NMR method for measuring carbon-13 isotopic enrichment of metabolites in complex solutions

Altered Fibroblast Glutamine Metabolism Is Linked to the Severity of Cardiac Dysfunction in DCMA, a Mitochondrial Cardiomyopathy

The dilated cardiomyopathy with ataxia (DCMA) syndrome is a rare mitochondrial disorder caused by mutations in the poorly understood DNAJC19 gene. Cardiac involvement in DCMA ranges from mild conduction abnormalities to early severe myocardial dysfunction. Although evidence suggests that DCMA is linked to abnormalities in mitochondrial function, the molecular underpinnings of this condition are unclear, and there is no way to predict which patients will develop life-threatening disease. To address this, we developed a metabolic flux assay for assessing the metabolic function of mitochondria in fibroblasts derived from DCMA patients. Using this approach, we discovered that DCMA fibroblasts have elevated glutamine uptake, increased glutamate and ammonium secretion, and elevated lactate production. Moreover, we observed that these cellular perturbations were closely correlated with cardiac dysfunction in a blinded cohort of patient cell lines. These findings suggest that glutamine catabolism is abnormal in DCMA and may serve as a predictor of clinical progression.

A simple 1H (12C/13C) filtered experiment to quantify and trace isotope enrichment in complex environmental and biological samples

Nuclear magnetic resonance (NMR) based 13C tracing has broad applications across medical and environmental research. As many biological and environmental samples are heterogeneous, they experience considerable spectral overlap and relatively low signal. Here a 1D 1H-12C/13C is introduced that uses "in-phase/opposite-phase" encoding to simultaneously detect and discriminate both protons attached to 12C and 13C at full 1H sensitivity in every scan. Unlike traditional approaches that focus on the 12C/13C satellite ratios in a 1H spectrum, this approach creates separate sub-spectra for the 12C and 13C bound protons. These spectra can be used for both quantitative and qualitative analysis of complex samples with significant spectral overlap. Due to the presence of the 13C dipole, faster relaxation of the 1H-13C pairs results in slight underestimation compared to the 1H-12C pairs. However, this is easily compensated for, by collecting an additional reference spectrum, from which the absolute percentage of 13C can be calculated by difference. When combined with the result, 12C and 13C percent enrichment in both 1H-12C and 1H-13C fractions are obtained. As the approach uses isotope filtered 1H NMR for detection, it retains nearly the same sensitivity as a standard 1H spectrum. Here, a proof-of-concept is performed using simple mixtures of 12C and 13C glucose, followed by suspended algal cells with varying 12C /13C ratios representing a complex mixture. The results consistently return 12C/13C ratios that deviate less than 1 % on average from the expected. Finally, the sequence was used to monitor and quantify 13C% enrichment in Daphnia magna neonates which were fed a 13C diet over 1 week. The approach helped reveal how the organisms utilized the 12C lipids they are born with vs. the 13C lipids they assimilate from their diet during growth. Given the experiments simplicity, versatility, and sensitivity, we anticipate it should find broad application in a wide range of tracer studies, such as fluxomics, with applications spanning various disciplines.

Exploring the potential application of alternative nuclei in NMR based metabolomics

INTRODUCTION

Nuclear magnetic resonance (NMR) is widely used in metabolomics but it focusses on ¹H over other NMR-active nuclei.

OBJECTIVES

To evaluate the potential of alternative NMR-sensitive nuclei to generate useful metabolomic data.

METHOD

Proton, carbon, phosphorus, and nitrogen-based NMR metabolomics was undertaken on extracts from mint and European honey bee tissue.

RESULTS

Carbon NMR provided useful information but required larger sample sizes. Phosphorus produced overlapping peaks in one dimensional (1D) analysis but showed potential in 2D experiments. ¹⁵N NMR was found to not be sensitive enough for general metabolomic work.

CONCLUSIONS

Alternative NMR active nuclei are useful for metabolomics.

Metabolomics and modelling approaches for systems metabolic engineering

Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.

