Phytopathology最新文献

Valsa pyri, the causal agent of pear canker disease, typically induces cankers on the bark of infected trees and even leads to tree mortality. Secondary metabolites produced by pathogenic fungi play a crucial role in the pathogenic process. In this study, secondary metabolic regulator VpLaeA was identified in V. pyri. VpLaeA was found to strongly affect the pathogenicity, fruiting body formation, and toxicity of secondary metabolites of V. pyri. Additionally, VpLaeA was found to be required for the response of V. pyri to some abiotic stresses. Transcriptome data analysis revealed that many of differentially expressed genes were involved in the secondary metabolite biosynthesis. Among them, about one third of secondary metabolite biosynthesis core genes were regulated by VpLaeA at different periods. Seven differentially expressed secondary metabolite biosynthesis core genes (VpPKS9, VpPKS10, VpPKS33, VpNRPS6, VpNRPS7, VpNRPS16, and VpNRPS17) were selected for knockout. Two modular polyketide synthase genes (VpPKS10 and VpPKS33) that were closely related to the virulence of V. pyri from the above seven genes were identified. Notably, VpPKS10 and VpPKS33 also affected the production of fruiting body of V. pyri but did not participate in the resistance of V. pyri to abiotic stresses. Overall, this study demonstrates the multifaceted biological functions of VpLaeA in V. pyri and identifies two toxicity-associated polyketide synthase genes in Valsa species fungi for the first time.

Net blotch disease caused by Drechslera teres is a major fungal disease that affects barley (Hordeum vulgare) plants and can result in significant crop losses. In this study, we developed a deep learning model to quantify net blotch disease symptoms on different days postinfection on seedling leaves using Cascade R-CNN (region-based convolutional neural network) and U-Net (a convolutional neural network) architectures. We used a dataset of barley leaf images with annotations of net blotch disease to train and evaluate the model. The model achieved an accuracy of 95% for Cascade R-CNN in net blotch disease detection and a Jaccard index score of 0.99, indicating high accuracy in disease quantification and location. The combination of Cascade R-CNN and U-Net architectures improved the detection of small and irregularly shaped lesions in the images at 4 days postinfection, leading to better disease quantification. To validate the model developed, we compared the results obtained by automated measurement with a classical method (necrosis diameter measurement) and a pathogen detection by real-time PCR. The proposed deep learning model could be used in automated systems for disease quantification and to screen the efficacy of potential biocontrol agents to protect against disease.

The southern corn leaf blight epidemic of 1970 caused estimated losses of about 16% for the U.S. corn crop, equivalent to about $8 billion in current terms. The epidemic was caused by the prevalence of Texas male sterile cytoplasm (cms-T), used to produce most of the hybrid corn seed planted that year, combined with the emergence of a novel race of the fungus Cochliobolus heterostrophus that was exquisitely virulent on cms-T corn. Remarkably, the epidemic lasted just a single year. This episode has often been portrayed in the literature and textbooks over the last 50 years as a catastrophic mistake perpetrated by corn breeders and seed companies of the time who did not understand or account for the dangers of crop genetic uniformity. In this perspective article, we aim to present an alternative interpretation of these events. First, we contend that, rather than being caused by a grievous error on the part of the corn breeding and seed industry, this epidemic was a particularly unfortunate, unusual, and unlucky consequence of a technological advancement intended to improve the efficiency of corn seed production for America's farmers. Second, we tell the story of the resolution of the epidemic as an example of timely, meticulously applied research in the public sector for the public good.

'Candidatus Liberibacter asiaticus', the putative causal agent of citrus greening disease, is transmitted by the Asian citrus psyllid, Diaphorina citri, in a propagative, circulative, and persistent manner. Unfortunately, 'Ca. L. asiaticus' is not yet available in pure culture to carry out Koch's postulates and to confirm its etiology. When a pure culture is available, an assay to test its infectivity in both the insect vector and the plant host will be crucial. Herein, we described a transmission assay based on the use of phloem sap extracted from infected citrus plants and topical feeding to D. citri nymphs. Phloem sap was collected by centrifugation, diluted with 0.1 M phosphate buffer pH 7.4 containing 20% (wt/vol) sucrose and 0.1% ascorbic acid (wt/vol) as an antioxidant, and delivered to third through fifth instar nymphs by placing droplets on the mouthparts. Nymphs unfolded the stylets and acquired the phloem sap containing the bacterial pathogen. Nymphs were then placed onto Citrus macrophylla seedlings (10 nymphs per seedling) for an inoculation period of 2 weeks. A transmission rate of up to 80% was recorded at 6 months postinoculation. The method could be a powerful tool to test the transmissibility of the bacterial pathogen after various treatments to reduce the viability of the bacteria or to block its transmission. In addition, it might be a potent assay to achieve Koch's postulates if a pure culture of 'Ca. L. asiaticus' becomes available.

