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

Multitissue 2H/13C flux analysis reveals reciprocal upregulation of renal gluconeogenesis in hepatic PEPCK-C-knockout mice

The liver is the major source of glucose production during fasting under normal physiological conditions. However, the kidney may also contribute to maintaining glucose homeostasis in certain circumstances. To test the ability of the kidney to compensate for impaired hepatic glucose production in vivo, we developed a stable isotope approach to simultaneously quantify gluconeogenic and oxidative metabolic fluxes in the liver and kidney. Hepatic gluconeogenesis from phosphoenolpyruvate was disrupted via liver-specific knockout of cytosolic phosphoenolpyruvate carboxykinase (PEPCK-C; KO). 2H/13C isotopes were infused in fasted KO and WT littermate mice, and fluxes were estimated from isotopic measurements of tissue and plasma metabolites using a multicompartment metabolic model. Hepatic gluconeogenesis and glucose production were reduced in KO mice, yet whole-body glucose production and arterial glucose were unaffected. Glucose homeostasis was maintained by a compensatory rise in renal glucose production and gluconeogenesis. Renal oxidative metabolic fluxes of KO mice increased to sustain the energetic and metabolic demands of elevated gluconeogenesis. These results show the reciprocity of the liver and kidney in maintaining glucose homeostasis by coordinated regulation of gluconeogenic flux through PEPCK-C. Combining stable isotopes with mathematical modeling provides a versatile platform to assess multitissue metabolism in various genetic, pathophysiological, physiological, and pharmacological settings.

Analysis of steady-state carbon tracer experiments using akaike information criteria

INTRODUCTION

Carbon isotope tracers have been used to determine relative rates of tricarboxylic acid cycle (TCA) cycle pathways since the 1950s. Steady-state experimental data are typically fit to a single mathematical model of metabolism to determine metabolic fluxes. Whether the chosen model is appropriate for the biological system has generally not been evaluated systematically. An overly-simple model omits known pathways while an overly-complex model may produce incorrect results due to overfitting.

OBJECTIVES

The objectives were to develop and study a method that systematically evaluates multiple TCA cycle mathematical models as part of the fitting process.

METHODS

The problem of choosing overly-simple or overly-complex models was approached by developing software that automatically explores all possible combinations of flux through pyruvate dehydrogenase, pyruvate kinase, pyruvate carboxylase and anaplerosis at propionyl-CoA carboxylase, and equivalent pathways, all relative to TCA cycle flux. Typical TCA cycle metabolic tracer experiments that use 13C nuclear magnetic resonance for detection and quantification of 13C-enriched glutamate products were simulated and analyzed. By evaluating the multiple model fits with both the conventional sum-of-squares residual error (SSRE) and the Akaike Information Criterion (AIC), the software helps the investigator understand the interaction between model complexity and goodness of fit.

RESULTS

When fitting alternative models of the TCA cycle metabolism, the SSRE may identify more than one model that fits the data well. Among those models, the AIC provides guidance as to which is the simplest of the candidate models is sufficient to describe the observed data. However under some conditions, AIC used alone inappropriately discriminates against necessary metabolic complexity.

CONCLUSION

In combination, the SSRE and AIC help the investigator identify the model that best describes the metabolism of a biological system.

Loss of mitochondrial aconitase promotes colorectal cancer progression via SCD1-mediated lipid remodeling

OBJECTIVE

Mitochondrial aconitase (ACO2) is an essential enzyme that bridges the TCA cycle and lipid metabolism. However, its role in cancer development remains to be elucidated. The metabolic subtype of colorectal cancer (CRC) was recently established. We investigated ACO2's potential role in CRC progression through mediating metabolic alterations.

METHODS

We compared the mRNA and protein expression of ACO2 between paired CRC and non-tumor tissues from 353 patients. Correlations between ACO2 levels and clinicopathological features were examined. CRC cell lines with knockdown or overexpression of ACO2 were analyzed for cell proliferation and tumor growth. Metabolomics and stable isotope tracing analyses were used to study the metabolic alterations induced by loss of ACO2.

RESULTS

ACO2 decreased in >50% of CRC samples compared with matched non-tumor tissues. Decreased ACO2 levels correlated with advanced disease stage (P < 0.001) and shorter patient survival (P < 0.001). Knockdown of ACO2 in CRC cells promoted cell proliferation and tumor formation, while ectopic expression of ACO2 restrained tumor growth. Specifically, blockade of ACO2 caused a reduction in TCA cycle intermediates and suppression of mitochondrial oxidative phosphorylation, resulting in an increase in glycolysis and elevated citrate flux for fatty acid and lipid synthesis. Increased citrate flux induced upregulation of stearoyl-CoA desaturase (SCD1), which enhanced lipid desaturation in ACO2-deficent cells to favor colorectal cancer growth. Pharmacological inhibition of SCD selectively reduced tumor formation of CRC with ACO2 deficiency.

CONCLUSIONS

Our study demonstrated that the rewiring metabolic pathway maintains CRC survival during compromised TCA cycles and characterized the therapeutic vulnerability of lipid desaturation in a meaningful subset of CRC with mitochondrial dysfunction.

The metabolic origins of non-photorespiratory CO2 release during photosynthesis: a metabolic flux analysis

Respiration in the light (RL) releases CO2 in photosynthesizing leaves and is a phenomenon that occurs independently from photorespiration. Since RL lowers net carbon fixation, understanding RL could help improve plant carbon-use efficiency and models of crop photosynthesis. Although RL was identified more than 75 years ago, its biochemical mechanisms remain unclear. To identify reactions contributing to RL, we mapped metabolic fluxes in photosynthesizing source leaves of the oilseed crop and model plant camelina (Camelina sativa). We performed a flux analysis using isotopic labeling patterns of central metabolites during 13CO2 labeling time course, gas exchange, and carbohydrate production rate experiments. To quantify the contributions of multiple potential CO2 sources with statistical and biological confidence, we increased the number of metabolites measured and reduced biological and technical heterogeneity by using single mature source leaves and quickly quenching metabolism by directly injecting liquid N2; we then compared the goodness-of-fit between these data and data from models with alternative metabolic network structures and constraints. Our analysis predicted that RL releases 5.2 μmol CO2 g-1 FW h-1 of CO2, which is relatively consistent with a value of 9.3 μmol CO2 g-1 FW h-1 measured by CO2 gas exchange. The results indicated that ≤10% of RL results from TCA cycle reactions, which are widely considered to dominate RL. Further analysis of the results indicated that oxidation of glucose-6-phosphate to pentose phosphate via 6-phosphogluconate (the G6P/OPP shunt) can account for >93% of CO2 released by RL.

In vivo2H/13C flux analysis in metabolism research

Identifying the factors and mechanisms that regulate metabolism under normal and diseased states requires methods to quantify metabolic fluxes of live tissues within their physiological milieu. A number of recent developments have expanded the reach and depth of isotope-based in vivo flux analysis, which have in turn challenged existing dogmas in metabolism research. First, minimally invasive techniques of intravenous isotope infusion and sampling have advanced in vivo metabolic tracer studies in animal models and human subjects. Second, recent breakthroughs in analytical instrumentation have expanded the scope of isotope labeling measurements and reduced sample volume requirements. Third, innovative modeling approaches and publicly available software tools have facilitated rigorous analysis of sophisticated experimental designs involving multiple tracers and expansive metabolomics datasets. These developments have enabled comprehensive in vivo quantification of metabolic fluxes in specific tissues and have set the stage for integrated multi-tissue flux assays.

