Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography–High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics

Testacosides A-D, glycoglycerolipids produced by Microbacterium testaceum isolated from Tedania brasiliensis

Marine bacteria living in association with marine sponges have proven to be a reliable source of biologically active secondary metabolites. However, no studies have yet reported natural products from Microbacterium testaceum spp. We herein report the isolation of a M. testaceum strain from the sponge Tedania brasiliensis. Molecular networking analysis of bioactive pre-fractionated extracts from culture media of M. testaceum enabled the discovery of testacosides A-D. Analysis of spectroscopic data and chemical derivatizations allowed the identification of testacosides A-D as glycoglycerolipids bearing a 1-[α-glucopyranosyl-(1 → 3)-(α-mannopyranosyl)]-glycerol moiety connected to 12-methyltetradecanoic acid for testacoside A (1), 14-methylpentadecanoic acid for testacoside B (2), and 14-methylhexadecanoic acid for testacosides C (3) and D (4). The absolute configuration of the monosaccharide residues was determined by 1H-NMR analysis of the respective diastereomeric thiazolidine derivatives. This is the first report of natural products isolated from cultures of M. testaceum. KEY POINTS: • The first report of metabolites produced by Microbacterium testaceum. • 1-[α-Glucopyranosyl-(1 → 3)-(α-mannopyranosyl)]-glycerol lipids isolated and identified. • Microbacterium testaceum strain isolated from the sponge Tedania brasiliensis.

Unambiguous Characterization of Commercial Natural (Dihydro)phenanthrene Compounds Is Vital in the Discovery of AMPK Activators

These days, easy access to commercially available (poly)phenolic compounds has expanded the scope of potential research beyond the field of chemistry, particularly in the area of their bioactivity. However, the quality of these compounds is often overlooked or not even considered. This issue is illustrated in this study through the example of (dihydro)phenanthrenes, a group of natural products present in yams, as AMP-activated protein kinase (AMPK) activators. A study conducted in our group on a series of compounds, fully characterized using a combination of chemical synthesis, NMR and MS techniques, provided evidence that the conclusions of a previous study were erroneous, likely due to the use of a misidentified commercial compound by its supplier. Furthermore, we demonstrated that additional representatives of the (dihydro)phenanthrene phytochemical classes were able to directly activate AMPK, avoiding the risk of misinterpretation of results based on analysis of a single compound alone.

Chemometric-based drug discovery approaches from natural origins using hyphenated chromatographic techniques

INTRODUCTION

Isolation and characterization of bioactive components from complex matrices of marine or terrestrial biological origins are the most challenging issues for natural product chemists. Biochemometric is a new potential scope in natural product analytical science, and it is a methodology to find the compound's correlation to their bioactivity with the help of hyphenated chromatographic techniques and chemometric tools.

OBJECTIVES

The present review aims to evaluate the application of chemometric tools coupled to chromatographic techniques for drug discovery from natural resources.

METHODS

The searching keywords "biochemometric," "chemometric," "chromatography," "natural products bioassay," and "bioassay" were selected to search the published articles between 2010-2023 using different search engines including "Pubmed", "Web of Science," "ScienceDirect," and "Google scholar."

RESULTS

An initial stage in natural product analysis is applying the chromatographic hyphenated techniques in conjunction with biochemometric approaches. Among the applied chromatographic techniques, liquid chromatography (LC) techniques, have taken up more than half (53%) and also, mass spectroscopy (MS)-based chromatographic techniques such as LC-MS are the most widely used techniques applied in combination with chemometric methods for natural products bioassay. Considering the complexity of dataset achieved from chromatographic hyphenated techniques, chemometric tools have been increasingly employed for phytochemical studies in the context of determining botanicals geographical origin, quality control, and detection of bioactive compounds.

CONCLUSION

Biochemometric application is expected to be further improved with advancing in data acquisition methods, new efficient preprocessing, model validation and variable selection methods which would guarantee that the applied model to have good prediction ability in compound relation to its bioactivity.

New piperazine derivatives helvamides B-C from the marine-derived fungus Penicillium velutinum ZK-14 uncovered by OSMAC (One Strain Many Compounds) strategy

Four extracts of the marine-derived fungus Penicillium velutinum J.F.H. Beyma were obtained via metal ions stress conditions based on the OSMAC (One Strain Many Compounds) strategy. Using a combination of modern approaches such as LC/UV, LC/MS and bioactivity data analysis, as well as in silico calculations, influence metal stress factors to change metabolite profiles Penicillium velutinum were analyzed. From the ethyl acetate extract of the P. velutinum were isolated two new piperazine derivatives helvamides B (1) and C (2) together with known saroclazin A (3) (4S,5R,7S)-4,11-dihydroxy-guaia-1(2),9(10)-dien (4). Their structures were established based on spectroscopic methods. The absolute configuration of helvamide B (1) as 2R,5R was determined by a combination of the X-ray analysis and by time-dependent density functional theory (TD-DFT) calculations of electronic circular dichroism (ECD) spectra. The cytotoxic activity of the isolated compounds against human prostate cancer PC-3 and human embryonic kidney HEK-293 cells and growth inhibition activity against yeast-like fungi Candida albicans were assayed.

Molecular networking-guided investigation of the secondary metabolome of four Morus species and their in vivo neuroprotective potential for the mitigation of Alzheimer's disease

Alzheimer's Disease (AD) is a fatal age-related neurodegenerative condition with a multifactorial etiology contributing to 70% of dementia globally. The search for a multi-target agent to hit different targets involved in the pathogenesis of AD is crucial. In the present study, the neuroprotective effects of four Morus extracts were assessed in LPS-induced AD in mice. Among the studied species, M. macroura exhibited a profound effect on alleviating the loss of cognitive function, improved the learning ability, restored the acetylcholine esterase (AChE) levels to normal, and significantly reduced the tumor necrosis factor alpha (TNF-α) brain content in LPS-treated mice. To investigate the secondary metabolome of the studied Morus species, ultra-performance liquid chromatography coupled to tandem mass spectrometry (UPLC-HRMS/MS), aided with feature-based molecular networking, was employed. Among the annotated features, aryl benzofurans and prenylated flavonoids were suggested as being responsible for the observed neuroprotective effect. Furthermore, some of the detected metabolites were proposed as new natural products such as moranoline di-O-hexoside (1), isomers of trimethoxy-dihydrochalcone-O-dihexoside (59 & 76), (hydroxy-dimethoxyphenyl)butenone-O-hexoside (82), and O-methylpreglabridin-O-sulphate (105). In conclusion, our findings advocate the potential usage of M. macroura leaves for the management of AD, yet after considering further clinical trials.

