Understanding metabolism with flux analysis: From theory to application

The Design of FluxML: A Universal Modeling Language for 13C Metabolic Flux Analysis

¹³C metabolic flux analysis (MFA) is the method of choice when a detailed inference of intracellular metabolic fluxes in living organisms under metabolic quasi-steady state conditions is desired. Being continuously developed since two decades, the technology made major contributions to the quantitative characterization of organisms in all fields of biotechnology and health-related research. ¹³C MFA, however, stands out from other "-omics sciences," in that it requires not only experimental-analytical data, but also mathematical models and a computational toolset to infer the quantities of interest, i.e., the metabolic fluxes. At present, these models cannot be conveniently exchanged between different labs. Here, we present the implementation-independent model description language FluxML for specifying ¹³C MFA models. The core of FluxML captures the metabolic reaction network together with atom mappings, constraints on the model parameters, and the wealth of data configurations. In particular, we describe the governing design processes that shaped the FluxML language. We demonstrate the utility of FluxML to represent many contemporary experimental-analytical requirements in the field of ¹³C MFA. The major aim of FluxML is to offer a sound, open, and future-proof language to unambiguously express and conserve all the necessary information for model re-use, exchange, and comparison. Along with FluxML, several powerful computational tools are supplied for easy handling, but also to maintain a maximum of flexibility. Altogether, the FluxML collection is an "all-around carefree package" for ¹³C MFA modelers. We believe that FluxML improves scientific productivity as well as transparency and therewith contributes to the efficiency and reproducibility of computational modeling efforts in the field of ¹³C MFA.

CYP2C19 and 3A4 Dominate Metabolic Clearance and Bioactivation of Terbinafine Based on Computational and Experimental Approaches

Lamisil (terbinafine) is an effective, widely prescribed antifungal drug that causes rare idiosyncratic hepatotoxicity. The proposed toxic mechanism involves a reactive metabolite, 6,6-dimethyl-2-hepten-4-ynal (TBF-A), formed through three N-dealkylation pathways. We were the first to characterize them using in vitro studies with human liver microsomes and modeling approaches, yet knowledge of the individual enzymes catalyzing reactions remained unknown. Herein, we employed experimental and computational tools to assess terbinafine metabolism by specific cytochrome P450 isozymes. In vitro inhibitor phenotyping studies revealed six isozymes were involved in one or more N-dealkylation pathways. CYP2C19 and 3A4 contributed to all pathways, and so, we targeted them for steady-state analyses with recombinant isozymes. N-Dealkylation yielding TBF-A directly was catalyzed by CYP2C19 and 3A4 similarly. Nevertheless, CYP2C19 was more efficient than CYP3A4 at N-demethylation and other steps leading to TBF-A. Unlike microsomal reactions, N-denaphthylation was surprisingly efficient for CYP2C19 and 3A4, which was validated by controls. CYP2C19 was the most efficient among all reactions. Nonetheless, CYP3A4 was more selective at steps leading to TBF-A, making it more effective in terbinafine bioactivation based on metabolic split ratios for competing pathways. Model predictions did not extrapolate to quantitative kinetic constants, yet some results for CYP3A4 and CYP2C19 agreed qualitatively with preferred reaction steps and pathways. Clinical data on drug interactions support the CYP3A4 role in terbinafine metabolism, while CYP2C19 remains understudied. Taken together, knowledge of P450s responsible for terbinafine metabolism and TBF-A formation provides a foundation for investigating and mitigating the impact of P450 variations in toxic risks posed to patients.

Proposing a validation scheme for 13C metabolite tracer studies in high-resolution mass spectrometry

¹³C metabolite tracer and metabolic flux analyses require upfront experimental planning and validation tools. Here, we present a validation scheme including a comparison of different LC methods that allow for customization of analytical strategies for tracer studies with regard to the targeted metabolites. As the measurement of significant changes in labeling patterns depends on the spectral accuracy, we investigate this aspect comprehensively for high-resolution orbitrap mass spectrometry combined with reversed-phase chromatography, hydrophilic interaction liquid chromatography, or anion-exchange chromatography. Moreover, we propose a quality control protocol based on (1) a metabolite containing selenium to assess the instrument performance and on (2) in vivo synthesized isotopically enriched Pichia pastoris to validate the accuracy of carbon isotopologue distributions (CIDs), in this case considering each isotopologue of a targeted metabolite panel. Finally, validation involved a thorough assessment of procedural blanks and matrix interferences. We compared the analytical figures of merit regarding CID determination for over 40 metabolites between the three methods. Excellent precisions of less than 1% and trueness bias as small as 0.01-1% were found for the majority of compounds, whereas the CID determination of a small fraction was affected by contaminants. For most compounds, changes of labeling pattern as low as 1% could be measured. Graphical abstract.

