Identification of constituents of Lamium amplexicaule applying UPLC‒QTOF MS analysis combined with feature-based molecular networking

The Global Natural Product Social Molecular Networking (GNPS) platform leverages tandem mass spectrometry (MS/MS) data for annotation of compounds. Molecular networks aid in the visualization of the chemical space within a metabolomics experiment. Recently, molecular networking has been combined with feature detection methods to yield Feature-Based Molecular Networking (FBMN). FBMN allows for the discrimination of isomers within the molecular network, incorporation of quantitative information generated by the feature detection tools into visualization of the molecular network, and compatibility with forthcoming in silico annotation tools. This chapter provides step-by-step methods for generating a molecular network to annotate microbial natural products using the Global Natural Product Social Molecular Networking (GNPS) Feature-Based Molecular Networking (FBMN) workflow.

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.

Ion mobility spectrometry (IMS) may improve feature-based molecular networking (FBMN) for annotating polyphenol isomers co-eluted in chromatography. The study aimed to assess the added value of trapped ion mobility spectrometry (TIMS) in metabolomics with FBMN for cocoa polyphenols. Untargeted analyses were performed on black and brown cocoa beans using a UHPLC-TIMS-Q-TOF system, with or without TIMS. The processed data underwent statistical and FBMN analyses. TIMS nearly doubled discriminating features in black beans and increased them by 50% in brown beans. It separated isomeric dimers, trimers, and C-(O-)glycosides (native or process-derived). The UHPLC-TIMS-HRMS-FBMN resolved these isomers into distinct molecular network clusters. New discriminant phenolics were reported compared to a previous non-IMS study: ethyl-bridged flavanols in black beans and dehydro(epi)catechins, (epi)catechin-C-glycosides, and B-type procyanidin trimer C-glycosides in brown beans. TIMS improved structural information from FBMN, enabling polyphenols isomer identification. This TIMS-based approach can improve the phenolic characterization of cocoa products.

Impact of Preprocessing and data analysis strategies on metabolite annotation in biological samples: A mass spectrometry-based metabolomics study using feature-based molecular networking.

BackgroundTrichoderma species, known as biocontrol agents against plant diseases, contain diverse compounds, especially terpenoids, with various bioactivities. To facilitate the exploration of bioactive secondary metabolites of Trichoderma harzianum NTU2180, the OSMAC approach MS/MS molecular networking was applied in the current study.ResultsThe feature-based molecular networking (FBMN) analysis showed that T. harzianum NTU2180 fermented on germinated brown rice (GBR) produced more terpenoids. Here, two new acorane-sesequiterpenes, trichospirols A (1) and B (2), and 12 known compounds (3 − 14) were isolated from the EtOAc layer of T. harzianum NTU2180 fermentation on GBR. Structures of these compounds were determined through NMR, UV, IR, and MS analyses. The absolute configuration of trichospirols A (1) was also elucidated by x-ray with Cu K-α radiation. Among them, six compounds (1, 2, 3, 4, 5, and 11) were annotated as terpenoids by the NPClassifier on FBMN. 5-Hydroxy-3-hydroxmethyl-2-methyl-7-methoxychromone (7) and ergosterol peroxide (11) showed significant anti-angiogenic activity in ex vivo experiments with respective 0.57 ± 0.12- and 0.20 ± 0.12-fold changes. In addition, compound 11 displayed cytotoxicity against Y79 retinoblastoma cells with IC50 value of 35.3 ± 6.9 µM.ConclusionsThe current study utilizes FBMN concept with OSMAC approach to accelerate the exploration of potential metabolites of the fungus Trichoderma harzianum NTU2180. Through a series of FBMN-guided isolation and purification, two new acorane-sesequiterpenes and 12 known compounds were obtained. The ex vivo and in vitro experiments were evaluated to assess anticancer isolates. It is worth noting that compound 11 was identified as a dual inhibitor targeting both angiogenesis and proliferation of retinoblastomas. Altogether, the results revealed the novel potential of T. harzianum for developing natural therapeutics against retinoblastomas.

Interpreting metabolic profiling of YIV906 in vivo: A Synergistic strategy combining LC-HRMS-based molecular networking and metabolomics thought integration.

Fungi have developed a wide array of defense strategies to overcome mechanical injuries and pathogen infections. Recently, photoactivity has been discovered by showing that pigments isolated from Cortinarius uliginosus produce singlet oxygen under irradiation. To test if this phenomenon is limited to dermocyboid Cortinarii, six colourful Cortinarius species belonging to different classical subgenera (i.e., Dermocybe, Leprocybe, Myxacium, Phlegmacium, and Telamonia) were investigated. Fungal extracts were explored by the combination of in vitro photobiological methods, UHPLC coupled to high-resolution tandem mass spectrometry (UHPLC-HRMS2), feature-based molecular networking (FBMN), and metabolite dereplication techniques. The fungi C. rubrophyllus (Dermocybe) and C. xanthophyllus (Phlegmacium) exhibited promising photobiological activity in a low concentration range (1–7 µg/mL). Using UHPLC-HRMS2-based metabolomic tools, the underlying photoactive principle was investigated. Several monomeric and dimeric anthraquinones were annotated as compounds responsible for the photoactivity. Furthermore, the results showed that light-induced activity is not restricted to a single subgenus, but rather is a trait of Cortinarius species of different phylogenetic lineages and is linked to the presence of fungal anthraquinones. This study highlights the genus Cortinarius as a promising source for novel photopharmaceuticals. Additionally, we showed that putative dereplication of natural photosensitizers can be done by FBMN.

