Bacterial Spot Research Articles

Xanthomonas euvesicatoria-Specific Bacteriophage BsXeu269p/3 Reduces the Spread of Bacterial Spot Disease in Pepper Plants.

The present study was focused on the pathosystem pepper plants (Capsicum annuum L.)-phytopathogenic bacterium X. euvesicatoria (wild strain 269p)-bacteriophage BsXeu269p/3 and the possibility of bacteriophage-mediated biocontrol of the disease. Two new model systems were designed for the monitoring of the effect of the phage treatment on the infectious process in vivo. The spread of the bacteriophage and the pathogen was monitored by qPCR. A new pair of primers for phage detection via qPCR was designed, as well as probes for TaqMan qPCR. The epiphytic bacterial population and the potential bacteriolytic effect of BsXeu269p/3 in vivo was observed by SEM. An aerosol-mediated transmission model system demonstrated that treatment with BsXeu269p/3 reduced the amount of X. euvesicatoria on the leaf surface five-fold. The needle-pricking model system showed a significant reduction of the amount of the pathogen in infectious lesions treated with BsXeu269p/3 (av. 59.7%), compared to the untreated control. We found that the phage titer is 10-fold higher in the infection lesions but it was still discoverable even in the absence of the specific host in the leaves. This is the first report of in vivo assessment of the biocontrol potential of locally isolated phages against BS pathogen X. euvesicatoria in Bulgaria.

Early detection of tomato bacterial spot disease in transplant tomato seedlings utilising remote sensing and artificial intelligence

Early detection of tomato bacterial spot disease in transplant tomato seedlings utilising remote sensing and artificial intelligence

Performance Comparison between SVM and LS-SVM for Rice Leaf Disease detection

INTRODUCTION: Automatic detection of rice plant diseases at early stage from its images is quite beneficial over traditional verification methods. OBJECTIVES: Recent years machine learning (ML) approaches are more efficient in disease classification task. In current generation the statistical machine learning algorithm which shows state-of-arts performance is Support Vector Machine (SVM) and variants of SVM. METHODS: SVM has an excellent learning performance for linear and non-linear data samples. It works for Quadratic Programming Problems (QPP) due to which it has the drawback of computational complexity. However QPP can be solved linearly with the help of Least Square SVM(LS-SVM) approach. In LS-SVM the epsilon tube and slack variables of SVM are replaced with error variables. The distance is calculated by error square value. RESULTS: In this research performance comparison is made between SVM and LS-SVM for rice leaf diseases such as Bacterial Leaf Blight (BLB), Brown spot(BS), Leaf smut(LS) and Leaf Blast using two datasets (DS1 and DS2).Accuracy of LS-SVM is found to be 91.3% and 98.87% for DS1 and DS2 respectively whereas accuracy of SVM is 83.3% and 98.75% for DS1 and DS2 respectively. CONCLUSION: Performance of LS-SVM outperformed than SVM in terms of accuracy.

Bacillus velezensis and Paenibacillus peoriae Strains Effective as Biocontrol Agents against Xanthomonas Bacterial Spot

Gram-negative bacteria belonging to the Xanthomonas genus include plant pathogens representing a major challenge in the field of agriculture for a wide variety of economically important crops, such as tomato, pepper, and lettuce. Due to the massive usage of agrochemicals, Xanthomonas spp. are developing resistance to copper pesticides typically used to control microbial infections. An interesting alternative approach to control bacterial phytopathogens consists of using eco-friendly biocontrol agents, often beneficial microorganisms. Here, following the targeted, broad-spectrum screening of thousands of microorganisms isolated from different environmental locations, we isolated Bacillus velezensis strain 71 and Paenibacillus peoriae strain To99 displaying potent antagonistic activity against Xanthomonas spp. We found that oxydifficidin and polymyxin A secreted by B. velezensis 71 and P. peoriae To99, respectively, are mainly responsible for the anti-Xanthomonas activity. We further evaluated the performance of cell suspensions and cell-free supernatants of these isolates in controlling tomato bacterial spot disease in growth chamber and greenhouse conditions to validate the in vitro results. The overall results demonstrate the potential of treatments based on the secondary metabolites from both isolates and their cells as an alternative to copper-based chemicals to control leaf spot diseases caused by Xanthomonas spp. phytopathogens.

