Recruitment of Arabidopsis RNA Helicase AtRH9 to the Viral Replication Complex by Viral Replicase to Promote Turnip Mosaic Virus Replication
Bibliographic record
Abstract
Positive-sense RNA viruses have a small genome with very limited coding capacity and are highly dependent on host components to fulfill their life cycle. Recent studies have suggested that DEAD-box RNA helicases play vital roles in many aspects of RNA metabolism. To explore the possible role of the RNA helicases in viral infection, we used the Turnip mosaic virus (TuMV)-Arabidopsis pathosystem. The Arabidopsis genome encodes more than 100 putative RNA helicases (AtRH). Over 41 Arabidopsis T-DNA insertion mutants carrying genetic lesions in the corresponding 26 AtRH genes were screened for their requirement in TuMV infection. TuMV infection assays revealed that virus accumulation significantly decreased in the Arabidopsis mutants of three genes, AtRH9, AtRH26, and PRH75. In the present work, AtRH9 was further characterized. Yeast two-hybrid and bimolecular fluorescence complementation (BiFC) assays showed that AtRH9 interacted with the TuMV NIb protein, the viral RNA-dependent RNA polymerase. Moreover, the subcellular distribution of AtRH9 was altered in the virus-infected cells, and AtRH9 was recruited to the viral replication complex. These results suggest that Arabidopsis AtRH9 is an important component of the TuMV replication complex, possibly recruited via its interaction with NIb.
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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 | 0.001 |
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".