Rapid quantitative 1H-13C two-dimensional NMR with high precision

Two dimensional (2D) 1H-13C heteronuclear single-quantum correlation (HSQC) spectroscopy has recently been proposed for quantitative determination of typical linear low density polyethylene (LLDPE) with high accuracy. It requires highly precise measurement to achieve further reliable quantification. In this context, this paper aims at determining conditions that allow the achievement of high precision. On the basis of the optimized parameters, two time-saving strategies, nonuniform sampling (NUS) and band-selective HSQC are evaluated on model polyolefins in terms of repeatability. Precision better than 0.3% and 5% for ethylene content (E mol%) and 1-hexene content (H mol%) of the model poly(ethylene-co-1-hexene)s are obtained with 50% NUS or band-selective HSQC. Moreover, dramatic precision enhancements can be achieved with the combination of band-selective HSQC and 50% NUS, in which repeatabilities better than 0.15% and 2.5% for E mol% and H mol% are observed. The experiment times are reduced to about 0.5 h. These methods open important perspectives for rapid, precise and accurate quantitative analysis of complex polymers.

NMR spectroscopy of wastewater: A review, case study, and future potential

NMR spectroscopy is arguably the most powerful tool for the study of molecular structures and interactions, and is increasingly being applied to environmental research, such as the study of wastewater. With over 97% of the planet's water being saltwater, and two thirds of freshwater being frozen in the ice caps and glaciers, there is a significant need to maintain and reuse the remaining 1%, which is a precious resource, critical to the sustainability of most life on Earth. Sanitation and reutilization of wastewater is an important method of water conservation, especially in arid regions, making the understanding of wastewater itself, and of its treatment processes, a highly relevant area of environmental research. Here, the benefits, challenges and subtleties of using NMR spectroscopy for the analysis of wastewater are considered. First, the techniques available to overcome the specific challenges arising from the nature of wastewater (which is a complex and dilute matrix), including an examination of sample preparation and NMR techniques (such as solvent suppression), in both the solid and solution states, are discussed. Then, the arsenal of available NMR techniques for both structure elucidation (e.g., heteronuclear, multidimensional NMR, homonuclear scalar coupling-based experiments) and the study of intermolecular interactions (e.g., diffusion, nuclear Overhauser and saturation transfer-based techniques) in wastewater are examined. Examples of wastewater NMR studies from the literature are reviewed and potential areas for future research are identified. Organized by nucleus, this review includes the common heteronuclei (¹³C, ¹⁵N, ¹⁹F, ³¹P, ²⁹Si) as well as other environmentally relevant nuclei and metals such as ²⁷Al, ⁵¹V, ²⁰⁷Pb and ¹¹³Cd, among others. Further, the potential of additional NMR methods such as comprehensive multiphase NMR, NMR microscopy and hyphenated techniques (for example, LC-SPE-NMR-MS) for advancing the current understanding of wastewater are discussed. In addition, a case study that combines natural abundance (i.e. non-concentrated), targeted and non-targeted NMR to characterize wastewater, along with in vivo based NMR to understand its toxicity, is included. The study demonstrates that, when applied comprehensively, NMR can provide unique insights into not just the structure, but also potential impacts, of wastewater and wastewater treatment processes. Finally, low-field NMR, which holds considerable future potential for on-site wastewater monitoring, is briefly discussed. In summary, NMR spectroscopy is one of the most versatile tools in modern science, with abilities to study all phases (gases, liquids, gels and solids), chemical structures, interactions, interfaces, toxicity and much more. The authors hope this review will inspire more scientists to embrace NMR, given its huge potential for both wastewater analysis in particular and environmental research in general.

Sensitive NMR method for detecting carbohydrate influx into competing chemocatalytic pathways

Mechanistic pathway studies in sustainable chemistry can be accelerated and have increased information content through the indirect detection of isotope-tracking experiments upon reduction of quaternary carbon sites.

Focusing on “the important” through targeted NMR experiments: an example of selective13C–12C bond detection in complex mixtures

Here, a targeted NMR experiment is introduced which selectively detects the formation of¹³C–¹²C bonds in mixtures.

An adapted isotope dilution 1H-13C heteronuclear single-quantum correlation (ID-HSQC) for rapid and accurate quantification of endogenous and exogenous plasma glucose

