Genetic diversity of orchardgrass

Biochemical Systematics and Ecology 54 (2014) 96–102

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Genetic diversity of orchardgrass (Dactylis glomerata L.) germplasms with resistance to rust diseases revealed by Start Codon Targeted (SCoT) markersq Bing Zeng b, *,1, Yu Zhang a,1, Lin-kai Huang a, **, Xiao-mei Jiang a, Deng Luo b, Guohua Yin c a

Department of Grassland Science, College of Animal Science and Technology College, Sichuan Agricultural University, Ya’an, Sichuan Province 625014, China Department of Animal Science, Southwest University, Rongchang, Chongqing Province 402460, China c College of Agriculture Life Science, The University of Arizona, Tucson 85721, United States b

a r t i c l e i n f o Article history: Received 26 September 2013 Accepted 26 December 2013 Available online Keywords: Dactylis glomerata L. Rust-resistance SCoT genetic diversity

1. Introduction Orchardgrass (Dactylis glomerata L.) is an important member of the family Poaceae. This grass has been grown as a major source of forage and hay for more than one hundred years (Casler et al., 2000; Lindner and Garcia, 1997). Orchardgrass has extensive variation in taxonomic characteristics, which distinguish them from other genera within Poaceae (Catalán et al., 2004). Orchardgrass includes diploid (2n ¼ 14) and tetraploid (2n ¼ 28) species (Lumaret and Barrientos, 1990). Diploids are the progenitors of tetraploid offspring through triploid backcrosses and non-gametic reduction (Borrill, 1977). These polyploid varieties have been widely utilized in cultivated pastures due to their good nutrition, high yield, and good shade tolerance; thus, they play an important role in animal husbandry (Stewart and Ellison, 2011). Orchardgrass germplasms have been studied extensively in China over the past 20 years (Shuai et al., 1997). Many natural populations have been collected, from which three cultivars (BaoXing, GuLin and ChuanDong) have been recently developed. In addition, five introduced varieties have been released (Zeng et al., 2008). However, the currently available orchardgrass species are susceptible to rust q This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-No Derivative Works License, which permits non-commercial use, distribution, and reproduction in any medium, provided the original author and source are credited. * Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (B. Zeng), [email protected] (L.-k. Huang). 1 These authors contributed equally. 0305-1978/$ – see front matter Ó 2014 The Authors. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.bse.2013.12.028

B. Zeng et al. / Biochemical Systematics and Ecology 54 (2014) 96–102

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Table 1 Orchardgrass (Dactylis glomerata L.) specimens examined in this study. Accession code

Accession name

Origin

Rust resistance in different year 2011

2012

Y10 Y11 Y22

98–102 1473 2122

HR HR HR

R R HR

Y24 Y52 Y53 Y62 Y66

947 02–114 02–115 1436 1824

HR HR HR HR HR

R HR R R HR

Y68

1993

HR

R

Y77 Y92 Y93 Y95 Y97 Y99 Y109 Y110 Y117 Y125 Y135 Y140 Y145 Y150 Y165 Y178 Y180 Y181 T185 Y186 Y197 Y199 Y202 Y213 Y221 Y223 Y229 Y234 Y264 Y265 Y266 Y267 Y268 Y272 Y273 Y277

PI441632 PI237602 PI295271 PI538922 PI441034 PI368880 PI610830 PI610822 PI173693 PI237590 PI578587 PI325293 PI312450 PI418672 PI305497 PI384018 PI399466 PI237268 PI598424 PI598423 PI231613 PI380812 PI308794 PI269885 PI237586 PI578667 PI231727 PI469234 Casfellafa Judacea Woronowii Parthiana BTN New Zealand Glorus Sweden California Oregon USA95

United States Australia Animal Husbandry Research Institute, Hubei, China Japan Qujing, Yunnan, China Deqin, Yunnan, China Jiangshu, China Animal Husbandry Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China Animal Husbandry Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China New Zealand Portugal India Russian Federation United Kingdom Algeria Spain Spain Turkey Israel Portugal Russian Federation Former Soviet Union Italy Poland Poland Finland Finland Bulgaria Ireland Iran Iran India Pakistan Tunisia Egypt Chile Australia New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand New Zealand

HR R HR HR HR HR HR HR HR HR R HR HR R R HR R R R HR R R HR HR R HR R HR HR R HR HR HR R HR R

HR R HR R R R HR HR R R R HR R R R HR HR R HR R R R HR R HR R R R R HR HR HR HR R R R

R: Resistance; HR: High Resistance.

