Allelism of <i>Rps3b</i> and <i>Rps11</i> revealed by NLR gene capture of resistance genes to <i>Phytophthora sojae</i> in soybean
Bibliographic record
Abstract
Exploitation of disease resistance genes in soybean (Glycine max (L.) Merr.), as an effective method for management of Phytophthora sojae (Kauf. & Gerd.), is on the verge of an impasse. Few of the known resistance genes are commercially exploited, and even fewer have been precisely identified. Therefore, little is known about the identities or relationships between those genes, a hindrance preventing optimal introgression of new sources of resistance into elite soybean lines. In this study, we have applied state-of-the-art nucleotide-binding and leucine-rich repeat gene capture (RenSeq) using a set of approximately 80,000 unique baits on near-isogenic lines, whole-genome resequencing, and bulked segregant analysis to uncover a resistance gene that has remained elusive for 40 years. This work highlights the reassessment of the Rps3b locus from Chr13 to Chr7 and the description of two alleles, from Turkish and Chinese landraces, of a sole candidate gene. We have identified Rps3b in four, fully resequenced, genetic backgrounds, including the original PI from 1985, in which the resistance gene was originally described. Specificity of the resistant alleles was achieved through phenotypic characterization with field isolates carrying virulent and avirulent forms of the corresponding effector, Avr3b. Surprisingly, these alleles showed extremely high synteny and sequence identity with Rps11 consistent with allelism, and conferred a resistance phenotype indistinguishable from that of the recently cloned Rps11. These results offer new sources of resistance for breeders that are effective against the current P. sojae pathotypes in the field.
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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 | 0.001 |
| 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".