Multiple Rice MicroRNAs Are Involved in Immunity against the Blast Fungus <i>Magnaporthe oryzae</i>
Bibliographic record
Abstract
MicroRNAs (miRNAs) are indispensable regulators for development and defense in eukaryotes. However, the miRNA species have not been explored for rice (Oryza sativa) immunity against the blast fungus Magnaporthe oryzae, the most devastating fungal pathogen in rice production worldwide. Here, by deep sequencing small RNA libraries from susceptible and resistant lines in normal conditions and upon M. oryzae infection, we identified a group of known rice miRNAs that were differentially expressed upon M. oryzae infection. They were further classified into three classes based on their expression patterns in the susceptible japonica line Lijiangxin Tuan Hegu and in the resistant line International Rice Blast Line Pyricularia-Kanto51-m-Tsuyuake that contains a single resistance gene locus, Pyricularia-Kanto 51-m (Pikm), within the Lijiangxin Tuan Hegu background. RNA-blot assay of nine of them confirmed sequencing results. Real-time reverse transcription-polymerase chain reaction assay showed that the expression of some target genes was negatively correlated with the expression of miRNAs. Moreover, transgenic rice plants overexpressing miR160a and miR398b displayed enhanced resistance to M. oryzae, as demonstrated by decreased fungal growth, increased hydrogen peroxide accumulation at the infection site, and up-regulated expression of defense-related genes. Taken together, our data indicate that miRNAs are involved in rice immunity against M. oryzae and that overexpression of miR160a or miR398b can enhance rice resistance to the disease.
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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 | 0.000 |
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".