RESEARCH ARTICLE A TaqMan-Based Multiplex qPCR Assay and DNA Extraction Method for Phylotype IIB Sequevars 1&2 (Select Agent) Strains of
Bibliographic record
Abstract
Ralstonia solanacearum race 3 biovar 2 strains belonging to phylotype IIB, sequevars 1 and 2 (IIB-1&2) cause brown rot of potato in temperate climates, and are quarantined pathogens in Canada and Europe. Since these strains are not established in the U.S. and because of their potential risk to the potato industry, the U.S. government has listed them as select agents. Cultivated geraniums are also a host and have the potential to spread the pathogen through trade, and its extracts strongly inhibits DNA-based detection methods. We designed four primer and probe sets for an improved qPCR method that targets stable regions of DNA. RsSA1 and RsSA2 recognize IIB-1&2 strains, RsII recognizes the current phylotype II (the newly proposed R. solanacearum species) strains (and a non-plant associ-ated R.mannitolilytica), and Cox1 recognizes eight plant species including major hosts of R. solanacearum such as potato, tomato and cultivated geranium as an internal plant con-trol. We multiplexed the RsSA2 with the RsII and Cox1 sets to provide two layers of detec-tion of a positive IIB-1&2 sample, and to validate plant extracts and qPCR reactions. The TaqMan-based uniplex and multiplex qPCR assays correctly identified 34 IIB-1&2 and 52 phylotype II strains out of 90R. solanacearum species complex strains. Additionally, the multiplex qPCR assay was validated successfully using 169 artificially inoculated symptomatic and asymptomatic plant samples frommultiple plant hosts including geranium. Furthermore, we developed an extraction buffer that allowed for a quick and easy DNA extraction from infected plants including geranium for detection of R. solanacearum by qPCR. Our multiplex qPCR assay, especially when coupled with the quick extraction buffer method, allows for quick, easy and reliable detection and differentiation of the IIB-1&2 strains of R. solanacearum.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".