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Study of Transferability of Rubus Microsatellite Markers to Hybrid Boysenberry
Plant Breeding and Biotechnology 2017;5:253-260
Published online December 1, 2017
© 2017 Korean Society of Breeding Science.

Jaihyunk Ryu3,†, Woon Ji Kim1,†, Juhyun Im1, Sang Hun Kim1, Seung Cheol Oh2, Lan Cho2, Si-Yong Kang3, and Bo-Keun Ha1,*

1Division of Plant Biotechnology, Chonnam National University, Gwangju 61186, Korea, 2Bioplus Co., Wanju 55310, Korea, 3Advanced Radiation Technology Institute, Korea Atomic Energy Research Institute, Jeongeup 56212, Korea
Correspondence to: Bo-Keun Ha, bkha@jnu.ac.kr, Tel: +82-62-530-2055, Fax: +82-62-530-2059
Received August 21, 2017; Revised October 13, 2017; Accepted October 16, 2017.
This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

Boysenberry, a Rubus hybrid between loganberry and a trailing blackberry, possesses distinctive polyphenol compounds, which have demonstrated positive biological effects on human health. Several new boysenberry genotypes have recently been developed from mutation breeding technology. In this study, a total of 103 SSR markers developed from expressed sequence tag (EST) and genomic libraries in blackberry and red raspberry were tested for cross-amplifications in 10 boysenberry genotypes. All primer pairs successfully produced amplification products, ranging from 1 to 4 loci per primer. Eleven polymorphic SSR markers (RH_MEa0007aB01, RH_MEa12cE03, RH_MEa14bF07, RH_MEa15aD04, RH_MEa13cF08, ERubLR_SQ01_N03, ERubLR_SQ053_H01, ERubLR_SQ191_A05, RubfruitG7, Rubusr43a, and RiM019) were detected among boysenberry genotypes, while polymorphic loci were not detected in 92 markers. Polymorphism information content (PIC) and genetic diversity (GD) values ranged from 0.160 to 0.580 and from 180 to 0.640, with average values of 0.359 and 0.407, respectively, in the 11 polymorphic markers. According to a cluster analysis, all the mutant boysenberry genotypes can be classified into one category. Although the level of genetic diversity revealed by SSR markers in 10 boysenberry genotypes was low, these SSR markers will be useful for future genetic diversity, cultivar identification, QTL mapping, and gene cloning studies in boysenberry.

Keywords : Boysenberry, SSR marker, Genetic diversity, Mutation, Gamma-ray
INTRODUCTION

Fruits of Rubus species are a rich source of anthocyanins and phenolics that provide a broad spectrum of biomedical functions in human health (Zafra-Stone et al. 2007). Boysenberry (Rubus ursinus Chamisso & Schlenhtendal) is a Rubus hybrid between loganberry (Rubus loganobaccus Bailey) and a trailing blackberry (Rubus baileyanus Britt.) (Wood et al. 1999). Boysenberry fruits, which are usually consumed in fresh and processed products in human diet, contain a unique mixture of polyphenols including ellagitannins, short oligomeric proanthocyanidins, anthocyanins, and ellagic acid (Furuuchi et al. 2011). Recent studies have indicated that the intake of polyphenols in boysenberry might offer positive effects such as reduction of oxidative stress (Barnett et al. 2007), prevention of liver injury (Igarashi et al. 2004), anti-hypertension (Furuuchi et al. 2012), and anti-obesity effects (Mineo et al. 2015). In addition, boysenberry had the highest level of free ellagic acid content among Rubus species (Wada and Ou 2002). The ellagic acid is well known as a potential anticarcinogenic compound against colon cancer, breast cancer, prostate cancer, and skin cancer (Zhang et al. 2014).

Generally, breeding progress in Rubus species has been limited by a lack of genetic variation for important agronomic traits in germplasms. Therefore, interspecific hybridization and mutation breeding constitute important breeding techniques in Rubus species. The inter-specific hybridization between red raspberry and blackberry have generated several new cultivars such as Loganberry, Nessberry, Phenomenal, and Brazos (Ipek et al. 2009). In addition, boysenberry is a “hybridberry.” Mutation breeding has been used to improve specific agronomic traits such as bigger fruit sizes, early maturation, higher disease resistance, and higher anthocyanin content in fruits (Ryu et al. 2012; Ryu et al. 2016). Recently, a new blackberry cultivar, Maple, with high sugar content in fruit was developed via gamma-irradiation (Kim et al. 2013).

The narrow range of variation of morphological traits in Rubus species has thus far limited the accurate characterization of Rubus germplasm and cultivars. DNA markers could be a useful tool for cultivar identification and for the assessment of genetic diversity in breeding programs. In red raspberry, highly polymorphic and co-dominant simple sequence repeat (SSR) markers have been developed from genomic and cDNA libraries (Graham et al. 2002; Graham et al. 2004; Woodhead et al. 2008). Castillo et al. (2010) also developed SSR markers from genomic libraries of red raspberry and blackberry. These SSR markers could be transferable across Rubus species and have been used to differentiate between wild and cultivated Andean blackberry (Rubus glaucus) (Marulanda et al. 2012), to study genetic diversity in wild and cultivated black raspberry accessions (Dossett et al. 2012), and to construct a genetic map of tetraploid blackberry (Castro et al. 2013). However, there are no studies on the development or application of SSR markers in boysenberry. Only dominant AFLP markers have been applied to assess the genetic relationship between some blackberry cultivars and boysenberry (Ipek et al. 2009).

