¹³C NMR metabolomics: applications at natural abundance

Identification of ADB-5'Br-BINACA in plant material and analytical characterization using GC-MS, LC-QTOF-MS, NMR and ATR-FTIR

Synthetic cannabinoids are a class of novel psychoactive substances that emerged in the drug market in the early 2010s. Since then, a wide range of different synthetic cannabinoids has been detected in drug materials and in biological specimens collected from intoxication cases. In general, synthetic cannabinoids are reported first in seized materials. In this study, the identification of the novel synthetic cannabinoid, ADB-5'Br-BINACA is reported. A plant material suspected to contain a synthetic cannabinoid was extracted and analyzed. Analyses were performed using gas chromatography-mass spectrometry (GC-MS), liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS), attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) and one dimensional and two-dimensional nuclear magnetic resonance (NMR) spectroscopy. An aliquot of the sample was extracted using methanol and deuterated chloroform, and analyzed via GC-MS and NMR, respectively. Further dilution of the methanolic extract was analyzed via LC-QTOF-MS. For ATR-FTIR analyses, a few drops of the extract in deuterated chloroform were analyzed. GC-MS, LC-QTOF-MS, and 1H NMRwere successfully used to elucidate and confirm the structure of ADB-5'Br-BINACA in the drug sample. ATR-FTIR and 13C NMR analyses of the extracts did not result in significant information for the confirmation of ADB-5'Br-BINACA in the plant material likely due to low amount of drug material and high background noise. The chemical characterization of ADB-5'Br-BINACA in an authentic sample is reported herein, and chromatographic, mass spectrometric and spectroscopic data are provided for use in future analysis of this drug in suspected samples.

Exploring the complementarity of fast multipulse and multidimensional NMR methods for metabolomics: a chemical ecology case study

This study investigates the potential and complementarity of high-throughput multipulse and multidimensional NMR methods for metabolomics. Through a chemical ecology case study, three methods are investigated, offering a continuum of methods with complementary features in terms of resolution, sensitivity and experiment time. Ultrafast 2D COSY, adiabatic INEPT and SYMAPS HSQC are shown to provide a very good classification ability, comparable to the reference 1D 1H NMR method. Moreover, a detailed analysis of discriminant buckets upon supervised statistical analysis shows that all methods are highly complementary, since they are able to highlight discriminant signals that could not be detected by 1D 1H NMR. In particular, fast 2D methods appear very efficient to discriminate signals located in highly crowded regions of the 1H spectrum. Overall, the combination of these recent methods within a single NMR metabolomics workflow allows to maximize the accessible metabolic information, and also raises exciting challenges in terms of NMR data analysis for chemical ecology.

PyINETA: Open-source platform for INADEQUATE-JRES integration in NMR metabolomics

Annotating compounds with high confidence is a critical element in metabolomics. 13C-detection NMR experiment INADEQUATE (incredible natural abundance double-quantum transfer experiment) stands out as a powerful tool for structural elucidation, whereas this valuable experiment is not often included in metabolomics studies. This is partly due to the lack of community platform that provides structural information based INADEQUATE. Also, it is often the case that a single study uses various NMR experiments synergistically to improve the quality of information or balance total NMR experiment time, but there is no public platform that can integrate the outputs of INADEQUATE and other NMR experiments either. Here, we introduce PyINETA, Python-based INADEQUATE network analysis. PyINETA is an open-source platform that provides structural information of molecules using INADEQUATE, conducts database search, and integrates information of INADEQUATE and a complementary NMR experiment 13C J-resolved experiment (13C-JRES). Those steps are carried out automatically, and PyINETA keeps track of all the pipeline parameters and outputs, ensuring the transparency of annotation in metabolomics. Our evaluation of PyINETA using a model mouse study showed that our pipeline successfully integrated INADEQUATE and 13C-JRES. The results showed that 13C-labeled amino acids that were fed to mice were transferred to different tissues, and, also, they were transformed to other metabolites. The distribution of those compounds was tissue-specific, showing enrichment of particular metabolites in liver, spleen, pancreas, muscle, or lung. The value of PyINETA was not limited to those known compounds; PyINETA also provided fragment information for unknown compounds. PyINETA is available on NMRbox.

HR-MAS DREAMTIME NMR for Slow Spinning ex Vivo and in Vivo Samples

HR-MAS NMR is a powerful tool, capable of monitoring molecular changes in intact heterogeneous samples. However, one of the biggest limitations of 1H NMR is its narrow spectral width which leads to considerable overlap in complex natural samples. DREAMTIME NMR is a highly selective technique that allows users to isolate suites of metabolites from congested spectra. This permits targeted metabolomics by NMR and is ideal for monitoring specific processes. To date, DREAMTIME has only been employed in solution-state NMR, here it is adapted for HR-MAS applications. At high spinning speeds (>5 kHz), DREAMTIME works with minimal modifications. However, spinning over 3-4 kHz leads to cell lysis, and if maintaining sample integrity is necessary, slower spinning (<2.5 kHz) is required. Very slow spinning (≤500 Hz) is advantageous for in vivo analysis to increase organism survival; however, sidebands from water pose a problem. To address this, a version of DREAMTIME, termed DREAMTIME-SLOWMAS, is introduced. Both techniques are compared at 2500, 500, and 50 Hz, using ex vivo worm tissue. Following this, DREAMTIME-SLOWMAS is applied to monitor key metabolites of anoxic stress in living shrimp at 500 Hz. Thus, standard DREAMTIME works well under MAS conditions and is recommended for samples reswollen in D2O or spun >2500 Hz. For slow spinning in vivo or intact tissue samples, DREAMTIME-SLOWMAS provides an excellent way to target process-specific metabolites while maintaining sample integrity. Overall, DREAMTIME should find widespread application wherever targeted molecular information is required from complex samples with a high degree of spectral overlap.

Metabolomics and mitochondrial dysfunction in cardiometabolic disease

Circulating metabolites are indicators of systemic metabolic dysfunction and can be detected through contemporary techniques in metabolomics. These metabolites are involved in numerous mitochondrial metabolic processes including glycolysis, fatty acid β-oxidation, and amino acid catabolism, and changes in the abundance of these metabolites is implicated in the pathogenesis of cardiometabolic diseases (CMDs). Epigenetic regulation and direct metabolite-protein interactions modulate metabolism, both within cells and in the circulation. Dysfunction of multiple mitochondrial components stemming from mitochondrial DNA mutations are implicated in disease pathogenesis. This review will summarize the current state of knowledge regarding: i) the interactions between metabolites found within the mitochondrial environment during CMDs, ii) various metabolites' effects on cellular and systemic function, iii) how harnessing the power of metabolomic analyses represents the next frontier of precision medicine, and iv) how these concepts integrate to expand the clinical potential for translational cardiometabolic medicine.

