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Abstract SummaryGenome-centric analysis of metagenomic samples is a powerful method for understanding the function of microbial communities. Calculating read coverage is a central part of analysis, enabling differential coverage binning for recovery of genomes and estimation of microbial community composition. Coverage is determined by processing read alignments to reference sequences of either contigs or genomes. Per-reference coverage is typically calculated in an ad-hoc manner, with each software package providing its own implementation and specific definition of coverage. Here we present a unified software package CoverM which calculates several coverage statistics for contigs and genomes in an ergonomic and flexible manner. It uses “Mosdepth arrays” for computational efficiency and avoids unnecessary I/O overhead by calculating coverage statistics from streamed read alignment results. Availability and implementationCoverM is free software available at https://github.com/wwood/coverm. CoverM is implemented in Rust, with Python (https://github.com/apcamargo/pycoverm) and Julia (https://github.com/JuliaBinaryWrappers/CoverM_jll.jl) interfaces. 

METHODS: Soil samples (6 total) were collected at the Stordalen Mire site in 2019 from two depths (1-5 & 20-24 cm below ground) across three habitats (Palsa, Bog, and Fen). DNA was extracted based on the protocol described by Li et al. (2024). For short reads, libraries were prepared at the Joint Genome Institute (JGI) with the KAPA Hyperprep kit, and sequenced with Illumina NovaSeq 6000. For long reads, libraries were prepared with the SMRTbell Express Template Prep Kit 2.0 (PacBio), then sequenced using PacBio Sequel IIe at JGI. PacBio data was processed at JGI to form filtered CCS (Circular Consensus Sequencing) reads.  Assemblies were generated with short-only, long-only, and hybrid read sources: Short-only was assembled with metaSPAdes (v3.15.4) using Aviary (v0.5.3) with default parameters. Long-only was assembled with metaFlye (v2.9-b1768) using Aviary (v0.5.3) with default parameters. Hybrid assembly was performed using Aviary v0.5.3 with default parameters. This involved a step-down procedure with long-read assembly through metaFlye (v2.9-b1768), followed by short-read polishing by Racon (v1.4.3), Pilon (v1.24) and then Racon again. Next, reads that didn't map to high-quality metaFlye contigs were hybrid assembled with SPAdes (--meta option) and binned out with MetaBAT2 (v2.1.5). For each bin, the reads within the bin were hybrid assembled using Unicycler (v0.4.8). The high-coverage metaFlye contigs and Unicycler contigs were then combined to form the assembly fasta file. Genome recovery was performed using Aviary v0.5.3 with samples chosen for differential abundance binning by Bin Chicken (v0.4.2) using SingleM metapackage S3.0.5. This involved initial read mapping through CoverM (v0.6.1) using minimap2 (v2.18) and binning by MetaBAT, MetaBAT2 (v2.1.5), VAMB (v3.0.2), SemiBin (v1.3.1), Rosella (v0.4.2), CONCOCT (v1.1.0) and MaxBin2 (v2.2.7). Genomes were analyzed using CheckM2 (v1.0.2) and clustered at 95% ANI using Galah (v0.4.0).   FILES: EMERGE_MAGs_2019_long-short-hybrid.tar.gz - Archive containing the MAG files (.fna). metadata_MAGs_2019_EMERGE.tsv - Table containing source sample names and accessions, GTDB classifications, CheckM2 quality information, NCBI GenomeBatch- and MIMAG(6.0)-formatted attributes, and other metadata for the MAGs.   FUNDING: This research is a contribution of the EMERGE Biology Integration Institute (https://emerge-bii.github.io/), funded by the National Science Foundation, Biology Integration Institutes Program, Award # 2022070. This study was also funded by the Genomic Science Program of the United States Department of Energy Office of Biological and Environmental Research, grant #s DE-SC0004632. DE-SC0010580. and DE-SC0016440. 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. Data from the Joint Genome Institute (JGI) was collected under BER Support Science Proposal 503530 (DOI: 10.46936/10.25585/60001148), conducted by the U.S. Department of Energy Joint Genome Institute (https://ror.org/04xm1d337), a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231. 

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. 

Advances in sequencing technologies and bioinformatics tools have dramatically increased the recovery rate of microbial genomes from metagenomic data. Assessing the quality of metagenome-assembled genomes (MAGs) is a critical step before downstream analysis. Here, we present CheckM2, an improved method of predicting genome quality of MAGs using machine learning. Using synthetic and experimental data, we demonstrate that CheckM2 outperforms existing tools in both accuracy and computational speed. In addition, CheckM2’s database can be rapidly updated with new high-quality reference genomes, including taxa represented only by a single genome. We also show that CheckM2 accurately predicts genome quality for MAGs from novel lineages, even for those with reduced genome size (for example, Patescibacteria and the DPANN superphylum). CheckM2 provides accurate genome quality predictions across bacterial and archaeal lineages, giving increased confidence when inferring biological conclusions from MAGs. 

