© 2013 Plant Management Network.
Characterization of Resistance of Peanut to Puccinia arachidis
Imana L. Power and Albert K. Culbreath, Department of Plant Pathology, The University of Georgia, Coastal Plain Experiment Station, 2360 Rainwater Road, Tifton, GA 31793; and Barry L. Tillman, North Florida Research and Extension Center, University of Florida, 3925 Highway 71, Marianna, FL 32446
Power, I. L., Culbreath, A. K., and Tillman, B. L. 2013. Characterization of resistance of peanut to Puccinia arachidis. Online. Plant Health Progress doi:10.1094/PHP-2013-1125-02-RS.
Peanut rust, caused by Puccinia arachidis Speg, is an important foliar disease of peanut (Arachis hypogaea L.) in tropical countries. The best option for disease management is host resistance. The objectives of this project included characterizing peanut genotypes for resistance to P. arachidis, assessing the genetic variation of newly developed Collaborative Research and Support Program (CRSP) peanut breeding lines, and assessing genetic variability among P. arachidis populations. In field studies conducted over 2010-2011, several CRSP breeding lines demonstrated varying levels of rust resistance. Detached leaf assays were used to examine the components of resistance to P. arachidis. Few significant differences were observed in these studies. We used SSR markers to characterize newly developed CRSP breeding lines, plant introductions, and commonly grown cultivars. The SSR markers used detected polymorphisms but were not able to distinguish resistant from susceptible peanut genotypes. Sequences of the 5.8S-ITS2-28S region of P. arachidis isolates collected from different regions in the United States and other countries do not indicate high genetic variability among the populations.
Peanut rust, caused by Puccinia arachidis Speg, is an important foliar disease of peanut (Arachis hypogaea L.) in mainly low-input peanut-producing countries with warm, tropical climates; it typically does not cause extensive losses in the southeastern United States. Under normal cultivation conditions, yield losses due to infection by the peanut rust fungus can be considerable (2,24,25,27,29). Subrahmanyam et al. (25,26,27) found yield losses as high as 50% in India. In areas where rust causes frequent problems, management methods include cultural practices to reduce the inoculum source, such as eradicating volunteer plants and allowing fallow periods of at least one month between crops, and multiple fungicidal sprays throughout the season (2,17,23,24). However, chemical applications increase the production costs and moreover, the fungus may develop resistance with frequent fungicide applications (23). In addition, rust is problematic in numerous production areas where fungicidal control is not an option due to cost or availability of fungicides. The use of resistant peanut cultivars is a promising alternative management approach, and can be beneficial to growers across a range of production levels.
Many germplasm accessions have been screened, and several peanut genotypes with resistance to peanut rust have been identified (10,11,12), with sources for resistance mainly originating from Peru, Bolivia, and India (2,24,29,34). However, very little new information on rust resistance has become available in the last two decades. More recently, several breeding lines were developed with a Bolivian landrace as parent, in the UF150 project of the Peanut Collaborative Research and Support Program (Peanut CRSP) as part of the United States Agency for International Development (USAID). These breeding lines are currently being screened for multiple disease resistance in the United States and several low input peanut producing countries in the Western Hemisphere.
There is no complete resistance to P. arachidis reported in cultivated peanut. Peanut rust resistance is partial and rate reducing, where several polygenic minor genes, the components of resistance, provide varying levels of partial resistance, leading to a reduced rate of the disease epidemic. The components of peanut rust resistance described are incubation period, latent period, infection frequency, pustule size, percent diseased area, spore production, and spore germination (2,3,27). In the 1980s and early 1990s Cook (3) and Subramanyam (27) characterized the components of peanut rust resistance for several genotypes, but little work has been reported on the more recently developed breeding lines.