A wide variety of methods, such as enzymatic methods, LC-MS, and LC-MS/MS, are currently available for the concentration determination of plasma glucose in studies of diabetes, obesity, exercise, etc. However, these methods rarely discriminate endogenous and exogenous glucose in plasma. A novel NMR strategy for discriminative quantification of the endogenous and exogenous glucose in plasma has been developed using an adapted isotope dilution ¹H-¹³C heteronuclear single-quantum correlation (ID-HSQC) with uniformly ¹³C-labeled glucose as a tracer of exogenous glucose. This method takes advantage of the distinct ¹H-¹³C chemical shifts of the hemiacetal group of the α-D-glucopyranose and makes use of the ¹³C-¹³C one-bond J-coupling (¹J_CC) in uniformly ¹³C-labeled glucose to differentiate the ¹H-¹³C HSQC signal of labeled glucose from that of its natural counterpart when data are acquired in high-resolution mode. The molar ratio between the endogenous and exogenous plasma glucose can then be calculated from the peak intensities of the natural and labeled glucose. The accuracy and precision of the method were evaluated using a series of standard mixtures of natural and uniformly ¹³C-labeled glucose with varied but known concentrations. Application of this method is demonstrated for the quantification of endogenous and exogenous glucose in plasma derived from healthy and diabetic cynomolgus monkeys. The results nicely agree with our previous LC-MS/MS results. Considering the natural abundance of ¹³C isotope at the level of 1.1% in endogenous glucose, comparable peak intensities of quantitatively measurable signals derived from natural and labeled glucose imply that the ID-HSQC can tolerate a significantly high ratio of isotope dilution, with labeled/natural glucose at ~ 1%. We expect that the ID-HSQC method can serve as an alternative approach to the biomedical or clinical studies of glucose metabolism.

Environmental metabolomics with data science for investigating ecosystem homeostasis

A natural ecosystem can be viewed as the interconnections between complex metabolic reactions and environments. Humans, a part of these ecosystems, and their activities strongly affect the environments. To account for human effects within ecosystems, understanding what benefits humans receive by facilitating the maintenance of environmental homeostasis is important. This review describes recent applications of several NMR approaches to the evaluation of environmental homeostasis by metabolic profiling and data science. The basic NMR strategy used to evaluate homeostasis using big data collection is similar to that used in human health studies. Sophisticated metabolomic approaches (metabolic profiling) are widely reported in the literature. Further challenges include the analysis of complex macromolecular structures, and of the compositions and interactions of plant biomass, soil humic substances, and aqueous particulate organic matter. To support the study of these topics, we also discuss sample preparation techniques and solid-state NMR approaches. Because NMR approaches can produce a number of data with high reproducibility and inter-institution compatibility, further analysis of such data using machine learning approaches is often worthwhile. We also describe methods for data pretreatment in solid-state NMR and for environmental feature extraction from heterogeneously-measured spectroscopic data by machine learning approaches.

Reprograming of Glucose Metabolism by Zerumbone Suppresses Hepatocarcinogenesis

Hepatocellular carcinoma (HCC) is the most prevalent and highly aggressive liver malignancy with limited therapeutic options. Here, the therapeutic potential of zerumbone, a sesquiterpene derived from the ginger plant Zingiber zerumbet, against HCC was explored. Zerumbone inhibited proliferation and clonogenic survival of HCC cells in a dose-dependent manner by arresting cells at the G₂-M phase and inducing apoptosis. To elucidate the underlying molecular mechanisms, a phosphokinase array was performed that showed significant inhibition of the PI3K/AKT/mTOR and STAT3 signaling pathways in zerumbone-treated HCC cells. Gene expression profiling using microarray and analysis of microarray data by Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) revealed that zerumbone treatment resulted in significant deregulation of genes regulating apoptosis, cell cycle, and metabolism. Indeed, tracing glucose metabolic pathways by growing HCC cells with ¹³C₆-glucose and measuring extracellular and intracellular metabolites by 2D nuclear magnetic resonance (NMR) spectroscopy showed a reduction in glucose consumption and reduced lactate production, suggesting glycolytic inhibition. In addition, zerumbone impeded shunting of glucose-6-phosphate through the pentose phosphate pathway, thereby forcing tumor cells to undergo cell-cycle arrest and apoptosis. Importantly, zerumbone treatment suppressed subcutaneous and orthotopic growth and lung metastasis of HCC xenografts in immunocompromised mice. In conclusion, these findings reveal a novel and potentially effective therapeutic strategy for HCC using a natural product that targets cancer cell metabolism.Implications: Dietary compounds, like zerumbone, that impact cell cycle, apoptosis, and metabolic processes may have therapeutic benefits for HCC patients. Mol Cancer Res; 16(2); 256-68. ©2017 AACR.

