Antiviral RNA interference in plants: Increasing complexity and integration with other biological processes
Bibliographic record
Abstract
RNA interference (RNAi, also known as RNA silencing) is one of the most important plant defense responses against viral invasion. Although major components of the RNAi pathway, steps leading to viral small interfering RNA biogenesis, and viral counterdefense strategies via RNAi suppressors have been well studied, the broader roles of RNAi in viral infection and seed transmission remain less thoroughly characterized. In particular, the increasing complexity of RNAi-associated mechanisms and their integration with other biological processes have not been comprehensively summarized. Increasing numbers of studies have identified non-canonical RNAi pathways, novel host factors involved in RNAi, and the possibility of small RNAs acting across kingdoms to modulate plant-virus-vector tritrophic interactions. In this review, we provide an overview of the roles of RNAi in plant viral infections and describe recent advances, with emphasis on the discoveries of novel positive and negative RNAi regulators, potential signaling pathways upstream and downstream of antiviral RNAi, and the prospects and challenges of double-stranded RNA applications, either expressed from transgenes or supplied exogenously via spraying. We also discuss how these findings reshape current views on antiviral RNAi, highlight remaining knowledge gaps, and examine how these advances influence plant-virus co-evolution while informing strategies for managing plant virus diseases and reducing their impact.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".