Ion identity molecular networking for mass spectrometry-based metabolomics in the GNPS environment

New N-acylated aminoalkanoic acids from tea roots derived biocontrol agent Clonostachys rosea 15020

Four new N-acylated aminoalkanoic acids, namely clonoroseins E-H (1-4), together with three previously identified analogs, clonoroseins A, B, and D (5-7), were identified from the endophytic fungus Clonostachys rosea strain 15020 (CR15020), using Feature-based Molecular Networking (FBMN). The elucidation of their chemical structures, including their absolute configurations, was achieved through spectroscopic analysis combined with quantum chemical calculations. Bioinformatics analyses suggested that an iterative type I HR-PKS (CrsE) generates the polyketide side chain of these clonoroseins. Furthermore, a downstream adenylate-forming enzyme of the PKS (CrsD) was suspected to function as an amide synthetase. CrsD potentially facilitates the transformation of the polyketide moiety into an acyl-AMP intermediate, followed by nucleophilic substitution with either β-alanine or γ-aminobutyric acid to produce amide derivatives. These findings significantly expand our understanding of PKS-related products originating from C. rosea and also underscore the powerful application of FBMN analytical methods in characterization of new compounds.

Targeted discovery of clerodane diterpenoids from Tinospora sinensis as immunomodulatory agents

Under the guidance of MS/MS-based molecular networking, five new clerodane diterpenoid glucosides, tinosinesides R-V (1-5), along with 15 known diterpenoids (6-20), were isolated from the stems of Tinospora sinensis. Compound 1 represents the first example of diterpenoid bearing a thio sugar and compound 5 is the first 18,19-dinor-clerodane with cis-fused A/B ring. The structures of the new compounds were elucidated by spectroscopic means, and their absolute configurations were established on the basis of time-dependent density functional theory (TD-DFT) based electronic circular dichroism (ECD) calculation and chemical methods. Selected compounds were evaluated for their immunomodulatory effect and several compounds could enhance the proliferation of B lymphocytes. Preliminary mechanistic studies disclosed that 3 could promote B cell generation and inhibit B cell differentiation.

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Feature-based molecular networking (FBMN) is a popular analysis approach for liquid chromatography-tandem mass spectrometry-based non-targeted metabolomics data. While processing liquid chromatography-tandem mass spectrometry data through FBMN is fairly streamlined, downstream data handling and statistical interrogation are often a key bottleneck. Especially users new to statistical analysis struggle to effectively handle and analyze complex data matrices. Here we provide a comprehensive guide for the statistical analysis of FBMN results, focusing on the downstream analysis of the FBMN output table. We explain the data structure and principles of data cleanup and normalization, as well as uni- and multivariate statistical analysis of FBMN results. We provide explanations and code in two scripting languages (R and Python) as well as the QIIME2 framework for all protocol steps, from data clean-up to statistical analysis. All code is shared in the form of Jupyter Notebooks ( https://github.com/Functional-Metabolomics-Lab/FBMN-STATS ). Additionally, the protocol is accompanied by a web application with a graphical user interface ( https://fbmn-statsguide.gnps2.org/ ) to lower the barrier of entry for new users and for educational purposes. Finally, we also show users how to integrate their statistical results into the molecular network using the Cytoscape visualization tool. Throughout the protocol, we use a previously published environmental metabolomics dataset for demonstration purposes. Together, the protocol, code and web application provide a complete guide and toolbox for FBMN data integration, cleanup and advanced statistical analysis, enabling new users to uncover molecular insights from their non-targeted metabolomics data. Our protocol is tailored for the seamless analysis of FBMN results from Global Natural Products Social Molecular Networking and can be easily adapted to other mass spectrometry feature detection, annotation and networking tools.

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.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

High-Resolution Tandem Mass Spectrometry-Based Analysis of Model Lignin-Iron Complexes: Novel Pipeline and Complex Structures

Understanding the chemical nature of soil organic carbon (SOC) with great potential to bind iron (Fe) minerals is critical for predicting the stability of SOC. Organic ligands of Fe are among the top candidates for SOCs able to strongly sorb on Fe minerals, but most of them are still molecularly uncharacterized. To shed insights into the chemical nature of organic ligands in soil and their fate, this study developed a protocol for identifying organic ligands using ultrahigh-performance liquid chromatography-high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) and metabolomic tools. The protocol was used for investigating the Fe complexes formed by model compounds of lignin-derived organic ligands, namely, caffeic acid (CA), p-coumaric acid (CMA), vanillin (VNL), and cinnamic acid (CNA). Isotopologue analysis of 54/56Fe was used to screen out the potential UHPLC-HRMS (m/z) features for complexes formed between organic ligands and Fe, with multiple features captured for CA, CMA, VNL, and CNA when 35/37Cl isotopologue analysis was used as supplementary evidence for the complexes with Cl. MS/MS spectra, fragment analysis, and structure prediction with SIRIUS were used to annotate the structures of mono/bidentate mono/biligand complexes. The analysis determined the structures of monodentate and bidentate complexes of FeLxCly (L: organic ligand, x = 1-4, y = 0-3) formed by model compounds. The protocol developed in this study can be used to identify unknown organic ligands occurring in complex environmental samples and shed light on the molecular-level processes governing the stability of the SOC.

Discovery of the Lipopeptides Albubactins A-H from Streptomyces albidoflavus RKJM0023 via Chemical Elicitation with Rhamnolipids and Synthesis of Albubactin A

The marine tunicate-derived Streptomyces albidoflavus RKJM0023 was cultured in the presence of a rhamnolipid mixture in an effort to elicit the production of silent natural products. MS/MS-based molecular networking analysis enhanced with nonparametric statistics highlighted the upregulation of a molecular cluster (Kruskal-Wallis p = 1.6 e-6 for 1) in which no MS/MS features had library matches. Targeted isolation of these features resulted in the discovery of nine new N-acylated lipopeptides, albubactins A-H (1-8) each containing a unique glutamine tripeptide and a C-terminal ethyl ester moiety. Three related albubactin acids A-C (9-11) lacking the ethyl ester were also identified. NMR spectroscopy and UPLC-HR-ESI-MS/MS demonstrated that the albubactins were obtained as mixtures that shared a common m/z and differed only in their acylated terminal groups. Due to the complex spectroscopic elucidation with many overlapping shifts, a total synthesis of albubactin A (1) was completed and used to determine the absolute configuration of the new albubactins.

Innovative molecular networking analysis of steroids and characterisation of the urinary steroidome

Steroids are cholesterol-derived biomolecules that play an essential role in biological processes. These substances used as growth promoters in animals are strictly regulated worldwide. Targeted assays are the conventional methods of monitoring steroid abuse, with limitations: only detect known metabolites. Metabolism leads to many potential compounds (isomers), which complicates the analysis. Thus, to overcome these limitations, non-targeted analysis offers new opportunities for a deeper understanding of metabolites related to steroid metabolism. Molecular networking (MN) appears to be an innovative strategy combining high-resolution mass spectrometry and specific data processing to study metabolic pathways. In the present study, two databases and networks of steroids were constructed to lay the foundations for the implementation of the GNPS-MN approach. Steroids of the same family were grouped together, nandrolone and testosterone were linked to other analogues. This network and associated database were then applied to a few urine samples in order to demonstrate the annotation capacity in steroidome study. The results show that MN strategy could be used to study steroid metabolism and highlight biomarkers.

