COLMAR Lipids Web Server and Ultrahigh-Resolution Methods for Two-Dimensional Nuclear Magnetic Resonance- and Mass Spectrometry-Based Lipidomics

Applications of chromatographic methods in metabolomics: A review

Chromatography is a robust and reliable separation method that can use various stationary phases to separate complex mixtures commonly seen in metabolomics. This review examines the types of chromatography and stationary phases that have been used in targeted or untargeted metabolomics with methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. General considerations for sample pretreatment and separations in metabolomics are considered, along with the various supports and separation formats for chromatography that have been used in such work. The types of liquid chromatography (LC) that have been most extensively used in metabolomics will be examined, such as reversed-phase liquid chromatography and hydrophilic liquid interaction chromatography. In addition, other forms of LC that have been used in more limited applications for metabolomics (e.g., ion-exchange, size-exclusion, and affinity methods) will be discussed to illustrate how these techniques may be utilized for new and future research in this field. Multidimensional LC methods are also discussed, as well as the use of gas chromatography and supercritical fluid chromatography in metabolomics. In addition, the roles of chromatography in NMR- vs. MS-based metabolomics are considered. Applications are given within the field of metabolomics for each type of chromatography, along with potential advantages or limitations of these separation methods.

Determination of fatty acid uptake and desaturase activity in mammalian cells by NMR-based stable isotope tracing

BACKGROUND

Mammalian cells both import exogenous fatty acids and synthesize them de novo. Palmitate, the end product of fatty acid synthase (FASN) is a substrate for stearoyl-CoA desaturases (Δ-9 desaturases) that introduce a single double bond into fatty acyl-CoA substrates such as palmitoyl-CoA and stearoyl-CoA. This process is particularly upregulated in lipogenic tissues and cancer cells. Tracer methodology is needed to determine uptake versus de novo synthesis of lipids and subsequent chain elongation and desaturation. Here we describe an NMR method to determine the uptake of 13C-palmitate from the medium into HCT116 human colorectal cancer cells, and the subsequent desaturation and incorporation into complex lipids.

RESULTS

Exogenous 13C16-palmitate was absorbed from the medium by HCT116 cells and incorporated primarily into complex glycerol lipids. Desaturase activity was determined from the quantification of double bonds in acyl chains, which was greatly reduced by ablation of the major desaturase SCD1.

SIGNIFICANCE

The NMR approach requires minimal sample preparation, is non-destructive, and provides direct information about the level of saturation and incorporation of fatty acids into complex lipids.

Twenty years of nmrshiftdb2: A case study of an open database for analytical chemistry

In October 2003, 20 years ago, the open-source and open-content database NMRshiftDB was announced. Since then, the database, renamed as nmrshiftdb2 later, has been continuously available and is one of the longer-running projects in the field of open data in chemistry. After 20 years, we evaluate the success of the project and present lessons learnt for similar projects.

1H NMR-based metabolomics study of the lipid profile of omega-3 fatty acid supplements and some vegetable oils

Omega-3 fatty acids, which consist of alpha-linolenic acid (ALA), docosahexaenoic acid (DHA), and eicosapentaenoic acid (EPA), are a type of polyunsaturated fatty acids that are crucial for enhancing human health. These three omega-3s are regarded as vital dietary nutrients because it cannot synthesize them on its own. Therefore, they must be obtained through dietary sources. On the other hands, there are concerns regarding the quality and quantity of omega-3 in dietary sources and supplements. In this study, 1H NMR spectroscopy and multivariate analysis were harnessed for non-destructive profiling of fatty acids in commercially available omega-3 supplements and plant-based oils. Results disclosed substantial disparities in omega-3 content from 8 to over 50 %, with some revealing unexpected adulteration. Notably, in one fish oil sample DHA could not be detected indicating the possibility of adulteration. Additionally, the research delineated the fatty acid composition of plant-based oils, emphasizing variations in alpha-linolenic acid (ALA) and linoleic acid (LA) content among flaxseed, chia seed, and walnut oils. Chia seeds emerged as a rich source of ALA (57-65 %mol), while walnuts contained considerable LA (44-53 % mol). The findings emphasize the power of metabolomics as a rapid and affordable tool for finding omega-3 fatty acids content and adulteration in commercial dietary products.

Artificial intelligence for natural product drug discovery

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Automated analysis for multiplet identification from ultra-high resolution 2D- 1H, 13C-HSQC NMR spectra

Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D- ¹H, ¹³C-HSQC NMR spectra arising from ¹³C - ¹³C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D- ¹H, ¹³C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D- ¹H, ¹³C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

NMR as a tool for compound identification in mixtures

INTRODUCTION

Natural products and metabolomics are intrinsically linked through efforts to analyze complex mixtures for compound annotation. Although most studies that aim for compound identification in mixtures use MS as the main analysis technique, NMR has complementary advances that are worth exploring for enhanced structural confidence.

