Metatranscriptomics and small RNA analysis revealed that viral covert coinfection resulted in disease symptoms reminiscent of rice sterility
Bibliographic record
Abstract
Abstract Background Viral pathogens are a major threat to stable crop production. The discovery of viral diseases traditionally concerns apparent infection that shows obvious symptoms in crop plants. However, little is known about the covert infection of crop plants by viruses. In this study, we used deep metatranscriptomic sequencing and small RNA analysis to identify covert infection of rice plants by viruses. Results Our results showed that introgression of the dominant brown planthopper (BPH) resistance gene Bph3 into the high-yielding but BPH-susceptible indica variety Ms55 via a backcross strategy significantly enhanced resistance to BPH. However, Bph3 -carrying backcross lines infested by BPH exhibited panicle enclosure and failed to produce seed at the mature stage, which are typical characteristics of sterile rice plants. Using a metatranscriptomic analysis, we identified six RNA viruses in backcross line Rby1 and eleven RNA viruses in backcross line Rby2, including eight novel viruses that fell within existing families and orders. Furthermore, our small RNA analysis revealed the biogenesis of viral small interfering RNAs that represented active virus infection in rice plants. Conclusion Bph3 -carrying backcross rice lines are resistant to BPH but are susceptible to viral infection. We identified viral covert coinfection in sterile rice plants by deep metatranscriptomic sequencing and small RNA analysis. Our results suggested that covert coinfection of rice plants by RNA viruses resulted in disease symptoms reminiscent of rice sterility. To develop rice varieties resistant to BPH, it is necessary to introgress genes resistant to not only BPH but also 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".