The Effects of Gluconacin on Bacterial Tomato Pathogens and Protection against Xanthomonas perforans, the Causal Agent of Bacterial Spot Disease.

As agricultural practices become more sustainable, adopting more sustainable practices will become even more relevant. Searching for alternatives to chemical compounds has been the focus of numerous studies, and bacteriocins are tools with intrinsic biotechnological potential for controlling plant diseases. We continued to explore the biotechnological activity of the bacteriocin Gluconacin from Gluconacetobacter diazotrophicus, PAL5 strain, by investigating this protein's antagonism against important tomato phytopathogens and demonstrating its effectiveness in reducing bacterial spots caused by Xanthomonas perforans. In addition to this pathogen, the bacteriocin Gluconacin demonstrated bactericidal activity in vitro against Ralstonia solanacearum and Pseudomonas syringae pv. tomato, agents that cause bacterial wilt and bacterial spots, respectively. Bacterial spot control tests showed that Gluconacin reduced disease severity by more than 66%, highlighting the biotechnological value of this peptide in ecologically correct formulations.

First report of Robbsia andropogonis causing bacterial leaf spot of bougainvilleas in Taiwan.

Bougainvilleas (Bougainvillea spp.) are popular ornamentals commonly grown as bushes, vines, or trees worldwide (Kobayashi et al. 2007). Leaf spot symptoms were observed on a bougainvillea hedge located in North District, Taichung, Taiwan during August of 2022. The lesions were brown, necrotic and had yellow halos (Fig. S1). All the plants at the location showed similar symptoms. Leaf samples were collected from five plants and symptomatic tissues were minced in 10 mM MgCl2. The samples were streaked onto nutrient agar (NA) and after culturing at 28°C for 2 days, small, round, creamy white colonies were consistently isolated from all the samples. A total of five strains (BA1 to BA5) were obtained; each of them was isolated from a different plant. All five strains induced hypersensitive response in tobacco leaves. Amplification and sequencing of the isolated strains' 16S rDNA using primers 27F and 1492R (Lane 1991) revealed that all five strains shared identical sequences (GenBank accession no. OQ053015) with Robbsia andropogonis LMG 2129T (formerly Burkholderia andropogonis and Pseudomonas andropogonis; GenBank accession no. NR104960; 1,393/1,393 bp). Further testing of BA1 to BA5's DNA samples using the pathogen's species-specific primers Pf (5'-AAGTCGAACGGTAACAGGGA-3') and Pr (5'-AAAGGATATTAGCCCTCGCC-3'; Bagsic et al. 1995) successfully amplified the expected 410-bp amplicon in all five samples; the sequences of the PCR products completely matched to those of BA1 to BA5's 16S rDNA. Strains BA1 to BA5 also tested negative for arginine dihydrolase and oxidase activity, and failed to grow at 40°C, all of which are consistent with descriptions of R. andropogonis (Schaad et al. 2001). Pathogenicity of the isolated bacteria was confirmed by spray inoculation. Three representative strains, BA1 to BA3, were used for the assay. Bacterial colonies were scraped from NA plates and suspended in 10 mM MgCl2 added with 0.02% Silwet L-77. The concentrations of the suspensions were adjusted to 4.4-5.8 x 108 cfu/ml. The suspensions were sprayed onto three-month-old, cutting-propagated bougainvillea plants (to runoff). Controls were treated with bacteria-free solutions. Three plants were used for each treatment group (and the controls). The plants were placed in a growth chamber (27/25°C, day/night; 14-hour photoperiod) and bagged for three days. Within 20 days post inoculation, brown, necrotic lesions resembling those observed in the sampling site were observed on all inoculated plants, but not on the controls. One strain was re-isolated for each treatment group and the re-isolated strains all shared the same colony morphology and 16S rDNA sequence with BA1 to BA5. Additional PCR testing of these re-isolated strains using Pf and Pr also produced the expected amplicon. This is the first formal report of R. andropogonis affecting bougainvilleas in Taiwan. The pathogen has been reported causing diseases of betel palm (Areca catechu), corn and sorghum in Taiwan (Hseu et al. 2007; Hsu et al. 1991), some of which are economically important (Lisowicz 2000; Navi et al. 2002). As such, infected bougainvilleas could potentially serve as an inoculum source for these diseases.

