A rare recombination allows the redefinition of a major avirulence gene in the <i>Phytophthora sojae</i> genome
Bibliographic record
Abstract
Abstract Significant yield losses in soybean production are imputable to Phytophthora root and stem rot (PRR) caused by Phytophthora sojae . Soybean resistance to the pathogen depends on the presence of resistance genes ( Rps ) that recognize key effectors from P. sojae , which are encoded by avirulence genes ( Avr ). A unique molecular signature associated with these genes allows the prediction of the outcome of infection with great accuracy making the interaction Rps - Avr central to reduce disease incidence. In this study, we reassessed the identity of the avirulence gene whose protein product is recognized by Rps6 . Following extensive soil sampling and single-spore isolation of a large population of P. sojae , we found two salient isolates carrying a rare recombination between two effectors, Avr3c and Avr4/6 . Using a PCR assay and molecular markers, we showed that only alleles at the Avr3c locus were in perfect association with the phenotypes of the isolates. Furthermore, whole-genome resequencing and de novo assembly of the two isolates revealed the full extent of this genomic rearrangement. These results bring to light an unsuspected connection between Avr3c and Rps6 and offer a more reliable target for the pathotyping of P. sojae , which ultimately leads to a better use of resistant soybean material.
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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".