NMR in metabolomics and natural products research: two sides of the same coin

Characterization of 37 enological tannins using a multiple technique approach: Linear sweep voltammetry as a rapid method both for classification and determination of antioxidant properties

In this work, 37 enological tannins of different classes were studied to investigate whether linear sweep voltammetry (LSV) could be a method to determine the family of a sample and its antioxidant capacity. A "wholistic" approach was used, combining LSV data with nuclear magnetic resonance (NMR), polyphenol quantification (Folin-Ciocalteu method and gravimetric analysis), antiradical activity (DPPH assay), and reducing capacity (FRAP assay). Voltammetric data were processed with statistical techniques and the results show the clustering of tannins in three different classes: ellagitannins, gallotannins, and condensed tannins. These findings were confirmed by NMR data treated with the same procedure. Finally, ellagitannins showed a high reducing capacity and gallotannins showed a high antiradical capacity. Importantly, LSV indices were shown to be significantly correlated with DPPH and FRAP parameters. Therefore, the hypothesis of LSV as a potentially useful technique to choose the most suitable tannin for a determined antioxidant purpose was successfully proved.

High-speed countercurrent chromatography with offline detection by electrospray mass spectrometry and nuclear magnetic resonance detection as a tool to resolve complex mixtures: A practical approach using Coffea arabica leaf extract

INTRODUCTION

Many secondary metabolites isolated from plants have been described in the literature owing to their important biological properties and possible pharmacological applications. However, the identification of compounds present in complex plant extracts has remained a great scientific challenge, is often laborious, and requires a long research time with high financial cost.

OBJECTIVES

The aim of this study was to develop a method that allows the identification of secondary metabolites in plant extracts with a high degree of confidence in a short period of time.

MATERIAL AND METHODS

In this study, an ethanolic extract of Coffea arabica leaves was used to validate the proposed method. Countercurrent chromatography was chosen as the initial step for extraction fractionation using gradient elution. Resulting fractions presented a variation of compounds concentrations, allowing for statistical total correlation spectroscopy (STOCSY) calculations between liquid chromatography coupled with high-resolution tandem mass spectrometry (LC-HRMS/MS) and NMR across fractions.

RESULTS

The proposed method allowed the identification of 57 compounds. Of the annotated compounds, 20 were previously described in the literature for the species and 37 were reported for the first time. Among the inedited compounds, we identified flavonoids, alkaloids, phenolic acids, coumarins, and terpenes.

CONCLUSION

The proposed method presents itself as a valid alternative for the study of complex extracts in an effective, fast, and reliable way that can be reproduced in the study of other extracts.

Use of carbon-13 NMR to identify known natural products by querying a nuclear magnetic resonance database-An assessment

The quick identification of known organic low molecular weight compounds, also known as structural dereplication, is a highly important task in the chemical profiling of natural resource extracts. To that end, a method that relies on carbon-13 nuclear magnetic resonance (NMR) spectroscopy, elaborated in earlier works of the author's research group, requires the availability of a dedicated database that establishes relationships between chemical structures, biological and chemical taxonomy, and spectroscopy. The construction of such a database, called acd_lotus, was reported earlier, and its usefulness was illustrated by only three examples. This article presents the results of structure searches carried out starting from 58 carbon-13 NMR data sets recorded on compounds selected in the metabolomics section of the biological magnetic resonance bank (BMRB). Two compound retrieval methods were employed. The first one involves searching in the acd_lotus database using commercial software. The second one operates through the freely accessible web interface of the nmrshiftdb2 database, which includes the compounds present in acd_lotus and many others. The two structural dereplication methods have proved to be efficient and can be used together in a complementary way.

A molting chemical cue (N-acetylglucosamine-6-phosphate) contributes to cannibalism of Chinese mitten crab Eriocheir sinensis

Under high-density culture, cannibalism occurs frequently during the molting of the Chinese mitten crabs Eriocheir sinensis, resulting in a large reduction in production. We found that the leakage of molting fluid from sexually immature crabs informs conspecifics that they are in a molting process. This hypothesis was verified through metabolomics analyses combined with behavioral experiments. The GlcNAc-6-P was identified as a molting biomarker from the differential metabolites by non-targeted metabolomics. In addition, we found that the concentration of GlcNAc-6-P in the molting fluid was significantly higher than other molting metabolites at different molting stages, reaching 5.84 μmol L-1, indicating that the molting fluid was the source of GlcNAc-6-P. Moreover, the behavioral experiments showed that crabs were actively approached to high concentrations of GlcNAc-6-P (1 μmol L-1), but had no obvious choice tendency at different concentrations of UTP, 20-HE and low concentrations of GlcNAc-6-P (0.1 μmol L-1, 0.01 μmol L-1) compared with the control groups. In conclusion, that E. sinensis by sensing the concentration change of GlcNAc-6-P can locate the source of GlcNAc-6-P release and actively approach the high concentration GlcNAc-6-P area and attack the molting crab, causing cannibalism. Blocking the reception pathway of molting chemical cues in E. sinensis, thereby preventing the perception of signals originating from conspecifics' molting in the vicinity, could lead to a reduction in cannibalistic behavior and an increase in overall production. Additionally, this method presents a prospective solution for addressing cannibalism in other crustacean species where such behavior is prevalent.

