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Global pattern of nitrogen metabolism in marine prokaryotes

Schickele, A.; Savioz, H.; Gruber, N.; Guidi, L.; Irisson, J.-O.; Vogt, M.

2025-10-28 biochemistry
10.1101/2025.10.28.685123 bioRxiv
Show abstract

The ocean nitrogen cycle is driven by an ensemble of metabolic processes sustaining marine ecosystems and ocean productivity. However, the spatial distribution and environmental drivers of its major pathways, i.e., nitrogen fixation, denitrification, assimilatory and dissimilatory nitrate reduction to ammonium (ANRA, DNRA), and nitrification are not well known. Furthermore, the taxonomic composition of the prokaryotes supporting each pathway remain incompletely understood. Leveraging newly assembled global marine metagenomic datasets and a state-of-the-art machine learning framework, we inferred the global biogeography of the genomic potential for key metabolic pathways of the marine nitrogen cycle. This was achieved using a multi-output regression of gene read counts against environmental climatologies. Our results reveal distinct biogeographic patterns of genomic potential: anaerobic or light-inhibited pathways are enriched in high-latitude regions, eastern boundary upwelling systems, and deeper ocean layers, while nitrogen fixation and ANRA dominate in oligotrophic gyres. These patterns are consistent with known metabolic strategies, model-based estimates, and underlying taxonomy. Indeed, we identify that Cyanobacteria associate primarily with aerobic, biosynthetic pathways, while Gammaproteobacteria and Nitrososphaeria encode for nitrogen transformations related to energy requirements. By coupling microbial community composition with genome-level information, our approach advances understanding of the microbial foundations of nitrogen transformation pathways and offers new insights on underrepresented processes into biogeochemical models. We highlight the growing value of omic data to better understand marine ecosystem function in relation to environmental gradients and community composition, and their use as a potential observation-based alternative or complement to biogeochemical models.

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