Bacterial spot of tomato caused by four taxonomically diverse Xanthomonas pathogens is one of the main seed-borne constraints of the tomato industry worldwide. Among the four bacterial spot xanthomonads, Xanthomonas euvesicatoria pv. perforans has a particular importance in terms of competitive aggressiveness and global distribution. Bacterial spot of tomato was observed for the first time in Iran in 2015, where X. euvesicatoria pv. perforans was identified as the causal agent. While occurrence of this disease appeared to have subsided in the following years, a resurgence of the disease in the main tomato-growing areas of Iran has raised serious concerns since 2023. In this study, we assessed the range of new bacterial spot outbreaks in the country during 2023-2024. Our data showed that the disease is currently present in five Iranian provinces, including the two provinces where it had originally been reported in 2015. Whole genome sequence-based phylogenomic investigations suggested that the causal agent of tomato bacterial spot in Iran is still X. euvesicatoria pv. perforans, while genetic diversity was observed in the recently emerging population of the pathogen compared to the old population. The bacterial strains isolated in 2023-2024 surveys were phylogenetically distinct from those isolated in 2014-2016 surveys. Furthermore, type III effector repertoires varied significantly among the strains isolated in this study, regardless of their geographic origin. We provide novel insights into the ever-changing nature of tomato bacterial spot pathogens, highlighting the impact of seed-borne lineages of X. euvesicatoria pv. perforans in the population dynamics of the bacterium.

Translating plant disease forecasts into profitable management decisions requires quantifying model prediction accuracy, yield response variability, and economic uncertainty. A Bayesian hierarchical framework employing Beta regression was recently used to develop models to predict severity of Stagonospora nodorum blotch in winter wheat. However, performance of these models (validation, yield loss quantification and stochastic fungicide profitability analysis) has not been conducted. Using 394 disease cases from 22 environments (2012-2014), the most complex weather-based model had the lowest root mean square error (RMSE = 11.7%) and near-nominal calibration predictive interval coverage probability (PICP = 100%), although systematic underestimation at high severity (mean error = -16.8% at high disease epidemic scenarios) was observed. Covariate shift analysis across 1,104 synthetic deployment environments identified divergence in some weather predictors. Population-level yield loss was modest (-13.6 kg ha[-1] per 1% severity increase) but with extreme environment-specific heterogeneity, indicating that disease severity alone is a poor predictor of yield impact in this pathosystem. A multi-trait Bayesian meta-analytic model jointly estimated disease suppression and yield response to a single fluxapyroxad + pyraclostrobin application at Feekes Growth Stage 8-10, which provided disease protection (46% efficacy) and a severity-independent benefit (+151.3 kg ha[-1]). Economic simulations showed that breakeven severity shifted from ~5% at low application costs to ~15% at moderate costs under typical wheat prices . Return on investment was more uncertain under higher disease pressure and premium market conditions, where potential gains were also the largest. The integrated framework developed here provides a template for risk-informed decision support system.

Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae, is a major threat to rice production. To characterize the field-scale spatial structure of BLB under commercial conditions, disease incidence and disease index in 11 naturally infected rice fields in Taizhou, Zhejiang Province, China. Disease incidence ranged from 51.54% to 93.56%, and disease index ranged from 41.59 to 73.75. The index of dispersion was >1.0 in all fields and differed significantly from a random binomial expectation (P < 0.0001), indicating overdispersion. Binary power law described the relationship between observed and binomial variances on logarithmic scales (R2 = 0.774); however, no consistent incidence-dependent aggregation pattern. Variance of moving window averages indicated positive spatial association in most fields at small to intermediate window sizes, particularly from 2×2 to 6×6 quadrats. Semivariogram analysis of trend-adjusted BLB incidence data detected significant spatial dependence in all 11 fields (P < 0.05), with estimated ranges from 1.57 to 6.27 m. Spherical models fitted the empirical semivariograms well in several fields (R2>0.90), and the proportion of structural variation varied among fields from 0.248 to 1.000. Semivariograms based on disease-index data produced spatial patterns comparable to those based on incidence. Overall, BLB was found to have field-scale overdispersion and local spatial dependence as expected due to fine-scale clustering which could be linked to localized spread, water-mediated dispersal, or spatial heterogeneity in the field. These results confirm spatially distributed sampling designs and indicate that localized surveillance can enhance BLB monitoring in commercial rice fields.

