An overview of methods using (13)C for improved compound identification in metabolomics and natural products

Automated Assignment of 15N And 13C Enrichment Levels in Doubly-Labeled Proteins

Uniform enrichment of 15N and 13C in proteins is commonly employed for 2D heteronuclear NMR measurements of the three-dimensional protein structure. Achieving a high degree of enrichment of both elements is important for obtaining high quality data. Uniform labeling of proteins and glycoproteins expressed in higher organisms (yeast or mammalian cell lines) is more challenging than expression in Escherichia coli, a prokaryote that grows on simple, chemically defined media but does not provide appropriate eukaryotic modifications. It is difficult to achieve complete incorporation of both heavy isotopes, and quality control measures are important for quantitating the level of their enrichment. Mass spectrometry measurements of the isotopic distribution of the intact protein or its proteolytic fragments provide the means to assess the enrichment level. A mass accuracy of 1 ppm or better is shown to be required to distinguish the correct combination of 13C and 15N enrichment due to subtle shifts in peak centroids with differences in the underlying, but unresolved, isotopic fine structure. A simple computer program was developed to optimize the fitting of experimental isotope patterns to statistically derived distributions. This method can determine the isotopic abundance from isotope patterns and isotopologue masses in conventional MS data for peptides, intact proteins, and glycans. For this purpose, MATLAB's isotope simulator, isotopicdist, has been modified to permit the variation of 15N and 13C enrichment levels and to perform a two-dimensional grid search of enrichment levels of both isotopes. We also incorporated an alternate isotope simulator, js-emass, into MATLAB for use in the same fitting program. Herein we benchmark this technique on natural abundance ubiquitin and uniformly [15N,13C]-labeled ubiquitin at both the intact and peptide level, outline considerations for data quality and mass accuracy, and report several improvements we have made to the previously reported analysis of the [15N,13C]-enriched human IgG Fc domain, a glycoprotein that has been expressed in Saccharomyces cerevisiae.

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.

An IROA Workflow for correction and normalization of ion suppression in mass spectrometry-based metabolomic profiling data

Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and signal-to-noise sensitivity. Here we report a new method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) plus novel companion algorithms to 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We have evaluated the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reverse phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibited ion suppression ranging from 1% to 90+% and coefficient of variations ranging from 1% to 20%, but the Workflow and companion algorithms were highly effective at nulling out that suppression and error. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.

Dissolved organic metabolite extraction from high-salt media

We describe considerations and strategies for developing a nuclear magnetic resonance (NMR) sample preparation method to extract low molecular weight metabolites from high-salt spent media in a model coculture system of phytoplankton and marine bacteria. Phytoplankton perform half the carbon fixation and oxygen generation on Earth. A substantial fraction of fixed carbon becomes part of a metabolite pool of small molecules known as dissolved organic matter (DOM), which are taken up by marine bacteria proximate to phytoplankton. There is an urgent need to elucidate these metabolic exchanges due to widespread anthropogenic transformations on the chemical, phenotypic, and species composition of seawater. These changes are increasing water temperature and the amount of CO₂ absorbed by the ocean at energetic costs to marine microorganisms. Little is known about the metabolite-mediated, structured interactions occurring between phytoplankton and associated marine bacteria, in part because of challenges in studying high-salt solutions on various analytical platforms. NMR analysis is problematic due to the high-salt content of both natural seawater and culture media for marine microbes. High-salt concentration degrades the performance of the radio frequency coil, reduces the efficiency of some pulse sequences, limits signal-to-noise, and prolongs experimental time. The method described herein can reproducibly extract low molecular weight DOM from small-volume, high-salt cultures. It is a promising tool for elucidating metabolic flux between marine microorganisms and facilitates genetic screens of mutant microorganisms.

