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Lipidomics of Environmental Microbial Communities. II: Characterization Using Molecular Networking and Information Theory
Ding, S.; Bale, N.J.; Hopmans, E.C.; Villanueva, L.; Arts, M.G.I.; Schouten, S.; Sinninghe Damsté, J.S (2021). Lipidomics of Environmental Microbial Communities. II: Characterization Using Molecular Networking and Information Theory. Front. Microbiol. 12: 659315. https://dx.doi.org/10.3389/fmicb.2021.659315
In: Frontiers in Microbiology. Frontiers Media: Lausanne. ISSN 1664-302X; e-ISSN 1664-302X, meer
Peer reviewed article  

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Author keywords
    microbial membrane lipids, lipidome, Black Sea; molecular network; information theory; mass spectometry; biomarker; intact polar lipids (IPLs)

Auteurs  Top 
  • Arts, M.G.I., meer
  • Schouten, S., meer
  • Sinninghe Damsté, J.S, meer

Abstract
    Structurally diverse, specialized lipids are crucial components of microbial membranes and other organelles and play essential roles in ecological functioning. The detection of such lipids in the environment can reveal not only the occurrence of specific microbes but also the physicochemical conditions to which they are adapted to. Traditionally, liquid chromatography coupled with mass spectrometry allowed for the detection of lipids based on chromatographic separation and individual peak identification, resulting in a limited data acquisition and targeting of certain lipid groups. Here, we explored a comprehensive profiling of microbial lipids throughout the water column of a marine euxinic basin (Black Sea) using ultra high-pressure liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS/MS). An information theory framework combined with molecular networking based on the similarity of the mass spectra of lipids enabled us to capture lipidomic diversity and specificity in the environment, identify novel lipids, differentiate microbial sources within a lipid group, and discover potential biomarkers for biogeochemical processes. The workflow presented here allows microbial ecologists and biogeochemists to process quickly and efficiently vast amounts of lipidome data to understand microbial lipids characteristics in ecosystems.

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