Método para la detección, identificación y cuantificación de Peronospora arborescens por PCR cuantitativa en tiempo real

Método para la detección, identificación y cuantificación de Peronospora arborescens por PCR cuantitativa en tiempo real. El método para la cuantificación de Peronospora arborescens por PCR cuantitativa (qPCR) en una muestra biológica, comprende extraer el ADN contenido en dicha muestra biológica y amplificarlo mediante qPCR. De aplicación en la cuantificación de P. arborescens.Peer reviewedConsejo Superior de Investigaciones Científicas (España), ALCALIBER SA, Universidad de CórdobaA1 Solicitud de patentes con informe sobre el estado de la técnic

Combined use of a new SNP-based assay and multilocus SSR markers to assess genetic diversity of Xylella fastidiosa subsp. pauca infecting citrus and coffee plants

Two haplotypes of Xylella fastidiosa subsp. pauca (Xfp) that correlated with their host of origin were identified in a collection of 90 isolates infecting citrus and coffee plants in Brazil, based on a single-nucleotide polymorphism in the gyrB sequence. A new single-nucleotide primer extension (SNuPE) protocol was designed for rapid identification of Xfp according to the host source. The protocol proved to be robust for the prediction of the Xfp host source in blind tests using DNA from cultures of the bacterium, infected plants, and insect vectors allowed to feed on Xfp-infected citrus plants. AMOVA and STRUCTURE analyses of microsatellite data separated most Xfp populations on the basis of their host source, indicating that they were genetically distinct. The combined use of the SNaPshot protocol and three previously developed multilocus SSR markers showed that two haplotypes and distinct isolates of Xfp infect citrus and coffee in Brazil and that multiple, genetically different isolates can be present in a single orchard or infect a single tree. This combined approach will be very useful in studies of the epidemiology of Xfp-induced diseases, host specificity of bacterial genotypes, the occurrence of Xfp host jumping, vector feeding habits, etc., in economically important cultivated plants or weed host reservoirs of Xfp in Brazil and elsewhere [Int Microbiol 2015; 18(1):13-24]Keywords: Citrus variegated chlorosis · coffee leaf scorch · vector transmission· xylem-limited bacteria · haplotype characterization · host-plant associatio

Combined use of a new SNP-based assay and multilocus SSR markers to assess genetic diversity of Xylella fastidiosa subsp. pauca infecting citrus and coffee plants

Two haplotypes of Xylella fastidiosa subsp. pauca (Xfp) that correlated with their host of origin were identified in a collection of 90 isolates infecting citrus and coffee plants in Brazil, based on a single-nucleotide polymorphism in the gyrB sequence. A new single-nucleotide primer extension (SNuPE) protocol was designed for rapid identification of Xfp according to the host source. The protocol proved to be robust for the prediction of the Xfp host source in blind tests using DNA from cultures of the bacterium, infected plants, and insect vectors allowed to feed on Xfp- infected citrus plants. AMOVA and STRUCTURE analyses of microsatellite data separated most Xfp populations on the basis of their host source, indicating that they were genetically distinct. The combined use of the SNaPshot protocol and three previously developed multilocus SSR markers showed that two haplotypes and distinct isolates of Xfp infect citrus and coffee in Brazil and that multiple, genetically different isolates can be present in a single orchard or infect a single tree. This combined approach will be very useful in studies of the epidemiology of Xfp- induced diseases, host specificity of bacterial genotypes, the occurrence of Xfp host jumping, vector feeding habits, etc., in economically important cultivated plants or weed host reservoirs of Xfp in Brazil and elsewhere [Int Microbiol 2015; 18(1):13-24].We acknowledge financial support from the EU grant ICA4-CT-2001-10005 and an ‘Intramural Project’ to B. B. Landa from the Spanish National Research Council (CSIC), as well as CNPq for a scholarship to J. R. S. Lopes in Brazil.Peer reviewe

Rapid screening tests for the assignment of X. fastidiosa genotypes to a subspecies cluster

Until now, different molecular tests can be used to assign novel X. fastidiosa isolates to subspecies clusters, among which MLST/MLSA represents the most common method. X. fastidiosa outbreaks in EU motivated the search for accurate and faster approaches to differentiate the X. fastidiosa isolates. Because MLST/MLSA requires PCR reactions and sequencing analyses, 2 independent approaches were recently developed and implemented for rapid taxonomic assignment of uncharacterized isolates: (1) single-nucleotide primer extension (SNuPE) method that allows to differentiate all subspecies and three genotypes within X. fastidiosa subsp. pauca including the typeisolate infecting olive in Italy and (2) high-resolution melting (HRM) analysis of the amplicon recovered from the gene encoding the conserved HL protein. Both assays were validated on a larger panel of isolates and proved to clearly differentiate X. fastidiosa isolates currently known to occur in the Italian, France and Spain outbreaks. These rapid approaches could represent a useful tool for prescreening of infected samples to be further analyzed by MLST or whole genome sequencing. In addition alternative genomic regions of X. fastidiosa are going to be analyzed to implement approaches aimed to assign genotypes to a subspecies cluster, with the purpose to support a rapid identification of genotypes/subspecies at interception places or when new findings occur in a pest free are

