Parallel adaptation and admixture drive the evolution of virulence in the grapevine downy mildew pathogen
Bibliographic record
Abstract
Abstract Plasmopara viticola is a biotrophic oomycete responsible for grapevine downy mildew, one of the most destructive diseases in viticulture. Breeding for resistant varieties relies on the introgression of partial resistance factors from wild grapes, but virulent strains are rapidly emerging. To decipher the genetic bases of the adaptation to plant resistance in P. viticola , we carried out a QTL mapping study using two F1 populations segregating for the ability to overcome Rpv3 . 1, Rpv10 and Rpv12 . Trajectories of virulence emergence were also compared by conducting a population structure analysis on a panel of diversity. We confirmed the position of AvrRpv3 . 1 and identified the AvrRpv12 locus, in which strains overcoming Rpv12 presented large deletions encompassing several RXLR genes. Distinct virulent alleles were selected independently in different winegrowing regions. Unlike this standard case of recessive virulence, partial breakdown of Rpv10 was determined by a dominant locus, suggesting a suppressor activity. The virulent haplotype exhibits structural rearrangements and an extended effector repertoire. It corresponds to an admixed genomic segment likely originating from a secondary introduction of P. viticola into Europe. On top of the identification of candidate effectors, these results illustrate the range of evolutionary pathways through which plant pathogen populations can adapt to plant resistances.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".