Journal of Phytopathology最新文献

The increasing occurrence of virus disease in cowpea raises concerns about the emergence of new or expanding viral threats in the production of vegetable and pulse crops. During the summer of 2022, plants showing veinal necrosis disease on cowpea were observed at the experimental farm of the Indian Agricultural Research Institute (IARI), New Delhi. To elucidate the natural occurrence of groundnut bud necrosis virus (GBNV) associated with vein necrosis disease in cowpea, this study employed biological and molecular approaches, including the characterization and in silico prediction of structural and functional features of the N and NSs proteins, as well as subcellular localization studies in both natural and experimental host systems. Mechanical transmission successfully reproduced the typical disease as observed in the field on cowpea plants (cv. Pusa Komal). Molecular diagnosis using RT-PCR indicated the presence of GBNV in the diseased plant samples. Cloning and sequencing of the near-complete small RNA segment of GBNV revealed 98.9% nucleotide and > 99% amino acid identity in the nucleocapsid (N) gene, and 98% nucleotide and 99% amino acid identity for the non-structural silencing suppressor (NSs) gene, compared to known GBNV isolates. Phylogenetic analysis further placed the associated virus strain within the major clade of GBNV in plants. Further, the N and NSs proteins were successfully transiently expressed in plant and found localized in the cytoplasm of cowpea and Nicotiana benthamiana, the natural and experimental host of the virus respectively. The experimental reproduction of the disease and characterization of the N and NSs genes of the virus associated with pathogenesis revealed the association of the GBNV strain in the occurrence of veinal necrosis of cowpea. For the first time, functional motifs within the N and NSs genes were comprehensively predicted through bioinformatic analyses and their subcellular localization was examined in both natural and experimental hosts using confocal microscopy.

The phyllody severity in sesame crop ranged from 9% to 20% under field conditions, and the maximum was recorded in Thiruvannamalai district of Tamil Nadu, India. The observed morphological characters of the transmitting insect vector Orosius albicinctus were light ochraceous colour with irregular striations and dark brown with black mottling. The infected sesame samples were subjected to PCR amplification by using universal primer pair P1/P6, followed by nested PCR using R16F2n/R16R2 primers, which amplified ~1.25 kb, which was also occurred in the Parthenium weed plant. Further, BLAST analysis showed that the sequences were aligned with the 16SrII-B group phytoplasma consisting of Candidatus Phytoplasma aurantifolia (94.87%), sesame phyllody (97.97%), Blainvillea acmella phytoplasma (97.70%) and Mollugo disticha phyllody phytoplasma (98.28%). Since there is no adorus impact despite the vast host range, polyphagous insect vectors, lack of environmentally acceptable effective insecticide, and unavailability of resistant sources, management of phyllody disease has been attempted with biotic inducers (salicylic acid [SA], methyl jasmonate [MeJA] and beta amino butyric acid [BABA]) individually (50, 100 and 150 ppm) and in combination. Among the inducers tested in vitro, SA 50 ppm showed a higher vigour index (1401) in the seed infiltration method of seed treatment, further confirmed in blotter paper technique. The same treatment showed a high germination percentage (86%) with more shoot length on the 60th day (67.0 cm) and five branches/plant under glasshouse experiment. In addition, SA 50/100 ppm increased the phenolic activity and plant defence enzymes; hence it has been further test verified at two different field locations, and the results revealed that seed infiltration treatment with SA 50/100 ppm, followed by foliar application of SA (50/100 ppm) at 30, 45 and 60 DAS, was shown to be effective by recording lesser disease severity compared to control (42.2%). Also, salicylic acid treatment showed higher germination (87.6%), more capsules/plant, thereby incorporation of inducers like salicylic acid in the integrated disease management will serve as a viable and sustainable management option.

Berkeleyomyces, a globally spread genus of soil-borne fungi comprising B. basicola and B. rouxiae, causes black root rot in over 170 plant species including the ornamental shrub Ilex crenata. These two species are morphologically indistinguishable and were previously grouped under Thielaviopsis basicola. Historical records did not account for hidden species diversity, including pathogenic diversity on specific host plant species such as lettuce. The first objective of this study was to determine whether strains of both Berkeleyomyces species affect I. crenata, as this knowledge could influence the selection of isolates for resistance breeding. We collected 33 isolates from I. crenata plants in gardens, parks and nurseries in Flanders (Belgium) and the Netherlands, and characterised this Berkeleyomyces collection to species and subspecies level using Genotyping-by-Sequencing (GBS). Most I. crenata isolates belonged to two near-clonal groups within B. basicola. Virulence testing revealed that B. basicola and B. rouxiae could incite similar disease levels in I. crenata, with smaller differences in virulence related to the host of origin. Our second objective was to develop novel qPCR assays, allowing fast species identification and quantification of both Berkeleyomyces species. We developed two sets of species-specific qPCR assays: one set on the 60S locus (with sensitivity down to 100 fg DNA), and another set on the rDNA ITS locus (with sensitivity down to 10 fg DNA), which can be run in duplex format. The qPCR assays were successfully used to quantify the pathogen. They show potential for use in other hosts as well as in epidemiological studies.

