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Targeted and non-targeted mass spectrometry to explore the chemical diversity of the genus Gambierdiscus in the Atlantic Ocean
Dinoflagellates of the genus Gambierdiscus have been associated with ciguatera, the most common non-bacterial fish-related intoxication in the world. Many studies report the presence of potentially toxic Gambierdiscus species along the Atlantic coasts including G. australes, G. silvae and G. excentricus. Estimates of their toxicity, as determined by bio-assays, vary substantially, both between species and strains of the same species. Therefore, there is a need for additional knowledge on the metabolite production of Gambierdiscus species and their variation to better understand species differences. Using liquid chromatography coupled to mass spectrometry, toxin and metabolomic profiles of five species of Gambierdiscus found in the Atlantic Ocean were reported. In addition, a molecular network was constructed aiming at annotating the metabolomes. Results demonstrated that G. excentricus could be discriminated from the other species based solely on the presence of MTX4 and sulfo-gambierones and that the variation in toxin content for a single strain could be up to a factor of two due to different culture conditions between laboratories. While untargeted analyses highlighted a higher variability at the metabolome level, signal correction was applied and supervised multivariate statistics performed on the untargeted data set permitted the selection of 567 features potentially useful as biomarkers for the distinction of G. excentricus, G. caribaeus, G. carolinianus, G. silvae and G. belizeanus. Further studies will be required to validate the use of these biomarkers in discriminating Gambierdiscus species.
The study also provided an overview about 17 compound classes present in Gambierdiscus, however, significant improvements in annotation are still required to reach a more comprehensive knowledge of Gambierdiscus’ metabolome.
Keyword(s)
(5 max), Toxin profile, Gambierdiscus sp., Metabolomics, Molecular networks, Chemotaxonomy