- Additional Plants: ornamentals
- Education and Training: technical assistance
- Pest Management: genetic resistance
- Sustainable Communities: sustainability measures
Perennial ryegrass seed, produced for sale as turf and forage grass seed, is grown in rural agricultural areas of northern Minnesota and contributes $15-20 million to rural economies annually. As a perennial, perennial ryegrass has many environmental benefits in a crop rotation including decreased erosion, reduced leaching, high organic matter production, and greater habitat for wildlife due to fewer tillage operations. From a farmers standpoint, perennial ryegrass is a desirable crop rotation option because it is profitable, tolerant of diverse weather conditions, requires less labor than annual crops, and harvest occurs earlier than for annual crops so labor requirements are spread throughout the season. Minnesota farmers have indicated that crown and stem rust pathogens are a severe issue in seed production fields (causing up to 80% yield reduction) and that new cultivars that are resistant to crown and stem rust pathogens would be desirable. Currently the only way to control rust pathogens is to spray fungicides which are costly and harmful to human and environmental health. The goal of the proposed research is to develop a method for rapid, and accurate selection for resistance to rust pathogens in perennial ryegrass germplasm based on plant chemical compounds (a “metabolic fingerprint”) associated with rust resistance. Termed metabolomics-assisted breeding, this technique will lead to faster rust resistant cultivar development, ultimately reducing fungicide use and making perennial ryegrass seed production a more profitable, marketable and sustainable option for farmers in rural communities in northern Minnesota as well as the Pacific Northwest and Canada.
Project objectives from proposal:
Objective 1: Quantify and verify levels of durable quantitative resistance to crown and stem rust in an advanced perennial ryegrass breeding population using a controlled environment.
Plant Material: A large perennial ryegrass population was field screened for crown rust resistance in 2009, 2010 and 2011 with 14 lines demonstrating consistent crown rust resistance rankings. Clones from the selected 14 lines are being propagated and in the greenhouse for future verification of crown rust resistance. In 2012, we will begin field screening for stem rust resistance at Roseau, MN for future development of a stem rust resistance model.
Pathogen Inoculum: Crown rust uredineospores were collected from 48 perennial ryegrass accessions in Becker and St Paul, MN in 2009 and 2011 as well as from a buckthorn nursery in 2011 and stored at -80°C. Stem rust uredineospores will be collected in 2012 from perennial ryegrass farm fields near Roseau, MN for future stem rust screening.
Resistance Verification: Replicated clones from the 14 lines will undergo artificial inoculation with the diverse collection of crown rust spores in a growth chamber to control environmental variation. At 14 days after inoculation, the number of rust pustules on 3 leaf sections measuring 4 cm each will be counted to quantify rust severity in each line. This will verify resistance rankings and develop a dataset that can be correlated with plant metabolic profiles in Objective 2.
Objective 2: Determining metabolic fingerprints associated with crown rust resistance.
Analysis of Plant Metabolic Profiles: Using reverse-phase ultra performance liquid chromatography (RP-UPLC) coupled to mass spectrometry (MS) we will detect a diverse mixture of secondary metabolites in the plant material used for resistance verification in Obj. 1. We have previously developed methods for extracting and analyzing ryegrass metabolic profiles using an RP-UPLC-MS approach. We will first identify a metabolic fingerprint associated with constitutive resistance then identify a metabolic fingerprint associated with pathogen induced resistance. All RP-UPLC-MS data will be processed and analyzed using GeneData Analyst® and RefinerMS® software available for a fee through the Minnesota Supercomputing Institute. A partial least squares regression model using the metabolic fingerprint data will be developed to explain resistance data from Obj. 1. Important metabolic features may be subject to further MS analysis techniques for compound ID.
Evaluation Plan: We will determine the accuracy of our predictive model on a ryegrass population that has been previously characterized for crown rust resistance. Metabolic fingerprints will be analyzed via stated methods and a rust resistance will be predicted using our model. We will determine the correlation between the predicted and actual rust resistance rankings in the testing population as a test of our model accuracy.
1) Thorough analysis and verification of rust resistance variability of important perennial ryegrass germplasm in our breeding program.
2) First development of metabolomics-assisted selection techniques for high-throughput, accurate, and dependable crown rust resistance screening in a plant breeding program.
3) Improved knowledge of the biological basis for plant resistance to rust pathogens plus greater farmer and researcher understanding of ryegrass and pathogen interactions.
1) Rust resistant perennial ryegrass lines identified in this study will be incorporated into our breeding program.
2) This research will result in our breeding program having a fast, reliable metabolomics-assisted selection method to supplement often unpredictable phenotypic screening methods leading to faster delivery rust resistant cultivars to farmers.
3) The crown rust resistance selection model will be adapted for stem rust resistance selection.
1) New crown and stem rust resistant cultivars will make perennial ryegrass seed crops a more profitable and environmentally sustainable crop rotation option for farmers in northern Minnesota.
2) Seed from rust resistant cultivars will be more marketable to end users and will result in more environmentally sustainable turfgrasses.
3) Our methods could be used as a model for metabolomics-assisted selection in other important agricultural crops or for other traits in perennial ryegrass.