Rapid probing of glucose influx into cancer cell metabolism: using adjuvant and a pH-dependent collection of central metabolites to improve in-cell D-DNP NMR

Changes to metabolism are a hallmark of many diseases. Disease metabolism under physiological conditions can be probed in real time with in-cell NMR assays. Here, we pursued a systematic approach towards improved in-cell NMR assays. Unambiguous identifications of metabolites and of intracellular pH are afforded by a comprehensive, downloadable collection of spectral data for central carbon metabolites in the physiological pH range (4.0-8.0). Chemical shifts of glycolytic intermediates provide unique pH dependent patterns akin to a barcode. Using hyperpolarized 13C1 enriched glucose as the probe molecule of central metabolism in cancer, we find that early glycolytic intermediates are detectable in PC-3 prostate cancer cell lines, concurrently yielding intracellular pH. Using non-enriched and non-enhanced pyruvate as an adjuvant, reactions of the pentose phosphate pathway become additionally detectable, without significant changes to the barriers in upper glycolysis and to intracellular pH. The scope of tracers for in-cell observations can thus be improved by the presence of adjuvants, showing that a recently proposed effect of pyruvate in the tumor environment is paralleled by a rerouting of cancer cell metabolism towards producing building blocks for proliferation. Overall, the combined use of reference data for compound identification, site specific labelling for reducing overlap, and use of adjuvant afford increasingly detailed insight into disease metabolism.

Comparison of the Metabolic Profile between Unstimulated and Stimulated Saliva Samples from Pregnant Women with/without Obesity and Periodontitis

This study aimed to compare the metabolic profile of unstimulated (US) and stimulated (SS) saliva samples from pregnant women with/without obesity and periodontitis. Ninety-six pregnant women were divided into: obesity + periodontitis (OP = 20); obesity/no periodontitis (OWP = 27); normal BMI + periodontitis (NP = 20); and normal BMI/no periodontitis (NWP = 29). US and SS samples were collected by expectoration and chewing of sterilized parafilm gum, respectively, and samples were individually analyzed by Proton Nuclear Magnetic Resonance (1H-NMR). Univariate (t test and correlations) and multivariate (Principal Component Analysis-PCA, and Partial Least Square-Discriminant Analysis-PLS-DA with Variance Importance Projection-VIP scores) and Metabolite Set Enrichment Analysis were done (p < 0.05). Metabolites commonly found in all groups in elevated concentration in US samples were 5-Aminopentoate, Acetic acid, Butyric acid, Propionic acid, Pyruvic acid, and Succinic acid. They were mainly related to the butyrate metabolism, citric acid cycle, amino sugar metabolism, fatty acids biosynthesis, pyruvate metabolism, glutamate metabolism, and Warburg effect. Metabolites commonly found in all groups that were in elevated concentration in SS samples were Citrulline, Fumaric acid, Histidine, N-acetyl glutamine, N-acetylneuraminic acid, para-hydroxyphenylacetic acid, Proline, Tyrosine. Although some differences were found between unstimulated and stimulated saliva samples from pregnant women with/without obesity and periodontitis, stimulated saliva collection seems adequate, demonstrating similar metabolic pathways to unstimulated saliva samples when groups are compared.

Targeted Compound Selection with Increased Sensitivity in 13C-Enriched Biological and Environmental Samples Using 13C-DREAMTIME in Both High-Field and Low-Field NMR

Chemical characterization of complex mixtures by Nuclear Magnetic Resonance (NMR) spectroscopy is challenging due to a high degree of spectral overlap and inherently low sensitivity. Therefore, NMR experiments that reduce overlap and increase signal intensity hold immense potential for the analysis of mixtures such as biological and environmental media. Here, we introduce a ¹³C version of DREAMTIME (Designed Refocused Excitation And Mixing for Targets In Vivo and Mixture Elucidation) NMR, which, when analyzing ¹³C-enriched materials, allows the user to selectively detect only the compound(s) of interest and remove all other peaks in a ¹³C spectrum. Selected peaks can additionally be "focused" into sharp "spikes" to increase sensitivity. ¹³C-DREAMTIME is first demonstrated at high field strength (500 MHz) with simultaneous selection of eight amino acids in a ¹³C-enriched cell free amino acid mixture and of six metabolites in an extract of ¹³C-enriched green algae and demonstrated at low field strength (80 MHz) with a standard solution of ¹³C-d-glucose and ¹³C-l-phenylalanine. ¹³C-DREAMTIME is then applied at high-field to analyze metabolic changes in ¹³C-enrichedDaphnia magna after exposure to polystyrene "microplastics," as well as at low-field to track fermentation of ¹³C-d-glucose using wine yeast. Ultimately, ¹³C-DREAMTIME reduces spectral overlap as only selected compounds are recorded, resulting in the detection of analyte peaks that may otherwise not have been discernable. In combination with focusing, up to a 6-fold increase in signal intensity can be obtained for a given peak. ¹³C-DREAMTIME is a promising experiment type for future reaction monitoring and for tracking metabolic processes with ¹³C-enriched compounds.

Investigation of synthesized ethyl-(2-(5-arylidine-2,4-dioxothiazolidin-3-yl) acetyl) butanoate as an effective corrosion inhibitor for mild steel in 1 M HCl: A gravimetric, electrochemical, and spectroscopic study

The corrosion inhibition effects of five concentrations (5E-5 M to 9E-5 M) of ethyl-(2-(5-arylidine-2,4-dioxothiazolidin-3-yl) acetyl) butanoate, a novel thiazolidinedione derivative code named B1, were investigated on mild steel in 1 M HCl using gravimetric analysis, electrochemical analysis and Fourier transform infrared spectroscopy. After synthesis and purification, B1 was characterized using nuclear magnetic resonance spectroscopy. All gravimetric analysis experiments were carried out at four different temperatures: 303.15 K, 313.15 K, 323.15 K and 333.15 K, achieving a maximum percentage inhibition efficiency of 92% at 303.15 K. The maximum percentage inhibition efficiency obtained from electrochemical analysis, conducted at 303.15 K, was 83%. Thermodynamic parameters such as ΔG°_ads showed that B1 adsorbs onto the MS surface via a mixed type of action at lower temperatures, transitioning to exclusively chemisorption at higher temperatures.

