Enhancing the UK diagnostic capabilities for Xylella fastidiosa

Reliable and accurate diagnostic tools are critical to enable effective response to Xylella fastidiosa in the event of an outbreak. Current testing procedures including different real-time PCR assays are being validated and optimised for use by accredited laboratories in the UK. Informal DNA based proficiency tests for real time PCR assays completed by four institutions gave consistent results. Three host species (Lavandula dentata, Nerium oleander, Coffea arabica) were inoculated with three subspecies of Xylella fastidiosa and kept in two different glasshouse environments (25°C and ambient temperature) where rates of colonisation and symptom expression were monitored to inform future sampling strategies. Symptoms that could be due to Xylella infections were not always associated with positive qPCR samples; oleander and coffee plants were successfully infected and, although most positive samples were close to inoculation points, some oleander samples taken up to 10cm away from the points were also positive showing that the infection had progressed. Other approaches, such as detection of volatile organic compounds (VOCs), are being assessed to target sampling. A proof of principle study using coffee plants showed that a large amount of VOCs can be retained on Mono Traps (a portable sorptive solid media) and a number of compounds were different between control and Xylella infected plants. Methods for identifying subspecies and strains are being developed to enable tracing of infection sources. Concatenated MLSA sequences were collected for all publicly available genomes of X. fastidiosa and trees built to visualise relationships between clonal complexes. Using public sequence data of over 200 genomes, a species-level cgMLST scheme was created for X. fastidiosa as well as schemes for three individual subspecies. As well as application to whole genomes, the resolution provided by the cgMLST approach has also been shown to be informative when applied to sparser data from infected plant metagenomes.GB; PDF; joana.vicente@fera.co.u