Alkaloids from Lepidium meyenii (Maca), structural revision of macaridine and UPLC-MS/MS feature-based molecular networking

Lanbuzheng (Geum japonicum Thunb. var. chinense Bolle), a plant found in Southwest China, is a traditional Chinese medicine that promotes hematopoiesis and antioxidant functions. Many of its chemical constituents remain unknown, posing challenges both to understanding its pharmacological mechanisms and to conducting quality control research. In this work, ultra-high performance liquid chromatography coupled with quadrupole Exactive Orbitrap high-resolution mass spectroscopy was used for profiling the composition of Lanbuzheng. Using positive ion mass spectrometry data enriched from Lanbuzheng extract, feature-based molecular networking (FBMN) was constructed and associated with Mass2Motifs substructures using MS2LDA. Prediction and validation of unknown constituents of Lanbuzheng using a custom-built compound library, SIRIUS, and network annotation propagation, achieved a semi-automated annotation of the molecular network. Based on the custom-built library comprising 206 compounds and the FBMN clustering results, the constituents in Lanbuzheng primarily include tannins, triterpenes, flavonoids, and phenolics. Using only 65 pre-identified compounds as references, 210 unknown compounds were annotated in various polarity regions of Lanbuzheng. Results of the current work indicate that molecular networks enable the efficient annotation of compounds in complex systems, laying the groundwork for the preliminary identification of pharmacologically active constituents of Lanbuzheng.

Integration of multidimensional data acquisition and data processing strategy for comprehensive characterization of chemical constituents in Qingre Sanjie capsule by UPLC-TWIMS-QTOF-MS and molecular networking.

Integrated molecular networking strategy enhance the accuracy and visualization of components identification: A case study of Ginkgo biloba leaf extract

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 70eV 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.

1. Feature-based molecular networking (FBMN), an advanced metabolomics tool leveraging MS/MS spectral similarity, was applied to update metabolite characterisation of fenbendazole (FBZ), a veterinary antiparasitic agent with emerging anticancer potential in humans. Despite its therapeutic promise, FBZ’s human metabolic pathways remain poorly understood. 2. In this study, FBMN was utilised for the comprehensive in vitro profiling of FBZ metabolites across species, employing high-resolution liquid chromatography-mass spectrometry (LC-HRMS) with data-dependant MS2 acquisition. 3. Nine metabolites, including two novel sulphate-conjugated forms (M2 sulphate and M7 sulphate), were identified and structurally characterised through integrated FBMN analysis. Oxidative metabolites (M1–M4) were found to be more abundant in rat liver microsomes, whereas monkey hepatocytes exhibited higher levels of most metabolites. Notably, hydrolysed FBZ (M5) dominated human samples, accounting for the largest proportion in both liver microsomes and hepatocytes, suggesting species-specific enzymatic activity. 4. The application of FBMN provided an enhanced, systematic approach for metabolite identification and inter-species comparison, revealing critical metabolic differences that support FBZ biotransformation. These findings offer novel insights into FBZ’s metabolic pathways, supporting its safety and efficacy assessment for potential human therapeutic applications.

Feature-based molecular networking (FBMN) is a powerful analytical tool for mass spectrometry (MS)-based untargeted metabolomics data analysis. FBMN plays an important role in drug metabolism studies, enabling the visualization of complex metabolomics data to achieve metabolite characterization. In this study, we propose a strategy for the characterization of glutathione (GSH) adducts formed via in vitro metabolic activation using FBMN assisted by multivariate analysis (MVA). Acetaminophen was used as a model substrate for method development, and the practical potential of the method was investigated by its application to 2-aminophenol (2-AP) and 2,4-dinitrochlorobenzene (DNCB). Two 2-AP GSH adducts and one DNCB GSH adduct were successfully characterized by forming networks with GSH even though the mass spectral information obtained for the parent compound was deficient. False positives were effectively filtered out by the variable influence on projection cutoff criteria obtained from orthogonal partial least-squares-discriminant analysis. The GSH adducts formed by enzymatic or nonenzymatic reactions were intuitively distinguished by the pie chart of FBMN results. In summary, our approach effectively characterizes GSH adducts, which serve as compelling evidence of bioactivation. It can be widely utilized to enhance risk assessment in the context of drug metabolism.

An enhanced chemical profiling characterization strategy integrating off-line two-dimensional counter-current chromatography × UHPLC-MS/MS and feature-based molecular networking: Comprehensive characterization of the fibrous root of Bletilla striata as a case study.