Ambient temperature CO2 fixation to pyruvate and subsequently to citramalate over iron and nickel nanoparticles

The chemical reactions that formed the building blocks of life at origins required catalysts, whereby the nature of those catalysts influenced the type of products that accumulated. Recent investigations have shown that at 100 °C awaruite, a Ni3Fe alloy that naturally occurs in serpentinizing systems, is an efficient catalyst for CO2 conversion to formate, acetate, and pyruvate. These products are identical with the intermediates and products of the acetyl-CoA pathway, the most ancient CO2 fixation pathway and the backbone of carbon metabolism in H2-dependent autotrophic microbes. Here, we show that Ni3Fe nanoparticles prepared via the hard-templating method catalyze the conversion of H2 and CO2 to formate, acetate and pyruvate at 25 °C under 25 bar. Furthermore, the 13C-labeled pyruvate can be further converted to acetate, parapyruvate, and citramalate over Ni, Fe, and Ni3Fe nanoparticles at room temperature within one hour. These findings strongly suggest that awaruite can catalyze both the formation of citramalate, the C5 product of pyruvate condensation with acetyl-CoA in microbial carbon metabolism, from pyruvate and the formation of pyruvate from CO2 at very moderate reaction conditions without organic catalysts. These results align well with theories for an autotrophic origin of microbial metabolism under hydrothermal vent conditions.

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 >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.

Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics

The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.

Isotopic Tracer for Absolute Quantification of Metabolites of the Pentose Phosphate Pathway in Bacteria

The pentose phosphate pathway (PPP) plays a key role in many metabolic functions, including the generation of NADPH, biosynthesis of nucleotides, and carbon homeostasis. In particular, the intermediates of PPP have been found to be significantly perturbed in bacterial metabolomic studies. Nonetheless, detailed analysis to gain mechanistic information of PPP metabolism remains limited as most studies are unable to report on the absolute levels of the metabolites. Absolute quantification of metabolites is a prerequisite to study the details of fluxes and its regulations. Isotope tracer or labeling studies are conducted in vivo and in vitro and have significantly improved the analysis and understanding of PPP. Due to the laborious procedure and limitations in the in vivo method, an in vitro approach known as Group Specific Internal Standard Technology (GSIST) has been successfully developed to measure the absolute levels of central carbon metabolism, including PPP. The technique adopts derivatization of an experimental sample and a corresponding internal standard with isotope-coded reagents to provide better precision for accurate identification and absolute quantification. In this review, we highlight bacterial studies that employed isotopic tracers as the tagging agents used for the absolute quantification analysis of PPP metabolites.

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.

NMR and Metabolomics-A Roadmap for the Future

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

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.

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.

Quantum Chemistry Calculations for Metabolomics

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

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.

Enhanced Metabolome Coverage and Evaluation of Matrix Effects by the Use of Experimental-Condition-Matched 13C-Labeled Biological Samples in Isotope-Assisted LC-HRMS Metabolomics

Stable isotope-assisted approaches can improve untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally ¹³C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve the detection of treatment-relevant metabolites and can aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly ¹³C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched ¹³C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.

Multi-class analysis of 46 antimicrobial drug residues in pond water using UHPLC-Orbitrap-HRMS and application to freshwater ponds in Flanders, Belgium

Increasing anthropogenic pressure and agricultural pollution raises concerns regarding antimicrobial resistance and biodiversity loss in aquatic environments. In order to protect and restore water resources and biodiversity, antimicrobial drug residues should be monitored in all aquatic environments including pond water. Consequently, the objective of this research was to develop and validate a novel multi-residue method for the simultaneous quantification of 46 targeted human and veterinary antimicrobial drugs in pond water. A suitable extraction method based on solid-phase extraction (SPE) was developed, assisted by a fractional factorial design. A broad polarity range of compounds was covered (log P from -4.05 to 4.38), including major representatives of the following classes: sulfonamides, tetracyclines, quinolones, macrolides, lincosamides, nitrofurans, penicillins, cephalosporins, diaminopyrimidines, pleuromutilins and phenicols. All analytes were separated using ultra-high performance liquid chromatography (UHPLC) and detected in full-scan by Orbitrap high resolution mass spectrometry (Orbitrap-HRMS). Good linearity was obtained for all compounds with R² ≥ 0.993 and goodness-of-fit coefficient (g) ≤ 11.56%. Method detection limits ranged from 10 to 50 ng L^-1 and method quantification limits were 50 ng L^-1 for all compounds. Acceptable values were obtained for within-day and between-day apparent recoveries (i.e. between 50 and 120%), precision (< 30% and < 45%) and measurement uncertainty (< 50%). Targeted analysis of 18 freshwater ponds throughout Flanders was performed to demonstrate the applicability of the newly developed UHPLC-HRMS method. Overall, 20 antimicrobial drugs were detected with highest concentrations observed for tetracyclines and their transformation products ranging between 51 and 248 ng L^-1. Finally, suspect screening was performed suggesting the presence of 14 additional pharmaceuticals including 3 antimicrobial degradation products (e.g. apo-oxytetracycline, amoxicillin penicilloic acid and penilloic acid) and 11 pesticides.

