Detection of the fire blight pathogen Erwinia amylovora from beeswith molecular tools as part of an alternative forecasting method

Abstract: Fire blight is considered the bacterial disease with the greatest economic impact inpome fruit production. The causal agent Erwinia amylovora can be dispersed by wind, rain,inadequate cultural measures and arthropod vectors e.g. honeybees (Apis mellifera). Honeybeesas main pollinators in fruit crops spread the pathogen while collecting nectar and pollen frominfected blossoms and could thus serve as early indicators for the presence of the bacterium inorchards. With regard to the expected global climate change, the infection conditions for E.amylovora could be favoured. Existing abiotic based fire blight forecasting systems would needadaptation, and monitoring of the pathogen’s presence on major vectors with molecular toolscould significantly improve precast of E. amylovora infections. As part of a master thesis,molecular tools for the detection of E. amylovora in honeybees were developed and tested. For areliable detection of the pathogen in honeybees with PCR a specific purification of the bacterialDNA is necessary. An existing method for DNA extraction of microorganisms was adapted forhoneybees. The sensitivity of the method could be proved by qualitative PCR in dilution series ofthe pathogen artificially injected in the vector. In addition, bees caught on pome fruit productionsites during the growing season were investigated with the established method. To quantify E.amylovora in honeybees and to determine the amount of bacteria on a single honeybee a real timePCR method was used. This information about bacterial quantity is considered crucial to monitorthe infection pressure on the flowering pome trees. In order to clarify the exact position of thepathogen on the vector individual parts of the bees were investigated for presence and quantity ofE. amylovora. It could be demonstrated that the developed methods allow both, a rapid and asensitive detection of E. amylovora directly from bees and that variable quantities of bacteria ondifferent parts of the vector occur. It is proposed that this newly developed system could beapplied to increase accuracy in infection prognosis of E. amylovora in orchards.