diseases caused by Puccinia graminis Pers.. Infection from rust diseases results in lower yield and poor-quality growth in subtropical regions of China (Zhang et al., 2012). Thus, orchardgrass rust diseases in China are a serious issue with potentially global implications. More recently, molecular markers linked to rust resistance have been identified and are being used by breeders for indirect selection of rust resistance in breeding populations. These technologies have also added to the breeder’s ability to do research on orchardgrass germplasms to achieve longer-term resistance to rust diseases in selected populations. Orchardgrass plays such an important role in stock farming that researchers have used numerous methods to study orchardgrass species. By introducing a foreign chimeric gene into orchardgrass protoplasts (Horn et al., 1988), chloroplast and ITS sequences have been sequenced to study the phylogenetic relationships of orchardgrass species (Lumaret et al., 1989; Catalán et al., 2004). Different molecular markers have been used to analyze orchardgrass genetic diversity and reveal a high level of genetic diversity. These results also reveal genetic relationships among and within wild-type specimens sampled from museum collections (Stuczynski, 1992; Peng et al., 2008; Xie et al., 2010b). Germplasm genetic diversity is the basis for the development and utilization of forages. The molecular marker technologies applied in orchardgrass research include

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amplified fragment length polymorphisms (AFLPs) (Reeves et al., 1998), random amplified polymorphic DNAs (RAPDs) (Tuna et al., 2004), sequence-related amplified polymorphisms (SRAPs) (Xie et al., 2010a), simple sequence repeats (SSRs) (Hirata et al., 2011), and inter-simple sequence repeats (ISSRs) (Zeng et al., 2006). In addition to the markers mentioned above, start codon targeted (SCoT) marker development introduced by Collard and Mackill (2009) is a novel and effective molecular marker technology. It is widely used for the generation of phylogenetic hypotheses as well as genetic variation studies (Luo et al., 2010; Xiong et al., 2009; Guo et al., 2012). Orchardgrass as a main forage grass is susceptible to rust, which negatively influence its yield and quality and restrict its agricultural use (Mizuno et al., 2000). For this reason, it is very important to breed new rust-resistant cultivars of orchardgrass. To improve the genetic resources of orchardgrass and related species, we investigated 45 rust-resistant orchardgrass specimens using SCoT markers. Our objectives were to assess the amplification efficiency and transferability of these molecular markers, to investigate the molecular variations of these markers, and to determine whether or not there exists high genetic diversity within and among orchardgrass populations. Our ultimate purpose is to provide information to assist with the development of orchardgrass breeding programs. 2. Materials and methods 2.1. Plant material A total of 45 orchardgrass specimens with varying levels of known resistance to rust diseases evaluated by Yan et al. (2013) were chosen as experimental materials. The orchardgrass germplasms originated from 28 regions around the world (Table 1). For each of the 45 specimens, 20 individuals were collected randomly from the Sichuan Agricultural University, and 0.5 g of young and clean leaves was selected per plant. Thirteen (98–102, 947, 02–115, PI237590, PI325293, PI312450, PI305497, PI384018, PI371948, PI308794, PI237586, judacea, California) of the 45 specimens received a low disease index (0 DI < 15) that scored them “HR” in both 2011 and 2012 (Yan et al., 2013). 2.2. DNA extraction Collected leaves were either immediately used for DNA extraction, or were stored at 20 C prior to DNA isolation. Genomic DNA was isolated from leaf tissues using a standard CTAB method (Porebski et al., 1997). DNA was quantified by comparing it to a known, diluted Lambda DNA run on 0.8% agarose gel. Quantified DNA was then stored at 20 C until use. 2.3. SCoT-PCR amplification Forty-eight SCoT primers synthetized by Shanghai Sangon Biological Engineering Technology and Service Company (Shanghai, China) were initially screened. Twenty-two primers clear, amplified bands were selected for genetic analysis (Table

Table 2 Sequences for 22 SCoT primers, and the number of scorable polymorphic bands of each primer. Primer

Primer sequence (50 –30 )

%G/C

TNB

NPB

PPB (%)

SCoT1 SCoT2 SCoT5 SCoT6 SCoT8 SCoT10 SCoT14 SCoT16 SCoT25 SCoT26 SCoT27 SCoT34 SCoT35 SCoT36 SCoT37 SCoT38 SCoT39 SCoT40 SCoT41 SCoT42 SCoT44 SCoT45 Total Average

