Unlocking the mechanisms involved in the control of Bacillus amyloliquefaciens against postharvest soft rot of tomato fruits.

GmIFS interacts with GmNFR1α and plays a positive role in soybean legume-rhizobia symbiosis.

The main goal of current research was to examine the students competence and academic achievement in higher education: a conditional process. The descriptive research design with quantitative study type was considered suitable for the current research. Population for the study was teachers and pupils of University of Sargodha. By using convenience sampling method, students and teachers from Sargodha Medical College, Department of Plant Pathology, Department of Social Work and Department of English were selected. Total sample was collected from 21 teacher and 320 students in total. Two adopted scales i.e., Teacher-Class Relationship Inventory and Generic Competence Scale were adopted. Both instruments were reliable according to the Cronbach’s’ value . By using the SPPS version 27, data was analyzed and moderation and mediation analysis techniques was utilized. The results revealed that the moderating and mediating effect of teacher-class relationship in the students competence as well as academic achievement of the respondents was found statistically insignificant. It was recommended on the basis of findings that teachers and experts need to conduct seminars and workshop to aware the students about the competencies importance as well as it will also improve their academic achievements.

In defending against pathogens, plants deploy diverse secondary metabolites and signaling molecules. Among these, melatonin orchestrates plant growth and development, modulates stress responses, and regulates intracellular redox homeostasis and signaling. However, the mechanisms of melatonin in plant-pathogen interaction are rarely reported. Using Pseudomonas syringae pv. tomato DC3000 (Pst DC3000) as model bacteria, we designed a two-step high-throughput screening strategy to screen the plant natural product library and the bacterial mutant library. This study reveals that melatonin is perceived by a bacterial receptor histidine kinase CpxA, which subsequently modulates bacterial virulence. In detail, bacterial CpxA senses melatonin through Glu48 and Thr51 sites located in the periplasmic sensor region. Thus, melatonin inhibits autophosphorylation of CpxA and decreases transphosphorylation of the response regulator CpxR. The DNA-binding capacity of CpxR to promoters of type III secretion system (T3SS) genes is weakened by reduced phosphorylation cascade of CpxA/R, inhibiting bacterial T3SS genes expression and virulence. We also showed that increasing melatonin synthesis in plants can enhance disease resistance and sustain crop productivity. This study illustrates a previously unknown mechanism by which plants disarm the pathogenicity of bacteria, as well as provide effective molecular targets for crop genetic improvement and biopesticides development.

Cross-kingdom RNA interference is an emerging concept in plant-pathogen interactions. Here we provide evidence that cross-kingdom RNA interference also occurs in a beneficial plant symbiosis called arbuscular mycorrhiza. The arbuscular mycorrhizal fungus Rhizophagus irregularis transfers small RNAs into plant cells, promoting the colonization of host roots. This finding establishes inter-organismal RNA communication as a new regulatory mechanism of this ancient and widespread symbiosis.

Hydroponic farming offers a sustainable alternative to traditional agriculture but is highly prone to rapid disease transmission due to its shared water systems. Timely detection of plant diseases is critical to prevent widespread crop loss. In this research, the YOLOv11n object detection model was evaluated in detail for the purpose of real-time hydroponic plant disease detection, and its accuracy, inference speed, power consumption, and resource utilization were compared through various edge devices such as Raspberry Pi 5 (Raspberry Pi Holdings, Cambridge, UK), NVIDIA Jetson Nano (NVIDIA Co., Santa Clara, CA, USA), and AMD Radeon Vega 8 (AMD Micro Devices, Inc., Santa Clara, CA, USA). The results not only confirm the applicability of light-weighted and quantized deep learning models for planting disease detection at early stages in controlled hydroponic environments but also give practical knowledge for hardware-model trade-offs in the context of sustainable edge AI-based smart farming systems.