Native metabolomics for mass spectrometry-based siderophore discovery

Microorganisms, plants, and animals alike have specialized acquisition pathways for obtaining metals, with microorganisms and plants biosynthesizing and secreting small molecule natural products called siderophores and metallophores with high affinities and specificities for iron or other non-iron metals, respectively. This chapter details a novel approach to discovering metal-binding molecules, including siderophores and metallophores, from complex samples ranging from microbial supernatants to biological tissue to environmental samples. This approach, called Native Metabolomics, is a mass spectrometry method in which pH adjustment and metal infusion post-liquid chromatography are interfaced with ion identity molecular networking (IIMN). This rule-based data analysis workflow that enables the identification of metal-binding species based on defined mass (m/z) offsets with the same chromatographic profiles and retention times. Ion identity molecular networking connects compounds that are structurally similar by their fragmentation pattern and species that are ion adducts of the same compound by chromatographic shape correlations. This approach has previously revealed new insights into metal binding metabolites, including that yersiniabactin can act as a biological zincophore (in addition to its known role as a siderophore), that the recently elucidated lepotchelin natural products are cyanobacterial metallophores, and that antioxidants in traditional medicine bind iron. Native metabolomics can be conducted on any liquid chromatography-mass spectrometry system to explore the binding of any metal or multiple metals simultaneously, underscoring the potential for this method to become an essential strategy for elucidating biological metal-binding molecules.

Unraveling the metabolomic architecture of autism in a large Danish population-based cohort

BACKGROUND

The prevalence of autism in Denmark has been increasing, reaching 1.65% among 10-year-old children, and similar trends are seen elsewhere. Although there are several factors associated with autism, including genetic, environmental, and prenatal factors, the molecular etiology of autism is largely unknown. Here, we use untargeted metabolomics to characterize the neonatal metabolome from dried blood spots collected shortly after birth.

METHODS

We analyze the metabolomic profiles of a subset of a large Danish population-based cohort (iPSYCH2015) consisting of over 1400 newborns, who later are diagnosed with autism and matching controls and in two Swedish population-based cohorts comprising over 7000 adult participants. Mass spectrometry analysis was performed by a timsTOF Pro operated in QTOF mode, using data-dependent acquisition. By applying an untargeted metabolomics approach, we could reproducibly measure over 800 metabolite features.

RESULTS

We detected underlying molecular perturbations across several metabolite classes that precede autism. In particular, the cyclic dipeptide cyclo-leucine-proline (FDR-adjusted p = 0.003) and the carnitine-related 5-aminovaleric acid betaine (5-AVAB) (FDR-adjusted p = 0.03), were associated with an increased probability for autism, independently of known prenatal and genetic risk factors. Analysis of genetic and dietary data in adults revealed that 5-AVAB was associated with increased habitual dietary intake of dairy (FDR-adjusted p < 0.05) and with variants near SLC22A4 and SLC22A5 (p < 5.0e - 8), coding for a transmembrane carnitine transporter protein involved in controlling intracellular carnitine levels.

CONCLUSIONS

Cyclo-leucine-proline and 5-AVAB are associated with future diagnosis of autism in Danish neonates, both representing novel early biomarkers for autism. 5-AVAB is potentially modifiable and may influence carnitine homeostasis.

Microbial metabolomics' latest SICRIT: Soft ionization by Chemical Reaction in-Transfer mass spectrometry

Microbial metabolomics studies are a common approach to identifying microbial strains that have a capacity to produce new chemistries both in vitro and in situ. A limitation to applying microbial metabolomics to the discovery of new chemical entities is the rediscovery of known compounds, or "known unknowns." One contributing factor to this rediscovery is the majority of laboratories use one ionization source-electrospray ionization (ESI)-to conduct metabolomics studies. Although ESI is an efficient, widely adopted ionization method, its widespread use may contribute to the re-identification of known metabolites. Here, we present the use of a dielectric barrier discharge ionization (DBDI) for microbial metabolomics applications through the use of soft ionization chemical reaction in-transfer (SICRIT). Additionally, we compared SICRIT to ESI using two different Vibrio species-Vibrio fischeri, a symbiotic marine bacterium, and Vibrio cholerae, a pathogenic bacterium. Overall, we found that the SICRIT source ionizes a different set of metabolites than ESI, and it has the ability to ionize lipids more efficiently than ESI in positive mode. This work highlights the value of using more than one ionization source for the detection of metabolites.

Discovering transformation products of pharmaceuticals in domestic wastewaters and receiving rivers by using non-target screening and machine learning approaches

Wastewater treatment plants (WWTPs) are an important source of pharmaceuticals in surface water, but information about their transformation products (TPs) is very limited. Here, we investigated occurrence and transformation of pharmaceuticals and TPs in WWTPs and receiving rivers by using suspect and non-target analysis as well as target analysis. Results showed identification of 113 pharmaceuticals and 399 TPs, including mammalian metabolites (n = 100), environmental microbial degradation products (n = 250), photodegradation products (n = 44) and hydrolysis products (n = 5). The predominant parent pharmaceuticals (n = 37) and transformation products (n = 68) were mainly derived from antimicrobials, accounting for 32.7 % and 17.0 %, respectively. The identified compounds were found in the influent (387-428) and effluent (227-400) of WWTPs, as well as upstream (290-451) and downstream (322-416) of receiving rivers, most predominantly from antimicrobials, followed by analgesic and antipyretic drugs. A total of 399 identified TPs were transformed by 110 pathways, of which the oxidation reaction was predominant (27.0 %), followed by photodegradation reaction (10.7 %). Of the 399 TPs, 49 (with lower PNECs) were predicted to be more toxic than their parents. Compounds with potential high risks (hazard quotient >1 and risk index (RI) > 0.1) were found in the WWTP influent (126), effluent (53) and river (61), and the majority were from the antimicrobial and antihypertensive classes. In particular, the potential risks (RI) of TPs from roxithromycin and irbesartan were found higher than those for their corresponding parents. The findings from this study highlight the need to monitor TPs from pharmaceuticals in the environment.

Characterization of Electrospray Ionization Complexity in Untargeted Metabolomic Studies

The annotation of metabolites detected in LC-MS-based untargeted metabolomics studies routinely applies accurate m/z of the intact metabolite (MS1) as well as chromatographic retention time and MS/MS data. Electrospray ionization and transfer of ions through the mass spectrometer can result in the generation of multiple "features" derived from the same metabolite with different m/z values but the same retention time. The complexity of the different charged and neutral adducts, in-source fragments, and charge states has not been previously and deeply characterized. In this paper, we report the first large-scale characterization using publicly available data sets derived from different research groups, instrument manufacturers, LC assays, sample types, and ion modes. 271 m/z differences relating to different metabolite feature pairs were reported, and 209 were annotated. The results show a wide range of different features being observed with only a core 32 m/z differences reported in >50% of the data sets investigated. There were no patterns reporting specific m/z differences that were observed in relation to ion mode, instrument manufacturer, LC assay type, and mammalian sample type, although some m/z differences were related to study group (mammal, microbe, plant) and mobile phase composition. The results provide the metabolomics community with recommendations of adducts, in-source fragments, and charge states to apply in metabolite annotation workflows.

Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products

Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.

Recent advances and challenges in the analysis of natural toxins

Natural toxins (NTs) are poisonous secondary metabolites produced by living organisms developed to ward off predators. Especially low molecular weight NTs (MW<∼1 kDa), such as mycotoxins, phycotoxins, and plant toxins, are considered an important and growing food safety concern. Therefore, accurate risk assessment of food and feed for the presence of NTs is crucial. Currently, the analysis of NTs is predominantly performed with targeted high pressure liquid chromatography tandem mass spectrometry (HPLC-MS/MS) methods. Although these methods are highly sensitive and accurate, they are relatively expensive and time-consuming, while unknown or unexpected NTs will be missed. To overcome this, novel on-site screening methods and non-targeted HPLC high resolution mass spectrometry (HRMS) methods have been developed. On-site screening methods can give non-specialists the possibility for broad "scanning" of potential geographical regions of interest, while also providing sensitive and specific analysis at the point-of-need. Non-targeted chromatography-HRMS methods can detect unexpected as well as unknown NTs and their metabolites in a lab-based approach. The aim of this chapter is to provide an insight in the recent advances, challenges, and perspectives in the field of NTs analysis both from the on-site and the laboratory perspective.

Accessing the specialized metabolome of actinobacteria from the bulk soil of Paullinia cupana Mart. on the Brazilian Amazon: a promising source of bioactive compounds against soybean phytopathogens

The Amazon rainforest, an incredibly biodiverse ecosystem, has been increasingly vulnerable to deforestation. Despite its undeniable importance and potential, the Amazonian microbiome has historically received limited study, particularly in relation to its unique arsenal of specialized metabolites. Therefore, in this study our aim was to assess the metabolic diversity and the antifungal activity of actinobacterial strains isolated from the bulk soil of Paullinia cupana, a native crop, in the Brazilian Amazon Rainforest. Extracts from 24 strains were subjected to UPLC-MS/MS analysis using an integrative approach that relied on the Chemical Structural and Compositional Similarity (CSCS) metric, GNPS molecular networking, and in silico dereplication tools. This procedure allowed the comprehensive understanding of the chemical space encompassed by these actinobacteria, which consists of features belonging to known bioactive metabolite classes and several unannotated molecular families. Among the evaluated strains, five isolates exhibited bioactivity against a panel of soybean fungal phytopathogens (Rhizoctonia solani, Macrophomina phaseolina, and Sclerotinia sclerotiorum). A focused inspection led to the annotation of pepstatins, oligomycins, hydroxamate siderophores and dorrigocins as metabolites produced by these bioactive strains, with potentially unknown compounds also comprising their metabolomes. This study introduces a pragmatic protocol grounded in established and readily available tools for the annotation of metabolites and the prioritization of strains to optimize further isolation of specialized metabolites. Conclusively, we demonstrate the relevance of the Amazonian actinobacteria as sources for bioactive metabolites useful for agriculture. We also emphasize the importance of preserving this biome and conducting more in-depth studies on its microbiota.

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.

Decoding the molecular interplay in the central dogma: An overview of mass spectrometry-based methods to investigate protein-metabolite interactions

With the emergence of next-generation nucleotide sequencing and mass spectrometry-based proteomics and metabolomics tools, we have comprehensive and scalable methods to analyze the genes, transcripts, proteins, and metabolites of a multitude of biological systems. Despite the fascinating new molecular insights at the genome, transcriptome, proteome and metabolome scale, we are still far from fully understanding cellular organization, cell cycles and biology at the molecular level. Significant advances in sensitivity and depth for both sequencing as well as mass spectrometry-based methods allow the analysis at the single cell and single molecule level. At the same time, new tools are emerging that enable the investigation of molecular interactions throughout the central dogma of molecular biology. In this review, we provide an overview of established and recently developed mass spectrometry-based tools to probe metabolite-protein interactions-from individual interaction pairs to interactions at the proteome-metabolome scale.

Environmental metabolomics characterization of modern stromatolites and annotation of ibhayipeptolides

Lithified layers of complex microbial mats known as microbialites are ubiquitous in the fossil record, and modern forms are increasingly identified globally. A key challenge to developing an understanding of microbialite formation and environmental role is how to investigate complex and diverse communities in situ. We selected living, layered microbialites (stromatolites) in a peritidal environment near Schoenmakerskop, Eastern Cape, South Africa to conduct a spatial survey mapping the composition and small molecule production of the microbial communities from environmental samples. Substrate core samples were collected from nine sampling stations ranging from the upper point of the freshwater inflow to the lower marine interface where tidal overtopping takes place. Substrate cores provided material for parallel analyses of microbial community diversity by 16S rRNA gene amplicon sequencing and metabolomics using LC-MS2. Species and metabolite diversities were correlated, and prominent specialized metabolites were targeted for preliminary characterization. A new series of cyclic hexadepsipeptides, named ibhayipeptolides, was most abundant in substrate cores of submerged microbialites. These results demonstrate the detection and identification of metabolites from mass-limited environmental samples and contribute knowledge about microbialite chemistry and biology, which facilitates future targeted studies of specialized metabolite function and biosynthesis.

B. Trivella D. NP3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics

Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP3 MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP3 MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP3 MS Workflow adds improved computing of the MS2 spectra in an LC-MS/MS data set and provides tools for automatic [M + H]+ ion deconvolution using fragmentation rules; chemical structural annotation against MS2 databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP3 MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.

Exploring the specialized metabolome of the plant pathogen Streptomyces sp. 11-1-2

Streptomyces bacteria are notable for producing chemically diverse specialized metabolites that exhibit various bioactivities and mediate interactions with different organisms. Streptomyces sp. 11-1-2 is a plant pathogen that produces nigericin and geldanamycin, both of which display toxic effects against various plants. Here, the 'One Strain Many Compounds' approach was used to characterize the metabolic potential of Streptomyces sp. 11-1-2. Organic extracts were prepared from 11-1-2 cultures grown on six different agar media, and the extracts were tested in antimicrobial and plant bioassays and were subjected to untargeted metabolomics and molecular networking. Most extracts displayed strong bioactivity against Gram-positive bacteria and yeast, and they exhibited phytotoxic activity against potato tuber tissue and radish seedlings. Several known specialized metabolites, including musacin D, galbonolide B, guanidylfungin A, meridamycins and elaiophylin, were predicted to be present in the extracts along with closely related compounds with unknown structure and bioactivity. Targeted detection confirmed the presence of elaiophylin in the extracts, and bioassays using pure elaiophylin revealed that it enhances the phytotoxic effects of geldanamycin and nigericin on potato tuber tissue. Overall, this study reveals novel insights into the specialized metabolites that may mediate interactions between Streptomyces sp. 11-1-2 and other bacteria and eukaryotic organisms.