Abstract Borgs are huge extrachromosomal elements (ECE) of anaerobic methane-consuming “CandidatusMethanoperedens” archaea. Here, we used nanopore sequencing to validate published complete genomes curated from short reads and to reconstruct new genomes. 13 complete and four near-complete linear genomes share 40 genes that define a largely syntenous genome backbone. We use these conserved genes to identify new Borgs from peatland soil and to delineate Borg phylogeny, revealing two major clades. Remarkably, Borg genes encoding nanowire-like electron-transferring cytochromes and cell surface proteins are more highly expressed than those of hostMethanoperedens, indicating that Borgs augment theMethanoperedensactivity in situ. We reconstructed the first complete 4.00 Mbp genome for aMethanoperedensthat is inferred to be a Borg host and predicted its methylation motifs, which differ from pervasive TC and CC methylation motifs of the Borgs. Thus, methylation may enableMethanoperedensto distinguish their genomes from those of Borgs. Very high Borg toMethanoperedensratios and structural predictions suggest that Borgs may be capable of encapsulation. The findings clearly define Borgs as a distinct class of ECE with shared genomic signatures, establish their diversification from a common ancestor with genetic inheritance, and raise the possibility of periodic existence outside of host cells. 

ABSTRACT While wetlands are major sources of biogenic methane (CH₄), our understanding of resident microbial metabolism is incomplete, which compromises the prediction of CH₄emissions under ongoing climate change. Here, we employed genome-resolved multi-omics to expand our understanding of methanogenesis in the thawing permafrost peatland of Stordalen Mire in Arctic Sweden. In quadrupling the genomic representation of the site’s methanogens and examining their encoded metabolism, we revealed that nearly 20% of the metagenome-assembled genomes (MAGs) encoded the potential for methylotrophic methanogenesis. Further, 27% of the transcriptionally active methanogens expressed methylotrophic genes; forMethanosarcinalesandMethanobacterialesMAGs, these data indicated the use of methylated oxygen compounds (e.g., methanol), while forMethanomassiliicoccales, they primarily implicated methyl sulfides and methylamines. In addition to methanogenic methylotrophy, >1,700 bacterial MAGs across 19 phyla encoded anaerobic methylotrophic potential, with expression across 12 phyla. Metabolomic analyses revealed the presence of diverse methylated compounds in the Mire, including some known methylotrophic substrates. Active methylotrophy was observed across all stages of a permafrost thaw gradient in Stordalen, with the most frozen non-methanogenic palsa found to host bacterial methylotrophy and the partially thawed bog and fully thawed fen seen to house both methanogenic and bacterial methylotrophic activities. Methanogenesis across increasing permafrost thaw is thus revised from the sole dominance of hydrogenotrophic production and the appearance of acetoclastic at full thaw to consider the co-occurrence of methylotrophy throughout. Collectively, these findings indicate that methanogenic and bacterial methylotrophy may be an important and previously underappreciated component of carbon cycling and emissions in these rapidly changing wetland habitats. IMPORTANCEWetlands are the biggest natural source of atmospheric methane (CH₄) emissions, yet we have an incomplete understanding of the suite of microbial metabolism that results in CH₄formation. Specifically, methanogenesis from methylated compounds is excluded from all ecosystem models used to predict wetland contributions to the global CH₄budget. Though recent studies have shown methylotrophic methanogenesis to be active across wetlands, the broad climatic importance of the metabolism remains critically understudied. Further, some methylotrophic bacteria are known to produce methanogenic by-products like acetate, increasing the complexity of the microbial methylotrophic metabolic network. Prior studies of Stordalen Mire have suggested that methylotrophic methanogenesis is irrelevantin situand have not emphasized the bacterial capacity for metabolism, both of which we countered in this study. The importance of our findings lies in the significant advancement toward unraveling the broader impact of methylotrophs in wetland methanogenesis and, consequently, their contribution to the terrestrial global carbon cycle. 

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 Northern post-glacial lakes are significant, increasing sources of atmospheric carbon through ebullition (bubbling) of microbially-produced methane (CH₄) from sediments. Ebullitive CH₄flux correlates strongly with temperature, reflecting that solar radiation drives emissions. However, here we show that the slope of the temperature-CH₄flux relationship differs spatially across two post-glacial lakes in Sweden. We compared these CH₄emission patterns with sediment microbial (metagenomic and amplicon), isotopic, and geochemical data. The temperature-associated increase in CH₄emissions was greater in lake middles—where methanogens were more abundant—than edges, and sediment communities were distinct between edges and middles. Microbial abundances, including those of CH₄-cycling microorganisms and syntrophs, were predictive of porewater CH₄concentrations. Results suggest that deeper lake regions, which currently emit less CH₄than shallower edges, could add substantially to CH₄emissions in a warmer Arctic and that CH₄emission predictions may be improved by accounting for spatial variations in sediment microbiota.