Metabolomic study of soft corals from the Colombian Caribbean: PSYCHE and 1H-NMR comparative analysis

New analytical methods focusing on polar metabolite analysis in mass spectrometry and NMR-based metabolomics

Following in the footsteps of genomics and proteomics, metabolomics has revolutionised the way we investigate and understand biological systems. Rapid development in the last 25 years has been driven largely by technical innovations in mass spectrometry and nuclear magnetic resonance spectroscopy. However, despite the modest size of metabolomes relative to proteomes and genomes, methodological capabilities for robust, comprehensive metabolite analysis remain a major challenge. Therefore, development of new methods and techniques remains vital for progress in the field. Here, we review developments in LC-MS, GC-MS and NMR methods in the last few years that have enhanced quantitative and comprehensive metabolome coverage, highlighting the techniques involved, their technical capabilities, relative performance, and potential impact.

1H-NMR Metabolomic Study of the Mushroom Pleurotus djamor for the Identification of Nematocidal Compounds

Due to the increasing populations of anthelmintic-resistant gastrointestinal nematodes and as a consequence of the adverse effects of synthetic drugs, this study focuses on the search for secondary metabolites with nematocidal activity from the edible mushroom Pleurotus djamor using The proton nuclear magnetic resonance (1H-NMR) metabolomics. The highest activity was shown by the ethyl acetate fractions of mycelium (EC50 290.8 µg/mL) and basidiomes (EC50 282.7 µg/mL). Principal component analysis (PCA) and hierarchical data analysis (HCA) of the 1H-NMR metabolic profiles data showed that the ethanolic extracts, the ethyl acetate, butanol, and water fractions from mycelium have different metabolic profiles than those from basidiomes, while low polarity (hexane) fractions from both stages of fungal development show similar profiles. Orthogonal partial least squares discriminant analysis (OPLS-DA) allowed the identification of signals in the 1H-NMR metabolic profile associated with nematocidal activity. The signals yielded via OPLS-DA and bidimensional NMR analysis allowed the identification of uracil as a component in the ethyl acetate fraction from basidiomes, with an EC50 of 237.7 µg/mL. The results obtained showed that chemometric analyses of the 1H-NMR metabolic profiles represent a viable strategy for the identification of bioactive compounds from samples with complex chemical profiles.

Advancing Natural Product Discovery: A Structure-Oriented Fractions Screening Platform for Compound Annotation and Isolation

Natural product discovery is hindered by the lack of tools that integrate untargeted nuclear magnetic resonance and mass spectrometry data on a library scale. This article describes the first application of the innovative NMR/MS-based machine learning tool, the "Structure-Oriented Fractions Screening Platform (SFSP)", enabling functional-group-guided fractionation and accelerating the discovery and characterization of undescribed natural products. The concept was applied to the extract of a marine fungus known to be a prolific producer of diverse natural products. With the assistance of SFSP, we isolated 24 flavipidin derivatives and five phenalenone analogues from Aspergillus sp. GE2-6, revealing 27 undescribed compounds. Compounds 7-22 were proposed as isomeric derivatives featuring a 5/6-ring fusion, formed by the dimerization of flavipidin E (5). Compounds 23 and 24 were envisaged as isomeric derivatives with a 6/5/6-ring fusion, generated through the degradation of two flavipidin E molecules. Furthermore, flavipidin A (1) and asperphenalenone E (28) exhibited potent anti-influenza (PR8) activities, with IC50 values of 21.9 ± 0.2 and 12.9 ± 0.1 μM, respectively. Meanwhile, asperphenalenone (26) and asperphenalenone P (27) treatments exhibited significant inhibition of HIV pseudovirus infection in 293FT cells, boasting IC50 values of 6.1 ± 0.9 and 4.6 ± 1.1 μM, respectively. Overall, SFSP streamlines natural product isolation through NMR and MS data integration, as showcased by the discovery of numerous undescribed flavipidins and phenalenones based on NMR olefinic signals and low-field hydroxy signals.