OBJECTIVE

This review aimed to showcase a portfolio of the main tools available for compound identification using NMR.

MATERIALS AND METHODS

COLMAR, SMART-NMR, MADByTE, and NMRfilter are presented using examples collected from real samples from the perspective of a natural product chemist. Data are also made available through Zenodo so that readers can test each case presented here.

CONCLUSION

The acquisition of ¹ H NMR, HSQC, TOCSY, HSQC-TOCSY, and HMBC data for all samples and fractions from a natural products study is strongly suggested. The same is valid for MS analysis to create a bridged analysis between both techniques in a complementary manner. The use of NOAH supersequences has also been suggested and demonstrated to save NMR time.

Advances in Microbial NMR Metabolomics

Confidently, nuclear magnetic resonance (NMR) is the most informative technique in analytical chemistry and its use as an analytical platform in metabolomics is well proven. This chapter aims to present NMR as a viable tool for microbial metabolomics discussing its fundamental aspects and applications in metabolomics using some chosen examples.

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.

Kövér K. Ultrahigh-Resolution Homo- and Heterodecoupled 1H and TOCSY NMR Experiments

The original homonuclear decoupled (pure shift) experiments provide ultrahigh-resolution ¹H spectra of compounds containing NMR-active heteronuclei of low natural isotopic abundance (e.g., ¹³C or ¹⁵N). In contrast, molecules containing highly abundant heteronuclei (like ³¹P or ¹⁹F) give doublets or a multiple of doublets in their homonuclear decoupled spectra, depending on the number of heteronuclear coupling partners and the magnitude of the respective coupling constants. In these cases, the complex and frequently overlapping signals may hamper the unambiguous assignment of resonances. Here, we present new heteronuclear decoupled (HD) PSYCHE ¹H and TOCSY experiments, which result in simplified spectra with significantly increased resolution, allowing the reliable assessment of individual resonances. The utility of the experiments has been demonstrated on a challenging stereoisomeric mixture of a platinum-phosphine complex, where ultrahigh resolution of the obtained HD PSYCHE spectra made the structure elucidation of the chiral products feasible. HD PSYCHE methods can be potentially applied to other important ³¹P- or ¹⁹F-containing compounds in medicinal chemistry and metabolomics.

Automated analysis for multiplet identification from ultra-high resolution 2D-1H,13C-HSQC NMR spectra

Background: Metabolism is essential for cell survival and proliferation. A deep understanding of the metabolic network and its regulatory processes is often vital to understand and overcome disease. Stable isotope tracing of metabolism using nuclear magnetic resonance (NMR) and mass spectrometry (MS) is a powerful tool to derive mechanistic information of metabolic network activity. However, to retrieve meaningful information, automated tools are urgently needed to analyse these complex spectra and eliminate the bias introduced by manual analysis. Here, we present a data-driven algorithm to automatically annotate and analyse NMR signal multiplets in 2D-1H,13C-HSQC NMR spectra arising from 13C -13C scalar couplings. The algorithm minimises the need for user input to guide the analysis of 2D-1H,13C-HSQC NMR spectra by performing automated peak picking and multiplet analysis. This enables non-NMR specialists to use this technology. The algorithm has been integrated into the existing MetaboLab software package. Methods: To evaluate the algorithm performance two criteria are tested: is the peak correctly annotated and secondly how confident is the algorithm with its analysis. For the latter a coefficient of determination is introduced. Three datasets were used for testing. The first was to test reproducibility with three biological replicates, the second tested the robustness of the algorithm for different amounts of scaling of the apparent J-coupling constants and the third focused on different sampling amounts. Results: The algorithm annotated overall >90% of NMR signals correctly with average coefficient of determination ρ of 94.06 ± 5.08%, 95.47 ± 7.20% and 80.47 ± 20.98% respectively. Conclusions: Our results indicate that the proposed algorithm accurately identifies and analyses NMR signal multiplets in ultra-high resolution 2D-1H,13C-HSQC NMR spectra. It is robust to signal splitting enhancement and up to 25% of non-uniform sampling.

