Deep metabolome annotation in natural products research: towards a virtuous cycle in metabolite identification

Current and emerging tools and strategies for the identification of bioactive natural products in complex mixtures

Covering: up to 2024The prompt identification of (bio)active natural products (NPs) from complex mixtures poses a significant challenge due to the presence of numerous compounds with diverse structures and (bio)activities. Thus, this review provides an overview of current and emerging tools and strategies for the identification of (bio)active NPs in complex mixtures. Traditional approaches of bioassay-guided fractionation (BGF), followed by nuclear magnetic resonance (NMR) and mass spectrometry (MS) analysis for compound structure elucidation, continue to play an important role in the identification of active NPs. However, recent advances (2018-2024) have led to the development of novel techniques such as (bio)chemometric analysis, dereplication and combined approaches, which allow efficient prioritization for the elucidation of (bio)active compounds. For researchers involved in the search for bioactive NPs and who want to speed up their discoveries while maintaining accurate identifications, this review highlights the strengths and limitations of each technique and provides up-to-date insights into their combined use to achieve the highest level of confidence in the identification of (bio)active natural products from complex matrices.

Recent Advancement in NMR Based Plant Metabolomics: Techniques, Tools, and Analytical Approaches

Plant metabolomics, a rapidly advancing field within plant biology, is dedicated to comprehensively exploring the intricate array of small molecules in plant systems. This entails precisely gathering comprehensive chemical data, detecting numerous metabolites, and ensuring accurate molecular identification. Nuclear magnetic resonance (NMR) spectroscopy, with its detailed chemical insights, is crucial in obtaining metabolite profiles. Its widespread application spans various research disciplines, aiding in comprehending chemical reactions, kinetics, and molecule characterization. Biotechnological advancements have further expanded NMR's utility in metabolomics, particularly in identifying disease biomarkers across diverse fields such as agriculture, medicine, and pharmacology. This review covers the stages of NMR-based metabolomics, including historical aspects and limitations, with sample preparation, data acquisition, spectral processing, analysis, and their application parts.

High-Throughput Mining of Novel Compounds from Known Microbes: A Boost to Natural Product Screening

Advanced techniques can accelerate the pace of natural product discovery from microbes, which has been lagging behind the drug discovery era. Therefore, the present review article discusses the various interdisciplinary and cutting-edge techniques to present a concrete strategy that enables the high-throughput screening of novel natural compounds (NCs) from known microbes. Recent bioinformatics methods revealed that the microbial genome contains a huge untapped reservoir of silent biosynthetic gene clusters (BGC). This article describes several methods to identify the microbial strains with hidden mines of silent BGCs. Moreover, antiSMASH 5.0 is a free, accurate, and highly reliable bioinformatics tool discussed in detail to identify silent BGCs in the microbial genome. Further, the latest microbial culture technique, HiTES (high-throughput elicitor screening), has been detailed for the expression of silent BGCs using 500-1000 different growth conditions at a time. Following the expression of silent BGCs, the latest mass spectrometry methods are highlighted to identify the NCs. The recently emerged LAESI-IMS (laser ablation electrospray ionization-imaging mass spectrometry) technique, which enables the rapid identification of novel NCs directly from microtiter plates, is presented in detail. Finally, various trending 'dereplication' strategies are emphasized to increase the effectiveness of NC screening.

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.

Mirror, mirror on the wall, which phytochemicals in Clinacanthus nutans inhibits advanced glycation end products of them all?

In our quest to discover advanced glycation end products (AGEs) inhibitors from Clinacanthus nutans (Burm.f.) Lindau leaves, we conducted a bioactivity-based molecular networking. This approach integrates LC-MS2 profiling and in vitro antiglycation data to predict bioactive compounds. We began by screening three extracts: 100% ethanol, 70% ethanol and 100% water alongside the in vitro antioxidant activity, total phenolics content (TPC) and schaftoside content. Among these extracts, 100% ethanol extract exhibited the highest total AGEs inhibition effects (IC50 = 80.18 ± 11.6 μg/mL), DPPH scavenging activity (IC50 = 747.40 ± 10.30 μg/mL) and TPC (26.54 ± 2.09 μg GAE /mg extract). Intriguingly, 100% ethanol extract contained the lowest amount of schaftoside, suggesting the involvement of other phytochemicals in the antiglycation effects. The molecular networking and in silico structural annotations of 401 LC-MS features detected in the fractions from 100% ethanol extract predicted 21 bioactive compounds (p < 0.05, r > 0.90), including several C40 carotenoids, alkaloids containing tetrapyrrole structures and fatty acids. On the contrary, all phenolics showed weak correlations with antiglycation effects. These predictions were further validated in vitro, where carotenoid lutein showed half maximal inhibitory concentration, IC50 = 96 ± 8 μM and selected flavonoid-C-glycosides exhibited weaker inhibitions (IC50 between 568 and 1922 μM). Notably, lutein content was higher in freeze-dried leaves (12.42 ± 0.82 mg/100 g) than oven-dried, although the former was associated with elevated mercury levels. In summary, C. nutans exhibited potential antiglycation and antioxidant activity, and lutein was identified as the main bioactive principle.

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.