Application and prospect of metabolomics-related technologies in food inspection

BACKGROUND

Food inspection covers a broad range of topics, including nutrient analysis, food pollutants, food auxiliary materials, additives, and food sensory identification. The foundation of diverse subjects like food science, nutrition, health research, and the food industry, as well as the desired reference for drafting trade and food legislation, makes food inspection highly significant. Because of their high efficiency, sensitivity, and accuracy, instrumental analysis methods have gradually replaced conventional analytical methods as the primary means of food hygiene inspection.

SCOPE AND APPROACH

Metabolomics-based analysis technology, such as nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), and capillary electrophoresis-mass spectrometry (CE-MS), has become a widely used analytics platform. This research provides a bird's eye view of the application and future of metabolomics-related technologies in food inspection.

KEY FINDINGS AND CONCLUSIONS

We have provided a summary of the features and the application range of various metabolomics techniques, the strengths and weaknesses of different metabolomics platforms, and their implementation in specific inspection procedures. These procedures encompass the identification of endogenous metabolites, the detection of exogenous toxins and food additives, analysis of metabolite alterations during processing and storage, as well as the recognition of food adulteration. Despite the widespread utilization and significant contributions of metabolomics-based food inspection technologies, numerous challenges persist as the food industry advances and technology continues to improve. Thus, we anticipate addressing these potential issues in the future.

DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data

The identification of molecular structure is essential for understanding chemical diversity and for developing drug leads from small molecules. Nevertheless, the structure elucidation of small molecules by Nuclear Magnetic Resonance (NMR) experiments is often a long and non-trivial process that relies on years of training. To achieve this process efficiently, several spectral databases have been established to retrieve reference NMR spectra. However, the number of reference NMR spectra available is limited and has mostly facilitated annotation of commercially available derivatives. Here, we introduce DeepSAT, a neural network-based structure annotation and scaffold prediction system that directly extracts the chemical features associated with molecular structures from their NMR spectra. Using only the 1H-13C HSQC spectrum, DeepSAT identifies related known compounds and thus efficiently assists in the identification of molecular structures. DeepSAT is expected to accelerate chemical and biomedical research by accelerating the identification of molecular structures.

High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery

Due to the continued emergence of resistance and a lack of new and promising antibiotics, bacterial infection has become a major public threat. High-throughput screening (HTS) allows rapid screening of a large collection of molecules for bioactivity testing and holds promise in antibacterial drug discovery. More than 50% of the antibiotics that are currently available on the market are derived from natural products. However, with the easily discoverable antibiotics being found, finding new antibiotics from natural sources has seen limited success. Finding new natural sources for antibacterial activity testing has also proven to be challenging. In addition to exploring new sources of natural products and synthetic biology, omics technology helped to study the biosynthetic machinery of existing natural sources enabling the construction of unnatural synthesizers of bioactive molecules and the identification of molecular targets of antibacterial agents. On the other hand, newer and smarter strategies have been continuously pursued to screen synthetic molecule libraries for new antibiotics and new druggable targets. Biomimetic conditions are explored to mimic the real infection model to better study the ligand-target interaction to enable the designing of more effective antibacterial drugs. This narrative review describes various traditional and contemporaneous approaches of high-throughput screening of natural products and synthetic molecule libraries for antibacterial drug discovery. It further discusses critical factors for HTS assay design, makes a general recommendation, and discusses possible alternatives to traditional HTS of natural products and synthetic molecule libraries for antibacterial drug discovery.

Demographic, Health and Lifestyle Factors Associated with the Metabolome in Older Women

Demographic and clinical factors influence the metabolome. The discovery and validation of disease biomarkers are often challenged by potential confounding effects from such factors. To address this challenge, we investigated the magnitude of the correlation between serum and urine metabolites and demographic and clinical parameters in a well-characterized observational cohort of 444 post-menopausal women participating in the Women’s Health Initiative (WHI). Using LC-MS and lipidomics, we measured 157 aqueous metabolites and 756 lipid species across 13 lipid classes in serum, along with 195 metabolites detected by GC-MS and NMR in urine and evaluated their correlations with 29 potential disease risk factors, including demographic, dietary and lifestyle factors, and medication use. After controlling for multiple testing (FDR < 0.01), we found that log-transformed metabolites were mainly associated with age, BMI, alcohol intake, race, sample storage time (urine only), and dietary supplement use. Statistically significant correlations were in the absolute range of 0.2–0.6, with the majority falling below 0.4. Incorporation of important potential confounding factors in metabolite and disease association analyses may lead to improved statistical power as well as reduced false discovery rates in a variety of data analysis settings.

Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR

Resolving small molecule mixtures by nuclear magnetic resonance (NMR) spectroscopy has been of great interest for a long time for its precision, reproducibility, and efficiency. However, spectral analyses for such mixtures are often highly challenging due to overlapping resonance lines and limited chemical shift windows. The existing experimental and theoretical methods to produce shift NMR spectra in dealing with the problem have limited applicability owing to sensitivity issues, inconsistency, and/or the requirement of prior knowledge. Recently, we resolved the problem by decoupling multiplet structures in NMR spectra by the wavelet packet transform (WPT) technique. In this work, we developed a scheme for deploying the method in generating highly resolved WPT NMR spectra and predicting the composition of the corresponding molecular mixtures from their 1H NMR spectra in an automated fashion. The four-step spectral analysis scheme consists of calculating the WPT spectrum, peak matching with a WPT shift NMR library, followed by two optimization steps in producing the predicted molecular composition of a mixture. The robustness of the method was tested on an augmented dataset of 1000 molecular mixtures, each containing 3 to 7 molecules. The method successfully predicted the constituent molecules with a median true positive rate of 1.0 against the varying compositions, while a median false positive rate of 0.04 was obtained. The approach can be scaled easily for much larger datasets.

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.

Data Fusion-based Discovery (DAFdiscovery) pipeline to aid compound annotation and bioactive compound discovery across diverse spectral data

INTRODUCTION

Data Fusion-based Discovery (DAFdiscovery) is a pipeline designed to help users combine mass spectrometry (MS), nuclear magnetic resonance (NMR), and bioactivity data in a notebook-based application to accelerate annotation and discovery of bioactive compounds. It applies Statistical Total Correlation Spectroscopy (STOCSY) and Statistical HeteroSpectroscopy (SHY) calculation in their data using an easy-to-follow Jupyter Notebook.

METHOD

Different case studies are presented for benchmarking, and the resultant outputs are shown to aid natural products identification and discovery. The goal is to encourage users to acquire MS and NMR data from their samples (in replicated samples and fractions when available) and to explore their variance to highlight MS features, NMR peaks, and bioactivity that might be correlated to accelerated bioactive compound discovery or for annotation-identification studies.

RESULTS

Different applications were demonstrated using data from different research groups, and it was shown that DAFdiscovery reproduced their findings using a more straightforward method.

CONCLUSION

DAFdiscovery has proven to be a simple-to-use method for different situations where data from different sources are required to be analyzed together.

Analysis of Small-Molecule Mixtures by Super-Resolved 1H NMR Spectroscopy

Analysis of small molecules is essential to metabolomics, natural products, drug discovery, food technology, and many other areas of interest. Current barriers preclude from identifying the constituent molecules in a mixture as overlapping clusters of NMR lines pose a major challenge in resolving signature frequencies for individual molecules. While homonuclear decoupling techniques produce much simplified pure shift spectra, they often affect sensitivity. Conversion of typical NMR spectra to pure shift spectra by signal processing without a priori knowledge about the coupling patterns is essential for accurate analysis. We developed a super-resolved wavelet packet transform based 1H NMR spectroscopy that can be used in high-throughput studies to reliably decouple individual constituents of small molecule mixtures. We demonstrate the efficacy of the method on the model mixtures of saccharides and amino acids in the presence of significant noise.

Leveraging the HMBC to Facilitate Metabolite Identification

The accuracy and ease of metabolite assignments from a complex mixture are expected to be facilitated by employing a multispectral approach. The two-dimensional (2D) 1H-13C heteronuclear single quantum coherence (HSQC) and 2D 1H-1H-total correlation spectroscopy (TOCSY) are the experiments commonly used for metabolite assignments. The 2D 1H-13C HSQC-TOCSY and 2D 1H-13C heteronuclear multiple-bond correlation (HMBC) are routinely used by natural products chemists but have seen minimal usage in metabolomics despite the unique information, the nearly complete 1H-1H and 1H-13C and spin systems provided by these experiments that may improve the accuracy and reliability of metabolite assignments. The use of a 13C-labeled feedstock such as glucose is a routine practice in metabolomics to improve sensitivity and to emphasize the detection of specific metabolites but causes severe artifacts and an increase in spectral complexity in the HMBC experiment. To address this issue, the standard HMBC pulse sequence was modified to include carbon decoupling. Nonuniform sampling was also employed for rapid data collection. A dataset of reference 2D 1H-13C HMBC spectra was collected for 94 common metabolites. 13C-13C spin connectivity was then obtained by generating a covariance pseudo-spectrum from the carbon-decoupled HMBC and the 1H-13C HSQC-TOCSY spectra. The resulting 13C-13C pseudo-spectrum provides a connectivity map of the entire carbon backbone that uniquely describes each metabolite and would enable automated metabolite identification.

Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

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.

Multiple-targeting NMR signal selection by optimal control of nuclear spin singlet

Selectively probing specific molecules in complex mixtures with nuclear magnetic resonance promises new insights into molecular structures or molecular interaction. Such a study often can be further facilitated when two or more objects in chemical moieties of interest can be precisely targeted. Herein, we proposed a novel method to implement the multiple-targeting signal selection by optimal control of the spin singlets of two or more targeted spin systems from one or more molecules. This method can endow the conventional nuclear magnetic resonance (NMR), magnetic resonance image (MRI) and magnetic resonance spectrum (MRS) with the multiple-targeting signal selectivity to selectively probe several targeted molecules and/or chemical groups simultaneously.

