The exchange of metabolites between microalgae and bacteria is essential for maintaining ecosystem structure and function in aquatic systems. Metabolomes of algal “holobionts” consist of thousands of compounds and are extremely diverse. This complexity has so far challenged our mass spectrometry (MS) based analytical methods and hampered our ability to assign structural characteristics to most metabolites detected by MS techniques. The goal of this study was to design an approach to more comprehensively describe the chemodiversity of this holometabolome than is afforded by approaches that rely on matching to existing mass spectral libraries. Our new workflow is presented here using the example of the ecological significant diatom genus Pseudo-nitzschia, which includes some members that produce the potent neurotoxin domoic acid that can result in mass mortality of marine mammals and human illness. Pseudo-nitzschia microbiomes have been shown to be species-specific and microbiomes influence the holometabolome, therefore, we hypothesized that the five different species that we cultured would exhibit distinct chemotypes. Our approach employed an untargeted metabolomics strategy, coupling liquid chromatography with high resolution tandem mass spectrometry (MS/MS) and feature-based molecular networking (GNPS) detecting 4826 metabolites produced in the cultures. As is typical for marine metabolite samples, we were only able to annotate 4.7% of all detected metabolites using publicly available compound libraries. In an attempt to better characterize unknown molecules, we first used the SIRIUS workflow to assign molecular formulas based on MS/MS spectra and parent ion isotope patterns. We then employed the machine learning tool CANOPUS, to predict compound class affiliations. Being conservative and only considering predictions assigned with high confidence, we obtained elemental and compound class level information for 56% of metabolites. Our findings revealed that Pseudo-nitzschia have species-specific metabolomes and the metabolites driving the distinction were structurally diverse nitrogen (N) compounds covering significant chemical space, from simple amines and amides to cyclic compounds such as imidazoles, pyrrolidines and lactams. Our findings add to the growing evidence that N containing metabolites play an important role in algae-microbiome interactions likely contributing to bloom formation and duration, especially when inorganic nutrients are depleted.

Illuminating the dark metabolome: Species-specific Pseudo-nitzschia metabolomes are distinguished by structurally diverse nitrogen compounds

ACS Fall 2021

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