Molecular Evolution of the<i>Pi-ta</i>Gene Resistant to Rice Blast in Wild Rice (<i>Oryza rufipogon</i>)
Bibliographic record
Abstract
Rice blast disease resistance to the fungal pathogen Magnaporthe grisea is triggered by a physical interaction between the protein products of the host R (resistance) gene, Pi-ta, and the pathogen Avr (avirulence) gene, AVR-pita. The genotype variation and resistant/susceptible phenotype at the Pi-ta locus of wild rice (Oryza rufipogon), the ancestor of cultivated rice (O. sativa), was surveyed in 36 locations worldwide to study the molecular evolution and functional adaptation of the Pi-ta gene. The low nucleotide polymorphism of the Pi-ta gene of O. rufipogon was similar to that of O. sativa, but greatly differed from what has been reported for other O. rufipogon genes. The haplotypes can be subdivided into two divergent haplogroups named H1 and H2. H1 is derived from H2, with nearly no variation and at a low frequency. H2 is common and is the ancestral form. The leucine-rich repeat (LRR) domain has a high pi(non)/pi(syn) ratio, and the low polymorphism of the Pi-ta gene might have primarily been caused by recurrent selective sweep and constraint by other putative physiological functions. Meanwhile, we provide data to show that the amino acid Ala-918 of H1 in the LRR domain has a close relationship with the resistant phenotype. H1 might have recently arisen during rice domestication and may be associated with the scenario of a blast pathogen-host shift from Italian millet to rice.
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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) | 0.000 | 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 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".