13C isotope-based metabolic flux analysis revealing cellular landscape of glucose metabolism in human liver cells exposed to perfluorooctanoic acid

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 ¹³C isotope-based metabolic flux analysis (MFA), i.e., pathway activities. The relative percentage of [U-¹³C₆] 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-¹³C₆] 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.

PI5P4Ks drive metabolic homeostasis through peroxisome-mitochondria interplay

PI5P4Ks are a class of phosphoinositide kinases that phosphorylate PI-5-P to PI-4,5-P₂. Distinct localization of phosphoinositides is fundamental for a multitude of cellular functions. Here, we identify a role for peroxisomal PI-4,5-P₂ generated by the PI5P4Ks in maintaining energy balance. We demonstrate that PI-4,5-P₂ regulates peroxisomal fatty acid oxidation by mediating trafficking of lipid droplets to peroxisomes, which is essential for sustaining mitochondrial metabolism. Using fluorescent-tagged lipids and metabolite tracing, we show that loss of the PI5P4Ks significantly impairs lipid uptake and β-oxidation in the mitochondria. Further, loss of PI5P4Ks results in dramatic alterations in mitochondrial structural and functional integrity, which under nutrient deprivation is further exacerbated, causing cell death. Notably, inhibition of the PI5P4Ks in cancer cells and mouse tumor models leads to decreased cell viability and tumor growth, respectively. Together, these studies reveal an unexplored role for PI5P4Ks in preserving metabolic homeostasis, which is necessary for tumorigenesis.

In Vivo Estimates of Liver Metabolic Flux Assessed by 13C-Propionate and 13C-Lactate Are Impacted by Tracer Recycling and Equilibrium Assumptions

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 [¹³C₃]lactate or [¹³C₃]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 ¹³C 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 [¹³C₃]lactate and [¹³C₃]propionate isotopes emanate, in part, from peripheral tracer recycling and incomplete isotope equilibration within the citric acid cycle.

Metabolic fluxes for nutritional flexibility of Mycobacterium tuberculosis

The co-catabolism of multiple host-derived carbon substrates is required by Mycobacterium tuberculosis (Mtb) to successfully sustain a tuberculosis infection. However, the metabolic plasticity of this pathogen and the complexity of the metabolic networks present a major obstacle in identifying those nodes most amenable to therapeutic interventions. It is therefore critical that we define the metabolic phenotypes of Mtb in different conditions. We applied metabolic flux analysis using stable isotopes and lipid fingerprinting to investigate the metabolic network of Mtb growing slowly in our steady-state chemostat system. We demonstrate that Mtb efficiently co-metabolises either cholesterol or glycerol, in combination with two-carbon generating substrates without any compartmentalisation of metabolism. We discovered that partitioning of flux between the TCA cycle and the glyoxylate shunt combined with a reversible methyl citrate cycle is the critical metabolic nodes which underlie the nutritional flexibility of Mtb. These findings provide novel insights into the metabolic architecture that affords adaptability of bacteria to divergent carbon substrates and expand our fundamental knowledge about the methyl citrate cycle and the glyoxylate shunt.

In Vivo Estimation of Ketogenesis Using Metabolic Flux Analysis-Technical Aspects and Model Interpretation

Ketogenesis occurs in liver mitochondria where acetyl-CoA molecules, derived from lipid oxidation, are condensed into acetoacetate (AcAc) and reduced to β-hydroxybutyrate (BHB). During carbohydrate scarcity, these two ketones are released into circulation at high rates and used as oxidative fuels in peripheral tissues. Despite their physiological relevance and emerging roles in a variety of diseases, endogenous ketone production is rarely measured in vivo using tracer approaches. Accurate determination of this flux requires a two-pool model, simultaneous BHB and AcAc tracers, and special consideration for the stability of the AcAc tracer and analyte. We describe the implementation of a two-pool model using a metabolic flux analysis (MFA) approach that simultaneously regresses liquid chromatography-tandem mass spectrometry (LC-MS/MS) ketone isotopologues and tracer infusion rates. Additionally, ¹H NMR real-time reaction monitoring was used to evaluate AcAc tracer and analyte stability during infusion and sample analysis, which were critical for accurate flux calculations. The approach quantifies AcAc and BHB pool sizes and their rates of appearance, disposal, and exchange. Regression analysis provides confidence intervals and detects potential errors in experimental data. Complications for the physiological interpretation of individual ketone fluxes are discussed.

13C Metabolic Flux Analysis Indicates Endothelial Cells Attenuate Metabolic Perturbations by Modulating TCA Activity

Disrupted endothelial metabolism is linked to endothelial dysfunction and cardiovascular disease. Targeted metabolic inhibitors are potential therapeutics; however, their systemic impact on endothelial metabolism remains unknown. In this study, we combined stable isotope labeling with ¹³C metabolic flux analysis (¹³C MFA) to determine how targeted inhibition of the polyol (fidarestat), pentose phosphate (DHEA), and hexosamine biosynthetic (azaserine) pathways alters endothelial metabolism. Glucose, glutamine, and a four-carbon input to the malate shuttle were important carbon sources in the baseline human umbilical vein endothelial cell (HUVEC) ¹³C MFA model. We observed two to three times higher glutamine uptake in fidarestat and azaserine-treated cells. Fidarestat and DHEA-treated HUVEC showed decreased ¹³C enrichment of glycolytic and TCA metabolites and amino acids. Azaserine-treated HUVEC primarily showed ¹³C enrichment differences in UDP-GlcNAc. ¹³C MFA estimated decreased pentose phosphate pathway flux and increased TCA activity with reversed malate shuttle direction in fidarestat and DHEA-treated HUVEC. In contrast, ¹³C MFA estimated increases in both pentose phosphate pathway and TCA activity in azaserine-treated cells. These data show the potential importance of endothelial malate shuttle activity and suggest that inhibiting glycolytic side branch pathways can change the metabolic network, highlighting the need to study systemic metabolic therapeutic effects.

Improved Yield of Recombinant Protein via Flagella Regulator Deletion in Escherichia coli

Protein production requires a significant amount of intracellular energy. Eliminating the flagella has been proposed to help Escherichia coli improve protein production by reducing energy consumption. In this study, the gene encoding a subunit of FlhC, a master regulator of flagella assembly, was deleted to reduce the expression of flagella-related genes. FlhC knockout in the ptsG-deleted strain triggered significant growth retardation with increased ATP levels and a higher NADPH/NADP⁺ ratio. Metabolic flux analysis using a ¹³C-labeled carbon substrate showed increased fluxes toward the pentose phosphate and tricarboxylic acid cycle pathways in the flhC- and ptsG-deleted strains. Introduction of a high copy number plasmid or overexpression of the recombinant protein in this strain restored growth rate without increasing glucose consumption. These results suggest that the metabolic burden caused by flhC deletion was resolved by recombinant protein production. The recombinant enhanced green fluorescent protein yield per glucose consumption increased 1.81-fold in the flhC mutant strain. Thus, our study demonstrates that high-yield production of the recombinant protein was achieved with reduced flagella formation.