Genetic variability in cultivated peanut is low. This is believed to be the result of the recent hybridization of the two diploid Arachis species A. duranensis and A. ipaensis, followed by chromosome doubling (15,16,18,31). In the last few years, hundreds of simple sequence repeat (SSR) markers have been developed by research groups including the University of Georgia and The International Crop Research Institute for the Semi-Arid Tropics (ICRISAT). Khedikhar et al. (15), Mace et al. (16), Mondal et al. (18), and Varshney et al. (31) identified SSR markers that were able to detect high levels of polymorphism in peanut recombinant inbred lines RILs and peanut genotypes from different geographical regions, of which several were able to distinguish rust resistant from susceptible genotypes. Information on whether these markers can identify peanut rust in the CRSP breeding lines would be beneficial.
The lifecycle of the peanut rust pathogen is incomplete; it is not known whether alternate hosts exist. Instead, the pathogen is highly host specific as there are no reports on hosts outside of the Arachis genus. There are no reports on the presence of basidiospores, pycniospores or aeciospores, and teliospores have been rarely observed (2,24). The asexually produced dikaryotic urediniospores are predominant. Little is known about the diversity of the P. arachidis fungus, and to our knowledge, little research is being conducted on this subject. Therefore, knowledge on the molecular variability of the pathogen will lay the groundwork in the population structure and evolution of the pathogen. Greater knowledge on the variability of the P. arachidis populations and the genetics of resistance to peanut rust will moreover enable us to effectively breed for resistance and thus effectively manage the peanut rust disease on the long run.
Although peanut rust is primarily a disease of the tropics and subtropics and has been sporadic in occurrence in the southeastern United States, global climate change may result in greater problems with this disease in the United States either through greater frequency of tropical storms that move inoculum from sources in the Caribbean to peanut production areas in the United States or by extending the range over which the pathogen can overwinter. Increased potential for rust epidemics should be addressed proactively, because most peanut cultivars in the United States currently grown have low disease resistance (2,9,24,25,26,27,29) or the level of resistance to the rust pathogen is not known.
With this paper we report preliminary results of: (i) rust resistance in newly developed peanut breeding lines; (ii) use of previously identified SSR markers for rust resistance genes to distinguish rust resistant genotypes from susceptible ones using genetic markers; and (iii) the genetic variation among P. arachidis populations.
Rust Resistance in CRSP Breeding Lines: Field Resistance
Field studies were conducted at the University of Florida, Plant Science and Education Unit, Citra, FL, in 2010 and 2011, and at the University of Georgia, Coastal Plain Experiment Station, Tifton, GA, in 2011, to evaluate the field resistance of the CRSP breeding lines. The experiments were organized in a randomized complete block with three replications. Twenty five and 19 genotypes were planted at a seeding rate of 20 seed/m of row, in two row plots (4.5 m × 6 m) at Citra and Tifton, respectively (Table 1). Disease severity was assessed weekly, using a modified nine-point ICRISAT scale based on lesion density and leaf necrosis (29): 1 = no disease and 9 = more than 50% of foliage damaged by the disease.
Disease severity data were used to calculate the area under disease progress curve (AUDPC) (22) for each plot. The effects of genotype on AUDPC were analyzed using the Proc GLM procedure (SAS v 9.2, SAS Institute Inc., Cary, NC). Replications were considered random effects, and genotype was considered a fixed effect. Differences among genotypes were determined using the "lsd" option included in each main effect. Fishers LSD (P ≤ 0.05) was used to determine significant differences between AUDPC. Genotypes with a disease severity score of three or lower were considered resistant (AUDPC < 5.8 in 2010 or AUDPC < 6.7 in 2011).
In 2010, 18 of the 25 genotypes demonstrated resistance to rust (Table 2). Of these genotypes, several could potentially be developed into cultivars, whereas others would be more suitable in rust resistance breeding programs. Disease severity was low in Citra in 2011, and few differences in rust severity were observed among genotypes. In Tifton, the disease epidemic started too late in the season be high enough to distinguish between genotypes by harvest.
