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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.
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Expanding standards in viromics: in silico evaluation of dsDNA viral genome identification, classification, and auxiliary metabolic gene curation
Background Viruses influence global patterns of microbial diversity and nutrient cycles. Though viral metagenomics (viromics), specifically targeting dsDNA viruses, has been critical for revealing viral roles across diverse ecosystems, its analyses differ in many ways from those used for microbes. To date, viromics benchmarking has covered read pre-processing, assembly, relative abundance, read mapping thresholds and diversity estimation, but other steps would benefit from benchmarking and standardization. Here we use in silico-generated datasets and an extensive literature survey to evaluate and highlight how dataset composition (i.e., viromes vs bulk metagenomes) and assembly fragmentation impact (i) viral contig identification tool, (ii) virus taxonomic classification, and (iii) identification and curation of auxiliary metabolic genes (AMGs). Results The in silico benchmarking of five commonly used virus identification tools show that gene-content-based tools consistently performed well for long (≥3 kbp) contigs, while k -mer- and blast-based tools were uniquely able to detect viruses from short (≤3 kbp) contigs. Notably, however, the performance increase of k -mer- and blast-based tools for short contigs was obtained at the cost of increased false positives (sometimes up to ∼5% for virome and ∼75% bulk samples), particularly when eukaryotic or mobile genetic element sequences were included in the test datasets. For viral classification, variously sized genome fragments were assessed using gene-sharing network analytics to quantify drop-offs in taxonomic assignments, which revealed correct assignations ranging from ∼95% (whole genomes) down to ∼80% (3 kbp sized genome fragments). A similar trend was also observed for other viral classification tools such as VPF-class, ViPTree and VIRIDIC, suggesting that caution is warranted when classifying short genome fragments and not full genomes. Finally, we highlight how fragmented assemblies can lead to erroneous identification of AMGs and outline a best-practices workflow to curate candidate AMGs in viral genomes assembled from metagenomes. Conclusion Together, these benchmarking experiments and annotation guidelines should aid researchers seeking to best detect, classify, and characterize the myriad viruses ‘hidden’ in diverse sequence datasets.
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- PAR ID:
- 10256499
- Date Published:
- Journal Name:
- PeerJ
- Volume:
- 9
- ISSN:
- 2167-8359
- Page Range / eLocation ID:
- e11447
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.7717/peerj.11447