Metabolic rewiring in MYC-driven medulloblastoma by BET-bromodomain inhibition

Medulloblastoma (MB) is the most common malignant brain tumour in children. High-risk MB patients harbouring MYC amplification or overexpression exhibit a very poor prognosis. Aberrant activation of MYC markedly reprograms cell metabolism to sustain tumorigenesis, yet how metabolism is dysregulated in MYC-driven MB is not well understood. Growing evidence unveiled the potential of BET-bromodomain inhibitors (BETis) as next generation agents for treating MYC-driven MB, but whether and how BETis may affect tumour cell metabolism to exert their anticancer activities remains unknown. In this study, we explore the metabolic features characterising MYC-driven MB and examine how these are altered by BET-bromodomain inhibition. To this end, we employed an NMR-based metabolomics approach applied to the MYC-driven MB D283 and D458 cell lines before and after the treatment with the BETi OTX-015. We found that OTX-015 triggers a metabolic shift in both cell lines resulting in increased levels of myo-inositol, glycerophosphocholine, UDP-N-acetylglucosamine, glycine, serine, pantothenate and phosphocholine. Moreover, we show that OTX-015 alters ascorbate and aldarate metabolism, inositol phosphate metabolism, phosphatidylinositol signalling system, glycerophospholipid metabolism, ether lipid metabolism, aminoacyl-tRNA biosynthesis, and glycine, serine and threonine metabolism pathways in both cell lines. These insights provide a metabolic characterisation of MYC-driven childhood MB cell lines, which could pave the way for the discovery of novel druggable pathways. Importantly, these findings will also contribute to understand the downstream effects of BETis on MYC-driven MB, potentially aiding the development of new therapeutic strategies to combat medulloblastoma.

Practical Aspects of NMR-Based Metabolomics

While NMR-based metabolomics is only about 20 years old, NMR has been a key part of metabolic and metabolism studies for &gt;40 years. Historically, metabolic researchers used NMR because of its high level of reproducibility, superb instrument stability, facile sample preparation protocols, inherently quantitative character, non-destructive nature, and amenability to automation. In this chapter, we provide a short history of NMR-based metabolomics. We then provide a detailed description of some of the practical aspects of performing NMR-based metabolomics studies including sample preparation, pulse sequence selection, and spectral acquisition and processing. The two different approaches to metabolomics data analysis, targeted vs. untargeted, are briefly outlined. We also describe several software packages to help users process NMR spectra obtained via these two different approaches. We then give several examples of useful or interesting applications of NMR-based metabolomics, ranging from applications to drug toxicology, to identifying inborn errors of metabolism to analyzing the contents of biofluids from dairy cattle. Throughout this chapter, we will highlight the strengths and limitations of NMR-based metabolomics. Additionally, we will conclude with descriptions of recent advances in NMR hardware, methodology, and software and speculate about where NMR-based metabolomics is going in the next 5-10 years.

Metabolomics in Cell Biology

Metabolomics has long been used in a biomedical context. The most typical samples are body fluids in which small molecules can be detected and quantified using technologies such as Nuclear Magnetic Resonance (NMR) and Mass Spectrometry (MS). Many studies, in particular in the wider field of cancer research, are based on cellular models. Different cancer cells can have vastly different ways of regulating metabolism and responses to drug treatments depend on specific metabolic mechanisms which are often cell type specific. This has led to a series of publications using metabolomics to study metabolic mechanisms. Cell-based metabolomics has specific requirements and allows for interesting approaches where metabolism is followed in real-time. Here applications of metabolomics in cell biology have been reviewed, providing insight into specific technologies used and showing exemplary case studies with an emphasis towards applications which help to understand drug mechanisms.

Metabolomic Profiles Associated with Obesity and Periodontitis during Pregnancy: Cross-Sectional Study with Proton Nuclear Magnetic Resonance (1H-NMR)-Based Analysis

This study aimed to elucidate the metabolomic signature associated with obesity and periodontitis during pregnancy in plasma and saliva biofluids. Ninety-eight pregnant women were divided into: with obesity and periodontitis (OP = 20), with obesity but without periodontitis (OWP = 27), with normal BMI but with periodontitis (NP = 21), with normal BMI and without periodontitis (NWP = 30). Saliva and plasma were analyzed by 1H-NMR for metabolites identification. Partial Least Squares-Discriminant Analysis (PLS-DA), Sparse PLS-DA (sPLS-DA), and Variable Importance of Projection (VIP) were performed. ANOVA and Pearson’s correlation were applied (p < 0.05). Plasmatic analysis indicated the levels of glucose (p = 0.041) and phenylalanine (p = 0.015) were positively correlated with periodontal parameters and BMI, respectively. In saliva, periodontitis was mainly associated with high levels of acetic acid (p = 0.024), isovaleric acid, butyric acid, leucine, valine, isoleucine, and propionic acid (p < 0.001). High salivary concentrations of glycine (p = 0.015), succinic acid (p = 0.015), and lactate (p = 0.026) were associated with obesity. Saliva demonstrated a more elucidative difference than plasma, indicating the glucose-alanine cycle, alanine metabolism, valine, leucine and isoleucine degradation, glutamate metabolism, and Warburg effect as the main metabolic pathways.

Fine optimization of a dissolution dynamic nuclear polarization experimental setting for 13C NMR of metabolic samples

NMR-based analysis of metabolite mixtures provides crucial information on biological systems but mostly relies on 1D 1 H experiments for maximizing sensitivity. However, strong peak overlap of 1 H spectra often is a limitation for the analysis of inherently complex biological mixtures. Dissolution dynamic nuclear polarization (d-DNP) improves NMR sensitivity by several orders of magnitude, which enables 13 C NMR-based analysis of metabolites at natural abundance. We have recently demonstrated the successful introduction of d-DNP into a full untargeted metabolomics workflow applied to the study of plant metabolism. Here we describe the systematic optimization of d-DNP experimental settings for experiments at natural 13 C abundance and show how the resolution, sensitivity, and ultimately the number of detectable signals improve as a result. We have systematically optimized the parameters involved (in a semi-automated prototype d-DNP system, from sample preparation to signal detection, aiming at providing an optimization guide for potential users of such a system, who may not be experts in instrumental development). The optimization procedure makes it possible to detect previously inaccessible protonated 13 C signals of metabolites at natural abundance with at least 4 times improved line shape and a high repeatability compared to a previously reported d-DNP-enhanced untargeted metabolomic study. This extends the application scope of hyperpolarized 13 C NMR at natural abundance and paves the way to a more general use of DNP-hyperpolarized NMR in metabolomics studies.

Implementing High Q-Factor HTS Resonators to Enhance Probe Sensitivity in 13C NMR Spectroscopy

Nuclear magnetic resonance spectroscopy (NMR) probes using thin-film high temperature superconducting (HTS) resonators provide exceptional mass sensitivity in small-sample NMR experiments for natural products chemistry and metabolomics. We report improvements in sensitivity to our 1.5 mm 13C-optimized NMR probe based on HTS resonators. The probe has a sample volume of 35 microliters and operates in a 14.1 T magnet. The probe also features HTS resonators for 1H transmission and detection and the 2H lock. The probe utilizes a 13C resonator design that provides greater efficiency than our previous design. The quality factor of the new resonator in the 14.1 T background field was measured to be 4,300, which is over 3x the value of the previous design. To effectively implement the improved quality factor, we demonstrate the effect of adding a shorted transmission line stub to increase the bandwidth and reduce the rise/fall time of 13C irradiation pulses. Initial NMR measurements verify 13C NMR sensitivity is significantly improved while preserving detection bandwidth. The probe will be used for applications in metabolomics.

