DATA AVAILABILITY STATEMENT : All Illumina sequences generated and analyzed in this study were deposited into the European Nucleotide Archive (accession number PRJEB55051).SUPPORTING INFORMATION 1 : FIGURE S1. Samples located in four inland areas of the Prince Charles Mountains (ME1 from Mount Rubin, ME2 and ME3 from Mawson Escarpment, MM1 and MM2 from Mount Menzies, LT1 and LT2 from Lake Terrasovoje), in the Reinbolt Hills (RH1), and in coastal sites in proximity of the Prince Charles Mountains (C1 and C2; see Table S1). Map was produced using MODIS mosaic (125 m) imagery distributed by Quantarctica (https://cmr.earthdata.nasa.gov/; https://www.npolar.no/quantarctica/).
FIGURE S2. Pearson's pairwise correlations between Bray–Curtis dissimilarity matrices calculated on relative abundance taxonomic dataset (genus level; A), and between Jaccard dissimilarity matrices calculated on presence/absence taxonomic dataset (genus level; B). Correlations were calculated for all the variable region datasets (V1–V3, V3–V4, V4, V4–V5 and V8–V9), and the mixed datasets (Mix 1, Mix 2 and Mix 3) constituted by randomly picked samples from V1–V3, V3–V4, V4, V4–V5 and V8–V9 (Table S4). Pearson's correlation coefficients (r) are reported only in case of significant correlation (p < 0.05).SUPPORTING INFORMATION 2 : TABLE S1. Sample specifics.
TABLE S2. Geochemical data.
TABLE S3. Relative abundance (%) of the taxonomic domains Bacteria and Archaea in sample (i.e., ME1, ME2, ME3, MM1, MM2, LT1, LT2, RH1, C1 and C2) for each variable region dataset (i.e., V1–V3, V3–V4, V4, V4–V5 and V8–V9).
TABLE S4. Composition of mixed communities.
TABLE S5. Number of reads at each step of the 16S rRNA gene processing pipeline. *counts reported as read pairs.
TABLE S6. Number and percentage of unknown amplicon sequence variants (ASVs) at genus level for each phylum.
TABLE S7. Relative abundance associated to unknown amplicon sequence variants at genus-level for each phylum.
TABLE S8. Pearson's correlations from pairwise comparisons of variable region datasets performed on number of genera (A), dominant genera (i.e., genera represented by a relative abundance higher than 1% in at least one sample) (B), rare genera (i.e., genera represented by a relative abundance lower than 0.1% in all samples (C), Shannon index (D) and unique genera (E).16S rRNA gene amplicon sequencing is routinely used in environmental surveys to identify microbial diversity and composition of the samples of interest. The dominant sequencing technology of the past decade (Illumina) is based on the sequencing of 16S rRNA hypervariable regions. Online sequence data repositories, which represent an invaluable resource for investigating microbial distributional patterns across spatial, environmental or temporal scales, contain amplicon datasets from diverse 16S rRNA gene variable regions. However, the utility of these sequence datasets is potentially reduced by the use of different 16S rRNA gene amplified regions. By comparing 10 Antarctic soil samples sequenced for five different 16S rRNA amplicons, we explore whether sequence data derived from diverse 16S rRNA variable regions can be validly used as a resource for biogeographical studies. Patterns of shared and unique taxa differed among samples as a result of variable taxonomic resolutions of the assessed 16S rRNA variable regions. However, our analyses also suggest that the use of multi-primer datasets for biogeographical studies of the domain Bacteria is a valid approach to explore bacterial biogeographical patterns due to the preservation of bacterial taxonomic and diversity patterns across different variable region datasets. We deem composite datasets useful for biogeographical studies.Australian Antarctic Division, Australian Research Council and NRF SANAP.http://wileyonlinelibrary.com/journal/emi4hj2023BiochemistryGeneticsMicrobiology and Plant Patholog
