysis

This viewpoint devises recommendations for future studies utilizing 13C isotopically non-stationary metabolic flux analysis to characterize plant metabolism. Most importantly, it highlights the necessity for model validation.

NaCT/SLC13A5 facilitates citrate import and metabolism under nutrient-limited conditions

Citrate lies at a critical node of metabolism, linking tricarboxylic acid metabolism and lipogenesis via acetyl-coenzyme A. Recent studies have observed that deficiency of the sodium-dependent citrate transporter (NaCT), encoded by SLC13A5, dysregulates hepatic metabolism and drives pediatric epilepsy. To examine how NaCT contributes to citrate metabolism in cells relevant to the pathophysiology of these diseases, we apply ¹³C isotope tracing to SLC13A5-deficient hepatocellular carcinoma (HCC) cells and primary rat cortical neurons. Exogenous citrate appreciably contributes to intermediary metabolism only under hypoxic conditions. In the absence of glutamine, citrate supplementation increases de novo lipogenesis and growth of HCC cells. Knockout of SLC13A5 in Huh7 cells compromises citrate uptake and catabolism. Citrate supplementation rescues Huh7 cell viability in response to glutamine deprivation or Zn²⁺ treatment, and NaCT deficiency mitigates these effects. Collectively, these findings demonstrate that NaCT-mediated citrate uptake is metabolically important under nutrient-limited conditions and may facilitate resistance to metal toxicity.

Measuring NQO1 Bioactivation Using [2H7]Glucose

Treatment of cancers with β-lapachone causes NAD(P)H: quinone oxidoreductase 1 (NQO1) to generate an unstable hydroquinone that regenerates itself in a futile cycle while producing reactive oxygen species (ROS) in the form of superoxide and subsequently hydrogen peroxide. Rapid accumulation of ROS damages DNA, hyperactivates poly-ADP-ribose polymerase-I, causes massive depletion of NAD+/ATP, and hampers glycolysis. Cells overexpressing NQO1 subsequently die rapidly through an NAD+-keresis mechanism. Assessing changes in glycolytic rates caused by NQO1 bioactivation would provide a means of assessing treatment efficacy, potentially lowering the chemotherapeutic dosage, and reducing off-target toxicities. NQO1-mediated changes in glycolytic flux were readily detected in A549 (lung), MiaPaCa2 (pancreatic), and HCT-116 (colon) cancer cell lines by 2H-NMR after administration of [2H7]glucose. The deuterated metabolic products 2H-lactate and HDO were quantified, and linear relationships with glucose consumption for both products were observed. The higher concentration of HDO compared to 2H-lactate allows for more sensitive measurement of the glycolytic flux in cancer. Gas chromatography-mass spectrometry analysis agreed with the NMR results and confirmed downregulated energy metabolism in NQO1+ cells after β-lapachone treatment. The demonstrated method is ideal for measuring glycolytic rates, the effects of chemotherapeutics that target glycolysis, and has the potential for in vivo translation.

mfapy: An open-source Python package for 13C-based metabolic flux analysis

¹³C-based metabolic flux analysis (¹³C-MFA) is an essential tool for estimating intracellular metabolic flux levels in metabolic engineering and biology. In ¹³C-MFA, a metabolic flux distribution that explains the observed isotope labeling data was computationally estimated using a non-linear optimization method. Herein, we report the development of mfapy, an open-source Python package developed for more flexibility and extensibility for ¹³C-MFA. mfapy compels users to write a customized Python code by describing each step in the data analysis procedures of the isotope labeling experiments. The flexibility and extensibility provided by mfapy can support trial-and-error performance in the routine estimation of metabolic flux distributions, experimental design by computer simulations of ¹³C-MFA experiments, and development of new data analysis techniques for stable isotope labeling experiments. mfapy is available to the public from the Github repository (https://github.com/fumiomatsuda/mfapy).

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An open-source Python package, mfapy, is developed for ¹³C-MFA.

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mfapy enables users to write Python codes for data analysis procedures of ¹³C-MFA.

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mfapy has a flexibility and extensibility to support various data analysis procedures.

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Computer simulations of ¹³C-MFA experiments is supported for experimental design.

