Exploring Genomic Diversity Using Metagenomics of Deep-Sea Subsurface Microbes from the Louisville Seamount and the South Pacific Gyre

Tuesday, 16 December 2014: 10:35 AM
Benjamin J Tully1,2, Jason B Sylvan2, John F Heidelberg2 and Julie A Huber3, (1)University of Southern California, Los Angeles, CA, United States, (2)USC-Biological Sciences, Los Angeles, CA, United States, (3)Josephine Bay Paul Center, Woods Hole, MA, United States
There are many limitations involved with sampling microbial diversity from deep-sea subsurface environments, ranging from physical sample collection, low microbial biomass, culturing at in situ conditions, and inefficient nucleic acid extractions. As such, we are continually modifying our methods to obtain better results and expanding what we know about microbes in these environments. Here we present analysis of metagenomes sequences from samples collected from 120 m within the Louisville Seamount and from the top 5-10cm of the sediment in the center of the south Pacific gyre (SPG). Both systems are low biomass with ~102 and ~104 cells per cm3 for Louisville Seamount samples analyzed and the SPG sediment, respectively. The Louisville Seamount represents the first in situ subseafloor basalt and the SPG sediments represent the first in situ low biomass sediment microbial metagenomes. Both of these environments, subseafloor basalt and sediments underlying oligotrophic ocean gyres, represent large provinces of the seafloor environment that remain understudied.

Despite the low biomass and DNA generated from these samples, we have generated 16 near complete genomes (5 from Louisville and 11 from the SPG) from the two metagenomic datasets. These genomes are estimated to be between 51-100% complete and span a range of phylogenetic groups, including the Proteobacteria, Actinobacteria, Firmicutes, Chloroflexi, and unclassified bacterial groups. With these genomes, we have assessed potential functional capabilities of these organisms and performed a comparative analysis between the environmental genomes and previously sequenced relatives to determine possible adaptations that may elucidate survival mechanisms for these low energy environments. These methods illustrate a baseline analysis that can be applied to future metagenomic deep-sea subsurface datasets and will help to further our understanding of microbiology within these environments.