In Silico-Predicted Dynamic Oxlipidomics MS/MS Library: High-Throughput Discovery and Characterization of Unknown Oxidized Lipids

Nontargeted lipidomics using liquid chromatography-tandem mass spectrometry can detect thousands of molecules in biological samples. However, the annotation of unknown oxidized lipids is limited to the structures present in libraries, restricting the analysis and interpretation of experimental data. Here, we describe Doxlipid, a computational tool for oxidized lipid annotation that predicts a dynamic MS/MS library for every experiment. Doxlipid integrates three key simulation algorithms to predict libraries and covers 32 subclasses of oxidized lipids from the three main classes. In the evaluation, Doxlipid achieves very high prediction and characterization performance and outperforms the current oxidized lipid annotation methods. Doxlipid, combined with a molecular network, further annotates unknown chemical analogs in the same reaction or pathway. We demonstrate the broad utility of Doxlipid by analyzing oxidized lipids in ferroptosis hepatocellular carcinoma, tissue samples, and other biological samples, substantially advancing the discovery of biological pathways at the trace oxidized lipid level.

Unraveling the Formation Mechanism of Egg's Unique Flavor via Flavoromics and Lipidomics

Egg products after thermal treatment possess a unique flavor and are favored by consumers. In this study, the key aroma-active compounds of egg yolk products and their formation mechanism during thermal treatment were investigated. The volatile aroma compounds in egg yolks were monitored using an electronic nose, gas chromatography-mass spectrometry (GC-MS) and gas chromatography-olfactometry-mass spectrometry (GC-O-MS), and the lipid molecular species were explored using ultra-high-performance liquid chromatography- mass spectrometry with a Q-Exactive HF-X Orbitrap (UPLC-Q-Exactive HF-X). A total of 68 volatile compounds were identified. Boiled eggs mainly derived their flavor from hexanal, 2-pentyl-furan, 2-butanone, 3-methyl-butanal and heptane. Meanwhile, fried eggs relied mainly on 14 compounds, the most important of which were 2-ethyl-3-methyl-pyrazine, 3-ethyl-2,5-dimethyl-pyrazine, 2-ethyl-3,5-dimethyl-pyrazine, nonanal and 2,3-diethyl-5-methyl-pyrazine, providing a baked and burnt sugar flavor. A total of 201 lipid molecules, belonging to 21 lipid subclasses, were identified in egg yolks, and 13 oxidized lipids were characterized using a molecular network. Phosphoethanolamines (PEs) containing polyunsaturated fatty acids were the primary flavor precursors contributing to the development of egg yolks' flavor, participating in lipid oxidation reactions and the Maillard reaction and regulating the production of aldehydes and pyrazine compounds. This study provides reference and guidance for the development of egg yolk flavor products.

Metabolite Profiling of Pleurotus ostreatus Grown on Sisal Agro-Industrial Waste Supplemented with Cocoa Almond Tegument and Wheat Bran

Pleurotus ostreatus is an edible fungus with high nutritional value that uses industrial and agricultural lignocellulosic residues as substrates for growth and reproduction. Understanding their growth metabolic dynamics on agro-industrial wastes would help to develop economically viable and eco-friendly biotechnological strategies for food production. Thus, we used UHPLC/MS/MS and GNPS as an innovative approach to investigate the chemical composition of two strains of P. ostreatus, coded as BH (Black Hirataki) and WH (White Hirataki), grown on sisal waste mixture (SW) supplemented with 20 % cocoa almond tegument (CAT) or 20 % of wheat bran (WB). Metabolite dereplication allowed the identification of 53 metabolites, which included glycerophospholipids, fatty acids, monoacylglycerols, steroids, carbohydrates, amino acids, and flavonoids. This is the first report of the identification of these compounds in P. ostreatus, except for the steroid ergosterol. Most of the metabolites described in this work possess potential biological activities, which support the nutraceutical properties of P. ostreatus. Thus, the results of this study provide essential leads to the understanding of white-rot fungi chemical plasticity aiming at developing alternative biotechnologies strategies for waste recycling.

Phytochemical Profiling, Antioxidant and Cognitive-Enhancing Effect of Helichrysum italicum ssp. italicum (Roth) G. Don (Asteraceae)

This study aimed at the evaluation of the antioxidant and cognitive-enhancing effect of methanol-aqueous extract from Helichrysum italicum ssp. italicum aerial parts. Significant radical scavenging activity (110.33 ± 3.47 and 234.70 ± 5.21 mg TE/g for DPPH and ABTS) and reducing power (354.23 ± 17.51 and 210.24 ± 8.68 mg TE/g for CUPRAC and FRAP) were observed. The extract showed average acetylcholinesterase and low butyrylcholinesterase inhibitory potential. H. italicum extract (200 mg/kg/po) administered in combination with galantamine (3 mg/kg/po) for 12 days significantly improved the memory and learning process compared with galantamine alone in the passive avoidance test. The effect was comparable to that of Ginkgo biloba extract (100 mg/kg/po). In deep secondary metabolite annotation of the extract by UHPLC-HRMS, more than 90 hydroxybenzoic and hydroxicinnamic acid-glycosides, phenylethanoid glycosides, a series of acylquinic and caffeoylhexaric acids, methoxylated derivatives of scutellarein, quercetagetin and 6-hydroxyluteolin, and prenylated phloroglucinol-α-pyrones were reported for the first time in H. italicum. Fragmentation patterns of four subclasses of heterodimer-pyrones were proposed. In-depth profiling of the pyrones revealed 23 compounds undescribed in the literature. Pyrones and acylphloroglucinols together with acylquinic acids could account for memory improvement. The presented research advanced our knowledge of H. italicum, highlighting the species as a rich source of secondary metabolites with cognitive-enhancing potential.