Itaconate Alters Succinate and Coenzyme A Metabolism via Inhibition of Mitochondrial Complex II and Methylmalonyl-CoA Mutase

Itaconate is a small molecule metabolite that is endogenously produced by cis-aconitate decarboxylase-1 (ACOD1) in mammalian cells and influences numerous cellular processes. The metabolic consequences of itaconate in cells are diverse and contribute to its regulatory function. Here, we have applied isotope tracing and mass spectrometry approaches to explore how itaconate impacts various metabolic pathways in cultured cells. Itaconate is a competitive and reversible inhibitor of Complex II/succinate dehydrogenase (SDH) that alters tricarboxylic acid (TCA) cycle metabolism leading to succinate accumulation. Upon activation with coenzyme A (CoA), itaconyl-CoA inhibits adenosylcobalamin-mediated methylmalonyl-CoA (MUT) activity and, thus, indirectly impacts branched-chain amino acid (BCAA) metabolism and fatty acid diversity. Itaconate, therefore, alters the balance of CoA species in mitochondria through its impacts on TCA, amino acid, vitamin B₁₂, and CoA metabolism. Our results highlight the diverse metabolic pathways regulated by itaconate and provide a roadmap to link these metabolites to potential downstream biological functions.

Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus

Anaerobic gut fungi in the phylum Neocallimastigomycota typically inhabit the digestive tracts of large mammalian herbivores, where they play an integral role in the decomposition of raw lignocellulose into its constitutive sugar monomers. However, quantitative tools to study their physiology are lacking, partially due to their complex and unresolved metabolism that includes the largely uncharacterized fungal hydrogenosome. Modern omics approaches combined with metabolic modeling can be used to establish an understanding of gut fungal metabolism and develop targeted engineering strategies to harness their degradation capabilities for lignocellulosic bioprocessing. Here, we introduce a high-quality genome of the anaerobic fungus Neocallimastix lanati from which we constructed the first genome-scale metabolic model of an anaerobic fungus. Relative to its size (200 Mbp, sequenced at 62× depth), it is the least fragmented publicly available gut fungal genome to date. Of the 1,788 lignocellulolytic enzymes annotated in the genome, 585 are associated with the fungal cellulosome, underscoring the powerful lignocellulolytic potential of N. lanati. The genome-scale metabolic model captures the primary metabolism of N. lanati and accurately predicts experimentally validated substrate utilization requirements. Additionally, metabolic flux predictions are verified by ¹³C metabolic flux analysis, demonstrating that the model faithfully describes the underlying fungal metabolism. Furthermore, the model clarifies key aspects of the hydrogenosomal metabolism and can be used as a platform to quantitatively study these biotechnologically important yet poorly understood early-branching fungi.

IMPORTANCE Recent genomic analyses have revealed that anaerobic gut fungi possess both the largest number and highest diversity of lignocellulolytic enzymes of all sequenced fungi, explaining their ability to decompose lignocellulosic substrates, e.g., agricultural waste, into fermentable sugars. Despite their potential, the development of engineering methods for these organisms has been slow due to their complex life cycle, understudied metabolism, and challenging anaerobic culture requirements. Currently, there is no framework that can be used to combine multi-omic data sets to understand their physiology. Here, we introduce a high-quality PacBio-sequenced genome of the anaerobic gut fungus Neocallimastix lanati. Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.

Exploring the metabolic fate of propanol in industrial erythromycin-producing strain via 13C labeling experiments and enhancement of erythromycin production by rational metabolic engineering of Saccharopolyspora erythraea

Propanol had been widely used as a precursor for erythromycin synthesis in industrial production. However, the knowledge on the exact metabolic fate of propanol was still unclear. In the present study, the metabolic fate of propanol in industrial erythromycin-producing strain Saccharopolyspora erythraea E3 was explored via 13C labeling experiments. An unexpected pathway in which propanol was channeled into tricarboxylic acid cycle was uncovered, resulting in uneconomic catabolism of propanol. By deleting the sucC gene, which encodes succinyl-CoA synthetase that catalyse a reaction in the unexpected propanol utilization pathway, a novel strain E3-ΔsucC was constructed. The strain E3-ΔsucC showed a significant enhancement in erythromycin production in the chemically defined medium compared to E3 (786.61 vs 392.94 mg/L). Isotopically nonstationary 13C metabolic flux analysis were employed to characterize the metabolic differences between Saccharopolyspora erythraea E3 and E3-ΔsucC. The results showed that compared with the starting strain E3, the fluxes of pentose phosphate pathway in E3-△sucC increased by almost 200%. The flux of the metabolic reaction catalyzed by succinyl-CoA synthetase in E3-ΔsucC was almost zero, while the glyoxylate bypass flux significantly increased. These new insights into the precursor utilization of antibiotic biosynthesis by rational metabolic engineering in Saccharopolyspora erythraea provided the new vision in increasing industrial production of secondary metabolites.

A Practical Guide to Metabolomics Software Development

A growing number of software tools have been developed for metabolomics data processing and analysis. Many new tools are contributed by metabolomics practitioners who have limited prior experience with software development, and the tools are subsequently implemented by users with expertise that ranges from basic point-and-click data analysis to advanced coding. This Perspective is intended to introduce metabolomics software users and developers to important considerations that determine the overall impact of a publicly available tool within the scientific community. The recommendations reflect the collective experience of an NIH-sponsored Metabolomics Consortium working group that was formed with the goal of researching guidelines and best practices for metabolomics tool development. The recommendations are aimed at metabolomics researchers with little formal background in programming and are organized into three stages: (i) preparation, (ii) tool development, and (iii) distribution and maintenance.

Preclinical techniques to investigate exercise training in vascular pathophysiology

Atherosclerosis is a dynamic process starting with endothelial dysfunction and inflammation and eventually leading to life-threatening arterial plaques. Exercise generally improves endothelial function in a dose-dependent manner by altering hemodynamics, specifically by increased arterial pressure, pulsatility, and shear stress. However, athletes who regularly participate in high-intensity training can develop arterial plaques, suggesting alternative mechanisms through which excessive exercise promotes vascular disease. Understanding the mechanisms that drive atherosclerosis in sedentary versus exercise states may lead to novel rehabilitative methods aimed at improving exercise compliance and physical activity. Preclinical tools, including in vitro cell assays, in vivo animal models, and in silico computational methods, broaden our capabilities to study the mechanisms through which exercise impacts atherogenesis, from molecular maladaptation to vascular remodeling. Here, we describe how preclinical research tools have and can be used to study exercise effects on atherosclerosis. We then propose how advanced bioengineering techniques can be used to address gaps in our current understanding of vascular pathophysiology, including integrating in vitro, in vivo, and in silico studies across multiple tissue systems and size scales. Improving our understanding of the antiatherogenic exercise effects will enable engaging, targeted, and individualized exercise recommendations to promote cardiovascular health rather than treating cardiovascular disease that results from a sedentary lifestyle.

