Back

Annual dynamics and distribution of Xylella fastidiosa in infected almond trees

Zecharia, N.; Vanunu, M.; Dror, O.; Hatib, K.; Holland, D.; Shtienberg, D.; Bahar, O.

2023-07-19 plant biology
10.1101/2023.07.17.549336 bioRxiv
Show abstract

This research focused on studying the dynamics of the bacterial pathogen Xylella fastidiosa in almond trees at different developmental stages and in various tree parts. The objective was to understand the annual distribution and concentration of X. fastidiosa within almond trees. Different tree parts, including leaf petioles, annual and perennial shoots, fruit parts, flowers, and roots, from ten X. fastidiosa-infected almond trees were sampled over two years. The distribution and concentration of X. fastidiosa were determined using qPCR and serial dilution plating. Throughout the study, X. fastidiosa was never found in the fruit, flowers, and roots of almond trees, but it was present in leaves and annual and perennial shoots. We show that the inability of X. fastidiosa to colonize roots is likely due to incompatibility with the GF677 rootstock. The presence of X. fastidiosa in shoots remained consistent throughout the year, while in leaf petioles it varied across developmental stages, with lower detection during early and late stages of the season. Similarly, viable X. fastidiosa cells could be isolated from shoots at all developmental stages, while in leaf petioles no successful isolations were achieved during the vegetative and nut growth stage. Examining the development of almond leaf scorch symptoms over time in trees with preliminary infections revealed that once symptoms have appeared on a single branch, other asymptomatic limbs were likely already colonized by the bacterium, hence, selective pruning of symptomatic branches is unlikely to cure the tree. Overall, this study enhances our understanding of X. fastidiosa dynamics in almonds and may have practical applications for its detection and control in almond orchards.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.