Plant Pathology Journal最新文献

Understanding and predicting epidemiological trends of plant viruses is essential for sustaining crop productivity and control strategies. The National Center for Biotechnology Information (NCBI) GenBank provides nucleotide sequences with metadata such as date, location, and host, offering valuable resources for research. However, GenBank lacks automated tools for visualizing temporal and spatial patterns. To address this limitation, we applied a vibe coding approach, a generative AI assisted method that enables non-programmers to process and visualize data efficiently. As a case study, we analyzed pepper (Capsicum spp.), a major East and Southeast Asian crop threatened by emerging viruses. Using vibe coding, we visualized reporting trends by country and year and mapped sequence variation and conserved regions of pepper-infecting viruses. This approach allowed rapid organization of large datasets and real-time utilization of newly deposited GenBank entries. NCBI-based plant virus analysis system provides automated analysis and visualization and is accessible at https://plantvirus-viewer.duckdns.org/.

Fire blight, caused by Erwinia amylovora, is an economically devastating disease affecting apple and pear orchards, and reliable detection is critical for effective management. However, field detection is challenging due to inhibitory compounds and the time-consuming nature of nucleic acid extraction, which limits the speed and accessibility of current diagnostic methods. Here, we present a CRISPR-Cas13a-based diagnostic platform designed for rapid, amplification-free, and extraction- free detection directly from plant material. In regions such as Korea where E. pyrifoliae is endemic, high genomic similarity between the two Erwinia species complicates accurate discrimination and poses a significant challenge for disease management. We identified E. amylovora-specific (EA-specific) single nucleotide polymorphisms and designed a panel of CRISPR RNAs (crRNAs) across multiple housekeeping genes and the 16S rRNA V3 region. Systematic screening with both synthetic RNA and mRNA revealed new crRNAs that maintained species specificity and sensitivity, achieving detection within minutes. To enable field-compatible sample processing, we developed and optimized a robust alkaline lysis workflow based on sequential NaOH lysis and HCl neutralization, which effectively released RNA from bacterial cells and remained compatible with crude Malus domestica leaf lysates. Under these extraction-free conditions, the assay achieved rapid, EA-specific detection of 1 × 106 CFUs/reaction within 15 minutes without nucleic acid purification or thermal cycling in the presence of plant material. This study establishes a practical framework for CRISPR-Cas13a diagnostics in plant pathology and provides a low-infrastructure strategy that can improve the speed and accuracy of fire blight surveillance and broader agricultural biosecurity efforts.

The Ralstonia solanacearum species complex (RSSC) is a major soil-borne pathogen of solanaceous crops. During a field experiment originally designed to monitor rhizosphere and episphere microbiomes in two pepper cultivars, a naturally emerging and asymptomatic Ralstonia dominance event was detected in the rhizosphere without visible wilt symptoms. This unexpected occurrence provided an opportunity to characterize asymptomatic RSSC dynamics and their microbial interactions under field conditions. Full-length 16S rRNA amplicon sequencing showed that one ASV (Sq_1) was nearly absent from the episphere but increased sharply in the rhizosphere from week 3 onward, dominating 20-80% of samples during weeks 7-10. Phylogenetic comparison with 93 historical Korean RSSC isolates placed Sq_1 within a 16S-defined lineage corresponding to pepper-associated R. pseudosolanacearum biovars 3 and 4. Sq_1 abundance accounted for a large portion of β-diversity turnover in the rhizosphere. After within-plot correlations were meta-analyzed, selected taxa were evaluated using a Bayesian pairwise compositional Lotka-Volterra (pcLV) model, which identified three taxa (Sq_272, TRA3-20; Sq_178, Bradyrhizobium; and Sq_124, Bryobacter) that consistently exerted inhibitory effects on Sq_1 per-interval growth. Supported by the longitudinal design and the high accuracy of PacBio full-length 16S sequencing, these findings highlight potential microbial suppressors of RSSC and demonstrate the utility of pcLV modeling for resolving directional interactions at the ASV level.

