Osa-miR162a fine-tunes rice resistance to Magnaporthe oryzae and yield
Bibliographic record
Abstract
Abstract MicroRNAs (miRNAs) play essential roles in rice immunity against Magnaporthe oryzae, the causative agent of rice blast disease. Here we demonstrate that Osa-miR162a fine-tunes rice immunity against M. oryzae and yield traits. Overexpression of Osa-miR162a enhances rice resistance to M. oryzae accompanying enhanced induction of defense-related genes and accumulation of hydrogen peroxide (H 2 O 2 ). In contrast, blocking Osa-miR162 by overexpressing a target mimic of Osa-miR162a enhances susceptibility to blast fungus associating with compromised induction of defense-related gene expression and H 2 O 2 accumulation. Moreover, the transgenic lines overexpressing Osa-miR162a display decreased seed setting rate resulting in slight reduced yield per plant, whereas the transgenic lines blocking Osa-miR162 show an increased number of grains per panicle, resulting in increased yield per plant. Altered accumulation of Osa-miR162 had a limited impact on the expression of rice Dicer-like 1 ( OsDCL1 ) in these transgenic lines showing normal gross morphology, and silencing of OsDCL1 led to enhanced resistance to blast fungus similar to that caused by overexpression of Osa-miR162a, suggesting the involvement of OsDCL1 in Osa-miR162a-regulated resistance. Together, our results indicate that Osa-miR162a is involved in rice immunity against M. oryzae and fine-tunes resistance and yield.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".