Different roles for RNA silencing and RNA processing components in virus recovery and virus-induced gene silencing in plants
Bibliographic record
Abstract
A major antiviral mechanism in plants is mediated by RNA silencing, which relies on the cleavage of viral dsRNA into virus-derived small interfering RNAs (vsiRNAs) by DICER-like enzymes. Members of the Argonaute (AGO) family of endonucleases then use these vsiRNA as guides to target viral RNA. This can result in a phenomenon known as recovery, whereby the plant silences viral gene expression and recovers from viral symptoms. Endogenous mRNAs can also be targeted by vsiRNAs in a phenomenon known as virus-induced gene silencing (VIGS). Although related to other RNA silencing mechanisms, it has not been established if recovery and VIGS are mediated by the same molecular mechanisms. We used tobacco rattle virus (TRV) carrying a fragment of the phytoene desaturase (PDS) gene (TRV-PDS) or expressing green fluorescent protein (TRV-GFP) as readouts for VIGS and recovery, respectively, in Arabidopsis ago mutants. Our results demonstrated roles for AGO2 and AGO4 in susceptibility to TRV, whereas VIGS of endogenous genes appeared to be largely mediated by AGO1. However, recovery appeared to be mediated by different components, as all the aforementioned mutants were able to recover from TRV-GFP inoculation. TRV RNAs from recovered plants associated less with ribosomes, suggesting that recovery involves translational repression of viral transcripts. Translationally repressed RNAs often accumulate in RNA processing bodies (PBs), where they are eventually processed by decapping enzymes. Consistent with this, we found that viral recovery induced increased PB formation and that a decapping mutant (DCP2) showed increased VIGS and virus RNA accumulation, indicating an important role for PBs in eliminating viral RNA.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".