LC-MS/DIA-based strategy for comprehensive flavonoid profiling: an Ocotea spp. applicability case

We introduce a liquid chromatography - mass spectrometry with data-independent acquisition (LC-MS/DIA)-based strategy, specifically tailored to achieve comprehensive and reliable glycosylated flavonoid profiling. This approach facilitates in-depth and simultaneous exploration of all detected precursors and fragments during data processing, employing the widely-used open-source MZmine 3 software. It was applied to a dataset of six Ocotea plant species. This framework suggested 49 flavonoids potentially newly described for these plant species, alongside 45 known features within the genus. Flavonols kaempferol and quercetin, both exhibiting O-glycosylation patterns, were particularly prevalent. Gas-phase fragmentation reactions further supported these findings. For the first time, the apigenin flavone backbone was also annotated in most of the examined Ocotea species. Apigenin derivatives were found mainly in the C-glycoside form, with O. porosa displaying the highest flavone : flavonol ratio. The approach also allowed an unprecedented detection of kaempferol and quercetin in O. porosa species, and it has underscored the untapped potential of LC-MS/DIA data for broad and reliable flavonoid profiling. Our study annotated more than 50 flavonoid backbones in each species, surpassing the current literature.

Metabolomics and Molecular Networking-Guided Screening of Bacillus-Derived Bioactive Compounds Against a Highly Lethal Vibrio Species

Microorganisms are important sources of bioactive natural products. However, the complexity of microbial metabolites and the low abundance of active compounds render the isolation and purification process laborious and inefficient. During our search for active substances capable of inhibiting the newly discovered highly lethal Vibrio strain vp-HL, we found that the fermentation broth of multiple Bacillus strains exhibited antibacterial activity. However, the substances responsible for the activity remained unclear. Metabolomics, molecular networking (MN), and the Structural similarity Network Annotation Platform for Mass Spectrometry (SNAP-MS) were employed in conjunction with bioactivity screening to predict the antibacterial compounds from Bacillus strains. The analysis of fractions, and their isolation, NMR-based annotation, and bioactivity evaluation of an amicoumacin compound partially confirmed the prediction from these statistical analyses. This work presents the potential of marine Bacillus in producing active substances against Vibrio species. Additionally, it highlighted the significance and feasibility of metabolomics and MN in the dereplication of compounds and the determination of isolation targets.

Decoding active compounds and molecular targets of herbal medicine by high-throughput metabolomics technology: A systematic review

Clinical experiences of herbal medicine (HM) have been used to treat a variety of human intractable diseases. As the treatment of diseases using HM is characterized by multi-components and multi-targets, it is difficult to determine the bio-active components, explore the molecular targets and reveal the mechanisms of action. Metabolomics is frequently used to characterize the effect of external disturbances on organisms because of its unique advantages on detecting changes in endogenous small-molecule metabolites. Its systematicity and integrity are consistent with the effective characteristics of HM. After HM intervention, metabolomics can accurately capture and describe the behavior of endogenous metabolites under the disturbance of functional compounds, which will be used to decode the bioactive ingredients of HM and expound the molecular targets. Metabolomics can provide an approach for explaining HM, addressing unclear clinical efficacy and undefined mechanisms of action. In this review, the metabolomics strategy and its applications in HM are systematically introduced, which offers valuable insights for metabolomics methods to characterizing the pharmacological effects and molecular targets of HM.

Medicinal plants meet modern biodiversity science

Plants have been an essential source of human medicine for millennia. In this review, we argue that a holistic, interdisciplinary approach to the study of medicinal plants that combines methods and insights from three key disciplines - evolutionary ecology, molecular biology/biochemistry, and ethnopharmacology - is poised to facilitate new breakthroughs in science, including pharmacological discoveries and rapid advancements in human health and well-being. Such interdisciplinary research leverages data and methods spanning space, time, and species associated with medicinal plant species evolution, ecology, genomics, and metabolomic trait diversity, all of which build heavily on traditional Indigenous knowledge. Such an interdisciplinary approach contrasts sharply with most well-funded and successful medicinal plant research during the last half-century, which, despite notable advancements, has greatly oversimplified the dynamic relationships between plants and humans, kept hidden the larger human narratives about these relationships, and overlooked potentially important research and discoveries into life-saving medicines. We suggest that medicinal plants and people should be viewed as partners whose relationship involves a complicated and poorly explored set of (socio-)ecological interactions including not only domestication but also commensalisms and mutualisms. In short, medicinal plant species are not just chemical factories for extraction and exploitation. Rather, they may be symbiotic partners that have shaped modern societies, improved human health, and extended human lifespans.

IMN4NPD: An Integrated Molecular Networking Workflow for Natural Product Dereplication

Molecular networking has emerged as a standard approach for natural product (NP) discovery. However, the current pipeline based on molecular networks tends to prioritize larger clusters comprising multiple nodes. To address this issue, we present the integrated molecular networking workflow for NP dereplication (IMN4NPD). This approach not only expedites the rapid dereplication of extensive clusters within the molecular network but also places specific emphasis on self-looped or pairs of nodes, which are often overlooked by the current methods. By amalgamating the outputs from various computational tools, we efficiently dereplicate compounds falling into specific categories and provide annotations for both large cluster nodes and self-looped or pair of nodes within the molecular network. Furthermore, we have incorporated several fundamentally distinct similarity algorithms, namely, Spec2Vec and MS2DeepScore, for constructing the t-SNE network. Through comparison with modified cosine similarity, we have observed that integrating additional diverse spectral similarity measures, the resulting t-SNE network enhanced the ability to dereplicate NPs. Demonstrating the use case of an ethanol extract of Plumula nelumbinis, we illustrate that an integration of multiple computational solutions with IMN4NPD aids the dereplication, especially self-looped nodes, and in the discovery of novel compounds in NPs.

Molecular networking unveils anti-SARS-CoV-2 constituents from traditionally used remedies

ETHNOPHARMACOLOGICAL RELEVANCE

Plants and fungi have a long tradition in ethnopharmacology for the treatment of infectious diseases including viruses. Many of these natural products have also been used to combat SARS-CoV-2 infections or symptoms of the post- and long-COVID form, owing to the scarcity of clinically approved therapeutics.

AIM OF THE STUDY

The ongoing threat posed by SARS-CoV-2, along with the rapidly evolving new variants, requires the development of new antiviral compounds. The aim of this study was to identify anti-SARS-CoV-2 herbal and fungal extracts used in traditional medicine against acute respiratory infection, inflammation, and related symptoms. Additionally, we sought to characterize their bioactive constituents.

MATERIALS AND METHODS

The antiviral activity and cell cytotoxicity of 179 herbal and fungal extracts were evaluated using two SARS-CoV-2 infection assays in Caco-2 cells. 19 plant extracts with and without anti-SARS-CoV-2 activity underwent detailed dereplication using molecular networking.

RESULTS

Extracts from Angelica sinensis (Oliv.) Diels roots, Annona squamosa L. seeds, Azadirachta indica A. Juss. fruits, Buddleja officinalis Maxim. flowers, Burkea africana Hook. bark and Clinopodium menthifolium (Host) Stace aerial parts showed a potent anti SARS-CoV-2 activity (IC50 < 5 μg/ml) with only moderate cytotoxicity (CC50 > 60 μg/ml, Caco-2). By performing the dereplication with a bioactivity-featured molecular network (MN) on the extract library level, rather than on the level of individual extracts, we could pinpoint compounds characteristic for active extracts. Thus, a straight-forward identification of potential anti-SARS-CoV-2 natural compounds was achieved prior to any fractionation or isolation efforts.

