Development and Application of qPCR and RPA Genus- and Species-Specific Detection of <i>Phytophthora sojae</i> and <i>P. sansomeana</i> Root Rot Pathogens of Soybean
Bibliographic record
Abstract
Phytophthora root rot of soybean, caused by Phytophthora sojae, is one of the most important diseases in the Midwestern United States, and is estimated to cause losses of up to 1.2 million metric tons per year. Disease may also be caused by P. sansomeana; however, the prevalence and damage caused by this species is not well known, partly due to limitations of current diagnostic tools. Efficient, accurate, and sensitive detection of pathogens is crucial for management. Thus, multiplex qPCR and isothermal RPA (recombinase polymerase amplification) assays were developed using a hierarchical approach to detect these Phytophthora spp. The assays consist of a genus-specific probe and two species-specific probes that target the atp9-nad9 region of the mitochondrial genome that is highly specific for the genus Phytophthora. The qPCR approach multiplexes the three probes and a plant internal control. The RPA assays run each probe independently with a plant internal control multiplexed in one amplification, obtaining a result in as little as 20 mins. The multicopy mitochondrial genome provides sensitivity with sufficient variability to discern among different Phytophthora spp. The assays were highly specific when tested against a panel of 100 Phytophthora taxa and range of Pythium spp. The consistent detection level of the assay was 100 fg for the qPCR assay and 10 pg for the RPA assay. The assays were validated on symptomatic plants collected from Michigan (U.S.) and Ontario (Canada) during the 2013 field season, showing correlation with isolation. In 2014, the assays were validated with samples from nine soybean producing states in the U.S. The assays are valuable diagnostic tools for detection of Phytophthora spp. affecting soybean.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".