Environmental drivers of viral community composition in Antarctic soils identified by viromics
Bibliographic record
Abstract
BACKGROUND: The Antarctic continent is considered the coldest and driest place on earth with simple ecosystems, devoid of higher plants. Soils in the ice-free regions of Antarctica are known to harbor a wide range of microorganisms from primary producers to grazers, yet their ecology and particularly the role of viruses is poorly understood. In this study, we examined the virus community structures of 14 soil samples from the Mackay Glacier region. METHODS: Viral communities were extracted from soil and the dsDNA was extracted, amplified using single-primer amplification, and sequenced using the Ion Torrent Proton platform. Metadata on soil physico-chemistry was collected from all sites. Both read and contig datasets were analyzed with reference-independent and reference-dependent methods to assess viral community structures and the influence of environmental parameters on their distribution. RESULTS: We observed a high heterogeneity in virus signatures, independent of geographical proximity. Tailed bacteriophages were dominant in all samples, but the incidences of the affiliated families Siphoviridae and Myoviridae were inversely correlated, suggesting direct competition for hosts. Viruses of the families Phycodnaviridae and Mimiviridae were present at significant levels in high-diversity soil samples and were found to co-occur, implying little competition between them. Combinations of soil factors, including pH, calcium content, and site altitude, were found to be the main drivers of viral community structure. CONCLUSIONS: The pattern of viral community structure with higher levels of diversity at lower altitude and pH, and co-occurring viral families, suggests that these cold desert soil viruses interact with each other, the host, and the environment in an intricate manner, playing a potentially crucial role in maintaining host diversity and functioning of the microbial ecosystem in the extreme environments of Antarctic soil.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".