Phallus rubrovolvatus is a valuable edible and medicinal mushroom, but its cultivation is severely hindered by yellow rot disease caused by Saccharomycopsis phalli, which shows a 60 to 80% incidence and causes severe fruiting body rot, yield loss, and quality decline. The molecular mechanisms, particularly the dynamic transcriptional and metabolic responses, underlying the defense of P. rubrovolvatus against S. phalli remain poorly understood, limiting the development of targeted control strategies. To address this gap, we conducted an integrated transcriptomic and metabolomic analysis, identifying differentially expressed genes (DEGs) and differentially accumulated metabolites (DAMs) at 8, 16, 32, and 48 hours post-inoculation (hpi). Results showed significant changes in the pentose and glucuronate interconversion pathway, with reduced expression of related genes (e.g., ARD1, XR) and decreased accumulation of metabolites (e.g., D-glucuronic acid). Conversely, the arginine biosynthesis pathway was significantly enriched, with accumulated intermediates (e.g., citrulline, argininosuccinic acid) and upregulated biosynthesis genes (e.g., glnA, rocF). Alanine, aspartate and glutamate metabolism was also implicated in the infection response, mediating carbon-nitrogen skeleton flux and energy allocation to meet physiological demands. Additionally, glutathione metabolism was significantly enriched, accompanied by upregulated glutathione S-transferase (GST) genes. Overall, this study reveals a multi-pathway defense response in P. rubrovolvatus against S. phalli across the infection time course, involving coordinated carbon-nitrogen metabolic reprogramming and antioxidant defense activation, offering theoretical insight into its disease resistance mechanisms and supporting breeding of yellow rot-resistant varieties.

Ralstonia solanacearum is a globally distributed, soil-borne species complex that causes bacterial wilt in many economically important crops. Race 3 biovar 2 (R3b2) is a cold-tolerant, highly aggressive subgroup within this complex that is responsible for an estimated $1 billion in annual global losses. Because R3b2 remains exotic to North America, it is classified as a USDA Select Agent due to its high potential to devastate U.S. solanaceous crops and ornamental industries. However, current diagnostic methods for this pathogen involve trade-offs among cost, sensitivity, specificity, and ease of use, particularly for point-of-care (POC) detection in field settings. CRISPR-based detection platforms have recently emerged as promising tools for high-sensitivity POC diagnostics. Here we developed an isothermal recombinase polymerase amplification (RPA)/Cas12a assay for specific, sensitive and rapid detection of R3b2. By targeting a genomic region associated with the cool-virulence phenotype, the assay reliably differentiates R3b2 from plant hosts and other diverse R. solanacearum strains. Incorporation of a dual-guide RNA system and an 8-mer single-stranded DNA (ssDNA) fluorescent reporter further improved sensitivity and reduced detection time. Using dilution series of R3b2-spiked Pelargonium DNA extracts, the assay detected as few as 4 CFU per reaction. To facilitate POC applications, the system was adapted into a one-pot, contamination-resistan¬t format compatible with portable fluorescence readers such as Genie III device and P51 viewer. By improving both sensitivity and portability, this assay enhances current diagnostic capabilities and, pending validation in real-world conditions, supports early surveillance efforts aimed at preventing the introduction of R3b2 into the United States.

Common bacterial blight of common beans caused by various Xanthomonas species is considered a significant threat to bean production worldwide. In this study, common bean-associated xanthomonads in Iran were investigated using a multilocus phylogeographic approach. Majority of the bacterial strains investigated in this study were isolated in 2023-2024, while a number of 1990s and 2000s Iranian strains were also included in the analyses. Phylogenic analyses using the sequences of four housekeeping genes showed that among 45 strains isolated from common bean across nine Iranian provinces, 44 strains belonged to Xanthomonas phaseoli pv. phaseoli, while one strain was identified as Xanthomonas citri pv. fuscans. Multilocus sequence analysis showed that all strains collected in 2023-2024 in Iran along with four strains isolated in 1999, 2006, and 2020 clustered into a monophyletic clade. Multilocus sequence typing of the Iranian strains along with 29 worldwide strains retrieved from the NCBI GenBank revealed low genetic diversity within the global population of X. phaseoli pv. phaseoli. Majority of the worldwide X. phaseoli pv. phaseoli strains belonged to the same haplotype. However, a distinct haplotype containing only the strains isolated in Iran was observed, with no members from the worldwide population. Our analysis provides novel insight into the population structure of common bacterial blight pathogen on a global scale. Emergence of a novel lineage of X. phaseoli pv. phaseoli in Iran raises concerns about further expansion of the pathogen and highlights the need for strict quarantine measures in a global scale.