Natural Products Are a Promising Source for Anthelmintic Drug Discovery

Parasitic nematodes infect almost all forms of life. In the human context, parasites are one of the major causative factors for physical and intellectual growth retardation in the developing world. In the agricultural setting, parasites have a great economic impact through a reduction in livestock performance or control cost. The main method of controlling these devastating conditions is the use of anthelmintic drugs. Unfortunately, there are only a few anthelmintic drug classes available in the market and significant resistance has developed in most of the parasitic species of livestock. Therefore, development of new anthelmintics with different modes of action is critical for sustainable parasitic control in the future. The drug development pipeline is broadly limited to two types of molecules, namely synthetic compounds and natural plant products. Compared to synthetic compounds, natural products are highly diverse, and many have historically proven valuable in folk medicine to treat various gastrointestinal ailments. This review focus on the use of traditional knowledge-based plant extracts in the development of new therapeutic leads, the approaches used as screening techniques, and common bottlenecks and opportunities in plant-based anthelmintic drug discovery.

The potential of nuclear magnetic resonance (NMR) in metabolomics and lipidomics of microalgae- a review

Microalgae biotechnology has made it possible to derive secondary bioactive metabolites from microalgae strains that have opened up their entire potential to uncover a wide range of novel metabolic capabilities and turn these into bio-products for the development of sustainable bio-refineries. Nuclear Magnetic Resonance Technology (NMR) has been one of the most successful and functional research technology over the past two decades to analyse the composition, structure and functionality of distinct metabolites in the different microalgae strains. This technology offers qualitative as well as quantitative knowledge about the endogenous metabolites and lipids of low molecular mass to offer a good picture of the physiological state of biological samples in metabolomics and lipidomics studies. Henceforth, this review is aimed at introducing the metabolomics and lipidomics studies into the field of NMR technology and also highlights the protocols for the isolation and metabolic measurements of metabolites from microalgae that should be redirected to resource recovery and value-added products with a systematic and holistic approach for scalability or sustainability.

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.

Development of an NMR-Based Platform for the Direct Structural Annotation of Complex Natural Products Mixtures

The development of new "omics" platforms is having a significant impact on the landscape of natural products discovery. However, despite the advantages that such platforms bring to the field, there remains no straightforward method for characterizing the chemical landscape of natural products libraries using two-dimensional nuclear magnetic resonance (2D-NMR) experiments. NMR analysis provides a powerful complement to mass spectrometric approaches, given the universal coverage of NMR experiments. However, the high degree of signal overlap, particularly in one-dimensional NMR spectra, has limited applications of this approach. To address this issue, we have developed a new data analysis platform for complex mixture analysis, termed MADByTE (Metabolomics and Dereplication by Two-Dimensional Experiments). This platform employs a combination of TOCSY and HSQC spectra to identify spin system features within complex mixtures and then matches spin system features between samples to create a chemical similarity network for a given sample set. In this report we describe the design and construction of the MADByTE platform and demonstrate the application of chemical similarity networks for both the dereplication of known compound scaffolds and the prioritization of bioactive metabolites from a bacterial prefractionated extract library.

Integrated molecular network and HPLC-UV-FLD analysis to explore antioxidant ingredients in camellia nitidissima Chi

At present, screening of active ingredients from natural products for pharmacological and clinical research is mostly time-consuming and costly. In this study, a molecular network (MN) guided high performance liquid chromatography-ultraviolet-fluorescence detector (HPLC-UV-FLD) method was carried out to profile the global antioxidant activity compounds, including the trace amount ingredients in Camellia nitidissima Chi (CNC). Firstly, HPLC-UV-FLD postcolumn derivatization system was utilized to screen the antioxidants. Then the MN of CNC was established via mass spectrometry (MS) data for getting the connection between ingredient structures. As a result, HPLC-UV-FLD indicated three antioxidant ingredients: gallic acid (126.3 mg/g), catechin (564.8 mg/g), and salicylic acid (24.3 mg/g). Combined with the MN, the actives' precise location and connection relationship were clarified based on the structural similarities. A new antioxidant ingredient, okicamelliaside, was suggested and evaluated at free radical scavenging and enzymatic protection. The novel method of activity and structural correlation analysis based on MN could provide a useful guide for screening trace active ingredients in natural products. PRACTICAL APPLICATION: Three main ingredients were screened out from Camellia nitidissima Chi by HPLC-UV-FLD postcolumn derivatization system. Integrated molecular network and HPLC-UV-FLD analysis, a new type of antioxidant okicamelliaside was selected. The novel method of activity and structural correlation analysis based on molecular network could provide a useful guide for screening trace active ingredients in natural products.

MicroED in natural product and small molecule research

Covering: 2013 to 2020The electron cryo-microscopy (cryo-EM) method Microcrystal Electron Diffraction (MicroED) allows the collection of high-resolution structural data from vanishingly small crystals that appear like amorphous powders or very fine needles. Since its debut in 2013, data collection and analysis schemes have been fine-tuned, and there are currently close to 100 structures determined by MicroED. Although originally developed to study proteins, MicroED is also very powerful for smaller systems, with some recent and very promising examples from the field of natural products. Herein, we review what has been achieved so far and provide examples of natural product structures, as well as demonstrate the expected future impact of MicroED to the field of natural product and small molecule research.