NMR-Based Metabolite Profiling and the Application of STOCSY toward the Quality and Authentication Assessment of European EVOOs

Extra virgin olive oil (EVOO) possesses a high-value rank in the food industry, thus making it a common target for adulteration. Hence, several methods have been essentially made available over the years. However, the issue of authentication remains unresolved with national and food safety organizations globally struggling to regulate and control its market. Over the course of this study, the aim was to determine the origin of EVOOs suggesting a high-throughput, state-of-the-art method that could be easily adopted. A rapid, NMR-based untargeted metabolite profiling method was applied and complemented by multivariate analysis (MVA) and statistical total correlation spectroscopy (STOCSY). STOCSY is a valuable statistical tool contributing to the biomarker identification process and was employed for the first time in EVOO analysis. Market samples from three Mediterranean countries of Spain, Italy, and Greece, blended samples from these countries, as well as monocultivar samples from Greece were analyzed. The NMR spectra were collected, with the help of chemometrics acting as "fingerprints" leading to the discovery of certain chemical classes and single biomarkers that were related to the classification of the samples into groups based on their origin.

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.

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.

Inventa: A computational tool to discover structural novelty in natural extracts libraries

Collections of natural extracts hold potential for the discovery of novel natural products with original modes of action. The prioritization of extracts from collections remains challenging due to the lack of a workflow that combines multiple-source information to facilitate the data interpretation. Results from different analytical techniques and literature reports need to be organized, processed, and interpreted to enable optimal decision-making for extracts prioritization. Here, we introduce Inventa, a computational tool that highlights the structural novelty potential within extracts, considering untargeted mass spectrometry data, spectral annotation, and literature reports. Based on this information, Inventa calculates multiple scores that inform their structural potential. Thus, Inventa has the potential to accelerate new natural products discovery. Inventa was applied to a set of plants from the Celastraceae family as a proof of concept. The Pristimera indica (Willd.) A.C.Sm roots extract was highlighted as a promising source of potentially novel compounds. Its phytochemical investigation resulted in the isolation and de novo characterization of thirteen new dihydro-β-agarofuran sesquiterpenes, five of them presenting a new 9-oxodihydro-β-agarofuran base scaffold.

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.

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.

Metabolomic Study of Dactylis glomerata Growing on Aeolian Archipelago (Italy)

The Aeolian Islands (Italy) are a volcanic archipelago in the Tyrrhenian Sea comprising seven main islands, among which are two active volcanoes. The peculiar geological features and the wide variety of environments and soils have an important impact on native plants, and in particular, the Aeolian populations of Dactylis glomerata (a perennial cool-season bunchgrass) exhibit remarkable phenotypic variability. Considering that environmental drivers also strongly affect the production of plant metabolites, this work aimed at comparing the metabolomic profiles of D. glomerata (leaves) harvested at different altitudes on four islands of the Aeolian archipelago, namely: Lipari, Vulcano, Stromboli and Panarea. Samples were analyzed by ¹H NMR profiling, and data were treated by PCA. Samples collected on Stromboli were very different from each other and from the samples collected in the other islands. Through an Orthogonal Partial Least Squares (OPLS) model, using altitude as the y variable, it emerged that the concentration of proline, glycine betaine, sucrose, glucose and chlorogenic acid of D. glomerata growing on Stromboli decreased at increasing altitude. Conversely, increasing altitude was associated with an increment in valine, asparagine, fumaric acid and phenylalanine.

Comparison of Illicit Drug Seizures Products of Natural Origin Using a Molecular Networking Approach

Drug powder composition analysis is of particular interest in forensic investigations to identify illicit substance content, cutting agents and impurities. Powder profiling is difficult to implement due to multiple analytical methods requirement and remains a challenge for forensic toxicology laboratories. Furthermore, visualization tools allowing seizure products identification appear to be under-used to date. The aim of this study is to present the utility of molecular networking for the composition establishment of natural origin drugs. A powder suspected to contain heroin and three powders suspected to contain cocaine obtained from law enforcement agency seizures were analyzed using untargeted screening by liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS/MS). Molecular networking and metabolite annotation applied to suspected heroin sample allowed rapid confirmation of its illicit content (heroin), the identification of structurally related major impurities (6-monoacetylmorphine, 6-monoacetylcodeine, noscapine, and papaverine), as well as cutting agents (acetaminophen and caffeine). The cocaine powder profiling allowed the comparison of its constituents in a semi-quantitative manner (cocaine, benzoylecgonine, trans/cis-cinnamoylcocaine, trimethoxycocaine, hexanoylecgonine methylester, caffeine, hydroxyzine, levamisole, and phenacetin), bringing additional information for their identification, including geographically sourcing of natural product and their putative place in the supply chain. Although this approach does not replace the profiling techniques used by forensic laboratories, the use of molecular networks provides a visual overview of structurally related constituents which aids the comparison and investigation of seizure powders. Molecular networks offers here an ideal way to depict structurally related and unrelated compounds in these often complex mixtures of chemicals.

Exploring the antibacterial, antibiofilm, and antivirulence activities of tea tree oil-containing nanoemulsion against carbapenem-resistant Serratia marcescens associated infections

Carbapenem-resistant Serratia marcescens (CRE-S. marcescens) has recently emerged as an opportunistic human pathogen that causes various nosocomial and respiratory tract infections. The prognosis for CRE-S. marcescens-related infections is very poor and these infections are difficult to treat. This study investigated the synthesis of tea tree oil nanoemulsion (TTO-NE) and its impact on CRE-S. marcescens both in vitro and in vivo. TTO-NE was characterized by dynamic light scattering (DLS) and effectively eradicated bacterial planktonic and sessile forms, reduced bacterial virulence factors, and generated reactive oxygen species (ROS) in the bacterial cell. Notably, TTO-NE was efficient in reducing the colonization of CRE-S. marcescens in a C. elegans in vivo model. The data suggest that TTO-NE might be an excellent tool to combat infections associated with CRE-S. marcescens.