Investigating metabolism by mass spectrometry: From steady state to dynamic view

Metabolism is the set of life-sustaining reactions in organisms. These biochemical reactions are organized in metabolic pathways, in which one metabolite is converted through a series of steps catalyzed by enzymes in another chemical compound. Metabolic reactions are categorized as catabolic, the breaking down of metabolites to produce energy, and/or anabolic, the synthesis of compounds that consume energy. The balance between catabolism of the preferential fuel substrate and anabolism defines the overall metabolism of a cell or tissue. Metabolomics is a powerful tool to gain new insights contributing to the identification of complex molecular mechanisms in the field of biomedical research, both basic and translational. The enormous potential of this kind of analyses consists of two key aspects: (i) the possibility of performing so-called targeted and untargeted experiments through which it is feasible to verify or formulate a hypothesis, respectively, and (ii) the opportunity to run either steady-state analyses to have snapshots of the metabolome at a given time under different experimental conditions or dynamic analyses through the use of labeled tracers. In this review, we will highlight the most important practical (e.g., different sample extraction approaches) and conceptual steps to consider for metabolomic analysis, describing also the main application contexts in which it is used. In addition, we will provide some insights into the most innovative approaches and progress in the field of data analysis and processing, highlighting how this part is essential for the proper extrapolation and interpretation of data.

Deuterium Tracing to Interrogate Compartment-Specific NAD(P)H Metabolism in Cultured Mammalian Cells

Oxidation-reduction (redox) reactions are ubiquitous in biology and typically occur in specific subcellular compartments. In cells, the electron transfer between molecules and organelles is commonly facilitated by pyridine nucleotides such as nicotinamide adenine dinucleotide phosphate (NADPH) and nicotinamide adenine dinucleotide (NADH). While often taken for granted, these metabolic reactions are critically important for maintaining redox homeostasis and biochemical potentials across membranes. While ¹³C tracing and metabolic flux analysis (MFA) have emerged as powerful tools to study intracellular metabolism, this approach is limited when applied to pathways catalyzed in multiple cellular compartments. To address this issue, we and others have applied ²H (deuterium) tracers to observe transfer of labeled hydride anions, which accompanies electron transfer. Furthermore, we have developed a reporter system for indirectly quantifying NADPH enrichment in specific subcellular compartments. Here, we provide a detailed description of ²H tracing applications and the interrogation of mitochondrial versus cytosolic NAD(P)H metabolism in cultured mammalian cells. Specifically, we describe the generation of reporter cell lines that express epitope-tagged R132H-IDH1 or R172K-IDH2 and produce (D)2-hydroxyglutarate in a doxycycline-dependent manner. These tools and methods allow for quantitation of reducing equivalent turnover rates, the directionality of pathways present in multiple compartments, and the estimation of pathway contributions to NADPH pools.

Cancer Cell Metabolites: Updates on Current Tracing Methods

Cancer is the second leading cause of death-1 in 6 deaths globally is due to cancer. Cancer metabolism is a complex and one of the most actively researched area in cancer biology. Metabolic reprogramming in cancer cells entails activities that involve several enzymes and metabolites to convert nutrient into building blocks that alter energy metabolism to fuel rapid cell division. Metabolic dependencies in cancer generate signature metabolites that have key regulatory roles in tumorigenesis. In this minireview, we highlight recent advances in the popular methods ingrained in biochemistry research such as stable and flux isotope analysis, as well as radioisotope labeling, which are valuable in elucidating cancer metabolites. These methods together with analytical tools such as chromatography, nuclear magnetic resonance spectroscopy and mass spectrometry have helped to bring about exploratory work in understanding the role of important as well as obscure metabolites in cancer cells. Information obtained from these analyses significantly contribute in the diagnosis and prognosis of tumors leading to potential therapeutic targets for cancer therapy.

Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms

Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.

Reductive tricarboxylic acid cycle enzymes and reductive amino acid synthesis pathways contribute to electron balance in a Rhodospirillum rubrum Calvin-cycle mutant

Purple non-sulfur bacteria (PNSB) use light for energy and organic substrates for carbon and electrons when growing photoheterotrophically. This lifestyle generates more reduced electron carriers than are required for biosynthesis, even during consumption of some of the most oxidized organic substrates like malate and fumarate. Reduced electron carriers not used in biosynthesis must still be oxidized for photoheterotrophic growth to occur. Diverse PNSB commonly rely on the CO₂-fixing Calvin cycle to oxidize reduced electron carriers. Some PNSB also produce H₂ or reduce terminal electron acceptors as alternatives to the Calvin cycle. Rhodospirillum rubrum Calvin-cycle mutants defy this trend by growing phototrophically on malate or fumarate without H₂ production or access to terminal electron acceptors. We used ¹³C-tracer experiments to examine how a Rs. rubrum Calvin-cycle mutant maintains electron balance under such conditions. We detected the reversal of some tricarboxylic acid cycle enzymes, carrying reductive flux from malate or fumarate to αKG. This pathway and the reductive synthesis of αKG-derived amino acids are likely important for electron balance, as supplementing the growth medium with αKG-derived amino acids prevented Rs. rubrum Calvin-cycle-mutant growth unless a terminal electron acceptor was provided. Flux estimates also suggested that the Calvin-cycle mutant preferentially synthesized isoleucine using the reductive threonine-dependent pathway instead of the less-reductive citramalate-dependent pathway. Collectively, our results suggest that alternative biosynthetic pathways can contribute to electron balance within the constraints of a relatively constant biomass composition.

Bedaquiline reprograms central metabolism to reveal glycolytic vulnerability in Mycobacterium tuberculosis

The approval of bedaquiline (BDQ) for the treatment of tuberculosis has generated substantial interest in inhibiting energy metabolism as a therapeutic paradigm. However, it is not known precisely how BDQ triggers cell death in Mycobacterium tuberculosis (Mtb). Using ¹³C isotopomer analysis, we show that BDQ-treated Mtb redirects central carbon metabolism to induce a metabolically vulnerable state susceptible to genetic disruption of glycolysis and gluconeogenesis. Metabolic flux profiles indicate that BDQ-treated Mtb is dependent on glycolysis for ATP production, operates a bifurcated TCA cycle by increasing flux through the glyoxylate shunt, and requires enzymes of the anaplerotic node and methylcitrate cycle. Targeting oxidative phosphorylation (OXPHOS) with BDQ and simultaneously inhibiting substrate level phosphorylation via genetic disruption of glycolysis leads to rapid sterilization. Our findings provide insight into the metabolic mechanism of BDQ-induced cell death and establish a paradigm for the development of combination therapies that target OXPHOS and glycolysis.

Metabolic Flux Analysis-Linking Isotope Labeling and Metabolic Fluxes

Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.

