Development of a Triplex Quantitative PCR System for the Detection of <i>Parastagonospora nodorum</i> , <i>Zymoseptoria tritici</i> , and <i>Pyrenophora tritici-repentis</i> from Wheat
Bibliographic record
Abstract
A triplex quantitative PCR (qPCR) system was developed for the simultaneous detection of the three most prevalent wheat leaf spot diseases: Septoria nodorum blotch caused by Parastagonospora nodorum, Septoria tritici blotch caused by Zymoseptoria tritici, and tan spot caused by Pyrenophora tritici-repentis. In this system, the primer set for P. tritici-repentis targets a species-specific multicopy genomic region, whereas the primer sets for the other two pathogens target the ribosomal DNA (rDNA) region. The specificity of the system was validated through sequence analysis using the currently available database and by testing against 24 DNA samples from nontarget species. Sensitivity testing on serial DNA dilutions from the three target species demonstrated that the system can detect as little as 2 fg of DNA of each species in a 20-μl reaction. For P. nodorum, the system was capable of detecting DNA extracted from a conidia suspension containing as few as 100 conidia. The system was further evaluated on 145 wheat leaf samples (45 symptomatic and 100 asymptomatic) collected from various fields in Alberta, Canada. At least one of the three pathogens was detected in 112 out of the 145 samples, with P. nodorum and/or P. tritici-repentis identified in 74 of the 100 asymptomatic samples. This triplex qPCR system offers a powerful tool for the diagnosis of wheat leaf spot diseases, surveillance, breeding for disease resistance, and research in epidemiology, population genetics, and host-pathogen interactions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".