Density functional theory calculations of δ(13 C) and δ(1 H) chemical shifts and 3 J(13 COO1 H) coupling constants as structural and analytical tools in hydroperoxides: Prospects and limitations of 1 H13 C heteronuclear multiple bond correlation experiments

Density functional theory (DFT) calculations of δ(¹³ C) and δ(¹ H) chemical shifts and ³ J(¹³ COO¹ H) coupling constants of three model hydroperoxides of the naturally occurring cis-11-OOH and trans-9-OOH isomers of oleate and 9-cis, 11-trans-16-OOH endo hydroperoxide of methyl linolenate are reported. The computational δ(OOH) for various functionals and basis sets were found to be nearly identical for the cis/trans geometric isomers. The chemical shifts of the methine CHOOH protons and carbons, on the contrary, are highly diagnostic for the identification of cis/trans geometric isomerism. The chemical shifts of the olefinic protons and carbons strongly depend on the orientation of the hydroperoxide unit relative to the double bond and, thus, of importance in conformational analysis. The results are in very good agreement with the available experimental data. For the various diastereomeric pairs of the model endo-hydroperoxide, the strongly deshielded OOH resonances, due to the presence of an intramolecular hydrogen bond between the hydroperoxide proton and an oxygen of the endo-peroxide ring, along with the δ(CHOOH), are highly diagnostic for identification and structure elucidation of complex erythro- and threo- diastereomeric pairs of endo-hydroperoxides; the computational results are in very good agreement with the available experimental data. The ³ J(¹³ COO¹ H) coupling constants were found to be <  2  Hz for the cis-trans geometric models and <  0.5  Hz for the endo-hydroperoxide and, thus, unimportant in stereochemical analysis. Sharp resonances of the hydroperoxide protons, with Δν_1/2  < 3 Hz, are required for the successful implementation of the ¹ H¹³ C heteronuclear multiple bond correlation (HMBC) technique.

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.

Exploring correlations between MS and NMR for compound identification using essential oils: A pilot study

INTRODUCTION

In this era of 'omics' technology in natural products studies, the complementary aspects of mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based techniques must be taken into consideration. The advantages of using both analytical platforms are reflected in a higher confidence of results especially when using replicated samples where correlation approaches can be used to statistically link results from MS to NMR.

OBJECTIVES

Demonstrate the use of Statistical Total Correlation (STOCSY) for linking results from MS and NMR data to reach higher confidence in compound identification.

METHODOLOGY

Essential oil samples of Melaleuca alternifolia and M. rhaphiophylla (Myrtaceae) were used as test objects. Aliquots of 10 samples were collected for GC-MS and NMR data acquisition [proton (¹ H)-NMR, and carbon-13 (¹³ C)-NMR as well as two-dimensional (2D) heteronuclear single quantum correlation (HSQC), heteronuclear multiple-bond correlation (HMBC), and HSQC-total correlated spectroscopy (TOCSY) NMR]. The processed data was imported to Matlab where STOCSY was applied.

RESULTS

STOCSY calculations led to the confirmation of the four main constituents of the sample-set. The identification of each was accomplished using; MS spectra, retention time comparison, ¹³ C-NMR data, and scalar correlations of the 2D NMR spectra.

CONCLUSION

This study provides a pipeline for high confidence in compound identification using a set of essential oils samples as test objects for demonstration.

Analytical and Structural Tools of Lipid Hydroperoxides: Present State and Future Perspectives

Mono- and polyunsaturated lipids are particularly susceptible to peroxidation, which results in the formation of lipid hydroperoxides (LOOHs) as primary nonradical-reaction products. LOOHs may undergo degradation to various products that have been implicated in vital biological reactions, and thus in the pathogenesis of various diseases. The structure elucidation and qualitative and quantitative analysis of lipid hydroperoxides are therefore of great importance. The objectives of the present review are to provide a critical analysis of various methods that have been widely applied, and more specifically on volumetric methods, applications of UV-visible, infrared, Raman/surface-enhanced Raman, fluorescence and chemiluminescence spectroscopies, chromatographic methods, hyphenated MS techniques, NMR and chromatographic methods, NMR spectroscopy in mixture analysis, structural investigations based on quantum chemical calculations of NMR parameters, applications in living cells, and metabolomics. Emphasis will be given to analytical and structural methods that can contribute significantly to the molecular basis of the chemical process involved in the formation of lipid hydroperoxides without the need for the isolation of the individual components. Furthermore, future developments in the field will be discussed.

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.

The exposome paradigm to predict environmental health in terms of systemic homeostasis and resource balance based on NMR data science

The environment, from microbial ecosystems to recycled resources, fluctuates dynamically due to many physical, chemical and biological factors, the profile of which reflects changes in overall state, such as environmental illness caused by a collapse of homeostasis. To evaluate and predict environmental health in terms of systemic homeostasis and resource balance, a comprehensive understanding of these factors requires an approach based on the "exposome paradigm", namely the totality of exposure to all substances. Furthermore, in considering sustainable development to meet global population growth, it is important to gain an understanding of both the circulation of biological resources and waste recycling in human society. From this perspective, natural environment, agriculture, aquaculture, wastewater treatment in industry, biomass degradation and biodegradable materials design are at the forefront of current research. In this respect, nuclear magnetic resonance (NMR) offers tremendous advantages in the analysis of samples of molecular complexity, such as crude bio-extracts, intact cells and tissues, fibres, foods, feeds, fertilizers and environmental samples. Here we outline examples to promote an understanding of recent applications of solution-state, solid-state, time-domain NMR and magnetic resonance imaging (MRI) to the complex evaluation of organisms, materials and the environment. We also describe useful databases and informatics tools, as well as machine learning techniques for NMR analysis, demonstrating that NMR data science can be used to evaluate the exposome in both the natural environment and human society towards a sustainable future.

