Location: Corn Insects and Crop Genetics Research
Project Number: 5030-21000-072-001-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 1, 2026
End Date: Jan 1, 2028
Objective:
To sequence, assemble, and annotate highly contiguous reference genomes of critical U.S. rust isolates infecting corn and sorghum; to map the transcriptomic expression of the host-pathogen interface across multiple stages of infection; to curate secretome and effector predictive resources; and to genotype a highly characterized set of resistant corn accessions for comprehensive allele mining, thereby equipping U.S. breeding programs with the genomic tools required to deploy durable rust resistance.
Approach:
The high-quality, telomere-to-telomere sequence assemblies of these vital agricultural pathogens, together with consolidated gene model annotations, will be made publicly available to the corn and plant pathology communities. The cooperator will extract high-molecular-weight nucleic acids from isolated Puccinia species and infected plant tissues to generate reference-quality genome assemblies and dual RNA-seq transcriptomic datasets. ARS will apply advanced bioinformatics pipelines to generate structural and functional annotations of pathogen genomes. A highly characterized set of rust-resistant corn accessions will also be genotyped to support allele mining and the discovery of host variants associated with resistance. These host genotyping data will be analyzed alongside pathogen genome resources, infection-stage transcriptomic profiles, secretome predictions, and protein structure and language model-based variant analyses to prioritize candidate resistance genes, alleles, and host-pathogen interactions. The mapped transcriptomic expression data, capturing changes in host defense and pathogen virulence over the course of infection, will be delivered through MaizeGDB as downloadable datasets, comparative expression views, differential expression tables, and genome browser tracks. Predicted fungal secretomes will be analyzed using established pipelines, and protein structure analysis combined with biological language model-based variant scoring will support prioritization of high-value gene and allele targets. Collectively, these resources will provide actionable trait, diagnostic, and breeding information for stakeholders working to strengthen disease resilience and protect the national grain supply.