CONCLUSIONS

A sophisticated hyphenation of empirical knowledge with MS-based bioinformatics and automated compound annotation was applied to decipher the chemical space of the investigated extracts. The correlation with experimentally assessed anti-SARS-CoV-2 activities helped in predicting compound classes and structural elements relevant for the antiviral activities. Consequently, this accelerated the identification of constituents from the investigated mixtures with inhibitory effects against SARS-CoV-2.

COLMARppm: A Web Server Tool for the Accurate and Rapid Prediction of 1H and 13C NMR Chemical Shifts of Organic Molecules and Metabolites

Despite rapid progress in metabolomics research, a major bottleneck is the large number of metabolites whose chemical structures are unknown or whose spectra have not been deposited in metabolomics databases. Nuclear magnetic resonance (NMR) spectroscopy has a long history of elucidating chemical structures from experimentally measured 1H and 13C chemical shifts. One approach to characterizing the chemical structures of an unknown metabolite is to predict the 1H and 13C chemical shifts of candidate compounds (e.g., metabolites from the Human Metabolome Database (HMDB)) and compare them with chemical shifts of the unknown. However, accurate prediction of NMR chemical shifts in aqueous solution is challenging due to limitations of experimental chemical shift libraries and the high computational cost of quantum chemical methods. To improve NMR prediction accuracy and applicability, an empirical prediction strategy is introduced here to provide an accurately predicted chemical shift for organic molecules and metabolites within seconds. Unique features of COLMARppm include (i) the training library exclusively consisting of high quality NMR spectra measured under standard conditions in aqueous solution, (ii) utilization of NMR motif information, and (iii) leveraging of the improved prediction accuracy for the automated assignment of experimental chemical shifts for candidate structures. COLMARppm is demonstrated in terms of accuracy and speed for a set of 20 compounds taken from the HMDB for chemical shift prediction and resonance assignment. COLMARppm is applicable to a wide range of small molecules and can be directly incorporated into metabolomics workflows.

Research progress of plant-derived natural products in thyroid carcinoma

Thyroid carcinoma (TC) is a prevalent malignancy of the endocrine system, with a notable rise in its detection rate in recent decades. The primary therapeutic approaches for TC now encompass thyroidectomy and radioactive iodine therapy, yielding favorable prognoses for the majority of patients. TC survivors may necessitate ongoing surveillance, remedial treatment, and thyroid hormone supplementation, while also enduring the adverse consequences of thyroid hormone fluctuations, surgical complications, or side effects linked to radioactive iodine administration, and encountering enduring physical, psychosocial, and economic hardships. In vitro and in vivo studies of natural products against TC are demonstrating the potential of these natural products as alternatives to the treatment of thyroid cancer. This therapy may offer greater convenience, affordability, and acceptability than traditional therapies. In the early screening of natural products, we mainly use a combination of database prediction and literature search. The pharmacological effects on TC of selected natural products (quercetin, genistein, apigenin, luteolin, chrysin, myricetin, resveratrol, curcumin and nobiletin), which hold promise for therapeutic applications in TC, are reviewed in detail in this article through most of the cell-level evidence, animal-level evidence, and a small amount of human-level evidence. In addition, this article explores possible issues, such as bioavailability, drug safety.

One-step Bio-guided Isolation of Secondary Metabolites from the Endophytic Fungus Penicillium crustosum Using High-resolution Semi-preparative HPLC

BACKGROUND

An endophytic fungal strain Penicillium crustosum was isolated from the seagrass Posidonia oceanica and investigated to identify its antimicrobial constituents and characterize its metabolome composition. The ethyl acetate extract of this fungus exhibited antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA) as well as an anti-quorum sensing effect against Pseudomonas aeruginosa.

METHODS

The crude extract was profiled by UHPLC-HRMS/MS, and the dereplication was assisted by feature-based molecular networking. As a result, more than twenty compounds were annotated in this fungus. To rapidly identify the active compounds, the enriched extract was fractionated by semipreparative HPLC-UV applying a chromatographic gradient transfer and dry load sample introduction to maximise resolution. The collected fractions were profiled by 1H-NMR and UHPLC-HRMS.

RESULTS

The use of molecular networking-assisted UHPLC-HRMS/MS dereplication allowed preliminary identification of over 20 compounds present in the ethyl acetate extract of P. crustosum. The chromatographic approach significantly accelerated the isolation of the majority of compounds present in the active extract. The one-step fractionation allowed the isolation and identification of eight compounds (1-8).

CONCLUSION

This study led to the unambiguous identification of eight known secondary metabolites as well as the determination of their antibacterial properties.

In Search of Authenticity Biomarkers in Food Supplements Containing Sea Buckthorn: A Metabolomics Approach

Sea buckthorn (Hippophae rhamnoides L.) (SB) is increasingly consumed worldwide as a food and food supplement. The remarkable richness in biologically active phytochemicals (polyphenols, carotenoids, sterols, vitamins) is responsible for its purported nutritional and health-promoting effects. Despite the considerable interest and high market demand for SB-based supplements, a limited number of studies report on the authentication of such commercially available products. Herein, untargeted metabolomics based on ultra-high-performance liquid chromatography coupled with quadrupole-time of flight mass spectrometry (UHPLC-QTOF-ESI+MS) were able to compare the phytochemical fingerprint of leaves, berries, and various categories of SB-berry herbal supplements (teas, capsules, tablets, liquids). By untargeted metabolomics, a multivariate discrimination analysis and a univariate approach (t-test and ANOVA) showed some putative authentication biomarkers for berries, e.g., xylitol, violaxanthin, tryptophan, quinic acid, quercetin-3-rutinoside. Significant dominant molecules were found for leaves: luteolin-5-glucoside, arginine, isorhamnetin 3-rutinoside, serotonin, and tocopherol. The univariate analysis showed discriminations between the different classes of food supplements using similar algorithms. Finally, eight molecules were selected and considered significant putative authentication biomarkers. Further studies will be focused on quantitative evaluation.

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.

Identification of Daphnane Diterpenoids from Wikstroemia indica Using Liquid Chromatography with Tandem Mass Spectrometry

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has emerged as a powerful tool for the rapid identification of compounds within natural resources. Daphnane diterpenoids, a class of natural compounds predominantly found in plants belonging to the Thymelaeaceae and Euphorbiaceae families, have attracted much attention due to their remarkable anticancer and anti-HIV activities. In the present study, the presence of daphnane diterpenoids in Wikstroemia indica, a plant belonging to the Thymelaeaceae family, was investigated by LC-MS/MS analysis. As a result, 21 daphnane diterpenoids (1-21) in the stems of W. indica were detected. Among these, six major compounds (12, 15, 17, 18, 20, and 21) were isolated and their structures were unequivocally identified through a comprehensive analysis of the MS and NMR data. For the minor compounds (1-11, 13, 14, 16, and 19), their structures were elucidated by in-depth MS/MS fragmentation analysis. This study represents the first disclosure of structurally diverse daphnane diterpenoids in W. indica, significantly contributing to our understanding of bioactive diterpenoids in plants within the Thymelaeaceae family.