Synthetic Biology Advanced Natural Product Discovery

A wide variety of bacteria, fungi and plants can produce bioactive secondary metabolites, which are often referred to as natural products. With the rapid development of DNA sequencing technology and bioinformatics, a large number of putative biosynthetic gene clusters have been reported. However, only a limited number of natural products have been discovered, as most biosynthetic gene clusters are not expressed or are expressed at extremely low levels under conventional laboratory conditions. With the rapid development of synthetic biology, advanced genome mining and engineering strategies have been reported and they provide new opportunities for discovery of natural products. This review discusses advances in recent years that can accelerate the design, build, test, and learn (DBTL) cycle of natural product discovery, and prospects trends and key challenges for future research directions.

Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

Covering: up to the end of 2020Recently introduced computational metabolome mining tools have started to positively impact the chemical and biological interpretation of untargeted metabolomics analyses. We believe that these current advances make it possible to start decomposing complex metabolite mixtures into substructure and chemical class information, thereby supporting pivotal tasks in metabolomics analysis including metabolite annotation, the comparison of metabolic profiles, and network analyses. In this review, we highlight and explain key tools and emerging strategies covering 2015 up to the end of 2020. The majority of these tools aim at processing and analyzing liquid chromatography coupled to mass spectrometry fragmentation data. We start with defining what substructures are, how they relate to molecular fingerprints, and how recognizing them helps to decompose complex mixtures. We continue with chemical classes that are based on the presence or absence of particular molecular scaffolds and/or functional groups and are thus intrinsically related to substructures. We discuss novel tools to mine substructures, annotate chemical compound classes, and create mass spectral networks from metabolomics data and demonstrate them using two case studies. We also review and speculate about the opportunities that NMR spectroscopy-based metabolome mining of complex metabolite mixtures offers to discover substructures and chemical classes. Finally, we will describe the main benefits and limitations of the current tools and strategies that rely on them, and our vision on how this exciting field can develop toward repository-scale-sized metabolomics analyses. Complementary sources of structural information from genomics analyses and well-curated taxonomic records are also discussed. Many research fields such as natural products discovery, pharmacokinetic and drug metabolism studies, and environmental metabolomics increasingly rely on untargeted metabolomics to gain biochemical and biological insights. The here described technical advances will benefit all those metabolomics disciplines by transforming spectral data into knowledge that can answer biological questions.

Kiwifruit Metabolomics-An Investigation of within Orchard Metabolite Variability of Two Cultivars of Actinidia chinensis

Plant metabolomics within field-based food production systems is challenging owing to environmental variability and the complex architecture and metabolic growth cycles of plants. Kiwifruit cultivars of Actinidia chinensis are vigorous perennial vines grown as clones in highly structured orchard environments, intensively managed to maximize fruit yield and quality. To understand the metabolic responses of vines to orchard management practices, we needed to better understand the various sources of metabolic variability encountered in the orchard. Triplicate composite leaf, internode and fruit (mature and immature) samples were collected from each of six Actinidia chinensis var. deliciosa 'Hayward' and A. chinensis var. chinensis 'Zesy002' kiwifruit vines at three times during the growing season and measured by LC-MS. In general, there was more variation in metabolite concentrations within vines than between vines, with 'Hayward' showing a greater percentage of within-vine variability than 'Zesy002' (c. 90 vs. 70% respectively). In specific tissues, the sampler, infection by Pseudomonas syringae var. actinidiae and the rootstock also influenced metabolite variability. A similar pattern of metabolic variability was observed from quantitative analysis of specific carbohydrates and phytohormones. High within-vine metabolic variability indicates that it is more important to obtain sufficient replicate samples than to sample from multiple vines. These data provide an objective basis for optimizing metabolite sampling strategies within kiwifruit orchards.

Untargeted metabolomics approach to discriminate mistletoe commercial products

Mistletoe (Viscum album L.) is used in German-speaking European countries in the field of integrative oncology linking conventional and complementary medicine therapies to improve quality of life. Various companies sell extracts, fermented or not, for injection by subcutaneous or intra-tumoral route with a regulatory status of anthroposophic medicinal products (European Medicinal Agency (EMA) assessment status). These companies as well as anthroposophical physicians argue that complex matrices composed of many molecules in mixture are necessary for activity and that the host tree of the mistletoe parasitic plant is the main determining factor for this matrix composition. The critical point is that parenteral devices of European mistletoe extracts do not have a standard chemical composition regulated by EMA quality guidelines, because they are not drugs, regulatory speaking. However, the mechanism of mistletoe's anticancer activity and its effectiveness in treating and supporting cancer patients are not fully understood. Because of this lack of transparency and knowledge regarding the matrix chemical composition, we undertook an untargeted metabolomics study of several mistletoe extracts to explore and compare their fingerprints by LC-(HR)MS(/MS) and 1H-NMR. Unexpectedly, we showed that the composition was primarily driven by the manufacturer/preparation method rather than the different host trees. This differential composition may cause differences in immunostimulating and anti-cancer activities of the different commercially available mistletoe extracts as illustrated by structure-activity relationships based on LC-MS/MS and 1H-NMR identifications completed by docking experiments. In conclusion, in order to move towards an evidence-based medicine use of mistletoe, it is a priority to bring rigor and quality, chemically speaking.