Grapevine trunk diseases (GTDs), such as esca, pose a major threat to viticulture worldwide and are associated with complex biochemical responses in woody tissues. Comprehensive metabolome coverage remains a challenge, as conventional methods often overlook non-polar metabolites critical to plant defense mechanisms. This study aimed to expand metabolome and lipidome coverage of grapevine wood by integrating complementary LC-MS approaches, in order to identify metabolic signatures linked to pathogenic fungi and to a biocontrol agent. Woody tissues of Vitis vinifera cv. Cabernet-Sauvignon were inoculated with Phaeomoniella chlamydospora, Phaeoacremonium minimum, and/or the biocontrol fungus Trichoderma atroviride (Vintec®). A biphasic extraction was coupled with three orthogonal LC-MS methods-reverse-phase (RP), hydrophilic interaction chromatography (HILIC), and lipidomics-focused RP. Data were processed through the MSCleanR workflow and integrated using the DIABLO multi-block statistical framework. Compound classification was performed with NPClassifier. The multiplexed strategy enabled the annotation of 1,425 unique features, representing an 83% increase compared to previous studies. Distinct metabolomic and lipidomic signatures were associated with fungal infection and biocontrol treatments. Lipidomic analysis highlighted oxidized fatty acids (oxylipins) -specifically hydroxy-eicosatetraenoic acids (13-HETE, 16(R)-HETE, and 11(R)-HETE)-as potential signaling molecules in defense responses. NPClassifier revealed diverse biosynthetic classes, including phenylpropanoids, terpenoids, and sphingolipids, underscoring the chemical heterogeneity of grapevine responses. This multiplexed LC-MS workflow provides a versatile analytical pipeline for untargeted metabolomics and lipidomics in plants. By integrating complementary methods, the study uncovered novel biomarkers of grapevine defense, particularly oxylipins, emphasizing the critical role of lipidomics in deciphering plant-pathogen interactions.

The LuxI/LuxR system, which produces and perceives N-acyl homoserine lactones (AHLs), plays a significant role in regulating pathogenesis and communication in gram-negative bacteria. A homologous system exists in Pseudomonas syringae pv. tomato (Pst) DC3000, which is encoded by the single-copy genes psyI/psyR. We created a double-knockout mutant of the AHL synthase (psyI) and the AHL receptor (psyR) genes in Pst DC3000 and a corresponding complemented strain to gain insights into their role in plant-pathogen interactions and the metabolic processes associated with this system in vitro. We observed that the mutant strain psyI-R- can overcome stomatal immunity as the parental strain Pst DC3000. However, the psyI-R- apoplastic population is significantly smaller than that of Pst DC3000 at 3 days postinoculation. Furthermore, PsyI and PsyR are required for necrosis but not chlorosis in infected tomato leaves, evidenced by the observed pharmacological and genetic complementation of the psyI-R- strain. By comparison, the coronatine-deficient mutant DB29 is unable to cause either necrosis or chlorosis on leaves. The reduced virulence of psyI-R- is associated with an intermediate induction level of the tomato PR2b marker gene for salicylic acid signaling and reduced expression of bacterial virulence genes in infected leaf tissue. Transcriptomic analysis of the psyI-R- and DB29 mutants showed misregulation of genes related to the bacterial secretion system, chemotaxis, flagellar motility, ABC transporters, and iron transporters and a partial overlap between metabolic processes regulated by PsyI/PsyR and coronatine. Altogether, these findings indicate that PsyI and PsyR are required for full bacterium virulence at the later stages of infection in tomato leaves.

Nicotinamide adenine dinucleotide (NAD) is an essential coenzyme in cellular metabolism with a long-established role in energy production, biosynthesis, and oxidative stress responses. Recent research demonstrates that NAD hydrolysis is a key step in immune signaling, beyond its primary metabolic functions. Here, we review how NAD and NAD-derived small molecules influence defense-related processes including reactive oxygen species production, calcium dynamics, and immune activation. We introduce diverse NAD-modifying enzymes in plants and discuss how they regulate immunity, with a special emphasis on Toll/interleukin 1 receptor (TIR) domain proteins, which hydrolyze NAD+ to produce immune-activating molecules. We also discuss how pathogens use NAD-modifying enzymes as virulence factors to manipulate host defenses, highlighting NAD metabolism as a newly emerged, critical battleground in the plant-pathogen arms race. Recent developments in this aspect of pathogenesis offer new opportunities to enhance disease resistance.

