Stable predictive markers for Phytophthora sojae avirulence genes that impair infection of soybean uncovered by whole genome sequencing of 31 isolates
Bibliographic record
Abstract
BACKGROUND: The interaction between oomycete plant pathogen Phytophthora sojae and soybean is characterized by the presence of avirulence (Avr) genes in P. sojae, which encode for effectors that trigger immune responses and resistance in soybean via corresponding resistance genes (Rps). A recent survey highlighted a rapid diversification of P. sojae Avr genes in soybean fields and the need to deploy new Rps genes. However, the full genetic diversity of P. sojae isolates remains complex and dynamic and is mostly characterized on the basis of phenotypic associations with differential soybean lines. RESULTS: We sequenced the genomes of 31 isolates of P. sojae, representing a large spectrum of the pathotypes found in soybean fields, and compared all the genetic variations associated with seven Avr genes (1a, 1b, 1c, 1d, 1k, 3a, 6) and how the derived haplotypes matched reported phenotypes in 217 interactions. We discovered new variants, copy number variations and some discrepancies with the virulence of previously described isolates with Avr genes, notably with Avr1b and Avr1c. In addition, genomic signatures revealed 11.5% potentially erroneous phenotypes. When these interactions were re-phenotyped, and the Avr genes re-sequenced over time and analyzed for expression, our results showed that genomic signatures alone accurately predicted 99.5% of the interactions. CONCLUSIONS: This comprehensive genomic analysis of seven Avr genes of P. sojae in a population of 31 isolates highlights that genomic signatures can be used as accurate predictors of phenotypes for compatibility with Rps genes in soybean. Our findings also show that spontaneous mutations, often speculated as a source of aberrant phenotypes, did not occur within the confines of our experiments and further suggest that epigenesis or gene silencing do not account alone for previous discordance between genotypes and phenotypes. Furthermore, on the basis of newly identified virulence patterns within Avr1c, our results offer an explanation why Rps1c has failed more rapidly in the field than the reported information on virulence pathotypes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".