A critical review of machine-learning for "multi-omics" marine metabolite datasets

During the last decade, genomic, transcriptomic, proteomic, metabolomic, and other omics datasets have been generated for a wide range of marine organisms, and even more are still on the way. Marine organisms possess unique and diverse biosynthetic pathways contributing to the synthesis of novel secondary metabolites with significant bioactivities. As marine organisms have a greater tendency to adapt to stressed environmental conditions, the chance to identify novel bioactive metabolites with potential biotechnological application is very high. This review presents a comprehensive overview of the available "-omics" and "multi-omics" approaches employed for characterizing marine metabolites along with novel data integration tools. The need for the development of machine-learning algorithms for "multi-omics" approaches is briefly discussed. In addition, the challenges involved in the analysis of "multi-omics" data and recommendations for conducting "multi-omics" study were discussed.

Chemical profiling and quantitative analysis on the aqueous extract of Pimpinella anisum fruit by liquid chromatography-tandem mass spectrometry

Anise fruit (Aniseed) has been used for many years as a traditional medicine in various countries throughout the world; however, the chemical material basis of Aniseed water extract (AWE) has not been examined in detail, limiting our understanding of its pharmacological mechanism and methods for practical quality control. A high-efficiency and high-sensitivity LC-triple time-of-flight tandem mass spectrometry (MS/MS) analysis method using data processing method combined with product ion and neutral loss filtering for systematic screening and identification of the constituents of AWE was established. A quantitative method was established by using LC-MS/MS with multiple reaction monitoring for 10 min to determine the concentration of 17 representative constituents. A total of 89 compounds were discovered in AWE, of which 31 were confirmed by the reference standards, while 24 were found in Aniseed for the first time. The qualification analysis results showed that chlorogenic acids and luteolin derivatives were the major compounds. The linearity, sensitivity, precision, stability, repeatability, and accuracy of the method were verified, which demonstrated that the method could meet the requirements for quantification. This work contributes to a better understanding of the chemical material basis of Aniseed and assists in the development of effective analytical methods for natural medicines.

Spatially resolved detection of small molecules from press-dried plant tissue using MALDI imaging

Premise

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a chemical imaging method that can visualize spatial distributions of particular molecules. Plant tissue imaging has so far mostly used cryosectioning, which can be impractical for the preparation of large-area imaging samples, such as full flower petals. Imaging unsectioned plant tissue presents its own difficulties in extracting metabolites to the surface due to the waxy cuticle.

Methods

We address this by using established delipidation techniques combined with a solvent vapor extraction prior to applying the matrix with many low-concentration sprays.

Results

Using this procedure, we imaged tissue from three different plant species (two flowers and one carnivorous plant leaf). Material factorization analysis of the resulting data reveals a wide range of plant-specific small molecules with varying degrees of localization to specific portions of the tissue samples, while facilitating detection and removal of signal from background sources.

Conclusions

This work demonstrates applicability of MALDI-MSI to press-dried plant samples without freezing or cryosectioning, setting the stage for spatially resolved molecule identification. Increased mass resolution and inclusion of tandem mass spectrometry are necessary next steps to allow more specific and reliable compound identification.

Mass Spectrometric Fingerprint Mapping Reveals Species-Specific Differences in Plant Polyphenols and Related Bioactivities

Plant species show large variation in the composition and content of their tannins and other polyphenols. These large metabolites are not easy to measure accurately, but they are important factors for species bioactivity and chemotaxonomy. Here, we used an automated group-specific UHPLC-DAD-MS/MS tool to detect and quantify eight most common polyphenol groups in 31 chemically diverse plant species representing many types of growth forms and evolutionary ages. Ten replicate plants were used for each species and two polyphenol-related bioactivities, i.e., protein precipitation capacity and oxidative activity were measured in all samples as well. By the help of a novel 2D fingerprint mapping tool we were able to visualize the qualitative and quantitative differences between the species in hydrolysable tannins (galloyl and hexahydroxydiphenoyl derivatives), proanthocyanidins (procyanidins and prodelphinidins), flavonols (kaempferol, quercetin and myricetin derivatives) and quinic acid derivatives together with the two bioactivities. The highest oxidative activities were found with species containing ellagitannins (e.g., Quercus robur, Geranium sylvaticum, Lythrum salicaria and Chamaenerion angustifolium) or prodelphinidin-rich proanthocyanidins (e.g., Ribes alpinum, Salix phylicifolia and Lysimachia vulgaris). The best species with high protein precipitation capacity were rich in gallotannins (Acer platanoides and Paeonia lactiflora) or oligomeric ellagitannins (e.g., Comarum palustre, Lythrum salicaria and Chamaenerion angustifolium). These types of tools could prove their use in many types of screening experiments and might reveal even unusually active polyphenol types directly from the crude plant extracts.

Reconciliation and evolution of Penicillium rubens genome-scale metabolic networks-What about specialised metabolism?

In recent years, genome sequencing of filamentous fungi has revealed a high proportion of specialised metabolites with growing pharmaceutical interest. However, detecting such metabolites through in silico genome analysis does not necessarily guarantee their expression under laboratory conditions. However, one plausible strategy for enabling their production lies in modifying the growth conditions. Devising a comprehensive experimental design testing in different culture environments is time-consuming and expensive. Therefore, using in silico modelling as a preliminary step, such as Genome-Scale Metabolic Network (GSMN), represents a promising approach to predicting and understanding the observed specialised metabolite production in a given organism. To address these questions, we reconstructed a new high-quality GSMN for the Penicillium rubens Wisconsin 54-1255 strain, a commonly used model organism. Our reconstruction, iPrub22, adheres to current convention standards and quality criteria, incorporating updated functional annotations, orthology searches with different GSMN templates, data from previous reconstructions, and manual curation steps targeting primary and specialised metabolites. With a MEMOTE score of 74% and a metabolic coverage of 45%, iPrub22 includes 5,192 unique metabolites interconnected by 5,919 reactions, of which 5,033 are supported by at least one genomic sequence. Of the metabolites present in iPrub22, 13% are categorised as belonging to specialised metabolism. While our high-quality GSMN provides a valuable resource for investigating known phenotypes expressed in P. rubens, our analysis identifies bottlenecks related, in particular, to the definition of what is a specialised metabolite, which requires consensus within the scientific community. It also points out the necessity of accessible, standardised and exhaustive databases of specialised metabolites. These questions must be addressed to fully unlock the potential of natural product production in P. rubens and other filamentous fungi. Our work represents a foundational step towards the objective of rationalising the production of natural products through GSMN modelling.

