Abstract
The Northern root-knot nematode (Meloidogyne hapla) is an important soilborne pathogen of numerous agricultural crops in temperate regions. Accurate detection and quantification is vital to supporting informed pest management decisions. However, traditional methods of manual nematode extraction and morphology-based identification are time-consuming and require highly specialized training. Molecular methods may expand the diagnostician's toolkit beyond those methods that rely on this disappearing specialized skillset. However, molecular assays targeting the internal transcribed spacer region may lead to inaccurate results because of intraspecific variability. The Meloidogyne spp. effector gene 16D10 was assessed as a target for a SYBR Green I quantitative PCR (qPCR) assay for detection and quantification of M. hapla. M. hapla-specific qPCR primers were developed and evaluated for specificity against five M. hapla isolates and 14 other plant-parasitic nematodes. A standard curve was generated by relating the quantification cycle (Cq) to the log of M. hapla population densities artificially introduced into soil. The influence of soil inhibitors on quantitative amplification was assessed by generating a dilution series from DNA extracted from pure nematode cultures and inoculated soil. Extracts from soil produced significantly higher Cq values than those produced from pure culture extracts. The utility of the qPCR was evaluated using soil samples collected from three naturally infested potato fields, resulting in a significant positive relationship between populations estimated using qPCR and populations derived from manual counting. The qPCR developed in this study provides a useful method for detecting and quantifying M. hapla in soil and demonstrates the utility of effector genes in plant-parasitic nematode diagnostics. The ability to use effector genes as targets for qPCR and other molecular detection and quantification methods may open additional avenues of novel research and support development of improved species-level diagnostics.
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