CAACAATGGCTACCACCA CAACAATGGCTACCACCC CAACAATGGCTACCACGA CAACAATGGCTACCACGC CAACAATGGCTACCACGT CAACAATGGCTACCAGCC ACGACATGGCGACCACGC ACCATGGCTACCACCGAC ACCATGGCTACCACCGGG ACCATGGCTACCACCGTC ACCATGGCTACCACCGTG ACCATGGCTACCACCGCA CATGGCTACCACCGGCCC GCAACAATGGCTACCACC ACGACATGGCGACCAGCG ACGACATGGCGACCACCG AACCATGGCTACCACCGC CAATGGCTACCACTACAG CAATGGCTACCACTGACA CAATGGCTACCATTAGCG CAATGGCTACCATTAGCG ACAATGGCTACCACTGAC

50 56 50 56 50 56 56 56 67 61 61 61 72 56 67 61 72 50 50 50 50 50

12 14 10 9 16 16 14 12 13 12 9 11 8 8 14 13 16 15 12 17 14 12 277 12.59

9 13 8 6 16 14 12 12 13 10 7 11 6 7 13 13 15 13 11 17 12 11 249 11.32

75.00 92.86 80.00 66.67 100.00 87.50 85.71 100.00 100.00 83.33 77.78 100.00 75.00 87.50 92.86 100.00 93.75 86.67 91.67 100.00 85.71 91.67 89.89 89.91

TNB: Total number of bands; NPB: Number of polymorphic bands; P: Polymorphic ratio.


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2). Each 20 mL amplification reaction consisted of 1.5 mM MgCl2, 0.4 mM dNTPs, 0.8 mM primers, 10 ng template DNA, and 1 U Taq DNA polymerase (Tiangen Biotech, Beijing, China). PCR amplification was using an initial denaturation step of 94 C for 3 min, followed by 36 cycles of the following: 94 C (denaturation) for 50 s, 50 C (annealing) for 1 min, and 72 C (extension) for 2 min. A final extension was conducted at 72 C (extension) for 5 min (Luo et al., 2010). Products were visualized on a 1.5% agarose gel.

2.4. Data analysis Only clear and reproducible bands were scored. The band patterns obtained by each SCoT primer were scored as absent (“0”) or present (“1”). Excel 2007 and POPGENE (version 1.32) (Yeh et al., 1997) were used to calculate the total number of bands (TNB), the number of polymorphic bands (NPB), the percentage of polymorphic bands (PPB), Nei’s Gene Diversity Index (H), and Shannon’s Information Index (I). The resulting 22 22 similarity matrix was subjected to multi-dimensional scaling (MDS) (Kruskal, 1964) to assess whether the molecular variation we observed suggested clustering among samples. The unweighted pair-group method with arithmetic average (UPGMA) (Rohlf, 2000) was used to independently confirm the clustering indicated by the two-dimensional MDS plot. A dendrogram was then constructed using the NTsys-pc V2.1 (Rohlf, 2000). Analysis of molecular variance (AMOVA) was used to partition the total SCoTs variation within and among geographical region components (Excoffier et al., 1992). POPGENE and AMOVA input files were prepared using the program DCFA1.1 (Zhang and Ge, 2002).

3. Results 3.1. SCoT polymorphisms Forty-eight SCoT primers were tested on DNA of 4 representative, rust-resistant samples, to select primers. All 48 primers generated polymorphic DNA amplification products; of the primers we used, 22 amplifying clear and reproducible bands were selected for use in genetic analysis of orchardgrass (Table 2). POPGEN software (version 1.32) analysis revealed a total of 277 reliable bands (TNB), with an average of 12.59 bands per primer, of which 249 were polymorphic (NPB). In total, this yielded an average polymorphism rate of 89.89% (PPB). The number of bands amplified by these 22 primers varied by sample from 8 (SCoT 35 and SCoT 36) to 17 (SCoT 42), with the number of polymorphic bands varying by sample from 6 (SCoT 6 and SCoT 35) to 17 (SCoT 42). These results indicate that SCoT primers we selected had high amplification efficiency and were able to recover a good amount of polymorphism (Fig. 1).

