Genetic diversity of an effector gene, <scp> <i>AvrPi9</i> </scp> , of rice blast pathogen in Thailand and characterization of its promoter
Bibliographic record
Abstract
Abstract Rice blast is one of the most destructive diseases of rice and is caused by the fungus Magnaporthe oryzae . The disease causes enormous yield losses in rice production worldwide. The rice blast fungus delivers effector proteins into rice cells. The effector proteins play an essential role in fungal virulence by manipulating and controlling host cellular pathways and inhibiting host immune responses to enhance pathogenicity. An effector gene, AvrPi9 , which corresponds to the resistance gene Pi9 , was cloned and characterized. However, a regulatory molecular mechanism for AvrPi9 gene expression has not been determined. In this study, the genetic variation of the AvrPi9 and its promoter function were characterized. The results showed that 98% (116/118) of the samples carried the AvrPi9 gene without any sequence variation, whilst two isolates, 10576 from Kalasin and NYK56003 from Nakhon Nayok, lacked the AvrPi9 gene. A homeobox domain‐containing protein (MoHOX6) was identified as a candidate transcription factor. The AvrPi9 gene expression was delayed in the MoHOX6 knockout mutant. Moreover, the AvrPi9 promoter was able to drive the expression of a luciferase gene in rice protoplasts. This study provides the first insight into the function and regulation of the AvrPi9 promoter of rice blast fungus.
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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".