• The Plant Pathology Journal
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• v.31 no.2
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• pp.123-131
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• 2015

Clavibacter michiganensis subsp. sepedonicus (Cms) multiplies very rapidly, passing through the vascular strands and into the stems and petioles of a diseased potato. Therefore, the rapid and specific detection of this pathogen is highly important for the effective control of the pathogen. Although several PCR assays have been developed for detection, they cannot afford specific detection of Cms. Therefore, in this study, a computational genome analysis was performed to compare the sequenced genomes of the C. michiganensis subspecies and to identify an appropriate gene for the development of a subspecies-specific PCR primer set (Cms89F/R). The specificity of the primer set based on the putative phage-related protein was evaluated using genomic DNA from seven isolates of Cms and 27 other reference strains. The Cms89F/R primer set was more specific and sensitive than the existing assays in detecting Cms in in vitro using Cms cells and its genomic DNA. This assay was also able to detect at least $1.47{\times}10^2copies/{\mu}l$ of cloned-amplified target DNA, 5 fg of DNA using genomic DNA or $10^{-6}$ dilution point of 0.12 at $OD_{600}$ units of cells per reaction using a calibrated cell suspension.

• Korean Journal of Agricultural Science
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• v.39 no.1
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• pp.117-123
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• 2012

Pathogenic fungi and bacteria such as Pectobacterium atrosepticum, Clavibacter michiganensis subsp. sepedonicus, Verticillium albo-atrum, and Rhizoctonia solani were the major microorganism which causes diseases in seed potato during postharvest process. Current detection method for disease-infected seed potato relies on human inspection, which is subjective, inaccurate and labor-intensive method. In this study, a reflectance spectroscopy was used to classify sound and disease-infected seed potatoes with the spectral range from 400 to 1100 nm. Partial least square discriminant analysis (PLS-DA) with various preprocessing methods was used to investigate the feasibility of classification between sound and disease-infected seed potatoes. The classification accuracy was above 97 % for discriminating disease seed potatoes from sound ones. The results show that Vis/NIR reflectance method has good potential for non-destructive sorting for disease-infected seed potatoes.