Exploring the Applications of Carbon-Detected NMR in Living and Dead Organisms Using a 13C-Optimized Comprehensive Multiphase NMR Probe

Comprehensive multiphase-nuclear magnetic resonance (CMP-NMR) is a non-invasive approach designed to observe all phases (solutions, gels, and solids) in intact samples using a single NMR probe. Studies of dead and living organisms are important to understand processes ranging from biological growth to environmental stress. Historically, such studies have utilized ¹H-based phase editing for the detection of soluble/swollen components and ¹H-detected 2D NMR for metabolite assignments/screening. However, living organisms require slow spinning rates (∼500 Hz) to increase survivability, but at such low speeds, complications from water sidebands and spectral overlap from the modest chemical shift window (∼0-10 ppm) make ¹H NMR challenging. Here, a novel ¹³C-optimized E-Free magic angle spinning CMP probe is applied to study all phases in ex vivo and in vivo samples. This probe consists of a two-coil design, with an inner single-tuned ¹³C coil providing a 113% increase in ¹³C sensitivity relative to a traditional multichannel single-CMP coil design. For organisms with a large biomass (∼0.1 g) like the Ganges River sprat (ex vivo), ¹³C-detected full spectral editing and ¹³C-detected heteronuclear correlation (HETCOR) can be performed at natural abundance. Unfortunately, for a single living shrimp (∼2 mg), ¹³C enrichment was still required, but ¹³C-detected HETCOR shows superior data relative to heteronuclear single-quantum coherence at low spinning speeds (due to complications from water sidebands in the latter). The probe is equipped with automatic-tuning-matching and is compatible with automated gradient shimming─a key step toward conducting multiphase screening of dead and living organisms under automation in the near future.

Application of Counter-wound Multi-arm Spirals in HTS Resonator Design

Significant sensitivity improvements have been achieved by utilizing high temperature superconducting (HTS) resonators in nuclear magnetic resonance (NMR) probes. Many nuclei such as ¹³C benefit from strong excitation fields which cannot be produced by traditional HTS resonator designs. We investigate the use of double-sided, counter-wound multi-arm spiral HTS resonators with the aim of increasing the excitation field at the required nuclear Larmor frequency for ¹³C. When compared to double-sided, counter-wound spiral resonators with similar geometry, simulations indicate that the multi-arm spiral version develops a more uniform current distribution. Preliminary tests of a two-arm resonator indicate that it may produce a stronger excitation field.

Fluxomics - New Metabolomics Approaches to Monitor Metabolic Pathways

Fluxomics is an innovative -omics research field that measures the rates of all intracellular fluxes in the central metabolism of biological systems. Fluxomics gathers data from multiple different -omics fields, portraying the whole picture of molecular interactions. Recently, fluxomics has become one of the most relevant approaches to investigate metabolic phenotypes. Metabolic flux using 13C-labeled molecules is increasingly used to monitor metabolic pathways, to probe the corresponding gene-RNA and protein-metabolite interaction networks in actual time. Thus, fluxomics reveals the functioning of multi-molecular metabolic pathways and is increasingly applied in biotechnology and pharmacology. Here, we describe the main fluxomics approaches and experimental platforms. Moreover, we summarize recent fluxomic results in different biological systems.

NMR Spectroscopy Identifies Chemicals in Cigarette Smoke Condensate That Impair Skeletal Muscle Mitochondrial Function

Tobacco smoke-related diseases such as chronic obstructive pulmonary disease (COPD) are associated with high healthcare burden and mortality rates. Many COPD patients were reported to have muscle atrophy and weakness, with several studies suggesting intrinsic muscle mitochondrial impairment as a possible driver of this phenotype. Whereas much information has been learned about muscle pathology once a patient has COPD, little is known about how active tobacco smoking might impact skeletal muscle physiology or mitochondrial health. In this study, we examined the acute effects of cigarette smoke condensate (CSC) on muscle mitochondrial function and hypothesized that toxic chemicals present in CSC would impair mitochondrial respiratory function. Consistent with this hypothesis, we found that acute exposure of muscle mitochondria to CSC caused a dose-dependent decrease in skeletal muscle mitochondrial respiratory capacity. Next, we applied an analytical nuclear magnetic resonance (NMR)-based approach to identify 49 water-soluble and 12 lipid-soluble chemicals with high abundance in CSC. By using a chemical screening approach in the Seahorse XF96 analyzer, several CSC-chemicals, including nicotine, o-Cresol, phenylacetate, and decanoic acid, were found to impair ADP-stimulated respiration in murine muscle mitochondrial isolates significantly. Further to this, several chemicals, including nicotine, o-Cresol, quinoline, propylene glycol, myo-inositol, nitrosodimethylamine, niacinamide, decanoic acid, acrylonitrile, 2-naphthylamine, and arsenic acid, were found to significantly decrease the acceptor control ratio, an index of mitochondrial coupling efficiency.

Metabolomics on the study of marine organisms

BACKGROUND

Marine ecosystems are hosts to a vast array of organisms, being among the most richly biodiverse locations on the planet. The study of these ecosystems is very important, as they are not only a significant source of food for the world but also have, in recent years, become a prolific source of compounds with therapeutic potential. Studies of aspects of marine life have involved diverse fields of marine science, and the use of metabolomics as an experimental approach has increased in recent years. As part of the "omics" technologies, metabolomics has been used to deepen the understanding of interactions between marine organisms and their environment at a metabolic level and to discover new metabolites produced by these organisms.

AIM OF REVIEW

This review provides an overview of the use of metabolomics in the study of marine organisms. It also explores the use of metabolomics tools common to other fields such as plants and human metabolomics that could potentially contribute to marine organism studies. It deals with the entire process of a metabolomic study, from sample collection considerations, metabolite extraction, analytical techniques, and data analysis. It also includes an overview of recent applications of metabolomics in fields such as marine ecology and drug discovery and future perspectives of its use in the study of marine organisms.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review covers all the steps involved in metabolomic studies of marine organisms including, collection, extraction methods, analytical tools, statistical analysis, and dereplication. It aims to provide insight into all aspects that a newcomer to the field should consider when undertaking marine metabolomics.