Non-negative Least Squares Approach to Quantification of 1H Nuclear Magnetic Resonance Spectra of Human Urine

Because of its quantitative character and capability for high-throughput screening, ¹H nuclear magnetic resonance (NMR) spectroscopy is used extensively in the profiling of biofluids such as urine and blood plasma. However, the narrow frequency bandwidth of ¹H NMR spectroscopy leads to a severe overlap of the spectra of components present in the complex mixtures such as biofluids. Therefore, ¹H NMR-based metabolomics analysis is focused on targeted studies related to concentrations of the small number of metabolites. Here, we propose a library-based approach to quantify proportions of overlapping metabolites from ¹H NMR mixture spectra. The method boils down to the linear non-negative least squares (NNLS) problem, whereas proportions of the pure components contained in the library stand for the unknowns. The method is validated on an estimation of the proportions of (i) the 78 pure spectra, presumably related to type 2 diabetes mellitus (T2DM), from their synthetic linear mixture; (ii) metabolites present in 62 ¹H NMR spectra of urine of subjects with T2DM and 62 ¹H NMR spectra of urine of control subjects. In both cases, the in-house library of 210 pure component ¹H NMR spectra represented the design matrix in the related NNLS problem. The proposed method pinpoints 63 metabolites that in a statistically significant way discriminate the T2DM group from the control group and 46 metabolites discriminating control from the T2DM group. For several T2DM-discriminative metabolites, we prove their presence by independent analytical determination or by pointing out the corresponding findings in the published literature.

Metabolite Structure Assignment Using In Silico NMR Techniques

A major challenge for metabolomic analysis is to obtain an unambiguous identification of the metabolites detected in a sample. Among metabolomics techniques, NMR spectroscopy is a sophisticated, powerful, and generally applicable spectroscopic tool that can be used to ascertain the correct structure of newly isolated biogenic molecules. However, accurate structure prediction using computational NMR techniques depends on how much of the relevant conformational space of a particular compound is considered. It is intrinsically challenging to calculate NMR chemical shifts using high-level DFT when the conformational space of a metabolite is extensive. In this work, we developed NMR chemical shift calculation protocols using a machine learning model in conjunction with standard DFT methods. The pipeline encompasses the following steps: (1) conformation generation using a force field (FF)-based method, (2) filtering the FF generated conformations using the ASE-ANI machine learning model, (3) clustering of the optimized conformations based on structural similarity to identify chemically unique conformations, (4) DFT structural optimization of the unique conformations, and (5) DFT NMR chemical shift calculation. This protocol can calculate the NMR chemical shifts of a set of molecules using any available combination of DFT theory, solvent model, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Our protocol reduces the overall computational time by 2 orders of magnitude over methods that optimize the conformations using fully ab initio methods, while still producing good agreement with experimental observations. The complete protocol is designed in such a manner that makes the computation of chemical shifts tractable for a large number of conformationally flexible metabolites.

NMR-TS: de novo molecule identification from NMR spectra

Nuclear magnetic resonance (NMR) spectroscopy is an effective tool for identifying molecules in a sample. Although many previously observed NMR spectra are accumulated in public databases, they cover only a tiny fraction of the chemical space, and molecule identification is typically accomplished manually based on expert knowledge. Herein, we propose NMR-TS, a machine-learning-based python library, to automatically identify a molecule from its NMR spectrum. NMR-TS discovers candidate molecules whose NMR spectra match the target spectrum by using deep learning and density functional theory (DFT)-computed spectra. As a proof-of-concept, we identify prototypical metabolites from their computed spectra. After an average 5451 DFT runs for each spectrum, six of the nine molecules are identified correctly, and proximal molecules are obtained in the other cases. This encouraging result implies that de novo molecule generation can contribute to the fully automated identification of chemical structures. NMR-TS is available at https://github.com/tsudalab/NMR-TS.

Classification of Flavonoid Metabolomes via Data Mining and Quantification of Hydroxyl NMR Signals

Utilizing the distinct HMBC cross-peak patterns of lower-field range (LFR; 11.80-14.20 ppm) hydroxyl singlets, presented NMR methodology characterizes flavonoid metabolomes both qualitatively and quantitatively. It enables simultaneous classification of the structural types of 5-OH flavonoids and biogenetically related 2'-OH chalcones, as well as quantification of individual metabolites from 1H NMR spectra, even in complex mixtures. Initially, metabolite-specific LFR 1D 1H and 2D HMBC patterns were established via literature mining and experimental data interpretation, demonstrating that LFR HMBC patterns encode the different structural types of 5-OH flavonoids/2'-OH chalcones. Taking advantage of the simplistic multiplicity of the H,H-uncoupled LFR 5-/2'-OH singlets, individual metabolites could subsequently be quantified by peak fitting quantitative 1H NMR (PF-qHNMR). Metabolomic analysis of enriched fractions from three medicinal licorice (Glycyrrhiza) species established proof-of-concept for distinguishing three major structural types and eight subtypes in biomedical applications. The method identified 15 G. uralensis (GU) phenols from the six possible subtypes of 5,7-diOH (iso)flav(an)ones with 6-, 8-, and nonprenyl substitution, including the new 6-prenyl-licoisoflavanone (1) and two previously unknown compounds (4 and 7). Relative (100%) qNMR established quantitative metabolome patterns suitable for species discrimination and plant metabolite studies. Absolute qNMR with combined external and internal (solvent) calibration (ECIC) identified and quantified 158 GU metabolites. HMBC-supported qHNMR analysis of flavonoid metabolomes ("flavonomics") empowers the exploration of structure-abundance-activity relationships of designated bioactivity. Its ability to identify and quantify numerous metabolites simultaneously and without identical reference materials opens new avenues for natural product discovery and botanical quality control and can be adopted to other flavonoid- and chalcone-containing taxa.