Detection of Bangladeshi-Produced Plant Disease Using a Transfer Learning Based on Deep Neural Model

Plant diseases pose a significant threat to agricultural productivity and food security in Bangladesh. In this research, we address the challenge of timely and accurate plant disease detection through the application of transfer learning with deep neural models. We curated a diverse dataset comprising 18 categories of plant leaf images, including Bell pepper Bacterial spot, Bell pepper Healthy, Peach Healthy, Potato Early Blight, Rice Leaf Blast, Rice Healthy, Rice Brown Spot, Potato Healthy, Peach Bacterial spot, Corn Blight, Potato Late blight, Corn Healthy, Tomato Bacterial spot, Strawberry Leaf Scorch, Tomato Early blight, Tomato Early blight, Strawberry Healthy, and Tomato Healthy. The dataset represents the most prevalent plant diseases observed in the Bangladeshi context. We employed three state-of-the-art deep learning algorithms, EfficientNetV2M, VGG-19, and NASNetLarge, to develop robust plant disease detection models. Through transfer learning, these pre-trained models were fine-tuned on our specialized dataset to adapt them for the task at hand. The performance evaluation revealed impressive results, with EfficientNetV2M achieving an accuracy rate of 99%, VGG-19 achieving 93%, and NASNetLarge attaining 83% accuracy. The high accuracy of EfficientNetV2M showcases its exceptional capability in accurately classifying plant diseases prevalent in Bangladesh. The success of these deep neural models in detecting various plant diseases signifies their potential in revolutionizing plant disease management and enhancing agricultural practices. Our research contributes valuable insights into the effective use of transfer learning for plant disease detection and emphasizes the significance of dataset curation for improved model performance. The developed models hold promise in providing timely and precise disease diagnosis to farmers and agricultural professionals, thereby facilitating prompt interventions and minimizing crop losses. Future research can explore the integration of these deep neural models into practical agricultural tools, enabling real-time disease detection and offering substantial benefits to the agricultural industry in Bangladesh.

Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique.

Rice is one of the most widely consumed foods all over the world. Various diseases and deficiency disorders impact the rice crop's growth, thereby hampering the rice yield. Therefore, proper crop monitoring is very important for the early diagnosis of diseases or deficiency disorders. Diagnosis of diseases and disorders requires specialized manpower, which is not scalable and accessible to all farmers. To address this issue, machine learning and deep learning (DL)-driven automated systems are designed, which may help the farmers in diagnosing disease/deficiency disorders in crops so that proper care can be taken on time. Various studies have used transfer learning (TL) models in the recent past. In recent studies, further improvement in rice disease and deficiency disorder diagnosis system performance is achieved by performing the ensemble of various TL models. However, in all these DL-based studies, the segmentation of the region of interest is not done beforehand and the infected-region extraction is left for the DL model to handle automatically. Therefore, this article proposes a novel framework for the diagnosis of rice-infected leaves based on DL-based segmentation with bitwise logical AND operation and DL-based classification. The rice diseases covered in this study are bacterial leaf blight, brown spot, and leaf smut. The rice nutrient deficiencies like nitrogen (N), phosphorous (P), and potassium (K) were also included. The results of the experiment conducted on these datasets showed that the performance of DeepBatch was significantly improved as compared to the conventional technique.

Diagnosis of soybean bacterial blight progress stage based on deep learning in the context of data-deficient