Extending the Scope of 1H NMR-Based Blood Metabolomics for the Analysis of Labile Antioxidants: Reduced and Oxidized Glutathione

Glutathione is a ubiquitous cellular antioxidant, which is critically required to protect cells from oxidative damage and free radical injury. It is practically impossible to analyze glutathione in its native form after isolation from biological mixtures since the active form (reduced glutathione, GSH) spontaneously gets converted to the oxidized form (oxidized glutathione, GSSG). To address this challenge, numerous highly sensitive detection methods, including mass spectrometry, have been used in conjunction with derivatization to block the oxidation of GSH. Efforts so far to quantitate GSH and GSSG using the nuclear magnetic resonance (NMR) spectroscopy method have remained unsuccessful. With a focus on addressing this challenge, in this study, we describe an extension to our recent whole blood analysis method [ Anal. Chem. 2017, 89, 4620-4627] that includes the important antioxidants GSH and GSSG. Fresh and frozen human whole blood specimens as well as standard GSH and GSSG were comprehensively investigated using NMR without and with derivatization using N-ethylmaleimide (NEM). NMR experiments detect two diastereomers, distinctly, for the derivatized GSH and enable the analysis of both GSH and GSSG in human whole blood with an accuracy of >99%. Interestingly, the excess (unreacted) NEM used for blocking the GSH can be removed from the samples during a drying step after extraction, with no need for additional processing. This is an important characteristic that offers an added advantage for simultaneous analysis of the antioxidants (GSH and GSSG), redox coenzymes (oxidized nicotinamide adenine dinucleotide (NAD⁺), reduced nicotinamide adenine dinucleotide (NADH), oxidized nicotinamide adenine dinucleotide phosphate (NADP⁺), reduced nicotinamide adenine dinucleotide phosphate (NADPH)), energy coenzymes (adenosine 5'-triphosphate (ATP), adenosine 5'-diphosphate (ADP), adenosine 5'-monophosphate (AMP)), and a large number of other blood metabolites using the same one-dimensional (1D) NMR spectrum. The presented method broadens the scope of global metabolite profiling and adds a new dimension to NMR-based blood metabolomics. Further, the method demonstrated here for human blood can be extended to virtually any biological specimen.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Progress Towards a Higher Sensitivity 13C-Optimized 1.5 mm HTS NMR Probe

Nuclear magnetic resonance (NMR) probes using thin-film high temperature superconducting (HTS) resonators offer high sensitivity and are particularly suitable for small-sample applications. We are developing an improved 1.5 mm HTS NMR probe designed for operation at 14.1 T and optimized for 13C detection. The total sample volume is about 35 μL and the active sample volume is 20 μL. The probe employs HTS resonators for 13C and 1H transmission and detection and the 2H lock. We examine the interactions of multiple superconducting resonators and normal metal tuning loops on coil resonance frequency and probe sensitivity. We test a recently introduced 13C resonator design, engineered to significantly increase 13C detection sensitivity over previous all-HTS probes. At zero field, we observe a 13C quality factor of 6000 which is several times higher than previous resonators. In this work the coil design considerations and probe build-out procedure are discussed.

Hyperpolarized NMR Metabolomics at Natural 13C Abundance

Metabolomics plays a pivotal role in systems biology, and NMR is a central tool with high precision and exceptional resolution of chemical information. Most NMR metabolomic studies are based on 1H 1D spectroscopy, severely limited by peak overlap. 13C NMR benefits from a larger signal dispersion but is barely used in metabolomics due to ca. 6000-fold lower sensitivity. We introduce a new approach, based on hyperpolarized 13C NMR at natural abundance, that circumvents this limitation. A new untargeted NMR-based metabolomic workflow based on dissolution dynamic nuclear polarization (d-DNP) for the first time enabled hyperpolarized natural abundance 13C metabolomics. Statistical analysis of resulting hyperpolarized 13C data distinguishes two groups of plant (tomato) extracts and highlights biomarkers, in full agreement with previous results on the same biological model. We also optimize parameters of the semiautomated d-DNP system suitable for high-throughput studies.

Metabolomic Approaches to Study Chemical Exposure-Related Metabolism Alterations in Mammalian Cell Cultures

Biological organisms are constantly exposed to an immense repertoire of molecules that cover environmental or food-derived molecules and drugs, triggering a continuous flow of stimuli-dependent adaptations. The diversity of these chemicals as well as their concentrations contribute to the multiplicity of induced effects, including activation, stimulation, or inhibition of physiological processes and toxicity. Metabolism, as the foremost phenotype and manifestation of life, has proven to be immensely sensitive and highly adaptive to chemical stimuli. Therefore, studying the effect of endo- or xenobiotics over cellular metabolism delivers valuable knowledge to apprehend potential cellular activity of individual molecules and evaluate their acute or chronic benefits and toxicity. The development of modern metabolomics technologies such as mass spectrometry or nuclear magnetic resonance spectroscopy now offers unprecedented solutions for the rapid and efficient determination of metabolic profiles of cells and more complex biological systems. Combined with the availability of well-established cell culture techniques, these analytical methods appear perfectly suited to determine the biological activity and estimate the positive and negative effects of chemicals in a variety of cell types and models, even at hardly detectable concentrations. Metabolic phenotypes can be estimated from studying intracellular metabolites at homeostasis in vivo, while in vitro cell cultures provide additional access to metabolites exchanged with growth media. This article discusses analytical solutions available for metabolic phenotyping of cell culture metabolism as well as the general metabolomics workflow suitable for testing the biological activity of molecular compounds. We emphasize how metabolic profiling of cell supernatants and intracellular extracts can deliver valuable and complementary insights for evaluating the effects of xenobiotics on cellular metabolism. We note that the concepts and methods discussed primarily for xenobiotics exposure are widely applicable to drug testing in general, including endobiotics that cover active metabolites, nutrients, peptides and proteins, cytokines, hormones, vitamins, etc.

Phosphorus NMR and Its Application to Metabolomics

Stable isotopes are routinely employed by NMR metabolomics to highlight specific metabolic processes and to monitor pathway flux. 13C-carbon and 15N-nitrogen labeled nutrients are convenient sources of isotope tracers and are commonly added as supplements to a variety of biological systems ranging from cell cultures to animal models. Unlike 13C and 15N, 31P-phosphorus is a naturally abundant and NMR active isotope that does not require an external supplemental source. To date, 31P NMR has seen limited usage in metabolomics because of a lack of reference spectra, difficulties in sample preparation, and an absence of two-dimensional (2D) NMR experiments, but 31P NMR has the potential of expanding the coverage of the metabolome by detecting phosphorus-containing metabolites. Phosphorylated metabolites regulate key cellular processes, serve as a surrogate for intracellular pH conditions, and provide a measure of a cell's metabolic energy and redox state, among other processes. Thus, incorporating 31P NMR into a metabolomics investigation will enable the detection of these key cellular processes. To facilitate the application of 31P NMR in metabolomics, we present a unified protocol that allows for the simultaneous and efficient detection of 1H-, 13C-, 15N-, and 31P-labeled metabolites. The protocol includes the application of a 2D 1H-31P HSQC-TOCSY experiment to detect 31P-labeled metabolites from heterogeneous biological mixtures, methods for sample preparation to detect 1H-, 13C-, 15N-, and 31P-labeled metabolites from a single NMR sample, and a data set of one-dimensional (1D) 31P NMR and 2D 1H-31P HSQC-TOCSY spectra of 38 common phosphorus-containing metabolites to assist in metabolite assignments.