Research Tales from the Clardy Laboratory: Function-Driven Natural Product Discovery

Over the past 70 years, the search for small molecules from nature has transformed biomedical research: natural products are the basis for half of all pharmaceuticals; the quest for total synthesis of natural products fueled development of methodologies for organic synthesis; and their biosynthesis presented unprecedented biochemical transformations, expanding our chemo-enzymatic toolkit. Initially, the discovery of small molecules was driven by bioactivity-guided fractionation. However, this approach yielded the frequent rediscovery of already known metabolites. As a result, focus shifted to identifying novel scaffolds through either structure-first methods or genome mining, relegating function as a secondary concern. Over the past two decades, the laboratory of Jon Clardy has taken an alternative route and focused on an ecology-driven, function-first approach in pursuit of uncovering bacterial small molecules with biological activity. In this review, we highlight several examples that showcase this ecology-first approach. Though the highlighted systems are diverse, unifying themes are (1) to understand how microbes interact with their host or environment, (2) to gain insights into the environmental roles of microbial metabolites, and (3) to explore pharmaceutical potential from these ecologically relevant metabolites.

NMR-Based Plant Metabolomics in Nutraceutical Research: An Overview

Few topics are able to channel the interest of researchers, the public, and industries, like nutraceuticals. The ever-increasing demand of new compounds or new sources of known active compounds, along with the need of a better knowledge about their effectiveness, mode of action, safety, etc., led to a significant effort towards the development of analytical approaches able to answer the many questions related to this topic. Therefore, the application of cutting edges approaches to this area has been observed. Among these approaches, metabolomics is a key player. Herewith, the applications of NMR-based metabolomics to nutraceutical research are discussed: after a brief overview of the analytical workflow, the use of NMR-based metabolomics to the search for new compounds or new sources of known nutraceuticals are reviewed. Then, possible applications for quality control and nutraceutical optimization are suggested. Finally, the use of NMR-based metabolomics to study the impact of nutraceuticals on human metabolism is discussed.

Mass spectrometry-based metabolomics approach in the isolation of bioactive natural products

Metabolomics is a powerful tool in the analysis and identification of metabolites responsible for biological properties. Regarding natural product chemistry, it constitutes a potential strategy to streamline the classic and laborious process of isolating natural products, which often involves the re-isolation and identification of known compounds. In this contribution, we establish a mass spectrometry-based metabolomics strategy to discover compounds with larvicidal activity against Aedes aegypti. We analyse the Brazilian plant Annona crassiflora using different platforms to annotate the active compounds in different extracts/fractions of various plant parts. The MetaboAnalyst and GNPS platforms, which consider LC-MS and LC-MS/MS data, respectively, were chosen to identify compounds that differentiate active and inactive samples. Bio-guided isolation was subsequently performed to confirm compound activity. Results proved the capacity of metabolomics to predict metabolite differences between active and inactive samples using LC-MS and LC-MS/MS data. Moreover, we discuss the limitations, possibilities, and strategies to have a broad view of vast data.

Metabolomics in the Context of Plant Natural Products Research: From Sample Preparation to Metabolite Analysis

Plant-derived natural products have long been considered a valuable source of lead compounds for drug development. Natural extracts are usually composed of hundreds to thousands of metabolites, whereby the bioactivity of natural extracts can be represented by synergism between several metabolites. However, isolating every single compound from a natural extract is not always possible due to the complex chemistry and presence of most secondary metabolites at very low levels. Metabolomics has emerged in recent years as an indispensable tool for the analysis of thousands of metabolites from crude natural extracts, leading to a paradigm shift in natural products drug research. Analytical methods such as mass spectrometry (MS) and nuclear magnetic resonance (NMR) are used to comprehensively annotate the constituents of plant natural products for screening, drug discovery as well as for quality control purposes such as those required for phytomedicine. In this review, the current advancements in plant sample preparation, sample measurements, and data analysis are presented alongside a few case studies of the successful applications of these processes in plant natural product drug discovery.

