Machine learning and metagenomics identifies uncharacterized taxa inferred to drive biogeochemical cycles in a subtropical hypereutrophic estuary

Abstract

Abstract Anthropogenic influences have drastically increased nutrient concentrations in many estuaries globally, and microbial communities have adapted to the resulting hypereutrophic ecosystems. However, our knowledge of the dominant microbial taxa and their potential functions in these ecosystems has remained sparse. Here, we study prokaryotic community dynamics in a temporal–spatial dataset, from a subtropical hypereutrophic estuary. Screening 54 water samples across brackish to marine sites revealed that nutrient concentrations and salinity best explained spatial community variations, whereas temperature and dissolved oxygen likely drive seasonal shifts. By combining short and long read sequencing data, we recovered 2,459 metagenome-assembled genomes, proposed new taxon names for previously uncharacterised lineages, and created an extensive, habitat specific genome reference database. Community profiling based on this genome reference database revealed a diverse prokaryotic community comprising 61 bacterial and 18 archaeal phyla, and resulted in an improved taxonomic resolution at lower ranks down to genus level. We found that the vast majority (61 out of 73) of abundant genera (>1% average) represented unnamed and novel lineages, and that all genera could be clearly separated into brackish and marine ecotypes with inferred habitat specific functions. Applying supervised machine learning and metabolic reconstruction, we identified several microbial indicator taxa responding directly or indirectly to elevated nitrate and total phosphorus concentrations. In conclusion, our analysis highlights the importance of improved taxonomic resolution, sheds light on the role of previously uncharacterised lineages in estuarine nutrient cycling, and identifies microbial indicators for nutrient levels crucial in estuary health assessments.