Metagenomic characterization of low-biomass Yellowstone National Park (YNP) hot spring waters remains challenging because microbial recovery is influenced by filtration methodology, sample preservation, DNA extraction, and sequencing strategy. We characterized thermophilic microbial communities in alkaline YNP hot spring waters (62-90.5{degrees}C) using three high-temperature-compatible filtration systems (Sterivex, Supor, and polycarbonate membranes), automated onsite DNA extraction (Titan), and shotgun metagenomic sequencing with Illumina short-read and Oxford Nanopore Technologies (ONT) long-read platforms. Across all filtration systems and sequencing workflows, microbial communities were consistently dominated by Bacteria (~90% of reads), whereas Archaea represented <10% of recovered sequences. Dominant microbial populations were reproducibly recovered across all approaches; however, recovery of lower-abundance taxa varied among methods. This variability was most evident in polycarbonate-filtered samples, which exhibited greater replicate-to-replicate variation and less consistent detection of microbial species. Thermocrinis ruber and related Aquificae-associated thermophiles dominated the hottest waters (78.5-90.5{degrees}C), whereas warmer effluent-channel waters (63.5-66.5{degrees}C) contained T. ruber together with photosynthetic taxa, including Synechococcus spp. and Candidatus Thermochlorobacter aerophilum. Archaeal communities were primarily represented by Pyrobaculum- and Thermoproteus-related taxa. Non-metric multidimensional scaling analyses indicated that overall community structure was largely unaffected by filtration or sequencing methodology, whereas alpha-diversity metrics showed that filter selection influenced richness and diversity estimates. These findings identify field-deployable workflows for metagenomic characterization of low-biomass thermophilic aquatic systems and demonstrate the importance of integrating filtration and sequencing strategies for studying extremophile microbiomes under remote sampling conditions.
Wood, J. M., Tighe, S., Urbaniak, C., Parker, C. W., Kumar Singh, N., Wong, S., Peyton, B. M., Venkateswaran, K.
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