Polymorphism of Avirulence Genes and Adaptation to <i>Brassica</i> Resistance Genes Is Gene-Dependent in the Phytopathogenic Fungus <i>Leptosphaeria maculans</i>
Bibliographic record
Abstract
The fungal phytopathogen Leptosphaeria maculans, which causes stem canker (blackleg) of rapeseed ( Brassica napus), is mainly controlled worldwide by genetic resistance, which includes major resistance genes ( Rlm). This model is one of those for which the highest number of avirulence genes ( AvrLm) has been cloned. In many systems, including the L. maculans–B. napus interaction, intense use of resistance genes exerts strong selection pressure on the corresponding avirulent isolates, and the fungi may rapidly escape resistance through various molecular events which modify the avirulence genes. In the literature, the study of polymorphism at avirulence loci is often focused on single genes under selection pressure. In this study, we investigate allelic polymorphism at 11 avirulence loci in a French population of 89 L. maculans isolates collected on a trap cultivar in four geographic locations in the 2017−2018 cropping season. The corresponding Rlm genes have been (i) used for a long time, (ii) recently used, or (iii) unused in agricultural practice. The sequence data generated indicate an extreme diversity of situations. For example, genes submitted to an ancient selection may have either been deleted in populations ( AvrLm1) or replaced by a single-nucleotide mutated virulent version ( AvrLm2, AvrLm5-9). Genes that have never been under selection may either be nearly invariant ( AvrLm6, AvrLm10A, AvrLm10B), exhibit rare deletions ( AvrLm11, AvrLm14), or display a high diversity of alleles and isoforms ( AvrLmS-Lep2). These data suggest that the evolutionary trajectory of avirulence/virulence alleles is gene-dependent and independent of selection pressure in L. maculans. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
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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 | 0.000 |
| 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".