Integrating an epidemic spread model with remote sensing for Xylella fastidiosa detection

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021.Xylella fastidiosa (Xf) causes plant diseases that lead to massive economic losses in agricultural crops, making it one of the pathogens of greatest concern to agriculture nowadays. Detecting Xf at early stages of infection is crucial to prevent and manage outbreaks of this vector-borne bacterium. Recent remote sensing (RS) studies at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, RS-based forecasting of Xf outbreaks requires tools that account for their spatiotemporal dynamics. Here, we show how coupling a spatial Xf-spread model with the probability of Xf-infection predicted by an RS-driven modeling algorithm based on a Support Vector Machine (RS-SVM) helps detecting the spatial Xf distribution in a landscape. To optimize such model, we investigated which RS plant traits (i.e., pigments, structural or leaf protein content) derived from high-resolution hyperspectral imagery and biophysical modelling are most responsive to Xf infection and damage. For that, we combined a field campaign in almond orchards in Alicante province (Spain) affected by Xf (n=1,426 trees), with an airborne campaign over the same area to acquire high-resolution thermal and hyperspectral images in the visible-near-infrared (400-850 nm) and short-wave infrared regions (SWIR, 950-1700 nm). We found that coupling the epidemic spread model and the RS-based model increased accuracy by around 5% (OA = 80%, kappa = 0.48 and AUC = 0.81); compared to the best performing RS-SVM model (OA = 75%; kappa = 0.50) that included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator, alongside pigments and structural parameters. The parameters with the greatest explanatory power of the RS model were leaf protein content together with NI (28%), followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. In the subset of almond trees where the presence of Xf was tested by qPCR (n=318 tress), the combined RS-spread model yielded the best performance (OA of 71% and kappa = 0.33). Conversely, the best-performing RS-SVM model and visual inspections produced OA and kappa values of 65% and 0.31, respectively. This study shows for the first time the potential of combining spatial epidemiological models and remote sensing to monitor Xf-disease distribution in almond trees

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.Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through grant agreements POnTE (635646) and XF-ACTORS (727987). R. Calderón was supported by a post-doctoral research fellowship from the Alfonso Martin Escudero Foundation (Spain)

Insights Into the Effect of Verticillium dahliae Defoliating-Pathotype Infection on the Content of Phenolic and Volatile Compounds Related to the Sensory Properties of Virgin Olive Oil

Verticillium wilt, caused by the defoliating pathotype of Verticillium dahliae, is the most devastating soil-borne fungal disease of olive trees, and leads to low yields and high rates of tree mortality in highly susceptible cultivars. The disease is widely distributed throughout the Mediterranean olive-growing region and is one of the major limiting factors of olive oil production. Other than effects on crop yield, little is known about the effect of the disease on the content of volatile compounds and phenolics that are produced during the oil extraction process and determine virgin olive oil (VOO) quality and commercial value. Here, we aim to study the effect of Verticillium wilt of the olive tree on the content of phenolic and volatile compounds related to the sensory properties of VOO. Results showed that synthesis of six and five straight-chain carbon volatile compounds were higher and lower, respectively, in oils extracted from infected trees. Pathogen infection affected volatile compounds known to be contributors to VOO aroma: average content of one of the main positive contributors to VOO aroma, (E)-hex-2-enal, was 38% higher in oils extracted from infected trees, whereas average content of the main unpleasant volatile compound, pent-1-en-3-one, was almost 50% lower. In contrast, there was a clear effect of pathogen infection on the content of compounds responsible for VOO taste, where average content of the main bitterness contributor, oleuropein aglycone, was 18% lower in oil extracted from infected plants, and content of oleocanthal, the main contributor to pungency, was 26% lower. We believe this is the first evidence of the effect of Verticillium wilt infection of olive trees on volatile compounds and phenolics that are responsible of the aroma, taste, and commercial value of VOO

Progress and achievements on the early detection of Xylella fastidiosa infection and symptom development with hyperspectral and thermal remote sensing imagery