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

Metabolic Effect of Estrogen Receptor Agonists on Breast Cancer Cells in the Presence or Absence of Carbonic Anhydrase Inhibitors

Metabolic shift is one of the major hallmarks of cancer development. Estrogen receptor (ER) activity has a profound effect on breast cancer cell growth through a number of metabolic changes driven by its effect on transcription of several enzymes, including carbonic anhydrases, Stearoyl-CoA desaturase-1, and oncogenes including HER2. Thus, estrogen receptor activators can be expected to lead to the modulation of cell metabolism in estrogen receptor positive cells. In this work we have investigated the effect of 17β-estradiol, an ER activator, and ferulic acid, a carbonic anhydrase inhibitor, as well as ER activator, in the absence and in the presence of the carbonic anhydrase inhibitor acetazolamide on the metabolism of MCF7 cells and MCF7 cells, stably transfected to express HER2 (MCF7HER2). Metabolic profiles were studied using 1D and 2D metabolomic Nuclear Magnetic Resonance (NMR) experiments, combined with the identification and quantification of metabolites, and the annotation of the results in the context of biochemical pathways. Overall changes in hydrophilic metabolites were largest following treatment of MCF7 and MC7HER2 cells with 17β-estradiol. However, the carbonic anhydrase inhibitor acetazolamide had the largest effect on the profile of lipophilic metabolites.

Suppression of phase and amplitude J(HH) modulations in HSQC experiments

The amplitude and the phase of cross peaks in conventional 2D HSQC experiments are modulated by both proton-proton, J(HH), and proton-carbon, (1)J(CH), coupling constants. It is shown by spectral simulation and experimentally that J(HH) interferences are suppressed in a novel perfect-HSQC pulse scheme that incorporates perfect-echo INEPT periods. The improved 2D spectra afford pure in-phase cross peaks with respect to (1)J(CH) and J(HH), irrespective of the experiment delay optimization. In addition, peak volumes are not attenuated by the influence of J(HH), rendering practical issues such as phase correction, multiplet analysis, and signal integration more appropriate.

Quantitative 2D liquid-state NMR

Two-dimensional (2D) liquid-state NMR has a very high potential to simultaneously determine the absolute concentration of small molecules in complex mixtures, thanks to its capacity to separate overlapping resonances. However, it suffers from two main drawbacks that probably explain its relatively late development. First, the 2D NMR signal is strongly molecule-dependent and site-dependent; second, the long duration of 2D NMR experiments prevents its general use for high-throughput quantitative applications and affects its quantitative performance. Fortunately, the last 10 years has witnessed an increasing number of contributions where quantitative approaches based on 2D NMR were developed and applied to solve real analytical issues. This review aims at presenting these recent efforts to reach a high trueness and precision in quantitative measurements by 2D NMR. After highlighting the interest of 2D NMR for quantitative analysis, the different strategies to determine the absolute concentrations from 2D NMR spectra are described and illustrated by recent applications. The last part of the manuscript concerns the recent development of fast quantitative 2D NMR approaches, aiming at reducing the experiment duration while preserving - or even increasing - the analytical performance. We hope that this comprehensive review will help readers to apprehend the current landscape of quantitative 2D NMR, as well as the perspectives that may arise from it.

Ultrafast 2D NMR: an emerging tool in analytical spectroscopy

Two-dimensional nuclear magnetic resonance (2D NMR) spectroscopy is widely used in chemical and biochemical analyses. Multidimensional NMR is also witnessing increased use in quantitative and metabolic screening applications. Conventional 2D NMR experiments, however, are affected by inherently long acquisition durations, arising from their need to sample the frequencies involved along their indirect domains in an incremented, scan-by-scan nature. A decade ago, a so-called ultrafast (UF) approach was proposed, capable of delivering arbitrary 2D NMR spectra involving any kind of homo- or heteronuclear correlation, in a single scan. During the intervening years, the performance of this subsecond 2D NMR methodology has been greatly improved, and UF 2D NMR is rapidly becoming a powerful analytical tool experiencing an expanded scope of applications. This review summarizes the principles and main developments that have contributed to the success of this approach and focuses on applications that have been recently demonstrated in various areas of analytical chemistry--from the real-time monitoring of chemical and biochemical processes, to extensions in hyphenated techniques and in quantitative applications.

