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Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

Drug monitoring is crucial for providing accurate and effective care; however, current methods (e.g., blood draws) are inconvenient and unpleasant. We aim to develop a non-invasive method for the detection and monitoring of drugs via human skin. The initial development toward this aim required information about which drugs, taken orally, can be detected via the skin. Untargeted liquid chromatography–mass spectrometry (LC–MS) was used as it was unclear if drugs, known drug metabolites, or other transformation products were detectable. In accomplishing our aim, we analyzed samples obtained by swabbing the skin of 15 kidney transplant recipients in five locations (forehead, nasolabial area, axillary, backhand, and palm), bilaterally, on two different clinical visits. Untargeted LC–MS data were processed using molecular networking via the Global Natural Products Social Molecular Networking platform. Herein, we report the qualitative detection and location of drugs and drug metabolites. For example, escitalopram/citalopram and diphenhydramine, taken orally, were detected in forehead, nasolabial, and hand samples, whereas N-acetyl-sulfamethoxazole, a drug metabolite, was detected in axillary samples. In addition, chemicals associated with environmental exposure were also detected from the skin, which provides insight into the multifaceted chemical influences on our health. The proof-of-concept results presented support the finding that the LC–MS and data analysis methodology is currently capable of the qualitative assessment of the presence of drugs directly via human skin.

The species Euphorbia pithyusaand Euphorbia cupaniiare two closely related Mediterranean spurges for which their taxonomic relationships are still being debated. Herein, the diterpene ester content of E. cupaniiwas investigated using liquid chromatography coupled to tandem mass spectrometry. The use of molecular networking coupled to unsupervised substructure annotation (MS2LDA) indicated the presence of new premyrsinane/myrsinane diterpene esters in the E. cupaniifractions. A structure-guided isolation procedure yielded 16 myrsinane (11a–h, 12, and 13) and premyrsinane esters (14a–cand 15a–c), along with four 4β-phorbol esters (16a–cand 17) that showed inhibitory activity against chikungunya virus replication. The structures of the 16 new compounds (11a–c, 11h, 12, 13, 14a–c, 15a–c, 16a–c, and 17) were characterized by NMR spectroscopy and X-ray crystallography. To further uncover the diterpene ester content of these two species, the concept of combinatorial network annotation propagation (C-NAP) was developed. By leveraging the fact that the diterpene esters of Euphorbiaspecies are made up of limited building blocks, a combinatorial database of theoretical structures was created and used for C-NAP that made possible the annotation of 123 premyrsinane or myrsinane esters, from which 74% are not found in any compound database.

The integration of LC–MS/MS molecular networking and in silicoMS/MS fragmentation is an emerging method for dereplication of natural products. In the present study, a targeted isolation of natural products using a new in silico-based annotation tool named Network Annotation Propagation (NAP) is described. NAP improves accuracy of in silicofragmentation analyses by reranking candidate structures based on the network topology from MS/MS-based molecular networking. Annotation for the MS/MS spectral network of the Sageratia theezanstwig extract was performed using NAP, and most molecular families within the network, including the known triterpenoids 1–7, could be putatively annotated, without relying on any previous reports of molecules from this species. Based on the in silicodereplication results, molecules were prioritized for isolation. In total, six dicoumaroyl 8-O-4′ neolignans (8–13) and three dicoumaroyl lignans (14–16) were isolated from the twigs of S. theezansand structurally characterized by spectroscopic analyses. Isolates were evaluated for their neuroprotective activity, and compounds 14–16showed potent protective effects against glutamate-induced oxidative stress in mouse HT22 cells at a concentration of 12.5 μM.

In order to expedite the rapid and efficient discovery and isolation of novel specialized metabolites, while minimizing the waste of resources on rediscovery of known compounds, it is crucial to develop efficient approaches for strain prioritization, rapid dereplication, and the assessment of favored cultivation and extraction conditions. Herein we interrogated bacterial strains by systematically evaluating cultivation and extraction parameters with LC-MS/MS analysis and subsequent dereplication through the Global Natural Product Social Molecular Networking (GNPS) platform. The developed method is fast, requiring minimal time and sample material, and is compatible with high-throughput extract analysis, thereby streamlining strain prioritization and evaluation of culturing parameters. With this approach, we analyzed 146 marine Salinisporaand Streptomycesstrains that were grown and extracted using multiple different protocols. In total, 603 samples were analyzed, generating approximately 1.8 million mass spectra. We constructed a comprehensive molecular network and identified 15 molecular families of diverse natural products and their analogues. The size and breadth of this network shows statistically supported trends in molecular diversity when comparing growth and extraction conditions. The network provides an extensive survey of the biosynthetic capacity of the strain collection and a method to compare strains based on the variety and novelty of their metabolites. This approach allows us to quickly identify patterns in metabolite production that can be linked to taxonomy, culture conditions, and extraction methods, as well as informing the most valuable growth and extraction conditions.

Understanding the microbial and chemical diversities, as well as what affects these diversities, is important for modern manufacturing of traditional fermented foods. In this work, Chinese dark teas (CDTs) that are traditional microbial fermented beverages with relatively high sample diversity were collected. Microbial DNA amplicon sequencing and mass spectrometry-based untargeted metabolomics show that the CDT microbial β diversity, as well as the nonvolatile chemical α and β diversities, is determined by the primary impact factors of geography and manufacturing procedures, in particular, latitude and pile fermentation after blending. A large number of metabolites sharing between CDTs and fungi were discovered by Feature-based Molecular Networking (FBMN) on the Global Natural Products Social Molecular Networking (GNPS) web platform. These molecules, such as prenylated cyclic dipeptides and B-vitamins, are functionally important for nutrition, biofunctions, and flavor. Molecular networking has revealed patterns in metabolite profiles on a chemical family level in addition to individual structures.