Metagenomic analysis of water column samples collected from Green Canyon 233 prior to the Deepwater Horizon incident

ABSTRACT The Gulf of Mexico/Gulf of America provides ecosystem services derived from marine biodiversity and oil and gas resources. Threats posed by unintended releases of oil and gas can be attenuated by microbial processes, necessitating the documentation of baseline microbial diversity to better understand spill dynamics and to inform bioremediation strategies. Here, we analyze metagenomic sequencing of 10 water column samples collected from the Green Canyon 233 (GC233) lease block near the mussel-fringed brine lake, Brine Pool NR-1. Bioinformatics processing produced 60 bacterial metagenome-assembled genomes (MAGs), 11 archaeal MAGs, 149 microbial taxa predicted from assembled full-length small subunit (SSU) rRNA genes, and 389 microbial genera predicted from single-copy marker genes. Abundant taxa classified from these analyses included archaeal Nitrosopumilaceae, Nitrosopelagicus, and Thalassarchaeaceae and the bacterial taxa Pelagibacteraceae and SAR324. The MAGs revealed genes that degrade gaseous and non-gaseous hydrocarbons, including methane, other alkanes, and aromatic compounds. These samples were collected in 2009, fortuitously prior to the 2010 Deepwater Horizon (DWH) oil spill. Therefore, we searched for members of the rare biosphere that dominated the DWH plume during the early phase of microbial succession. Sequences related to Bermanella spp. were not detected initially. The search was expanded by mapping reads from ours and an additional 55 metagenomic libraries to two Bermanella MAGs. Read recruitment to Bermanella sp913054445 enriched in DWH plume samples was low (<1%) for our samples, those collected after the spill, and most experimental samples compared to samples collected outside (3%) and inside the DWH plume (19%–23%) during the spill. IMPORTANCE Microbes execute oil spill biodegradation through complex interactions involving whole microbiome communities by harnessing genes distributed across multiple taxa. Therefore, metagenomic data sets provide taxonomic and functional annotations to aid in understanding spill dynamics. Although the Deepwater Horizon oil spill provided opportunities to observe ecosystem recovery, data about the microbiome prior to the spill are scarce and limited to amplicon sequencing. Our metagenomic libraries, although not derived from the same lease block as the blowout, contribute linkages between microbial taxonomy and function in an area of active oil and gas production. This analysis can aid microbial indicator development, machine learning, and modeling efforts to bioremediate hydrocarbon influxes in marine environments.