Osa-miR162a balances rice yield and resistance to Magnaporthe oryzae
Bibliographic record
Abstract
Abstract MicroRNAs (miRNAs) play essential roles in rice immunity against Magnaporthe oryzae, the causative agent of rice blast disease. Osa-miR162a targets Dicer-like 1 (DCL1) genes, which play vital roles in miRNA biogenesis and act as negative regulators in rice immunity. Here we demonstrate that Osa-miR162a improves rice immunity against M. oryzae and balances the trade-off between rice yield and resistance. Overexpression of Osa-miR162a compromises rice susceptibility to M. oryzae accompanying enhanced induction of defense-related genes and accumulation of hydrogen peroxide (H2O2). In contrast, blocking miR162 by overexpressing a target mimic of miR162 enhances susceptibility to blast fungus associating with compromised induction of defense-related gene expression and H2O2 accumulation. Moreover, the transgenic lines overexpressing Osa-miR162a display decreased seed setting rate resulting in reduced yield per plant, whereas blocking miR162 leads to an increased number of grains per panicle, resulting in increased yield per plant. Altered accumulation of miR162 had limited impact on the expression of OsDCL1. Together, our results indicate that Osa-miR162a improves rice blast resistance and plays a role in the balance of trade-off between resistance and yield.
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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) | 0.002 | 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".