Untargeted metabolomics to expand the chemical space of the marine diatom Skeletonema marinoi

Front Microbiol. 2023 Dec 5:14:1295994. doi: 10.3389/fmicb.2023.1295994. eCollection 2023.

Abstract

Diatoms (Bacillariophyceae) are aquatic photosynthetic microalgae with an ecological role as primary producers in the aquatic food web. They account substantially for global carbon, nitrogen, and silicon cycling. Elucidating the chemical space of diatoms is crucial to understanding their physiology and ecology. To expand the known chemical space of a cosmopolitan marine diatom, Skeletonema marinoi, we performed High-Resolution Liquid Chromatography-Tandem Mass Spectrometry (LC-MS2) for untargeted metabolomics data acquisition. The spectral data from LC-MS2 was used as input for the Metabolome Annotation Workflow (MAW) to obtain putative annotations for all measured features. A suspect list of metabolites previously identified in the Skeletonema spp. was generated to verify the results. These known metabolites were then added to the putative candidate list from LC-MS2 data to represent an expanded catalog of 1970 metabolites estimated to be produced by S. marinoi. The most prevalent chemical superclasses, based on the ChemONT ontology in this expanded dataset, were organic acids and derivatives, organoheterocyclic compounds, lipids and lipid-like molecules, and organic oxygen compounds. The metabolic profile from this study can aid the bioprospecting of marine microalgae for medicine, biofuel production, agriculture, and environmental conservation. The proposed analysis can be applicable for assessing the chemical space of other microalgae, which can also provide molecular insights into the interaction between marine organisms and their role in the functioning of ecosystems.

Keywords: Skeletonema; Skeletonema marinoi; chemical classification; diatom; metabolome annotation; untargeted metabolomics.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy—EXC 2051—Project-ID 390713860.