Strategies for the discovery of potential anticancer agents from plants collected from Southeast Asian tropical rainforests as a case study

Covering up to early 2023The present review summarizes recent accomplishments made as part of a multidisciplinary, multi-institutional anticancer drug discovery project, wherein samples comprising higher plants were collected primarily from Southeast Asia, and also from Central America, and the West Indies. In the introductory paragraphs, a short perspective is provided on the current importance of plants in the discovery of cancer therapeutic agents, and the contributions of other groups working towards this objective are mentioned. For our own investigations, following their collection, tropical plants have been subjected to solvent extraction and biological evaluation for their antitumor potential. Several examples of purified plant lead bioactive compounds were obtained and characterized, and found to exhibit diverse structures, including those of the alkaloid, cardiac glycoside, coumarin, cucurbitacin, cyclobenzofuran (rocaglate), flavonoid, lignan, and terpenoid types. In order to maximize the efficiency of work on drug discovery from tropical plant species, strategies to optimize various research components have been developed, including those for the plant collections and taxonomic identification, in accordance with the requirements of contemporary international treaties and with a focus on species conservation. A major component of this aspect of the work is the development of collaborative research agreements with representatives of the source countries of tropical rainforest plants. The phytochemical aspects have included the preparation of plant extracts for initial screening and the selection of promising extracts for activity-guided fractionation. In an attempt to facilitate this process, a TOCSY-based NMR procedure has been applied for the determination of bioactive rocaglate derivatives in samples of Aglaia species (Meliaceae) collected for the project. Preliminary in vitro and in vivo mechanistic studies carried out by the authors are described for two tropical plant-derived bioactive lead compounds, corchorusoside C and (+)-betulin, including work conducted with a zebrafish (Danio rerio) model. In the concluding remarks, a number of lessons are summarized that our group has learned as a result of working on anticancer drug discovery using tropical plants, which we hope will be of interest to future workers.

Mass spectrometry-based metabolomic analysis as a tool for quality control of natural complex products

Metabolomics is an area of intriguing and growing interest. Since the late 1990s, when the first Omic applications appeared to study metabolite's pool ("metabolome"), to understand new aspects of the global regulation of cellular metabolism in biology, there have been many evolutions. Currently, there are many applications in different fields such as clinical, medical, agricultural, and food. In our opinion, it is clear that developments in metabolomics analysis have also been driven by advances in mass spectrometry (MS) technology. As natural complex products (NCPs) are increasingly used around the world as medicines, food supplements, and substance-based medical devices, their analysis using metabolomic approaches will help to bring more and more rigor to scientific studies and industrial production monitoring. This review is intended to emphasize the importance of metabolomics as a powerful tool for studying NCPs, by which significant advantages can be obtained in terms of elucidation of their composition, biological effects, and quality control. The different approaches of metabolomic analysis, the main and basic techniques of multivariate statistical analysis are also briefly illustrated, to allow an overview of the workflow associated with the metabolomic studies of NCPs. Therefore, various articles and reviews are illustrated and commented as examples of the application of MS-based metabolomics to NCPs.

A Mass Spectrometry Database for Sea Cucumber Triterpene Glycosides

Sea cucumber triterpene glycosides are a class of secondary metabolites that possess distinctive chemical structures and exhibit a variety of biological and pharmacological activities. The application of MS-based approaches for the study of triterpene glycosides allows rapid evaluation of the structural diversity of metabolites in complex mixtures. However, the identification of the detected triterpene glycosides can be challenging. The objective of this study is to establish the first spectral library containing the mass spectra of sea cucumber triterpene glycosides using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. The library contains the electrospray ionization tandem mass spectra and retention times of 191 triterpene glycosides previously isolated from 15 sea cucumber species and one starfish at the Laboratory of the Chemistry of Marine Natural Products of the G.B. Elyakov Pacific Institute of Bioorganic Chemistry. In addition, the chromatographic behavior and some structure-related neutral losses in tandem MS are discussed. The obtained data will accelerate the accurate dereplication of known triterpene glycosides and the annotation of novel compounds, as we demonstrated by the processing of LC-MS/MS data of Eupentacta fraudatrix extract.

Identification and characterization of phenolamides in tea (Camellia sinensis) flowers using ultra-high-performance liquid chromatography/Q-Exactive orbitrap mass spectrometry

Phenolamides (PAs) are important secondary metabolites present in plants with multiple bioactivities. This study aims to comprehensively identify and characterize PAs in tea (Camellia sinensis) flowers using ultra-high-performance liquid chromatography/Q-Exactive orbitrap mass spectrometry based on a lab-developed in-silico accurate-mass database. The PAs found in tea flowers were conjugates of Z/E-hydroxycinnamic acids (p-coumaric, caffeic and ferulic acids) with polyamines (putrescine, spermidine and agmatine). The positional and Z/E isomers were distinguished through characteristic MS² fragmentation rules and chromatographic retention behavior summarized from some synthetic PAs. 21 types of PAs consisting of over 80 isomers were identified, and the majority of them were found in tea flowers for the first time. Among 12 tea flower varieties studied, they all possessed tris-(p-coumaroyl)-spermidine with the highest relative content, and C. sinensis 'Huangjinya' had the highest total relative contents of PAs. This study shows the richness and structural diversity of PAs in tea flowers.

Contrastive Learning-Based Embedder for the Representation of Tandem Mass Spectra

Tandem mass spectrometry (MS/MS) shows great promise in the research of metabolomics, providing an abundance of information on compounds. Due to the rapid development of mass spectrometric techniques, a large number of MS/MS spectral data sets have been produced from different experimental environments. The massive data brings great challenges into the spectral analysis including compound identification and spectra clustering. The core challenge in MS/MS spectral analysis is how to describe a spectrum more quantitatively and effectively. Recently, emerging deep-learning-based technologies have brought new opportunities to handle this challenge in which high-quality descriptions of MS/MS spectra can be obtained. In this study, we propose a novel contrastive learning-based method for the representation of MS/MS spectra, called CLERMS, which is based on transformer architecture. Specifically, an optimized model architecture equipped with a sinusoidal embedder and a novel loss function composed of InfoNCE loss and MSE loss has been proposed for the attainment of good embedding from the peak information and the metadata. We evaluate our method using a GNPS data set, and the results demonstrate that the learned embedding can not only distinguish spectra from different compounds but also reveal the structural similarity between them. Additionally, the comparison between our method and other methods on the performance of compound identification and spectra clustering shows that our method can achieve significantly better results.