It is essential to have a thorough knowledge of the genetic variation among different strains of Xanthomonas citri pv. citri, which is responsible for causing citrus bacterial canker. This understanding is important for studying disease characteristics, population structure, and evolution and ultimately for developing sustainable methods of control. A total of 48 strains obtained from citrus production areas in Burkina Faso in 2012, 2020, and 2021 were subjected to Polymerase Chain reaction (PCR) tests using specific primers. The aim was to examine the distribution of type 3 effectors and determine the geographical origins of the strains. The examination of the distribution of type 3 non-transcription-activator-like effectors (TALEs) revealed a broader range of strains obtained in 2020 and 2021 than in 2012. However, all the strains possessed a shared set of three genes, specifically, XopE2, XopN, and AvrBs2. Furthermore, all examined effectors were observed in the Bobo-Dioulasso region. Regarding the characterization of TALEs, two profiles containing two to three TALEs were discovered. Profile 1, consisting of two TALEs, was found in 37 X. citri pv. citri strains, whereas Profile 2, comprising three TALEs, was detected in 11 strains. Among the three TALEs (A, B, and C) that were identified, TALEs B and C were present in all the strains. The correlation matrix indicated a positive association between the type 3 effector content of strains and the duration of their isolation. Principal component analysis revealed a limited organization of the strains under investigation. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Mixtures of fungicides with different modes of action are commonly used as disease and resistance management tools, but little is known of mixtures of natural and synthetic products. In this study, mixtures of metabolites from the rhizobacterium Pseudomonas chlororaphis strain ASF009 formulated as Howler EVO with below-label rates (50 µg/ml) of conventional sterol demethylation inhibitor (DMI) fungicides were investigated for control of anthracnose of cherry (Prunus avium) caused by Colletotrichum siamense. Howler mixed with metconazole or propiconazole synergistically reduced disease severity through lesion growth. Real-time PCR showed that difenoconazole, flutriafol, metconazole, and propiconazole induced the expression of DMI target genes CsCYP51A and CsCYP51B in C. siamense. The addition of Howler completely suppressed the DMI fungicide-induced expression of both CYP51 genes. We hypothesize that the downregulation of DMI fungicide-induced expression of the DMI target genes may, at least in part, explain the synergism observed in detached fruit assays.

Yellow dwarf viruses (YDVs) spread by aphids are some of the most economically important barley (Hordeum vulgare) virus-vector complexes worldwide. Detection and control of these viruses are critical components in the production of barley, wheat, and numerous other grasses of agricultural importance. Genetic control of plant diseases is often preferable to chemical control to reduce the environmental and economic cost of foliar insecticides. Accordingly, the objectives of this work were to (i) screen a barley population for resistance to YDVs under natural infection using phenotypic assessment of disease symptoms, (ii) implement drone imagery to further assess resistance and test its utility as a disease screening tool, (iii) identify the prevailing virus and vector types in the experimental environment, and (iv) perform a genome-wide association study to identify genomic regions associated with measured traits. Significant genetic differences were found in a population of 192 barley inbred lines regarding their YDV symptom severity, and symptoms were moderately to highly correlated with grain yield. The YDV severity measured with aerial imaging was highly correlated with on-the-ground estimates (r = 0.65). Three aphid species vectoring three YDV species were identified with no apparent genotypic influence on their distribution. A quantitative trait locus impacting YDV resistance was detected on chromosome 2H, albeit undetected using aerial imaging. However, quantitative trait loci for canopy cover and mean normalized difference vegetation index were successfully mapped using the drone. This work provides a framework for utilizing drone imagery in future resistance breeding efforts for YDVs in cereals and grasses, as well as in other virus-vector disease complexes.

