Identification of a candidate rice blast resistance gene, <i>Pior4</i>(t), in an introgression line of <i>Oryza rufipogon</i> using CRISPR/Cas9-mediated genome editing
Bibliographic record
Abstract
Resistance breeding for rice blast is an economic strategy for protecting rice crops against this disease. Genes with nucleotide-binding site leucine-rich repeat (NBS-LRR) structures are known to contribute to disease resistance. Here, we identified a candidate resistance gene, named Pior4(t), associated with leaf and panicle blasts in an introgression line carrying the chromosome 4 segment of wild rice (Oryza rufipogon Griff.) backcrossed with the cultivar ‘Nipponbare’ (Oryza sativa L.). Mapping analysis based on leaf blast severity confirmed that Pior4(t) was localized in the 177-kb NBS-LRR cluster region. To identify the Pior4(t) sequence, mutant lines were generated by knocking out a candidate NBS-LRR gene in a homozygous line carrying Pior4(t), M18, using CRISPR/Cas9-mediated genome editing. Leaf blast resistance was lost in the mutant lines lacking the corresponding Os04g0620950 N-terminal sequence of the M18 line. The result suggested that the counterpart NBS-LRR gene in the M18 line is involved in resistance to leaf blast. Pior4(t) showed homology to Pi63 in the resistant cultivar ‘Kahei’, and an NBS-LRR gene in the resistant cultivar ‘Mine-haruka’ carrying Pi39(t). These results suggest that the NBS-LRR gene is a candidate gene of Pior4(t) and is present on the long arm of chromosome 4.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".