A Negative Feedback Loop Compromises NMD‐Mediated Virus Restriction by the Autophagy Pathway in Plants
Bibliographic record
Abstract
Nonsense-mediated decay (NMD) and autophagy play pivotal roles in restricting virus infection in plants. However, the interconnection between these two pathways in viral infections has not been explored. Here, it is shown that overexpression of NbSMG7 and NbUPF3 attenuates cucumber green mottle mosaic virus (CGMMV) infection by recognizing the viral internal termination codon and vice versa. NbSMG7 is subjected to autophagic degradation, which is executed by its interaction with one of the autophagy-related proteins, NbATG8i. Mutation of the ATG8 interacting motif (AIM) in NbSMG7 (SMG7mAIM1) abolishes the interaction and comprises its autophagic degradation. Silencing of NbSMG7 and NbATG8i, or NbUPF3 and NbATG8i, compared to silencing each gene individually, leads to more virus accumulations, but overexpression of NbSMG7 and NbATG8i fails to achieve more potent virus inhibition. When CGMMV is co-inoculated with NbSMG7mAIM1 or with NbUPF3, compared to co-inoculating with NbSMG7 in NbATG8i transgene plants, the inoculated plants exhibit milder viral phenotypes. These findings reveal that NMD-mediated virus inhibition is impaired by the autophagic degradation of SMG7 in a negative feedback loop, and a novel regulatory interplay between NMD and autophagy is uncovered, providing insights that are valuable in optimizing strategies to harness NMD and autophagy for combating viral infections.
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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) | 0.000 | 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 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".