Toxicant-Induced Metabolic Alterations in Lipid and Amino Acid Pathways Are Predictive of Acute Liver Toxicity in Rats

Liver disease and disorders associated with aberrant hepatocyte metabolism can be initiated via drug and environmental toxicant exposures. In this study, we tested the hypothesis that gene and metabolic profiling can reveal commonalities in liver response to different toxicants and provide the capability to identify early signatures of acute liver toxicity. We used Sprague Dawley rats and three classical hepatotoxicants: acetaminophen (2 g/kg), bromobenzene (0.4 g/kg), and carbon tetrachloride (0.3 g/kg), to identify early perturbations in liver metabolism after a single acute exposure dose. We measured changes in liver genes and plasma metabolites at two time points (5 and 10 h) and used genome-scale metabolic models to identify commonalities in liver responses across the three toxicants. We found strong correlations for gene and metabolic profiles between the toxicants, indicative of similarities in the liver response to toxicity. We identified several injury-specific pathways in lipid and amino acid metabolism that changed similarly across the three toxicants. Our findings suggest that several plasma metabolites in lipid and amino acid metabolism are strongly associated with the progression of liver toxicity, and as such, could be targeted and clinically assessed for their potential as early predictors of acute liver toxicity.

A guide to metabolic flux analysis in metabolic engineering: Methods, tools and applications

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. ¹³C-metabolic flux analysis (¹³C-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.

Induced pluripotent stem cells can utilize lactate as a metabolic substrate to support proliferation

Human-induced pluripotent stem cells (iPSCs) hold the promise to improve cell-based therapies. Yet, to meet rising demands and become clinically impactful, sufficient high-quality iPSCs in quantity must be generated, a task that exceeds current capabilities. In this study, K3 iPSCs cultures were examined using parallel-labeling metabolic flux analysis (¹³ C-MFA) to quantify intracellular fluxes at relevant bioprocessing stages: glucose concentrations representative of initial media concentrations and high lactate concentrations representative of fed-batch culture conditions, prior to and after bolus glucose feeds. The glucose and lactate concentrations are also representative of concentrations that might be encountered at different locations within 3D cell aggregates. Furthermore, a novel method was developed to allow the isotopic tracer [U-¹³ C₃ ] lactate to be used in the ¹³ C-MFA model. The results indicated that high extracellular lactate concentrations decreased glucose consumption and lactate production, while glucose concentrations alone did not affect rates of aerobic glycolysis. Moreover, for the high lactate cultures, lactate was used as a metabolic substrate to support oxidative mitochondrial metabolism. These results demonstrate that iPSCs have metabolic flexibility and possess the capacity to metabolize lactate to support exponential growth, and that high lactate concentrations alone do not adversely impact iPSC proliferation.

Sterile Spikelets Contribute to Yield in Sorghum and Related Grasses

Sorghum (Sorghum bicolor) and its relatives in the grass tribe Andropogoneae bear their flowers in pairs of spikelets in which one spikelet (seed-bearing or sessile spikelet [SS]) of the pair produces a seed and the other is sterile or male (staminate). This division of function does not occur in other major cereals such as wheat (Triticum aestivum) or rice (Oryza sativa). Additionally, one bract of the SS spikelet often produces a long extension, the awn, that is in the same position as, but independently derived from, that of wheat and rice. The function of the sterile spikelet is unknown and that of the awn has not been tested in Andropogoneae. We used radioactive and stable isotopes of carbon, RNA sequencing of metabolically important enzymes, and immunolocalization of ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco) to show that the sterile spikelet assimilates carbon, which is translocated to the largely heterotrophic SS. The awn shows no evidence of photosynthesis. These results apply to distantly related species of Andropogoneae. Removal of sterile spikelets in sorghum significantly decreases seed weight (yield) by ∼9%. Thus, the sterile spikelet, but not the awn, affects yield in the cultivated species and fitness in the wild species.

Dynamic Inventory of Intermediate Metabolites of Cyanobacteria in a Diurnal Cycle

Cyanobacteria are gaining importance both as hosts for photoautotrophic production of chemicals and as model systems for studies of diurnal lifestyle. The proteome and transcriptome of cyanobacteria have been closely examined under diurnal growth, whereas the downstream effects on the intermediary metabolism have not received sufficient attention. The present study focuses on identifying the cellular metabolites whose inventories undergo dramatic changes in a fast-growing cyanobacterium, Synechococcus elongatus PCC 11801. We identified and quantified 67 polar metabolites, whose inventory changes significantly during diurnal growth, with some metabolites changing by 100-fold. The Calvin-Benson-Bassham cycle intermediates peak at midday to support fast growth. The hitherto unexplored γ-glutamyl peptides act as reservoirs of amino acids. Interestingly, several storage molecules or their precursors accumulate during the dark phase, dispelling the notion that all biosynthetic activity takes place in the light phase. Our results will guide metabolic modeling and strain engineering of cyanobacteria.

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We identify and quantify 67 polar intermediate metabolites in cyanobacteria via LC-MS

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A number of metabolites show large variations during the diurnal cycle

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Intermediates of the CBB cycle peak at midday, coinciding with peak in growth rate

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Gamma-glutamyl dipeptides identified as new storage compounds that peak at dawn

Autotrophic and mixotrophic metabolism of an anammox bacterium revealed by in vivo 13C and 2H metabolic network mapping

Anaerobic ammonium-oxidizing (anammox) bacteria mediate a key step in the biogeochemical nitrogen cycle and have been applied worldwide for the energy-efficient removal of nitrogen from wastewater. However, outside their core energy metabolism, little is known about the metabolic networks driving anammox bacterial anabolism and use of different carbon and energy substrates beyond genome-based predictions. Here, we experimentally resolved the central carbon metabolism of the anammox bacterium Candidatus ‘Kuenenia stuttgartiensis’ using time-series ¹³C and ²H isotope tracing, metabolomics, and isotopically nonstationary metabolic flux analysis. Our findings confirm predicted metabolic pathways used for CO₂ fixation, central metabolism, and amino acid biosynthesis in K. stuttgartiensis, and reveal several instances where genomic predictions are not supported by in vivo metabolic fluxes. This includes the use of the oxidative branch of an incomplete tricarboxylic acid cycle for alpha-ketoglutarate biosynthesis, despite the genome not having an annotated citrate synthase. We also demonstrate that K. stuttgartiensis is able to directly assimilate extracellular formate via the Wood–Ljungdahl pathway instead of oxidizing it completely to CO₂ followed by reassimilation. In contrast, our data suggest that K. stuttgartiensis is not capable of using acetate as a carbon or energy source in situ and that acetate oxidation occurred via the metabolic activity of a low-abundance microorganism in the bioreactor’s side population. Together, these findings provide a foundation for understanding the carbon metabolism of anammox bacteria at a systems-level and will inform future studies aimed at elucidating factors governing their function and niche differentiation in natural and engineered ecosystems.

Chronic cold exposure enhances glucose oxidation in brown adipose tissue

The cultured brown adipocytes can oxidize glucose in vitro, but it is still not fully clear whether brown adipose tissue (BAT) could completely oxidize glucose in vivo. Although positron emission tomography (PET) with ¹⁸F‐fluorodeoxyglucose (¹⁸F‐FDG) showed a high level of glucose uptake in the activated BAT, the non‐metabolizable ¹⁸F‐FDG cannot fully demonstrate intracellular glucose metabolism. Through in vivo [U‐¹³C]glucose tracing, here we show that chronic cold exposure dramatically activates glucose oxidation in BAT and the browning/beiging subcutaneous white adipose tissue (sWAT). Specifically, chronic cold exposure enhances glucose flux into the mitochondrial TCA cycle. Metabolic flux analysis models that β3‐adrenergic receptor (β3‐AR) agonist significantly enhances the flux of mitochondrial pyruvate uptake through mitochondrial pyruvate carrier (MPC) in the differentiated primary brown adipocytes. Furthermore, in vivo MPC inhibition blocks cold‐induced glucose oxidation and impairs body temperature maintenance in mice. Together, mitochondrial pyruvate uptake and oxidation serve an important energy source in the chronic cold exposure activated BAT and beige adipose tissue, which supports a role for glucose oxidation in brown fat thermogenesis.

