Agrobacterium tumefaciens is a plant-pathogenic bacterium capable of secreting several virulence factors into extracellular space or the host cell. In this study, we used shotgun proteomics analysis to investigate the secretome of A. tumefaciens, which resulted in identification of 12 proteins, including 1 known secretory protein (VirB1*) and 11 potential secretory proteins. Interestingly, one unknown protein, which we designated hemolysin-coregulated protein (Hcp), is a predicted soluble protein without a recognizable N-terminal signal peptide. Western blot analysis revealed that A. tumefaciens Hcp is expressed and secreted when cells are grown in both minimal and rich media. Further biochemical and immunoelectron microscopy analysis demonstrated that intracellular Hcp is localized mainly in the cytosol, with a small portion in the membrane system. To investigate the mechanism of secretion of Hcp in A. tumefaciens, we generated mutants with deletions of a conserved gene, icmF, or the entire putative operon encoding a recently identified type VI secretion system (T6SS). Western blot analysis indicated that Hcp was expressed but not secreted into the culture medium in mutants with deletions of icmF or the t6ss operon. The secretion deficiency of Hcp in the icmF mutant was complemented by heterologous trans expression of icmF, suggesting that icmF is required for Hcp secretion. In tumor assays with potato tuber disks, deletion of hcp resulted in approximately 20 to 30% reductions in tumorigenesis efficiency, while no consistent difference was observed when icmF or the t6ss operon was deleted. These results increase our understanding of the conserved T6SS used by both plant-and animal-pathogenic bacteria.
Strawberry is a small fruit crop with high economic value. Anthracnose caused by Colletotrichum spp. poses a serious threat to strawberry production, particularly in warm and humid climates, but knowledge of pathogen populations in tropical and subtropical regions is limited. to investigate the diversity of infectious agents causing strawberry anthracnose in taiwan, a disease survey was conducted from 2010 to 2018, and Colletotrichum spp. were identified through morphological characterization and multilocus phylogenetic analysis with internal transcribed spacer, glyceraldehyde 3-phosphate dehydrogenase, chitin synthase, actin, beta-tubulin, calmodulin, and the intergenic region between Apn2 and MAT1-2-1 (ApMAT). Among 52 isolates collected from 24 farms/nurseries in taiwan, a new species, Colletotrichum miaoliense sp. nov. (6% of all isolates), a species not previously known to be associated with strawberry, Colletotrichum karstii (6%), and three known species, Colletotrichum siamense (75%), Colletotrichum fructicola (11%), and Colletotrichum boninense (2%), were identified. The predominant species C. siamense and C. fructicola exhibited higher mycelial growth rates on potato dextrose agar and caused larger lesions on wounded and non-wounded detached strawberry leaves. Colletotrichum boninense, C. karstii, and C. miaoliense only caused lesions on wounded leaves. Understanding the composition and biology of the pathogen population will help in disease management and resistance breeding. Strawberry (Fragaria × ananassa Duch.) is a popular small fruit crop with high economic and nutritive value. Strawberry is in high demand globally. From 2008 to 2018, the annual worldwide cultivation of strawberries increased from approximately 400 to 483 thousand hectares 1. Although strawberries are native to temperate regions, they can also be grown in tropical and subtropical regions (sometimes under high-altitude conditions). The land areas devoted to strawberry cultivation in Colombia,
The strawberry (Fragaria × ananassa Duch.) is a high-value crop with an annual cultivated area of ~500 ha in Taiwan. Over 90% of strawberry cultivation is in Miaoli County. Unfortunately, various diseases significantly decrease strawberry production. The leaf and fruit disease became an epidemic in 1986. From 2010 to 2016, anthracnose crown rot caused the loss of 30–40% of seedlings and ~20% of plants after transplanting. The automation of agriculture and image recognition techniques are indispensable for detecting strawberry diseases. We developed an image recognition technique for the detection of strawberry diseases using a convolutional neural network (CNN) model. CNN is a powerful deep learning approach that has been used to enhance image recognition. In the proposed technique, two different datasets containing the original and feature images are used for detecting the following strawberry diseases—leaf blight, gray mold, and powdery mildew. Specifically, leaf blight may affect the crown, leaf, and fruit and show different symptoms. By using the ResNet50 model with a training period of 20 epochs for 1306 feature images, the proposed CNN model achieves a classification accuracy rate of 100% for leaf blight cases affecting the crown, leaf, and fruit; 98% for gray mold cases, and 98% for powdery mildew cases. In 20 epochs, the accuracy rate of 99.60% obtained from the feature image dataset was higher than that of 1.53% obtained from the original one. This proposed model provides a simple, reliable, and cost-effective technique for detecting strawberry diseases.
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