In Vitro Metabolism Study of Seongsanamide A in Human Liver Microsomes Using Non-Targeted Metabolomics and Feature-Based Molecular Networking

Seongsanamide A is a bicyclic peptide with an isodityrosine residue discovered in Bacillus safensis KCTC 12796BP which exhibits anti-allergic activity in vitro and in vivo without significant cytotoxicity. The purpose of this study was to elucidate the in vitro metabolic pathway and potential for drug interactions of seongsanamide A in human liver microsomes using non-targeted metabolomics and feature-based molecular networking (FBMN) techniques. We identified four metabolites, and their structures were elucidated by interpretation of high-resolution tandem mass spectra. The primary metabolic pathway associated with seongsanamide A metabolism was hydroxylation and oxidative hydrolysis. A reaction phenotyping study was also performed using recombinant cytochrome P450 isoforms. CYP3A4 and CYP3A5 were identified as the major metabolic enzymes responsible for metabolite formation. Seongsanamide A did not inhibit the cytochrome P450 isoforms commonly involved in drug metabolism (IC₅₀ > 10 µM). These results will contribute to further understanding the metabolism and drug interaction potential of various bicyclic peptides.

Synthetic biology approaches to actinomycete strain improvement

Their biochemical versatility and biotechnological importance make actinomycete bacteria attractive targets for ambitious genetic engineering using the toolkit of synthetic biology. But their complex biology also poses unique challenges. This mini review discusses some of the recent advances in synthetic biology approaches from an actinomycete perspective and presents examples of their application to the rational improvement of industrially relevant strains.

In vivo and in vitro α-amanitin metabolism studies using molecular networking

Amanitin poisonings are among the most life-threatening mushroom poisonings, and are mainly caused by the genus Amanita. Hepatotoxicity is the hallmark of amanitins, powerful toxins contained in these mushrooms, and can require liver transplant. Among amatoxins, α-amanitin is the most studied. However, the hypothesis of a possible metabolism of amanitins is still controversial in this pathophysiology. Therefore, there is a need of clarification using cutting-edge tools allowing metabolism study. Molecular network has emerged as powerful tool allowing metabolism study through organization and representation of untargeted tandem mass spectrometry (MS/MS) data in a graphical form. The aim of this study is to investigate amanitin metabolism using molecular networking. In vivo (four positive amanitin urine samples) and in vitro (differentiated HepaRG cells supernatant incubated with α-amanitin 2 μM for 24 h) samples were extracted and analyzed by LC-HRMS/MS using a Q Exactive™ Orbitrap mass spectrometer. Using molecular networking on both in vitro and in vivo, we have demonstrated that α-amanitin does not undergo metabolism in human. Thus, we provide solid evidence that a possible production of amanitin metabolites cannot be involved in its toxicity pathways. These findings can help to settle the debate on amanitin metabolism and toxicity.

Natural products in drug discovery: advances and opportunities

Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments - including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances - are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities.

Curcumin Improves Chronic Pain Induced Depression Through Regulating Serum Metabolomics in a Rat Model of Trigeminal Neuralgia

Background

Depression is a prevalent and complex psychiatric disorder with high incidence in patients with chronic pain. The underlying pathogenesis of chronic pain-induced depression is complicated and remains largely unclear. An integrated analysis of endogenous substance-related metabolisms would help to understand the molecular mechanism of chronic pain-induced depression. Curcumin was reported to exert various health benefits, such as anti-depression, antioxidant, antineoplastic, analgesia, and anti-inflammation.

Objective

The aim of this study was to analyze the biomarkers related to depression in serum and to evaluate the anti-depression properties of curcumin in a chronic pain-induced depression model of rats.

Design

This is a randomized, controlled experiment.

Setting

This study was conducted at the Experimental Animal Center, Beijing Friendship Hospital, Capital Medical University.

Methods

Trigeminal neuralgia (TN) was produced by injecting 4 µL, 10% cobra venom saline solution into the infraorbital nerve (ION). Curcumin was administered by gavage twice a day from post-operation day (POD) 15 to POD 42. Mechanical allodynia was assessed using von Frey filaments. Sucrose preference and forced swimming tests were performed to evaluate depression-like behaviors. The metabolomics analysis was preceded by LCMS-IT-TOF and multivariate statistical methods for sample detection and biomarker screening.

Results

Cobra venom intra-ION injection led to chronic mechanical allodynia, reduced sucrose preference, and prolonged immobility during forced swimming. Curcumin treatment alleviated chronic mechanical allodynia, regained sucrose preference, and reduced immobility time. Differential analysis identified 30 potential metabolites changed under TN condition. The integrated analyses further revealed two major metabolic changes by comparing the serums from sham operated rats, TN rats, and TN rats treated with curcumin: 1) ether lipid metabolism; and 2) glycerophospholipid metabolism, and suggested that curcumin may improve chronic pain-induced depression by regulating these two types of lipid metabolisms.

Conclusion

Ether lipid and glycerophospholipid metabolism might be two of the pathways with the most potential related to chronic pain induced-depression; and curcumin could alleviate chronic pain induced-depression by modulating these two pathways. These results provide further insights into the mechanisms of chronic pain-induced depression and may help to identify potential targets for anti-depression properties of curcumin.

