Occurrence of Neopestalotiopsis clavispora Causing Leaf Spot on Dendrobium officinale in China

Dendrobium officinale Kimura L., an endangered orchid plant, is a rare and precious Chinese herb and widely used to prepare Chinese traditional medicine (Zheng et al. 2005). In August 2021, significant indications of an unknown leaf spot disease were observed on greenhouse-grown D. officinale in Yueqing of Wenzhou (28.39°N, 121.04°E), Zhejiang Province, China, the main producing location of this orchid plant. Approximately twenty percent of plants surveyed showed typical infection symptoms. Initially, the symptoms appeared as small, circular black spots. As the disease developed, the center of the lesions was sunken with a black border. To determine the causal agent, 10 symptomatic plant samples were collected and all pieces from symptomatic plant leaves were used for isolating pathogen. Tissues between healthy and necrotic area were cut into pieces (5 × 5 mm, n=10), disinfected with 10% sodium hypochlorite for 1 minute, rinsed 3 times with sterile water, and dried on sterile tissue. Samples were then placed on potato dextrose agar medium (PDA) for 1 piece per plate, and incubated at 25℃ in a dark biochemical incubator. After 3 days, hyphal tips growing from the disinfected tissues were individually transferred to new PDA plates and incubated at 25℃ in the dark. Twelve same fungal isolates were obtained from all symptomatic leave fragments, then DDO11 was chosen as a representative isolate for further study. The colonies showed white aerial mycelium after 5 days culture at 25°C on PDA. Black viscous acervuli appeared and scattered on the surface of the colony after 8-12 days culture. Conidia were spindle shape, five cells, four septa, average 29.3 × 8.5 μm (n = 30; length × width). The apical and basal cells were lighter in color, and most of them were hyaline. The middle three cells were darker in color, and mostly brown. There are 2 to 4 colorless and transparent unbranched accessory filaments at the top, 32.5 µm in average length, and the basal cell has a small appendage, 9.2 µm in average length, n=30. For fungal identification to species level, Internal transcribed spacer (ITS) region, β-tubulin gene (TUB2) and translation elongation factor-1α (TEF-1α) were amplified (Qiu et al. 2020), respectively. The ITS, TUB2 and TEF-1α gene sequences of the representative isolate DDO11 were deposited in NCBI GenBank nucleotide database with accession numbers OK631881, OK655895 and OK655896, respectively. BLASTn analysis respectively showed 100%, 100% and 99.6% nucleotide sequence identity with Neopestalotiopsis clavispora strain accessions MG729690, MG740736 and MH423940, which indicated that the pathogen belonged N. clavispora. A maximum-likelihood phylogenetic analysis based on multi-locus sequence (ITS, TUB2, and TEF-1α) using MEGA X showed the similar result (Kumar et al. 2018). To verify pathogenicity, thirty 1-year-old healthy D. officinale plants of cultivar Yandang1 were used for inoculation tests. Spores of N. clavispora DDO11 were produced on PDA for 7 days at 28°C and washed with sterile distilled water, and the concentrations were adjusted to 1 × 106 spores/ml using a hemocytometer. Fifteen surface disinfected healthy plants were inoculated by spraying the suspension (2 ml, 1 × 106 spores/ml) and covered with plastic bags for 24 h, and another 15 plants treated with sterile distilled water were used as control. The plants were placed in a humidified chamber (>95% relative humidity) at 25°C for 48 h after inoculation and kept in a growth chamber (Kiangnan, China) at 25°C with 12-h day/night cycle for 8 days (Cao et al. 2019). All inoculated leaves showed symptoms identical to those observed in the field. No disease occurred on the controls. The Neopestalotiopsis isolate was reisolated from the symptomatic leaves, and species identification was confirmed by the morphological and molecular method described above. N. clavispora has been reported to cause diseases on a variety of plants all over the world, such as strawberry (Gilardi et al. 2019), blue berry (Shi et al. 2021), Syzygium cumini (Banerjee et al. 2020), Macadamia (Qiu et al. 2020), and so on. To the best of our knowledge, this is the first report of N. clavispora causing leaf spot on D. officinale in China. This report will help us to recognize the leaf spot disease of D. officinale and establish a foundation for future studies on N. clavispora to address effective management strategies.

