A proteomic evaluation of <b><i>Pyrenophora tritici‐repentis</i></b>, causal agent of tan spot of wheat, reveals major differences between virulent and avirulent isolates
Bibliographic record
Abstract
Pyrenophora tritici-repentis causes tan spot, an important foliar disease of wheat. The fungus produces multiple host-specific toxins, including Ptr ToxB, a chlorosis-inducing protein encoded by the ToxB gene. A homolog of ToxB is also found in avirulent isolates of the fungus. In order to improve understanding of the role of this homolog and evaluate the general pathogenic ability of P. tritici-repentis, we compared the proteomes of avirulent race 4 and virulent race 5 isolates of the pathogen. Western blotting analysis revealed the presence of Ptr ToxB in spore germination and culture fluids of race 5 but not race 4. A comprehensive proteome-level comparison by 2-DE indicated 133 differentially abundant proteins in the secretome (29 proteins) and mycelium (104 proteins) of races 4 and 5, of which 63 were identified by MS/MS. A number of the proteins found to be up-regulated in race 5 have been implicated in microbial virulence in other pathosystems, and included the secreted enzymes alpha-mannosidase and exo-beta-1,3-glucanase, heat-shock and BiP proteins, and various metabolic enzymes. These proteome-level differences suggest a reduced general pathogenic ability in race 4 of P. tritici-repentis, irrespective of toxin production. Such differences may reflect an adaptation to a saprophytic habit.
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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".