A MassQL-Integrated Molecular Networking Approach for the Discovery and Substructure Annotation of Bioactive Cyclic Peptides

The marine sponge-derived fungus Stachylidium bicolor 293 K04 is a prolific producer of specialized metabolites, including certain cyclic tetrapeptides called endolides, which are characterized by the presence of the unusual amino acid N-methyl-3-(3-furyl)-alanine. This rare feature can be used as bait to detect new endolide-like analogs through customized fragment pattern searches of tandem mass spectrometry data using the Mass Spec Query Language (MassQL). Here, we integrate endolide-specific MassQL queries with molecular networking to obtain substructural information guiding the targeted isolation and structure elucidation of the new proline-containing endolides E (1) and F (2). We showed that endolide F (but not E) is a moderate antagonist of the arginine vasopressin V1A receptor, a member of the G protein-coupled receptor superfamily.

Comprehensive mass spectrometric metabolomic profiling of a chemically diverse collection of plants of the Celastraceae family

Natural products exhibit interesting structural features and significant biological activities. The discovery of new bioactive molecules is a complex process that requires high-quality metabolite profiling data to properly target the isolation of compounds of interest and enable their complete structural characterization. The same metabolite profiling data can also be used to better understand chemotaxonomic links between species. This Data Descriptor details a dataset resulting from the untargeted liquid chromatography-mass spectrometry metabolite profiling of 76 natural extracts of the Celastraceae family. The spectral annotation results and related chemical and taxonomic metadata are shared, along with proposed examples of data reuse. This data can be further studied by researchers exploring the chemical diversity of natural products. This can serve as a reference sample set for deep metabolome investigation of this chemically rich plant family.

MIST-CF: Chemical Formula Inference from Tandem Mass Spectra

Chemical formula annotation for tandem mass spectrometry (MS/MS) data is the first step toward structurally elucidating unknown metabolites. While great strides have been made toward solving this problem, the current state-of-the-art method depends on time-intensive, proprietary, and expert-parametrized fragmentation tree construction and scoring. In this work, we extend our previous spectrum Transformer methodology into an energy-based modeling framework, MIST-CF: Metabolite Inference with Spectrum Transformers for Chemical Formula prediction, for learning to rank chemical formula and adduct assignments given an unannotated MS/MS spectrum. Importantly, MIST-CF learns in a data-dependent fashion using a Formula Transformer neural network architecture and circumvents the need for fragmentation tree construction. We train and evaluate our model on a large open-access database, showing an absolute improvement of 10% top 1 accuracy over other neural network architectures. We further validate our approach on the CASMI2022 challenge data set, achieving nearly equivalent performance to the winning entry within the positive mode category without any manual curation or postprocessing of our results. These results demonstrate an exciting strategy to more powerfully leverage MS2 fragment peaks for predicting MS1 precursor chemical formulas with data-driven learning.

Integration of Wnt-inhibitory activity and structural novelty scoring results to uncover novel bioactive natural products: new Bicyclo[3.3.1]non-3-ene-2,9-diones from the leaves of Hymenocardia punctata

In natural products (NPs) research, methods for the efficient prioritization of natural extracts (NEs) are key for discovering novel bioactive NPs. In this study a biodiverse collection of 1,600 NEs, previously analyzed by UHPLC-HRMS2 metabolite profiling was screened for Wnt pathway regulation. The results of the biological screening drove the selection of a subset of 30 non-toxic NEs with an inhibitory IC50 ≤ 5 μg/mL. To increase the chance of finding structurally novel bioactive NPs, Inventa, a computational tool for automated scoring of NEs based on structural novelty was used to mine the HRMS2 analysis and dereplication results. After this, four out of the 30 bioactive NEs were shortlisted by this approach. The most promising sample was the ethyl acetate extract of the leaves of Hymenocardia punctata (Phyllanthaceae). Further phytochemical investigations of this species resulted in the isolation of three known prenylated flavones (3, 5, 7) and ten novel bicyclo[3.3.1]non-3-ene-2,9-diones (1, 2, 4, 6, 8-13), named Hymenotamayonins. Assessment of the Wnt inhibitory activity of these compounds revealed that two prenylated flavones and three novel bicyclic compounds showed interesting activity without apparent cytotoxicity. This study highlights the potential of combining Inventa's structural novelty scores with biological screening results to effectively discover novel bioactive NPs in large NE collections.

Harnessing the potential: advances in cyanobacterial natural product research and biotechnology

Covering: 2000 to 2023Cyanobacteria produce a variety of bioactive natural products that can pose a threat to humans and animals as environmental toxins, but also have potential for or inspire pharmaceutical use. As oxygenic phototrophs, cyanobacteria furthermore hold great promise for sustainable biotechnology. Yet, the necessary tools for exploiting their biotechnological potential have so far been established only for a few model strains of cyanobacteria, while large untapped biosynthetic resources are hidden in slow-growing cyanobacterial genera that are difficult to access by genetic techniques. In recent years, several approaches have been developed to circumvent the bottlenecks in cyanobacterial natural product research. Here, we summarize current progress that has been made in unlocking or characterizing cryptic metabolic pathways using integrated omics techniques, orphan gene cluster activation, use of genetic approaches in original producers, heterologous expression and chemo-enzymatic techniques. We are mainly highlighting genomic mining concepts and strategies towards high-titer production of cyanobacterial natural products from the last 10 years and discuss the need for further research developments in this field.

Functional metabolomics of the human scalp: a metabolic niche for Staphylococcus epidermidis

Although metabolomics data acquisition and analysis technologies have become increasingly sophisticated over the past 5-10 years, deciphering a metabolite's function from a description of its structure and its abundance in a given experimental setting is still a major scientific and intellectual challenge. To point out ways to address this "data to knowledge" challenge, we developed a functional metabolomics strategy that combines state-of-the-art data analysis tools and applied it to a human scalp metabolomics data set: skin swabs from healthy volunteers with normal or oily scalp (Sebumeter score 60-120, n = 33; Sebumeter score > 120, n = 41) were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS), yielding four metabolomics data sets for reversed phase chromatography (C18) or hydrophilic interaction chromatography (HILIC) separation in electrospray ionization (ESI) + or - ionization mode. Following our data analysis strategy, we were able to obtain increasingly comprehensive structural and functional annotations, by applying the Global Natural Product Social Networking (M. Wang, J. J. Carver, V. V. Phelan, L. M. Sanchez, et al., Nat Biotechnol 34:828-837, 2016, https://doi.org/10.1038/nbt.3597), SIRIUS (K. Dührkop, M. Fleischauer, M. Ludwig, A. A. Aksenov, et al., Nat Methods 16:299-302, 2019, https://doi.org/10.1038/s41592-019-0344-8), and MicrobeMASST (S. ZuffaS, R. Schmid, A. Bauermeister, P. W, P. Gomes, et al., bioRxiv:rs.3.rs-3189768, 2023, https://doi.org/10.21203/rs.3.rs-3189768/v1) tools. We finally combined the metabolomics data with a corresponding metagenomic sequencing data set using MMvec (J. T. Morton, A. A. Aksenov, L. F. Nothias, J. R. Foulds, et. al., Nat Methods 16:1306-1314, 2019, https://doi.org/10.1038/s41592-019-0616-3), gaining insights into the metabolic niche of one of the most prominent microbes on the human skin, Staphylococcus epidermidis.IMPORTANCESystems biology research on host-associated microbiota focuses on two fundamental questions: which microbes are present and how do they interact with each other, their host, and the broader host environment? Metagenomics provides us with a direct answer to the first part of the question: it unveils the microbial inhabitants, e.g., on our skin, and can provide insight into their functional potential. Yet, it falls short in revealing their active role. Metabolomics shows us the chemical composition of the environment in which microbes thrive and the transformation products they produce. In particular, untargeted metabolomics has the potential to observe a diverse set of metabolites and is thus an ideal complement to metagenomics. However, this potential often remains underexplored due to the low annotation rates in MS-based metabolomics and the necessity for multiple experimental chromatographic and mass spectrometric conditions. Beyond detection, prospecting metabolites' functional role in the host/microbiome metabolome requires identifying the biological processes and entities involved in their production and biotransformations. In the present study of the human scalp, we developed a strategy to achieve comprehensive structural and functional annotation of the metabolites in the human scalp environment, thus diving one step deeper into the interpretation of "omics" data. Leveraging a collection of openly accessible software tools and integrating microbiome data as a source of functional metabolite annotations, we finally identified the specific metabolic niche of Staphylococcus epidermidis, one of the key players of the human skin microbiome.