Rust Resistance in CRSP Breeding Lines: Components of Resistance
To assess the components of resistance, a detached leaf experiment was carried out as described by Cook (3). Peanut plants were grown at 25°C from seed in the greenhouse in 15 cm pots filled with potting soil. The youngest fully expanded leaves of six- to eight-week-old plants were collected, the leaflets detached, and placed on sterile damp filters in a Petri dish (15 cm diameter) with the abaxial side up. The leaflets were then inoculated by spraying them for one second using an aerosol sprayer containing an uredinial spore suspension (40,000 spores per ml). Spore suspensions were made with a peanut rust isolate collected from fields in Georgia, and increased in the greenhouse on cultivars Florida-07 and Georgia Green. There were three replicates per genotype. The Petri dishes containing inoculated leaflets were arranged in a randomized complete block, and incubated in darkness for 16 h at 25°C. After inoculation, the closed Petri dishes were incubated at 25°C, 12h photoperiod for 31 days. Leaflets were examined for the numbers of pustules at 20 and 31 days after inoculation (DAI), and pustule size at 31 DAI. Pustule size was determined by measuring the largest diameter of each pustule, using a dissecting microscope at 5× magnification.
The effects of genotype on numbers of pustules and pustule size were analyzed using the Proc GLM procedure (SAS v 9.2, SAS Institute Inc., Cary, NC). Replications were considered random effects, and number of pustules, pustule size, and genotype were considered fixed effects. Differences among genotypes were determined using the "lsd" option included in each main effect. Fishers LSD (P ≤ 0.05) was used to determine significant differences among genotypes for numbers of pustules, and pustule size.
There were few significant differences between genotypes for numbers of pustules developed and pustule size in both Spring and Fall (Table 3). In the Spring, genotypes NC3033 and SunOleic 97R had the highest pustule numbers and SunOleic 97R had the largest pustule size. In the Fall genotype PI568164 had both the highest pustule number and largest lesion diameters.
SSR Markers to Identify Rust Resistance Genes in Peanut Genotypes
Total genomic DNA of 41 genotypes (Table 1) was extracted from fresh unfolded
leaves of eight- week-old greenhouse-grown plants, following a CTAB protocol
(19). A set of seven SSR markers − GM431, GM457, GM496, GM518, GM553, GM567, and GM591 (5) − previously identified as being able to detect high levels of
polymorphism in peanut RILs and peanut genotypes from different geographical
regions (15,16,18,31), were used for preliminary characterization of the 41
genotypes. The SSR markers were selected based on their ability to
distinguish between rust resistant and susceptible genotypes in previous reports
(16). PCR amplifications were performed in 10 µl total volumes (8) containing
2.5-ng DNA template, using a 64°C-58°C touchdown PCR amplification program
(4). PCR products were sent to the Georgia Genomics Facility (Athens, GA) for
Peak analysis was conducted using GeneMapper software v4.0 (Applied Biosystems, Forest City, CA). The alleles of each SSR locus from the 41 peanut genotypes were scored as presence or absence of the allele. Analysis of molecular variance (AMOVA), principal component analysis and estimates of genetic distances between the genotypes were calculated with GenAlEx6.4 (20) to analyze differences between the resistant and susceptible peanut genotypes. The Jaccard coefficient was used to compute the genetic similarity matrix of the genotypes based on the SSR data using DendroUPGMA (genomes.urv.es/UPGMA) (6). An unweighted pair group method of arithmetic means (UPGMA) dendrogram was constructed using the PhyloWidget program (www.phylowidget.org) (14).
A total of 15 polymorphic alleles were generated for the seven loci across the peanut genotypes, with an average of 2.1 alleles per locus. Although polymorphisms were detected no distinction between resistant and susceptible peanut genotypes was observed. No distinct resistant or susceptible clades were observed in the neighbor joining analysis (Fig. 1). A similar lack of grouping of resistant or susceptible genotypes was observed in the principal components analysis (Fig 2). These results are in contrast with those reported by Mace et al. (16), who found SSR markers associated with rust resistance genes. In their study, GM431, GM496, GM 553, and GM567 were present only in resistant genotypes and absent in susceptible ones. They also found marker GM518 absent in resistant and present in susceptible genotypes. In our study, no marker alleles were consistently associated with resistant or susceptible genotypes. The AMOVA indicated that a low percentage of the genetic variation was associated with disease resistance and the genotypes. Fifteen% of the observed variation is accounted for by "among resistant and susceptible populations," whereas 85% is accounted for "within resistant and susceptible groups" (Table 4). The observed low polymorphism is consistent with previous reports (15,16,18,31).
