Xylella fastidiosa inoculation behaviors (EPG X wave) are performed differently by blue-green sharpshooters based on infection status of prior probing host

Abstract

Does Xylella fastidiosa, a bacterial plant pathogen with noncirculative foregut-borne transmission, manipulate behavior of its sharpshooter vector to facilitate its own inoculation? To answer this question, blue-green sharpshooters, Graphocephala atropunctata (Signoret), were reared on basil to clean their foreguts, then removed from the colony and given one of four pre-electropenetrography (EPG) treatments: i) old colony adults on basil, ii) young colony adults on basil, iii) young colony adults held on healthy grapevine for 4 days, and iv) young colony adults held on Xf-infected (symptomatic) grapevine for 4 days. After treatments, stylet probing behaviors were recorded on healthy grapevine via AC-DC electropenetrography. Waveforms representing putative Xf inoculation (XB1 [salivation and rinsing egestion] and XC1 [discharging egestion]) and other behaviors were statistically compared among treatments. Mean number of events per insect and ‘total’ duration per insect of XB1 and XC1 were highest for insects from healthy grape, lowest for basil (regardless of insect age), and intermediate for Xf-infected grape. The surprising results showed that prior exposure to healthy grapevines had a stronger effect on subsequent performance of inoculation behaviors on healthy grapevine than did prior exposure to Xf-infected grapevine. It is hypothesized that non-Xf microbes were acquired from healthy grapevine, causing greater clogging of the precibarium, leading to more performance of inoculation behaviors. This study shows for the first time that presence of noncirculative, foregut-borne microbes can directly manipulate a vector’s behavior to increase inoculation. Also, EPG can uniquely visualize the dynamic interactions between vectors and the microbes they carry.

Novel program for automatic calculation of EPG variables

“…The program is able to calculate up to 121 sequential and non-sequential variables and also identifies common errors in the data file’s waveform sequence, such as a waveform other than C following np, or an E2 that is not preceded by E1. In the present work, we describe our NPAC-EPGv, which imports data from Stylet+ and Windaq Pro+, which are the 2 most common programs currently used for EPG data acquisition (e.g., Roddee et al 2023 , Zippari et al 2024 ). NPAC-EPGv calculates up to 127 EPG variables and identifies common errors that often occur when marking EPG data files.…”