Development of Defense Signaling Pathways Against Bacterial Blight Disease in Rice Using Genome-Wide Transcriptome Data
Bibliographic record
Abstract
Bacterial blight (BB) disease caused by Xanthomonas oryzae pv. oryzae (Xoo) drives severe yield and quality losses in rice (Oryza sativa Xa1, Xa3/Xa26, xa5, xa13, Xa21, and Xa27). Here we employ a transcriptomics approach to elucidate the Xa21-, NH1- (NPR1 homolog 1) (NH1)-, and NRR- (negative regulator of disease resistance) mediated defense response to Xoo. Among the candidate genes, we focused on 288 genes showing significant change in at least two of the above comparisons to support the association with an enhanced defense response. Gene Ontology enrichment analysis for this gene list revealed that response to biotic stimulus was 25.0-fold more enriched compared to the control, well qualifying the candidate genes for enhanced resistant response. The biotic stress overview installed in the MapMan toolkit was used to identify diverse components consisting of defense signaling pathways such as genes involved in disease resistance, redox, signaling, regulation of transcription, pathogenesis-related functions, secondary metabolism, and protein degradation. Of these, we validated the expression patterns of genes related to regulation of transcription and pathogenesis-related functions and suggest a functional network model for WRKY transcription factors mediating defense signaling pathways against Xoo. We expect that our analysis will contribute to increasing the depth of knowledge on the molecular mechanism for enhanced disease resistance against bacterial blight disease in rice.
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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 | 0.002 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".