Consecutive Queries to Assess Biological Correlation in NMR Metabolomics: Performance of Comprehensive Search of Multiplets over Typical 1D 1H NMR Database Search

NMR-based metabolomics requires proper identification of metabolites to draw conclusions from the system under study. Normally, multivariate data analysis is performed using 1D ¹H NMR spectra, and identification of peaks (and then compounds) relevant to the classification is accomplished using database queries as a first step. 1D ¹H NMR spectra of complex mixtures often suffer from peak overlap. To overcome this issue, several studies employed the projections of the (tilted and symmetrized) 2D ¹H J-resolved (JRES) spectra, p-JRES, which are similar to 1D ¹H decoupled spectra. Nonetheless, there are no public databases available that allow searching for chemical shift spectral data for multiplets. We present the Chemical Shift Multiplet Database (CSMDB), built utilizing JRES spectra obtained from the Birmingham Metabolite Library. The CSMDB provides scoring accounting for both matched and unmatched peaks from a query list and the database hits. This input list is generated from a projection of a 2D statistical correlation analysis on the JRES spectra, p-(JRES-STOCSY), being able to compare the multiplets for the matched peaks, in essence, the f1 traces from the JRES-STOCSY spectrum and from the database hit. The inspection of the unmatched peaks for the database hit allows the retrieval of peaks in the query list that have a decreased correlation coefficient due to low intensities. The CSMDB is coupled to "ConQuer ABC", which permits the assessment of biological correlation by means of consecutive queries with the unmatched peaks in the first and subsequent queries.

NMR-based metabolomics and fluxomics: developments and future prospects

Recent NMR developments are acting as game changers for metabolomics and fluxomics – a critical and perspective review.

NMR Spectroscopy-Based Metabolic Profiling of Biospecimens

Metabolomics refers to study of metabolites in biospecimens such as blood serum, tissues, and urine. Nuclear magnetic resonance (NMR) spectroscopy and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS; mass spectrometry coupled with liquid chromatography) are most frequently employed to analyze complex biological/clinical samples. NMR is a relatively insensitive tool compared to UPLC-MS/MS but offers straightforward quantification and identification and easy sample processing. One-dimensional ¹ H NMR spectroscopy is inherently quantitative and can be readily used for metabolite quantification without individual metabolite standards. Two-dimensional spectroscopy is most commonly used for identification of metabolites but can also be used quantitatively. Although NMR experiments are unbiased regarding the chemical nature of the analyte, it is crucial to adhere to the proper metabolite extraction protocol for optimum results. Selection and implementation of appropriate NMR pulse programs are also important. Finally, employment of the correct metabolite quantification strategy is crucial as well. In this unit, step-by-step guidance for running an NMR metabolomics experiment from typical biospecimens is presented. The unit describes an optimized metabolite extraction protocol, followed by implementation of NMR experiments and quantification strategies using the so-called "targeted profiling" technique. This approach relies on an underlying basis set of metabolite spectra acquired under similar conditions. Some strategies for statistical analysis of the data are also presented. Overall, this set of protocols should serve as a guide for anyone who wishes to enter the world of NMR-based metabolomics analysis. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: Metabolite extraction from different biospecimens Basic Protocol 2: Preparation of dried upper fraction for NMR analysis Alternate Protocol: Preparation of urine samples for NMR analysis Basic Protocol 3: NMR experiments Basic Protocol 4: Spectral processing and quantification of metabolites Basic Protocol 5: Statistical analysis of the data.

Challenges in Identifying the Dark Molecules of Life

Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.

Age-dependent changes in metabolite profile and lipid saturation in dystrophic mice

Duchenne Muscular Dystrophy (DMD) is a fatal X-linked genetic disorder. In DMD, the absence of the dystrophin protein causes decreased sarcolemmal integrity resulting in progressive replacement of muscle with fibrofatty tissue. The effects of lacking dystrophin on muscle and systemic metabolism are still unclear. Therefore, to determine the impact of the absence of dystrophin on metabolism, we investigated the metabolic and lipid profile at two different, well-defined stages of muscle damage and stabilization in mdx mice. We measured NMR-detectable metabolite and lipid profiles in the serum and muscles of mdx mice at 6 and 24 weeks of age. Metabolites were determined in muscle in vivo using ¹ H MRI/MRS, in isolated muscles using ¹ H-HR-MAS NMR, and in serum using high resolution ¹ H/¹³ C NMR. Dystrophic mice were found to have a unique lipid saturation profile compared with control mice, revealing an age-related metabolic change. In the 6-week-old mdx mice, serum lipids were increased and the degree of lipid saturation changed between 6 and 24 weeks. The serum taurine-creatine ratio increased over the life span of mdx, but not in control mice. Furthermore, the saturation index of lipids increased in the serum but decreased in the tissue over time. Finally, we demonstrated associations between MRI-T₂ , a strong indicator of inflammation/edema, with tissue and serum lipid profiles. These results indicate the complex temporal changes of metabolites in the tissue and serum during repetitive bouts of muscle damage and regeneration that occur in dystrophic muscle.

Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures

NMR and LC-MS are two powerful techniques for metabolomics studies. In NMR spectra and LC-MS data collected on a series of metabolite mixtures, signals of the same individual metabolite are quantitatively correlated, based on the fact that NMR and LC-MS signals are derived from the same metabolite covary. Deconvoluting NMR spectra and LC-MS data of the mixtures through this kind of statistical correlation, NMR and LC-MS spectra of individual metabolites can be obtained as if the specific metabolite is virtually isolated from the mixture. Integrating NMR and LC-MS spectra, more abundant and orthogonal information on the same compound can significantly facilitate the identification of individual metabolites in the mixture. This strategy was demonstrated by deconvoluting 1D ¹³C, DEPT, HSQC, TOCSY, and LC-MS spectra acquired on 10 mixtures consisting of 6 typical metabolites with varying concentration. Based on statistical correlation analysis, NMR and LC-MS signals of individual metabolites in the mixtures can be extracted as if their spectra are acquired on the purified metabolite, which notably facilitates structure identification. Statistically correlating NMR spectra and LC-MS data (CoNaM) may represent a novel approach to identification of individual compounds in a mixture. The success of this strategy on the synthetic metabolite mixtures encourages application of the proposed strategy of CoNaM to biological samples (such as serum and cell extracts) in metabolomics studies to facilitate identification of potential biomarkers.

