Uncovering the hidden RNA virus diversity in Lake Nam Co: Evolutionary insights from an extreme high-altitude environment
Bibliographic record
Abstract
Alpine lakes, characterized by isolation, low temperatures, oligotrophic conditions, and intense ultraviolet radiation, remain a poorly explored ecosystem for RNA viruses. Here, we present the first comprehensive metatranscriptomic study of RNA viruses in Lake Nam Co, a high-altitude alkaline saline lake on the Tibetan Plateau. Using a combination of sequence- and structure-based homology searches, we identified 742 RNA virus species, including 383 novel genus-level groups and 84 novel family-level groups exclusively found in Lake Nam Co. These findings significantly expand the known diversity of the Orthornavirae , uncovering evolutionary adaptations such as permutated RNA-dependent RNA polymerase motifs and distinct RNA secondary structures. Notably, 14 additional RNA virus families potentially infecting prokaryotes were predicted, broadening the known host range of RNA viruses and questioning the traditional assumption that RNA viruses predominantly target eukaryotes. The presence of auxiliary metabolic genes in viral genomes suggested that RNA viruses (families f . 0102 and Nam-Co_family_51) exploit host energy production mechanisms in energy-limited alpine lakes. Low nucleotide diversity, single nucleotide polymorphism frequencies, and pN/pS ratios indicate strong purifying selection in Nam Co viral populations. Our findings offer insights into RNA virus evolution and ecology, highlighting the importance of extreme environments in uncovering hidden viral diversity and further shed light into their potential ecological implications, particularly in the context of climate change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".