Tracking active heterotrophic microbial communities in the Guaymas Basin deep biosphere with BONCAT-FACS
The marine deep biosphere harbors microbial communities that drive organic matter transformations and biogeochemical cycles. Previous work on these communities has focused either on genomic characterization or metabolic activity measurements. However, to understand microbial ecophysiology in the deep biosphere taxonomic identity and metabolic function must be connected on both single-cell and ecosystem scales. In this work, we optimized a bioorthogonal non-canonical amino acid tagging fluorescence-activated cell sorting (BONCAT-FACS) workflow for low-biomass deep-biosphere sediments obtained during International Ocean Discovery Program Expedition 385 (IODP 385). BONCAT-FACS with 16S rRNA gene amplicon sequencing as well as metagenomics of sediment communities was applied to characterize translationally active communities in hydrothermally altered subsurface sediments of the Guaymas Basin. Our results revealed a heterotrophic microbial population throughout all sediments examined, with taxa translationally active down to our deepest sampling point, 154 meters below the seafloor. Based on 16S rRNA gene identities, the translationally active microbial community was dominated by heterotrophic members of the Gammaproteobacteria, Bacilli, Deinococci, and Alphaproteobacteria. These taxa are likely key contributors to cycling the large quantities of hydrothermally altered organic matter in Guaymas Basin sediments. To further elucidate the metabolic capacity of active taxa, we mapped 16S rRNA gene amplicons to metagenome assembled genomes (MAGs) previously obtained from IODP 385. These MAGs contained genes associated with C1 metabolism, carbohydrate degradation, and fermentation, indicating that active taxa leverage these metabolisms for energy conservation. Our results demonstrate that BONCAT-FACS provides high-throughput and single-cell insights into the metabolic activity of microbes in the low-biomass marine subsurface.