Soybean bacterial spot disease is a predominant disease that impairs the quality and yield of soybean produce. In order to minimize production losses to the greatest extent possible, rapid detection and identification of disease progression stages are crucial. However, the conventional techniques used for identifying bacterial spot disease exhibit limitations in terms of providing real-time and objective analysis for dynamically identifying significant differences in the disease manifestation. In the present study, a novel sliding segmentation method was proposed for solving the problem of insufficient data volume, which can diagnose soybean disease stages in a timely and accurate manner, take measures to reduce the impact of diseases on soybeans, and solve the problem of difficulty in obtaining disease datasets. Further, a convolutional neural network (CNN) based image recognition method for soybean leaves at different disease stages was proposed. Different depth learning classical network models were used for transfer learning, and the characteristics of soybean leaf spots in different stages and the differences between diseased leaves and normal leaves were considered. The focus of the experiment was on optimizing a network model by adjusting three factors: the optimization algorithm, epoch number, and batch size number. The aim was to enhance the recognition accuracy of the model. The experimental results show that the optimal network model was based on Swin Transformer, with an optimization algorithm of SGD, epoch, and batch size of 200 and 8, respectively. In the experiment, the optimal model achieved an accuracy of 99.64 % in identifying soybean leaf disease stages. The model can accurately identify soybean leaf diseases at different stages, meet practical production needs, prevent pesticide misuse, reduce yield losses caused by soybean leaf diseases, provide a reference for identifying the development process of leaf diseases, and also provide theoretical support for the establishment of soybean disease warning systems.

Bacterial Leaf Spot of Plum Caused by Sphingomonas spermidinifaciens in Guangxi, China.

Plum (Prunus salicina L.) is a traditional fruit in Southern China and is ubiquitous throughout the world. In August 2021, leaves of plum trees showed water-soaking spots and light yellow-green halos with incidence exceeding 50% in Babu district in Hezhou, Guangxi (N23°49'-24°48', E111°12'-112°03'). To isolate the causal agent, three diseased leaves collected from three different trees growing in different orchards were cut into 5 mm × 5 mm pieces, disinfected with 75% ethanol for 10 sec, 2% sodium hypochlorite for 1 min and rinsed three times in sterile water. The diseased pieces were ground in sterile water and then kept static for about 10 min. Ten-fold serial dilutions in water were prepared and 100 µL of each dilution from 10-1 to 10-6 were plated on Luria-Bertani (LB) Agar. After incubation at 28℃ for 48 h, the proportion of isolates with similar morphology was 73%. Three representative isolates (GY11-1, GY12-1 and GY15-1) were selected for further study. The colonies were non-spore-forming, yellow, round, opaque, rod shaped, convex with smooth and bright neat edges. Biochemical test results showed that the colonies were strictly aerobic and gram-negative. The isolates were able to grow on LB agar containing 0-2% (w/v) NaCl and could utilize glucose, lactose, galactose, mannose, sucrose, maltose and rhamnose as a carbon source. They displayed a positive reaction for H2S production, oxidase, catalase and gelatin, but negative for starch. Genomic DNA of the three isolates was extracted for amplification of the 16S rDNA with primers 27F and 1492R. The resulting amplicons were sequenced. Additionally, five housekeeping genes atpD, dnaK, gap, recA, and rpoB of the three isolates were amplified using the corresponding primer pairs and sequenced. The sequences were deposited in GenBank (16S rDNA, OP861004-OP861006; atpD, OQ703328-OQ703330; dnaK, OQ703331-OQ703333; gap, OQ703334-OQ703336; recA, OQ703337-OQ703339; and rpoB, OQ703340-OQ703342). The isolates were identified as Sphingomonas spermidinifaciens based on the phylogenetic tree inferred by maximum-likelihood using MegaX 7.0 of the concatenated six sequences (multilocus sequence analysis, MLSA) compared with sequences from different Sphingomonas type strains . Pathogenicity of the isolates was tested on healthy leaves of the two-year-old plum plants in a greenhouse. The leaves were wounded by a sterilized needle and sprayed with bacterial suspensions prepared in PBS (Phosphate buffer saline) at OD600=0.5. PBS buffer solution was used as negative control. Each isolate was used to inoculate on 20 leaves per plum tree. The plants were covered with plastic bags to maintain high humidity. Dark brown-to-black lesions were observed on leaves 3 days post incubation at 28℃ with constant light. The average diameter of lesions was 1 cm after seven days, but the negative controls were symptomless. Bacteria reisolated from the diseased leaves were the same as the ones used for inoculation on the basis of morphological and molecular identification, fulfilling Koch's postulates. Plant disease caused by a Sphingomonas species has been reported on mango, pomeand Spanish melon. However, this is the first report of S. spermidinifaciens causing leaf spot disease of plum in China. This report will help to develop effective disease control strategies in the future.

