Natural self-attenuation of pathogenic viruses by deleting the silencing suppressor coding sequence for long-term plant-virus coexistence
Bibliographic record
Abstract
Potyviridae is the largest family of plant-infecting RNA viruses. All members of the family (potyvirids) have single-stranded positive-sense RNA genomes, with polyprotein processing as the expression strategy. The 5'-proximal regions of all potyvirids, except bymoviruses, encode two types of leader proteases: the serine protease P1 and the cysteine protease HCPro. However, their arrangement and sequence composition vary greatly among genera or even species. The leader proteases play multiple important roles in different potyvirid-host combinations, including RNA silencing suppression and virus transmission. Here, we report that viruses in the genus Arepavirus, which encode two HCPro leader proteases in tandem (HCPro1-HCPro2), can naturally lose the coding sequences for these two proteins during infection. Notably, this loss is associated with a shift in foliage symptoms from severe necrosis to mild chlorosis or even asymptomatic infections. Further analysis revealed that the deleted region is flanked by two short repeated sequences in the parental isolates, suggesting that recombination during virus replication likely drives this genomic deletion. Reverse genetic approaches confirmed that the loss of leader proteases weakens RNA silencing suppression and other critical functions. A field survey of areca palm trees displaying varied symptom severity identified a transitional stage in which full-length viruses and deletion mutants coexist in the same tree. Based on these findings, we propose a scenario in which full-length isolates drive robust infections and facilitate plant-to-plant transmission, eventually giving rise to leader protease-less variants that mitigate excessive damage to host trees, allowing long-term coexistence with the perennial host. To our knowledge, this is the first report of potyvirid self-attenuation via coding sequence loss.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".