Integration of LC/MS-based molecular networking and classical phytochemical approach allows in-depth annotation of the metabolome of non-model organisms - The case study of the brown seaweed Taonia atomaria.

Untargeted LC-MS based metabolomics is a useful approach in many research areas such as medicine, systems biology, environmental sciences or even ecology. In such an approach, annotation of metabolomes of non-model organisms remains a significant challenge. In this study, an analytical workflow combining a classical phytochemical approach, using the isolation and the full characterization of the chemical structure of natural products, together with the use of MS/MS-based molecular networking with various levels of restrictiveness was developed. This protocol was applied to the marine brown seaweed Taonia atomaria, a cosmopolitan algal species, and allowed to annotate more than 200 metabolites. First, the algal organic crude extracts were fractionated by flash-chromatography and the chemical structure of eight of the main chemical constituents of this alga were fully characterized by means of spectroscopic methods (1D and 2D NMR, HRMS). These compounds were further used as chemical standards. In a second step, the main fractions of the algal extracts were analyzed by UHPLC-MS/MS and the resulting data were uploaded to the Global Natural Products Social Molecular Networking platform (GNPS) to create several molecular networks (MNs). A first MN (MN-1) was built with restrictive parameters and allowed the creation of clusters composed by nodes with highly similar MS/MS spectra. Then, using database hits and chemical standards as "seed" nodes and/or similarity between MS/MS fragmentation pattern, the main clusters were easily annotated as common glycerolipids and phospholipids, much rare lipids -such as acylglycerylhydroxymethyl-N,N,N-trimethyl-ß-alanines or fulvellic acid derivatives- but also new glycerolipids bearing a terpene moiety. Lastly, the use of less and less constrained MNs allowed to further increase the number of annotated metabolites.