Transcriptomics Analysis and Candidate Genes Associated With Xinjiang Jujubes in Response to Alternaria Alternata Infection

BackgroundBlack spot disease, caused by Alternaria altrenata, is one of the most destructive diseases of jujube worldwide. To better understand the resistance mechanisms of jujube to A. altrenata infection to be able to improve disease control and resistance breeding. Two different cultivars, Zizyphus jujuba Mill. var. Jun jujube (susceptible) and Zizyphus jujuba Mill. var. Hui jujube (resistant), were tested. ResultsIn this study, we identified 2235 differentially expressed genes (DEGs) in the disease-resistant cultivar and 4958 in the susceptible cultivar. To better understand these DEGs, the datasets were analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genome (KEGG) database. Most of them were associated with plant phytohormone synthesis and signal transduction, flavonoid synthesis, and glutathione metabolism. The expression of 6 DEGs associated with disease resistance were detected by real time-quantitative polymerase chain reaction (RT-qPCR), consistent with the results of Illumina transcriptome sequencing. Moreover, the expression level of the six DEGs differently in Jun jujube and Hui jujube, verified they are defense response factors. ConclusionsThe present study identified several candidate resistance genes and signal transduction pathways that may contribute to black spot disease resistance in jujube, which will assist the investigation of resistance mechanisms in the response of jujube to A. altrenata infection.

Early-Stage Brown Spot Disease Recognition in Paddy Using Image Processing and Deep Learning Techniques

India is an agricultural country. Paddy is the main crop here on which the livelihood of millions of people depends. Brown spot disease caused by fungus is the most predominant infection that appears as oval and round lesions on the paddy leaves. If not addressed on time, it might result in serious crop loss. Pesticide use for plant disease treatment should be limited because it raises costs and pollutes the environment. Usage of pesticide and crop loss both can be minimized if we recognize the disease in a timely manner. Our aim is to develop a simple, fast, and effective deep learning structure for early-stage brown spot disease detection by utilizing infection severity estimation using image processing techniques. The suggested approach consists of two phases. In the first phase, the brown spot infected leaf image dataset is partitioned into two sets named as early-stage brown spot and developed stage brown spot. This partition is done on the basis of calculated infection severity. Infection severity is computed as a ratio of infected pixel count to total leaf pixel count. Total leaf pixel counts are determined by segmenting the leaf region from the background image using Otsu's thresholding technique. Infected pixel counts are determined by segmenting infected regions from leaf regions using Triangle thresholding segmentation. In the second phase, a fully connected CNN architecture is built for automatic feature extraction and classification. The CNN-based classification model is trained and validated using early-stage brown spot, developed stage brown spot, and healthy leaves images of rice plants. Early-stage brown spot and developed stage brown spot images used in training and validation are the same images that are obtained in phase 1. The experimental analysis shows that the proposed fully connected CNN-based early-stage brown spot disease recognition model is an effective approach. The classification accuracy of the suggested model is found to be 99.20%. The result of the suggested method is compared with those existing CNN-based disease recognition and classification methods that have used leaf images to recognize the diseases. It is observed that the performance of our method is significantly better than compared methods.

Occurrence of Leaf Spot Disease on Watermelon Caused by Pseudomonas syringae pv. syringae

Typical bacterial symptoms, water-soaking brown and black leaf spots with yellow halo, were observed on watermelon seedlings in nursery and field of Gyeongnam and Jeonnam provinces. Bacterial isolates from the lesion showed strong pathogenicity on watermelon and zucchini. One of them was rod-shaped with 4 polar flagella by observation of transmission electron microscopy. They belonged to LOPAT group 1. The phylogenical trees with nucleotide sequences of 16S rRNA and multi-locus sequencing typing with the 4 house-keeping genes (gapA, gltA, gyrB, and rpoD) of the isolates showed they were highly homologous to Pseudomonas syringae pv. syringae and grouped together with them, indicating that they were appeared as P. syringae genomospecies group 1. Morphological, physiological, and genetical characteristics of the isolates suggested they are P. syringae pv. syringae. We believe this is the first report that P. syringae pv. syringae caused leaf spot disease on watermelon in the Republic of Korea.

Pathogenic Fungi Diversity of ‘CuiXiang’ Kiwifruit Black Spot Disease during Storage

Kiwifruit black spot disease has become increasingly widespread in many ‘CuiXiang’ kiwifruit plantings regions. This research was aimed at the pathogenic microorganisms of black spot of the ‘CuiXiang’ cultivar. Physiological, morphological and transcriptional characteristics between black spot fruit and healthy fruits were evaluated. Then, it applied a high-throughput internal transcribed spacer (ITS) sequencing to analyze the black spot disease microbial community. The cell structure showed that mycelium was attached to the surface of the kiwifruit through black spot, and that consequently the mitochondria were damaged, starch particles were reduced, and shelf life was shortened. Transcriptome revealed that different genes in kiwifruit with black spot disease were involved in cell wall modification, pathogen perception, and signal transduction. ITS sequencing results described the disease-causing fungi and found that the microbial diversity of black spot-diseased fruit was lower than that of healthy fruit. We predict that candidate pathogenic fungi Cladosporium cladosporioides, Diaporthe phaseolorum, Alternaria alternata, and Trichothecium roseum may cause black spot. This study was to explore the pathogenic fungal community of ‘CuiXiang’ kiwifruit black spot disease and to provide essential information for field prevention.