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Wheat stem rust is one of the wheat diseases caused by Puccinia graminis Pers. f. sp. tritici (Pgt). This disease has been responsible for major losses to wheat production worldwide. Currently used methods for controlling this disease include fungicides, the breeding of stem rust-resistant cultivars, and preventive agricultural measures. However, the excessive use of fungicides can have various deleterious effects on the environment. A hyperparasitic fungus with white mycelia and oval conidia, Simplicillium lanosoniveum, was isolated from the urediniospores of Pgt. When Pgt-infected wheat leaves were inoculation with isolates of S. lanosoniveum, it was found that S. lanosoniveum inoculation inhibited the production and germination of urediniospores, suggesting that S. lanosoniveum could inhibit the growth and spread of Pgt. Scanning electron microscopy revealed that S. lanosoniveum could inactivate the urediniospores by inducing structural damage. Overall, findings indicate that S. lanosoniveum might provide an effective biological agent for the control of Pgt. Full article

Stem rust is one wheat’s most dangerous fungal diseases. Yield losses caused by stem rust have been significant enough to cause famine in the past. Some races of stem rust are considered to be a threat to food security even nowadays. Resistance genes are considered to be the most rational environment-friendly and widely used way to control the spread of stem rust and prevent yield losses. More than 60 genes conferring resistance against stem rust have been discovered so far (so-called Sr genes). The majority of the Sr genes discovered have lost their effectiveness due to the emergence of new races of stem rust. There are some known resistance genes that have been used for over 50 years and are still effective against most known races of stem rust. The goal of this article is to outline the different types of resistance against stem rust as well as the effective and noneffective genes, conferring each type of resistance with a brief overview of their origin and usage. Full article

Diseases caused by Puccinia graminis are some of the most devastating diseases of wheat. Extensive genomic understanding of the pathogen has proven helpful not only in understanding host- pathogen interaction but also in finding appropriate control measures. In the present study, whole-genome sequencing of four diverse P. graminis pathotypes was performed to understand the genetic variation and evolution. An average of 63.5 Gb of data per pathotype with about 100× average genomic coverage was achieved with 100-base paired-end sequencing performed with Illumina Hiseq 1000. Genome structural annotations collectively predicted 9273 functional proteins including ~583 extracellular secreted proteins. Approximately 7.4% of the genes showed similarity with the PHI database which is suggestive of their significance in pathogenesis. Genome-wide analysis demonstrated pathotype 117-6 as likely distinct and descended through a different lineage. The 3–6% more SNPs in the regulatory regions and 154 genes under positive selection with their orthologs and under negative selection in the other three pathotypes further supported pathotype 117-6 to be highly diverse in nature. The genomic information generated in the present study could serve as an important source for comparative genomic studies across the genus Puccinia and lead to better rust management in wheat. Full article

Wheat rust diseases, including yellow rust (Yr; also known as stripe rust) caused by Puccinia striiformis Westend. f. sp. tritici, leaf rust (Lr) caused by Puccinia triticina Eriks. and stem rust (Sr) caused by Puccinia graminis Pres f. sp. tritici are major threats to wheat production all around the globe. Durable resistance to wheat rust diseases can be achieved through genomic-assisted prediction of resistant accessions to increase genetic gain per unit time. Genomic prediction (GP) is a promising technology that uses genomic markers to estimate genomic-assisted breeding values (GBEVs) for selecting resistant plant genotypes and accumulating favorable alleles for adult plant resistance (APR) to wheat rust diseases. To evaluate GP we compared the predictive ability of nine different parametric, semi-parametric and Bayesian models including Genomic Unbiased Linear Prediction (GBLUP), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (LASSO), Elastic Net (EN), Bayesian Ridge Regression (BRR), Bayesian A (BA), Bayesian B (BB), Bayesian C (BC) and Reproducing Kernel Hilbert Spacing model (RKHS) to estimate GEBV’s for APR to yellow, leaf and stem rust of wheat in a panel of 363 bread wheat landraces of Afghanistan origin. Based on five-fold cross validation the mean predictive abilities were 0.33, 0.30, 0.38, and 0.33 for Yr (2016), Yr (2017), Lr, and Sr, respectively. No single model outperformed the rest of the models for all traits. LASSO and EN showed the lowest predictive ability in four of the five traits. GBLUP and RR gave similar predictive abilities, whereas Bayesian models were not significantly different from each other as well. We also investigated the effect of the number of genotypes and the markers used in the analysis on the predictive ability of the GP model. The predictive ability was highest with 1000 markers and there was a linear trend in the predictive ability and the size of the training population. The results of the study are encouraging, confirming the feasibility of GP to be effectively applied in breeding programs for resistance to all three wheat rust diseases. Full article