Reverse metabolomics for the discovery of chemical structures from humans

Determining the structure and phenotypic context of molecules detected in untargeted metabolomics experiments remains challenging. Here we present reverse metabolomics as a discovery strategy, whereby tandem mass spectrometry spectra acquired from newly synthesized compounds are searched for in public metabolomics datasets to uncover phenotypic associations. To demonstrate the concept, we broadly synthesized and explored multiple classes of metabolites in humans, including N-acyl amides, fatty acid esters of hydroxy fatty acids, bile acid esters and conjugated bile acids. Using repository-scale analysis1,2, we discovered that some conjugated bile acids are associated with inflammatory bowel disease (IBD). Validation using four distinct human IBD cohorts showed that cholic acids conjugated to Glu, Ile/Leu, Phe, Thr, Trp or Tyr are increased in Crohn's disease. Several of these compounds and related structures affected pathways associated with IBD, such as interferon-γ production in CD4+ T cells3 and agonism of the pregnane X receptor4. Culture of bacteria belonging to the Bifidobacterium, Clostridium and Enterococcus genera produced these bile amidates. Because searching repositories with tandem mass spectrometry spectra has only recently become possible, this reverse metabolomics approach can now be used as a general strategy to discover other molecules from human and animal ecosystems.

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.

Enhancing Metabolome Annotation by Electron Impact Excitation of Ions from Organics-Molecular Networking

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is widely used in untargeted metabolomics, but large-scale and high-accuracy metabolite annotation remains a challenge due to the complex nature of biological samples. Recently introduced electron impact excitation of ions from organics (EIEIO) fragmentation can generate information-rich fragment ions. However, effective utilization of EIEIO tandem mass spectrometry (MS/MS) is hindered by the lack of reference spectral databases. Molecular networking (MN) shows great promise in large-scale metabolome annotation, but enhancing the correlation between spectral and structural similarity is essential to fully exploring the benefits of MN annotation. In this study, a novel approach was proposed to enhance metabolite annotation in untargeted metabolomics using EIEIO and MN. MS/MS spectra were acquired in EIEIO and collision-induced dissociation (CID) modes for over 400 reference metabolites. The study revealed a stronger correlation between the EIEIO spectra and metabolite structure. Moreover, the EIEIO spectral network outperformed the CID spectral network in capturing structural analogues. The annotation performance of the structural similarity network for untargeted LC-MS/MS was evaluated. For the spiked NIST SRM 1950 human plasma, the annotation coverage and accuracy were 72.94 and 74.19%, respectively. A total of 2337 metabolite features were successfully annotated in NIST SRM 1950 human plasma, which was twice that of LC-CID MS/MS. Finally, the developed method was applied to investigate prostate cancer. A total of 87 significantly differential metabolites were annotated. This study combining EIEIO and MN makes a valuable contribution to improving metabolome annotation.

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.

Spatialized Metabolomic Annotation Combining MALDI Imaging and Molecular Networks

MALDI mass spectrometry imaging has gained major interest in the field of chemical imaging. This technique makes it possible to locate tens to hundreds of ionic signals on the sample surface without any a priori. One of the current challenges is still the limited ability to annotate signals in order to convert m/z values into probable chemical structures. At the same time, data obtained by LC-MS/MS have benefited from the development of numerous chemoinformatics tools, in particular molecular networks, for their efficient annotation. For the first time, we present here the combination of MALDI-FT-ICR imaging with molecular networks from MALDI-MS/MS data directly acquired on plant tissue sections. Annotation improvements are demonstrated, paving the way for new annotation pipelines for MALDI imaging.

Succession of microbial community composition and secondary metabolism during marine biofilm development

In nature, secondary metabolites mediate interactions between microorganisms residing in complex microbial communities. However, the degree to which community dynamics can be linked to secondary metabolite potential remains largely unknown. In this study, we address the relationship between community succession and secondary metabolism variation. We used 16S and 18S rRNA gene and adenylation domain amplicon sequencing, genome-resolved metagenomics, and untargeted metabolomics to track the taxons, biosynthetic gene clusters, and metabolome dynamics in situ of microorganisms during marine biofilm succession over 113 days. Two phases were identified during the community succession, with a clear shift around Day 29, where the alkaloid secondary metabolites, pseudanes, were also detected. The microbial secondary metabolite potential changed between the phases, and only a few community members, including Myxococotta spp., were responsible for the majority of the biosynthetic gene cluster potential in the early succession phase. In the late phase, bryozoans and benthic copepods were detected, and the microbial nonribosomal peptide potential drastically decreased in association with a reduction in the relative abundance of the prolific secondary metabolite producers. Conclusively, this study provides evidence that the early succession of the marine biofilm community favors prokaryotes with high nonribosomal peptide synthetase potential. In contrast, the late succession is dominated by multicellular eukaryotes and a reduction in bacterial nonribosomal peptide synthetase potential.

OSMAC Method to Assess Impact of Culture Parameters on Metabolomic Diversity and Biological Activity of Marine-Derived Actinobacteria

Actinobacteria are known for their production of bioactive specialized metabolites, but they are still under-exploited. This study uses the "One Strain Many Compounds" (OSMAC) method to explore the potential of three preselected marine-derived actinobacteria: Salinispora arenicola (SH-78) and two Micromonospora sp. strains (SH-82 and SH-57). Various parameters, including the duration of the culture and the nature of the growth medium, were modified to assess their impact on the production of specialized metabolites. This approach involved a characterization based on chemical analysis completed with the construction of molecular networks and biological testing to evaluate cytotoxic and antiplasmodial activities. The results indicated that the influence of culture parameters depended on the studied species and also varied in relation with the microbial metabolites targeted. However, common favorable parameters could be observed for all strains such as an increase in the duration of the culture or the use of the A1 medium. For Micromonospora sp. SH-82, the solid A1 medium culture over 21 days favored a greater chemical diversity. A rise in the antiplasmodial activity was observed with this culture duration, with a IC50 twice as low as for the 14-day culture. Micromonospora sp. SH-57 produced more diverse natural products in liquid culture, with approximately 54% of nodes from the molecular network specifically linked to the type of culture support. Enhanced biological activities were also observed with specific sets of parameters. Finally, for Salinispora arenicola SH-78, liquid culture allowed a greater diversity of metabolites, but intensity variations were specifically observed for some metabolites under other conditions. Notably, compounds related to staurosporine were more abundant in solid culture. Consequently, in the range of the chosen parameters, optimal conditions to enhance metabolic diversity and biological activities in these three marine-derived actinobacteria were identified, paving the way for future isolation works.