Bioassay- and metabolomics-guided screening of bioactive soil actinomycetes from the ancient city of Ihnasia, Egypt

Literature surveys, taxonomical differences, and bioassay results have been utilized in the discovery of new natural products to aid in Actinomycetes isolate-selection. However, no or less investigation have been done on establishing the differences in metabolomic profiles of the isolated microorganisms. The study aims to utilise bioassay- and metabolomics-guided tools that included dereplication study and multivariate analysis of the NMR and mass spectral data of microbial extracts to assist the selection of isolates for scaling-up the production of antimicrobial natural products. A total of 58 actinomycetes were isolated from different soil samples collected from Ihnasia City, Egypt and screened for their antimicrobial activities against indicator strains that included Bacillus subtilis, Escherichia coli, methicillin-resistant Staphylococcus aureus and Candida albicans. A number of 25 isolates were found to be active against B. subtilis and/or to at least one of the tested indicator strains. Principal component analyses showed chemical uniqueness for four outlying bioactive actinomycetes extracts. In addition, Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) and dereplication study led us to further select two outlying anti-MRSA active isolates MS.REE.13 and 22 for scale-up work. MS.REE.13 and 22 exhibited zones of inhibition at 19 and 13 mm against MRSA, respectively. A metabolomics-guided approach provided the steer to target the bioactive metabolites (P<0.01) present in a crude extract or fraction even at nanogram levels but it was a challenge that such low-yielding bioactive natural products would be feasible to isolate. Validated to occur only on the active side of OPLS-DA loadings plot, the isolated compounds exhibited medium to weak antibiotic activity with MIC values between 250 and 800 μM. Two new compounds, P_24306 (C₁₀H₁₃N₂) and N_12799 (C₁₈H₃₂O₃) with MICs of 795 and 432 μM, were afforded from the scale-up of MS.REE. 13 and 22, respectively.

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.

Metabolomic variability of four macroalgal species of the genus Lobophora using diverse approaches

Among comparative metabolomic studies used in marine sciences, only few of them are dedicated to macroalgae despite their ecological importance in marine ecosystems. Therefore, experimental data are needed to assess the scopes and limitations of different metabolomic techniques applied to macroalgal models. Species of the genus Lobophora belong to marine brown algae (Family: Dictyotaceae) and are widely distributed, especially in tropical coral reefs. The species richness of this genus has only been unveiled recently and it includes species of diverse morphologies and habitats, with some species interacting with corals. This study aims to assess the potential of different metabolomic fingerprinting approaches in the discrimination of four well known Lobophora species (L. rosacea, L. sonderii, L. obscura and L. monticola). These species present distinct morphologies and are found in various habitats in the New Caledonian lagoon (South-Western Pacific). We compared and combined different untargeted metabolomic techniques: liquid chromatography-mass spectrometry (LC-MS), nuclear magnetic resonance (1H-NMR) and gas chromatography (GC-MS). Metabolomic separations were observed between each Lobophora species, with significant differences according to the techniques used. LC-MS was the best approach for metabotype distinction but a combination of approaches was also useful and allowed identification of chemomarkers for some species. These comparisons provide important data on the use of metabolomic approaches in the Lobophora genus and will pave the way for further studies on the sources of metabolomic variations for this ecologically important macroalgae.

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.

Ultrafast Maximum-Quantum NMR Spectroscopy for the Analysis of Aromatic Mixtures

Maximum-quantum (MaxQ) NMR experiments have been introduced to overcome issues related to peak overlap and high spectral density in the NMR spectra of aromatic mixtures. In MaxQ NMR, spin systems are separated on the basis of the highest-quantum coherence that they can form. MaxQ experiments are however time consuming and methods have been introduced to accelerate them. In this article, we demonstrate the ultrafast, single-scan acquisition of MaxQ NMR spectra using spatial encoding of the multiple-quantum dimension. So far, the spatial encoding methodology has been applied only for the encoding of up to double-quantum coherences, and here we show that it can be extended to higher coherence orders, to yield a massive reduction of the acquisition time of multi-quantum spectra of aromatic mixtures, and also to monitor chemical reactions.

Non-Uniform and Absolute Minimal Sampling for High-Throughput Multidimensional NMR Applications

Many biomolecular NMR applications can benefit from the faster acquisition of multidimensional NMR data with high resolution and their automated analysis and interpretation. In recent years, a number of non-uniform sampling (NUS) approaches have been introduced for the reconstruction of multidimensional NMR spectra, such as compressed sensing, thereby bypassing traditional Fourier-transform processing. Such approaches are applicable to both biomacromolecules and small molecules and their complex mixtures and can be combined with homonuclear decoupling (pure shift) and covariance processing. For homonuclear 2D TOCSY experiments, absolute minimal sampling (AMS) permits the drastic shortening of measurement times necessary for high-throughput applications for identification and quantification of components in complex biological mixtures in the field of metabolomics. Such TOCSY spectra can be comprehensively represented by graphic theoretical maximal cliques for the identification of entire spin systems and their subsequent query against NMR databases. Integration of these methods in webservers permits the rapid and reliable identification of mixture components. Recent progress is reviewed in this Minireview.

The use of 1 H-NMR Metabolomics to Optimise the Extraction and Preliminary Identification of Anthelmintic Products from the Leaves of Lysiloma latisiliquum

INTRODUCTION

Tannin-rich forages are recognised as an important alternative for the control of gastrointestinal nematodes in small ruminants. Lysiloma latisiliquum, a forage commonly consumed by goats and sheep, has shown anthelmintic activity when tested against Haemonchus contortus. However, to date, the metabolites responsible for the activity are not known.

