Divergent RNA viruses infecting sea lice, major ectoparasites of fish
Bibliographic record
Abstract
Sea lice, the major ectoparasites of fish, have significant economic impacts on wild and farmed finfish, and have been implicated in the decline of wild salmon populations. As blood-feeding arthropods, sea lice may also be reservoirs for viruses infecting fish. However, except for two groups of negative-strand RNA viruses within the order Mononegavirales, nothing is known about viruses of sea lice. Here, we used transcriptomic data from three key species of sea lice (Lepeophtheirus salmonis, Caligus clemensi, and Caligus rogercresseyi) to identify 32 previously unknown RNA viruses. The viruses encompassed all the existing phyla of RNA viruses, with many placed in deeply branching lineages that likely represent new families and genera. Importantly, the presence of canonical virus-derived small interfering RNAs (viRNAs) indicates that most of these viruses infect sea lice, even though in some cases their closest classified relatives are only known to infect plants or fungi. We also identified both viRNAs and PIWI-interacting RNAs (piRNAs) from sequences of a bunya-like and two qin-like viruses in C. rogercresseyi. Our analyses showed that most of the viruses found in C. rogercresseyi occurred in multiple life stages, spanning from planktonic to parasitic stages. Phylogenetic analysis revealed that many of the viruses infecting sea lice were closely related to those that infect a wide array of eukaryotes with which arthropods associate, including fungi and parasitic tapeworms, implying that over evolutionary time there has been cross-phylum and cross-kingdom switching of viruses between arthropods and other eukaryotes. Overall, this study greatly expands our view of virus diversity in crustaceans, identifies viruses that infect and replicate in sea lice, and provides evidence that over evolutionary time, viruses have switched between arthropods and eukaryotic hosts in other phyla and kingdoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".