Quantification of <i>Plasmodiophora brassicae</i> Resting Spores in Soils Using Droplet Digital PCR (ddPCR)
Bibliographic record
Abstract
Plasmodiophora brassicae, an obligate soilborne pathogen that causes clubroot on Brassica crops, is spreading rapidly in western Canada, threatening canola production in the region. Bioassays and molecular assays have been used to estimate the concentration of P. brassicae resting spores in soil, which can affect clubroot incidence and severity on crops. Droplet digital PCR (ddPCR) is a promising new approach for quantification of pathogen inoculum owing to its low sensitivity to inhibitors and consistency at low target concentrations. The objective of this study was to assess ddPCR against existing quantitative PCR (qPCR) for potential advantage and/or improvement in quantifying P. brassicae resting spores in soil. The new protocol enumerated resting spores accurately in spiked potting mix or soil samples ranging from 10 2 to 10 7 spores per gram. At a spore concentration ≥10 7 spores per gram, however, ddPCR became less accurate, with a tendency of overestimation. The protocol was validated by quantifying the resting spores in spiked brown, dark brown, and black soils using both ddPCR and qPCR simultaneously. These soil types are found commonly on the Canadian Prairies, and they vary in texture, pH, and organic content. ddPCR showed similar results among the different soil types, whereas qPCR often displayed lower counts for the same spore concentration, with the amplification of DNA inhibited completely in black soil samples. The inhibition can be removed by a 10-fold dilution of DNA samples. The results show that ddPCR can be a more versatile tool than qPCR for detection and quantification of P. brassicae resting spores in soil samples.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".