A versatile synthetic route to the preparation of 15 N heterocycles

A robust medium-scale (approximately 3 g) synthetic method for 15 N labeling of pyridine (15 N-Py) is reported based on the Zincke reaction. 15 N enrichment in excess of 81% was achieved with approximately 33% yield. 15 N-Py serves as a standard substrate in a wide range of studies employing a hyperpolarization technique for efficient polarization transfer from parahydrogen to heteronuclei; this technique, called SABRE (signal amplification by reversible exchange), employs a simultaneous chemical exchange of parahydrogen and a to-be-hyperpolarized substrate (e.g., pyridine) on metal centers. In studies aimed at the development of hyperpolarized contrast agents for in vivo molecular imaging, pyridine is often employed either as a model substrate (for hyperpolarization technique development, quality assurance, and phantom imaging studies) or as a co-substrate to facilitate more efficient hyperpolarization of a wide range of emerging contrast agents (e.g., nicotinamide). Here, the produced 15 N-Py was used for the feasibility study of spontaneous 15 N hyperpolarization at high magnetic (HF) fields (7 T and 9.4 T) of an NMR spectrometer and an MRI scanner. SABRE hyperpolarization enabled acquisition of 2D MRI imaging of catalyst-bound 15 N-pyridine with 75 × 75 mm2 field of view (FOV), 32 × 32 matrix size, demonstrating the feasibility of 15 N HF-SABRE molecular imaging with 2.4 × 2.4 mm2 spatial resolution.

From genomics to metabolomics, moving toward an integrated strategy for the discovery of fungal secondary metabolites

Fungal secondary metabolites are defined by bioactive properties that ensure adaptation of the fungus to its environment. Although some of these natural products are promising sources of new lead compounds especially for the pharmaceutical industry, others pose risks to human and animal health. The identification of secondary metabolites is critical to assessing both the utility and risks of these compounds. Since fungi present biological specificities different from other microorganisms, this review covers the different strategies specifically used in fungal studies to perform this critical identification. Strategies focused on the direct detection of the secondary metabolites are firstly reported. Particularly, advances in high-throughput untargeted metabolomics have led to the generation of large datasets whose exploitation and interpretation generally require bioinformatics tools. Then, the genome-based methods used to study the entire fungal metabolic potential are reported. Transcriptomic and proteomic tools used in the discovery of fungal secondary metabolites are presented as links between genomic methods and metabolomic experiments. Finally, the influence of the culture environment on the synthesis of secondary metabolites by fungi is highlighted as a major factor to consider in research on fungal secondary metabolites. Through this review, we seek to emphasize that the discovery of natural products should integrate all of these valuable tools. Attention is also drawn to emerging technologies that will certainly revolutionize fungal research and to the use of computational tools that are necessary but whose results should be interpreted carefully.

Isotopic ratio outlier analysis improves metabolomics prediction of nitrogen treatment in maize

We evaluated Isotope Ratio Outlier Analysis (IROA) as a metabolome-wide internal standard approach to improve the quality of LC/MS data collected from a large-scale greenhouse experiment designed to metric the ability of metabolomics to model quantitatively nitrogen treatments. We further looked at how IROA would be incorporated into a metabolomics workflow. For this we compared IROA processed data with that generated without the benefit of metabolome-wide internal standards using our current tool, Genedata Expressionist, from the same raw LC/MS data files. In our experiment, 367 maize plants were grown from kernel in a greenhouse under controlled conditions. Plants were treated from germination on with varying concentrations of nutrient nitrogen as one (treatment) variable. A second variable was the presence of one of two transgenes. Metabolomics analysis of leaves was performed by LC/MS positive and negative electrospray ionization modes, and raw data were processed with both our routine and IROA protocols. IROA data analysis detected 184 metabolites in each ionization mode. Analysis without IROA yielded 281 metabolites in positive ionization mode and 172 in negative ionization mode. Data from both protocols were normalized for sample dry weight, location in the greenhouse, extraction batch, sample run order, and internal standard. Normalized results were subjected to partial least squares (PLS) analysis to model the relationship between the metabolome and nitrogen treatment. Without IROA, regression coefficients of 0.819 and 0.849 for positive and negative modes, respectively were achieved. The IROA protocol improved on the values, yielding regression coefficients of 0.876 and 0.879 for positive and negative modes, respectively. In addition, IROA corrected for detector saturation for several high abundant peaks. Our experiment demonstrates that incorporating IROA into an LC/MS metabolomics experiment improves data quality and facilitates more precise modeling of a biological response.