Modeling disease spread and risk is crucial for disease management, with numerous options for models. Multiple factors affect model selection, including the model's spatial and temporal dimensions, the quantity and quality of infection data, management objectives, and the research questions being addressed. We developed a workflow to filter model types based on pathogen biology and infection data availability, then select the appropriate model for the research question and management objectives. We applied the workflow to two case study pathosystems (late blight and sudden oak death) and evaluated three modeling approaches representing different spatial and temporal dimensions: a temporally focused weather-risk model (WRM), a spatially focused species distribution model (SDM), and a spatiotemporal spread model, the Pest or Pathogen Spread (PoPS) model. We qualitatively compared how each model's spatiotemporal outputs affected predicted disease risk or spread. Additionally, we conducted a simulation study in which infection data were reduced to determine the minimum data density required to obtain reliable parameter estimates with PoPS. Our results suggest that the temporal WRM model is suitable for disease management in agricultural pathosystems, where stakeholders possess sufficient spatial knowledge. In the natural pathosystem, the spatiotemporal model's greater accuracy reduced the total high-risk infection area by >99% compared with the other models. We found that although a spatiotemporal model is more accurate and helps with resource allocation, performance dropped significantly when data density fell below 50%. The effect of decreasing data density on SDM performance varied with pathosystem. Our work underscores the need for scalable disease-detection methods.

Powdery mildew associated with Fragaria × ananassa Duch. has recently been reinterpreted as a species complex comprising Podosphaera fragariae in Eurasia and P. shepherdiae in the Americas; however, molecular data for South American populations remain scarce, and the few South American specimens sequenced to date have been assigned to P. shepherdiae. In this study, the causal agent of powdery mildew on strawberry cultivar 'Sabrina' grown in Lima, Peru, was characterized through morphological analysis, pathogenicity tests, and multilocus phylogenetic inference based on ITS, GAPDH, and RPB2. The fungal material examined developed mycelial colonies typical of powdery mildew and consistently reproduced the symptoms observed in the field. Morphologically, the pathogen exhibited characteristics consistent with Podosphaera sect. Sphaerotheca, including ellipsoidal conidia with conspicuous fibrosin bodies, chained conidiogenesis, and Fibroidium-type lateral germination. Sequences were obtained for ITS, GAPDH, and RPB2. The concatenated ITS+GAPDH+RPB2 phylogeny resolved two major lineages corresponding to P. fragariae (95%) and P. shepherdiae (98%), placing the Peruvian isolate PUCP-Pa001 within the P. fragariae clade with high statistical support and close affinity to Eurasian accessions. These results provide the first multilocus evidence of P. fragariae on cultivated strawberry in the Americas and support the hypothesis of an anthropogenic introduction associated with the international movement of propagative material. Furthermore, the study demonstrates the value of multilocus approaches for resolving cryptic taxa within Erysiphaceae and provides the first South American reference sequences for this pathogen, supporting future epidemiological and phytosanitary research on powdery mildews associated with Fragaria in South America.

Blast disease of rice caused by the fungus Magnaporthe oryzae is one of the most damaging diseases of rice worldwide. Major blast resistance genes have been deployed for broad-spectrum resistance. However, the minor genes and epistatic interactions contributing to the broad-spectrum resistance are largely uncharacterized. Using a mapping population of 272 individuals derived from the blast resistant variety 'Katy' and blast susceptible 'PI312777', we identified and verified resistance spectra of major genes Pi-ta/Ptr and Pi-ks and further identified one additional major and 13 minor resistance quantitative trait loci (QTLs) for 10 of the 11 pathogen races tested and detected additive-by-additive epistasis for 32 pairs of QTLs. We used Bayesian network (BN) structure learning to model associations between QTLs, blast resistance, and agronomic traits, revealing four traits associated with resistance to specific pathogen races. The major gene cluster Pita/Ptr conferred resistance to only 7 of 11 races, highlighting the need for multiple smaller effect resistance QTL to achieve broad-spectrum resistance. Genomic predictive abilities (GPAs) were calculated and were lowest for resistance to race IE1k (for which Pita/Ptr is not effective) and highest for IA45. Reciprocal GPAs were calculated for prediction across pairs of blast races and were lowest for pathogen races IB1, ID1 and IE1k, moderate for races IB49, IE1, IB54, IH1 and highest among IC17, IG1, IA45, IB45. A genomic selection rank sum index (GSRI) of genomic estimated breeding values (GEBVs) for overall resistance to 11 pathogen races distinctly separated high and low resistant lines for all races except IE1k.

