Characterization of Metabolically Active Bacterial Populations in Subseafloor Nankai Trough Sediments above, within, and below the Sulfate-Methane Transition Zone.

A remarkable number of microbial cells have been enumerated within subseafloor sediments, suggesting a biological impact on geochemical processes in the subseafloor habitat. However, the metabolically active fraction of these populations is largely uncharacterized. In this study, an RNA-based molecular approach was used to determine the diversity and community structure of metabolically active bacterial populations in the upper sedimentary formation of the Nankai Trough seismogenic zone. Samples used in this study were collected from the slope apron sediment overlying the accretionary prism at Site C0004 during the Integrated Ocean Drilling Program Expedition 316. The sediments represented microbial habitats above, within, and below the sulfate-methane transition zone (SMTZ), which was observed approximately 20 m below the seafloor (mbsf). Small subunit ribosomal RNA were extracted, quantified, amplified, and sequenced using high-throughput 454 pyrosequencing, indicating the occurrence of metabolically active bacterial populations to a depth of 57 mbsf. Transcript abundance and bacterial diversity decreased with increasing depth. The two communities below the SMTZ were similar at the phylum level, however only a 24% overlap was observed at the genus level. Active bacterial community composition was not confined to geochemically predicted redox stratification despite the deepest sample being more than 50 m below the oxic/anoxic interface. Genus-level classification suggested that the metabolically active subseafloor bacterial populations had similarities to previously cultured organisms. This allowed predictions of physiological potential, expanding understanding of the subseafloor microbial ecosystem. Unique community structures suggest very diverse active populations compared to previous DNA-based diversity estimates, providing more support for enhancing community characterizations using more advanced sequencing techniques.