New insights into quetiapine metabolism using molecular networking

Metabolism is involved in both pharmacology and toxicology of most xenobiotics including drugs. Yet, visualization tools facilitating metabolism exploration are still underused, despite the availibility of pertinent bioinformatics solutions. Since molecular networking appears as a suitable tool to explore structurally related molecules, we aimed to investigate its interest in in vitro metabolism exploration. Quetiapine, a widely prescribed antipsychotic drug, undergoes well-described extensive metabolism, and is therefore an ideal candidate for such a proof of concept. Quetiapine was incubated in metabolically competent human liver cell models (HepaRG) for different times (0 h, 3 h, 8 h, 24 h) with or without cytochrom P450 (CYP) inhibitor (ketoconazole as CYP3A4/5 inhibitor and quinidine as CYP2D6 inhibitor), in order to study its metabolism kinetic and pathways. HepaRG culture supernatants were analyzed on an ultra-high performance liquid chromatography coupled with tandem mass spectrometry (LC-HRMS/MS). Molecular networking approach on LC-HRMS/MS data allowed to quickly visualize the quetiapine metabolism kinetics and determine the major metabolic pathways (CYP3A4/5 and/or CYP2D6) involved in metabolite formation. In addition, two unknown putative metabolites have been detected. In vitro metabolite findings were confirmed in blood sample from a patient treated with quetiapine. This is the first report using LC-HRMS/MS untargeted screening and molecular networking to explore in vitro drug metabolism. Our data provide new evidences of the interest of molecular networking in drug metabolism exploration and allow our in vitro model consistency assessment.

MetWork: a web server for natural products anticipation

MOTIVATION

The annotation of natural products and more generally small molecules is one of the major drawbacks in untargeted mass spectrometry analysis. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments. Despite the great potential of this tool, the annotation is usually performed manually by the expert as only few spectral libraries are available.

RESULTS

Herein we propose a web server of in silico metabolization of metabolites that represents a full implementation of the metabolome consistency concept. The workflow is based on MS/MS data, organized in molecular network using the Global Natural Products Social Molecular Networking (GNPS) platform, a collaborative library of reactions and a MS/MS spectra prediction module. Having one node identified in the molecular network, the server generates putative structures and predict the associated MS/MS spectra when the exact mass is detected in the network. A similarity comparison between the MS/MS spectra is then performed in order to annotate the node.

AVAILABILITY AND IMPLEMENTATION

The web server is available at: https://metwork.pharmacie.parisdescartes.fr.

Lipid Annotation by Combination of UHPLC-HRMS (MS), Molecular Networking, and Retention Time Prediction: Application to a Lipidomic Study of In Vitro Models of Dry Eye Disease

Annotation of lipids in untargeted lipidomic analysis remains challenging and a systematic approach needs to be developed to organize important datasets with the help of bioinformatic tools. For this purpose, we combined tandem mass spectrometry-based molecular networking with retention time (t_R) prediction to annotate phospholipid and sphingolipid species. Sixty-five standard compounds were used to establish the fragmentation rules of each lipid class studied and to define the parameters governing their chromatographic behavior. Molecular networks (MNs) were generated through the GNPS platform using a lipid standards mixture and applied to lipidomic study of an in vitro model of dry eye disease, i.e., human corneal epithelial (HCE) cells exposed to hyperosmolarity (HO). These MNs led to the annotation of more than 150 unique phospholipid and sphingolipid species in the HCE cells. This annotation was reinforced by comparing theoretical to experimental t_R values. This lipidomic study highlighted changes in 54 lipids following HO exposure of corneal cells, some of them being involved in inflammatory responses. The MN approach coupled to t_R prediction thus appears as a suitable and robust tool for the discovery of lipids involved in relevant biological processes.

HPLC-based activity profiling for pharmacologically and toxicologically relevant natural products - principles and recent examples

CONTEXT

Discovery of pharmacologically active natural products as starting points for drug development remains important and, for reasons of consumer safety, the identification of toxicologically relevant compounds in herbal drugs.

OBJECTIVE

To explain, with the aid of relevant examples from our own research, how these goals can be achieved.

METHODS

An in-house technology platform comprising pre-formatted extract libraries in 96-well format, miniaturized tracking of activity in extracts via HPLC-activity profiling, structure elucidation with microprobe NMR, and in vitro and in vivo pharmacological methods were used.

RESULTS

Piperine was identified as a new scaffold for allosteric GABA_A receptor modulators with in vivo activity that interacts at a benzodiazepine-independent binding site. Selectivity and potency were improved by iterative optimization towards synthetic piperine analogues. Dehydroevodiamine and hortiamine from the traditional Chinese herbal drug Evodiae fructus were identified as potent hERG channel blockers in vitro. The compounds induced torsades de pointes arrhythmia in animal models.

CONCLUSIONS

The allosteric binding site for piperine analogues remains to be characterized and cardiac risks of herbal drugs need to be further evaluated to ensure consumer safety.

Chemical Diversity and Classification of Secondary Metabolites in Nine Bryophyte Species

The central aim in ecometabolomics and chemical ecology is to pinpoint chemical features that explain molecular functioning. The greatest challenge is the identification of compounds due to the lack of constitutive reference spectra, the large number of completely unknown compounds, and bioinformatic methods to analyze the big data. In this study we present an interdisciplinary methodological framework that extends ultra-performance liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry (UPLC/ESI-QTOF-MS) with data-dependent acquisition (DDA-MS) and the automated in silico classification of fragment peaks into compound classes. We synthesize findings from a prior study that explored the influence of seasonal variations on the chemodiversity of secondary metabolites in nine bryophyte species. Here we reuse and extend the representative dataset with DDA-MS data. Hierarchical clustering, heatmaps, dbRDA, and ANOVA with post-hoc Tukey HSD were used to determine relationships of the study factors species, seasons, and ecological characteristics. The tested bryophytes showed species-specific metabolic responses to seasonal variations (50% vs. 5% of explained variation). Marchantia polymorpha, Plagiomnium undulatum, and Polytrichum strictum were biochemically most diverse and unique. Flavonoids and sesquiterpenoids were upregulated in all bryophytes in the growing seasons. We identified ecological functioning of compound classes indicating light protection (flavonoids), biotic and pathogen interactions (sesquiterpenoids, flavonoids), low temperature and desiccation tolerance (glycosides, sesquiterpenoids, anthocyanins, lactones), and moss growth supporting anatomic structures (few methoxyphenols and cinnamic acids as part of proto-lignin constituents). The reusable bioinformatic framework of this study can differentiate species based on automated compound classification. Our study allows detailed insights into the ecological roles of biochemical constituents of bryophytes with regard to seasonal variations. We demonstrate that compound classification can be improved with adding constitutive reference spectra to existing spectral libraries. We also show that generalization on compound classes improves our understanding of molecular ecological functioning and can be used to generate new research hypotheses.