Integrative deep learning framework predicts lipidomics-based investigation of preservatives on meat nutritional biomarkers and metabolic pathways

Preservatives are added as antimicrobial agents to extend the shelf life of meat. Adding preservatives to meat products can affect their flavor and nutrition. This review clarifies the effects of preservatives on metabolic pathways and network molecular transformations in meat products based on lipidomics, metabolomics and proteomics analyses. Preservatives change the nutrient content of meat products via altering ionic strength and pH to influence enzyme activity. Ionic strength in salt triggers muscle triglyceride hydrolysis by causing phosphorylation and lipid droplet splitting in adipose tissue hormone-sensitive lipase and triglyceride lipase. DisoLipPred exploiting deep recurrent networks and transfer learning can predict the lipid binding trend of each amino acid in the disordered region of input protein sequences, which could provide omics analyses of biomarkers metabolic pathways in meat products. While conventional meat quality assessment tools are unable to elucidate the intrinsic mechanisms and pathways of variables in the influences of preservatives on the quality of meat products, the promising application of omics techniques in food analysis and discovery through multimodal learning prediction algorithms of neural networks (e.g., deep neural network, convolutional neural network, artificial neural network) will drive the meat industry to develop new strategies for food spoilage prevention and control.

Open access repository-scale propagated nearest neighbor suspect spectral library for untargeted metabolomics

Despite the increasing availability of tandem mass spectrometry (MS/MS) community spectral libraries for untargeted metabolomics over the past decade, the majority of acquired MS/MS spectra remain uninterpreted. To further aid in interpreting unannotated spectra, we created a nearest neighbor suspect spectral library, consisting of 87,916 annotated MS/MS spectra derived from hundreds of millions of MS/MS spectra originating from published untargeted metabolomics experiments. Entries in this library, or "suspects," were derived from unannotated spectra that could be linked in a molecular network to an annotated spectrum. Annotations were propagated to unknowns based on structural relationships to reference molecules using MS/MS-based spectrum alignment. We demonstrate the broad relevance of the nearest neighbor suspect spectral library through representative examples of propagation-based annotation of acylcarnitines, bacterial and plant natural products, and drug metabolism. Our results also highlight how the library can help to better understand an Alzheimer's brain phenotype. The nearest neighbor suspect spectral library is openly available for download or for data analysis through the GNPS platform to help investigators hypothesize candidate structures for unknown MS/MS spectra in untargeted metabolomics data.

Unravelling key enzymatic steps in C-ring cleavage during angucycline biosynthesis

Angucyclines are type II polyketide natural products, often characterized by unusual structural rearrangements through B- or C-ring cleavage of their tetracyclic backbone. While the enzymes involved in B-ring cleavage have been extensively studied, little is known of the enzymes leading to C-ring cleavage. Here, we unravel the function of the oxygenases involved in the biosynthesis of lugdunomycin, a highly rearranged C-ring cleaved angucycline derivative. Targeted deletion of the oxygenase genes, in combination with molecular networking and structural elucidation, showed that LugOI is essential for C12 oxidation and maintaining a keto group at C6 that is reduced by LugOII, resulting in a key intermediate towards C-ring cleavage. An epoxide group is then inserted by LugOIII, and stabilized by the novel enzyme LugOV for the subsequent cleavage. Thus, for the first time we describe the oxidative enzymatic steps that form the basis for a wide range of rearranged angucycline natural products.

Comprehensive metabolomics of Philippine Stichopus cf. horrens reveals diverse classes of valuable small molecules for biomedical applications

Stichopus cf. horrens is an economically important sea cucumber species in Southeast Asia due to their presumed nutritional and medicinal benefits. However, compared to other sea cucumbers such as Apostichopus japonicus, there are no biochemical studies on which compounds contribute to the purported bioactivities of S. cf. horrens. To address this, a high-throughput characterization of the global metabolite profile of the species was performed through LC-MS/MS experiments and utilizing open-access platforms such as GNPS, XCMS, and metaboAnalyst. Bioinformatics-based molecular networking and chemometrics revealed the abundance of phospholipids such as phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), phosphatidylinositols (PIs), and phosphatidylserines (PSs) in the crude samples. Body wall extracts were observed to have higher levels of structural, diacylated PCs, while the viscera have higher relative abundance of single-tail PCs and PEs that could be involved in digestion via nutrient absorption and transport for sea cucumbers. PEs and sphingolipids could also be implicated in the ecological response and morphological transformations of S. cf. horrens in the presence of predatory and other environmental stress. Interestingly, terpenoid glycosides and saponins with reported anti-cancer benefits were significantly localized in the body wall. The sulfated alkanes and sterols present in S. cf. horrens bear similarity to known kairomones and other signaling molecules. All in all, the results provide a baseline metabolomic profile of S. cf. horrens that may further be used for comparative and exploratory studies and suggest the untapped potential of S. cf. horrens as a source of bioactive molecules.

Subplenones A-J: Dimeric Xanthones with Antibacterial Activity from the Endophytic Fungus Subplenodomus sp. CPCC 401465

Subplenones A-J (1-10), 10 new xanthone dimers, have been isolated and characterized from the endophytic fungus Subplenodomus sp. CPCC 401465, which resides within the Chinese medicinal plant Gentiana straminea. The isolation process was guided by antibacterial assays and molecular-networking-based analyses. The chemical structures of these compounds were elucidated through the interpretation of nuclear magnetic resonance (NMR) and high-resolution electrospray ionization mass spectrometry (HRESIMS) data. Furthermore, the relative configuration of the compounds was determined using NMR and single-crystal X-ray diffraction analyses, and the absolute configuration was established using electronic circular dichroism calculations. All of the isolated compounds exhibited significant inhibitory activity against Gram-positive bacteria. Notably, compounds 1, 5, and 7 displayed remarkable inhibitory activity against methicillin-resistant Staphylococcus aureus (MRSA) ATCC 700698, with a minimum inhibitory concentration (MIC) of 0.25 μg/mL, and against vancomycin-resistant Enterococcus faecium (VRE) ATCC 700221, with MIC values ranging from 0.5 to 1.0 μg/mL.

