Simultaneous tracing of carbon and nitrogen isotopes in human cells

One-shot 13 C15 N-metabolic flux analysis for simultaneous quantification of carbon and nitrogen flux

Metabolic flux is the final output of cellular regulation and has been extensively studied for carbon but much less is known about nitrogen, which is another important building block for living organisms. For the tuberculosis pathogen, this is particularly important in informing the development of effective drugs targeting the pathogen's metabolism. Here we performed ¹³ C¹⁵ N dual isotopic labeling of Mycobacterium bovis BCG steady state cultures, quantified intracellular carbon and nitrogen fluxes and inferred reaction bidirectionalities. This was achieved by model scope extension and refinement, implemented in a multi-atom transition model, within the statistical framework of Bayesian model averaging (BMA). Using BMA-based ¹³ C¹⁵ N-metabolic flux analysis, we jointly resolve carbon and nitrogen fluxes quantitatively. We provide the first nitrogen flux distributions for amino acid and nucleotide biosynthesis in mycobacteria and establish glutamate as the central node for nitrogen metabolism. We improved resolution of the notoriously elusive anaplerotic node in central carbon metabolism and revealed possible operation modes. Our study provides a powerful and statistically rigorous platform to simultaneously infer carbon and nitrogen metabolism in any biological system.

Validation-based model selection for 13C metabolic flux analysis with uncertain measurement errors

Accurate measurements of metabolic fluxes in living cells are central to metabolism research and metabolic engineering. The gold standard method is model-based metabolic flux analysis (MFA), where fluxes are estimated indirectly from mass isotopomer data with the use of a mathematical model of the metabolic network. A critical step in MFA is model selection: choosing what compartments, metabolites, and reactions to include in the metabolic network model. Model selection is often done informally during the modelling process, based on the same data that is used for model fitting (estimation data). This can lead to either overly complex models (overfitting) or too simple ones (underfitting), in both cases resulting in poor flux estimates. Here, we propose a method for model selection based on independent validation data. We demonstrate in simulation studies that this method consistently chooses the correct model in a way that is independent on errors in measurement uncertainty. This independence is beneficial, since estimating the true magnitude of these errors can be difficult. In contrast, commonly used model selection methods based on the χ²-test choose different model structures depending on the believed measurement uncertainty; this can lead to errors in flux estimates, especially when the magnitude of the error is substantially off. We present a new approach for quantification of prediction uncertainty of mass isotopomer distributions in other labelling experiments, to check for problems with too much or too little novelty in the validation data. Finally, in an isotope tracing study on human mammary epithelial cells, the validation-based model selection method identified pyruvate carboxylase as a key model component. Our results argue that validation-based model selection should be an integral part of MFA model development.

Measuring metabolic reaction fluxes in living cells is difficult, yet important. The gold standard is to label extracellular metabolites with ¹³C, to use mass spectrometry to find out where the ¹³C-atoms ends up, and finally use mathematical modelling to calculate how quickly each reaction must have flowed, for the ¹³C-atoms to end up like that. This measurement thus relies on usage of the right mathematical model, which must be selected among various candidate models. In this manuscript, we present a new way to do this model selection step, utilizing validation data. Using an adopted approach to calculate the uncertainty of model predictions, we identify new validation experiments, which are neither too similar, nor too dissimilar, compared to the previous training data. The model candidate that is best at predicting this new validation data is the one chosen. Tests on simulated data where the true model is known, shows that the validation-based method is robust when the magnitude of the error in the measurement uncertainty is unknown, something that conventional methods are not. This improvement is important since true uncertainties can be difficult to estimate for these data. Finally, we demonstrate how the new method can be used on real data, to identify fluxes and important reactions.

Gas chromatography-mass spectrometry based 18O stable isotope labeling of Krebs cycle intermediates