Insights into the Diversity of Secondary Metabolites of Planktothrix Using a Biphasic Approach Combining Global Genomics and Metabolomics

Cyanobacteria are an ancient lineage of slow-growing photosynthetic bacteria and a prolific source of natural products with diverse chemical structures and potent biological activities and toxicities. The chemical identification of these compounds remains a major bottleneck. Strategies that can prioritize the most prolific strains and novel compounds are of great interest. Here, we combine chemical analysis and genomics to investigate the chemodiversity of secondary metabolites based on their pattern of distribution within some cyanobacteria. Planktothrix being a cyanobacterial genus known to form blooms worldwide and to produce a broad spectrum of toxins and other bioactive compounds, we applied this combined approach on four closely related strains of Planktothrix. The chemical diversity of the metabolites produced by the four strains was evaluated using an untargeted metabolomics strategy with high-resolution LC-MS. Metabolite profiles were correlated with the potential of metabolite production identified by genomics for the different strains. Although, the Planktothrix strains present a global similarity in terms of a biosynthetic cluster gene for microcystin, aeruginosin, and prenylagaramide for example, we found remarkable strain-specific chemodiversity. Only few of the chemical features were common to the four studied strains. Additionally, the MS/MS data were analyzed using Global Natural Products Social Molecular Networking (GNPS) to identify molecular families of the same biosynthetic origin. In conclusion, we depict an efficient, integrative strategy for elucidating the chemical diversity of a given genus and link the data obtained from analytical chemistry to biosynthetic genes of cyanobacteria.

Chromatographic editing enhances natural product discovery

Fungi are known for their diverse biologically active secondary metabolites, compounds that have provided the basis for many landmark therapeutics in the last century. Due to ease of collection and culturing, the existing fungal chemical literature is vast, and fungal natural product isolation can often be hindered by the numerous nuisance and pan-toxic compounds that many strains produce. Dereplication efforts, aimed at identifying such compounds early in the purification, are imperative to reduce time and expense of rediscovery of known metabolites. The common practice of dereplication then deprioritizes samples containing nuisance compounds and often excludes them from the drug discovery workflow. We have implemented a two-step dereplication protocol that uses tandem mass spectrometry to identify nuisance compounds, followed by mass-directed chromatographic editing to remove them while leaving the remaining 'edited extract' in the drug discovery workflow. This two-step strategy facilitates rapid and more accurate evaluation of the chemical potential of high-throughput extract screening campaigns by consideration of bioactivity beyond that triggered by known metabolites. We demonstrate the isolation of a new natural product antibiotic from an otherwise toxic extract using the technique.

CANPA: Computer-Assisted Natural Products Anticipation

Traditional natural products discovery workflows implying a combination of different targeting strategies, including structure- and/or bioactivity-based approaches, afford no information about new compound structures until late in the discovery pipeline. By integrating a MS/MS prediction module and a collaborative library of (bio)chemical transformations, we have developed a new platform, coined MetWork, that is capable of anticipating the structural identity of metabolites starting from any identified compound. In our quest to discover new monoterpene indole alkaloids, we demonstrate the utility of the MetWork platform by anticipating the structures of five previously undescribed sarpagine-like N-oxide alkaloids that have been targeted and isolated from the leaves of Alstonia balansae using a molecular networking-based dereplication strategy fueled by computer-generated annotations. This study constitutes the first example of nonpeptidic molecular networking-based natural product discovery workflow, in which the targeted structures were initially generated, and therefore anticipated by a computer prior to their isolation.

Metabolic reaction network-based recursive metabolite annotation for untargeted metabolomics

Large-scale metabolite annotation is a challenge in liquid chromatogram-mass spectrometry (LC-MS)-based untargeted metabolomics. Here, we develop a metabolic reaction network (MRN)-based recursive algorithm (MetDNA) that expands metabolite annotations without the need for a comprehensive standard spectral library. MetDNA is based on the rationale that seed metabolites and their reaction-paired neighbors tend to share structural similarities resulting in similar MS2 spectra. MetDNA characterizes initial seed metabolites using a small library of MS2 spectra, and utilizes their experimental MS2 spectra as surrogate spectra to annotate their reaction-paired neighbor metabolites, which subsequently serve as the basis for recursive analysis. Using different LC-MS platforms, data acquisition methods, and biological samples, we showcase the utility and versatility of MetDNA and demonstrate that about 2000 metabolites can cumulatively be annotated from one experiment. Our results demonstrate that MetDNA substantially expands metabolite annotation, enabling quantitative assessment of metabolic pathways and facilitating integrative multi-omics analysis.

