Dereplication represents a key step for rapidly identifying known secondary metabolites in complex biological matrices. In this context, liquid-chromatography coupled to high resolution mass spectrometry (LC-HRMS) is increasingly used and, via untargeted data-dependent MS/MS experiments, massive amounts of detailed information on the chemical composition of crude extracts can be generated. An efficient exploitation of such data sets requires automated data treatment and access to dedicated fragmentation databases. Various novel bioinformatics approaches such as molecular networking (MN) and in-silico fragmentation tools have emerged recently and provide new perspective for early metabolite identification in natural products (NPs) research. Here we propose an innovative dereplication strategy based on the combination of MN with an extensive in-silico MS/MS fragmentation database of NPs. Using two case studies, we demonstrate that this combined approach offers a powerful tool to navigate through the chemistry of complex NPs extracts, dereplicate metabolites, and annotate analogues of database entries.

Methanolic and ethanolic crude extracts of Vitis vinifera canes exhibited significant antifungal activity against the three major fungal pathogens affecting grapevines, Plasmopara viticola, Erysiphe necator and Botrytis cinerea. The active extracts were analyzed by LC-PDA-ESI-MS, and selected compounds were identified. Efficient targeted isolation using medium-pressure liquid chromatography afforded six pure constituents in one step. The structures of the isolated compounds were elucidated by NMR and HRMS. Six identified compounds (ampelopsin A, hopeaphenol, trans-resveratrol, ampelopsin H, ε-viniferin, and E-vitisin B) presented antifungal activities against P. viticola. ε-Viniferin also exhibited a low antifungal activity against B. cinerea. None of the identified compounds inhibited the germination of E. necator. The potential to develop a novel natural fungicide against the three major fungal pathogens affecting V. vinifera from viticulture waste material is discussed.

Natural products represent an inexhaustible source of novel therapeutic agents. Their complex and constrained three-dimensional structures endow these molecules with exceptional biological properties, thereby giving them a major role in drug discovery programs. However, the search for new bioactive metabolites is hampered by the chemical complexity of the biological matrices in which they are found. The purification of single constituents from such matrices requires such a significant amount of work that it should be ideally performed only on molecules of high potential value (i.e., chemical novelty and biological activity). Recent bioinformatics approaches based on mass spectrometry metabolite profiling methods are beginning to address the complex task of compound identification within complex mixtures. However, in parallel to these developments, methods providing information on the bioactivity potential of natural products prior to their isolation are still lacking and are of key interest to target the isolation of valuable natural products only. In the present investigation, we propose an integrated analysis strategy for bioactive natural products prioritization. Our approach uses massive molecular networks embedding various informational layers (bioactivity and taxonomical data) to highlight potentially bioactive scaffolds within the chemical diversity of crude extracts collections. We exemplify this workflow by targeting the isolation of predicted active and nonactive metabolites from two botanical sources (Bocquillonia nervosa and Neoguillauminia cleopatra) against two biological targets (Wnt signaling pathway and chikungunya virus replication). Eventually, the detection and isolation processes of a daphnane diterpene orthoester and four 12-deoxyphorbols inhibiting the Wnt signaling pathway and exhibiting potent antiviral activities against the CHIKV virus are detailed. Combined with efficient metabolite annotation tools, this bioactive natural products prioritization pipeline proves to be efficient. Implementation of this approach in drug discovery programs based on natural extract screening should speed up and rationalize the isolation of bioactive natural products.