Bayesian vector transmission model detects conflicting interactions from transgenic disease‐resistant grapevines

Abstract

Effective management of vector‐borne plant pathogens often relies on disease‐resistant cultivars. While heterogeneity in host resistance and in pathogen population density at the host population level plays important and well‐recognized roles in epidemiology, the effects of resistance traits on pathogen distribution at the individual host level, and the epidemiological consequences in turn, are poorly understood. Transgenic disease‐resistant plants that produce bacterial diffusible signaling factor (DSF) could provide resistance to the vector‐borne bacterium Xylella fastidiosa by impeding plant colonization and reducing virulence. However, the effects of constitutive in planta production of DSF on insect vector transmission have remained unresolved. We investigated the transmission biology of X. fastidiosa in DSF and wild‐type (WT) grapevines with the efficient vector Graphocephala atropunctata. We also developed a novel Bayesian hierarchical model to improve statistical inference on the multiple components of the vector transmission process. We found that insect vectors had a greater colonization efficiency on DSF plants—meaning they acquired a greater population size of X. fastidiosa—than on WT plants. However, DSF plants also maintained much lower X. fastidiosa populations. These apparently conflicting processes resulted in a lower but highly variable probability of transmission from DSF plants compared to WT plants. Our Bayesian model improved statistical inference compared to widely used frequentist statistics in part by estimating and correcting for imperfect detection of X. fastidiosa in plant and insect tissues. Overall, our results support current models on the roles that DSF plays in vector transmission of X. fastidiosa. In line with our hypothesis, DSF production reduced mean X. fastidiosa population density but increased heterogeneity within host plants. While DSF‐producing plants could potentially improve disease management, our results suggest that they could, under some conditions, facilitate X. fastidiosa spread.

Bacterial Vector-Borne Plant Diseases: Unanswered Questions and Future Directions

“…In the case of X. fastidiosa , it is clear that time after infection is key to determining vector behavior, ultimately driving a decrease in pathogen transmission: plants become better sources of inoculum, but that is offset by vector discrimination against infected and symptomatic plants ( Daugherty et al, 2017 ). In addition, experimental and modeling work has revealed distinct transmission dynamics in tolerant and susceptible plants ( Zeilinger et al, 2018 ). Studying how time after plant infection with a pathogen affects vector behavior and the design of experiments with mathematical models in mind should provide a more holistic understanding of disease dynamics and translate results more effectively to field situations.…”