Disruption of redox homeostasis for combinatorial drug efficacy in K-Ras tumors as revealed by metabolic connectivity profiling

Abstract

Background

Rewiring of metabolism induced by oncogenic K-Ras in cancer cells involves both glucose and glutamine utilization sustaining enhanced, unrestricted growth. The development of effective anti-cancer treatments targeting metabolism may be facilitated by the identification and rational combinatorial targeting of metabolic pathways.

Methods

We performed mass spectrometric metabolomics analysis in vitro and in vivo experiments to evaluate the efficacy of drugs and identify metabolic connectivity.

Results

We show that K-Ras-mutant lung and colon cancer cells exhibit a distinct metabolic rewiring, the latter being more dependent on respiration. Combined treatment with the glutaminase inhibitor CB-839 and the PI3K/aldolase inhibitor NVP-BKM120 more consistently reduces cell growth of tumor xenografts. Maximal growth inhibition correlates with the disruption of redox homeostasis, involving loss of reduced glutathione regeneration, redox cofactors, and a decreased connectivity among metabolites primarily involved in nucleic acid metabolism.

Conclusions

Our findings open the way to develop metabolic connectivity profiling as a tool for a selective strategy of combined drug repositioning in precision oncology.

Intracellular Mycobacterium tuberculosis Exploits Multiple Host Nitrogen Sources during Growth in Human Macrophages

Nitrogen metabolism of Mycobacterium tuberculosis (Mtb) is crucial for the survival of this important pathogen in its primary human host cell, the macrophage, but little is known about the source(s) and their assimilation within this intracellular niche. Here, we have developed ¹⁵N-flux spectral ratio analysis (¹⁵N-FSRA) to explore Mtb’s nitrogen metabolism; we demonstrate that intracellular Mtb has access to multiple amino acids in the macrophage, including glutamate, glutamine, aspartate, alanine, glycine, and valine; and we identify glutamine as the predominant nitrogen donor. Each nitrogen source is uniquely assimilated into specific amino acid pools, indicating compartmentalized metabolism during intracellular growth. We have discovered that serine is not available to intracellular Mtb, and we show that a serine auxotroph is attenuated in macrophages. This work provides a systems-based tool for exploring the nitrogen metabolism of intracellular pathogens and highlights the enzyme phosphoserine transaminase as an attractive target for the development of novel anti-tuberculosis therapies.

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Mycobacterium tuberculosis utilizes multiple amino acids as nitrogen sources in human macrophages

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¹⁵N-FSRA tool identified the intracellular nitrogen sources

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Glutamine is the predominant nitrogen donor for M. tuberculosis

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Serine biosynthesis is essential for the survival of intracellular M. tuberculosis

Branched chain amino acids and carbohydrate restriction exacerbate ketogenesis and hepatic mitochondrial oxidative dysfunction during NAFLD

Mitochondrial adaptation during non-alcoholic fatty liver disease (NAFLD) include remodeling of ketogenic flux and sustained tricarboxylic acid (TCA) cycle activity, which are concurrent to onset of oxidative stress. Over 70% of obese humans have NAFLD and ketogenic diets are common weight loss strategies. However, the effectiveness of ketogenic diets toward alleviating NAFLD remains unclear. We hypothesized that chronic ketogenesis will worsen metabolic dysfunction and oxidative stress during NAFLD. Mice (C57BL/6) were kept (for 16-wks) on either a low-fat, high-fat, or high-fat diet supplemented with 1.5X branched chain amino acids (BCAAs) by replacing carbohydrate calories (ketogenic). The ketogenic diet induced hepatic lipid oxidation and ketogenesis, and produced multifaceted changes in flux through the individual steps of the TCA cycle. Higher rates of hepatic oxidative fluxes fueled by the ketogenic diet paralleled lower rates of de novo lipogenesis. Interestingly, this metabolic remodeling did not improve insulin resistance, but induced fibrogenic genes and inflammation in the liver. Under a chronic "ketogenic environment," the hepatocyte diverted more acetyl-CoA away from lipogenesis toward ketogenesis and TCA cycle, a milieu which can hasten oxidative stress and inflammation. In summary, chronic exposure to ketogenic environment during obesity and NAFLD has the potential to aggravate hepatic mitochondrial dysfunction.

Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases

Metabolic flux analysis requires both a reliable metabolic model and reliable metabolic profiles in characterizing metabolic reprogramming. Advances in analytic methodologies enable production of high-quality metabolomics datasets capturing isotopic flux. However, useful metabolic models can be difficult to derive due to the lack of relatively complete atom-resolved metabolic networks for a variety of organisms, including human. Here, we developed a neighborhood-specific graph coloring method that creates unique identifiers for each atom in a compound facilitating construction of an atom-resolved metabolic network. What is more, this method is guaranteed to generate the same identifier for symmetric atoms, enabling automatic identification of possible additional mappings caused by molecular symmetry. Furthermore, a compound coloring identifier derived from the corresponding atom coloring identifiers can be used for compound harmonization across various metabolic network databases, which is an essential first step in network integration. With the compound coloring identifiers, 8865 correspondences between KEGG (Kyoto Encyclopedia of Genes and Genomes) and MetaCyc compounds are detected, with 5451 of them confirmed by other identifiers provided by the two databases. In addition, we found that the Enzyme Commission numbers (EC) of reactions can be used to validate possible correspondence pairs, with 1848 unconfirmed pairs validated by commonality in reaction ECs. Moreover, we were able to detect various issues and errors with compound representation in KEGG and MetaCyc databases by compound coloring identifiers, demonstrating the usefulness of this methodology for database curation.

Serine restriction alters sphingolipid diversity to constrain tumour growth

Serine, glycine, and other non-essential amino acids are critical for tumor progression, and strategies to limit their availability are emerging as potential cancer therapies^1–3. However, the molecular mechanisms driving this response remain unclear, and the impact on lipid metabolism is relatively unexplored. Serine palmitoyltransferase (SPT) catalyzes the de novo biosynthesis of sphingolipids but also produces non-canonical 1-deoxysphingolipids (doxSLs) when using alanine as a substrate^4,5. DoxSLs accumulate in the context of SPTLC1 or SPTLC2 mutations^6,7 or low serine availability^8,9 to drive neuropathy, and deoxysphinganine (doxSA) has been investigated as an anti-cancer agent¹⁰. Here we exploit amino acid metabolism and SPT promiscuity to modulate the endogenous synthesis of toxic doxSLs and slow tumor progression. Anchorage-independent growth reprograms a metabolic network involving serine, alanine, and pyruvate resulting in increased susceptibility to endogenous doxSL synthesis. Targeting the mitochondrial pyruvate carrier (MPC) promotes alanine oxidation to mitigate doxSL synthesis and improves spheroid growth, while direct inhibition of doxSL synthesis drives similar phenotypes. Restriction of dietary serine/glycine potently induces accumulation of doxSLs in xenografts while decreasing tumor growth. Pharmacological modulation of SPT rescues xenograft growth on serine/glycine-restricted diets, while reduction of circulating serine by inhibition of phosphoglycerate dehydrogenase (PHGDH) leads to doxSL accumulation and mitigates tumor growth. SPT promiscuity therefore links serine and mitochondrial alanine metabolism to membrane lipid diversity, which sensitizes tumors to metabolic stress.