New technologies permit determining metabolomic profiles of human diseases by fingerprinting metabolites levels. However, to fully understand metabolomic phenotypes, metabolite levels and turnover rates are necessary to know. Krebs cycle is the major hub of energy metabolism and cell signaling. Traditionally, ¹³C stable isotope labeled substrates were used to track the carbon turnover rates in Krebs cycle metabolites. In this study, for the first time we introduce H₂[¹⁸O] based stable isotope marker that permit tracking oxygen exchange rates in separate segments of Krebs cycle. The chromatographic and non-chromatographic parameters were systematically tested on the effect of labeling ratio of Krebs cycle mediators to increase selectivity and sensitivity of the method. We have developed a rapid, precise, and robust GC-MS method for determining the percentage of ¹⁸O incorporation to Krebs cycle metabolites. The developed method was applied to track the cancer-induced shift in the Krebs cycle dynamics of Caco-2 cells as compared to the control FHC cells revealing Warburg effects in Caco-2 cells. We demonstrate that unique information could be obtained using this newly developed ¹⁸O-labeling analytical technology by following the oxygen exchange rates of Krebs cycle metabolites. Thus, ¹⁸O-labeling of Krebs cycle metabolites expands the arsenal of techniques for monitoring the dynamics of cellular metabolism. Moreover, the developed method will allow to apply the ¹⁸O-labeling technique to numerous other metabolic pathways where oxygen exchange with water takes place.

Chemical Isotope Labeling LC-MS for Metabolomics

Due to the great diversity of chemical and physical properties of metabolites as well as a wide range of concentrations of metabolites present in metabolomic samples, performing comprehensive and quantitative metabolome analysis is a major analytical challenge. Conventional approach of combining various techniques and methods with each detecting a fraction of the metabolome can lead to the increase in overall metabolomic coverage. However, this approach requires extensive investment in equipment and analytical expertise with still relatively low coverage and low sample throughput. Chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) offers an alternative means of increasing metabolomic coverage while maintaining high quantification precision and accuracy. This chapter describes the CIL LC-MS method and its key features for metabolomic analysis.

Cellular sensing of extracellular purine nucleosides triggers an innate IFN-β response

Mechanisms linking immune sensing of DNA danger signals in the extracellular environment to innate pathways in the cytosol are poorly understood. Here, we identify a previously unidentified immune-metabolic axis by which cells respond to purine nucleosides and trigger a type I interferon-β (IFN-β) response. We find that depletion of ADA2, an ectoenzyme that catabolizes extracellular dAdo to dIno, or supplementation of dAdo or dIno stimulates IFN-β. Under conditions of reduced ADA2 enzyme activity, dAdo is transported into cells and undergoes catabolysis by the cytosolic isoenzyme ADA1, driving intracellular accumulation of dIno. dIno is a functional immunometabolite that interferes with the cellular methionine cycle by inhibiting SAM synthetase activity. Inhibition of SAM-dependent transmethylation drives epigenomic hypomethylation and overexpression of immune-stimulatory endogenous retroviral elements that engage cytosolic dsRNA sensors and induce IFN-β. We uncovered a previously unknown cellular signaling pathway that responds to extracellular DNA-derived metabolites, coupling nucleoside catabolism by adenosine deaminases to cellular IFN-β production.

Metabolic flux analysis and fluxomics-driven determination of reaction free energy using multiple isotopes

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

Glutamine supports the protection of tissue cells against the damage caused by cholesterol-dependent cytolysins from pathogenic bacteria

Pathogenic bacteria often damage tissues by secreting toxins that form pores in cell membranes, and the most common pore-forming toxins are cholesterol-dependent cytolysins. During bacterial infections, glutamine becomes a conditionally essential amino acid, and glutamine is an important nutrient for immune cells. However, the role of glutamine in protecting tissue cells against pore-forming toxins is unclear. Here we tested the hypothesis that glutamine supports the protection of tissue cells against the damage caused by cholesterol-dependent cytolysins. Stromal and epithelial cells were sensitive to damage by the cholesterol-dependent cytolysins, pyolysin and streptolysin O, as determined by leakage of potassium and lactate dehydrogenase from cells, and reduced cell viability. However, glutamine deprivation increased the leakage of lactate dehydrogenase and reduced the viability of cells challenged with cholesterol-dependent cytolysins. Without glutamine, stromal cells challenged with pyolysin leaked lactate dehydrogenase (control vs. pyolysin, 2.6 ± 0.6 vs. 34.4 ± 4.5 AU, n = 12), which was more than three-fold the leakage from cells supplied with 2 mM glutamine (control vs. pyolysin, 2.2 ± 0.3 vs. 9.4 ± 1.0 AU). Glutamine cytoprotection did not depend on glutaminolysis, replenishing the Krebs cycle via succinate, changes in cellular cholesterol, or regulators of cell metabolism (AMPK and mTOR). In conclusion, although the mechanism remains elusive, we found that glutamine supports the protection of tissue cells against the damage caused by cholesterol-dependent cytolysins from pathogenic bacteria.