Modulation of ethylene and ROS-scavenging enzymes by multifarious plant growth-promoting endophytes in tomato (Solanum lycopersicum) plants to combat Xanthomonas -induced stress

The purpose of the current study was to explore root endophytes- Priestia megaterium T3 and Bacillus cereus T4 from Moringa olefiera for the suppression of leaf spot disease in tomato plants challenged with Xanthomonas vesicatoria. Both strains had plant growth-stimulating characteristics including auxin production, solubilization of inorganic phosphate and zinc complexes, and production of ammonia, siderophore, as well as hydrolytic enzymes. An agar well diffusion and fluorescence viability assay have validated the antibacterial effect of the cell-free culture supernatant of strains T3 and T4. Liquid chromatography-mass spectrometry (LC-MS) profiling has identified the secondary metabolites in the cell-free supernatant of strains T3 and T4. The bio-priming of tomato seeds with a consortium of T3 and T4 strains has significantly declined ethylene (by 0.61-fold) and hydrogen peroxide (H2O2, 0.64-fold) concentration thus, maintaining a lower content of ROS-induced malondialdehyde (MDA, 0.91-fold) as compared to control counterparts. Consequently, the leaf spot disease severity was reduced by ∼70% in consortium-treated tomato plants in contrast to their pathogen-challenged control. The consortia (T3+T4) treatment has facilitated induced systemic resistance by enhancing enzymatic activities of phenylalanine ammonia-lyase (PAL), peroxidase (PO), polyphenol oxidase (PPO), catalase (CAT), and ascorbate oxidase (AO) to detoxify the excessive Xanthomonas-induced ROS accumulation in tomato plants. Conclusively, bacterial endophytes modulate X. vesicatoria-induced ROS response and ethylene levels in tomato plants. The current findings indicate that plant growth-promoting endophytic bacterial strains hold the potential to sustainably enhance plant growth and suppress bacterial leaf spot disease in tomato plants.

Enhancing host-pathogen phenotyping dynamics: early detection of tomato bacterial diseases using hyperspectral point measurement and predictive modeling.

Early diagnosis of plant diseases is needed to promote sustainable plant protection strategies. Applied predictive modeling over hyperspectral spectroscopy (HS) data can be an effective, fast, cost-effective approach for improving plant disease diagnosis. This study aimed to investigate the potential of HS point-of-measurement (POM) data for in-situ, non-destructive diagnosis of tomato bacterial speck caused by Pseudomonas syringae pv. tomato (Pst), and bacterial spot, caused by Xanthomonas euvesicatoria (Xeu), on leaves (cv. cherry). Bacterial artificial infection was performed on tomato plants at the same phenological stage. A sensing system composed by a hyperspectral spectrometer, a transmission optical fiber bundle with a slitted probe and a white light source were used for spectral data acquisition, allowing the assessment of 3478 spectral points. An applied predictive classification model was developed, consisting of a normalizing pre-processing strategy allied with a Linear Discriminant Analysis (LDA) for reducing data dimensionality and a supervised machine learning algorithm (Support Vector Machine - SVM) for the classification task. The predicted model achieved classification accuracies of 100% and 74% for Pst and Xeu test set assessments, respectively, before symptom appearance. Model predictions were coherent with host-pathogen interactions mentioned in the literature (e.g., changes in photosynthetic pigment levels, production of bacterial-specific molecules, and activation of plants' defense mechanisms). Furthermore, these results were coherent with visual phenotyping inspection and PCR results. The reported outcomes support the application of spectral point measurements acquired in-vivo for plant disease diagnosis, aiming for more precise and eco-friendly phytosanitary approaches.

Isolation, characterization, and evaluation of putative new bacteriophages for controlling bacterial spot on tomato in Brazil.