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.

Use of isotopically labeled substrates reveals kinetic differences between human and bacterial serine palmitoyltransferase

Isotope labels are frequently used tools to track metabolites through complex biochemical pathways and to discern the mechanisms of enzyme-catalyzed reactions. Isotopically labeled l-serine is often used to monitor the activity of the first enzyme in sphingolipid biosynthesis, serine palmitoyltransferase (SPT), as well as labeling downstream cellular metabolites. Intrigued by the effect that isotope labels may be having on SPT catalysis, we characterized the impact of different l-serine isotopologues on the catalytic activity of recombinant SPT isozymes from humans and the bacterium Sphingomonas paucimobilis Our data show that S. paucimobilis SPT activity displays a clear isotope effect with [2,3,3-D]l-serine, whereas the human SPT isoform does not. This suggests that although both human and S. paucimobilis SPT catalyze the same chemical reaction, there may well be underlying subtle differences in their catalytic mechanisms. Our results suggest that it is the activating small subunits of human SPT that play a key role in these mechanistic variations. This study also highlights that it is important to consider the type and location of isotope labels on a substrate when they are to be used in in vitro and in vivo studies.

13C Labeling of Nematode Worms to Improve Metabolome Coverage by Heteronuclear Nuclear Magnetic Resonance Experiments

Nuclear magnetic resonance (NMR) spectroscopy is widely used as a metabolomics tool, and 1D spectroscopy is overwhelmingly the commonest approach. The use of 2D spectroscopy could offer significant advantages in terms of increased spectral dispersion of peaks, but has a number of disadvantages—in particular, heteronuclear 2D spectroscopy is often much less sensitive than 1D NMR. One factor contributing to this low sensitivity in ¹³C/¹H heteronuclear NMR is the low natural abundance of the ¹³C stable isotope; as a consequence, where it is possible to label biological material with ¹³C, there is a potential enhancement of sensitivity of up to around 90fold. However, there are some problems that can reduce the advantages otherwise gained—in particular, the fine structure arising from ¹³C/¹³C coupling, which is essentially non-existent at natural abundance, can reduce the possible sensitivity gain and increase the chances of peak overlap. Here, we examined the use of two different heteronuclear single quantum coherence (HSQC) pulse sequences for the analysis of fully ¹³C-labeled tissue extracts from Caenorhabditis elegans nematodes. The constant time ct-HSQC had improved peak shape, and consequent better peak detection of metabolites from a labeled extract; matching this against reference spectra from the HMDB gave a match to about 300 records (although fewer actual metabolites, as some of these represent false positive matches). This approach gives a rapid and automated initial metabolome assignment, forming an ideal basis for further manual curation.

1D "Spikelet" Projections from Heteronuclear 2D NMR Data-Permitting 1D Chemometrics While Preserving 2D Dispersion

Nuclear magnetic resonance (NMR) spectroscopy is a powerful tool for the non-targeted metabolomics of intact biofluids and even living organisms. However, spectral overlap can limit the information that can be obtained from 1D 1H NMR. For example, magnetic susceptibility broadening in living organisms prevents any metabolic information being extracted from solution-state 1D 1H NMR. Conversely, the additional spectral dispersion afforded by 2D 1H-13C NMR allows a wide range of metabolites to be assigned in-vivo in 13C enriched organisms, as well as a greater depth of information for biofluids in general. As such, 2D 1H-13C NMR is becoming more and more popular for routine metabolic screening of very complex samples. Despite this, there are only a very limited number of statistical software packages that can handle 2D NMR datasets for chemometric analysis. In comparison, a wide range of commercial and free tools are available for analysis of 1D NMR datasets. Overtime, it is likely more software solutions will evolve that can handle 2D NMR directly. In the meantime, this application note offers a simple alternative solution that converts 2D 1H-13C Heteronuclear Single Quantum Correlation (HSQC) data into a 1D “spikelet” format that preserves not only the 2D spectral information, but also the 2D dispersion. The approach allows 2D NMR data to be converted into a standard 1D Bruker format that can be read by software packages that can only handle 1D NMR data. This application note uses data from Daphnia magna (water fleas) in-vivo to demonstrate how to generate and interpret the converted 1D spikelet data from 2D datasets, including the code to perform the conversion on Bruker spectrometers.