Revisiting Protocols for the NMR Analysis of Bacterial Metabolomes

Over the past decade, metabolomics has emerged as an important technique for systems biology. Measuring all the metabolites in a biological system provides an invaluable source of information to explore various cellular processes, and to investigate the impact of environmental factors and genetic modifications. Nuclear magnetic resonance (NMR) spectroscopy is an important method routinely employed in metabolomics. NMR provides comprehensive structural and quantitative information useful for metabolomics fingerprinting, chemometric analysis, metabolite identification and metabolic pathway construction. A successful metabolomics study relies on proper experimental protocols for the collection, handling, processing and analysis of metabolomics data. Critically, these protocols should eliminate or avoid biologically-irrelevant changes to the metabolome. We provide a comprehensive description of our NMR-based metabolomics procedures optimized for the analysis of bacterial metabolomes. The technical details described within this manuscript should provide a useful guide to reliably apply our NMR-based metabolomics methodology to systems biology studies.

Addressing the current bottlenecks of metabolomics: Isotopic Ratio Outlier Analysis™, an isotopic-labeling technique for accurate biochemical profiling

Metabolomics or biochemical profiling is a fast emerging science; however, there are still many associated bottlenecks to overcome before measurements will be considered robust. Advances in MS resolution and sensitivity, ultra pressure LC-MS, ESI, and isotopic approaches such as flux analysis and stable-isotope dilution, have made it easier to quantitate biochemicals. The digitization of mass spectrometers has simplified informatic aspects. However, issues of analytical variability, ion suppression and metabolite identification still plague metabolomics investigators. These hurdles need to be overcome for accurate metabolite quantitation not only for in vitro systems, but for complex matrices such as biofluids and tissues, before it is possible to routinely identify biomarkers that are associated with the early prediction and diagnosis of diseases. In this report, we describe a novel isotopic-labeling method that uses the creation of distinct biochemical signatures to eliminate current bottlenecks and enable accurate metabolic profiling.

Semiautomated device for batch extraction of metabolites from tissue samples

Metabolomics has become a mainstream analytical strategy for investigating metabolism. The quality of data derived from these studies is proportional to the consistency of the sample preparation. Although considerable research has been devoted to finding optimal extraction protocols, most of the established methods require extensive sample handling. Manual sample preparation can be highly effective in the hands of skilled technicians, but an automated tool for purifying metabolites from complex biological tissues would be of obvious utility to the field. Here, we introduce the semiautomated metabolite batch extraction device (SAMBED), a new tool designed to simplify metabolomics sample preparation. We discuss SAMBED’s design and show that SAMBED-based extractions are of comparable quality to extracts produced through traditional methods (13% mean coefficient of variation from SAMBED versus 16% from manual extractions). Moreover, we show that aqueous SAMBED-based methods can be completed in less than a quarter of the time required for manual extractions.

ECOMICS: a web-based toolkit for investigating the biomolecular web in ecosystems using a trans-omics approach

Ecosystems can be conceptually thought of as interconnected environmental and metabolic systems, in which small molecules to macro-molecules interact through diverse networks. State-of-the-art technologies in post-genomic science offer ways to inspect and analyze this biomolecular web using omics-based approaches. Exploring useful genes and enzymes, as well as biomass resources responsible for anabolism and catabolism within ecosystems will contribute to a better understanding of environmental functions and their application to biotechnology. Here we present ECOMICS, a suite of web-based tools for ECosystem trans-OMICS investigation that target metagenomic, metatranscriptomic, and meta-metabolomic systems, including biomacromolecular mixtures derived from biomass. ECOMICS is made of four integrated webtools. E-class allows for the sequence-based taxonomic classification of eukaryotic and prokaryotic ribosomal data and the functional classification of selected enzymes. FT2B allows for the digital processing of NMR spectra for downstream metabolic or chemical phenotyping. Bm-Char allows for statistical assignment of specific compounds found in lignocellulose-based biomass, and HetMap is a data matrix generator and correlation calculator that can be applied to trans-omics datasets as analyzed by these and other web tools. This web suite is unique in that it allows for the monitoring of biomass metabolism in a particular environment, i.e., from macromolecular complexes (FT2DB and Bm-Char) to microbial composition and degradation (E-class), and makes possible the understanding of relationships between molecular and microbial elements (HetMap). This website is available to the public domain at: https://database.riken.jp/ecomics/.

