Dataset: IODP360 - iTAG and metatranscriptome data
Deployment: IODP-360

Rock material was crushed while still frozen in a Progressive Exploration Jaw Crusher (Model 150) whose surfaces were sterilized with 70% ethanol and RNase AWAY (Thermo Fisher Scientific, USA) inside a laminar flow hood. Powdered rock material was returned to the -80°C freezer until extraction.

DNA was extracted from 20, 30, or 40 grams of powdered rock material, depending on the quantity of rock available. A DNeasy PowerMax Soil Kit (Qiagen, USA) was used following the manufacturer’s protocol modified to included three freeze/thaw treatments prior to the addition of Soil Kit solution C1. Each treatment consisted of 1 minute in liquid nitrogen followed by 5 minutes at 65 °C. DNA extracts were concentrated by isopropanol precipitation overnight at 4°C.

The low biomass in our samples required whole genome amplification (WGA) prior to PCR amplification of marker genes. Genomic DNA was amplified by Multiple Displacement Amplification (MDA) using the REPLI-g Single Cell Kit (Qiagen) as directed. MDA bias was minimized by splitting each WGA sample into triplicate 16 μL reactions after 1 hr of amplification and then resuming amplification for the manufacturer-specified 7 hrs (8 hrs total).

DNA was also recovered from samples of drilling mud and drilling fluid (surface water collected during the coring process) for negative controls, as well as two “kit control” samples, in which no sample was added, to account for any contaminants originating from either the DNeasy PowerMax Soil Kit or the REPLI-g Single Cell Kit.

Bacterial SSU rRNA gene fragments were PCR amplified from MDA samples and sequenced at Georgia Genomics and Bioinformatics Core (Univ. of Georgia). The primers used were: Bac515-Y and Bac926R. Dual-indexed libraries were prepared with (HT) iTruS (Kappa Biosystems) chemistry and sequencing was performed on an Illumina MiSeq 2 x 300 bp system with all samples combined equally on a single flow cell.

Raw sequence reads were processed through Trim Galore [http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/], FLASH (ccb.jhu.edu/software/FLASH/) and FASTX Toolkit [http://hannonlab.cshl.edu/fastx_toolkit/] for trimming and removal of low quality/short reads.

Quality filtering included requiring a minimum average quality of 25 and rejection of paired reads less than 250 nucleotides.

Operational Taxonomic Unit (OTU) clusters were constructed at 99% similarity with the script pick_otus.py within the Quantitative Insights Into Microbial Ecology (QIIME) v.1.9.1 software and ‘uclust’. Any OTU that matched an OTU in one of our control samples (drilling fluids, drilling mud, extraction and WGA controls) was removed (using filter_otus_from_otu_table.py) along with any sequences of land plants and human pathogens that may have survived the control filtering due to clustering at 99% (filter_taxa_from_otu_table.py). As an additional quality control measure, genera that are commonly identified as PCR contaminants were removed. Unclassified OTUs were queried using BLAST against the GenBank nr database and further information about these OTUs is provided in the Supplementary Discussion text under the section “Taxonomic diversity information from iTAGs.” OTUs that could not be assigned to Bacteria or Archaea were removed from further analysis. For downstream analyses, any OTUs not representing more than 0.01% of relative abundance of sequences overall were removed as those are unlikely to contribute significantly to in situ communities. The OTU data table was transformed to a presence/absence table and the Jaccard method was used to generate a distance matrix using the dist.binary() function in the R package ade4.