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.

Enhanced Isotopic Ratio Outlier Analysis (IROA) Peak Detection and Identification with Ultra-High Resolution GC-Orbitrap/MS: Potential Application for Investigation of Model Organism Metabolomes

Identifying non-annotated peaks may have a significant impact on the understanding of biological systems. In silico methodologies have focused on ESI LC/MS/MS for identifying non-annotated MS peaks. In this study, we employed in silico methodology to develop an Isotopic Ratio Outlier Analysis (IROA) workflow using enhanced mass spectrometric data acquired with the ultra-high resolution GC-Orbitrap/MS to determine the identity of non-annotated metabolites. The higher resolution of the GC-Orbitrap/MS, together with its wide dynamic range, resulted in more IROA peak pairs detected, and increased reliability of chemical formulae generation (CFG). IROA uses two different ¹³C-enriched carbon sources (randomized 95% ¹²C and 95% ¹³C) to produce mirror image isotopologue pairs, whose mass difference reveals the carbon chain length (n), which aids in the identification of endogenous metabolites. Accurate m/z, n, and derivatization information are obtained from our GC/MS workflow for unknown metabolite identification, and aids in silico methodologies for identifying isomeric and non-annotated metabolites. We were able to mine more mass spectral information using the same Saccharomyces cerevisiae growth protocol (Qiu et al. Anal. Chem 2016) with the ultra-high resolution GC-Orbitrap/MS, using 10% ammonia in methane as the CI reagent gas. We identified 244 IROA peaks pairs, which significantly increased IROA detection capability compared with our previous report (126 IROA peak pairs using a GC-TOF/MS machine). For 55 selected metabolites identified from matched IROA CI and EI spectra, using the GC-Orbitrap/MS vs. GC-TOF/MS, the average mass deviation for GC-Orbitrap/MS was 1.48 ppm, however, the average mass deviation was 32.2 ppm for the GC-TOF/MS machine. In summary, the higher resolution and wider dynamic range of the GC-Orbitrap/MS enabled more accurate CFG, and the coupling of accurate mass GC/MS IROA methodology with in silico fragmentation has great potential in unknown metabolite identification, with applications for characterizing model organism networks.

Plant metabolism as studied by NMR spectroscopy

The study of plant metabolism impacts a broad range of domains such as plant cultural practices, plant breeding, human or animal nutrition, phytochemistry and green biotechnologies. Plant metabolites are extremely diverse in terms of structure or compound families as well as concentrations. This review attempts to illustrate how NMR spectroscopy, with its broad variety of experimental approaches, has contributed widely to the study of plant primary or specialized metabolism in very diverse ways. The review presents recent developments of one-dimensional and multi-dimensional NMR methods to study various aspects of plant metabolism. Through recent examples, it highlights how NMR has proved to be an invaluable tool for the global characterization of sample composition within metabolomic studies, and shows some examples of use for targeted phytochemistry, with a special focus on compound identification and quantitation. In such cases, NMR approaches are often used to provide snapshots of the plant sample composition. The review also covers dynamic aspects of metabolism, with a description of NMR techniques to measure metabolic fluxes - in most cases after stable isotope labelling. It is mainly intended for NMR specialists who would be interested to learn more about the potential of their favourite technique in plant sciences and about specific details of NMR approaches in this field. Therefore, as a practical guide, a paragraph on the specific precautions that should be taken for sample preparation is also included. In addition, since the quality of NMR metabolic studies is highly dependent on approaches to data processing and data sharing, a specific part is dedicated to these aspects. The review concludes with perspectives on the emerging methods that could change significantly the role of NMR in the field of plant metabolism by boosting its sensitivity. The review is illustrated throughout with examples of studies selected to represent diverse applications of liquid-state or HR-MAS NMR.