Integrated transcriptomic and metabolomic analyses reveal hormone-mediated crosstalk during potato virus Y and potato spindle tuber viroid co-infection.

Genomic insights into tomato-Fusarium relationship: Plant-pathogen interactions and virulence mechanisms

Burkholderia seminalis suppresses Fusarium wilt infection in banana by modulating cell wall integrity.

The banana MaAGP15 negatively regulates resistance to Fusarium oxysporum in Arabidopsis thaliana.

Corrigendum to “Antagonistic activity and application of lipopeptide-producing Bacillus velezensis as biocontrol agent against rice bacterial leaf blight in vivo”[Physiological and Molecular Plant Pathology 142 (2026) 103054

Ambient pH affects the virulence of Phytophthora nicotianae and activates NtFERL2 to enhance the resistance of tobacco to Phytophthora nicotianae. Tobacco is a globally important economic crop and plays a crucial role in agricultural production and rural development. Tobacco black shank, caused by Phytophthora nicotianae, results in severe biomass and yield losses across all major tobacco-growing regions. Variations in soil pH are known to reshape crop-pathogen interactions and pose a threat to productivity, yet how ambient pH affects the occurrence of diseases in plants remains poorly understood. Here, we observed that acidic ambient pH was more conducive to the growth and pathogenicity of P. nicotianae, which was correlated with promoted sporulation and mycelial bulges under laboratory conditions. In tobacco plants, acidic ambient pH increased susceptibility to the pathogen, whereas alkaline pH reduced disease severity. Transcriptome analysis with tobacco plants under different pH regimes for 4weeks showed that genes involved in the plant-pathogen interaction, oxidative phosphorylation and mitogen-activated protein kinase (MAPK) signaling pathway were differentially expressed. We identified a receptor-like kinase, FERONIA-like 2 (FERL2), as a resistance factor exhibiting pH-dependent expression variations. Overexpression of FERL2 attenuated resistance differences across pH conditions by activating downstream defense signaling pathways, suggesting its essential role in pH-modulated immunity. Our study demonstrates that acidic pH enhances P. nicotianae virulence and compromises resistance, potentially through impairing FERL2-mediated signaling, providing strategic insights for controlling tobacco black shank under varying soil pH conditions.

Accurate identification of apple leaf diseases in field conditions is essential for sustaining crop yield and supporting precision agriculture. Variable illumination, cluttered backgrounds, and co-occurring symptoms complicate diagnosis in real orchards. This study applies a deep learning approach using a fine-tuned MobileNetV2 model to classify apple leaf diseases from a heterogeneous dataset derived from the Plant Pathology 2021 (FGVC8) benchmark. The original five labels were expanded by subdividing the "multiple disease" category into expert-defined compound subclasses, yielding 12 disease categories encompassing both single and compound infections. Data augmentation and transfer learning were employed to improve robustness, while interpretability was assessed through Grad-CAM and LIME visualizations. Results show that the model performs well on distinct single-disease categories such as rust, scab, and frogeye leaf spot, but struggles to detect overlapping or compound infections. These findings highlight both the potential and the challenges of lightweight CNN architectures for agricultural image classification. The study contributes evidence that explainable, compact deep learning models can support future efforts to build reliable tools for plant health monitoring in diverse field conditions.

The common bean (Phaseolus vulgaris L.) is a legume that faces numerous biotic constraints that hinder its production. Among these are fungal diseases that attack both leaves and pods, reducing yields. The objective of this study is to contribute to improving green bean production in the Gourma province. Macroscopic and microscopic identifications were carried out using plant pathology tools. As a result of the work, four pathogens were identified: Aspergillus niger, Botrytis cinerea, Colletotrichum lindemuthianum, and Alternaria alternata. These pathogens are responsible for various symptoms observed on the leaves and fruits, all contributing to a decrease in green bean productivity. This study allowed us to identify the different fungal pathogens of the common bean, which opens up possibilities for developing an appropriate control method.