Development of biomarkers to distinguish different origins of red seabreams (Pagrus major) from Korea and Japan by fatty acid, amino acid, and mineral profiling

Red seabream (Pagrus major) has been one of the most popular fish in East Asia since early times. However, the discharge of nuclear wastewater into the sea following the Fukushima nuclear disaster in Japan has led to violations of the country of origin labeling. Therefore, the aim of the present study was to determine the origin of fish based on fatty acid, amino acid, and mineral analyses, and to develop biomarkers that can discriminate between Japanese and Korean red seabream. To identify the differences between the two groups, 29 fatty acid families, 17 amino acids, and 4 minerals were analyzed in 60 fish samples (standard sample collected in autumn), and fatty acid profiles were analyzed using heatmap with hierarchical clustering analysis and orthogonal projections to latent structures discriminant analysis. The top 10 fatty acids that were different between the two groups were selected from all seasonal fish samples by combining variable importance in projection scores and p-values. According to the receiver operating characteristic curve analysis results, we proposed percentage linoleic acid (C18:2n-6, cis) as a candidate biomarker with excellent sensitivity and specificity. This study introduces a strategy to identify the origins of red seabream using linoleic acid obtained from fatty acid analysis.

Discovery of candidate biomarkers to discriminate between Korean and Japanese red seabream (Pagrus major) using metabolomics

Red seabream (Pagrus major) is widely consumed in East Asia. As nuclear wastewater is discharged into Japanese waterbodies, the country of origin of marine products must be accurately labeled. Here, we aimed to discover candidate metabolite biomarkers to discriminate between Korean and Japanese red seabream using LC-Orbitrap mass spectrometry. In total, 95 and 138 putative metabolites were detected via chromatographic separation of fish sampled in the warm and cold seasons, respectively. The spectrometric and chromatographic data were analyzed using principal component analysis and orthogonal partial least squares discriminant analysis. We identified 12 and 19 influential metabolites to discriminate between each origin fish in the warm and cold seasons, respectively, using variable importance in projection scores and p values. Anserine was further selected as a candidate biomarker based on receiver operating characteristic curve analysis. This study provides a basis for using anserine to determine the geographic origin of red seabream.

Present and future of pure shift NMR in metabolomics

NMR is one of the most powerful techniques for the analysis of biological samples in the field of metabolomics. However, the high complexity of fluids, tissues, or other biological materials taken from living organisms is still a challenge for state-of-the-art pulse sequences, thereby limiting the detection, the identification, and the quantification of metabolites. In this context, the resolution enhancement provided by broadband homonuclear decoupling methods, which allows for simplifying 1 H multiplet patterns into singlets, has placed this so-called pure shift technique as a promising approach to perform metabolic profiling with unparalleled level of detail. In recent years, the many advances achieved in the design of pure shift experiments has paved the way to the analysis of a wide range of biological samples with ultra-high resolution. This review leads the reader from the early days of the main pure shift methods that have been successfully developed over the last decades to address complex samples, to the most recent and promising applications of pure shift NMR to the field of NMR-based metabolomics.

Analysis of metabolite differences between South Korean and Chinese yellow goosefish (Lophius litulon) using capillary electrophoresis time-of-flight mass spectrometry

The yellow goosefish is a benthic fish that belongs to the family Lophiidae and order Lophiiformes and is distributed in the Yellow and East China Seas. This study aimed to distinguish between yellow goosefish from different geographical origins by analyzing their metabolites. Capillary electrophoresis time-of-flight mass spectrometry was used to analyze metabolite profiles in the muscle tissues of yellow goosefish to distinguish between Korean and Chinese yellow goosefish. In total, 271 putative metabolites were extracted using 50% acetonitrile in water. Principal component analysis and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to distinguish different geographical origins using the metabolite profiles obtained. The R2 and Q2 values of the OPLS-DA model were 0.856 and 0.695, respectively, indicating that the model was well-fitted and had good predictability. The heat map revealed that nucleic acid and amino compounds differed between the Korean and Chinese fish, and the variable importance in the projection scores obtained from OPLS-DA showed that there were geographical differences in the primary metabolites (5'-methylthioadenosine, adenosine, uridine 5-diphosphate, guanosine 5-diphosphate, urea, homocarnosine, O-acetylcarnitine, cycloleucine, cycloleucine S-adenosylmethionine, S-adenosylhomocysteine, ethanolamine, myo-inositol 1-phosphate), which were identified as potential candidate biomarkers.