Reaction of the Phytochemistry Community to Green Chemistry: Insights Obtained Since 1990

This review article aims to study how phytochemists have reacted to green chemistry insights since 1990, the year when the U.S. Environmental Protection Agency launched the "Pollution Prevention Act". For each year in the period 1990 to 2019, three highly cited phytochemistry papers that provided enough information about the experimental procedures utilized were sampled. The "greenness" of these procedures was assessed, particularly for the use of solvents. The highly hazardous diethyl ether, benzene, and carbon tetrachloride did not appear in the papers sampled after 2010. Advances in terms of sustainability were observed mainly in the extraction stage. Similar progress was not observed in purification procedures, where chloroform, dichloromethane, and hexane regularly have been employed. Since replacing such solvents in purification procedures should be a major goal, potential alternative approaches are discussed. Moreover, some current initiatives toward a more sustainable phytochemical research considering aspects other than only solvents are highlighted. Although some advances have been achieved, it is believed that natural products chemists can play a major role in developing a novel ecological paradigm in chemistry. To contribute to this objective, six principles for performing natural products chemistry consistent with the guidelines of green chemistry are proposed.

Two new di-tert-butyl-type compounds from a saline-lake derived Streptomyces sp. XZB42

Two novel di-tert-butyl-type structures (1-2), and five known compounds (3-7) were isolated from the chemical investigations of a saline lake actinomycete, Streptomyces sp. XZB42. The structures of the new compounds were elucidated by extensive NMR spectroscopic analysis, HRESIMS data, GIAO (gauge-including atomic orbitals) NMR and specific optical rotation (SOR).

Direct introduction MALDI FTICR MS based on dried droplet deposition applied to non-targeted metabolomics on Pisum Sativum root exudates

Non-targeted metabolomic approaches based on direct introduction (DI) through a soft ionization source are nowadays used for large-scale analysis and wide cover-up of metabolites in complex matrices. When coupled with ultra-high-resolution Fourier-Transform ion cyclotron resonance (FTICR MS), DI is generally performed through electrospray (ESI), which, despite the great analytical throughput, can suffer of matrix effects due to residual salts or charge competitors. In alternative, matrix assisted laser desorption ionization (MALDI) coupled with FTICR MS offers relatively high salt tolerance but it is mainly used for imaging of small molecule within biological tissues. In this study, we report a systematic evaluation on the performance of direct introduction ESI and MALDI coupled with FTICR MS applied to the analysis of root exudates (RE), a complex mixture of metabolites released from plant root tips and containing a relatively high salt concentration. Classic dried droplet deposition followed by screening of best matrices and ratio allowed the selection of high ranked conditions for non-targeted metabolomics on RE. Optimization of MALDI parameters led to improved reproducibility and precision. A RE desalted sample was used for comparison on ionization efficiency of the two sources and ion enhancement at high salinity was highlighted in MALDI by spiking desalted solution with inorganic salts. Application of a true lyophilized RE sample exhibited the complementarity of the two sources and the ability of MALDI in the detection of undisclosed metabolites suffering of matrix effects in ESI mode.

Deciphering interactions between the marine dinoflagellate Prorocentrum lima and the fungus Aspergillus pseudoglaucus

The comprehension of microbial interactions is one of the key challenges in marine microbial ecology. This study focused on exploring chemical interactions between the toxic dinoflagellate Prorocentrum lima and a filamentous fungal species, Aspergillus pseudoglaucus, which has been isolated from the microalgal culture. Such interspecies interactions are expected to occur even though they were rarely studied. Here, a co-culture system was designed in a dedicated microscale marine-like condition. This system allowed to explore microalgal-fungal physical and metabolic interactions in presence and absence of the bacterial consortium. Microscopic observation showed an unusual physical contact between the fungal mycelium and dinoflagellate cells. To delineate specialized metabolome alterations during microalgal-fungal co-culture metabolomes were monitored by high-performance liquid chromatography coupled to high-resolution mass spectrometry. In-depth multivariate statistical analysis using dedicated approaches highlighted (1) the metabolic alterations associated with microalgal-fungal co-culture, and (2) the impact of associated bacteria in microalgal metabolome response to fungal interaction. Unfortunately, only a very low number of highlighted features were fully characterized. However, an up-regulation of the dinoflagellate toxins okadaic acid and dinophysistoxin 1 was observed during co-culture in supernatants. Such results highlight the importance to consider microalgal-fungal interactions in the study of parameters regulating toxin production.

Advances in Mass Spectrometry-Metabolomics Based Approaches

Highly selective and sensitive analytical techniques are necessary for microbial metabolomics due to the complexity of the microbial sample matrix. Hence, mass spectrometry (MS) has been successfully applied in microbial metabolomics due to its high precision, versatility, sensitivity, and wide dynamic range. The different analytical tools using MS have been employed in microbial metabolomics investigations and can contribute to the discovery or accelerate the search for bioactive substances. The coupling with chromatographic and electrophoretic separation techniques has resulted in more efficient technologies for the analysis of microbial compounds occurring in trace levels. This book chapter describes the current advances in the application of mass spectrometry-based metabolomics in the search for new biologically active agents from microbial sources; the development of new approaches for in silico annotation of natural products; the different technologies employing mass spectrometry imaging to deliver more comprehensive analysis and elucidate the metabolome involved in ecological interactions as they enable visualization of the spatial dispersion of small molecules. We also describe other ambient ionization techniques applied to the fingerprint of microbial natural products and modern techniques such as ion mobility mass spectrometry used to microbial metabolomic analyses and the dereplication of natural microbial products through MS.

Metabolomics: Going Deeper, Going Broader, Going Further

Metabolomics is a continuously dynamic field of research that is driven by demanding research questions and technological advances alike. In this review we highlight selected recent and ongoing developments in the area of mass spectrometry-based metabolomics. The field of view that can be seen through the metabolomics lens can be broadened by adoption of separation techniques such as hydrophilic interaction chromatography and ion mobility mass spectrometry (going broader). For a given biospecimen, deeper metabolomic analysis can be achieved by resolving smaller entities such as rare cell populations or even single cells using nano-LC and spatially resolved metabolomics or by extracting more useful information through improved metabolite identification in untargeted metabolomic experiments (going deeper). Integration of metabolomics with other (omics) data allows researchers to further advance in the understanding of the complex metabolic and regulatory networks in cells and model organisms (going further). Taken together, diverse fields of research from mechanistic studies to clinics to biotechnology applications profit from these technological developments.

Mass spectrometry-based strategies for single-cell metabolomics

Single cell analysis has drawn increasing interest from the research community due to its capability to interrogate cellular heterogeneity, allowing refined tissue classification and facilitating novel biomarker discovery. With the advancement of relevant instruments and techniques, it is now possible to perform multiple omics including genomics, transcriptomics, metabolomics or even proteomics at single cell level. In comparison with other omics studies, single-cell metabolomics (SCM) represents a significant challenge since it involves many types of dynamically changing compounds with a wide range of concentrations. In addition, metabolites cannot be amplified. Although difficult, considerable progress has been made over the past decade in mass spectrometry (MS)-based SCM in terms of processing technologies and biochemical applications. In this review, we will summarize recent progress in the development of promising MS platforms, sample preparation methods and SCM analysis of various cell types (including plant cell, cancer cell, neuron, embryo cell, and yeast cell). Current limitations and future research directions in the field of SCM will also be discussed.