Rice bacterial leaf blight caused by Xanthomonas oryzae pv. oryzae (Xoo) remains a serious yield threat. To identify selective antibacterials, we synthesized a small library of naphthalene-2-acyl imidazolium salts (NAIMSs) and evaluated their anti-Xoo activity. Diskdiffusion assays of 18 analogues revealed a clear structure-activity relationship: isopentyloxy-substituted derivatives were active, whereas others were not. Among them, NAIMS1f exhibited potent inhibition with a minimum inhibitory concentration of 0.75 μg mL-1. Growth kinetics demonstrated bacteriostatic action, completely suppressing low-inoculum growth but not killing high-density cultures. NAIMS1f showed higher sensitivity toward Xoo than Escherichia coli, Bacillus pumilus, or Pseudomonas syringae, defining a useful sensitivity window. These findings identify NAIMS1f as a promising lead for Xoo-focused inhibition in vitro and provide a basis for future mechanistic and pre-field validation studies.

Soybean (Glycine max [L.] Merr.) is a globally important crop; however, its productivity is severely constrained by the soybean cyst nematode (Heterodera glycines Ichinohe). This nematode often remains undetected during early infection and persists in the soil as dormant cysts, causing long-term yield losses. Although conventional detection methods, such as microscopic inspection and polymerase chain reaction assays, provide accuracy, they are labor-intensive and unsuitable for large-scale monitoring. Therefore, an artificial intelligence-based framework was established for the classification and segmentation of female soybean cyst nematodes using advanced deep learning architectures. Soil samples were collected from infected fields in South Korea and female nematodes were imaged with red-green-blue cameras under a dissecting microscope. Instance segmentation was benchmarked across YOLOv5, YOLOv8, YOLOv11, and Detectron2. The finetuned YOLOv11 model achieved the best performance, with a precision of 0.977, a recall of 0.980, and a mean Average Precision at 50% intersection-over-union of 0.988. Additionally, color-based phenotyping using hue-saturation-value thresholds classified 4,392 nematode images into yellow, orange, and brown groups, representing the reproductive and developmental stages. Consequently, this integrated framework highlights the potential of artificial intelligence-driven detection systems to reduce labor-intensive practices and support sustainable soybean production through the improved management of nematode-induced yield losses.

Cassava mosaic disease, caused by Cassava mosaic begomoviruses in the family Geminiviridae, poses a major threat to cassava production, with Sri Lankan cassava mosaic virus (SLCMV) being the dominant strain in Southeast Asia. Transmitted via infected propagative stems and whiteflies (Bemisia tabaci), SLCMV's impact on cassava metabolite dynamics remains poorly understood. This study investigated metabolite profile changes in resistant, tolerant, and susceptible cassava cultivars at 1, 3, and 7 days after inoculation by viruliferous whiteflies. Distinct metabolite patterns were observed among cultivars, with several pathways linked to plant defense identified, including flavonoid biosynthesis, phenylpropanoid biosynthesis, and purine metabolism. Secondary metabolite pathways, such as the energy-signaling SnRK1/AMPK-liked proteins, alpha-linolenic acid metabolism, and starch and sucrose metabolism, were also implicated. The results provide insights into metabolite-mediated defense mechanisms during early and late infection, enhancing understanding of cassava's responses to SLCMV inoculation after exposure to viruliferous whitefly infestation. This study supports the development of SLCMV-resistant cassava cultivars.