Exploring Proton-Only NMR Experiments and Filters for Daphnia In Vivo: Potential and Limitations

Environmental metabolomics provides insight into how anthropogenic activities have an impact on the health of an organism at the molecular level. Within this field, in vivo NMR stands out as a powerful tool for monitoring real-time changes in an organism's metabolome. Typically, these studies use 2D ¹³C-¹H experiments on ¹³C-enriched organisms. Daphnia are the most studied species, given their widespread use in toxicity testing. However, with COVID-19 and other geopolitical factors, the cost of isotope enrichment increased ~6-7 fold over the last two years, making ¹³C-enriched cultures difficult to maintain. Thus, it is essential to revisit proton-only in vivo NMR and ask, "Can any metabolic information be obtained from Daphnia using proton-only experiments?". Two samples are considered here: living and whole reswollen organisms. A range of filters are tested, including relaxation, lipid suppression, multiple-quantum, J-coupling suppression, 2D ¹H-¹H experiments, selective experiments, and those exploiting intermolecular single-quantum coherence. While most filters improve the ex vivo spectra, only the most complex filters succeed in vivo. If non-enriched organisms must be used, then, DREAMTIME is recommended for targeted monitoring, while IP-iSQC was the only experiment that allowed non-targeted metabolite identification in vivo. This paper is critically important as it documents not just the experiments that succeed in vivo but also those that fail and demonstrates first-hand the difficulties associated with proton-only in vivo NMR.

A One-Dimensional Phase-Modulated (PM) NMR Experiment for Differentiating Spin Systems in Small Molecules Mixtures: With Application to Chiral Discrimination and Bicomponent Organic Systems

A one-dimensional phase-modulated NMR experiment, which distinguishes the partially resolved peaks and accelerates the data acquisition due to reduced dimensionality, is reported for differentiating spin systems, with application to chiral discrimination. The multifarious utility of the technique is demonstrated in plenteous examples.

Marine natural products

Covering: 2020This review covers the literature published in 2020 for marine natural products (MNPs), with 757 citations (747 for the period January to December 2020) referring to compounds isolated from marine microorganisms and phytoplankton, green, brown and red algae, sponges, cnidarians, bryozoans, molluscs, tunicates, echinoderms, mangroves and other intertidal plants and microorganisms. The emphasis is on new compounds (1407 in 420 papers for 2020), together with the relevant biological activities, source organisms and country of origin. Pertinent reviews, biosynthetic studies, first syntheses, and syntheses that led to the revision of structures or stereochemistries, have been included. A meta analysis of bioactivity data relating to new MNPs reported over the last five years is also presented.

Ultrahigh-Resolution NMR with Water Signal Suppression for a Deeper Understanding of the Action of Antimetabolic Drugs on Diffuse Large B-Cell Lymphoma

Ultrahigh-resolution NMR has recently attracted considerable attention in the field of complex samples analysis. Indeed, the implementation of broadband homonuclear decoupling techniques has allowed us to greatly simplify crowded ¹H spectra, yielding singlets for almost every proton site from the analyzed molecules. Pure shift methods have notably shown to be particularly suitable for deciphering mixtures of metabolites in biological samples. Here, we have successfully implemented a new pure shift pulse sequence based on the PSYCHE method, which incorporates a block for solvent suppression that is suitable for metabolomics analysis. The resulting experiment allows us to record ultrahigh-resolution 1D NOESY ¹H spectra of biofluids with suppression of the water signal, which is a crucial step for highlighting metabolite mixtures in an aqueous phase. We have successfully recorded pure shift spectra on extracellular media of diffuse large B-cell lymphoma (DLBCL) cells. Despite a lower sensitivity, the resolution of pure shift data was found to be better than that of the standard approach, which provides a more detailed vision of the exo-metabolome. The statistical analyses carried out on the resulting metabolic profiles allow us to successfully highlight several metabolic pathways affected by these drugs. Notably, we show that Kidrolase plays a major role in the metabolic pathways of this DLBCL cell line.

The dolabellane diterpenes as potential inhibitors of the SARS-CoV-2 main protease: molecular insight of the inhibitory mechanism through computational studies

An investigation has been carried out on natural products from dolabellane derivatives to understand their potential in inhibiting the SARS-CoV-2 main protease (3CLpro) using an in silico approach. Inhibition of the 3CLpro enzyme is a promising target in stopping the replication of the SARS-CoV-2 virus through inhibition of the subsite binding pocket. The redocking process aims to determine the 3CLpro active sites. The redocking requirement showed a good pose with an RMSD value of 1.39 Å. The combination of molecular docking and MD simulation shows the results of DD13 as a candidate which had a good binding affinity (kcal mol-1) to inhibit the 3CLpro enzyme activity. Prediction of binding free energy (kcal mol-1) of DD13 using the Molecular Mechanics-Poisson Boltzmann/Generalized Born Surface Area (MM-PB/GBSA) approach shows the results ΔG bind(MM-GBSA): -52.33 ± 0.34 and ΔG bind(MM-PBSA): -43.52 ± 0.42. The key residues responsible for the inhibition mechanism are Hie41, Ser46, Met49, Asn142, Cys145, Hie163, Met165, and Gln189. Additionally, pharmacokinetic prediction recommended that DD13 had promising criteria as a drug candidate. The results demonstrated in this study provide theoretical information to obtain a potential inhibitor against the SARS-CoV-2 main protease.

