Expansion of the plant-parasitic nematode RNA viruses: Unprecedented diversity, intron-bearing viruses, and cross-kingdom evolutionary links
Bibliographic record
Abstract
Plant-parasitic nematodes (PPNs) pose a significant threat to global crop production, yet their associated viral diversity remains poorly characterized, limiting potential virus-mediated biocontrol strategies. In this study, we investigated PPN-associated viruses using both virome data obtained from ten field populations of potato rot nematode (Ditylenchus destructor) collected in Lulong County (Qinhuangdao city, China), a major sweet potato-producing region, along with 536 publicly available transcriptome datasets from the Sequence Read Archive (SRA) database, collectively encompassing twenty-five PPN species. We identified 94 PPN-associated viruses, representing a 7.9-fold increase over prior records. These viruses span eighteen established families and six unclassified viral groups, including the first discovery of orthomyxo-like viruses, Jingmen viruses, and ormycoviruses in PPNs or nematodes, expanding the possible host ranges of these viral groups. Notably, a clade of yue-like viruses harbored up to 10 introns, surpassing 2-3 introns that were only observed in orthomyxoviruses and certain members of the Mononegavirales. Furthermore, we identified two larger nematode-associated bunyaviruses with the L segments exceeding 12,000 bp, which appear to have acquired a putative cysteine proteinase gene potentially originating from their nematode hosts (possibly Pristionchus spp.). Our findings reveal that natural PPN populations could host an unexpectedly high diversity of RNA viruses, higher than previously recognized. Exploring these viruses provides novel insights into viral evolution and establishes a foundation for utilizing viruses as a potential method for controlling PPN diseases.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".