Leaf rust, caused by Puccinia triticina (Pt), poses a serious threat to sustainable wheat production globally, and identification of leaf rust resistance genes that can be readily used in cultivar development is of agronomic importance. A recombinant inbred line (RIL) population was developed from Graecum 48 × TAM 110 to characterize a leaf rust resistance gene in Graecum 48, a winter wheat cultivar developed in Kazakhstan, and was evaluated for responses to Pt race MMPSD (isolate: Pt52-2). Together with Graecum 48 and TAM 110, a subset of 20 RILs with contrasting phenotypes were genotyped using genotyping-by-sequencing, leading to the identification of 20,379 single nucleotide polymorphic (SNP) markers. Allele distribution analyses of each SNP in the sequenced RILs identified a set of SNPs closely associated with leaf rust resistance in the terminal region of chromosome arm 2DS. Four SNPs in this region were converted to kompetitive allele-specific PCR (KASP) markers, and these markers, together with those previously mapped in the target region, were used to genotype the RIL population. Linkage analysis delimited the leaf rust resistance gene in Graecum 48, designated LrGR, to an interval of 53 Kb between 64.46 and 64.51 Mb in the Chinese Spring IWGSC RefSeq 2.1, in which Lr2 resides. A test of allelism indicated LrGR is a novel gene, and the estimated recombination frequency and genetic distance between LrGR and Lr2 were 7.87% and 7.95 cM, respectively. KASP markers flanking LrGR such as kwm1620 and kwm1632 can be used to tag LrGR in marker-assisted selection.

The Polerovirus Sugarcane yellow leaf virus (SCYLV) causes one of the most economically important viral diseases of sugarcane worldwide, including Colombia, where sugarcane is a strategic crop and the virus has high phytosanitary relevance but limited epidemiological characterization. This study provides the first integrative quantitative and multimodel epidemiological assessment of SCYLV in the country, using a 22-year surveillance dataset (1999 to 2020) from Colombia's main sugarcane-producing region. The analysis integrated data science, classical statistics, machine learning, and spatial epidemiology to identify temporal trends, spatiotemporal aggregation, and environmental and agronomic drivers of disease risk. Data science procedures supported data curation, exploratory analysis, and detection of patterns in SCYLV presence-absence, incidence, and prevalence across the historical series. Risk factor identification used logistic regression modeling of presence-absence data to estimate adjusted associations, and the machine learning framework modeled both presence-absence (classification) and incidence (regression) to capture nonlinear interactions. Spatiotemporal dynamics were assessed using presence-absence data in a space-time permutation model fitted to a Poisson-type distribution and the Knox test. SCYLV presence-absence and incidence were significantly associated with varietal susceptibility, with CC 01-1940 and SP 71-6949 showing the highest case burden climatic variability, with major outbreak clusters coinciding with strong El Niño-Southern Oscillation anomalies of both phases (El Niño in 2007 and 2015/2016 and the strong 2010/2011 La Niña), meteorological conditions (temperature, wind speed, and vapor pressure), vegetation greenness (normalized difference vegetation index), and soil properties (pH, cation exchange capacity, sand content, and nitrogen). The Knox test identified a high-risk spatiotemporal window for SCYLV spread, with relative risk > 10 concentrated within 4.5 km and 4 days after a confirmed positive case, highlighting a critical range for targeted surveillance and rapid preventive action. This integrative workflow provides a reproducible, data-driven foundation for delineating differential risk zones and supports evidence-based strategies for the integrated management of SCYLV in Colombian sugarcane systems, with transferable methodological value for long-term surveillance analyses of other aphid-transmitted viruses.

Crown gall disease, caused by Agrobacterium vitis, is a global threat to viticulture that severely compromises vine vigor and berry quality. To elucidate the molecular mechanisms underlying grapevine resistance, five cultivars were inoculated with A. vitis strain A10 under controlled greenhouse conditions. Pathological assessments revealed divergent disease progression patterns among cultivars in which Yan73 (Y73) and Muscat Blanc (MB) exhibited higher resistance, whereas Y73 displayed mild disease development. Conversely, Merlot (ML), Cabernet Sauvignon, and Chardonnay showed rapid symptom development, and ML was identified as the most susceptible, characterized by the highest incidence and severity. Given that bacterial pathogens such as A. vitis are notoriously difficult to eradicate after infection, characterizing the early infection phase is critical for effective management. Comparative transcriptional analysis (RNA-seq) was conducted to capture the early molecular responses across these cultivars. Differentially expressed genes shared among cultivars were significantly enriched in pathways related to plant-pathogen interaction, stilbene biosynthesis, and responses to oxygen levels. Notably, ethylene signaling and response pathways were predominantly enriched in the relatively resistant cultivars Y73 and MB, suggesting their pivotal role in early defense. Further investigation into the stilbene synthase (STS) pathway revealed that its primary catalytic product, resveratrol (Res), serves as a key defensive metabolite. In vivo assays demonstrated that exogenous Res treatments significantly attenuated disease incidence and severity in a dose-dependent manner. Overall, this study outlines the early immune response of grapevine to A. vitis infection and highlights the importance of STS and ethylene pathways in resistance, providing potential targets for breeding crown gall-resistant grape cultivars.

