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Spring barley production and quality in the United States is severely impacted by Fusarium head blight (FHB), primarily caused by Fusarium graminearum. Management of FHB is best accomplished using an integrated approach including resistant varieties and fungicides. Field experiments were established from 2014 to 2019 to quantify the effects of host resistance, fungicide, and fungicide timing on management of FHB and deoxynivalenol (DON) and yield protection in spring barley. Three separate research objectives were investigated using multiyear and multilocation experiments. These included investigating the efficacy of prothioconazole + tebuconazole applied at either Feekes growth stage (FGS) 10.5 (full head) or 3 to 7 days after FGS 10.5; the efficacy of sequential fungicide applications occurring at both FGS 10.5 and 3 to 7 days after FGS 10.5 using prothioconazole + tebuconazole, metconazole, prothioconazole, or tebuconazole; and the efficacy and timing of pydiflumetofen + propiconazole. Pooled analysis results suggest that metconazole, prothioconazole + tebuconazole, and pydiflumetofen + propiconazole are all effective fungicides for reducing DON, reducing FHB, and protecting yield. The use of sequential fungicide applications with one occurring at FGS 10.5 and another at 3 to 7 days after FGS 10.5 provided greater suppression than a single application at FGS 10.5. The greatest DON reduction from pydiflumetofen + propiconazole occurred when applied at full head or 3 to 7 days after full head. Across all research objectives, the most effective single fungicide application timing to reduce FHB, reduce DON, and protect yield in barley is 3 to 7 days after FGS 10.5.

Plant breeding aims to develop elite crop varieties appropriate for various environments with higher quality and quantity of production. Researchers use quantitative trait loci (QTL) mapping and association studies to identify regions in the genome responsible for the variation of the quantitative traits of interest. However, mapped regions do not always translate to functional proteins, which makes it challenging to identify genes associated with traits of interest. The biological functions of proteins are strongly dependent on their 3D structure. Alternatively, if proteins can be directly linked with the phenotypes, the effect of mutations on phenotypic changes can be assessed. Innovation of deep learning models in biology opens new avenues of exploration. AlphaFold is an AI system that predicts the 3D structure of a protein from its amino acid sequence with near experimental accuracy and was used in this study. Point mutations with a significant influence on the 3D structure of a protein can capture the effect on phenotypes through association study, and this provides insights into the regions that are of functional importance. In the current study, 534 plants were selected based on plant vigor, and 154 missense variants that change amino acid sequences, including 5 significant hits from previous study, were included. The changes in protein 3D structure were assessed by association with the phenotype. The analysis identified five significant associations, four of which were also identified in previous study of SNPs GWAS, however, a new fifth association was also identified which was annotated as disease resistance gene in Medicago truncatula. This study helps to associate SNPs that could be missed by GWAS due to stringent Bonferroni corrected p-values by providing a more robust filter for SNPs using features from predicted protein 3D structures.

Alfalfa primarily produces seeds through cross-pollination among different individuals. Self-pollination results in severe inbreeding depression, such as weak seedlings and termination of growth. Identifying the genetic loci associated with vigorous plant growth could enable the elimination of deleterious alleles and eventually develop inbred alfalfa lines for hybrid production. In this study, 215 alfalfa accessions were self-pollinated for three generations. Within accessions, pairs of weak and strong plants were sampled and bulked for exome sequencing. We extracted individual DNA from 534 plants that included parental clones, strong and weak pairs of plants, and plants selected based on the number of seeds produced. Among them, we formed 42 pools, including 16 with strong plants and 17 with weak plants, 3 top-seeded plants, 3 low-seeded plants, and 3 no-seeded plants. Along with 79 individuals, these pools were sequenced in the target regions covering 112,626 contigs across the entire alfalfa genome. From the 121 samples, (42+79) genotyped, 13.2 million SNPs including indels were generated. After filtering for MAF (>5%), depth (