High Throughput Complementary Analysis and Quantitation of Metabolites by MALDI- and Silicon Nanopost Array-Laser Desorption/Ionization-Mass Spectrometry

Silicon nanopost array (NAPA) structures have been shown to be effective substrates for laser desorption/ionization-mass spectrometry (LDI-MS) and have been used to analyze a variety of samples including peptides, metabolites, drugs, explosives, and intact cells, as well as to image lipids and metabolites in tissue sections. However, no direct comparison has yet been conducted between NAPA-MS and the most commonly used LDI-MS technique, matrix-assisted laser desorption/ionization (MALDI)-MS. In this work, we compare the utility of NAPA-MS to that of MALDI-MS using two common matrices for the analysis of metabolites in cellular extracts and human urine. Considerable complementarity of molecular coverage was observed between the two techniques. Of 178 total metabolites assigned from cellular extracts, 68 were uniquely detected by NAPA-MS and 62 were uniquely detected by MALDI-MS. NAPA-MS was found to provide enhanced coverage of low-molecular weight compounds such as amino acids, whereas MALDI afforded better detection of larger, labile compounds including nucleotides. In the case of urine, a sample largely devoid of higher-mass labile compounds, 88 compounds were uniquely detected by NAPA-MS and 13 by MALDI-MS. NAPA-MS also favored more extensive alkali metal cation adduction relative to MALDI-MS, with the [M + 2Na/K - H]⁺ species accounting for as much as 97% of the total metabolite ion signal in positive mode. The capability of NAPA-MS for targeted quantitation of endogenous metabolites in urine via addition of isotopically labeled standards was also examined. Both NAPA-MS and MALDI-MS provided quantitative results in good agreement with one another and the concentrations reported in the literature, as well as good sample-to-sample reproducibility (RSD < 10%).

High metabolic variation for seaweeds in response to environmental changes: a case study of the brown algae Lobophora in coral reefs

In the marine environment, macroalgae face changing environmental conditions and some species are known for their high capacity to adapt to the new factors of their ecological niche. Some macroalgal metabolites play diverse ecological functions and belong to the adaptive traits of such species. Because algal metabolites are involved in many processes that shape marine biodiversity, understanding their sources of variation and regulation is therefore of utmost relevance. This work aims at exploring the possible sources of metabolic variations with time and space of four common algal species from the genus Lobophora (Dictyotales, Phaeophyceae) in the New Caledonian lagoon using a UHPLC-HRMS metabolomic fingerprinting approach. While inter-specific differences dominated, a high variability of the metabolome was noticed for each species when changing their natural habitats and types of substrates. Fatty acids derivatives and polyolefins were identified as chemomarkers of these changing conditions. The four seaweeds metabolome also displayed monthly variations over the 13-months survey and a significant correlation was made with sea surface temperature and salinity. This study highlights a relative plasticity for the metabolome of Lobophora species.

Application of a molecular networking approach for clinical and forensic toxicology exemplified in three cases involving 3-MeO-PCP, doxylamine, and chlormequat

Untargeted toxicological screening is an analytical challenge, given the high number of molecules and metabolites to be detected and the constant appearance of new psychoactive substances (NPS). The combination of liquid chromatography with high-resolution tandem mass spectrometry (HRMS/MS) in a data-dependent acquisition mode generates a large volume of high quality spectral data. Commercial software for processing MS data acquired during untargeted screening experiments usually compare measured features (mass, retention time, and fragmentation spectra) against a predefined list of analytes. However, there is a lack of tools for visualizing and organizing MS data of unknown compounds. Here, we applied molecular networking to untargeted toxicological screening. This bioinformatic tool allows the exploration and organization of MS/MS data without prior knowledge of the sample's chemical composition. The organization of spectral data is based on spectral similarity. Hence, important information can be obtained even before the annotation step. The link established between molecules enables the propagation of structural information. We applied this approach to three clinical and forensic cases with various matrices: (a) blood and a syringe content in a forensic case of death by self-injection, (b) hair segments in a case of drug-facilitated assault, and (c) urine and blood samples in a case of 3-methoxyphencyclidine intoxication. Data preprocessing with MZmine allows sample-to-sample comparison and generation of multisample molecular networks. Our present study shows that molecular networking can be a useful complement to conventional approaches for untargeted screening interpretation, for example for xenobiotics identification or NPS metabolism elucidation.

Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation

Mass spectrometry (MS) offers unrivalled sensitivity for the metabolite profiling of complex biological matrices encountered in natural products (NP) research. The massive and complex sets of spectral data generated by such platforms require computational approaches for their interpretation. Within such approaches, computational metabolite annotation automatically links spectral data to candidate structures via a score, which is usually established between the acquired data and experimental or theoretical spectral databases (DB). This process leads to various candidate structures for each MS features. However, at this stage, obtaining high annotation confidence level remains a challenge notably due to the extensive chemodiversity of specialized metabolomes. The design of a metascore is a way to capture complementary experimental attributes and improve the annotation process. Here, we show that integrating the taxonomic position of the biological source of the analyzed samples and candidate structures enhances confidence in metabolite annotation. A script is proposed to automatically input such information at various granularity levels (species, genus, and family) and complement the score obtained between experimental spectral data and output of available computational metabolite annotation tools (ISDB-DNP, MS-Finder, Sirius). In all cases, the consideration of the taxonomic distance allowed an efficient re-ranking of the candidate structures leading to a systematic enhancement of the recall and precision rates of the tools (1.5- to 7-fold increase in the F1 score). Our results clearly demonstrate the importance of considering taxonomic information in the process of specialized metabolites annotation. This requires to access structural data systematically documented with biological origin, both for new and previously reported NPs. In this respect, the establishment of an open structural DB of specialized metabolites and their associated metadata, particularly biological sources, is timely and critical for the NP research community.