Protective role of foliar application of green-synthesized silver nanoparticles against wheat stripe rust disease caused by Puccinia striiformis

Green-synthesized nanoparticles have a tremendous antimicrobial potential to be used as an alternative to hazardous fungicides. In this study, the green synthesis of silver nanoparticles (AgNPs) was performed by using Moringa oleifera leaf extract as a reducing and stabilizing agent. The synthesized AgNPs were subjected to different characterization techniques. UV-visible spectroscopy confirmed the surface plasmon resonance band in the range of 400–450 nm, and zeta analysis revealed that the synthesized AgNPs ranged 4–30 nm in size. Scanning electron microscopy depicted tiny fused rectangular segments and the crystalline nature of the synthesized AgNPs was confirmed using X-ray diffraction. Energy dispersive X-ray (EDX) detector confirmed the presence of metallic silver ions. Fourier-transform infrared analysis revealed the presence of phenols as main reducing agents in the plant extract. Foliar application of different concentrations (25, 50, 75, and 100 ppm) of AgNPs was applied on wheat plants inoculated with Puccinia striiformis to assess the disease incidence against stripe rust disease. AgNPs at a conc. of 75 ppm were found to be more effective against wheat stripe rust disease. Furthermore, the application of AgNPs enhanced morpho-physiological attributes and reduced nonenzymatic compounds and antioxidant enzymes in wheat. The present study highlights the potential role of the green-synthesized AgNPs as a biological control of yellow rust disease.

Wheat Yellow Rust Disease Infection Type Classification Using Texture Features

Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20–30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield.

Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

Whole genome resequencing and comparative genome analysis of three Puccinia striiformis f. sp. tritici pathotypes prevalent in India

Stripe rust disease of wheat, caused by Puccinia striiformis f. sp. tritici, ( Pst ) is one of the most serious diseases of wheat worldwide. In India, virulent stripe rust races have been constantly evolving in the North-Western Plains Zone leading to the failure of some of the most widely grown resistant varieties in the region. With the goal of studying the recent evolution of virulent races in this region, we conducted whole-genome sequencing of three prevalent Indian Pst pathotypes Pst46S119, Pst78S84 and Pst110S119. We assembled 58.62, 58.33 and 55.78 Mb of Pst110S119, Pst46S119 and Pst78S84 genome, respectively. Pathotypes were found to be highly heterozygous. Comparative phylogenetic analysis indicated the recent evolution of pathotypes Pst110S119 and Pst78S84 from Pst46S119. Pathogenicity-related genes classes (CAZyme, proteases, effectors, and secretome proteins) were identified and found to be under positive selection. Higher rate of gene family expansion was also observed in the three pathotypes. A strong association between the effector genes and transposable elements may be the source of the rapid evolution of these strains. Phylogenetic analysis differentiated the Indian races in this study from other known US, European, African and Asian races. Diagnostic markers developed for the identification of different Pst pathotypes will help tracking of yellow rust at farmers’ field and strategizing resistance gene deployment.

Isolation and Identification Rust Pathogens and the Study of Antioxidant Enzyme Activity and Gene Expression under Rust Infection in Zoysia japonica

The goal of this study was to identify the zoysiagrass rust pathogens and to analyze the differences in rust-resistant and rust-susceptible Zoysia japonica germplasm upon inoculation. Based on the assessment of spore morphology and 18S ribosomal DNA (rDNA) molecular identification, the zoysiagrass rust pathogen was identified as Puccinia zoysiae Diet. The development of mycelium, the rate of spreading, and the timing of spore production were more delayed in the rust-resistant (RR) genotype than the rust-susceptible (RS) genotype. After inoculation, the activities of superoxide dismutase (SOD), catalase (CAT), and ascorbate peroxidase (APX) initially decreased, then increased in both the RR and RS genotypes, but the increased enzyme activities were faster in the RR than in the RS genotype. Rust resistance was positively correlated with antioxidant enzyme activity. The observed changes in CAT, POD and APX activity corresponded to their gene expression levels. The results of this study may be utilized in accurately evaluating the damage of rust disease and rust-resistance in zoysiagrass germplasm aimed at breeding the rust-resistant zoysiagrass varieties and improving disease management of zoysiagrass turf.

Genetic diversity, antifungal evaluation and molecular docking studies of Cu-chitosan nanoparticles as prospective stem rust inhibitor candidates among some Egyptian wheat genotypes

Wheat has a remarkable importance among cereals worldwide. Wheat stem and leaf rust constitute the main threats that destructively influence grain quality and yield production. Pursuing resistant cultivars and developing new genotypes including resistance genes is believed to be the most effective tool to overcome these challenges. This study is the first to use molecular markers to evaluate the genetic diversity of eighteen Egyptian wheat genotypes. Moreover, the molecular docking analysis was also used to assess the Cu-chitosan nanoparticle (CuChNp) treatment and its mode of action in disease control management. The tested genotypes were categorized into two main cluster groups depending on the similarity matrix, i.e the most resistant and susceptible genotypes to stem and leaf rust races. The results of SCoT primers revealed 140 polymorphic and 5 monomorphic bands with 97% polymorphism. While 121 polymorphic and 74 monomorphic bands were scored for SRAP primers (99% polymorphism). The genotypes Sakha 94, Sakha 95, Beni Sweif 4, Beni Sweif 7, Sohag 4 and Sohag 5 were resistant, while Giza 160 was highly susceptible to all stem rust races at the seedling stage. However, in the adult stage, the 18 genotypes were evaluated for stem and leaf rust-resistant in two different locations, i.e. Giza and Sids. In this investigation, for the first time, the activity of CuChNp was studied and shown to have the potential to inhibit stem and leaf rust in studied Egyptian wheat genotypes. The Spraying Cu-chitosan nanoparticles showed that the incubation and latent periods were increased in treated plants of the tested genotypes. Molecular modeling revealed their activity against the stem and leaf rust development. The SRAP and SCoT markers were highly useful tools for the classification of the tested wheat genotypes, although they displayed high similarities at the morphological stage. However, Cu-chitosan nanoparticles have a critical and effective role in stem and leaf rust disease control.