Advances in understanding the biology and epidemiology of rust diseases of cereals

Rust fungi (order: Pucciniales) constitute the largest group of plant parasitic fungi and include many species of agricultural importance. This includes the three wheat rust fungi (Puccinia graminis f. sp. tritici, Puccinia striiformis f. sp. tritici and Puccinia triticina) that have posed a threat to crop production throughout history. This chapter provides an overview of the wheat rust pathogen lifecycle that has been critical to the design of effective disease management strategies and discusses recent integration of basic biological knowledge and genomic-led tools within an epidemiological framework. Furthermore, we include a case study on the “field pathogenomics” technique, illustrating the value of genomic-based tools in disease surveillance activities. Bringing together advances in understanding basic pathogen biology, developments in modelling for disease forecasting and identification, alongside genomic-led advances in surveillance and resistance gene cloning, holds great promise for curtailing the threat of these notorious pathogens.

Association of Polyphenol Oxidase Activities with Molecular Markers and Yellow Rust Resistance in Diverse Bread Wheat Genotypes

Polyphenol Oxidase (PPO) catalyses the undesirable browning of wheat products which is of significant concern in consumer acceptance perspectives. Another important yield-limiting cause for wheat crops is wheat rust (e.g., yellow rust), a source of great economic loss worldwide. The purpose of the current research was to screen conventional and synthetic bread wheat genotypes for their PPO activity and yellow rust resistance. Different genotypes differed significantly in total PPO activity and in their activities against different substrates (L-DOPA and Catechol). The synthetically derived bread wheat genotypes 1-279, showed the lowest (39.2 units/min/g) cumulative PPO activity. Ten genotypes each with the highest and lowest PPO activities were selected for testing their association with seven reported molecular markers. Intriguingly the PPO markers reported in literature could not clearly differentiate between contrasting cultivars. Association with yellow rust resistance was also investigated. Interestingly, the rust-resistant genotypes, including 1-263, Ch-43, 1-57 and Emat (all synthetic-derived), exhibited low PPO activity. The current study underpins that there is a need to search for more reliable PPO markers and to further validate association of low PPO activity with yellow rust.

Genome-Wide Analysis of Four Pathotypes of Wheat Rust Pathogen (Puccinia graminis) Reveals Structural Variations and Diversifying Selection

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.

WHEAT RUST IDENTIFICATION USING PHYTOPATHOLOGICAL RESEARCH AND MACHINE VISION TECHNOLOGIES

Wheat is the most economically important and valuable food crop cultivated in most regions of the world, and various diseases have a significant impact on yield parameters. Particular attention in wheat protection technologies from phytopathogens is given to rust, since yield losses, depending on the weather conditions of the season and the resistance of the sown varieties, can range from 30 to 100%. The article provides brief information on wheat rust diseases (yellow, brown, stem rust), as well as on current methods of their identification. Accurate and timely identification of rust pathogens is a key step in making decisions on application of plant protection products in the battle against diseases, which prevents their further development, spread and the occurrence of epiphytoties. The article describes the main method for identification and further record of yellow, brown, stem rust - this is a classic phytopathological study based on usage of human resources. The advantage of this method is its accuracy and versatility. Among the drawbacks, one should single out the labor intensity and the need for a staff of qualified phytopathologists. In view of intensive development of computer technologies and agriculture digitalization, the possibility of using machine vision based on programming of neural networks and their training in identifying the main causative agents of diseases is acquiring scientific and practical interest. A promising methodological approach to identification of phytopathogens when providing phytosanitary monitoring and algorithms used for training of neural networks and applied in machine vision technologies are presented.

Comparison of Genomic Prediction Methods for Yellow, Stem, and Leaf Rust Resistance in Wheat Landraces from Afghanistan

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.

The branched-chain amino acid aminotransferase TaBCAT1 modulates amino acid metabolism and positively regulates wheat rust susceptibility

Plant pathogens suppress defense responses to evade recognition and promote successful colonization. Although identifying the genes essential for pathogen ingress has traditionally relied on screening mutant populations, the post-genomic era provides an opportunity to develop novel approaches that accelerate identification. Here, RNA-seq analysis of 68 pathogen-infected bread wheat (Triticum aestivum) varieties, including three (Oakley, Solstice and Santiago) with variable levels of susceptibility, uncovered a branched-chain amino acid aminotransferase (termed TaBCAT1) as a positive regulator of wheat rust susceptibility. We show that TaBCAT1 is required for yellow and stem rust infection and likely functions in branched-chain amino acid (BCAA) metabolism, as TaBCAT1 disruption mutants had elevated BCAA levels. TaBCAT1 mutants also exhibited increased levels of salicylic acid (SA) and enhanced expression of associated defense genes, indicating that BCAA regulation, via TaBCAT1, has a key role in SA-dependent defense activation. We also identified an association between the levels of BCAAs and resistance to yellow rust infection in wheat. These findings provide insight into SA-mediated defense responses in wheat and highlight the role of BCAA metabolism in the defense response. Furthermore, TaBCAT1 could be manipulated to potentially provide resistance to two of the most economically damaging diseases of wheat worldwide.