Discovering New Natural Products Using Metabolomics-Based Approaches

The incessant search for new natural molecules with biological activities has forced researchers in the field of chemistry of natural products to seek different approaches for their prospection studies. In particular, researchers around the world are turning to approaches in metabolomics to avoid high rates of re-isolation of certain compounds, something recurrent in this branch of science. Thanks to the development of new technologies in the analytical instrumentation of spectroscopic and spectrometric techniques, as well as the advance in the computational processing modes of the results, metabolomics has been gaining more and more space in studies that involve the prospection of natural products. Thus, this chapter summarizes the precepts and good practices in the metabolomics of microbial natural products using mass spectrometry and nuclear magnetic resonance spectroscopy, and also summarizes several examples where this approach has been applied in the discovery of bioactive molecules.

Drug discovery inspired by bioactive small molecules from nature

Natural products (NPs) have greatly contributed to the development of novel treatments for human diseases such as cancer, metabolic disorders, and infections. Compared to synthetic chemical compounds, primary and secondary metabolites from medicinal plants, fungi, microorganisms, and our bodies are promising resources with immense chemical diversity and favorable properties for drug development. In addition to the well-validated significance of secondary metabolites, endogenous small molecules derived from central metabolism and signaling events have shown great potential as drug candidates due to their unique metabolite-protein interactions. In this short review, we highlight the values of NPs, discuss recent scientific and technological advances including metabolomics tools, chemoproteomics approaches, and artificial intelligence-based computation platforms, and explore potential strategies to overcome the current challenges in NP-driven drug discovery.

NMR-Based Chromatography Readouts: Indispensable Tools to “Translate” Analytical Features into Molecular Structures

Gaining structural information is a must to allow the unequivocal structural characterization of analytes from natural sources. In liquid state, NMR spectroscopy is almost the only possible alternative to HPLC-MS and hyphenating the effluent of an analyte separation device to the probe head of an NMR spectrometer has therefore been pursued for more than three decades. The purpose of this review article was to demonstrate that, while it is possible to use mass spectrometry and similar methods to differentiate, group, and often assign the differentiating variables to entities that can be recognized as single molecules, the structural characterization of these putative biomarkers usually requires the use of NMR spectroscopy.

New insights into the Van Krevelen diagram: Automated molecular formula determination from HRMS for a large chemical profiling of lichen extracts

INTRODUCTION

In recent years, LC-MS has become the golden standard for metabolomic studies. Indeed, LC is relatively easy to couple with the soft electrospray ionization. As a consequence, many tools have been developed for the structural annotation of tandem mass spectra. However, it is sometimes difficult to do data-dependent acquisition (DDA), especially when developing new methods that stray from the classical LC-MS workflow.

OBJECTIVE

An old tool from petroleomics that has recently gained popularity in metabolomics, the Van Krevelen diagram, is adapted for an overview of the molecular diversity profile in lichens through high-resolution mass spectrometry (HRMS).

METHODS

A new method is benchmarked against the state-of-the-art classification tool ClassyFire using a database containing most known lichen metabolites (n ≈ 2,000). Four lichens known for their contrasted chemical composition were selected, and extractions with apolar, aprotic polar, and protic polar solvents were performed to cover a wide range of polarities. Extracts were analyzed with direct infusion electrospray ionization mass spectrometry (DI-ESI-MS) and atmospheric solids analysis probe mass spectrometry (ASAP-MS) techniques to be compared with the chemical composition described in the literature.

RESULTS

The most common lichen metabolites were efficiently classified, with more than 90% of the molecules in some classes being matched with ClassyFire. Results from this method are consistent with the various extraction protocols in the present case study.

CONCLUSION

This approach is a rapid and efficient tool to gain structural insight regarding lichen metabolites analyzed by HRMS without relying on DDA by LC-MS/MS analysis. It may notably be of use during the development phase of novel MS-based metabolomic approaches.

Molecular Network Guided Cataloging of the Secondary Metabolome of Selected Egyptian Red Sea Soft Corals

Soft corals are recognized as an abundant source of diverse secondary metabolites with unique chemical features and physiologic capabilities. However, the discovery of these metabolites is usually hindered by the traditional protocol which requires a large quantity of living tissue for isolation and spectroscopic investigations. In order to overcome this problem, untargeted metabolomics protocols have been developed. The latter have been applied here to study the chemodiversity of common Egyptian soft coral species, using only minute amounts of coral biomass. Spectral similarity networks, based on high-resolution tandem mass spectrometry data, were employed to explore and highlight the metabolic biodiversity of nine Egyptian soft coral species. Species-specific metabolites were highlighted for future prioritization of soft coral species for MS-guided chemical investigation. Overall, 79 metabolites were tentatively assigned, encompassing diterpenes, sesquiterpenes, and sterols. Simultaneously, the methodology assisted in shedding light on newly-overlooked chemical diversity with potential undescribed scaffolds. For instance, glycosylated fatty acids, nitrogenated aromatic compounds, and polyketides were proposed in Sinularia leptoclados, while alkaloidal terpenes and N-acyl amino acids were proposed in both Sarcophyton roseum and Sarcophyton acutum.

Integrative omics approaches for biosynthetic pathway discovery in plants

Covering: up to 2022With the emergence of large amounts of omics data, computational approaches for the identification of plant natural product biosynthetic pathways and their genetic regulation have become increasingly important. While genomes provide clues regarding functional associations between genes based on gene clustering, metabolome mining provides a foundational technology to chart natural product structural diversity in plants, and transcriptomics has been successfully used to identify new members of their biosynthetic pathways based on coexpression. Thus far, most approaches utilizing transcriptomics and metabolomics have been targeted towards specific pathways and use one type of omics data at a time. Recent technological advances now provide new opportunities for integration of multiple omics types and untargeted pathway discovery. Here, we review advances in plant biosynthetic pathway discovery using genomics, transcriptomics, and metabolomics, as well as recent efforts towards omics integration. We highlight how transcriptomics and metabolomics provide complementary information to link genes to metabolites, by associating temporal and spatial gene expression levels with metabolite abundance levels across samples, and by matching mass-spectral features to enzyme families. Furthermore, we suggest that elucidation of gene regulatory networks using time-series data may prove useful for efforts to unwire the complexities of biosynthetic pathway components based on regulatory interactions and events.

Mass spectrometry data on specialized metabolome of medicinal plants used in East Asian traditional medicine

Traditional East Asian medicine not only serves as a potential source of drug discovery, but also plays an important role in the healthcare systems of Korea, China, and Japan. Tandem mass spectrometry (MS/MS)-based untargeted metabolomics is a key methodology for high-throughput analysis of the complex chemical compositions of medicinal plants used in traditional East Asian medicine. This Data Descriptor documents the deposition to a public repository of a re-analyzable raw LC-MS/MS dataset of 337 medicinal plants listed in the Korean Pharmacopeia, in addition to a reference spectral library of 223 phytochemicals isolated from medicinal plants. Enhanced by recently developed repository-level data analysis pipelines, this information can serve as a reference dataset for MS/MS-based untargeted metabolomic analysis of plant specialized metabolites.

