A qPCR assay for the detection of <i>Phytophthora abietivora</i> , an emerging pathogen on fir species cultivated as Christmas trees
Bibliographic record
Abstract
Abstract Emerging species of the Phytophthora genus are among the most important threats to global plant biodiversity. For instance, Phytophthora root rot (PRR) of Christmas trees is responsible for 10% of the observed mortality rate in nurseries. Diagnosis of PRR involves isolation followed by morphological and molecular identification of the causal agents. However, these methods are rarely adapted to larger scale experiments such as in situ detection. For these applications, molecular detection of environmental DNA (eDNA) provides the high-throughput and the fast result generation needed. Phytophthora abietivora was associated to PRR in firs cultivated as Christmas trees in the province of Québec (Canada). This study focused on developing a sensitive and specific qPCR assay targeting P. abietivora and validating its efficiency on eDNA samples. A set of primers and probe was designed for this assay, and parameters such as the limit of detection (LoD 95% ) and limit of quantification (LoQ) were measured. The assay was tested on eDNA obtained from healthy-looking and PRR symptomatic firs. The assay was shown to be semi-specific because it cross-reacted with P. abietivora, and four phylogenetically close species unrelated to fir diseases. The limit of detection (LOD 95% ) was estimated at 10 copies per reaction (C q of 35.7). The assay showed reliable detection down to 33 P. abietivora oospores per gram of soil. Out of 488 eDNA samples from soil, 68 tested positive for P. abietivora. While factors such as the tree species, the sampled region or the year of sampling did not affect the proportion of positive results, samples from trees showing PRR-like symptoms had significantly higher odds of testing positive compared to healthy-looking trees. This assay will be useful for rapid diagnostics of P. abietivora infected trees and as a prospecting tool to better characterize the natural distribution and dissemination of the disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".