Detection and quantification of airborne spores from six important wheat fungal pathogens in southern Alberta

Wheat is affected by many fungal diseases that can cause severe yield and quality losses. Disease prediction models generally employ weather data to estimate potential for infection to determine timing for fungicide applications, but these models fail to account for the presence and quantity of pathogen inoculum. This study adapted highly specific qPCR primers to identify and quantify, in real-time, inoculum present in air for the six most important wheat pathogens in Canada. Fungal spores were collected using either Burkard spore collectors and quantified using qPCR or microscope slides covered with adhesive tape and identified and quantified using microscopy. Samples were collected from seven different sites in southern Alberta throughout the 2015-2017 growing seasons. The results demonstrated that qPCR can reliably identify and quantify spores from Puccinia striiformis f. sp. tritici, P. triticina, P. graminis f. sp. tritici, Blumeria graminis f. sp. tritici, Pyrenophora tritici-repentis, and Fusarium graminearum. The limits of detection of DNA for primer pairs in singleplex tests ranged from 0.0001 ng for P. graminis to 0.001 ng for P. tritici-repentis, which corresponded to approximately 3 spores for P. tritici-repentis and F. graminearum and 1 spore for the other pathogens. Conversely, microscopy permitted identification of rusts to the genus but not to the species level and was ineffective in quantification of the remainder of the wheat pathogens. This study will contribute to the development of a fast and reliable forecasting system that will enable identification and quantification of airborne pathogens in real-time before initial disease symptoms appear.

Automated summary

This study adapted qPCR primers to identify and quantify wheat pathogens in air, demonstrating the reliability of qPCR in detecting spores and the limitations of microscopy in species-level identification. This research will contribute to developing a fast and reliable forecasting system for real-time identification and quantification of airborne pathogens.