RXLR effector gene <i>Avr3a</i> from <i>Phytophthora sojae</i> is recognized by <i>Rps8</i> in soybean
Bibliographic record
Abstract
Abstract The use of resistance genes in elite soybean cultivars is one of the most widely used methods to manage Phytophthora sojae . This method relies on effector‐triggered immunity, where a Resistant to P. sojae ( Rps ) gene product from the plant recognizes a specific effector from the pathogen, encoded by an avirulence ( Avr ) gene. Many Avr genes from P . sojae have been identified in the last decade, allowing a better exploitation of this type of resistance. The objective of the present study was to identify the Avr gene triggering immunity derived from the soybean resistance gene Rps8 . The analysis of a segregating F 2 progeny coupled with a genotyping‐by‐sequencing approach led to the identification of a putative Avr8 locus. The investigation of this locus using whole‐genome sequencing data from 31 isolates of P . sojae identified Avr3a as the likely candidate for Avr8 . Long‐read sequencing also revealed that P . sojae isolates can carry up to five copies of the Avr3a gene, compared to the four previously reported. Haplotype and transcriptional analyses showed that amino acid changes and absence of Avr3a transcripts from P. sojae isolates caused changes in virulence towards Rps8 . Functional analyses using CRISPR/Cas9 knockout and constitutive expression demonstrated that Rps8 interacted with Avr3a . We also showed that a specific allele of Avr3a is recognized by Rps3a but not Rps8 . While Rps3a and Rps8 have been previously described as closely linked, this is the first report of a clear distinction hitherto undefined between these two resistance genes.
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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".