Bacterial Substrate Transformation Tracked by Stable-Isotope-Guided NMR Metabolomics: Application in a Natural Aquatic Microbial Community

The transformation of organic substrates by heterotrophic bacteria in aquatic environments constitutes one of the key processes in global material cycles. The development of procedures that would enable us to track the wide range of organic compounds transformed by aquatic bacteria would greatly improve our understanding of material cycles. In this study, we examined the applicability of nuclear magnetic resonance spectroscopy coupled with stable-isotope labeling to the investigation of metabolite transformation in a natural aquatic bacterial community. The addition of a model substrate (¹³C₆–glucose) to a coastal seawater sample and subsequent incubation resulted in the detection of >200 peaks and the assignment of 22 metabolites from various chemical classes, including amino acids, dipeptides, organic acids, nucleosides, nucleobases, and amino alcohols, which had been identified as transformed from the ¹³C₆–glucose. Additional experiments revealed large variability in metabolite transformation and the key compounds, showing the bacterial accumulation of glutamate over the incubation period, and that of 3-hydroxybutyrate with increasing concentrations of ¹³C₆–glucose added. These results suggest the potential ability of our approach to track substrate transformation in aquatic bacterial communities. Further applications of this procedure may provide substantial insights into the metabolite dynamics in aquatic environments.

Whole Blood Metabolomics by 1H NMR Spectroscopy Provides a New Opportunity To Evaluate Coenzymes and Antioxidants

Conventional human blood metabolomics employs serum or plasma and provides a wealth of metabolic information therein. However, this approach lacks the ability to measure and evaluate important metabolites such as coenzymes and antioxidants that are present at high concentrations in red blood cells. As an important alternative to serum/plasma metabolomics, we show here that a simple 1H NMR experiment can simultaneously measure coenzymes and antioxidants in extracts of whole human blood, in addition to the nearly 70 metabolites that were shown to be quantitated in serum/plasma recently [ Anal. Chem. 2015 , 87 , 706 - 715 ]. Coenzymes of redox reactions: oxidized/reduced nicotinamide adenine dinucleotide (NAD+ and NADH) and nicotinamide adenine dinucleotide phosphate (NADP+ and NADPH); coenzymes of energy including adenosine triphosphate (ATP), adenosine diphosphate (ADP), and adenosine monophosphate (AMP); and antioxidants, the sum of oxidized and reduced glutathione (GSSG and GSH) can be measured with essentially no additional effort. A new method was developed for detecting many of these unstable species without affecting other blood/blood plasma metabolites. The identities of coenzymes and antioxidants in blood NMR spectra were established combining 1D/2D NMR techniques, chemical shift databases, pH measurements and, finally, spiking with authentic compounds. This is the first study to report identification of major coenzymes and antioxidants and quantify them, simultaneously, with the large pool of other metabolites in human blood using NMR spectroscopy. Considering that the levels of coenzymes and antioxidants represent a sensitive measure of cellular functions in health and numerous diseases, the NMR method presented here potentially opens a new chapter in the metabolomics of blood.

Real-Time Analysis of Tenofovir Release Kinetics Using Quantitative Phosphorus (31P) Nuclear Magnetic Resonance Spectroscopy

The dialysis method is classically used for drug separation before analysis, but does not provide direct and real-time drug quantification and has limitations affecting the dialysis rate. In this study, a phosphorus nuclear magnetic resonance (³¹P-qNMR) method is developed for the real-time quantification of therapeutic molecules in vitro. The release kinetics of model drug, tenofovir (anti-HIV microbicide), was analyzed in vaginal fluid simulant (VFS), seminal fluid simulant (SFS), and human plasma (HP) from chitosan nanofibers (size ∼100-200 nm) using the NMR (direct) method and compared with dialysis/UV-Vis (indirect) method. The assay was linear in VFS/SFS (0.20-5.0 mM), HP (0.30-5.0 mM of drug concentration range) and specific no drug ³¹P-qNMR chemical shift [∼15 ppm] interference with formulation/media components. Limit of detection values were 0.075/0.10/0.20 mM, whereas limit of quantification values were 0.20/0.20/0.30 mM in VFS/SFS/HP, respectively. The method was robust, precise (%RSE <2%), and accurate (%mean recovery 90%-110%). After 12 h, ∼77%/72%/70% wt/wt of tenofovir release was observed with direct, compared to ∼47%/52%/52% wt/wt by indirect method in VFS/SFS/HP, respectively. Approximately 20% decrease in %drug release observed with dialysis method suggested an interference with drug transport process due to the dialysis membrane and the Gibbs-Donnan effect. Overall, ³¹P-qNMR provides more accurate, real-time, and direct drug quantification for effective in vitro-in vivo correlation.