The present study was conducted at the Department of Plant Pathology and Nematology, RPCAU, Pusa, during the year 2022–2023. It demonstrates the in vitro antagonistic effect of native Trichoderma isolates and fungal endophytes, as well as the inhibitory effects of volatile organic compounds (VOCs) from four selected bacterial endophytes against Fusarium oxysporum f. sp. cubense (Foc TR4). The results revealed that Trichoderma isolates, i.e. T1-1723; T2-1723; T3-1723, in which T-3-1723 and Trichoderma asperellum showed maximum per cent mycelium inhibition of 88.89 and 88.92 respectively, whereas fungal endophytes LfFE1 exhibited inhibition per cent of 44.78 %. In addition, bacterial endophytes (GNBEF-14) have shown an inhibition of 40.13 % over control. Overall, it was found that among all biocontrol agents, only Trichoderma isolates showed best antagonistic effect against pathogen. Thus, it can be used further to test their efficacy and develop consortia to manage this devastating pathogen at the field level.

Plant diseases are a persistent constraint on crop productivity and food security, with fungal and oomycete pathogens causing significant yield and quality losses even under intensive management. This review analyzes how climate change is reconfiguring plant disease epidemiology by altering the host–pathogen–environment relationship through warming, shifts in precipitation, rising atmospheric CO₂, and an increasing frequency of extreme events. The objective is to synthesize evidence on (i) climate-driven redistribution and instability of plant diseases, (ii) climate-mediated host vulnerability and potential breakdown of resistance, (iii) pathogen adaptation and the emergence or re-emergence of diseases, and (iv) agroecosystems as dynamic disease networks. A narrative and integrative literature review was conducted using peer-reviewed studies and foundational references in plant pathology, emphasizing mechanistic and cross-scale interpretations. The synthesis indicates that climate change promotes spatial range shifts, episodic outbreaks, and greater uncertainty in disease risk, while also challenging conventional forecasting and management schedules. Moreover, climatic variability can reduce the reliability of chemical control and intensify selection for resistance, reinforcing the need for adaptive, integrated approaches. The review concludes that plant pathology must advance toward climate-informed, systems-based frameworks that integrate epidemiology, host resistance, environmental monitoring, and evolutionary processes to strengthen resilience and safeguard food systems under accelerating environmental change.

Plant pathogens are a major cause of crop yield loss, making disease resistance breeding crucial for crop improvement. Plants have evolved innate immune systems, mediated by immune-related genes such as nucleotide-binding site leucine-rich repeat (NLR), pattern-recognition receptors (PRR) and susceptibility genes, which are essential resources for breeding disease-resistant plants. To identify immunity genes, extensive genetic approaches that examine the association between resistance phenotypes and genomic regions have been applied with great success. While genetic methods remain important for identifying immunity genes, novel strategies that rely on functional rather than genetic association with disease resistance offer unique advantages. For example, mutagenesis with R gene enrichment sequencing (MutRenSeq) enabled the identification of wheat resistance genes Sr22 and Sr45 by comparing the NLRomes of resistant and susceptible lines while single-cell RNA sequencing resolved cell-type-specific responses to pathogen infection and revealed ZmChit7, especially in maize epidermal and guard cells. These approaches reach beyond existing natural variation, can accelerate experimental timelines, reduce the experimental scale, and provide mechanistic insights into pathogen resistance. This review discusses emerging techniques that generate focused candidate immunity gene lists or accelerate their validation, as both are required to identify causal variants for resistance breeding. We consider advances in RenSeq-derived methods, spatial omics, proximity labelling, computational prediction, Clustered regularly interspaced short palindromic repeats (CRISPR) screens, and cell death assays. These approaches are reshaping resistance breeding pipelines beyond association-based discovery. By discussing the strengths and limitations of these emerging methods and their combinations, we outline current opportunities and future directions to help plant pathologists to more effectively identify and validate plant immunity genes.