Endophytic fungi are known to improve plant resistance to stress and enhance plant performance. A comprehensive understanding of the mechanisms underlying endophytic fungi-mediated systemic resistance is crucial for the widespread adoption of these fungi as beneficial biological control agents. To unravel the complex interactions between tomato and endophytic fungi, we compared the transcriptomic responses of tomato plants induced with Trichoderma harzianum, T. afroharzianum, T. atroviride and Pochonia chlamydosporia. RNA-seq datasets were used to assess the common expression patterns in defence-related pathways. Our analysis revealed that a group of common key genes were significantly induced in all investigations examined. Additionally, we observed that 20 transcripts related to anion transport, which is crucial for early plant immune responses, were consistently enriched in all the studies. These findings highlight the conserved and specific nature of plant–endophyte interactions and their potential for enhancing plant resistance through targeted genetic manipulation and breeding for long-lasting resistance.

Achachairu (Garcinia humilis) is a fruit crop with its centre of origin in the Amazon rainforest region, which has gained prominence in commercialization in several countries in America. In addition to a sweet flavour, it is rich in several vitamins and fibres, making it of interest in the industrial sector. Fungal isolates of the Botryosphaeriaceae were obtained from fruits of G. humilis with symptoms of rot and were identified by concatenated phylogenetic analyses of ITS, TEF1-α, Tub-2 and RPB2 loci. We identified nine isolates as Lasiodiplodia theobromae, nine isolates as Lasiodiplodia pseudotheobromae, and one isolate as Neofusicoccum batangarum. Three representative isolates of these species were selected for pathogenicity tests in fruits and stems of seedlings. Typical rot symptoms appeared on the inoculated fruits and stems, confirming pathogenicity. To our knowledge, this is the first report of L. theobromae, L. pseudotheobromae, and N. batangarum as the causal agents of fruit rot of G. humilis worldwide.

Seed samples of rice were collected from north India during two consecutive crop seasons, 2022 and 2023. Isolations of fungi associated with discoloured seed samples were performed and various species of Aspergillus obtained from 1966 samples (the large majority from the state of Punjab) were characterised morphologically for their identification. A total of 109 isolates of six different species of Aspergillus were obtained from 22 popular rice varieties. Species level identification was confirmed by using ITS and β tubulin gene loci. A. flavus and A. niger were the most prevalent species having frequencies of occurrence of 33.0% and 29.9% respectively. Approximately 8.3% of the isolates were predicted to be positive for production of aflatoxins and 1.8% of the isolates were predicted positive for production of ochratoxin based on PCR screening. Quantitative analysis of toxin production by selected isolates using liquid chromatographic tandem mass spectrometry (LC–MS/MS) revealed that isolate Af22 of A. flavus obtained from rice variety PR 126 produced 20.7 μg/kg aflatoxin B1 and 28.7 μg/kg aflatoxin B2 and isolate Ao5 of A. ochraceus obtained from variety PR 121 produced ochratoxin A (8.6 μg/kg) were the most toxigenic isolates. Pathogenicity tests on susceptible rice variety PR 114 demonstrated that all toxigenic isolates induced distinct discoloration patterns on rice grains after an incubation period of 5–7 days. The study underscores the importance of continuous monitoring of Aspergillus contamination and calls for stringent post-harvest management practices to reduce the risk of mycotoxin contamination in rice.

Plant diseases are considered one of the most serious problems in world agricultural production. Regular monitoring and detection are essential to control plant diseases, and effective management methods are used to prevent disease spread and lower pesticide costs. Smart agriculture techniques are one of the key solutions in plant disease prediction and improving crop productivity. Even though various papers have been published on the model for plant disease prediction based on smart agriculture, there is still a lack of an overall systematic model. The proposed approach has been developed to overcome the challenges faced by the existing method. This presented approach uses deep learning and meta-heuristic techniques to detect and classify crop diseases, providing an accurate and efficient solution for farmers to improve crop yield. The process begins with collecting crop disease images from the Kaggle database. Initially, noise removal and contrast enhancement are performed using a Gaussian Amended Wiener Filter (GAWF). Next, the Modified Residual U-Net (MRU-Net) model extracts significant disease regions from the images. Effective features are collected from these segments using a convolutional neural network (CNN) and an improved vision transformer model (IViT). Finally, classification is performed with a stacking ensemble model that incorporates XGBoost (XGB), Gradient Boosting (GB) and AdaBoost-Decision Tree (AdB-DT). The proposed model achieved an accuracy of 99.74% on the PlantVillage dataset, 99.51% on the PlantDoc dataset and 99.57% on the Pigeonpea Leaf Disease dataset, demonstrating its robustness and generalizability across both curated and real-world agricultural image conditions. Also, the proposed approach provided insights into disease identification by utilising Grad-CAM to provide visual explanations.