Bacterial spot is a highly damaging tomato disease caused by members of several species of the genus Xanthomonas. Bacteriophages have been studied for their potential use in the biological control of bacterial diseases. In the current study, bacteriophages were obtained from soil and tomato leaves in commercial fields in Brazil with the aim of obtaining biological control agents against bacterial spot. Phage isolation was carried out by co-cultivation with isolates of Xanthomonas euvesicatoria pv. perforans, which was prevalent in the collection areas. In a host range evaluation, none of the phage isolates was able to induce a lytic cycle in all of the bacterial isolates tested. In in vivo tests, treatment of susceptible bacterial isolates with the corresponding phage prior to application to tomato plants led to a reduction in the severity of the resulting disease. The level of disease control provided by phage application was equal to or greater than that achieved using copper hydroxide. Electron microscopy analysis showed that all of the phages had similar morphology, with head and tail structures similar to those of viruses belonging to the class Caudoviricetes. The presence of short, non-contractile tubular tails strongly suggested that these phages belong to the family Autographiviridae. This was confirmed by phylogenetic analysis, which further revealed that they all belong to the genus Pradovirus. The phages described here are closely related to each other and potentially belong to a new species within the genus. These phages will be evaluated in future studies against other tomato xanthomonad strains to assess their potential as biological control agents.

Parametric evaluation of segmentation techniques for paddy diseases analysis

In most paddy plant diseases, the leaf is the primary source of information for image-based disease identification and classification. Image segmentation is an important step in the plant disease analysis process. It is used to separate the normal part of the leaf from the disease-affected part of the leaf. In this paper diseases like Bacterial leaf blight, Brown spot, and Leaf smut are segmented using existing, K-means clustering, the Otsu thresholding method. Color space-based segmentation is newly proposed for paddy disease analysis. The intelligence of segmentation techniques is evaluated using the Error Rate and Overlap Rate across the three paddy diseases namely, Bacterial Leaf Blight (BLB), Brown Spot (BS) and Leaf Smut (LS). The results were compared among the Otsu, K-means and color thresholding segmentation techniques. The results revealed that, the color thresholding method using the Lab model emerged as a novel segmentation method for all three paddy diseases with an average Error Rate (ER) and Overlap Rate (OR) of [0.36, 0.95]. The proposed work is carried out in the department of Electronics and Communication research centre at Ballari Institute of Technology and Management, Ballari, Karnataka during the period from August 2022 to February 2023 with the expert suggestions of the plant pathologist, from the University of Agricultural Science, Dharwad, Karnataka.

A Review on Disease Management in Millets

Millets, both major and minor, are important source of human diet. They are rich in proteins, carbohydrates, fat, fiber, micro nutrients including vitamins and minerals. Production of millets increase every year as they have high capacity to withstand biotic and abiotic stresses. However, in recent times, they are subjected to all type of bacterial, viral infections and fungal diseases. Blast, downy mildew, ergot, grain mold, smut, rust, bacterial leaf strike, bacterial leaf spot, Maize stripe virus (MStV-S), Maize streak virus, Maize mosaic virus (MMV-S) are common among all millets. Integrated crop management and pest control through agronomic cultural practices, use of resistant tolerant cultivars, chemical and biological control measures reduce the spread of diseases leading to high crop yield.

First Report of Bacterial Leaf Spot of Red Maple Caused by Pseudomonas syringae pv. syringae in Tennessee.

