Supplemental Information
Methods
Chromium distillation for determining rates of sulfate reduction
Using this method, 1 gram (wet weight) of crushed mineral (about 60% mineral, 30%
interstitial fluid) was added to 10 mL of a 1:1 ethanol to water solution in the chromium
distillation apparatus, and then degassed with nitrogen for 15 minutes to achieve anaerobicity.
8 mL of 12 N HCl and 16 mL of 1 M reduced chromium chloride was added anaerobically to the
chamber and gently heated to a slow boil for 3 hours to evolve hydrogen sulfide gas. The
resulting sulfide gas was carried via nitrogen gas through a condenser to remove any ethanol or
water vapor, and was then trapped as zinc sulfide in a 25% zinc acetate solution. The
radioactivity of the resulting sulfide (Zn35S) and the remaining sulfate from the supernatant
(35SO42-) were measured via liquid scintillation counter in Ultima Gold scintillation cocktail
(ThermoFisher Inc., Waltham, MA).
Conversion of rates previously described in sediments.
Conventionally, published sulfate reduction rates from sediment systems are reported
as volumetric rates in units of nmol S cm3
-1
day-1. Due to the challenges associated with
working with crushed mineral, accurate volumetric subsampling for each sample was difficult to
replicate. Because of this, each incubation was subsampled by mass (wet weight). Published
volumetric rates (mol/cm3day) were converted to units of mol g-1 day-1 using either published
density values of associated sediment samples or assuming an average sediment density of
approximately 2 g/cm3.
Plasmid generation for qPCR standard curves
Standard curves for total bacterial and archaeal 16S rRNA genes were constructed as
above from serial dilutions (108 to 101 copies µl–1 TE) of plasmids containing inserts of the 16S
rRNA gene from Arcobacter nitrofigulis (ATCC 33309) and Methanosarcina acidovorans
respectively. Standards for functional genes of interest were generated using Desulfovibrio
vulgaris Hildenborough (ATCC 29579/ NCIMB 8303/ AE017285) as template for amplification of
target genes, which were then cloned using the Strataclone vector (Agilent Technologies Inc.)
following the manufacturer’s instructions. Plasmids were extracted using the Qiaprep
Minispin™ kit (Qiagen Inc), quantified with Quanti-iT™ PicoGreen (Life Technologies) on a
Spectramax™ Gemini XS fluorometer (Molecular Devices Inc.), linearized and serially diluted
into aliquots from 108 to 101 copies µL-1 in 10 mM Tris-EDTA buffer.
Sequencing and Phylogenetic Analysis via 454 pyrosequencing
The resulting 454 datasets were processed via Mothur (Schloss et al., 2009). Sequencing
error was reduced by running the Mothur implementation of the AmpliconNoise algorithm
(shhh.seqs). The 16S rRNA gene sequence data was aligned to the SILVA-compatible alignment
database reference alignment (Schloss, 2009). Sequences were screened and selected to start
at the same position, then those sequences within 2 bp of a more abundant sequence were
merged in a pre clustering step, in which a difference of 1 bp per 100 bp of sequence length
was allowed. Chimeric sequences were identified and removed using the command
chimera.uchime, which uses the abundant sequences in the sample set as a reference
database. Sequences were classified using the Mothur version of the “Bayesian” classifier
against the Grenegenes99 database (Werner et al., 2012). Contaminating sequences, those
affiliated with organelles such as chloroplasts and mitochondria, were removed. Operational
taxanomic units (OTUs) were built by generating a distance matrix with pairwise distance
lengths smaller than 0.15. The data were then clustered and each OTU was classified.
References:
Schloss, P. D. (2009). A high-throughput DNA sequence aligner for microbial ecology studies.
PloS one, 4(12), e8230.
Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R. A., Hartmann, M., Hollister, E. B., Lesniewski,
R. a, et al. (2009). Introducing mothur: open-source, platform-independent, communitysupported software for describing and comparing microbial communities. Appl. Environ.
Microbiol., 75(23), 7537-7541.
Werner, J. J., Koren, O., Hugenholtz, P., DeSantis, T. Z., Walters, W. a, Caporaso, J. G., Angenent,
L. T., et al. (2012). Impact of training sets on classification of high-throughput bacterial 16s
rRNA gene surveys. ISME J, 6(1), 94-103.