RNA decay is an antiviral defense in plants that is counteracted by viral RNA silencing suppressors
Bibliographic record
Abstract
Exonuclease-mediated RNA decay in plants is known to be involved primarily in endogenous RNA degradation, and several RNA decay components have been suggested to attenuate RNA silencing possibly through competing for RNA substrates. In this paper, we report that overexpression of key cytoplasmic 5'-3' RNA decay pathway gene-encoded proteins (5'RDGs) such as decapping protein 2 (DCP2) and exoribonuclease 4 (XRN4) in Nicotiana benthamiana fails to suppress sense transgene-induced post-transcriptional gene silencing (S-PTGS). On the contrary, knock-down of these 5'RDGs attenuates S-PTGS and supresses the generation of small interfering RNAs (siRNAs). We show that 5'RDGs degrade transgene transcripts via the RNA decay pathway when the S-PTGS pathway is disabled. Thus, RNA silencing and RNA decay degrade exogenous gene transcripts in a hierarchical and coordinated manner. Moreover, we present evidence that infection by turnip mosaic virus (TuMV) activates RNA decay and 5'RDGs also negatively regulate TuMV RNA accumulation. We reveal that RNA silencing and RNA decay can mediate degradation of TuMV RNA in the same way that they target transgene transcripts. Furthermore, we demonstrate that VPg and HC-Pro, the two known viral suppressors of RNA silencing (VSRs) of potyviruses, bind to DCP2 and XRN4, respectively, and the interactions compromise their antiviral function. Taken together, our data highlight the overlapping function of the RNA silencing and RNA decay pathways in plants, as evidenced by their hierarchical and concerted actions against exogenous and viral RNA, and VSRs not only counteract RNA silencing but also subvert RNA decay to promote viral infection.
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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".