Protocol: rhPCR and SNaPshot assays to distinguish Plasmodiophora brassicae pathotype clusters
Bibliographic record
Abstract
BACKGROUND: Clubroot of canola (Brassica napus), caused by the soilborne pathogen Plasmodiophora brassicae, has become a serious threat to canola production in Canada. The deployment of clubroot-resistant (CR) cultivars is the most commonly used management strategy; however, the widespread cultivation of CR canola has resulted in the emergence of new pathotypes of P. brassicae capable of overcoming resistance. Several host differential sets have been reported for pathotype identification, but such testing is time-consuming, labor-intensive, and based on phenotypic classifications. The development of rapid and objective methods that allow for efficient, cost-effective and convenient pathotyping would enable testing of a much larger number of samples in shorter times. The aim of this study was to develop two pathotyping assays, an RNase H2-dependent PCR (rhPCR) assay and a SNaPshot assay, which could quickly differentiate P. brassicae pathotypes. RESULTS: Both assays clearly distinguished between pathotype clusters in a collection of 38 single-spore isolates of P. brassicae. Additional isolates pathotyped from clubbed roots and samples from blind testing also were correctly clustered. The rhPCR assay generated clearly differentiating electrophoretic bands without non-specific amplification. The SNaPshot assay was able to detect down to a 10% relative allelic proportion in a 10:90 template mixture with both single-spore isolates and field isolates when evaluated in a relative abundance test. CONCLUSIONS: This study describes the development of two rapid and sensitive technologies for P. brassicae pathotyping. The high-throughput potential and accuracy of both assays makes them promising as SNP-based pathotype identification tools for clubroot diagnostics. rhPCR is a highly sensitive approach that can be optimized into a quantitative assay, while the main advantages of SNaPshot are its ability to multiplex samples and alleles in a single reaction and the detection of up to four allelic variants per target site.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.022 |
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".