Back

Metagenomic Discovery of Neutral Lipid Metabolism Pathways in the Arctic Ocean Microbiomes Suggests a Potential New Role in Survival and Oceanic Carbon Cycling

Grevesse, T.; Walsh, D. A.; McLatchie, S.; Onana, V. E.

2026-07-27 microbiology
10.64898/2026.07.24.740503 bioRxiv
Show abstract

The Arctic Ocean microbiomes experience extreme seasonal fluctuations in light, nutrient availability, and organic carbon supply. In this environment, neutral lipid storage may provide a key survival strategy. Here, we investigated the diversity, distribution, and ecological role of neutral lipid metabolism in Arctic microbiomes using metagenome-resolved analyses and global ocean comparisons. Arctic photic-zone microbiomes were strongly enriched in triacylglycerol (TAG) biosynthesis genes relative to other oceans, primarily due to picoeukaryotic phytoplankton, including the ecologically dominant Micromonas and Bathycoccus. In contrast, prokaryotic communities exhibited diverse TAG-degrading taxa and fatty acid transport systems, supporting a previously unrecognized lipotrophic bacterial guild exploiting phytoplankton-derived lipids as carbon and energy sources. Genome-resolved analyses further revealed distinct bacterial lipid-storage strategies: TAG-producing taxa preferentially encoded fatty acid uptake and carbohydrate utilization pathways, whereas polyhydroxyalkanoate-producing taxa were associated with aromatic compound degradation, linking terrestrial organic matter to lipid storage. Our results expand the known diversity of marine microbes capable of neutral lipid metabolism and identify microbial lipid cycling as a previously overlooked component of the Arctic carbon cycle. We propose that neutral lipid storage and turnover support microbial survival through the polar night while enhancing carbon transfer within Arctic microbial food webs under ongoing climate change.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.