NMR Methods for Determining Lipid Turnover via Stable Isotope Resolved Metabolomics

Lipids comprise diverse classes of compounds that are important for the structure and properties of membranes, as high-energy fuel sources and as signaling molecules. Therefore, the turnover rates of these varied classes of lipids are fundamental to cellular function. However, their enormous chemical diversity and dynamic range in cells makes detailed analysis very complex. Furthermore, although stable isotope tracers enable the determination of synthesis and degradation of complex lipids, the numbers of distinguishable molecules increase enormously, which exacerbates the problem. Although LC-MS-MS (Liquid Chromatography-Tandem Mass Spectrometry) is the standard for lipidomics, NMR can add value in global lipid analysis and isotopomer distributions of intact lipids. Here, we describe new developments in NMR analysis for assessing global lipid content and isotopic enrichment of mixtures of complex lipids for two cell lines (PC3 and UMUC3) using both 13C6 glucose and 13C5 glutamine tracers.

Metabolomics: small molecules that matter more

Metabolomics, an analytical study with high-throughput profiling, helps to understand interactions within a biological system. Small molecules, called metabolites or metabolomes with the size of <1500 Da, depict the status of a biological system in a different manner. Currently, we are in need to globally analyze the metabolome and the pathways involved in healthy, as well as diseased conditions, for possible therapeutic applications. Metabolome analysis has revealed high-abundance molecules during different conditions such as diet, environmental stress, microbiota, and disease and treatment states. As a result, it is hard to understand the complete and stable network of metabolites of a biological system. This review helps readers know the available techniques to study metabolomics in addition to other major omics such as genomics, transcriptomics, and proteomics. This review also discusses the metabolomics in various pathological conditions and the importance of metabolomics in therapeutic applications.

NMR and Computational Studies as Analytical and High-Resolution Structural Tool for Complex Hydroperoxides and Diastereomeric Endo-Hydroperoxides of Fatty Acids in Solution-Exemplified by Methyl Linolenate

A combination of selective 1D Total Correlation Spectroscopy (TOCSY) and 1H-13C Heteronuclear Multiple Bond Correlation (HMBC) NMR techniques has been employed for the identification of methyl linolenate primary oxidation products without the need for laborious isolation of the individual compounds. Complex hydroperoxides and diastereomeric endo-hydroperoxides were identified and quantified. Strongly deshielded C-O-O-H 1H-NMR resonances of diastereomeric endo-hydroperoxides in the region of 8.8 to 9.6 ppm were shown to be due to intramolecular hydrogen bonding interactions of the hydroperoxide proton with an oxygen atom of the five-member endo-peroxide ring. These strongly deshielded resonances were utilized as a new method to derive, for the first time, three-dimensional structures with an assignment of pairs of diastereomers in solution with the combined use of 1H-NMR chemical shifts, Density Functional Theory (DFT), and Our N-layered Integrated molecular Orbital and molecular Mechanics (ONIOM) calculations.

DFT Calculations of 1H- and 13C-NMR Chemical Shifts of Geometric Isomers of Conjugated Linoleic Acid (18:2 ω-7) and Model Compounds in Solution

A density functional theory (DFT) study of the ¹H- and ¹³C-NMR chemical shifts of the geometric isomers of 18:2 ω-7 conjugated linoleic acid (CLA) and nine model compounds is presented, using five functionals and two basis sets. The results are compared with available experimental data from solution high resolution nuclear magnetic resonance (NMR). The experimental ¹H chemical shifts exhibit highly diagnostic resonances due to the olefinic protons of the conjugated double bonds. The "inside" olefinic protons of the conjugated double bonds are deshielded than those of the "outside" protons. Furthermore, in the cis/trans isomers, the signals of the cis bonds are more deshielded than those of the trans bonds. These regularities of the experimental ¹H chemical shifts of the olefinic protons of the conjugated double bonds are reproduced very accurately for the lowest energy DFT optimized single conformer, for all functionals and basis sets used. The other low energy conformers have negligible effects on the computational ¹H-NMR chemical shifts. We conclude that proton NMR chemical shifts are more discriminating than carbon, and DFT calculations can provide a valuable tool for (i) the accurate prediction of ¹H-NMR chemical shifts even with less demanding functionals and basis sets; (ii) the unequivocal identification of geometric isomerism of CLAs that occur in nature, and (iii) to derive high resolution structures in solution.