Bacterial osmoregulation: a paradigm for the study of cellular homeostasis

To thrive, cells must control their own physical and chemical properties. This process is known as cellular homeostasis. The dilute solutions traditionally favored by experimenters do not simulate the cytoplasm, where macromolecular crowding and preferential interactions among constituents may dominate critical processes. Solutions that do simulate cytoplasmic conditions are now being characterized. Corresponding cytoplasmic properties can be varied systematically by imposing osmotic stress. This osmotic stress approach is revealing how cytoplasmic properties modulate protein folding and protein?nucleic acid interactions. Results suggest that cytoplasmic homeostasis may require adjustments to multiple, interwoven cytoplasmic properties. Osmosensory transporters with diverse structures and bioenergetic mechanisms activate in response to osmotic stress as other proteins inactivate. These transporters are serving as paradigms for the study of in vivo protein-solvent interactions. Experimenters have proposed three different osmosensory mechanisms. Distinct mechanisms may exist, or these proposals may reflect different perceptions of a single, unifying mechanism.

Isotope enhanced approaches in metabolomics

The rapidly growing area of "metabolomics," in which a large number of metabolites from body fluids, cells or tissue are detected quantitatively, in a single step, promises immense potential for a number of disciplines including early disease diagnosis, therapy monitoring, systems biology, drug discovery and nutritional science. Because of its ability to detect a large number of metabolites in intact biological samples reproducibly and quantitatively, nuclear magnetic resonance (NMR) spectroscopy has emerged as one of the most powerful analytical techniques in metabolomics. NMR spectroscopy of biological samples with isotope labeling of metabolites using nuclei such as (2)H, (13)C, (15)N and (31)P, either in vivo or ex vivo, has dramatically improved our ability to identify low concentrated metabolites and trace important metabolic pathways. Considering the somewhat limited sensitivity and high complexity of NMR spectra of biological samples, efforts have been made to increase sensitivity and selectivity through isotope labeling methods, which pave novel avenues to unravel biological complexity and understand cellular functions in health and various disease conditions. This chapter describes current developments in isotope labeling of metabolites in vivo as well as ex vivo, and their potential metabolomics applications.

Metabolomics of oxidative stress in recent studies of endogenous and exogenously administered intermediate metabolites

Aerobic metabolism occurs in a background of oxygen radicals and reactive oxygen species (ROS) that originate from the incomplete reduction of molecular oxygen in electron transfer reactions. The essential role of aerobic metabolism, the generation and consumption of ATP and other high energy phosphates, sustains a balance of approximately 3000 essential human metabolites that serve not only as nutrients, but also as antioxidants, neurotransmitters, osmolytes, and participants in ligand-based and other cellular signaling. In hypoxia, ischemia, and oxidative stress, where pathological circumstances cause oxygen radicals to form at a rate greater than is possible for their consumption, changes in the composition of metabolite ensembles, or metabolomes, can be associated with physiological changes. Metabolomics and metabonomics are a scientific disciplines that focuse on quantifying dynamic metabolome responses, using multivariate analytical approaches derived from methods within genomics, a discipline that consolidated innovative analysis techniques for situations where the number of biomarkers (metabolites in our case) greatly exceeds the number of subjects. This review focuses on the behavior of cytosolic, mitochondrial, and redox metabolites in ameliorating or exacerbating oxidative stress. After reviewing work regarding a small number of metabolites—pyruvate, ethyl pyruvate, and fructose-1,6-bisphosphate—whose exogenous administration was found to ameliorate oxidative stress, a subsequent section reviews basic multivariate statistical methods common in metabolomics research, and their application in human and preclinical studies emphasizing oxidative stress. Particular attention is paid to new NMR spectroscopy methods in metabolomics and metabonomics. Because complex relationships connect oxidative stress to so many physiological processes, studies from different disciplines were reviewed. All, however, shared the common goal of ultimately developing “omics”-based, diagnostic tests to help influence therapies.

Dynamic omics approach identifies nutrition-mediated microbial interactions

"Omics" studies reported to date have dealt with either thoroughly characterized single species or poorly explored meta-microbial communities. However, these techniques are capable of producing highly informative data for the analysis of interactions between two organisms. We examined the bacterial interaction between Escherichia coli O157:H7 (O157) and Bifidobacterium longum (BL) as a pathogenic-commensal bacterial model creating a minimum microbial ecosystem in the gut using dynamic omics approaches, consisting of improved time-lapse 2D-nuclear magnetic resonance (NMR) metabolic profiling, transcriptomic, and proteomic analyses. Our study revealed that the minimum ecosystem was established by bacterial adaptation to the changes in the extracellular environment, primarily by O157, but not by BL. Additionally, the relationship between BL and O157 could be partially regarded as that between a producer and a consumer of nutrients, respectively, especially with regard to serine and aspartate metabolism. Taken together, our profiling system can provide a new insight into the primary metabolic dynamics in microbial ecosystems.