Breeding for Phytophthora fruit rot (PFR) resistance caused by the devastating pathogen Phytophthora capsici remains challenging in watermelon mainly due to phenotyping constraints. To overcome these bottlenecks, we developed and validated genomic selection models using an interspecific recombinant inbred line (RIL, F11) population derived from USVL531-MDR (resistant, Citrullus mucosospermus) × USVL677-PMS (susceptible, Citrullus lanatus) and a segregating F2 population (USVL531-MDR × 'Calhoun Grey'). Mature fruits from both populations were artificially inoculated under controlled conditions and phenotyped for lesion diameter, visible pathogen growth diameter, and sporulation intensity. Fourteen genomic selection models comprising ten parametric approaches (Bayesian and regression-based), one semi-parametric model (RKHS), and three machine learning algorithms (Random Forest, SVM, XGBoost) were evaluated for their performance. Parametric models achieved high predictive accuracy in the RIL population (r = 0.700-0.718 in 10-fold cross-validation; r = 0.694-0.713 in 5-fold cross-validation), while the F2 population showed moderate accuracies, with GBLUP performing well under 10-fold cross-validation (r = 0.476) and Bayesian ridge regression under 5-fold cross-validation (r = 0.433). Coincidence analysis demonstrated that genomic prediction effectively captured top-performing RILs, with 10% and 20% selection intensities showing strong overlap with phenotypic selection and Bayesian models achieving up to 85% coincidence. These results establish a solid framework for application of genomic selection in watermelon PFR resistance breeding programs.

Methyl-accepting chemotaxis proteins (MCPs) are bacterial chemoreceptors that sense environmental and intracellular signals and regulate flagellar rotation through interactions with Che proteins. Some MCPs interact with ATP-binding cassette (ABC) transporters, which import and export various compounds across cell membranes. Using annotated prokaryotic genome assemblies, we assessed the abundance and organization of MCPs and ABC transporters in plant pathogenic Pectobacteriaceae and compared these features with those in other members of the Enterobacterales order, including Escherichia, Yersinia, Pectobacterium, Salmonella, Erwinia, and Sodalis. Soft rot Pectobacteriaceae encode at least 20 MCP genes per strain and contain diverse ligand-binding domains, including TarH, 4HB, HBM, NIT, dCache-1, sCache_2, Cache_3-Cache_2, FIST, Chase3, CZB, NIT, and Pas domains. Their genomes are also enriched in ABC transporters that transport sugars, peptides, amino acids, iron-siderophores, and the family 5, 6 and 9 transporters compared to other Enterobacterales. Some MCPs are clustered in Pectobacteriaceae, and some are collocated with ABC transporters. A subset of substrate-binding proteins of ABC transporters that bind to sugar, amino acid, mineral salt, iron, and polyamine may interact with MCPs in Pectobacterium to co-sense signals. Our findings suggest that soft rot Pectobacteriaceae are enriched in both MCPs and ABC transporters relative to many other members of Enterobacterales, supporting the potential importance of these proteins in adaptation to diverse environments and pathogenesis within the context of shared phylogenetic history. MCPs and ABC transporters therefore provide candidates for future functional studies of soft rot pathogenesis and may help identify strategies for plant disease management.

Pectate lyases (Pels) are critical cell wall-degrading enzymes that facilitate plant infection by many phytopathogens. However, their functional contributions in Fusarium graminearum remain largely unexplored. Here, we identified and functionally characterized FgPel2, a pectate lyase in F. graminearum. FgPel2 possesses a functional signal peptide that confers secretion capability, and its transcript levels was significantly upregulated during wheat spike infection. Subcellular localization analyses revealed that FgPel2 associated with the cell wall. Overexpression of FgPel2 (OE‑FgPel2) significantly enhanced fungal virulence on wheat seedling leaves , suggesting a virulence-associated function. A silencing strain (FgPel2‑S) generated by RNA interference exhibited normal vegetative growth, conidiation, and sexual reproduction but displayed markedly increased sensitivity to cell wall stress, particularly to Calcofluor White (CFW). Importantly, the FgPel2‑S strain did not show reduced pathogenicity compared with the wild type, whereas OE‑FgPel2 led to enhanced pathogen virulence. These findings demonstrate that FgPel2 acts as a virulence factor in F. graminearum and imply functional redundancy with other pectate lyase family members.