Antiviral RNA interference in plants: Increasing complexity and integration with other biological processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it