3.2. Genetic diversity revealed by SCoT Nei’s Gene Diversity Index (H) not only reflects the abundance and uniformity of the allele, but also measures genetic diversity among different experimental materials. The H value of these 45 specimens was 0.361, and the Shannon’s Information Index (I) was 0.526 (Table 3). The genetic similarity coefficient (GS) between pairs of samples was obtained from the marker data and was based on simple matching (SM) coefficients. The analysis of NTsys-pc V2.1 showed a GS of 0.545–0.801, with an average of 0.702. The highest observed GS value (0.801) was found between samples PI610830 and PI61082. The lowest observed GS value (0.545) was found between samples 2122 and Oregon USA95. Analysis of molecular variation (AMOVA) of the 45 rust-resistant orchardgrass genotypes revealed that only a small proportion of the total genetic variation was associated with resistance (R) or a high level of resistance (HR). In total, 7.06% of the genetic variation we observed could be associated with resistant/high resistant populations, whereas the majority of the variation (92.94%) accounted for the resistance found within populations.

Fig. 1. Results of PCR reactions using primer SCoT41.

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Table 3 SCoT analysis of two populations of orchardgrass. Population

NPB

PPB (%)

H

I

HR R Accession level

205 240 249

74.01 86.64 89.89

0.300 0.352 0.361

0.436 0.513 0.526

HR: High resistance in 2011 and 2012; R: Resistance in 2011 or 2012; NPB: Number of polymorphic bands; PPB: Percentage of polymorphic bands; H: Nei’s (1973) Gene Diversity Index; I: Shannon’s Information Index.

3.3. Cluster analysis UPGMA clustering reflects the history of breeding and selection of the specimens we examined, and grouped the specimens according to geographical origin and type (Viruel et al., 2005). The 45 samples we examined clustered into 5 groups with a similarity index of 0.580. We used an UPGMA clustering algorithm from SCoT markers The five groups we identified with our cluster analyses were named Ⅰ, Ⅱ, Ⅲ, Ⅳ, and Ⅴ (Fig. 2). Twenty-two samples from 14 countries were distributed in group Ⅰ, 10 of which were from Asia, and 6 of which were HR in 2011 and 2012. Group Ⅱ consisted of 19 specimens, 4 of which were HR in 2011 and 2012. Interestingly, these 19 samples originated from 12 different countries. PI305497, which originated from Poland, was recovered in group Ⅲ, and it was R in 2011 and 2012. Group Ⅳ included two specimens from New Zealand. The most distinct group, Ⅴ, included only one sample 2122, which was HR in both 2011 and 2012 as well. 4. Discussion 4.1. Variation of SCoT In our study, the SCoT marker was successfully used to study the genetic diversity of 45 orchardgrass specimens with resistance to rust diseases. Twenty-two out of the 48 primers we tested were used to amplify a total of 277 scorable bands, with an average of 12.59 bands for each primer. Of these bands, 249 (89.89%) were polymorphic. Our results can serve as a valuable resource for genetic and genomic analysis of orchardgrass specimens. AFLPs, RAPDs, SSRs, ISSRs, SRAPs, and the newly developed SCoT molecular marker technology have all been widely used to characterize genetic diversity. AFLPs, RAPDs, SSRs, and ISSRs are traditional, random molecular markers, while SRAPs and SCoTs are considered “molecular marker genes”, which are thought to be more useful for informing breeding programs (Bhattacharyya et al., 2013; Liu et al., 2013). Our results ensure that the evaluation of the genetic diversity of orchardgrass germplasm resources is no longer confined to morphological, cytological, or biochemical markers alone. Previous studies of orchardgrass that used genetic and genomicscale molecular markers showed abundant diversity within the orchardgrass genus Dactylis and its related genera. For example, Reeves and colleagues chose two populations selected from French and Italian transects to determine if there was a significantly negative correlation between DNA C-value and altitude among experimental populations of D. glomerata L. Their AFLP results showed that these populations were genetically distinct (Reeves et al., 1998). ISSR molecular markers have been used to detect the genetic diversity among 50 samples of D. glomerata collected from China and other countries. The range of GS values was 0.6116–0.9290, indicating that D. glomerata possesses rich genetic diversity (Zeng et al., 2006). Xie and colleagues investigated the molecular variation and structure of cultivars, subspecies, and advanced breeding lines to determine whether there sufficient genetic diversity still existed within commonly used cultivars. Their SSR results (114 easily-scored bands) were generated from 15 SSR primer pairs. The polymorphic rate was 100% among the 120 individuals, reflecting a high degree of genetic diversity in the samples they examined (Xie et al., 2012). Traditional plant taxonomy based on morphology, physiology, and biochemistry to analyze diversity is likely affected by both environment and growth stages. In addition, it is challenging to determine the relationships between samples because of the close relationship between the sources and the fact that agronomic traits are difficult to distinguish (Di et al., 2006; Trimech et al., 2013). SCoT analysis can potentially overcome this challenge as it is a low-cost and highly effective approach that can be used to reveal real genetic diversity between samples. 4.2. Genetic diversity within and among populations Orchardgrass is a common forage in temperate zones and possesses high infection rate of rust diseases. Rust-resistant orchardgrass can also be susceptible to diseases when it is exposed to a certain environment or pathogen variant (Yan et al., 2013). Selecting high-rust-resistant orchardgrass and improving cultivation technologies are both critical, and selecting for disease resistance genes is highly dependent upon available germplasm resources. Ittu and Kellner (1977) found a higher rate of rust-resistant orchardgrass germplasm varieties, most generated from native Italian populations found in lower latitudes, than that mentioned in our discussion. All of the 45 rust-resistant orchardgrass specimens in this study could be efficient for screening high-disease-resistance genes. These specimens were scored as “R” or “HR” according to their performance with rust-disease-resistance in 2011 and 2012. Samples 2122, 02–114, 1824, PI295271, PI325293, PI610830, PI610822, PI385018, woronowii, parthiana, and BTN New Zealand were HR in both 2011 and 2012, which were grouped as a population artificially. The other 34 specimens we examined formed another population. The high percentage of polymorphic


