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].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

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

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

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

Unraveling the nature of suppressiveness to Verticillium wilt of specific olive orchard soils

Ponencia presentada en el 11th International Verticillium Symposium, celebrado en Göttingen (Alemania) del 5 al 8 de mayo de 2013.Plants have evolved strategies of stimulating and supporting specific groups of antagonistic microorganisms in the rhizosphere as a defense against diseases caused by soilborne plant pathogens. Disease suppressive soils provide the best examples of this strategy. Suppressive soils have been described worldwide for many different pathogens, however for the fungal pathogen Verticillum dahliae the information is very scarce. Olive (Olea europaea L.) is one of the most important crops in Spain with > 2.4 million ha. During the last two decades the phytosanitary status of olive orchards is being threatened mainly due to Verticillium wilt. In previous studies a collection of rhizosphere soils from 90 olive orchards under different management systems and three rhizosphere soils from wild olive havens in Andalusia were characterized by their level of suppressiveness to Verticillium wilt. Results indicated that at least 25% of soils showed a high level of suppressiveness to Verticillium wilt. The objective of this study was to unravel the biotic and abiotic factors that may be associated with this phenomenon. For that purpose we selected a set of suppressive and conducive soils and have performed some “Classical” approaches to identify the biotic factors (microorganisms) involved in this specific suppression that included: 1) Transferring suppressiveness by adding small amounts of suppressive soil to conducive soil which have confirmed its biological nature. 2) Treating suppressive soil with heat to eliminate specific groups of microorganism which have demonstrated loss of suppressiveness at a certain level. 3) Isolating different microbial groups and correlating their presence with suppressiveness. 4) Screening representatives of microbial groups for in vitro and biocontrol activity against the target pathogen. In a second step using molecular approaches such as bar-coded pyrosequencing we have characterized all bacterial communities associated to the rhizosphere of plants growing in those selected soils. Sequences generated from pyrosequencing of rRNA gene amplicons with the GS Junior system (Roche) were processed using the Quantitative Insights Into Microbial Ecology (QIIME 1.6.0) pipeline. Flowgrams were clustered into OTUs at 97% pairwise identity using the seed-based UCLUST algorithm, and representative sequences from each OTU were aligned to the Greengenes bacteria database using PyNAST. In addition, α diversity and β diversity metrics together with rarefaction plots were also calculated to determine the bacterial population structure in those soils. Different multivariate analyses have allowed identifying some climatic parameters and physicochemical soil characteristics that are differentially associated to the level of suppressiveness of those soils as well as to determine which OTUs are specially enriched in the suppressive soils. Furthermore, specific OTUs have been identified as being transferred from the suppressive into the conducive soils in the ‘transferability experiment’ which might be associated as the OTUs being responsible of this transferable suppressive effect. Finally, experiments are being conducted to determine the influence of the indigenous microbiota on inducing plant defense mechanisms in olive plants grown in those suppressive soils. For that purpose both microarray and metagenomic sequencing of total DNA will be used to unravel genes differentially expressed or present under the suppressive conditions.Peer Reviewe

Στρατηγικές αποτιμήσεις του εκπαιδευτικού έργου στη χώρα μας στην Πρωτ/θμια και Δευτ/θμια εκπαίδευση, στο διάστημα 1995-2004 : (εργαλεία και μέσα)

BACKGROUND: In the last years, many olive plantations in southern Spain have been mediated by the use of self-rooted planting stocks, which have incorporated commercial AMF during the nursery period to facilitate their establishment. However, this was practised without enough knowledge on the effect of cropping practices and environment on the biodiversity of AMF in olive orchards in Spain. METHODOLOGY/PRINCIPAL FINDINGS: Two culture-independent molecular methods were used to study the AMF communities associated with olive in a wide-region analysis in southern Spain including 96 olive locations. The use of T-RFLP and pyrosequencing analysis of rDNA sequences provided the first evidence of an effect of agronomic and climatic characteristics, and soil physicochemical properties on AMF community composition associated with olive. Thus, the factors most strongly associated to AMF distribution varied according to the technique but included among the studied agronomic characteristics the cultivar genotype and age of plantation and the irrigation regimen but not the orchard management system or presence of a cover crop to prevent soil erosion. Soil physicochemical properties and climatic characteristics most strongly associated to the AMF community composition included pH, textural components and nutrient contents of soil, and average evapotranspiration, rainfall and minimum temperature of the sampled locations. Pyrosequencing analysis revealed 33 AMF OTUs belonging to five families, with Archaeospora spp., Diversispora spp. and Paraglomus spp., being first records in olive. Interestingly, two of the most frequent OTUs included a diverse group of Claroideoglomeraceae and Glomeraceae sequences, not assigned to any known AMF species commonly used as inoculants in olive during nursery propagation. CONCLUSIONS/SIGNIFICANCE: Our data suggests that AMF can exert higher host specificity in olive than previously thought, which may have important implications for redirecting the olive nursery process in the future as well as to take into consideration the specific soils and environments where the mycorrhized olive trees will be established