A rapid molecular diagnostic tool to discriminate alleles of avirulence genes and haplotypes of <i>Phytophthora sojae</i> using high‐resolution melting analysis
Bibliographic record
Abstract
Effectors encoded by avirulence genes (Avr) interact with the Phytophthora sojae resistance gene (Rps) products to generate incompatible interactions. The virulence profile of P. sojae is rapidly evolving as a result of the large-scale deployment of Rps genes in soybean. For a successful exploitation of Rps genes, it is recommended that soybean growers use cultivars containing the Rps genes corresponding to Avr genes present in P. sojae populations present in their fields. Determination of the virulence profile of P. sojae isolates is critical for the selection of soybean cultivars. High-resolution melting curve (HRM) analysis is a powerful tool, first applied in medicine, for detecting mutations with potential applications in different biological fields. Here, we report the development of an HRM protocol, as an original approach to discriminate effectors, to differentiate P. sojae haplotypes for six Avr genes. An HRM assay was performed on 24 P. sojae isolates with different haplotypes collected from soybean fields across Canada. The results clearly confirmed that the HRM assay discriminated different virulence genotypes. Moreover, the HRM assay was able to differentiate multiple haplotypes representing small allelic variations. HRM-based prediction was validated by phenotyping assays. This HRM assay provides a unique, cost-effective and efficient tool to predict virulence pathotypes associated with six different Avr (1b, 1c, 1d, 1k, 3a and 6) genes from P. sojae, which can be applied in the deployment of appropriate Rps genes in soybean fields.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".