In Situ Capture and Real-Time Enrichment of Marine Chemical Diversity

Analyzing the chemical composition of seawater to understand its influence on ecosystem functions is a long-lasting challenge due to the inherent complexity and dynamic nature of marine environments. Describing the intricate chemistry of seawater requires optimal in situ sampling. Here is presented a novel underwater hand-held solid-phase extraction device, I-SMEL (In Situ Marine moleculELogger), which aims to concentrate diluted molecules from large volumes of seawater in a delimited zone targeting keystone benthic species. Marine benthic holobionts, such as sponges, can impact the chemical composition of their surroundings possibly through the production and release of their specialized metabolites, hence termed exometabolites (EMs). I-SMEL was deployed in a sponge-dominated Mediterranean ecosystem at a 15 m depth. Untargeted MS-based metabolomics was performed on enriched EM extracts and showed (1) the chemical diversity of enriched seawater metabolites and (2) reproducible recovery and enrichment of specialized sponge EMs such as aerothionin, demethylfurospongin-4, and longamide B methyl ester. These EMs constitute the chemical identity of each targeted species: Aplysina cavernicola, Spongia officinalis, and Agelas oroides, respectively. I-SMEL concentrated sponge EMs from 10 L of water in a 10 min sampling time. The present proof of concept with I-SMEL opens new research perspectives in marine chemical ecology and sets the stage for further sustainable efforts in natural product chemistry.

Automated Composition Assessment of Natural Extracts: Untargeted Mass Spectrometry-Based Metabolite Profiling Integrating Semiquantitative Detection

Recent developments in mass spectrometry-based metabolite profiling allow unprecedented qualitative coverage of complex biological extract composition. However, the electrospray ionization used in metabolite profiling generates multiple artifactual signals for a single analyte. This leads to thousands of signals per analysis without satisfactory means of filtering those corresponding to abundant constituents. Generic approaches are therefore needed for the qualitative and quantitative annotation of a broad range of relevant constituents. For this, we used an analytical platform combining liquid chromatography-mass spectrometry (LC-MS) with Charged Aerosol Detection (CAD). We established a generic metabolite profiling for the concomitant recording of qualitative MS data and semiquantitative CAD profiles. The MS features (recorded in high-resolution tandem MS) are grouped and annotated using state-of-the-art tools. To efficiently attribute features to their corresponding extracted and integrated CAD peaks, a custom signal pretreatment and peak-shape comparison workflow is built. This strategy allows us to automatically contextualize features at both major and minor metabolome levels, together with a detailed reporting of their annotation including relevant orthogonal information (taxonomy, retention time). Signals not attributed to CAD peaks are considered minor metabolites. Results are illustrated on an ethanolic extract of Swertia chirayita (Roxb.) H. Karst., a bitter plant of industrial interest, exhibiting the typical complexity of plant extracts as a proof of concept. This generic qualitative and quantitative approach paves the way to automatically assess the composition of single natural extracts of interest or broader collections, thus facilitating new ingredient registrations or natural-extracts-based drug discovery campaigns.

Putative Identification of New Phragmaline-Type Limonoids from the Leaves of Swietenia macrophylla King: A Case Study Using Mass Spectrometry-Based Molecular Networking

Swietenia macrophylla King is a plant commonly known as Brazilian mahogany. The wood from its stem is highly prized for its exceptional quality, while its leaves are valued for their high content of phragmalin-type limonoids, a subclass of compounds known for their significant biological activities, including antimalarial, antitumor, antiviral, and anti-inflammatory properties. In this context, twelve isolated limonoids from S. macrophylla leaves were employed as standards in mass spectrometry-based molecular networking to unveil new potential mass spectrometry signatures for phragmalin-type limonoids. Consequently, ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was utilized for data acquisition. Subsequently, the obtained data were analyzed using the Global Natural Products Social Molecular Networking platform based on spectral similarity. In summary, this study identified 24 new putative phragmalin-type limonoids for the first time in S. macrophylla. These compounds may prove valuable in guiding future drug development efforts, leveraging the already established biological activities associated with limonoids.

Metabolomics and molecular networking approach for exploring the anti-diabetic activity of medicinal plants

Metabolomics and molecular networking approaches have expanded rapidly in the field of biological sciences and involve the systematic identification, visualization, and high-throughput characterization of bioactive metabolites in natural products using sophisticated mass spectrometry-based techniques. The popularity of natural products in pharmaceutical therapies has been influenced by medicinal plants with a long history of ethnobotany and a vast collection of bioactive compounds. Here, we selected four medicinal plants Cleistocalyx operculatus, Terminalia chebula, Ficus lacor, and Ficus semicordata, the biochemical characteristics of which remain unclear owing to the inherent complexity of their plant metabolites. In this study, we aimed to evaluate the potential of these aforementioned plant extracts in inhibiting the enzymatic activity of α-amylase and α-glucosidase, respectively, followed by the annotation of secondary metabolites. The methanol extract of Ficus semicordata exhibited the highest α-amylase inhibition with an IC50 of 46.8 ± 1.8 μg mL-1, whereas the water fraction of Terminalia chebula fruits demonstrated the most significant α-glucosidase inhibition with an IC50 value of 1.07 ± 0.01 μg mL-1. The metabolic profiling of plant extracts was analyzed through Liquid Chromatography-Mass Spectrometry (LC-HRMS) of the active fractions, resulting in the annotation of 32 secondary metabolites. Furthermore, we applied the Global Natural Product Social Molecular Networking (GNPS) platform to evaluate the MS/MS data of Terminalia chebula (bark), revealing that there were 205 and 160 individual ion species observed as nodes in the methanol and ethyl acetate fractions, respectively. Twenty-two metabolites were tentatively identified from the network map, of which 11 compounds were unidentified during manual annotation.

G protein-coupled receptors: A target for microbial metabolites and a mechanistic link to microbiome-immune-brain interactions

Human-microorganism interactions play a key role in human health. However, the underlying molecular mechanisms remain poorly understood. Small-molecules that offer a functional readout of microbe-microbe-human relationship are of great interest for deeper understanding of the inter-kingdom crosstalk at the molecular level. Recent studies have demonstrated that small-molecules from gut microbiota act as ligands for specific human G protein-coupled receptors (GPCRs) and modulate a range of human physiological functions, offering a mechanistic insight into the microbe-human interaction. To this end, we focused on analysis of bacterial metabolites that are currently recognized to bind to GPCRs and are found to activate the known downstream signaling pathways. We further mapped the distribution of these molecules across the public mass spectrometry-based metabolomics data, to identify the presence of these molecules across body sites and their association with health status. By combining this with RNA-Seq expression and spatial localization of GPCRs from a public human protein atlas database, we inferred the most predominant GPCR-mediated microbial metabolite-human cell interactions regulating gut-immune-brain axis. Furthermore, by evaluating the intestinal absorption properties and blood-brain barrier permeability of the small-molecules we elucidated their molecular interactions with specific human cell receptors, particularly expressed on human intestinal epithelial cells, immune cells and the nervous system that are shown to hold much promise for clinical translational potential. Furthermore, we provide an overview of an open-source resource for simultaneous interrogation of bioactive molecules across the druggable human GPCRome, a useful framework for integration of microbiome and metabolite cataloging with mechanistic studies for an improved understanding of gut microbiota-immune-brain molecular interactions and their potential therapeutic use.

