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ARS Home » Northeast Area » Ithaca, New York » Robert W. Holley Center for Agriculture & Health » Emerging Pests and Pathogens Research » Research » Research Project #429748

Research Project: Development of Pre-Plant DNA Tests for the Prediction of Risk of Damage from Root-Knot Nematode in Potato

Location: Emerging Pests and Pathogens Research

Project Number: 8062-22000-022-04-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Sep 1, 2015
End Date: Dec 31, 2018

1) To develop quantitative real-time PCR tests to quantify the amount of DNA of RKN (M. hapla) affecting potato in a range of soil types, and 2) to use these tests to quantify the relationship between nematode DNA and tuber damage.

We will use ITS sequences as well as DNA sequences of effector genes (e.g., chorismate mutase gene and 16D10 gene) to develop a TaqMan qPCR assay for the identification of root-knot nematode (RKN) species. The developed tests will be used to assess the effectiveness of several methods for DNA extraction from soil samples across a range of soil types. Once effective DNA extraction methods are established, we will use the developed qPCR tests to determine the infestation levels of RKN in a given soil sample or field. To test the relationship between nematode DNA and tuber damage, three commercial fields within New York with a history of high populations of RKN (M. hapla) will be intensively sampled prior to planting potato. Soil will be taken along a diagonal transect in each of 30 plots (5 m2)/field. Subsamples will be taken for manual nematode extraction and greenhouse bioassay. A further subsample will be used for DNA testing. In the bioassay, 2 tomato seedlings will be placed as bait plants in soil collected from each location and after sufficient time for one generation of RKN (~ 6 weeks) roots will be examined for galls. Relationships between nematode populations (including DNA levels) and potato tuber damage within and across fields will be analyzed using receiver operating characteristic (ROC) curves to develop appropriate thresholds and evaluate predictive value.