How close are we to complete annotation of metabolomes?

The metabolome describes the full complement of the tens to hundreds of thousands of low molecular weight metabolites present within a biological system. Identification of the metabolome is critical for discovering the maximum amount of biochemical knowledge from metabolomics datasets. Yet no exhaustive experimental characterisation of any organismal metabolome has been reported to date, dramatically contrasting with the genome sequencing of thousands of plants, animals and microbes. Here, we review the status of metabolome annotation and describe advances in the analytical methodologies being applied. In part through new international coordination, we conclude that we are now entering a new era of metabolome annotation.

NMR window of molecular complexity showing homeostasis in superorganisms

NMR offers tremendous advantages in the analyses of molecular complexity. The “big-data” are produced during the acquisition of fingerprints that must be stored and shared for posterior analysis and verifications.

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in 92% of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0.

Natural products from aquatic eukaryotic microorganisms for cancer therapy: Perspectives on anti-tumour properties of ciliate bioactive molecules

Several modern drugs, including those for cancer therapy, have been isolated from natural sources, are based on natural products and its derivatives, or mime natural products. Some of them are in clinical use, others in clinical trials. The success of natural products in drug discovery is related to their biochemical characteristics and to the technologic methods used to study their feature. Natural compounds may acts as chemo-preventive agents and as factors that increase therapeutic efficacy of existing drugs, thus overcoming cancer cell drug resistance that is the main factor determining the failure in conventional chemotherapy. Water environment, because of its physical and chemical conditions, shows an extraordinary collection of natural biological substances with an extensive structural and functional diversity. The isolation of bioactive molecules has been reported from a great variety of aquatic organisms; however, the therapeutic application of molecules from eukaryotic microorganisms remains inadequately investigated and underexploited on a systematic basis. Herein we describe the biological activities in mammalian cells of selected substances isolated from ciliates, free-living protozoa common almost everywhere there is water, focusing on their anti-tumour actions and their possible therapeutic activity. In particular, we unveil the cellular and molecular machine mediating the effects of cell type-specific signalling protein pheromone Er-1 and secondary metabolites, i.e. euplotin C and climacostol, in cancer cells. To support the feasibility of climacostol-based approaches, we also present novel findings and report additional mechanisms of action using both in vitro and in vivo models of mouse melanomas, with the scope of highlighting new frontiers that can be explored also in a therapeutic perspective. The high skeletal chemical difference of ciliate compounds, their sustainability and availability, also through the use of new organic synthesis/modifications processes, and the results obtained so far in biological studies provide a rationale to consider some of them a potential resource for the design of new anti-cancer drugs.

Recent advances in stable isotope-enabled mass spectrometry-based plant metabolomics

Methods employing isotope labeled compounds have been an important part of the bioanalytical canon for many decades. The past fifteen years have seen the development of many new approaches using stable (non-radioactive) isotopes as labels for high-throughput bioanalytical, 'omics-scale' measurements of metabolites (metabolomics) and proteins (proteomics). This review examines stable isotopic labeling approaches that have been developed for labeling whole intact plants, plant tissues, or crude extracts of plant materials with stable isotopes (mainly using ²H, ¹³C, ¹⁵N, ¹⁸O or ³⁴S). The application of metabolome-scale labeling for improving metabolite annotation, metabolic pathway elucidation, and relative quantification in mass spectrometry-based metabolomics of plants is also reviewed.