Polygalacturonase inhibiting proteins (PGIPs) are cell wall-anchored proteins that inhibit pathogen polygalacturonases (PGs). PGIPs, like other resistance genes, contain extracytoplasmic leucine-rich repeats (eLRRs), which are required for pathogen PG recognition. The importance of these PGIPs in plant defense has been well documented. This study focuses on chickpea (Cicer arietinum) PGIPs (CaPGIPs) owing to limited information available on this important crop. In this study, we identified and characterized two novelCapgips(Capgip3andCapgip4) in addition to the previously reportedCapgip1andCapgip2. Our analysis showed that CaPGIP1, CaPGIP3, and CaPGIP4 proteins contain N-terminal signal peptides, ten LRRs, theoretical molecular mass, and isoelectric points like other legume PGIPs. However, the previously reported CaPGIP2 cannot be classified as a true PGIP since it lacked a signal peptide, more than half of the LRRs, and other characteristics of a typical PGIP. Phylogenetic analysis and multiple sequence alignment revealed that theCapgipamino acid sequences are similar to the other reported legumes. Several cis-acting elements that are typical of pathogen response, tissue-specific activity, hormone response, and abiotic stress-related are present in the promoters ofCapgip1, Capgip3, andCapgip4. Localization experiments showed thatCapgip1, Capgip3, andCapgip4are located in the cell wall or membrane, whereasCapgip2is found in the endoplasmic reticulum.Capgiptranscript levels analyzed at normal conditions show constitutive tissue specific expression and heterozygous expression patterns analogous to other defense related gene families. All these findings suggest that CaPGIPs could have the potential to combat chickpea pathogens.

A diverse sexual population of wheat stem rust, Puccinia graminis f. sp. tritici, exists in the Pacific Northwest region of the United States because of the natural presence of Mahonia spp. that serves as alternate hosts to complete its sexual life cycle. The region appears to be a center of stem rust diversity in North America where novel virulence gene combinations can emerge that could overcome deployed barley and wheat stem rust resistances. A total of 100 single pustule isolates derived from stem rust samples collected from barley in Eastern Washington during the 2019 growing season were assayed for virulence on the two known effective barley stem rust resistance genes/loci, Rpg1 and the rpg4/5-mediated resistance locus (RMRL) at the seedling stage. Interestingly, 99% of the P. graminis f. sp. tritici isolates assayed were virulent on barley variety Morex carrying the Rpg1 gene, and 62% of the isolates were virulent on the variety Golden Promise transformant (H228.2c) that carries a single-copy insertion of the Rpg1 gene from Morex and is more resistant than Morex to many Rpg1 avirulent isolates. Also, 16% of the isolates were virulent on the near isogenic line HQ-1, which carries the RMRL introgression from the barley line Q21861 in the susceptible Harrington background. Alarmingly, 10% of the isolates were virulent on barley line Q21861, which contains both Rpg1 and RMRL. Thus, we report on the first P. graminis f. sp. tritici isolates worldwide with virulence on both Rpg1 and RMRL when stacked together, representing the most virulent P. graminis f. sp. tritici isolates reported on barley.

Stripe rust ​​(caused by Puccinia striiformis f. sp. tritici) is one of the most devastating diseases of wheat and causes large-scale epidemics and severe yield loss. Applying fungicides during early epidemic development is crucial to controlling the disease but is often challenged by resource-limited human visual scouting. Deep learning has the potential to process images and videos captured from affordable devices to empower high-throughput phenotyping for early detection of stripe rust for timely application of fungicides and improve control efficiency. Here, we developed RustNet, a neural network-based image classifier, for efficiently monitoring fields for stripe rust. RustNet was built on a ResNet-18 architecture pre-trained with ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) dataset using transfer learning. RGB images and videos of multiple wheat fields with different wheat types (winter and spring wheat), conditions (irrigated and non-irrigated), and locations were acquired using smartphones or unmanned aerial vehicles near the canopy. A semi-automated image labeling approach was conducted to improve labeling efficiency by combining automated machine labeling and human correction. Cross-validations across multiple categories (sensor platforms, wheat types, and locations) achieved Area Under Curve from 0.72 to 0.87. Independent validation on a published dataset from Germany achieved accuracies ranging from 0.79 to 0.86. The visualization of the last convolutional layer of RustNet demonstrated the identification of pixels with stripe rust. RustNet is freely available at https://zzlab.net/RustNet.