Northern corn leaf blight (NCLB), caused by Exserohilum turcicum (Setosphaeria turcica), is a major disease that negatively impacts the yield and quality of sweet corn. Plant-associated microbes hold great potential for enhancing crop productivity and sustainability. This study investigated the fungal and bacterial communities associated with NCLB in resistant and susceptible sweet corn cultivars using amplicon metagenomic sequencing. The structural composition and diversity of the fungal community in symptomatic NCLB-susceptible cultivars differed significantly from those in asymptomatic NCLB-resistant cultivars. In contrast, the bacterial communities showed no significant differences between resistant and susceptible cultivars in both the phyllosphere and rhizosphere. Exserohilum and Alternaria were significantly more abundant in the phyllosphere of symptomatic NCLB-susceptible plants, while fungal genera such as Sporobolomyces and Aureobasidium, along with the order Dothideales and the bacteria Bacillus, were significantly more abundant in the phyllosphere of asymptomatic NCLB-resistant cultivars. Microbial metabolic functions related to sugar metabolism-including sucrose biosynthesis and the degradation of glucose and xylose, compounds abundant in plant cell walls-were enriched in the phyllosphere of symptomatic NCLB-susceptible plants. In contrast, functions associated with detoxification and defense responses to plant phenolic compounds were enriched in microbes from asymptomatic NCLB-resistant cultivars. Additionally, Bacillus, identified ash part of the core microbiome, and the epiphytic yeast Sporobolomyces, identified as a hub in the microbial network, exhibited antimicrobial activity that may suppress E. turcicum. These findings offer valuable insights into the role of microbial communities in plant health and disease resistance, with promising implications for developing microbiome-based strategies to manage NCLB.

Abiotic stresses, such as drought and high salinity, threaten global food security by severely limiting crop yields. Among diverse agricultural practices, the usage of plant growth-promoting rhizobacteria has been expanding to enhance plant resilience against environmental stresses. In this study, we examined the effects of Bacillus subtilis W1-like strain (BsW1L) on increasing plant tolerance in lettuce plants (Lactuca sativa) grown under drought and high-salt stresses. BsW1L-treated plants exhibited improved tolerance to both stresses, as indicated by increased shoot and root growth, leaf area, and chlorophyll content. Application of the BsW1L strain enhanced the mRNA expression and activity of key antioxidant enzymes, catalase and ascorbate peroxidase. This facilitated the detoxification of reactive oxygen species, leading to decreased hydrogen peroxide levels, reduced malondialdehyde accumulation, and increased total soluble sugars. Notably, treatment with the BsW1L strain elevated proline levels in the leaves of lettuce plants grown under drought stress but reduced them in plants exposed to salt stress. Taken together, these findings suggest that BsW1L can serve as an ecofriendly biostimulant for improving plant tolerance to abiotic stresses, contributing to sustainable agricultural practices.

This study presents a comprehensive, long-term assessment of the performance and economic impact of virus-free (VF) apple seedlings distributed to commercial orchards in Korea. We compared VF and virus-infected (VI) 'Hongro' and 'Fuji' apple trees over five years, starting from four to five years after planting. VF trees maintained no reinfection for up to nine years, while VI trees showed a high infection rate (78.6%). VF trees consistently showed higher fruit set, greater yield, and superior fruit quality-including size, color, and soluble solids-than VI trees. VI trees produced more unmarketable fruit in both cultivars. Economic analysis showed that adopting VF seedlings increased net grower income by KRW 825,000 per 10 a. These results demonstrate that VF apple trees provide sustained improvements in productivity, fruit quality, and profitability in orchard conditions, supporting the continued expansion of VF seedling programs for sustainable apple production in Korea.

Fire blight, caused by Erwinia amylovora, is a highly destructive bacterial disease that affects apple and pear orchards worldwide, leading to significant economic losses. In this study, we isolated and characterized endophytic bacterial strains from apple trees in Gyeongsangbuk-do, South Korea, to identify potential biocontrol agents against E. amylovora. Among the five antagonistic strains identified, Bacillus velezensis JE80 and JE250 exhibited the strongest inhibitory effects. Further analysis using culture filtrates (CFs) from these strains demonstrated that the CFs of JE80 and JE250 not only suppressed E. amylovora growth in a growth-phase-dependent manner but also significantly impaired bacterial motility and biofilm formation. Notably, in planta assays revealed that JE250 effectively reduced fire blight symptoms in apple blossoms, performing comparably to streptomycin sulfate. Whole-genome sequencing of JE250 identified biosynthetic gene clusters associated with the production of antimicrobial compounds, including difficidin, fengycin, bacillaene, macrolactin, bacillibactin, and bacilysin, further supporting its strong antagonistic potential. These findings suggest that B. velezensis JE250 is a promising biocontrol agent for sustainable fire blight management. Future research should focus on optimizing formulation methods for field application, characterizing specific antimicrobial compounds, and evaluating its long-term efficacy in orchard environments.