Two Xylella fastidiosa subsp. multiplex Strains Isolated from Almond in Spain Differ in Plasmid Content and Virulence Traits.

The plant-pathogenic bacterium Xylella fastidiosa is a major threat to agriculture and the environment worldwide. Recent devastating outbreaks in Europe highlight the potential of this pathogen to cause emergent diseases. X. fastidiosa subsp. multiplex ESVL and IVIA5901 strains that belong to sequence type 6 were isolated from almond orchards within the outbreak area in Alicante province (Spain). Both strains share more than 99% of the chromosomal sequences (average nucleotide identity), but the ESVL strain harbors two plasmids (pXF64-Hb_ESVL and pUCLA-ESVL). Here, virulence phenotypes and genome content were compared between both strains, using three strains from the United States as a reference for the phenotypic analyses. Experiments in microfluidic chambers, used as a simulation of xylem vessels, showed that twitching motility was absent in the IVIA5901 strain, whereas the ESVL strain had reduced twitching motility. In general, both Spanish strains had less biofilm formation, less cell aggregation, and lower virulence in tobacco compared with U.S. reference strains. Genome analysis of the two plasmids from ESVL revealed 51 unique coding sequences that were absent in the chromosome of IVIA5901. Comparison of the chromosomes of both strains showed some unique coding sequences and single-nucleotide polymorphisms in each strain, with potential deleterious mutations. Genomic differences found in genes previously associated with adhesion and motility might explain the differences in the phenotypic traits studied. Although additional studies are necessary to infer the potential role of X. fastidiosa plasmids, our results indicate that the presence of plasmids should be considered in the study of the mechanisms of pathogenicity and adaptation in X. fastidiosa to new environments. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits.

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64-65% and kappa = 0.26-31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.

Divergent abiotic spectral pathways unravel pathogen stress signals across species.

Plant pathogens pose increasing threats to global food security, causing yield losses that exceed 30% in food-deficit regions. Xylella fastidiosa (Xf) represents the major transboundary plant pest and one of the world's most damaging pathogens in terms of socioeconomic impact. Spectral screening methods are critical to detect non-visual symptoms of early infection and prevent spread. However, the subtle pathogen-induced physiological alterations that are spectrally detectable are entangled with the dynamics of abiotic stresses. Here, using airborne spectroscopy and thermal scanning of areas covering more than one million trees of different species, infections and water stress levels, we reveal the existence of divergent pathogen- and host-specific spectral pathways that can disentangle biotic-induced symptoms. We demonstrate that uncoupling this biotic-abiotic spectral dynamics diminishes the uncertainty in the Xf detection to below 6% across different hosts. Assessing these deviating pathways against another harmful vascular pathogen that produces analogous symptoms, Verticillium dahliae, the divergent routes remained pathogen- and host-specific, revealing detection accuracies exceeding 92% across pathosystems. These urgently needed hyperspectral methods advance early detection of devastating pathogens to reduce the billions in crop losses worldwide.