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Fig. 2. Dendrogram representing relationship (UPGMA cluster) of 45 orchardgrass specimens based on SCoT markers.

bands observed within populations, and the low genetic similarity among bulked samples within populations suggested a high level of within-population heterogeneity. The AMOVA analysis we conducted also showed a significant amount of intrapopulation variation (92.61%). 4.3. Classification with molecular data Based on SCoT molecular data and an UPGMA clustering algorithm (with Nei’s genetic distances), the 45 samples we examined could be divided into main five groups. Group I was composed of 22 samples from 14 countries, of which 10 were from Asia, and others were 1 from American, 1 from Australian, 2 from Portugal, 2 from Russian, 1 from Great Britain, 1 from Algerian, 2 from Spain, 1 from the former Soviet Union, and 1 from Italy. Group Ⅱ consisted of 19 specimens that originated from 12 different countries: 5 from Europe, 4 from Asia, 2 from Africa, 1 from Chile, and 7 from New Zealand. PI305497, which originated from Poland, formed group Ⅲ. Group Ⅳ included ‘California’ and Oregon USA95, both of which were from New Zealand. The most distinct group, Ⅴ, was formed by sample 2122. The eleven HR samples from 2011 to 2012 were dispersed in these five groups. 02–114, 1824, PI295271, PI325293, PI610830, and PI610822 clustered with group I; PI384018, Woronowii, Parthiana, and BTN New Zealand fell into group Ⅱ; and 2122 alone formed group Ⅴ. The relationships recovered using SCoT data were congruent with those generated using morphological and agronomic data. The groups that we recovered using SCoT data could be useful for selecting and developing new lines of orchardgrass. Data from our study will provide information useful for improvement of orchardgrass varieties, and both updates and enhances the diversity of existing germplasm resources. Acknowledgments This study was supported by the National Natural Science Foundation of China (NSFC) (No. 31101760; 31201845), Fundamental Research Funds for the Central Universities of China (XDJK2013C140) and the Funding Project of 2013 Chongqing Universities Innovation Team Building Plan “Modern Technology in Beef Cattle Production”. References Bhattacharyya, P., Kumaria, S., Kumar, S., Tandon, P., 2013. Gene 529 (1), 21. Borrill, M., 1977. Ann. Rep. Welsh Pl. Breed. Stat. 1977, 190. Casler, M.D., Fales, S.L., McElroy, A.R., Hall, M.H., Hoffman, L.D., Leath, K.T., 2000. Crop Sci. 40 (4), 1019. Catalán, P., Torrecilla, P., Rodríguez, J.Á.L., Olmstead, R.G., 2004. Mol. Phylogene. Evol. 31 (2), 517. Collard, B.C.Y., Mackill, D.J., 2009. Plant Mol. Biol. Report. 27 (1), 86. Di, H., Liu, Z.J., Chen, Y.L., 2006. Mol. Plant Breed. 14 (2), 238. Excoffier, L., Smouse, P.E., Quattro, J.M., 1992. Genetics 131 (2), 479. Guo, D.L., Zhang, J.Y., Liu, C.H., 2012. Mol. Biol. Report. 39 (5), 5307. Hirata, M., Yuyama, N., Cai, H., 2011. Plant Breed. 130 (4), 503. Horn, M.E., Shillito, R.D., Conger, B.V., Harms, C.T., 1988. Plant Cell. Report. 7 (7), 469.

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