AbstractPhenotypic screening for resistance to crown rust (Puccinia coronata) and stem rust (Puccinia graminis) in a perennial ryegrass (Lolium perenne) breeding program can be time consuming, costly, and unpredictable do to pathogen and environment variability.  Turfgrass breeders need a technique to select for rust resistance in the absence of the actual pathogen in order to be able to select for other traits simultaneously and speed cultivar development.  Metabolomics-assisted breeding has recently emerged a technique to select for desired traits based on plant chemical profiles that have been previously correlated with a trait of interest. Chemical compounds within plants, specifically secondary metabolites, are the end products of complex biochemical pathways that are regulated by genes and are often used directly as stress mediation or plant defense mechanisms making them closely associated to plant phenotypes.  The potential to use secondary metabolites as biomarkers for selecting desired traits in plants has been demonstrated in Arabidopsis, grapes, wheat, and conifer. In perennial ryegrass, using reverse-phase ultra performance liquid chromatography (RP-UPLC) coupled to mass spectrometry (MS), we have developed high-throughput methods to detect a diverse mixture of secondary metabolites which could potentially be correlated with rust resistance levels.  In addition, 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 14 selected lines were propagated in the greenhouse and were additionally screened for rust resistance via artificial inoculation in a growth chamber using a diverse collection of crown rust spores to control environmental variation.  The rust resistance data from the greenhouse and field will be correlated with the plant metabolic profiles to develop a model to help predict rust resistance levels in our perennial ryegrass population.

Introduction

Results• Based on initial screening, 14 perennial ryegrass lines from our breeding program could be classified as consistently resistant, moderately resistant, or susceptible to crown rust (Figure 1).

•In order to develop and accurate data set on crown rust resistance levels and rankings, clones from the most resistant or susceptible plant in the 14 stable lines were collected and grown in identical resistance verification trials located in both the field and growth chamber. A good range of disease severity was observed in both the final 2011 field resistance screening trial and the 2012 growth chamber screening trial (Figure 3). Spearman rank correlation between the 2011 field screening trial and 2012 growth chamber trial indicated that the resistance rankings of lines in the two trials were similar (Figure 3).

•We feel that our rust resistance rankings for our 14 selected lines are very accurate and our results indicate that rust resistance rankings determined in a growth chamber, using a diverse collection of crown rust inoculum, are highly correlated with resistance rankings observed in the field (Figure 3).

• In order to identify metabolic biomarkers that are predictive of crown rust resistance or susceptibility we harvested leaf tissue from each clone representing its respective line prior to inoculating with crown rust in the 2012 growth chamber experiment. Tissue samples within a line were pooled, stored at -80C and will be subject to HPLC-

MS analysis similar to Figure 5. for metabolic biomarker identification.

•Using our extraction and HPLC-MS instrument methods were able to detect over 700 unique metabolic features in a composite perennial ryegrass sample (Figure 5). These methods are currently being used to identify metabolic features that are predictive of crown rust resistance levels.

Results

Funding and Acknowledgments•Funded by the Minnesota Agricultural Experiment Station Rapid Agricultural Response Fund as well as a 2012 North Central Sustainable Agriculture Research and Education graduate student grant.

Short-term outcomes: 1) Thorough analysis and verification of rust resistance variability of important perennial ryegrass germplasm in our breeding program.2) First demonstration of using metabolomics-assisted selection for high-throughput, accurate, and dependable crown rust resistance screening in a plant breeding program.Improved knowledge of the biological basis for plant resistance to rust pathogens plus greater farmer and researcher understanding of ryegrass and pathogen interactions.

Intermediate-term outcomes: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 faster, more reliable method for rust resistance screening 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. Long-term outcomes: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.

Stem rust (Puccinia graminis) and crown rust (Puccinia coronata) are serious fungal diseases of most agriculturally important grass species and currently affect all 72,000 hectares of perennial ryegrass seed production within the United States. Rust pathogens also affect perennial ryegrass when grown as a turfgrass and can reduce turf quality and lead to increased water and nutrient use (Dracatos, et al). Phenotypic screening for resistance to rust pathogens can be time consuming, costly, and unpredictable due to pathogen and environment unpredictability (Kimbeng, 1999). In addition we need a way to select for rust resistance in the absence of the actual pathogen in order to be able to select for other traits simultaneously and speed cultivar development. Metabolomics-assisted breeding has recently emerged a technique to select for desired traits based on chemical profiles that have been previously correlated with the trait of interest. The goal of this research is to develop a method for rapid, and accurate selection of 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 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.