Identification and Prioritization of Environmental Organic Pollutants: From an Analytical and Toxicological Perspective

Exposure to environmental organic pollutants has triggered significant ecological impacts and adverse health outcomes, which have been received substantial and increasing attention. The contribution of unidentified chemical components is considered as the most significant knowledge gap in understanding the combined effects of pollutant mixtures. To address this issue, remarkable analytical breakthroughs have recently been made. In this review, the basic principles on recognition of environmental organic pollutants are overviewed. Complementary analytical methodologies (i.e., quantitative structure-activity relationship prediction, mass spectrometric nontarget screening, and effect-directed analysis) and experimental platforms are briefly described. The stages of technique development and/or essential parts of the analytical workflow for each of the methodologies are then reviewed. Finally, plausible technique paths and applications of the future nontarget screening methods, interdisciplinary techniques for achieving toxicant identification, and burgeoning strategies on risk assessment of chemical cocktails are discussed.

Illuminating the oral microbiome and its host interactions: recent advancements in omics and bioinformatics technologies in the context of oral microbiome research

The oral microbiota has an enormous impact on human health, with oral dysbiosis now linked to many oral and systemic diseases. Recent advancements in sequencing, mass spectrometry, bioinformatics, computational biology, and machine learning are revolutionizing oral microbiome research, enabling analysis at an unprecedented scale and level of resolution using omics approaches. This review contains a comprehensive perspective of the current state-of-the-art tools available to perform genomics, metagenomics, phylogenomics, pangenomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics analysis on (all) microbiomes, and then provides examples of how the techniques have been applied to research of the oral microbiome, specifically. Key findings of these studies and remaining challenges for the field are highlighted. Although the methods discussed here are placed in the context of their contributions to oral microbiome research specifically, they are pertinent to the study of any microbiome, and the intended audience of this includes researchers would simply like to get an introduction to microbial omics and/or an update on the latest omics methods. Continued research of the oral microbiota using omics approaches is crucial and will lead to dramatic improvements in human health, longevity, and quality of life.

Portosystemic shunt placement reveals blood signatures for the development of hepatic encephalopathy through mass spectrometry

Elective transjugular intrahepatic portosystemic shunt (TIPS) placement can worsen cognitive dysfunction in hepatic encephalopathy (HE) patients due to toxins, including possible microbial metabolites, entering the systemic circulation. We conducted untargeted metabolomics on a prospective cohort of 22 patients with cirrhosis undergoing elective TIPS placement and followed them up to one year post TIPS for HE development. Here we suggest that pre-existing intrahepatic shunting predicts HE severity post-TIPS. Bile acid levels decrease in the peripheral vein post-TIPS, and the abundances of three specific conjugated di- and tri-hydroxylated bile acids are inversely correlated with HE grade. Bilirubins and glycerophosphocholines undergo chemical modifications pre- to post-TIPS and based on HE grade. Our results suggest that TIPS-induced metabolome changes can impact HE development, and that pre-existing intrahepatic shunting could be used to predict HE severity post-TIPS.

Evaluation of Data-Dependent MS/MS Acquisition Parameters for Non-Targeted Metabolomics and Molecular Networking of Environmental Samples: Focus on the Q Exactive Platform

Non-targeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) is a widely used tool for metabolomics analysis, enabling the detection and annotation of small molecules in complex environmental samples. Data-dependent acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage, and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with classical and feature-based molecular networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality-related metrics such as annotation and singleton rates, MS/MS placement, and coverage. Our results show that automatic gain control, microscans, mass resolving power, and dynamic exclusion are the most critical parameters, whereas collision energy, TopN, and isolation width had moderate and apex trigger, monoisotopic selection, and isotopic exclusion minor effects. The insights into the data acquisition ergonomics of the Q Exactive platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.

Differential Volatile Organic Compound Expression in the Interaction of Daldinia eschscholtzii and Mycena citricolor

Fungi exhibit a wide range of ecological guilds, but those that live within the inner tissues of plants (also known as endophytes) are particularly relevant due to the benefits they sometimes provide to their hosts, such as herbivory deterrence, disease protection, and growth promotion. Recently, endophytes have gained interest as potential biocontrol agents against crop pathogens, for example, coffee plants (Coffea arabica). Published results from research performed in our laboratory showed that endophytic fungi isolated from wild Rubiaceae plants were effective in reducing the effects of the American leaf spot of coffee (Mycena citricolor). One of these isolates (GU11N) from the plant Randia grandifolia was identified as Daldinia eschscholtzii (Xylariales). Its antagonism mechanisms, effects, and chemistry against M. citricolor were investigated by analyzing its volatile profile alone and in the presence of the pathogen in contactless and dual culture assays. The experimental design involved direct sampling of agar plugs in vials for headspace (HS) and headspace solid-phase microextraction (HS-SPME) gas chromatography-mass spectrometry (GC-MS) analysis. Additionally, we used ultrahigh-performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS/MS) to identify nonvolatile compounds from organic extracts of the mycelia involved in the interaction. Results showed that more volatile compounds were identified using HS-SPME (39 components) than those by the HS technique (13 components), sharing only 12 compounds. Statistical tests suggest that D. eschscholtzii inhibited the growth of M. citricolor through the release of VOCs containing a combination of 1,8-dimethoxynapththalene and terpene compounds affecting M. citricolor pseudopilei. The damaging effects of 1,8-dimethoxynaphthalene were corroborated in an in vitro test against M. citricolor pseudopilei; scanning electron microscopy (SEM) photographs confirmed structural damage. After analyzing the UHPLC-HRMS/MS data, a predominance of fatty acid derivatives was found among the putatively identified compounds. However, a considerable proportion of features (37.3%) remained unannotated. In conclusion, our study suggests that D. eschscholtzii has potential as a biocontrol agent against M. citricolor and that 1,8-dimethoxynaphthalene contributes to the observed damage to the pathogen's reproductive structures.

Application of feature-based molecular networking and MassQL for the MS/MS fragmentation study of depsipeptides

The Feature-based Molecular Networking (FBMN) is a well-known approach for mapping and identifying structures and analogues. However, in the absence of prior knowledge about the molecular class, assessing specific fragments and clusters requires time-consuming manual validation. This study demonstrates that combining FBMN and Mass Spec Query Language (MassQL) is an effective strategy for accelerating the decoding mass fragmentation pathways and identifying molecules with comparable fragmentation patterns, such as beauvericin and its analogues. To accomplish this objective, a spectral similarity network was built from ESI-MS/MS experiments of Fusarium oxysporum at various collision energies (CIDs) and paired with a MassQL search query for conserved beauvericin ions. FBMN analysis revealed that sodiated and protonated ions clustered differently, with sodiated adducts needing more collision energy and exhibiting a distinct fragmentation pattern. Based on this distinction, two sets of particular fragments were discovered for the identification of these hexadepsipeptides: ([M + H]+) m/z 134, 244, 262, and 362 and ([M + Na]+) m/z 266, 284 and 384. By using these fragments, MassQL accurately found other analogues of the same molecular class and annotated beauvericins that were not classified by FBMN alone. Furthermore, FBMN analysis of sodiated beauvericins at 70 eV revealed subclasses with distinct amino acid residues, allowing distinction between beauvericins (beauvericin and beauvericin D) and two previously unknown structural isomers with an unusual methionine sulfoxide residue. In summary, our integrated method revealed correlations between adduct types and fragmentation patterns, facilitated the detection of beauvericin clusters, including known and novel analogues, and allowed for the differentiation between structural isomers.