Characterization of <i>Pyrenophora tritici-repentis</i> in Tunisia and Comparison with a Global Pathogen Population
Bibliographic record
Abstract
Pyrenophora tritici-repentis causes tan spot, an important foliar disease of wheat. A collection of P. tritici-repentis isolates from Tunisia, located in one of the main secondary centers of diversification of durum wheat, was tested for phenotypic race classification based on virulence on a host differential set and for the presence of the necrotrophic effector (NE) genes ToxA, ToxB, and toxb by PCR analysis. While races 2, 4, 5, 6, 7, and 8 were identified according to their virulence phenotypes, PCR testing indicated the presence of “atypical” isolates that induced necrosis on the wheat differential ‘Glenlea,’ but lacked the expected ToxA gene, suggesting the involvement of other NEs in the P. tritici-repentis/wheat interaction. Genetic diversity and the P. tritici-repentis population structure were explored further by examining 59 Tunisian isolates and 35 isolates from Algeria, Azerbaijan, Canada, Iran, and Syria using 24 simple sequence repeat markers. Average genetic diversity, overall gene flow, and percentage polymorphic loci were estimated as 0.58, 2.09, and 87%, respectively. Analysis of molecular variance showed that 81% of the genetic variance occurred within populations and 19% occurred between populations. Cluster analysis by the unweighted pair group method indicated that ToxB– isolates grouped together and were distantly related to ToxB+ isolates. Based on Nei’s analysis, the global collection clustered into two distinct groups according to their region of origin. The results suggest that geographic origin and the host specificity imposed by different NEs can lead to differentiation among P. tritici-repentis populations.
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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 | 0.001 |
| 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".