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

Dynamic Flux Analysis: An Experimental Approach of Fluxomics

Metabolic flux analysis represents an essential perspective to understand cellular physiology and offers quantitative information to guide pathway engineering. A valuable approach for experimental elucidation of metabolic flux is dynamic flux analysis, which estimates the relative or absolute flow rates through a series of metabolic intermediates in a given pathway. It is based on kinetic isotope labeling experiments, liquid chromatography-mass spectrometry (LC-MS), and computational analysis that relate kinetic isotope trajectories of metabolites to pathway activity. Herein, we illustrate the mathematic principles underlying the dynamic flux analysis and mainly focus on describing the experimental procedures for data generation. This protocol is exemplified using cyanobacterial metabolism as an example, for which reliable labeling data for central carbon metabolites can be acquired quantitatively. This protocol is applicable to other microbial systems as well and can be readily adapted to address different metabolic processes.

Systematic identification and elimination of flux bottlenecks in the aldehyde production pathway of Synechococcus elongatus PCC 7942

Isotopically nonstationary metabolic flux analysis (INST-MFA) provides a versatile platform to quantitatively assess in vivo metabolic activities of autotrophic systems. By applying INST-MFA to recombinant aldehyde-producing cyanobacteria, we identified metabolic alterations that correlated with increased strain performance in order to guide rational metabolic engineering. We identified four reactions adjacent to the pyruvate node that varied significantly with increasing aldehyde production: pyruvate kinase (PK) and acetolactate synthase (ALS) fluxes were directly correlated with product formation, while pyruvate dehydrogenase (PDH) and phosphoenolpyruvate carboxylase (PPC) fluxes were inversely correlated. Overexpression of enzymes for PK or ALS did not result in further improvements to the previous best-performing strain, while downregulation of PDH expression (through antisense RNA expression) or PPC flux (through expression of the reverse reaction, phosphoenolpyruvate carboxykinase) provided significant improvements. These results illustrate the potential of INST-MFA to enable a systematic approach for iterative identification and removal of pathway bottlenecks in autotrophic host cells.

Enzymes as Parts in Need of Replacement - and How to Extend Their Working Life

Enzymes catalyze reactions in vivo at different rates and each enzyme molecule has a lifetime limit before it is degraded and replaced to enable catalysis to continue. Considering these rates together as a unitless ratio of catalytic cycles until replacement (CCR) provides a new quantitative tool to assess the replacement schedule of and energy investment into enzymes as they relate to function. Here, we outline the challenges of determining CCRs and new approaches to overcome them and then assess the CCRs of selected enzymes in bacteria and plants to reveal a range of seven orders of magnitude for this ratio. Modifying CCRs in plants holds promise to lower cellular costs, to tailor enzymes for particular environments, and to breed enzyme improvements for crop productivity.

Parallel isotope differential modeling for instationary 13C fluxomics at the genome scale

BACKGROUND

A precise map of the metabolic fluxome, the closest surrogate to the physiological phenotype, is becoming progressively more important in the metabolic engineering of photosynthetic organisms for biofuel and biomass production. For photosynthetic organisms, the state-of-the-art method for this purpose is instationary 13C fluxomics, which has arisen as a sibling of transcriptomics or proteomics. Instationary 13C data processing requires solving high-dimensional nonlinear differential equations and leads to large computational and time costs when its scope is expanded to a genome-scale metabolic network.

RESULT

Here, we present a parallelized method to model instationary 13C labeling data. The elementary metabolite unit (EMU) framework is reorganized to allow treating individual mass isotopomers and breaking up of their networks into strongly connected components (SCCs). A variable domain parallel algorithm is introduced to process ordinary differential equations in a parallel way. 15-fold acceleration is achieved for constant-step-size modeling and ~ fivefold acceleration for adaptive-step-size modeling.

CONCLUSION

This algorithm is universally applicable to isotope granules such as EMUs and cumomers and can substantially accelerate instationary 13C fluxomics modeling. It thus has great potential to be widely adopted in any instationary 13C fluxomics modeling.

Resolution of NASH and hepatic fibrosis by the GLP-1R/GcgR dual-agonist Cotadutide via modulating mitochondrial function and lipogenesis

Non-alcoholic fatty liver disease and steatohepatitis are highly associated with obesity and type 2 diabetes mellitus. Cotadutide, a GLP-1R/GcgR agonist, was shown to reduce blood glycemia, body weight and hepatic steatosis in patients with T2DM. Here, we demonstrate that the effects of Cotadutide to reduce body weight, food intake and improve glucose control are predominantly mediated through the GLP-1 signaling, while, its action on the liver to reduce lipid content, drive glycogen flux and improve mitochondrial turnover and function are directly mediated through Gcg signaling. This was confirmed by the identification of phosphorylation sites on key lipogenic and glucose metabolism enzymes in liver of mice treated with Cotadutide. Complementary metabolomic and transcriptomic analyses implicated lipogenic, fibrotic and inflammatory pathways, which are consistent with a unique therapeutic contribution of GcgR agonism by Cotadutide in vivo. Significantly, Cotadutide also alleviated fibrosis to a greater extent than Liraglutide or Obeticholic acid (OCA), despite adjusting dose to achieve similar weight loss in 2 preclinical mouse models of NASH. Thus Cotadutide, via direct hepatic (GcgR) and extra-hepatic (GLP-1R) effects, exerts multi-factorial improvement in liver function and is a promising therapeutic option for the treatment of steatohepatitis.

α-ketobutyrate links alterations in cystine metabolism to glucose oxidation in mtDNA mutant cells

Pathogenic mutations in the mitochondrial genome (mtDNA) impair organellar ATP production, requiring mutant cells to activate metabolic adaptations for survival. Understanding how metabolism adapts to clinically relevant mtDNA mutations may provide insight into cellular strategies for metabolic flexibility. In this study, we use ¹³C isotope tracing and metabolic flux analysis to investigate central carbon and amino acid metabolic reprogramming in isogenic cells containing mtDNA mutations. We identify alterations in glutamine and cystine transport which indirectly regulate mitochondrial metabolism and electron transport chain function. Metabolism of cystine can promote glucose oxidation through the transsulfuration pathway and the production of α-ketobutyrate. Intriguingly, activating or inhibiting α-ketobutyrate production is sufficient to modulate both glucose oxidation and mitochondrial respiration in mtDNA mutant cells. Thus, cystine-stimulated transsulfuration serves as an adaptive mechanism linking glucose oxidation and amino acid metabolism in the setting of mtDNA mutations.