Extending the Scope of 1H NMR Spectroscopy for the Analysis of Cellular Coenzyme A and Acetyl Coenzyme A

Coenzyme A (CoA) and acetyl-coenzyme A (acetyl-CoA) are ubiquitous cellular molecules, which mediate hundreds of anabolic and catabolic reactions including energy metabolism. Highly sensitive methods including absorption spectroscopy and mass spectrometry enable their analysis, albeit with many limitations. To date, however, NMR spectroscopy has not been used to analyze these important molecules. Building on our recent efforts, which enabled simultaneous analysis of a large number of metabolites in tissue and blood including many coenzymes and antioxidants ( Anal. Chem. 2016, 88, 4817-24; ibid 2017, 89, 4620-4627), we describe here a new method for identification and quantitation of CoA and acetyl-CoA ex vivo in tissue. Using mouse heart, kidney, liver, brain, and skeletal tissue, we show that a simple ¹H NMR experiment can simultaneously measure these molecules. Identification of the two species involved a comprehensive analysis of the different tissue types using 1D and 2D NMR, in combination with spectral databases for standards, as well as spiking with authentic compounds. Time dependent studies showed that while the acetyl-CoA levels remain unaltered, CoA levels diminish by more than 50% within 24 h, which indicates that CoA is labile in solution; however, degassing the sample with helium gas halted its oxidation. Further, interestingly, we also identified endogenous coenzyme A glutathione disulfide (CoA-S-S-G) in tissue for the first time by NMR and show that CoA, when oxidized in tissue extract, also forms the same disulfide metabolite. The ability to simultaneously visualize absolute concentrations of CoA, acetyl-CoA, and endogenous CoA-S-S-G along with redox coenzymes (NAD⁺, NADH, NADP⁺, NADPH), energy coenzymes (ATP, ADP, AMP), antioxidants (GSH, GSSG), and a vast pool of other metabolites using a single 1D NMR spectrum offers a new avenue in the metabolomics field for investigation of cellular function in health and disease.

Practical Guidelines for 13C-Based NMR Metabolomics

We present an overview of ¹³C-based NMR metabolomics. At first glance, the low sensitivity of ¹³C relative to ¹H NMR might seem like too great an obstacle to use this approach. However, there are several advantages to ¹³C NMR, whether samples can be isotopically enriched or not. At natural abundance, peaks are sharp and largely resolved, and peak frequencies are more stable to pH and other sample conditions. Statistical approaches can be used to obtain C-C and C-H correlation maps, which greatly aid in compound identification. With ¹³C isotopic enrichment, other experiments are possible, including both ¹³C-J-RES and INADEQUATE, which can be used for de novo identification of metabolites not in databases.NMR instrumentation and software has significantly improved, and probes are now commercially available that can record useful natural abundance 1D ¹³C spectra from real metabolomics samples in 2 h or less. Probe technology continues to improve, and the next generation should be even better. Combined with new methods of simultaneous data acquisition, which allows for two or more 1D or 2D NMR experiments to be collected using multiple receivers, very rich datasets can be collected in a reasonable amount of time that should improve metabolomics data analysis and compound identification.

Profiling Redox and Energy Coenzymes in Whole Blood, Tissue and Cells Using NMR Spectroscopy

Coenzymes of cellular redox reactions and cellular energy, as well as antioxidants mediate biochemical reactions fundamental to the functioning of all living cells. Conventional analysis methods lack the opportunity to evaluate these important redox and energy coenzymes and antioxidants in a single step. Major coenzymes include redox coenzymes: NAD⁺ (oxidized nicotinamide adenine dinucleotide), NADH (reduced nicotinamide adenine dinucleotide), NADP⁺ (oxidized nicotinamide adenine dinucleotide phosphate) and NADPH (reduced nicotinamide adenine dinucleotide phosphate); energy coenzymes: ATP (adenosine triphosphate), ADP (adenosine diphosphate) and AMP (adenosine monophosphate); and antioxidants: GSSG (oxidized glutathione) and GSH (reduced glutathione). We show here that a simple ¹H NMR experiment can measure these coenzymes and antioxidants in tissue and whole blood apart from a vast pool of other metabolites. In addition, focused on the goal of identification of coenzymes in subcellular fractions, we demonstrate analysis of coenzymes in the cytoplasm using breast cancer cells. Owing to their unstable nature, or low concentrations, most of the coenzymes either evade detection or lose their integrity when established sample preparation and analysis methods are used. To overcome this challenge, here we describe the development of new methods to detect these molecules without affecting the integrity of other metabolites. We used an array of 1D and 2D NMR methods, chemical shift databases, pH measurements and spiking with authentic compounds to establish the identity of peaks for the coenzymes and antioxidants in NMR spectra. Interestingly, while none of the coenzymes and antioxidants were detected in plasma, they were abundant in whole blood. Considering that the coenzymes and antioxidants represent a sensitive measure of human health and risk for numerous diseases, the presented NMR methods to measure them in one step potentially open new opportunities in the metabolomics field.

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.

Advances in Computational Analysis of Metabolomic NMR Data

In this chapter we discuss some of the more recent developments in preprocessing and statistical analysis of NMR spectra in metabolomics. Bayesian methods for analyzing NMR spectra are summarized and we describe one particular approach, BATMAN, in more detail. We consider techniques based on statistical associations, such as correlation spectroscopy (e.g. STOCSY and recent variants), as well as approaches that model the associations as a network and how these change under different biological conditions. The link between metabolism and genotype is explored by looking at metabolic GWAS and related techniques. Finally, we describe the relevance and current status of data standards for NMR metabolomics.

Fast Metabolite Identification in Nuclear Magnetic Resonance Metabolomic Studies: Statistical Peak Sorting and Peak Overlap Detection for More Reliable Database Queries

A lot of time is spent by researchers in the identification of metabolites in NMR-based metabolomic studies. The usual metabolite identification starts employing public or commercial databases to match chemical shifts thought to belong to a given compound. Statistical total correlation spectroscopy (STOCSY), in use for more than a decade, speeds the process by finding statistical correlations among peaks, being able to create a better peak list as input for the database query. However, the (normally not automated) analysis becomes challenging due to the intrinsic issue of peak overlap, where correlations of more than one compound appear in the STOCSY trace. Here we present a fully automated methodology that analyzes all STOCSY traces at once (every peak is chosen as driver peak) and overcomes the peak overlap obstacle. Peak overlap detection by clustering analysis and sorting of traces (POD-CAST) first creates an overlap matrix from the STOCSY traces, then clusters the overlap traces based on their similarity and finally calculates a cumulative overlap index (COI) to account for both strong and intermediate correlations. This information is gathered in one plot to help the user identify the groups of peaks that would belong to a single molecule and perform a more reliable database query. The simultaneous examination of all traces reduces the time of analysis, compared to viewing STOCSY traces by pairs or small groups, and condenses the redundant information in the 2D STOCSY matrix into bands containing similar traces. The COI helps in the detection of overlapping peaks, which can be added to the peak list from another cross-correlated band. POD-CAST overcomes the generally overlooked and underestimated presence of overlapping peaks and it detects them to include them in the search of all compounds contributing to the peak overlap, enabling the user to accelerate the metabolite identification process with more successful database queries and searching all tentative compounds in the sample set.