LINT-Web: A Web-Based Lipidomic Data Mining Tool Using Intra-Omic Integrative Correlation Strategy

Lipidomics is a younger member of the "omics" family. It aims to profile lipidome alterations occurring in biological systems. Similar to the other "omics", lipidomic data is highly dimensional and contains a massive amount of information awaiting deciphering and data mining. Currently, the available bioinformatic tools targeting lipidomic data processing and lipid pathway analysis are limited. A few tools designed for lipidomic analysis perform only basic statistical analyses, and lipid pathway analyses rely heavily on public databases (KEGG, Reactome, and HMDB). Due to the inadequate understanding of lipid signaling and metabolism, the use of public databases for lipid pathway analysis can be biased and misleading. Instead of using public databases to interpret lipidomic ontology, the authors introduce an intra-omic integrative correlation strategy for lipidomic data mining. Such an intra-omic strategy allows researchers to unscramble and predict lipid biological functions from correlated genomic ontological results using statistical approaches. To simplify and improve the lipidomic data processing experience, they designed an interactive web-based tool: LINT-web (http://www.lintwebomics.info/) to perform the intra-omic analysis strategy, and validated the functions of LINT-web using two biological systems. Users without sophisticated statistical experience can easily process lipidomic datasets and predict the potential lipid biological functions using LINT-web.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Comparison of Regular, Pure Shift, and Fast 2D NMR Experiments for Determination of the Geographical Origin of Walnuts

¹H NMR spectroscopy, in combination with chemometric methods, was used to analyze the methanol/acetonitrile (1:1) extract of walnut (Juglans Regia L.) regarding the geographical origin of 128 authentic samples from different countries (France, Germany, China) and harvest years (2016–2019). Due to the large number of different metabolites within the acetonitrile/methanol extract, the one-dimensional (1D) ¹H NOESY (nuclear Overhauser effect spectroscopy) spectra suffer from strongly overlapping signals. The identification of specific metabolites and statistical analysis are complicated. The use of pure shift ¹H NMR spectra such as PSYCHE (pure shift yielded by chirp excitation) or two-dimensional ASAP-HSQC (acceleration by sharing adjacent polarization-heteronuclear single quantum correlation) spectra for multivariate analysis to determine the geographical origin of foods may be a promising method. Different types of NMR spectra (1D ¹H NOESY, PSYCHE, and ASAP-HSQC) were acquired for each of the 128 walnut samples and the results of the statistical analysis were compared. A support vector machine classifier was applied for differentiation of samples from Germany/China, France/Germany, and France/China. The models obtained by conduction of a repeated nested cross-validation showed accuracies from 58.9% (±1.3%) to 95.9% (±0.8%). The potential of the ¹H-¹³C HSQC as a 2D NMR experiment for metabolomics studies was shown.

PSYCHE-A Valuable Experiment in Plant NMR-Metabolomics

¹H-NMR is a very reproducible spectroscopic method and, therefore, a powerful tool for the metabolomic analysis of biological samples. However, due to the high complexity of natural samples, such as plant extracts, the evaluation of spectra is difficult because of signal overlap. The new NMR “Pure Shift” methods improve spectral resolution by suppressing homonuclear coupling and turning multiplets into singlets. The PSYCHE (Pure Shift yielded by Chirp excitation) and the Zangger–Sterk pulse sequence were tested. The parameters of the more suitable PSYCHE experiment were optimized, and the extracts of 21 Hypericum species were measured. Different evaluation criteria were used to compare the suitability of the PSYCHE experiment with conventional ¹H-NMR. The relationship between the integral of a signal and the related bin value established by linear regression demonstrates an equal representation of the integrals in binned PSYCHE spectra compared to conventional ¹H-NMR. Using multivariate data analysis based on both techniques reveals comparable results. The obtained data demonstrate that Pure Shift spectra can support the evaluation of conventional ¹H-NMR experiments.