Modern mass spectrometry for synthetic biology and structure-based discovery of natural products

Covering: up to 2016In this highlight, we describe the current landscape for dereplication and discovery of natural products based on the measurement of the intact mass by LC-MS. Often it is assumed that because better mass accuracy (provided by higher resolution mass spectrometers) is necessary for absolute chemical formula determination (≤1 part-per-million), that it is also necessary for dereplication of natural products. However, the average ability to dereplicate tapers off at ∼10 ppm, with modest improvement gained from better mass accuracy when querying focused databases of natural products. We also highlight some recent examples of how these platforms are applied to synthetic biology, and recent methods for dereplication and correlation of substructures using tandem MS data. We also offer this highlight to serve as a brief primer for those entering the field of mass spectrometry-based natural products discovery.

ADAP-GC 3.0: Improved Peak Detection and Deconvolution of Co-eluting Metabolites from GC/TOF-MS Data for Metabolomics Studies

ADAP-GC is an automated computational pipeline for untargeted, GC/MS-based metabolomics studies. It takes raw mass spectrometry data as input and carries out a sequence of data processing steps including construction of extracted ion chromatograms, detection of chromatographic peak features, deconvolution of coeluting compounds, and alignment of compounds across samples. Despite the increased accuracy from the original version to version 2.0 in terms of extracting metabolite information for identification and quantitation, ADAP-GC 2.0 requires appropriate specification of a number of parameters and has difficulty in extracting information on compounds that are in low concentration. To overcome these two limitations, ADAP-GC 3.0 was developed to improve both the robustness and sensitivity of compound detection. In this paper, we report how these goals were achieved and compare ADAP-GC 3.0 against three other software tools including ChromaTOF, AnalyzerPro, and AMDIS that are widely used in the metabolomics community.

Structure and Metabolic-Flow Analysis of Molecular Complexity in a (13) C-Labeled Tree by 2D and 3D NMR

Improved signal identification for biological small molecules (BSMs) in a mixture was demonstrated by using multidimensional NMR on samples from (13) C-enriched Rhododendron japonicum (59.5 atom%) cultivated in air containing (13) C-labeled carbon dioxide for 14 weeks. The resonance assignment of 386 carbon atoms and 380 hydrogen atoms in the mixture was achieved. 42 BSMs, including eight that were unlisted in the spectral databases, were identified. Comparisons between the experimental values and the (13) C chemical shift values calculated by density functional theory supported the identifications of unlisted BSMs. Tracing the (13) C/(12) C ratio by multidimensional NMR spectra revealed faster and slower turnover ratios of BSMs involved in central metabolism and those categorized as secondary metabolites, respectively. The identification of BSMs and subsequent flow analysis provided insight into the metabolic systems of the plant.

Isotopic Ratio Outlier Analysis of the S. cerevisiae Metabolome Using Accurate Mass Gas Chromatography/Time-of-Flight Mass Spectrometry: A New Method for Discovery

Isotopic ratio outlier analysis (IROA) is a (13)C metabolomics profiling method that eliminates sample to sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass liquid chromatography/mass spectrometry (LC/MS). This is the first report using IROA technology in combination with accurate mass gas chromatography/time-of-flight mass spectrometry (GC/TOF-MS), here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% (13)C, or 5%(13)C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose. When these IROA experiments are combined, the abundance of the heavy isotopologues in the 5%(13)C extracts, or light isotopologues in the 95%(13)C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the (12)C monoisotopic and the (13)C monoisotopic equals the number of carbons in the molecules. The IROA-GC/MS protocol developed, using both chemical and electron ionization, extends the information acquired from the isotopic peak patterns for formulas generation. The process that can be formulated as an algorithm, in which the number of carbons, as well as the number of methoximations and silylations are used as search constraints. In electron impact (EI/IROA) spectra, the artifactual peaks are identified and easily removed, which has the potential to generate "clean" EI libraries. The combination of chemical ionization (CI) IROA and EI/IROA affords a metabolite identification procedure that enables the identification of coeluting metabolites, and allowed us to characterize 126 metabolites in the current study.

Emerging new strategies for successful metabolite identification in metabolomics

This review discusses strategies for the identification of metabolites in complex biological mixtures, as encountered in metabolomics, which have emerged in the recent past. These include NMR database-assisted approaches for the identification of commonly known metabolites as well as novel combinations of NMR and MS analysis methods for the identification of unknown metabolites. The use of certain chemical additives to the NMR tube can permit identification of metabolites with specific physical chemical properties.