The future perspective: metabolomics in laboratory medicine for inborn errors of metabolism

Metabolomics can be described as a simultaneous and comprehensive analysis of small molecules in a biological sample. Recent technological and bioinformatics advances have facilitated large-scale metabolomic studies in many areas, including inborn errors of metabolism (IEMs). Despite significant improvements in the diagnosis and treatment of some IEMs, it is still challenging to understand how genetic variation affects disease progression and susceptibility. In addition, a search for new more personalized therapies and a growing demand for tools to monitor the long-term metabolic effects of existing therapies set the stage for metabolomics integration in preclinical and clinical studies. While targeted metabolomics approach is a common practice in biochemical genetics laboratories for biochemical diagnosis and monitoring of IEMs, applications of untargeted metabolomics in the clinical laboratories are still in infancy, facing some challenges. It is however, expected in the future to dramatically change the scope and utility of the clinical laboratory playing a significant role in patient management. This review provides an overview of targeted and global, large-scale metabolomic studies applied to investigate various IEMs. We discuss an existing and prospective clinical applications of metabolomics in IEMs for better diagnosis and deep understanding of complex metabolic perturbations associated with the etiology of inherited metabolic disorders.

Computer-Aided 13C NMR Chemical Profiling of Crude Natural Extracts without Fractionation

A computer-aided, ¹³C NMR-based dereplication method is presented for the chemical profiling of natural extracts without any fractionation. An algorithm was developed in order to compare the ¹³C NMR chemical shifts obtained from a single routine spectrum with a set of predicted NMR data stored in a natural metabolite database. The algorithm evaluates the quality of the matching between experimental and predicted data by calculating a score function and returns the list of metabolites that are most likely to be present in the studied extract. The proof of principle of the method is demonstrated on a crude alkaloid extract obtained from the leaves of Peumus boldus, resulting in the identification of eight alkaloids, including isocorydine, rogersine, boldine, reticuline, coclaurine, laurotetanine, N-methylcoclaurine, and norisocorydine, as well as three monoterpenes, namely, p-cymene, eucalyptol, and α-terpinene. The results were compared to those obtained with other methods, either involving a fractionation step before the chemical profiling process or using mass spectrometry detection in the infusion mode or coupled to gas chromatography.

Chiral Recognition by Dissolution DNP NMR Spectroscopy of 13C-Labeled dl-Methionine

A method based on d-DNP NMR spectroscopy to study chiral recognition is described for the first time. The enantiodifferentiation of a racemic metabolite in a millimolar aqueous solution using a chiral solvating agent was performed. Hyperpolarized ¹³C-labeled dl-methionine enantiomers were differently observed with a single-scan ¹³C NMR experiment, while the chiral auxiliary at thermal equilibrium remained unobserved. The method developed entails a step forward in the chiral recognition of small molecules by NMR spectroscopy, opening new possibilities in situations where the sensitivity is limited, for example, when a low concentration of analyte is available or when the measurement of an insensitive nucleus, like ¹³C, is required. The advantages and current limitations of the method, as well as future perspectives, are discussed.

Challenges and perspectives in quantitative NMR

This perspective article summarizes, from the author's point of view at the beginning of 2016, the major challenges and perspectives in the field of quantitative NMR. The key concepts in quantitative NMR are first summarized; then, the most recent evolutions in terms of resolution and sensitivity are discussed, as well as some potential future research directions in this field. A particular focus is made on methodologies capable of boosting the resolution and sensitivity of quantitative NMR, which could open application perspectives in fields where the sample complexity and the analyte concentrations are particularly challenging. These include multi-dimensional quantitative NMR and hyperpolarization techniques such as para-hydrogen-induced polarization or dynamic nuclear polarization. Because quantitative NMR cannot be dissociated from the key concepts of analytical chemistry, i.e. trueness and precision, the methodological developments are systematically described together with their level of analytical performance. Copyright © 2016 John Wiley & Sons, Ltd.

Novel selective TOCSY method enables NMR spectral elucidation of metabolomic mixtures

Complex mixture analysis is routinely encountered in NMR-based investigations. With the aim of component identification, spectral complexity may be addressed chromatographically or spectroscopically, the latter being favored to reduce sample handling requirements. An attractive experiment is selective total correlation spectroscopy (sel-TOCSY), which is capable of providing tremendous spectral simplification and thereby enhancing assignment capability. Unfortunately, isolating a well resolved resonance is increasingly difficult as the complexity of the mixture increases and the assumption of single spin system excitation is no longer robust. We present TOCSY optimized mixture elucidation (TOOMIXED), a technique capable of performing spectral assignment particularly in the case where the assumption of single spin system excitation is relaxed. Key to the technique is the collection of a series of 1D sel-TOCSY experiments as a function of the isotropic mixing time (τ_m), resulting in a series of resonance intensities indicative of the underlying molecular structure. By comparing these τ_m-dependent intensity patterns with a library of pre-determined component spectra, one is able to regain assignment capability. After consideration of the technique's robustness, we tested TOOMIXED firstly on a model mixture. As a benchmark we were able to assign a molecule with high confidence in the case of selectively exciting an isolated resonance. Assignment confidence was not compromised when performing TOOMIXED on a resonance known to contain multiple overlapping signals, and in the worst case the method suggested a follow-up sel-TOCSY experiment to confirm an ambiguous assignment. TOOMIXED was then demonstrated on two realistic samples (whisky and urine), where under our conditions an approximate limit of detection of 0.6mM was determined. Taking into account literature reports for the sel-TOCSY limit of detection, the technique should reach on the order of 10μM sensitivity. We anticipate this technique will be highly attractive to various analytical fields facing mixture analysis, including metabolomics, foodstuff analysis, pharmaceutical analysis, and forensics.

The future of NMR-based metabolomics

The two leading analytical approaches to metabolomics are mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. Although currently overshadowed by MS in terms of numbers of compounds resolved, NMR spectroscopy offers advantages both on its own and coupled with MS. NMR data are highly reproducible and quantitative over a wide dynamic range and are unmatched for determining structures of unknowns. NMR is adept at tracing metabolic pathways and fluxes using isotope labels. Moreover, NMR is non-destructive and can be utilized in vivo. NMR results have a proven track record of translating in vitro findings to in vivo clinical applications.

Highly Repeatable Dissolution Dynamic Nuclear Polarization for Heteronuclear NMR Metabolomics

At natural (13)C abundance, metabolomics based on heteronuclear NMR is limited by sensitivity. We have recently demonstrated how hyperpolarization by dissolution dynamic nuclear polarization (D-DNP) assisted by cross-polarization (CP) provides a reliable way of enhancing the sensitivity of heteronuclear NMR in dilute mixtures of metabolites. In this Technical Note, we evaluate the precision of this experimental approach, a critical point for applications to metabolomics. The higher the repeatability, the greater the likelihood that one can detect small biologically relevant differences between samples. The average repeatability of our state-of-the-art D-DNP NMR equipment for samples of metabolomic relevance (20 mg dry weight tomato extracts) is 3.6% for signals above the limit of quantification (LOQ) and 6.4% when all the signals above the limit of detection (LOD) are taken into account. This first report on the repeatability of D-DNP highlights the compatibility of the technique with the requirements of metabolomics and confirms its potential as an analytical tool for such applications.