Mass Difference Matching Unfolds Hidden Molecular Structures of Dissolved Organic Matter

Ultrahigh-resolution Fourier transform mass spectrometry (FTMS) has revealed unprecedented details of natural complex mixtures such as dissolved organic matter (DOM) on a molecular formula level, but we lack approaches to access the underlying structural complexity. We here explore the hypothesis that every DOM precursor ion is potentially linked with all emerging product ions in FTMS² experiments. The resulting mass difference (Δm) matrix is deconvoluted to isolate individual precursor ion Δm profiles and matched with structural information, which was derived from 42 Δm features from 14 in-house reference compounds and a global set of 11 477 Δm features with assigned structure specificities, using a dataset of ∼18 000 unique structures. We show that Δm matching is highly sensitive in predicting potential precursor ion identities in terms of molecular and structural composition. Additionally, the approach identified unresolved precursor ions and missing elements in molecular formula annotation (P, Cl, F). Our study provides first results on how Δm matching refines structural annotations in van Krevelen space but simultaneously demonstrates the wide overlap between potential structural classes. We show that this effect is likely driven by chemodiversity and offers an explanation for the observed ubiquitous presence of molecules in the center of the van Krevelen space. Our promising first results suggest that Δm matching can both unfold the structural information encrypted in DOM and assess the quality of FTMS-derived molecular formulas of complex mixtures in general.

Expanding the Coverage of Metabolic Landscape in Cultivated Rice with Integrated Computational Approaches

Genome-scale metabolomics analysis is increasingly used for pathway and function discovery in the post-genomics era. The great potential offered by developed mass spectrometry (MS)-based technologies has been hindered, since only a small portion of detected metabolites were identifiable so far. To address the critical issue of low identification coverage in metabolomics, we adopted a deep metabolomics analysis strategy by integrating advanced algorithms and expanded reference databases. The experimental reference spectra and in silico reference spectra were adopted to facilitate the structural annotation. To further characterize the structure of metabolites, two approaches were incorporated into our strategy, i.e., structural motif search combined with neutral loss scanning and metabolite association network. Untargeted metabolomics analysis was performed on 150 rice cultivars using ultra-performance liquid chromatography coupled with quadrupole-Orbitrap MS. Consequently, a total of 1939 out of 4491 metabolite features in the MS/MS spectral tag (MS2T) library were annotated, representing an extension of annotation coverage by an order of magnitude in rice. The differential accumulation patterns of flavonoids between indica and japonica cultivars were revealed, especially O-sulfated flavonoids. A series of closely-related flavonolignans were characterized, adding further evidence for the crucial role of tricin-oligolignols in lignification. Our study provides an important protocol for exploring phytochemical diversity in other plant species.

Use of Diethanolamine as a Viscous Solvent for Mixture Analysis by Multidimensional Heteronuclear ViscY NMR Experiments

Diethanolamine/DMSO-d₆ as a viscous binary solvent is first reported for the individualization of low-polarity mixture components by multidimensional heteronuclear ViscY NMR experiments under spin diffusion conditions. Solvent viscosity induces the slowing down of molecular tumbling, hence promoting magnetization transfer by dipolar longitudinal cross-relaxation. As a result, all ¹H nuclei resonances within the same molecule may correlate in a 2D nuclear Overhauser effect spectroscopy (NOESY) spectrum, giving access to mixture analysis. We offer a new way to analyze mixtures by considering 3D heteronuclear heteronuclear single-quantum coherence-NOESY (HSQC-NOESY) experiments under viscous conditions. We state the individualization of four low-polarity chemical compounds dissolved in the diethanolamine/DMSO-d₆ solvent blend using homonuclear selective 1D, 2D ¹H-¹H NOESY experiments and heteronuclear 1D, 2D ¹H-¹⁹F heteronuclear Overhauser effect spectroscopy, 2D ¹H-¹⁹F, ¹H-³¹P HSQC-NOESY, and 3D ¹H-¹⁹F-¹H, ¹H-³¹P-¹H HSQC-NOESY experiments by taking profit from spin diffusion.

Metabolomics with multi-block modelling of mass spectrometry and nuclear magnetic resonance in order to discriminate Haplosclerida marine sponges

A comprehensive metabolomic strategy, integrating ¹H NMR and MS-based multi-block modelling in conjunction with multi-informational molecular networking, has been developed to discriminate sponges of the order Haplosclerida, well known for being taxonomically contentious. An in-house collection of 33 marine sponge samples belonging to three families (Callyspongiidae, Chalinidae, Petrosiidae) and four different genera (Callyspongia, Haliclona, Petrosia, Xestospongia) was investigated using LC-MS/MS, molecular networking, and the annotations processes combined with NMR data and multivariate statistical modelling. The combination of MS and NMR data into supervised multivariate models led to the discrimination of, out of the four genera, three groups based on the presence of metabolites, not necessarily previously described in the Haplosclerida order. Although these metabolomic methods have already been applied separately, it is the first time that a multi-block untargeted approach using MS and NMR has been combined with molecular networking and statistically analyzed, pointing out the pros and cons of this strategy.

Application of MS-Based Metabolomic Approaches in Analysis of Starfish and Sea Cucumber Bioactive Compounds

Today, marine natural products are considered one of the main sources of compounds for drug development. Starfish and sea cucumbers are potential sources of natural products of pharmaceutical interest. Among their metabolites, polar steroids, triterpene glycosides, and polar lipids have attracted a great deal of attention; however, studying these compounds by conventional methods is challenging. The application of modern MS-based approaches can help to obtain valuable information about such compounds. This review provides an up-to-date overview of MS-based applications for starfish and sea cucumber bioactive compounds analysis. While describing most characteristic features of MS-based approaches in the context of starfish and sea cucumber metabolites, including sample preparation and MS analysis steps, the present paper mainly focuses on the application of MS-based metabolic profiling of polar steroid compounds, triterpene glycosides, and lipids. The application of MS in metabolomics studies is also outlined.

Studying Metabolism by NMR-Based Metabolomics

During the past few decades, the direct analysis of metabolic intermediates in biological samples has greatly improved the understanding of metabolic processes. The most used technologies for these advances have been mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy. NMR is traditionally used to elucidate molecular structures and has now been extended to the analysis of complex mixtures, as biological samples: NMR-based metabolomics. There are however other areas of small molecule biochemistry for which NMR is equally powerful. These include the quantification of metabolites (qNMR); the use of stable isotope tracers to determine the metabolic fate of drugs or nutrients, unravelling of new metabolic pathways, and flux through pathways; and metabolite-protein interactions for understanding metabolic regulation and pharmacological effects. Computational tools and resources for automating analysis of spectra and extracting meaningful biochemical information has developed in tandem and contributes to a more detailed understanding of systems biochemistry. In this review, we highlight the contribution of NMR in small molecule biochemistry, specifically in metabolic studies by reviewing the state-of-the-art methodologies of NMR spectroscopy and future directions.