Plant defenses are conserved among closely related species, but domestication can alter host genotypes through artificial selection with potential losses in host defenses. Therefore, both domestication and host phylogenetic structure may influence plant virus infection outcomes. Here, we examined the association of phylogeny and domestication with the fitness of infected plants. We inoculated three pairs of domesticated and wild/noncultivated squash (Cucurbita spp.) with a combination of two viruses commonly found to coinfect cucurbits, zucchini yellow mosaic virus and squash mosaic virus, and recorded fitness traits related to flowers, pollination, fruit, and seed viability in the field over 2 separate years. In an additional field experiment, we recorded the relative abundance of both viruses via RT-qPCR. We found a gradient of susceptibility across the six tested lineages, and phylogenetic structure, but not domestication, contributed to differences in infection outcomes and impacts on several fitness traits, including fruit number, fruit weight, and germination. Plant virus infection also impacted the quantity and quality of floral rewards and visitation rates of specialist bee pollinators. There were no detectable differences in viral load between the six host taxa for either virus individually or the ratio of zucchini yellow mosaic virus to squash mosaic virus. Our results highlight the importance of phylogenetic structure in predicting host susceptibility to disease across wild and domesticated plants and the ability of several hosts to maintain fitness in the field despite infection. Broader consequences of plant pathogens for beneficial insects, such as pollinators, should also be considered in future research.

Timely and accurate identification of peanut pests and diseases, coupled with effective countermeasures, is pivotal for ensuring high-quality and efficient peanut production. Despite the prevalence of pests and diseases in peanut cultivation, challenges such as minute disease spots, the elusive nature of pests, and intricate environmental conditions often lead to diminished identification accuracy and efficiency. Moreover, continuous monitoring of peanut health in real-world agricultural settings demands solutions that are computationally efficient. Traditional deep learning models often require substantial computational resources, limiting their practical applicability. In response to these challenges, we introduce LSCDNet (Lightweight Sandglass and Coordinate Attention Network), a streamlined model derived from DenseNet. LSCDNet preserves only the transition layers to reduce feature map dimensionality, simplifying the model's complexity. The inclusion of a sandglass block bolsters features extraction capabilities, mitigating potential information loss due to dimensionality reduction. Additionally, the incorporation of coordinate attention addresses issues related to positional information loss during feature extraction. Experimental results showcase that LSCDNet achieved impressive metrics with accuracy, precision, recall, and Fl score of 96.67, 98.05, 95.56, and 96.79%, respectively, while maintaining a compact parameter count of merely 0.59 million. When compared with established models such as MobileNetV1, MobileNetV2, NASNetMobile, DenseNet-121, InceptionV3, and X-ception, LSCDNet outperformed with accuracy gains of 2.65, 4.87, 8.71, 5.04, 6.32, and 8.2%, respectively, accompanied by substantially fewer parameters. Lastly, we deployed the LSCDNet model on Raspberry Pi for practical testing and application and achieved an average recognition accuracy of 85.36%, thereby meeting real-world operational requirements.

Wheat blast, caused by Pyricularia oryzae (syn. Magnaporthe oryzae) pathotype Triticum (MoT), is a devastating disease that can result in up to 100% yield loss in affected fields. To find new resistance genes against wheat blast, we screened 199 accessions of Aegilops tauschii, the D genome progenitor of common wheat (Triticum aestivum), by seedling inoculation assays with Brazilian MoT isolate Br48 and found 14 resistant accessions. A synthetic hexaploid wheat line (Ldn/KU-2097) derived from a cross between the T. turgidum 'Langdon' (Ldn) and resistant A. tauschii accession KU-2097 exhibited resistance in seedlings and spikes against Br48. In an F₂ population derived from 'Chinese Spring' × Ldn/KU-2097, resistant and susceptible individuals segregated in a 3:1 ratio, suggesting that the resistance from KU-2097 is controlled by a single dominant gene. We designated this gene Rmg10. Genetic mapping using an F_2:3 population from the same cross mapped the RMG10 locus to the short arm of chromosome 2D. Rmg10 was ineffective against Bangladesh isolates but effective against Brazilian isolates. Field tests in Bolivia showed increased spike resistance in a synthetic octaploid wheat line produced from a cross between common wheat cultivar 'Gladius' and KU-2097. These results suggest that Rmg10 would be beneficial in farmers' fields in South America.