A framework for tracer-based metabolism in mammalian cells by NMR

Metabolism changes extensively during the normal proliferation and differentiation of mammalian cells, and in cancer and inflammatory diseases. Since changes in the metabolic network reflect interactions between genetic, epigenetic and environmental changes, it is helpful to study the flow of label from isotopically labelled precursors into other metabolites rather than static metabolite levels. For this Nuclear Magnetic Resonance (NMR) spectroscopy is an attractive technique as it can quantify site-specific label incorporation. However, for applications using human cells and cell lines, the challenge is to optimize the process to maximize sensitivity and reproducibility. Here we present a new framework to analyze metabolism in mammalian cell lines and primary cells, covering the workflow from the preparation of cells to the acquisition and analysis of NMR spectra. We have applied this new approach in hematological and liver cancer cell lines and confirm the feasibility of tracer-based metabolism in primary liver cells.

Metabolic partitioning of sucrose and seasonal changes in fat turnover rate in ruby-throated hummingbirds (Archilochus colubris)

Hummingbirds fuel their high energy needs with the fructose and glucose in their nectar diets. These sugars are used to fuel both immediate energy needs and to build fat stores to fuel future fasting periods. Fasting hummingbirds can deplete energy stores in only hours and need to be continuously replacing these stores while feeding and foraging. If and how hummingbirds partition dietary fructose and glucose towards immediate oxidation versus fat storage is unknown. Using a chronic stable isotope tracer methodology, we examined if glucose or fructose are preferentially used for de novo lipogenesis in ruby-throated hummingbirds (Archilochus colubris.) Potential seasonal changes were correlated with variation in the overall daily energy expenditure. We fed ruby-throated hummingbirds sucrose-based diets enriched with 13C on either the glucose or the fructose portion of the disaccharide for 5 days. Isotopic incorporation into fat stores was measured via the breath 13C signature while fasting (oxidizing fat) during the winter and summer seasons. We found greater isotopic enrichment of fat stores when glucose was labelled compared to fructose, suggesting preference for glucose as a substrate for fatty acid synthesis. We also found a seasonal effect on fat turnover rate. Faster turnover rates occurred during the summer months when birds maintained lower body mass, fat stores and exhibited higher daily nectar intake compared to winter. This demonstrates that fat turnover rate can substantially vary with changing energy expenditure and body composition, however the partitioning of sucrose towards de novo fatty acid synthesis remains constant.

Dynamic flux balance analysis for microbial conversion of glycerol into 1,3-propanediol by Klebsiella pneumoniae

To investigate the relationship between the yield of 1,3-propanediol (1,3-PD) and the flux variation in metabolic pathways of Klebsiella pneumoniae, an optimized calculation method was constructed on basis of dynamic flux balance analysis by combining genome-scale flux balance analysis with a kinetic model of extracellular metabolites. Through optimizing calculations, a more completely expanded metabolic pathway was obtained, which includes the previously reported metabolic pathway and additional three pathways or site: a pentose phosphate pathway (PPP) elicited at the dihydroxyacetone (DHA) node to provide more reducing equivalents; a branch of synthetic amino acids at the 3-phosphoglycerate (3PG) node; and the α-ketoglutarate site in the tricarboxylic acid (TCA) cycle leading to anabolic pathways for glutamate and other amino acids. On this basis, the relationships between the dynamic flux distribution of the important nodes in the metabolic pathway and the yield of 1,3-propanediol were analyzed. First, dynamic flux change from DHA to the PPP is positively correlated with the yield. Second, variation in flux in the TCA cycle is also positively correlated with the yield of 1,3-propanediol. In addition, the influence of the feedback loop formed by the cofactor tetrahydrofolate on the flux change of TCA in the amino acid anabolic pathway was examined. These results are of important reference value and have guiding significance for the extension of the glycerol metabolism pathway in K. pneumoniae, the rational transformation of genetic engineering in bacteria, and the optimization of metabolic pathways for industrial production.

Microenvironmental regulation of cancer cell metabolism: implications for experimental design and translational studies

Cancers have an altered metabolism, and there is interest in understanding precisely how oncogenic transformation alters cellular metabolism and how these metabolic alterations can translate into therapeutic opportunities. Researchers are developing increasingly powerful experimental techniques to study cellular metabolism, and these techniques have allowed for the analysis of cancer cell metabolism, both in tumors and in ex vivo cancer models. These analyses show that, while factors intrinsic to cancer cells such as oncogenic mutations, alter cellular metabolism, cell-extrinsic microenvironmental factors also substantially contribute to the metabolic phenotype of cancer cells. These findings highlight that microenvironmental factors within the tumor, such as nutrient availability, physical properties of the extracellular matrix, and interactions with stromal cells, can influence the metabolic phenotype of cancer cells and might ultimately dictate the response to metabolically targeted therapies. In an effort to better understand and target cancer metabolism, this Review focuses on the experimental evidence that microenvironmental factors regulate tumor metabolism, and on the implications of these findings for choosing appropriate model systems and experimental approaches.