ScalaFlux: A scalable approach to quantify fluxes in metabolic subnetworks

13C-metabolic flux analysis (13C-MFA) allows metabolic fluxes to be quantified in living organisms and is a major tool in biotechnology and systems biology. Current 13C-MFA approaches model label propagation starting from the extracellular 13C-labeled nutrient(s), which limits their applicability to the analysis of pathways close to this metabolic entry point. Here, we propose a new approach to quantify fluxes through any metabolic subnetwork of interest by modeling label propagation directly from the metabolic precursor(s) of this subnetwork. The flux calculations are thus purely based on information from within the subnetwork of interest, and no additional knowledge about the surrounding network (such as atom transitions in upstream reactions or the labeling of the extracellular nutrient) is required. This approach, termed ScalaFlux for SCALAble metabolic FLUX analysis, can be scaled up from individual reactions to pathways to sets of pathways. ScalaFlux has several benefits compared with current 13C-MFA approaches: greater network coverage, lower data requirements, independence from cell physiology, robustness to gaps in data and network information, better computational efficiency, applicability to rich media, and enhanced flux identifiability. We validated ScalaFlux using a theoretical network and simulated data. We also used the approach to quantify fluxes through the prenyl pyrophosphate pathway of Saccharomyces cerevisiae mutants engineered to produce phytoene, using a dataset for which fluxes could not be calculated using existing approaches. A broad range of metabolic systems can be targeted with minimal cost and effort, making ScalaFlux a valuable tool for the analysis of metabolic fluxes.

Cystine transporter regulation of pentose phosphate pathway dependency and disulfide stress exposes a targetable metabolic vulnerability in cancer

SLC7A11-mediated cystine uptake is critical for maintaining redox balance and cell survival. Here, we show that this comes at a significant cost for cancer cells with high SLC7A11 expression. Actively importing cystine is potentially toxic due to its low solubility, forcing SLC7A11-high cancer cells to constitutively reduce cystine to the more soluble cysteine. This presents a substantial drain on the cellular NADPH pool and renders such cells dependent on the pentose phosphate pathway (PPP). Limiting glucose supply to SLC7A11-high cancer cells results in marked accumulation of intracellular cystine, redox system collapse, and rapid cell death, which can be rescued by treatments that prevent disulfide accumulation. We further show that glucose transporter (GLUT) inhibitors selectively kill SLC7A11-high cancer cells and suppress SLC7A11-high tumor growth. Our results identify a coupling between SLC7A11-associated cystine metabolism and the PPP, and uncover an accompanying metabolic vulnerability for therapeutic targeting in SLC7A11-high cancers.

Robust Moiety Model Selection Using Mass Spectrometry Measured Isotopologues

Stable isotope resolved metabolomics (SIRM) experiments use stable isotope tracers to provide superior metabolomics datasets for metabolic flux analysis and metabolic modeling. Since assumptions of model correctness can seriously compromise interpretation of metabolic flux results, we have developed a metabolic modeling software package specifically designed for moiety model comparison and selection based on the metabolomics data provided. Here, we tested the effectiveness of model selection with two time-series mass spectrometry (MS) isotopologue datasets for uridine diphosphate N-acetyl-d-glucosamine (UDP-GlcNAc) generated from different platforms utilizing direct infusion nanoelectrospray and liquid chromatography. Analysis results demonstrate the robustness of our model selection methods by the successful selection of the optimal model from over 40 models provided. Moreover, the effects of specific optimization methods, degree of optimization, selection criteria, and specific objective functions on model selection are illustrated. Overall, these results indicate that over-optimization can lead to model selection failure, but combining multiple datasets can help control this overfitting effect. The implication is that SIRM datasets in public repositories of reasonable quality can be combined with newly acquired datasets to improve model selection. Furthermore, curation efforts of public metabolomics repositories to maintain high data quality could have a huge impact on future metabolic modeling efforts.

In Vivo Thermodynamic Analysis of Glycolysis in Clostridium thermocellum and Thermoanaerobacterium saccharolyticum Using 13C and 2H Tracers

Thermodynamics constitutes a key determinant of flux and enzyme efficiency in metabolic networks. Here, we provide new insights into the divergent thermodynamics of the glycolytic pathways of C. thermocellum and T. saccharolyticum, two industrially relevant thermophilic bacteria whose metabolism still is not well understood. We report that while the glycolytic pathway in T. saccharolyticum is as thermodynamically favorable as that found in model organisms, such as E. coli or Saccharomyces cerevisiae, the glycolytic pathway of C. thermocellum operates near equilibrium. The use of a near-equilibrium glycolytic pathway, with potentially increased ATP yield, by this cellulolytic microbe may represent an evolutionary adaptation to growth on cellulose, but it has the drawback of being highly susceptible to product feedback inhibition. The results of this study will facilitate future engineering of high-performance strains capable of transforming cellulosic biomass to biofuels at high yields and titers.

Clostridium thermocellum and Thermoanaerobacterium saccharolyticum are thermophilic anaerobic bacteria with complementary metabolic capabilities that utilize distinct glycolytic pathways for the conversion of cellulosic sugars to biofuels. We integrated quantitative metabolomics with ²H and ¹³C metabolic flux analysis to investigate the in vivo reversibility and thermodynamics of the central metabolic networks of these two microbes. We found that the glycolytic pathway in C. thermocellum operates remarkably close to thermodynamic equilibrium, with an overall drop in Gibbs free energy 5-fold lower than that of T. saccharolyticum or anaerobically grown Escherichia coli. The limited thermodynamic driving force of glycolysis in C. thermocellum could be attributed in large part to the small free energy of the phosphofructokinase reaction producing fructose bisphosphate. The ethanol fermentation pathway was also substantially more reversible in C. thermocellum than in T. saccharolyticum. These observations help explain the comparatively low ethanol titers of C. thermocellum and suggest engineering interventions that can be used to increase its ethanol productivity and glycolytic rate. In addition to thermodynamic analysis, we used our isotope tracer data to reconstruct the T. saccharolyticum central metabolic network, revealing exclusive use of the Embden-Meyerhof-Parnas (EMP) pathway for glycolysis, a bifurcated tricarboxylic acid (TCA) cycle, and a sedoheptulose bisphosphate bypass active within the pentose phosphate pathway.

IMPORTANCE Thermodynamics constitutes a key determinant of flux and enzyme efficiency in metabolic networks. Here, we provide new insights into the divergent thermodynamics of the glycolytic pathways of C. thermocellum and T. saccharolyticum, two industrially relevant thermophilic bacteria whose metabolism still is not well understood. We report that while the glycolytic pathway in T. saccharolyticum is as thermodynamically favorable as that found in model organisms, such as E. coli or Saccharomyces cerevisiae, the glycolytic pathway of C. thermocellum operates near equilibrium. The use of a near-equilibrium glycolytic pathway, with potentially increased ATP yield, by this cellulolytic microbe may represent an evolutionary adaptation to growth on cellulose, but it has the drawback of being highly susceptible to product feedback inhibition. The results of this study will facilitate future engineering of high-performance strains capable of transforming cellulosic biomass to biofuels at high yields and titers.