Search for: All records

Stordalen Mire sample metadata from a mire-wide survey (2015) and co-analyzed autochamber site samples (2014-2015). These samples were analyzed by 16S rRNA amplicon sequencing, and the 16S data is available under NCBI BioProject PRJNA1236848. Column descriptions for this metadata file: The first 4 columns (sample_name, SRA library_ID, SRA accession, BioSample) include sample & library names and accessions in NCBI. The sample_name column also matches the SampleID__ attribute in the EMERGE Database (EMERGE-DB; https://emerge-db.asc.ohio-state.edu/). The next 7 columns (SampleID, Habitat, Depth, Description, Source, Site, Origin) are the metadata used for the 16S data analysis (results available at https://doi.org/10.5281/zenodo.15047596 and https://doi.org/10.5281/zenodo.15047715). The final 9 columns (Latitude, Longitude, Date, Full Site Name, Core #, DepthMin (cm), DepthMax (cm), DepthAvg (cm), pH_porewater) provide other metadata, including latitude/longitude, sampling dates, full site and core names, depths, and porewater pH, standardized to match the nomenclature in the EMERGE-DB. FUNDING: National Aeronautics and Space Administration, Interdisciplinary Science program: From Archaea to the Atmosphere (award # NNX17AK10G). National Science Foundation, Biology Integration Institutes Program: EMERGE Biology Integration Institute (award # 2022070). United States Department of Energy Office of Biological and Environmental Research, Genomic Science Program: The IsoGenie Project (grant #s DE-SC0004632, DE-SC0010580, and DE-SC0016440). Sequencing was performed using startup funding from the University of Arizona to Virginia Rich. We thank the Swedish Polar Research Secretariat and SITES for the support of the work done at the Abisko Scientific Research Station. SITES is supported by the Swedish Research Council's grant 4.3-2021-00164. 

Recovered microbial community structure is known to be influenced by sample storage conditions and nucleic acid extraction methods, and the impact varies by sample type. Peat soils store a large portion of soil carbon and their microbiomes mediate climate feedbacks. Here, we tested three storage conditions and five extraction protocols on peat soils from three physicochemically distinct habitats in Stordalen Mire, Sweden, revealing significant methodological impacts on microbial (here, meaning bacteria and archaea) community structure. Initial preservation method impacted alpha but not beta diversity, with in-field storage in LifeGuard buffer yielding roughly two-thirds the richness of in-field flash-freezing or transport from the field on ice (all samples were stored at −80 °C after return from the field). Nucleic acid extraction method impacted both alpha and beta diversity; one method (the PowerSoil Total RNA Isolation kit with DNA Elution Accessory kit) diverged from the others (PowerMax Soil DNA Isolation kit-High Humic Acid Protocol, and three variations of a modifiedPowerMax Soil DNA/RNA isolation kit), capturing more diverse microbial taxa, with divergent community structures. Although habitat and sample depth still consistently dominated community variation, method-based biases in microbiome recovery for these climatologically-relevant soils are significant, and underscore the importance of methodological consistency for accurate inter-study comparisons, long-term monitoring, and consistent ecological interpretations. 

Abstract Climate change is disproportionately warming northern peatlands, which may release large carbon stores via increased microbial activity. While there are many unknowns about such microbial responses, virus roles are especially poorly characterized with studies to date largely restricted to “bycatch” from bulk metagenomes. Here, we used optimized viral particle purification techniques on 20 samples along a highly contextualized peatland permafrost thaw gradient, extracted and sequenced viral particle DNA using two library kits to capture single-stranded (ssDNA) and double-stranded (dsDNA) virus genomes (40 total viromes), and explored their diversity and potential ecosystem impacts. Both kits recovered similar dsDNA virus numbers, but only one also captured thousands of ssDNA viruses. Combining these data, we explored population-level ecology using genomic representation from 9,560 viral operational taxonomic units (vOTUs); nearly a 4-fold expansion from permafrost-associated soils, and 97% of which were novel when compared against large datasets from soils, oceans, and the human gut.In silicopredictions identified putative hosts for 44% (4,149 dsDNA + 17 ssDNA) of the identified vOTUs spanning 2 eukaryotic, 12 archaeal, and 30 bacterial phyla. The recovered vOTUs encoded 1,684 putative auxiliary metabolic genes (AMGs) and other metabolic genes carried by ∼10% of detected vOTUs, of which 46% were related to carbon processing and 644 were novel. These AMGs grouped into five functional categories and 11 subcategories, and nearly half (47%) of the AMGs were involved in carbon utilization. Of these, 112 vOTUs encoded 123 glycoside hydrolases spanning 15 types involved in the degradation of polysaccharides (e.g., cellulose) to monosaccharides (e.g., galactose), or further monosaccharide degradation, which suggests virus involvement in myriad metabolisms including fermentation and central carbon metabolism. These findings expand the scope of viral roles in microbial carbon processing and suggest viruses may be critical for understanding the fate of soil organic carbon in peatlands. 

Abstract Viral metagenomics (viromics) has reshaped our understanding of DNA viral diversity, ecology, and evolution across Earth’s ecosystems. However, viromics now needs approaches to link newly discovered viruses to their host cells and characterize them at scale. This study adapts one such method, sequencing-enabled viral tagging (VT), to establish “Viral Tag and Grow” (VT + Grow) to rapidly capture and characterize viruses that infect a cultivated target bacterium, Pseudoalteromonas. First, baseline cytometric and microscopy data improved understanding of how infection conditions and host physiology impact populations in VT flow cytograms. Next, we extensively evaluated “and grow” capability to assess where VT signals reflect adsorption alone or wholly successful infections that lead to lysis. Third, we applied VT + Grow to a clonal virus stock, which, coupled to traditional plaque assays, revealed significant variability in burst size—findings that hint at a viral “individuality” parallel to the microbial phenotypic heterogeneity literature. Finally, we established a live protocol for public comment and improvement via protocols.io to maximally empower the research community. Together these efforts provide a robust foundation for VT researchers, and establish VT + Grow as a promising scalable technology to capture and characterize viruses from mixed community source samples that infect cultivable bacteria.