Convergent Adaptation to Quantitative Host Resistance in a Major Plant Pathogen
Bibliographic record
Abstract
Plant pathogens can adapt to quantitative resistance, eroding its effectiveness. The aim of this work was to reveal the genomic basis of adaptation to such a resistance in populations of the fungus Pseudocercospora fijiensis, a major devastating pathogen of banana, by studying convergent adaptation on different cultivars. Samples from P. fijiensis populations showing a local adaptation pattern on new banana hybrids with quantitative resistance were compared, based on a genome scan approach, with samples from traditional and more susceptible cultivars in Cuba and the Dominican Republic. Whole-genome sequencing of pools of P. fijiensis isolates (pool-seq) sampled from three locations per country was conducted according to a paired population design. The findings of different combined analyses highly supported the existence of convergent adaptation on the study cultivars between locations within but not between countries. Five to six genomic regions involved in this adaptation were detected in each country. An annotation analysis and available biological data supported the hypothesis that some genes within the detected genomic regions may play a role in quantitative pathogenicity, including gene regulation. The results suggested that the genetic basis of fungal adaptation to quantitative plant resistance is at least oligogenic, while highlighting the existence of specific host-pathogen interactions for this kind of resistance.IMPORTANCE Understanding the genetic basis of pathogen adaptation to quantitative resistance in plants has a key role to play in establishing durable strategies for resistance deployment. In this context, a population genomic approach was developed for a major plant pathogen (the fungus Pseudocercospora fijiensis causing black leaf streak disease of banana) whereby samples from new resistant banana hybrids were compared with samples from more susceptible conventional cultivars in two countries. A total of 11 genomic regions for which there was strong evidence of selection by quantitative resistance were detected. An annotation analysis and available biological data supported the hypothesis that some of the genes within these regions may play a role in quantitative pathogenicity. These results suggested a polygenic basis of quantitative pathogenicity in this fungal pathogen and complex molecular plant-pathogen interactions in quantitative disease development involving several genes on both sides.
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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 | 0.000 |
| 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".