Development of a Duplex qPCR System for Detection and Quantification of the Two Canola Blackleg Pathogens <i>Leptosphaeria biglobosa</i> and <i>L. maculans</i>
Bibliographic record
Abstract
Two probe-based qPCR systems, namely P-Lb and P-Lm, specific to the canola blackleg pathogens Leptosphaeria biglobosa and L. maculans, respectively, were developed, and their efficiencies were tested. Each of the two systems targets a single-copy gene exclusively present in the corresponding species. The specificities of the two systems on the species level and their ubiquities on the subspecies level were confirmed by in silico sequence analyses and testing on L. biglobosa (17 strains), L. maculans (10 strains), and other plant pathogens (31 species). For sensitivities, the two systems were tested on synthesized DNA fragments (gBlock) of the targeted regions, from which a standard curve was generated for each system. In addition, standard curves were also generated on gBlocks for duplex qPCR in which the two systems were used in the same reaction. The two systems were further tested in both singleplex and duplex qPCR on DNA samples extracted from fungal spores, inoculated canola cotyledons, and naturally infected canola stubble samples collected from commercial fields. Our data indicated that the two systems are specific to L. biglobosa and L. maculans, respectively, and one reaction could detect as few as 200 spores of either species. When used in duplex qPCR on DNA samples with various origins, the two systems generated similar results as in singleplex qPCR. The duplex qPCR system, along with the sample preparation and DNA extraction specified in this study, constituted a first-reported duplex qPCR protocol for detection and quantification of the two blackleg pathogens from field samples.
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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) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".