A pyrosequencing‐based method to quantify genetic substitutions associated with resistance to succinate dehydrogenase inhibitor fungicides in <i>Botrytis</i> spp. populations
Bibliographic record
Abstract
BACKGROUND: The genetic underlying resistance mechanisms in the population of the phytopathogenic fungus Botrytis cinerea are well documented. Specifically, several genetic substitutions associated with succinate dehydrogenase inhibitor (SDHI)-based fungicide resistance have been identified in the succinate dehydrogenase gene. The objective of the present work was to develop a molecular tool for accurate quantification of these genetic substitutions within Botrytis populations. A test using the PyroMark Q24 instrument was designed to detect and quantify five genetic substitutions associated with SDHI resistance. RESULTS: The technique is based on sequencing by synthesis, and it generated quantitative and accurate data with a limit of quantification of a minimum of 500 spores. There was a linear relationship between the known and estimated percentages of spores with the targeted genetic substitutions and wild-type strains at ratios of 0-100%, with a 20% increment. CONCLUSION: With the pyrosequencing assay developed in this study, a large number of Botrytis spp. individuals can be characterised in a timely fashion with greater accuracy than by commonly used methods. Hence, pyrosequencing-based methods will be useful for improving our understanding of fungicide resistance, detecting the arrival of new genetic substitutions, monitoring shifts in fungal populations and assessing the effectiveness of antiresistance strategies, and for routine monitoring of fungicide resistance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".