Analysing central metabolism in ultra-high resolution: At the crossroads of carbon and nitrogen

BACKGROUND

Cancer cell metabolism can be characterised by adaptive metabolic alterations, which support abnormal proliferative cell growth with high energetic demand. De novo nucleotide biosynthesis is essential for providing nucleotides for RNA and DNA synthesis, and drugs targeting this biosynthetic pathway have proven to be effective anticancer therapeutics. Nevertheless, cancers are often able to circumvent chemotherapeutic interventions and become therapy resistant. Our understanding of the changing metabolic profile of the cancer cell and the mode of action of therapeutics is dependent on technological advances in biochemical analysis.

SCOPE OF REVIEW

This review begins with information about carbon- and nitrogen-donating pathways to build purine and pyrimidine moieties in the course of nucleotide biosynthesis. We discuss the application of stable isotope resolved metabolomics to investigate the dynamics of cancer cell metabolism and outline the benefits of high-resolution accurate mass spectrometry, which enables multiple tracer studies.

CONCLUSION

With the technological advances in mass spectrometry that allow for the analysis of the metabolome in high resolution, the application of stable isotope resolved metabolomics has become an important technique in the investigation of biological processes. The literature in the area of isotope labelling is dominated by 13C tracer studies. Metabolic pathways have to be considered as complex interconnected networks and should be investigated as such. Moving forward to simultaneous tracing of different stable isotopes will help elucidate the interplay between carbon and nitrogen flow and the dynamics of de novo nucleotide biosynthesis within the cell.

Stable Isotopes for Tracing Mammalian-Cell Metabolism In Vivo

Metabolism is at the cornerstone of all cellular functions and mounting evidence of its deregulation in different diseases emphasizes the importance of a comprehensive understanding of metabolic regulation at the whole-organism level. Stable-isotope measurements are a powerful tool for probing cellular metabolism and, as a result, are increasingly used to study metabolism in in vivo settings. The additional complexity of in vivo metabolic measurements requires paying special attention to experimental design and data interpretation. Here, we review recent work where in vivo stable-isotope measurements have been used to address relevant biological questions within an in vivo context, summarize different experimental and data interpretation approaches and their limitations, and discuss future opportunities in the field.

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.

Profiling the Metabolism of Human Cells by Deep 13C Labeling

Studying metabolic activities in living cells is crucial for understanding human metabolism, but facile methods for profiling metabolic activities in an unbiased, hypothesis-free manner are still lacking. To address this need, we here introduce the deep-labeling method, which combines a custom ¹³C medium with high-resolution mass spectrometry. A proof-of-principle study on human cancer cells demonstrates that deep labeling can identify hundreds of endogenous metabolites as well as active and inactive pathways. For example, protein and nucleic acids were almost exclusively de novo synthesized, while lipids were partly derived from serum; synthesis of cysteine, carnitine, and creatine was absent, suggesting metabolic dependencies; and branched-chain keto acids (BCKAs) were formed and metabolized to short-chain acylcarnitines, but did not enter the tricarboxylic acid cycle. Remarkably, BCKAs could substitute for essential amino acids to support growth. The deep-labeling method may prove useful to map metabolic phenotypes across a range of cell types and conditions.

Stable isotope compounds - production, detection, and application

Stable isotopes are used in wide fields of application from natural tracers in biology, geology and archeology through studies of metabolic fluxes to their application as tracers in quantitative proteomics and structural biology. We review the use of stable isotopes of biogenic elements (H, C, N, O, S, Mg, Se) with the emphasis on hydrogen and its heavy isotope deuterium. We will discuss the limitations of enriching various compounds in stable isotopes when produced in living organisms. Finally, we overview methods for measuring stable isotopes, focusing on methods for detection in single cells in situ and their exploitation in modern biotechnologies.