OBJECTIVE

To use ¹ H-NMR metabolomics in the extraction and identification of anthelmintic metabolites from L. latisiliquum.

METHODOLOGY

Eight different solvent systems were compared for the optimum extraction of anthelmintic metabolites from L. latisiliquum. ¹ H-NMR spectra of the tannin-free extracts were measured in methanol-d₄ using trimethylsilylpropanoic acid (TSP) as internal standard. Extracts were also evaluated for their anthelmintic activity using the larval exsheathment inhibition assay against H. contortus. These data were correlated by multivariate analysis [principal component analysis (PCA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA)] and analysed. To validate the results obtained after the OPLS-DA, a bioassay-guided isolation of bioactive metabolites was conducted.

RESULTS

The PCA of the ¹ H-NMR data allowed the identification of hydrophilic solvents as those best suited for the extraction of anthelmintics from L. latisiliquum and indicated that the bioactive metabolites are high-polarity, glycosylated products. Similarly, OPLS-DA of the data enabled the detection of activity-related signals, assigned to the glycosylated metabolites quercitrin and arbutin obtained from the bioassay-guided purification of the extract.

CONCLUSION

The results of this investigation confirm metabolomics as a useful tool in the detection of bioactive metabolites in plants without previous phytochemical studies. Copyright © 2017 John Wiley & Sons, Ltd.

Aspergillus flavus Secondary Metabolites: More than Just Aflatoxins

Aspergillus flavus is best known for producing the family of potent carcinogenic secondary metabolites known as aflatoxins. However, this opportunistic plant and animal pathogen also produces numerous other secondary metabolites, many of which have also been shown to be toxic. While about forty of these secondary metabolites have been identified from A. flavus cultures, analysis of the genome has predicted the existence of at least 56 secondary metabolite gene clusters. Many of these gene clusters are not expressed during growth of the fungus on standard laboratory media. This presents researchers with a major challenge of devising novel strategies to manipulate the fungus and its genome so as to activate secondary metabolite gene expression and allow identification of associated cluster metabolites. In this review, we discuss the genetic, biochemical and bioinformatic methods that are being used to identify previously uncharacterized secondary metabolite gene clusters and their associated metabolites. It is important to identify as many of these compounds as possible to determine their bioactivity with respect to fungal development, survival, virulence and especially with respect to any potential synergistic toxic effects with aflatoxin.

Foodomics for human health: current status and perspectives

INTRODUCTION

In the post-genomic era, the opportunity to combine and integrate cutting-edge analytical platforms and data processing systems allowed the birth of foodomics, 'a discipline that studies the Food and Nutrition domains through the application of advanced omics technologies to improve consumer's well-being, health, and confidence'. Since then, this discipline has rapidly evolved and researchers are now facing the daunting tasks to meet consumers' needs in terms of food traceability, sustainability, quality, safety and integrity. Most importantly, today it is imperative to provide solid evidence of the mechanisms through which food can promote human health and well-being. Areas covered: In this review, the complex relationships connecting food, nutrition and human health will be discussed, with emphasis on the relapses for the development of functional foods and nutraceuticals, personalized nutrition approaches, and the study of the interplay among gut microbiota, diet and health/diseases. Expert commentary: Evidence has been provided supporting the role of various omic platforms in studying the health-promoting effects of food and customized dietary interventions. However, although associated to major analytical challenges, only the proper integration of multi-omics studies and the implementation of bioinformatics tools and databases will help translate findings from clinical practice into effective personalized treatment strategies.

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.

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.

Time resolved and label free monitoring of extracellular metabolites by surface enhanced Raman spectroscopy

Metabolomics is an emerging field of cell biology that aims at the comprehensive identification of metabolite levels in biological fluids or cells in a specific functional state. Currently, the major tools for determining metabolite concentrations are mass spectrometry coupled with chromatographic techniques and nuclear magnetic resonance, which are expensive, time consuming and destructive for the samples. Here, we report a time resolved approach to monitor metabolite dynamics in cell cultures, based on Surface Enhanced Raman Scattering (SERS). This method is label-free, easy to use and provides the opportunity to simultaneously study a broad range of molecules, without the need to process the biological samples. As proof of concept, NIH/3T3 cells were cultured in vitro, and the extracellular medium was collected at different time points to be analyzed with our engineered SERS substrates. By identifying individual peaks of the Raman spectra, we showed the simultaneous detection of several components of the conditioned medium, such as L-tyrosine, L-tryptophan, glycine, L-phenylalanine, L-histidine and fetal bovine serum proteins, as well as their intensity changes during time. Furthermore, analyzing the whole Raman data set with the Principal Component Analysis (PCA), we demonstrated that the Raman spectra collected at different days of culture and clustered by similarity, described a well-defined trajectory in the principal component plot. This approach was then utilized to determine indirectly the functional state of the macrophage cell line Raw 264.7, stimulated with the lipopolysaccharide (LPS) for 24 hours. The collected spectra at different time points, clustered by the PCA analysis, followed a well-defined trajectory, corresponding to the functional change of cells toward the activated pro-inflammatory state induced by the LPS. This study suggests that our engineered SERS surfaces can be used as a versatile tool both for the characterization of cell culture conditions and the functional state of cells over time.