Genetic Variation Among P. arachidis Populations
We used P. arachidis field isolates, consisting of a collection of
spores, collected in North America, South America, Central America and Asia
(Table 5), in different years. Genomic DNA of isolates collected in GA was
extracted from 10-25 mg of urediniospores per field isolate, by grinding the
spores in a bead beater for 5 min with glass beads, followed by the Omniprep for
fungi extraction kit (G-Biosciences, St. Louis, MO) according to the
instructions. DNA of isolates collected outside of the state of GA, was
extracted using the Qiagen REPLI-g Ultrafast mini kit (Qiagen, Valencia, CA), as
described by Wang et al. (33) with minor revisions: Several spores per isolate
were added to a 2.5 µl mixture that contained 1 µl phosphate buffered saline
(PBS) and 1.5 µl denaturing buffer (D2). After ice-incubation, the
manufacturers protocol was followed. DNA quality was examined on a 1% agarose
gel, and the quantity was determined with nanodrop.
The primers Rust2inv (1) and LR6 (32) were used to amplify the complete ribosomal 5.8S subunit, the internal transcribed spacer region 2 (ITS 2) and the 28S subunit, in 25 µl total volumes (1). Cleaned PCR products were sequenced with Rust2inv by Eurofins MWG Operon (Albany, GA). To confirm rust specificity, all DNA sequences were subjected to BLAST search. Nucleotide sequences generated were aligned and analyzed using the software Geneious v6.05 using default settings.
We have collected 33 isolates from the US, Bolivia, Guyana, Haiti, Nicaragua and the Philippines (Table 5), and extracted genomic DNA, PCR-amplified and sequenced the ribosomal 5.8S-ITS2-28S region of 33 isolates. Based on the preliminary results from the sequenced region, no distinct, well-supported groups could be identified, as there was no distinction among geographic regions or collection dates (Fig. 3). This high degree of genetic similarity in the ITS region of the isolates studied, indicates low molecular variability within the populations, which may indicate that the isolates shared a common origin. The homogeneity within these populations may furthermore indicate a lack of sexual recombination, as is suspected due to the absence of the sexual teliospores.
We conducted field experiments, growth chamber experiments, and genetics of P. arachidis and its host to characterize peanut rust resistance. Though preliminary, this research indicates the existence of field resistance to the peanut rust disease in the newly developed CRPS breeding lines. Including more components will most likely enable us to better explain the mechanism behind the peanut rust resistance.
All SSR markers used were polymorphic, however, very few alleles were present per locus, which is consistent with low polymorphism reported in peanut (15,16,18,31). No distinction between resistance and susceptibility was observed in the studied genotypes. We will include more polymorphic SSR markers to allow us to better distinguish peanut rust resistant genotypes from susceptible ones, on the genetic level.
ITS regions are useful for identifying molecular variability within populations of the same species (7,13). Our data indicate that P. arachidis populations are highly homogeneous for those regions. Other loci will be examined to determine levels of variability among isolates within P. arachidis. To our knowledge, no information on the population structure of P. arachidis has ever been published.
The authors thank USAID, Peanut CRSP, the National Peanut Board and the APS Foundation for funding the research; Dr. Timothy Brenneman, Dr. Robert Kemerait Jr., Dr. Katherine Stevenson, Pablo Navia, Marian Luis, and Abraham Fulmer for collection of P. arachidis isolates; Michael Heath, Ronald Hooks, Matthew Wiggins, Samuel Holbrook, Patricia Hilton, Miranda Goodman, Stephen Mullis, Justin McKinney, Dr. Sergio Morichetti, Mike Giomillion, Dr. Graeme Wright, Jeff Tatnell, and Alyssa Cho for assistance in field and growth chamber studies; Kippy Lewis, Dr. Venkatsan Parkunan, Dr. Bhabesh Dutta, Dr. Peggy Ozias-Akins, Dr. Ye Chu, and Rattandeep Gill for molecular studies and molecular data analysis.
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