Red maple (Acer rubrum L.) is an economically important ornamental nursery plant grown for its aesthetic value. In May 2022, field and container-grown red maple 'October Glory' plants exhibited severe leaf spots in a commercial nursery in Warren Co., Tennessee. Leaf spots were brown-to-black color with a yellow halo (Fig. 1a). Disease severity was about 40% of leaf area and incidence was 60-70% of 10,000 plants. Symptomatic leaf tissues were surface sterilized with 0.525% sodium hypochlorite for 1 min and washed twice with sterilized water. Bacterial colonies, cream colored and circular with smooth margins, were obtained on King's B (KB) and nutrient agar media after 3 days of incubation at 28°C. Bacteria were gram-negative and fluorescent on KB under UV light. The biochemical and physiological test results were negative for cytochrome C oxidase, pectolytic activity on potato slices, and arginine dehydrolase, but positive for gelatin liquefaction, aesculin hydrolysis, and levan production. The BIOLOG test was positive for the utilization of D-galactose, D-galacturonic acid, D-galactonic acid lactone, D-gluconic acid, and was negative for the utilization of β-methyl-D-glucoside, N-acetyl-D-glucosamine, α-hydroxybutyric acid, D-glucose-6-phosphate, α-keto-butyric acid, and α-keto-glutaric acid. To confirm the bacterial identity, total genomic DNA was extracted using DNeasy PowerLyzer Microbial Kit directly from pure cultures (strains FBG1662 and FBG4230). The small subunit ribosomal RNA (16S rRNA), RNA polymerase sigma factor (rpoDp and rpoDs), citrate synthase (gltA), DNA gyrase (gyrB) genes were amplified and sequenced by primers 8F/1492R (Galkiewicz et al. 2008), rpoDpF/R, rpoDsF/R, gltAF/R, and gyrBF/R (Sarkar and Guttman 2004), respectively. The sequences of the two strains (GenBank accession nos. 16S: OP962145 and OP948281; rpoDp: OP998258 and OP957300; rpoDs: OP998259 and OP957299: gltA: OP998256 and OP957301; gyrB: OP998257 and OP957302) were >99% similar (100% coverage) to the complete genome of Pseudomonas syringae pv. syringae (CP026568) in the NCBI database. A phylogenetic analysis was performed and confirmed the identity using concatenated sequences of gltA, gyrB, rpoDp, rpoDs, and 16S of P. syringae pv. syringae and other closely related taxa retrieved from GenBank (Fig. 2). Based on morphological and molecular identification, both bacterial strains were identified as P. syringae pv. syringae. Pathogenicity test was conducted by spray inoculation of ten one-year-old red maple 'October Glory' with bacterial suspension (107 CFU/ml) using bacterial strain FBG4230. Ten plants were sprayed with sterilized water as control. All plants were covered with clear plastic for 24 h and incubated in a greenhouse at 21 to 23°C, 70%RH, 16-h photoperiod. At seven days after inoculation, brown-to-black leaf spots surrounded by yellow halo were developed on all inoculated plants (Fig. 1b), while the control plants remained symptomless. The bacterium was re-isolated from the inoculated plants and it was 100% identical to P. syringae pv. syringae using biochemical tests as well as sequence analysis. P. syringae has been reported pathogenic in red maple causing leaf spot in Oregon (Malvick and Moore, 1988). To our knowledge, this is the first report of bacterial leaf spot caused by P. syringae pv. syringae in red maple in Tennessee. Identification of this bacterial pathogen on red maple is crucial in developing timely management practices.

A Brief Evaluation of a Copper Resistance Mobile Genetic Island in the Bacterial Leaf Spot Pathogen Xanthomonas euvesicatoria pv. perforans.

Due to the continuous use of copper containing bactericides without effective alternative bactericides, copper resistance has become more prevalent in plant pathogens, including Xanthomonas euvesicatoria pv. perforans (formerly Xanthomonas perforans), a predominant cause of bacterial leaf spot disease of tomato and pepper in the Southeastern United States. Previously, reports of copper resistance have been associated with a large conjugative plasmid. However, we have characterized a copper resistance genomic island located within the chromosome of multiple X. euvesicatoria pv. perforans strains. The island is distinct from a previously described chromosomally encoded copper resistance island in X. vesicatoria strain XVP26. Computational analysis revealed the genomic island to contain multiple genes associated with genetic mobility, including both phage-related genes and transposase. Among copper-tolerant strains of X. euvesicatoria pv. perforans isolated from Florida, the majority of strains were found to have the copper resistance chromosomally encoded rather than plasmid borne. Our results suggest that this copper resistance island may have two modes of horizontal gene transfer and that chromosomally encoded copper resistance genes may provide a fitness advantage over plasmid-borne resistance.

Functional characterization of VirB/VirD4 and Icm/Dot type IV secretion systems from the plant-pathogenic bacterium Xanthomonas euvesicatoria.