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021.Remote sensing efforts made as part of European initiatives via POnTE, XF-ACTORS and the JRC, as well as through regional programs, have focused, among others, on the development of algorithms for the early detection of Xylella fastidiosa (Xf)-induced symptoms. Airborne campaigns carried out between 2016 and 2019 collected high-resolution hyperspectral and thermal images from infected areas in the Apulia region (Italy), in the province of Alicante and on the island of Mallorca (Spain). The remote sensing imagery collections were performed alongside field surveys and laboratory analyses to assess the presence of Xf, and the severity and incidence of disease in olive and almond trees. Radiative transfer models and machine learning algorithms were used to quantify spectral plant traits for each individual infected tree, assessing their importance as pre visual indicators of Xf-induced stress. These studies conducted across species have demonstrated that specific spectral plant traits successfully revealed Xf induced symptoms at early stages, i.e., before visual symptoms appear. The results show that spectral plant traits contribute differently to symptom detection across host species (olive vs. almond), and that abiotic-induced stress affects the performance of the algorithms used for detecting infected trees. Together, the different European initiatives studying the use of remote sensing to support the monitoring of landscapes for Xylella fastidiosa detection lead us to conclude that the early detection of Xf-induced symptoms is feasible when high-resolution hyperspectral imagery and physically-based plant trait retrievals are used, obtaining accuracies exceeding 92% (kappa>0.8). These results are essential to enable the implementation of effective control and management of plant diseases using airborne- droneand satellite-based remote sensing technologies. Moreover, these large-scale hyperspectral and thermal imaging methods greatly contribute to the future operational monitoring of infected areas at large scales, well beyond what is possible from field surveys and laboratory analyses alone

Detecting Xylella fastidiosa in a machine learning framework using Vcmax and leaf biochemistry quantified with airborne hyperspectral imagery

The bacterium Xylella fastidiosa (Xf) is a plant pathogen that can block the flow of water and nutrients through the xylem. Xf symptoms may be confounded with generic water stress responses. Here, we assessed changes in biochemical, biophysical and photosynthetic traits, inferred using biophysical models, in Xf-affected almond orchards under rainfed and irrigated conditions on the Island of Majorca (Balearic Islands, Spain). Recent research has demonstrated the early detection of Xf-infections by monitoring spectral changes associated with pigments, canopy structural traits, fluorescence emission and transpiration. Nevertheless, there is still a need to make further progress in monitoring physiological processes (e.g., photosynthesis rate) to be able to efficiently detect when Xf-infection causes subtle spectral changes in photosynthesis. This paper explores the ability of parsimonious machine learning (ML) algorithms to detect Xf-infected trees operationally, when considering a proxy of photosynthetic capacity, namely the maximum carboxylation rate (Vcmax), along with carbon-based constituents (CBC, including lignin), and leaf biochemical traits and tree-crown temperature (Tc) as an indicator of transpiration rates. The ML framework proposed here reduced the uncertainties associated with the extraction of reflectance spectra and temperature from individual tree crowns using high-resolution hyperspectral and thermal images. We showed that the relative importance of Vcmax and leaf biochemical constituents (e.g., CBC) in the ML model for the detection of Xf at early stages of development were intrinsically associated with the water and nutritional conditions of almond trees. Overall, the functional traits that were most consistently altered by Xf-infection were Vcmax, pigments, CBC, and Tc, and, particularly in rainfed-trees, anthocyanins, and Tc. The parsimonious ML model for Xf detection yielded accuracies exceeding 90% (kappa = 0.80). This study brings progress in the development of an operational ML framework for the detection of Xf outbreaks based on plant traits related to photosynthetic capacity, plant biochemistry and structural decay parameters.This research was supported by grant: ITS2017-095: Design and Implementation of control strategies for Xylella fastidiosa, Project 5. Government of the Balearic Islands, Spain. Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through gran agreement XF-ACTORS (727987).Peer reviewe

Disentangling Peronospora on Papaver: Phylogenetics, taxonomy, nomenclature and host range of downy mildew of opium poppy (Papaver somniferum) and related species

Based on sequence data from ITS rDNA, cox1 and cox2, six Peronospora species are recognised as phylogenetically distinct on various Papaver species. The host ranges of the four already described species P. arborescens, P. argemones, P. cristata and P. meconopsidis are clarified. Based on sequence data and morphology, two new species, P. apula and P. somniferi, are described from Papaver apulum and P. somniferum, respectively. The second Peronospora species parasitizing Papaver somniferum, that was only recently recorded as Peronospora cristata from Tasmania, is shown to represent a distinct taxon, P. meconopsidis, originally described from Meconopsis cambrica. It is shown that P. meconopsidis on Papaver somniferum is also present and widespread in Europe and Asia, but has been overlooked due to confusion with P. somniferi and due to less prominent, localized disease symptoms. Oospores are reported for the first time for P. meconopsidis from Asian collections on Papaver somniferum. Morphological descriptions, illustrations and a key are provided for all described Peronospora species on Papaver. cox1 and cox2 sequence data are confirmed as equally good barcoding loci for reliable Peronospora species identification, whereas ITS rDNA does sometimes not resolve species boundaries. Molecular phylogenetic data reveal high host specificity of Peronospora on Papaver, which has the important phytopathological implication that wild Papaver spp. cannot play any role as primary inoculum source for downy mildew epidemics in cultivated opium poppy crops. © 2014 Voglmayr et al.Peer Reviewe