2009 Field Crown Rust Screening

Line Rust SeverityAUDPC†

34 6612 65*10 64*19 6361 6314 62*73 62*44 61*26 6020 584 566 56

18 5667 55*92 5436 543 54

32 5333 5122 5027 492 48

76 4850 4857 4875 48*11 4816 4837 4847 4774 479 47*7 46

88 46*87 4630 4651 46*23 458 44

86 4342 435 43

13 4393 4391 4245 4258 4141 4154 4156 41*1 39

39 3825 38*38 37*89 3752 3749 37*48 3568 33

2010 Field Crown Rust ScreeningLine Rust Severity

1-10 Scale8 2.9

73* 2.710* 2.612* 2.614* 2.542 2.422 2.444* 2.454 2.375* 2.367* 2.25 2.2

89 2.288* 2.14 2.09* 2.0

51* 2.056* 2.049* 1.938* 1.957 1.974 1.861 1.711 1.725* 1.5

Susceptible

Resistant

Medium

Resistant

Medium

Susceptible

Crown Rust Resistance Range and Rating Key

1

2

3

4

5

6

72009 Clones 2010 Clones

Figure 1. Crown rust severity rankings for initial field screening of perennial ryegrass germplasm from our breeding program in 2009 and 2010.

Figure 2. Clones from the most resistant or susceptible plant in stable lines (indicated by *) from each replication in 2009 and 2010 were collected and used in replicated trials for final resistance classification and HPLC-MS analysis.

Figure 3. Final crown rust resistance severity and rank data for 14 perennial ryegrass lines to be used for identifying metabolic biomarkers associated with crown rust resistance.

Figure 4. Range of disease severity and key for the modified Horsefall-Barret rating scale. 1= no rust, 2 = <10%, 3 = 11-25%, 4 = 26-40%, 5 = 41-60%, 6 = 61-70%, 7 = 71-80% 8 = 81-90% 9 = >90%, and 10 = 100% coverage of rust pustules (Helgeson et al., 1998).

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Figure 5. A. HPLC-MS chromatogram of perennial ryegrass extract showing retention time and base peak intensity. B. Base peak intensity with metabolic feature detection. C. Relative abundance and mass to charge ratio for metabolic feature detected at retention time 4.10-4.15.

Expected Outcomes

Materials and MethodsRust Screening:

Plant Material•59 perennial ryegrass breeding lines previously selected for horizontal growth, turf quality, winterhardiness (Minnesota spreading germplasm x Rutgers high quality germplasm).

•Planted in St. Paul nursery for crown rust resistance screening in a field trial in 2009 and 2010, RCB, 4 replications.

•14 lines that demonstrated stable resistance, moderate resistance, or susceptible rankings in 2009 and 2010 were selected for final resistance classification and HPLC-MS analysis using clones of the most resistant or susceptible plants (Figure 2).

•Replicated clones taken from the 14 selected lines were screened in a field trial in 2011 and a growth chamber in 2012 using a RCB design with 4 replications. Crown rust severity was rated on a 1-10 scale (See Fig. 4). The growth chamber experiment was artificially inoculated using crown rust uredineospores that were collected from 48 perennial ryegrass accessions grown in Becker and St Paul, MN in 2009 and 2010 as well as from a buckthorn (Rhamnus cathartica) nursery in 2011. Collected spores were desiccated and stored at -80°C until needed.

Metabolite Extraction and Analysis:

•40 mg of perennial ryegrass leaf tissue was snap frozen in liquid N lyophilized and pulverized in a bead mill.

•Room temperature 90% methanol extraction (10 min shaking in bead mill).

•Samples were dried in SpeedVac and reconstituted in 100 µl 5% acetonitrile and 95% dd H2O.

•Samples subject to reverse phase UPLC-MS (Thermo Accela-Thermo LTQ Orbitrap).

•UPLC Column: Acquity UPLC BEH C18 1.7 um 2.1 x 100mm.

•Samples analyzed using negative electrospray ionization (ESI) .

•UPLC conditions: 10μl injection volume, .5 ml min-1 flow rate, 18 min gradient, mobile phase A: 0.1% FA, mobile phase B: 100% acetonitrile.

•Individual metabolite features were Thermo Sieve software.