A New Multiplex TaqMan qPCR for Precise Detection and Quantification of <i>Clavibacter michiganensis</i> in Seeds and Plant Tissue
Bibliographic record
Abstract
ABSTRACT Bacterial canker of tomato caused by Clavibacter michiganensis ( Cm ) is one of the most devastating bacterial diseases affecting the tomato industry worldwide. As the result of Cm colonization of the xylem, the susceptible host shows typical symptoms of wilt, marginal leaf necrosis, stem cankers, and ultimately plant death. However, is the ability of Cm to infect seeds and plants without causing symptoms what makes it an even more dangerous pathogen. Unfortunately, there are no resistant cultivars or effective chemical or biological control methods available to growers against Cm . Its control relies heavily on prevention. The implementation of a rapid and accurate detection tool is imperative to monitor the presence of Cm and prevent its spread. In this study, we developed a specific and sensitive multiplex TaqMan qPCR assay to detect Cm and distinguish it from related bacterial species that affect tomato plants. Two Cm chromosomal virulence-related genes, rhu M and tom A, were used as specific targets. The plant internal control tubulin alpha-3 was included in each of the multiplexes to improve the reliability of the assay. Specificity was evaluated with 37 bacterial strains and more than 120 samples, including other Clavibacter spp. and related and unrelated bacterial pathogens from different geographic locations affecting a wide variety of hosts. Results showed that the assay was able to screen all Cm strains against other related bacteria. The assay was validated on tissue and seed samples following artificial infection and all tested samples accurately detected the presence of Cm . The tool described here is highly specific, sensitive, and reliable for the detection of Cm and allows the quantification of Cm in seeds, roots, stems, and leaves, finding a lower abundance of Cm in the roots compared to the other parts of the plant. The diagnostic assay can also be adapted for multiple purposes such as seed certification programs, surveillance, biosafety, the effectiveness of control methods, border protection, and epidemiological studies.
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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 | 0.001 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".