Isotopically nonstationary metabolic flux analysis (INST-MFA): putting theory into practice

Typically, ¹³C flux analysis relies on assumptions of both metabolic and isotopic steady state. If metabolism is steady but isotope labeling is not allowed to fully equilibrate, isotopically nonstationary metabolic flux analysis (INST-MFA) can be used to estimate fluxes. This requires solution of differential equations that describe the time-dependent labeling of network metabolites, while iteratively adjusting the flux and pool size parameters to match the transient labeling measurements. INST-MFA holds a number of unique advantages over approaches that rely solely upon steady-state isotope enrichments. First, INST-MFA can be applied to estimate fluxes in autotrophic systems, which consume only single-carbon substrates. Second, INST-MFA is ideally suited to systems that label slowly due to the presence of large intermediate pools or pathway bottlenecks. Finally, INST-MFA provides increased measurement sensitivity to estimate reversible exchange fluxes and metabolite pool sizes, which represents a potential framework for integrating metabolomic analysis with ¹³C flux analysis. This review highlights the unique capabilities of INST-MFA, describes newly available software tools that automate INST-MFA calculations, presents several practical examples of recent INST-MFA applications, and discusses the technical challenges that lie ahead.

DeltaMS: a tool to track isotopologues in GC- and LC-MS data

INTRODUCTION

Stable isotopic labeling experiments are powerful tools to study metabolic pathways, to follow tracers and fluxes in biotic and abiotic transformations and to elucidate molecules involved in metal complexing.

OBJECTIVE

To introduce a software tool for the identification of isotopologues from mass spectrometry data.

METHODS

DeltaMS relies on XCMS peak detection and X¹³CMS isotopologue grouping and then analyses data for specific isotope ratios and the relative error of these ratios. It provides pipelines for recognition of isotope patterns in three experiment types commonly used in isotopic labeling studies: (1) search for isotope signatures with a specific mass shift and intensity ratio in one sample set, (2) analyze two sample sets for a specific mass shift and, optionally, the isotope ratio, whereby one sample set is isotope-labeled, and one is not, (3) analyze isotope-guided perturbation experiments with a setup described in X¹³CMS.

RESULTS

To illustrate the versatility of DeltaMS, we analyze data sets from case-studies that commonly pose challenges in evaluation of natural isotopes or isotopic signatures in labeling experiment. In these examples, the untargeted detection of sulfur, bromine and artificial metal isotopic patterns is enabled by the automated search for specific isotopes or isotope signatures.

CONCLUSION

DeltaMS provides a platform for the identification of (pre-defined) isotopologues in MS data from single samples or comparative metabolomics data sets.

New concepts in feedback regulation of glucose metabolism

Glycolysis, the breakdown of glucose, is one of the most conserved and extensively studied biochemical pathways. Designing principles from chemistry and thermodynamics allow for energy production, biosynthesis and cellular communication. However, the kinetics or metabolic flux through the pathway also determines its function. Recently, there have been numerous developments that establish new allosteric interactions of glycolytic enzymes with small molecule metabolites and other mechanisms that may cooperate to allow for addition complex regulation of glycolysis. This review surveys these newfound sources of glycolysis regulation and discusses their possible roles in establishing kinetic design principles of glycolysis.

The impact of cellular metabolism on chromatin dynamics and epigenetics

The substrates used to modify nucleic acids and chromatin are affected by nutrient availability and the activity of metabolic pathways. Thus, cellular metabolism constitutes a fundamental component of chromatin status and thereby of genome regulation. Here we describe the biochemical and genetic principles of how metabolism can influence chromatin biology and epigenetics, discuss the functional roles of this interplay in developmental and cancer biology, and present future directions in this rapidly emerging area.

Glutamine Metabolism in Cancer: Understanding the Heterogeneity

Reliance on glutamine has long been considered a hallmark of cancer cell metabolism. However, some recent studies have challenged this notion in vivo, prompting a need for further clarifications on the role of glutamine metabolism in cancer. We find that there is ample evidence of an essential role for glutamine in tumors and that a variety of factors, including tissue type, the underlying cancer genetics, the tumor microenvironment and other variables such as diet and host physiology collectively influence the role of glutamine in cancer. Thus the requirements for glutamine in cancer are overall highly heterogeneous. In this review, we discuss the implications both for basic science and for targeting glutamine metabolism in cancer therapy.