Field Sampling

Microbial mats and soils were sampled at 17 sites between December 2019 and February 2020 within the Ross (n=8) and Taylor tills (n=8) of Taylor Valley and Beacon Valley (n=1). These sites were along stream channels, pond margins, and snow melt patches where dense microbial mats were present. The Beacon Valley sample is better defined as a desiccated, patchy community found downhill from a snow patch. Dominant color composition (orange or black mats) was noted for each location. We identified a 0.5 x 0.5 m area of microbial mat at each site, and we sampled 5 plugs of mat material (from the center and the four corners) using a sterilized #13 brass cork borer (2.27 cm2) for ash-free dry mass (AFDM), pigment analysis, and molecular analysis, separately. Directly beneath each sampled microbial mat, we sampled the underlying soil (0 - 5 cm depth) using a sterilized cork borer for later geochemical and molecular analysis. The 5 microbial mat and underlying soil samples were composited into 1 soil and 1 microbial mat sample per site, per analysis. All samples were kept frozen at -20°C in complete darkness until subsequent analyses were performed.

Biophysicochemical Analysis

Gravimetric water content (GWC): Water content was determined as the mass of water per mass of dry soil. This was conducted by weighing field moist soil, drying in an oven at 105°C for 24 hr, and reweighing the soil after.

Electrical conductivity (EC): We measured electrical conductivity using a 1:5 soil to DI H2O slurry using a benchtop conductivity probe.

pH: We measured pH using a 1:2 soil to DI H2O slurry using a benchtop pH probe.

Inorganic N and P: We extracted inorganic N (NH₄⁺ and NO₃^-) in 2 M potassium chloride and inorganic P (PO₄^3-) in 0.5 M sodium bicarbonate. Inorganic N and P were measured on extracts using a Lachat flow injection analyzer (Knepel, 2003; Prokopy, 1995). 

Sulfate and Chloride Ions: Sulfate (SO₄^2-) and chloride (Cl^-) were extracted using a 1:5 soil to DI H₂O slurry. Sulfate and chloride were measured on extracts using a Lachat flow injection analyzer (Knepel, 2003; Prokopy, 1995).

Total N (TN) and soil organic carbon (SOC): We measured SOC and TN using an Elementar Vario MAX Cube analyzer after fumigating samples with concentrated hydrochloric acid to remove the influence of carbonates on SOC values (Walthert et al., 2010).

Ash-free dry mass (AFDM): The microbial mat samples were measured for organic matter content as AFDM by oven drying at 100°C for 24 hr, weighing dry mass, combusting at 550°C for 15 hr using a muffle furnace, and weighing ashed mass after cooling in a desiccator.

Pigment Analysis

Microbial mat samples were shipped to the Paerl Lab at the University of North Carolina - Chapel Hill Institute of Marine Sciences, where a suite of pigments was extracted and analyzed using high performance liquid chromatography (HPLC). Samples were not exposed to direct light throughout the entire process. Pigments were extracted from the microbial mat samples in 100% acetone for 24 hr before analysis on their Shimadzu HPLC system. The HPLC separated the pigments and measured absorbance of the extracts by scanning 340 - 700 nm every 1.28 sec. These data were analyzed using Shimadzu’s LabSolutions Lite software by K. Rossignol, who identified the various pigments using a combination of peak retention time, absorbance spectrum shape/signature, maximum wavelength, and the similarity match of the unknown pigment to a standard. Pigments were then quantified from their peak areas, calculated at 388 nm for scytonemin and reduced scytonemin and 440 nm for all other pigments.

Nif Gene Abundance

As indicators of nitrogen fixation potential, we determined the relative abundance of all nif genes using shotgun metagenome sequencing and the absolute abundance of nifH specifically using qPCR for both the microbial mats and underlying soils.

DNA Extraction: DNA was extracted from ~1.5 g soil and ~ 0.5 – 1.0 g microbial mat material using the DNeasy PowerSoil kit (Qiagen), and the extracts were quantified using a Qubit 2.0 Fluorometer (Thermo Fisher Inc.).

qPCR for nifH: We used the IGK3/DVV primer set (Ando et al. 2005) because it exhibits less bias than other nifH primer sets (Gaby and Buckley 2017). Samples were amplified in triplicate and PCR reactions contained 10 µL Quantitect SYBR Green qPCR Mastermix (QIAGEN), 3 µL of 10 µM working stocks of each of the forward and reverse primers (for final primer concentrations of 1.5 µM), and 4 µL of DNA template for a total reaction volume of 20 µL. The high primer concentration we used was intended to maximize the efficiency of the qPCR reactions (Gaby and Buckley, 2017). Thermal cycling conditions were as follows: 15 min at 95°C followed by 40 cycles of 15 sec at 95°C, 30 sec at 56°C, and 30 sec at 72°C. Standard curves were generated by amplifying serial dilutions of the target regions, with amplification efficiencies of 84.5% for the microbial mat samples and 74.2% for the soil samples, and both R2 values > 0.99. For the qPCR gene standard, we used the nifH gene sequence from Nostoc punctiforme strain PCC 73102, which has a high similarity (99%) to Antarctic Nostoc sequences (Jungblut and Neilan 2010). Amplification specificity was assessed using melt curve analysis. The nifH gene copy numbers were corrected for dry soil mass and wet mat mass.

Shotgun Metagenome Sequencing for nif and nifH: The metagenomic sequence data are archived in MG-RAST under project (MCM_MG mgp95386). Libraries were sequenced on an Illumina NextSeq500 in a 150 bp paired-end sequencing run at the Duke University School of Medicine Sequencing and Genomic Technologies Shared Resource. Raw sequence reads were uploaded to MG-RAST (accession# mgp95386) for merging of paired ends, quality control, and annotation using the recommended MG-RAST default parameters (Meyer et al., 2008). Functional annotations were performed by aligning the metagenomic sequences to the SEED Subsystems database (Overbeek et al., 2014) while taxonomic annotations were performed using the RefSeq and SILVA databases. For all functional and taxonomic annotations, we used an evalue cutoff of 1e-5, a sequence similarity threshold of 80% and a minimum alignment length of 20 bp. After processing with MG-RAST, we retained an average of ~7.2 million sequences per sample, ranging from ~4.3 million to ~ 8.9 million. Of these sequences, an average of 3.91% failed QC, 1.10% were rRNA genes, 41.25% were annotated proteins, and 57.65% were unknown proteins. To quantify N fixation gene relative abundances, we summed all sequences annotated as nif genes for each sample and expressed nif gene relative abundance as the percentage of all annotated reads.