High-Speed Tracer Analysis of Metabolism (HS-TrAM)

Tracing the fate of stable isotopically-enriched nutrients is a sophisticated method of describing and quantifying the activity of metabolic pathways. Nuclear Magnetic Resonance (NMR) offers high resolution data, yet is under-utilised due to length of time required to collect the data, quantification requiring multiple samples and complicated analysis. Here we present two techniques, quantitative spectral filters and enhancement of the splitting due to J-coupling in 1H,13C-HSQC NMR spectra, which allow the rapid collection of NMR data in a quantitative manner on a single sample. The reduced duration of HSQC spectra data acquisition opens up the possibility of real-time tracing of metabolism including the study of metabolic pathways in vivo. We show how these novel techniques can be used to trace the fate of labelled nutrients in a whole organ model of kidney preservation prior to transplantation using a porcine kidney as a model organ, and also show how the use of multiple nutrients, differentially labelled with 13C and 15N, can be used to provide additional information with which to profile metabolic pathways.

High-Speed Tracer Analysis of Metabolism (HS-TrAM)

Tracing the fate of stable isotopically-enriched nutrients is a sophisticated method of describing and quantifying the activity of metabolic pathways. Nuclear Magnetic Resonance (NMR) spectroscopy offers high resolution data in terms of resolving metabolic pathway utilisation. Despite this, NMR spectroscopy is under-utilised due to length of time required to collect the data, quantification requiring multiple samples and complicated analysis. Here we present two techniques, quantitative spectral filters and enhancement of the splitting of  ¹³C signals due to homonuclear  ¹³C, ¹³C or heteronuclear  ¹³C, ¹⁵N J-coupling in  ¹H, ¹³C-HSQC NMR spectra. Together, these allow the rapid collection of NMR spectroscopy data in a quantitative manner on a single sample. The reduced duration of HSQC spectra data acquisition opens up the possibility of real-time tracing of metabolism including the study of metabolic pathways  in vivo. We show how these techniques can be used to trace the fate of labelled nutrients in a whole organ model of kidney preservation prior to transplantation using a porcine kidney as a model organ. In addition, we show how the use of multiple nutrients, differentially labelled with  ¹³C and  ¹⁵N, can be used to provide additional information with which to profile metabolic pathways.

Modeling framework for isotopic labeling of heteronuclear moieties

BACKGROUND

Isotopic labeling is an analytic technique that is used to track the movement of isotopes through reaction networks. In general, the applicability of isotopic labeling techniques is limited to the investigation of reaction networks that consider homonuclear moieties, whose atoms are of one tracer element with two isotopes, distinguished by the presence of one additional neutron.

RESULTS

This article presents a reformulation of the modeling framework for isotopic labeling, generalized to arbitrarily large, heteronuclear moieties, arbitrary numbers of isotopic tracer elements, and arbitrary numbers of isotopes per element, distinguished by arbitrary numbers of additional neutrons.

CONCLUSIONS

With this work, it is now possible to simulate the isotopic labeling states of metabolites in completely arbitrary biochemical reaction networks.

Estimation of flux ratios without uptake or release data: Application to serine and methionine metabolism

Model-based metabolic flux analysis (MFA) using isotope-labeled substrates has provided great insight into intracellular metabolic activities across a host of organisms. One challenge with applying MFA in mammalian systems, however, is the need for absolute quantification of nutrient uptake, biomass composition, and byproduct release fluxes. Such measurements are often not feasible in complex culture systems or in vivo. One way to address this issue is to estimate flux ratios, the fractional contribution of a flux to a metabolite pool, which are independent of absolute measurements and yet informative for cellular metabolism. Prior work has focused on "local" estimation of a handful of flux ratios for specific metabolites and reactions. Here, we perform systematic, model-based estimation of all flux ratios in a metabolic network using isotope labeling data, in the absence of uptake/release data. In a series of examples, we investigate what flux ratios can be well estimated with reasonably tight confidence intervals, and contrast this with confidence intervals on normalized fluxes. We find that flux ratios can provide useful information on the metabolic state, and is complementary to normalized fluxes: for certain metabolic reactions, only flux ratios can be well estimated, while for others normalized fluxes can be obtained. Simulation studies of a large human metabolic network model suggest that estimation of flux ratios is technically feasible for complex networks, but additional studies on data from actual isotopomer labeling experiments are needed to validate these results. Finally, we experimentally study serine and methionine metabolism in cancer cells using flux ratios. We find that, in these cells, the methionine cycle is truncated with little remethylation from homocysteine, and polyamine synthesis in the absence of methionine salvage leads to loss of 5-methylthioadenosine, suggesting a new mode of overflow metabolism in cancer cells. This work highlights the potential for flux ratio analysis in the absence of absolute quantification, which we anticipate will be important for both in vitro and in vivo studies of cancer metabolism.