A multiplex PCR assay for the detection and quantification of <i>Sclerotinia sclerotiorum</i> and <i>Botrytis cinerea</i>
Bibliographic record
Abstract
UNLABELLED: Traditional culture methods for identifying the plant fungal pathogens Sclerotinia sclerotiorum (Lib.) de Bary and Botrytis cinerea Pers.:Fr. are slow and laborious. The goal of this study was to develop a multiplex real-time PCR (qPCR) assay to detect and quantify DNA from S. sclerotiorum and B. cinerea. A primer set (SsIGS_5) for S. sclerotiorum was designed that targeted the intergenic spacer (IGS) regions of the ribosomal DNA. Addition of a probe to the assay increased its specificity: when the primer/probe set was tested against 21 fungal species (35 strains), amplification was detected from all S. sclerotiorum strains and no other species. For qPCR, the SsIGS_5 primer and probe set exhibited a linear range from 7·0 ng to 0·07 pg target DNA (R(2) = 0·99). SsIGS_5 was then multiplexed with a previously published primer/probe set for B. cinerea to develop a high-throughput method for the detection and quantification of DNA from both pathogens. When multiplexed, the sensitivity and specificity of both assays were not different from individual qPCR reactions. The multiplex assay is currently being used to detect and quantify S. sclerotiorum and B. cinerea DNA from aerosol samples collected in commercial seed alfalfa fields. SIGNIFICANCE AND IMPACT OF THE STUDY: A primer and probe set for the quantification of Sclerotinia sclerotiorum DNA in a PCR assay was developed. The probe-based nature of this assay signifies an improvement over previous assays for this species by allowing multiplex reactions while maintaining high sensitivity. The primer/probe set was used in a multiplex real-time PCR assay for the quantification of S. sclerotiorum and Botrytis cinerea DNA, enabling rapid analysis of environmental samples. In crops susceptible to both pathogens, this multiplex assay can be used to quickly quantify the presence of each pathogen.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.003 |
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".