Ins and Outs of the TCA Cycle: The Central Role of Anaplerosis

The reactions of the tricarboxylic acid (TCA) cycle allow the controlled combustion of fat and carbohydrate. In principle, TCA cycle intermediates are regenerated on every turn and can facilitate the oxidation of an infinite number of nutrient molecules. However, TCA cycle intermediates can be lost to cataplerotic pathways that provide precursors for biosynthesis, and they must be replaced by anaplerotic pathways that regenerate these intermediates. Together, anaplerosis and cataplerosis help regulate rates of biosynthesis by dictating precursor supply, and they play underappreciated roles in catabolism and cellular energy status. They facilitate recycling pathways and nitrogen trafficking necessary for catabolism, and they influence redox state and oxidative capacity by altering TCA cycle intermediate concentrations. These functions vary widely by tissue and play emerging roles in disease. This article reviews the roles of anaplerosis and cataplerosis in various tissues and discusses how they alter carbon transitions, and highlights their contribution to mechanisms of disease. Expected final online publication date for the Annual Review of Nutrition, Volume 41 is September 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Hyperpolarized Dihydroxyacetone Is a Sensitive Probe of Hepatic Gluconeogenic State

Type II diabetes and pre-diabetes are widely prevalent among adults. Elevated serum glucose levels are commonly treated by targeting hepatic gluconeogenesis for downregulation. However, direct measurement of hepatic gluconeogenic capacity is accomplished only via tracer metabolism approaches that rely on multiple assumptions, and are clinically intractable due to expense and time needed for the studies. We previously introduced hyperpolarized (HP) [2-13C]dihydroxyacetone (DHA) as a sensitive detector of gluconeogenic potential, and showed that feeding and fasting produced robust changes in the ratio of detected hexoses (6C) to trioses (3C) in the perfused liver. To confirm that this ratio is robust in the setting of treatment and hormonal control, we used ex vivo perfused mouse livers from BLKS mice (glucagon treated and metformin treated), and db/db mice. We confirm that the ratio of signal intensities of 6C to 3C in 13C nuclear magnetic resonance spectra post HP DHA administration is sensitive to hepatic gluconeogenic state. This method is directly applicable in vivo and can be implemented with existing technologies without the need for substantial modifications.

Metabolic control of nitrogen fixation in rhizobium-legume symbioses

Rhizobia induce nodule formation on legume roots and differentiate into bacteroids, which catabolize plant-derived dicarboxylates to reduce atmospheric N₂ into ammonia. Despite the agricultural importance of this symbiosis, the mechanisms that govern carbon and nitrogen allocation in bacteroids and promote ammonia secretion to the plant are largely unknown. Using a metabolic model derived from genome-scale datasets, we show that carbon polymer synthesis and alanine secretion by bacteroids facilitate redox balance in microaerobic nodules. Catabolism of dicarboxylates induces not only a higher oxygen demand but also a higher NADH/NAD⁺ ratio than sugars. Modeling and ¹³C metabolic flux analysis indicate that oxygen limitation restricts the decarboxylating arm of the tricarboxylic acid cycle, which limits ammonia assimilation into glutamate. By tightly controlling oxygen supply and providing dicarboxylates as the energy and electron source donors for N₂ fixation, legumes promote ammonia secretion by bacteroids. This is a defining feature of rhizobium-legume symbioses.

Hierarchical Harmonization of Atom-Resolved Metabolic Reactions across Metabolic Databases

Metabolic models have been proven to be useful tools in system biology and have been successfully applied to various research fields in a wide range of organisms. A relatively complete metabolic network is a prerequisite for deriving reliable metabolic models. The first step in constructing metabolic network is to harmonize compounds and reactions across different metabolic databases. However, effectively integrating data from various sources still remains a big challenge. Incomplete and inconsistent atomistic details in compound representations across databases is a very important limiting factor. Here, we optimized a subgraph isomorphism detection algorithm to validate generic compound pairs. Moreover, we defined a set of harmonization relationship types between compounds to deal with inconsistent chemical details while successfully capturing atom-level characteristics, enabling a more complete enabling compound harmonization across metabolic databases. In total, 15,704 compound pairs across KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc databases were detected. Furthermore, utilizing the classification of compound pairs and EC (Enzyme Commission) numbers of reactions, we established hierarchical relationships between metabolic reactions, enabling the harmonization of 3856 reaction pairs. In addition, we created and used atom-specific identifiers to evaluate the consistency of atom mappings within and between harmonized reactions, detecting some consistency issues between the reaction and compound descriptions in these metabolic databases.