Molecular networking as a novel approach to unravel toxin diversity of four strains of the dominant Dinophysis species from French coastal waters

Sibat, Manoëlla; Réveillon, Damien; Antoine, Chloé; Carpentier, Liliane; Rovillon, Georges Augustin; Sechet, Véronique; Bertrand, Samuel

doi:10.1016/j.hal.2021.102026

Cited by 5 publications

(3 citation statements)

References 42 publications

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“…Recently, metabolomic molecular networking using an untargeted approach that clusters chemical species with similar MS/MS spectrums has been used to identify new toxins in Dinophysis species [ 30 ]. Applying this method under different chemical stress conditions would improve our knowledge of the set of metabolites known to be involved in the stress response of dinoflagellates.…”

Section: Metabolomic Approachesmentioning

confidence: 99%

OMICS Approaches to Assess Dinoflagellate Responses to Chemical Stressors

Roussel,

Mériot,

Jauffrais

et al. 2023

Biology

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Dinoflagellates are important primary producers known to form Harmful Algae Blooms (HABs). In water, nutrient availability, pH, salinity and anthropogenic contamination constitute chemical stressors for them. The emergence of OMICs approaches propelled our understanding of dinoflagellates’ responses to stressors. However, in dinoflagellates, these approaches are still biased, as transcriptomic approaches are largely conducted compared to proteomic and metabolomic approaches. Furthermore, integrated OMICs approaches are just emerging. Here, we report recent contributions of the different OMICs approaches to the investigation of dinoflagellates’ responses to chemical stressors and discuss the current challenges we need to face to push studies further despite the lack of genomic resources available for dinoflagellates.

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Section: Metabolomic Approachesmentioning

confidence: 99%

OMICS Approaches to Assess Dinoflagellate Responses to Chemical Stressors

Roussel,

Mériot,

Jauffrais

et al. 2023

Biology

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“…The Global Natural Products Social Molecular Networking Platform (GNPS) is based on spectral alignment to assess the similarities and relationships among molecules (Wang et al, 2016) and visualized using a molecular network. While GNPS is mainly used for biodiscovery of natural products (Teta et al, 2015;Naman et al, 2017;Ding et al, 2018;Via et al, 2018), it has been recently applied to dinoflagellate metabolites and demonstrated the unique metabolites from these organisms (Fiorini et al, 2020;Wu et al, 2020;Sibat et al, 2021). Molecular networking of five Dinophysis species identified the characteristic toxin profile for each strain and identified five new putative pectenotoxins (Sibat et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…While GNPS is mainly used for biodiscovery of natural products (Teta et al, 2015;Naman et al, 2017;Ding et al, 2018;Via et al, 2018), it has been recently applied to dinoflagellate metabolites and demonstrated the unique metabolites from these organisms (Fiorini et al, 2020;Wu et al, 2020;Sibat et al, 2021). Molecular networking of five Dinophysis species identified the characteristic toxin profile for each strain and identified five new putative pectenotoxins (Sibat et al, 2021). Differences in the metabolites of Pseudonitzchia during the reproductive stages were evident from the GNPS-based analysis (Fiorini et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Global Mass Spectrometric Analysis Reveals Chemical Diversity of Secondary Metabolites and 44-Methylgambierone Production in Philippine Gambierdiscus Strains

et al. 2022

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Surveillance and characterization of emerging marine toxins and toxigenic dinoflagellates are warranted to evaluate their associated health risks. Here, we report the occurrence of the ciguatera poisoning-causative dinoflagellate Gambierdiscus balechii in the Philippines. Toxin production and chemical diversity of secondary metabolites in G. balechii GtoxSAM092414, G. balechii Gtox112513, and the recently reported Gambierdiscus carpenteri Gam1BOL080513 were assessed using targeted and untargeted UPLC-MS/MS analysis and radioligand receptor-binding assay (RBA). 44-methylgambierone was produced by all three strains, albeit with different levels based on RBA and UPLC-HRMS/MS analysis. The fatty acid composition was similar in all strains, while subtle differences in monosaccharide content were observed, related to the collection site rather than the species. Molecular networking using the GNPS database identified 45 clusters belonging to at least ten compound classes, with terpene glycosides, carbohydrate conjugates, polyketides, and macrolides as major convergence points. Species-specific peptides and polyhydroxylated compounds were identified in G. balechii GtoxSAM092414 and G. carpenteri Gam1BOL080513, respectively. These provide a glimpse of the uncharacterized biosynthetic potential of benthic dinoflagellates and highlight the intricate and prolific machinery for secondary metabolites production in these organisms.

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