Louis Backmann,* Katharina Schmidtmann, Pascal Wegmann-Herr, Andreas Jürgens, and Maren Scharfenberger-Schmeer
*Institute for Viticulture and Oenology, Dienstleistungszentrum Ländlicher Raum (DLR) Rheinpfalz, Breitenweg 71, Neustadt, 67435, Germany (louis.backmann@dlr.rlp.de)

The fungal pathogen Botrytis cinerea has a tremendous impact on many crops, including winegrapes. It causes specific off-flavors, brownish color, and poor filtrability in must and wine, and can lead to total loss of harvest. Climate change and development of strains resistant to fungicides make it even more challenging to control the disease. To adapt to those problems, it is important to find and develop new methods to detect Botrytis and differentiate strains. Some of these methods are strain differentiation, classification by simple sequence repeats (SSRs), and early detection of the fungus by qPCR. In this ongoing study, strains from different regions, years, and grape varieties were analyzed using SSR markers and evaluated using either agarose gel electrophoresis or capillary sequencer via PCR. Furthermore, a sensitive qPCR method was refined to achieve early detection of the pathogen. In addition, cross-contamination with other grape pathogens, Penicillium expansum, Trichothecium roseum, and Cladosporium spp., was tested to exclude false quantification of the biomass. The qPCR method was also tested with different B. cinerea strains, to test for potential quantification differences between strains. The results demonstrate promising ways to distinguish between strains using both agarose gel electrophoresis and capillary sequencing, as well as to detect an infection with qPCR before it becomes visible on grapes. Cross contamination could be excluded with the tested pathogens. The different Botrytis strains tested in qPCR had no significant affect on quantification of the biomass. The results can be used to further understand and analyze different B. cinerea strain characteristics, such as laccase activity, regional, or annual effects. The early detection method can be used to better prepare growers for an impending infection so that targeted efforts can be made.

Funding Support: Forschungskreis der Ernährungsindustrie E.V. (FEI)