Many Gram-negative plant- and animal-pathogenic bacteria employ type IV secretion (T4S) systems to transport proteins or DNA/protein complexes into eukaryotic or bacterial target cells. T4S systems have been divided into minimized and expanded T4S systems and resemble the VirB/VirD4 T4S system from the plant pathogen Agrobacterium tumefaciens and the Icm/Dot T4S system from the human pathogen Legionella pneumophila, respectively. The only known plant pathogen with both types of T4S systems is Xanthomonas euvesicatoria which is the causal agent of bacterial spot disease on pepper and tomato plants. In the present study, we show that virB/virD4 and icm/dot T4S genes are expressed and encode components of oligomeric complexes corresponding to known assemblies of VirB/VirD4 and Icm/Dot proteins. Both T4S systems are dispensable for the interaction of X. euvesicatoria with its host plants and do not seem to confer contact-dependent lysis of other bacteria, which was previously shown for the chromosomally encoded VirB/VirD4 T4S system from Xanthomonas axonopodis pv. citri. The corresponding chromosomal T4S gene cluster from X. euvesicatoria is incomplete, however, the second plasmid-localized vir gene cluster encodes a functional VirB/VirD4 T4S system which contributes to plasmid transfer. In agreement with this finding, we identified the predicted relaxase TraI as substrate of the T4S systems from X. euvesicatoria. TraI and additional candidate T4S substrates with homology to T4S effectors from X. axonopodis pv. citri interact with the T4S coupling protein VirD4. Interestingly, however, the predicted C-terminal VirD4-binding sites are not sufficient for T4S, suggesting the contribution of additional yet unknown mechanisms to the targeting of T4S substrates from X. euvesicatoria to both VirB/VirD4 and Icm/Dot T4S systems.

Determining the Efficacy of Plant Extracts and Chemicals against Bacterial Leaf Spot of Spinach

Bacterial leaf spot of spinach is an emerging problem causing significant losses worldwide, particularly in Pakistan. The current study was subjected to mitigate this arising issue by the exploitation of four chemicals and four plant extracts in vitro and in green house at three different concentrations. The experiments were performed under CRD through inhibition zone technique and foliar spray method. Laboratory experiments concluded aqueous extract of Acacia nilotica with maximum inhibition zone (10.4 mm) followed by Calotropis gigantea (5.0 mm), Citrullus colocynthis (3.6 mm), and Moringa oleifera (1.6 mm) respectively as compared to control (0.00 mm). In vitro results of chemicals showed Streptomycin sulphate (13.9 mm) with highest inhibition zone followed by Oxytetracycline (10.2 mm), Kasumin (4.7 mm), and Copper oxychloride (3.2 mm) respectively. Most effective Phytoextracts and chemicals observed in vitro were selected for greenhouse evaluation. In greenhouse results Streptomycin sulphate (5.2%) showed as most significant antibacterial agent in managing the disease severity followed by Acacia nilotica (10.1%) as compared to control. The current findings would be the valuable insights for researchers and the farming community, assisting in the improved management of bacterial leaf spot of spinach.

Characterization of Xanthomonas hortorum isolates causing leaf spot on Primula vulgaris

AbstractThe bacterial leaf spot of Primula vulgaris was reported from forest locations in Mazandaran province, Iran in 1995. The causative bacterium was initially identified as Xanthomonas campestris based on sequences of ITS and 16S rRNA regions. On the basis of sequences of gyrB and dnaK housekeeping genes, the isolates resembled strains of Xanthomonas hortorum. The present study was performed to clarify the taxonomic status of the incitant bacterium. Samples of P. vulgaris with leaf spot symptoms were collected from the Darabkola forest (Mazandaran province) and the bacterial strains were isolated on plates of glucose nutrient agar. Some isolates only induced hypersensitive reaction on geranium (Pelargonium × hortorum) leaves but a few isolates incited leaf spot symptoms when inoculated on P. vulgaris, P. heterochroma and P. × polyantha plants. The isolates were Gram stain, oxidase and urease negative but positive in tests for catalase and hydrolysis of gelatin, aesculin and Tween 80. Several unique nonpathogenic isolates were recovered with varying carbon source utilization profiles and different molecular features, including distinct ERIC‐PCR profiles. Representative gyrB, rpoD, dnaK and fyuA housekeeping genes were amplified by PCR and sequenced for comparison with Xanthomonas species in the GenBank database using maximum‐likelihood, maximum‐parsimony and Bayesian algorithms to construct phylogenetic trees. The pathogenic and nonpathogenic strains were closely related to X. hortorum pathovars. Therefore, it is likely that these isolates belong to a new pathovar of X. hortorum. Based on previous studies and our findings, we propose naming the strain causing primrose leaf spot as X. hortorum pv. primulicola.