Recently, several boysenberry mutant lines showing high anthocyanin and ellagic acid contents have been developed via gamma-irradiation (Ryu et al. 2017). In this study, 103 SSR markers previously developed for other Rubus species were tested to determine their transferability in boysenberry and were used to assess the genetic diversity in boysenberry mutant genotypes.

MATERIALS AND METHODS

Plant materials and DNA extraction

Ten boysenberry genotypes were used in this study (Table 1). These genotypes were used to analyze fruit quality and antioxidant components in previous study (Ryu et al. 2017). The BS_PI genotype, which is spiny, was introduced from Japan, and others included stabilized lines from advanced generations, all of which are thornless. The BS_Hybrid was developed from the cross between the thornless blackberry (R. fruticosus L. ‘V3’) and the BS_PI which was introduced from Japan (Shin et al. 2008). Six mutant genotypes (BSA-036 to BSA-144) were developed from 20 Gy gamma-ray treatments on explants of the hybrid boysenberry. Two mutant genotypes (BSB-032 and BSB-127) were developed from 40 Gy gamma-ray treatments on explants of the hybrid boysenberry. Freshly growing young leaves from each genotype were harvested for DNA samples. DNA was extracted using a DNeasy plant mini kit (Qiagen, Hilden, Germany) and quantified using a UV-Vis spectrophotometer (NanoDrop 2000, Thermo Scientific, DE, USA).

SSR analysis

A total of 103 SSR markers previously developed from expressed sequence tag (EST) and genomic libraries in blackberry (Rubus fruticosus L.) and red raspberry (Rubus idaeus L.) were used in this study. These primers are summarized in Table 2 and Supplementary Table S1. PCR amplification used a 10 μL reaction mix containing 2 μL of 40 ng template DNA, 1.0 × PCR buffer, 3.0 mM MgCl2, 100 μM of each dNTP, 0.2 μM each of forward and reverse primers, and 0.5 units of GoTaq DNA polymerase (Promega, Madison, USA). PCR cycling conditions were 30 second DNA denaturation step at 94°C, a 30 second annealing step at 50°C, and a 1 minute extension step at 72°C for 35 cycles on a TProfessional Basic thermocycler (Biometra, Göttingen, Germany). PCR amplifications were separated by capillary electrophoresis using a Fragment Analyzer system (Advanced Analytical Technologies, IA, USA) with dsDNA 900 Reagent Kit. After capillary electrophoresis separation, the SSR alleles were scored using PROSize 2.0 software (Advanced analytical Technologies, IA, USA).

Data analysis

Polymorphic validation and genetic diversity assessments, including polymorphism, gene diversity (GD), and polymorphism information content (PIC) of the alleles revealed by each primer pair were calculated from the genotype data from 10 boysenberry individuals using PowerMarker v3.25 software (Liu and Muse 2005). A phylogenetic dendrogram was constructed using the un-weighted pair-group method with arithmetical algorithms averages (UPGMA) method by NTSYS PC 2.1 software (Exeter Software, NY, USA). Genetic distance matrix was calculated using Nei (1978)’s unbiased genetic distance measure.

RESULTS

SSR marker selection

A total of 103 primer pairs were tested against 10 boysenberry genotypes (Table 2). These SSR markers were selected from previous works. Among those, 53 SSR markers were developed from a blackberry and red raspberry genomic library, and 50 SSR markers were derived from a blackberry and red raspberry EST library (Graham et al. 2004; Lewers et al. 2008; Castillo et al. 2010; Castro et al. 2013; Woodhead et al. 2008). All primer pairs amplified a product in each of the 10 boysenberry genotypes that showed 100% transferability in boysenberry. Of these, only eleven SSR markers (10.7%) revealed genetic differentiation among boysenberry genotypes, while polymorphic loci were not detected in 92 SSR primers (Table 2). Two out of 53 SSR markers (3.8%) derived from the genomic library were found to be polymorphic for the 10 genotypes. However, 9 out of 50 SSR markers (18%) derived from the EST library were polymorphic. Therefore, the polymorphic rate of EST-SSR markers was approximately 4.7 times higher than that of genomic-SSR markers in this study. In addition, the blackberry-derived EST-SST markers showed the highest polymorphic rates (26.3%) among boysenberry genotypes.

The number of loci, number of polymorphic loci, and polymorphic rate of boysenberry genotypes are presented in Supplementary Table S1. Polymorphism analysis detected 123 loci generated from all 103 SSR primer set, of which 27 loci were polymorphic, representing 21.9% of the total generated loci. The total number of loci per primer ranged from 1 to 4. Eight SSR primers (ERubLR_SQ01_N03, ERubLR_SQ191_A05, RH_MEa0007aB01, RiM019, Rubusr43a, RH_MEa12cE03, RH_MEa15aD04, and RH_MEa13cF08) gave the highest percentage of polymorphic loci (100%).

The observed number of alleles (Na), effective number of alleles (Ne), gene diversity (GD) and PIC of boysenberry genotypes were analyzed with 11 polymorphic SSR markers (Table 3). The mean Na value was 1.909 and ranged from 1.5 to 2.0. The mean Ne value was 1.421 and ranged from 1.220 (ERubLR_SQ01_N03) to 1.584 (RH_MEa14bF07). The GD for 11 polymorphic SSR markers ranged from 0.180 to 0.640, with a mean of 0.407. The PIC of the 11 polymorphic SSR markers ranged from 0.160 to 0.580, with an average of 0.359. The maximum values of GD (0.640) and PIC (0.580) occurred with the RH_MEa0007aB01 and RH_MEa14bF07 primers that were derived from the EST library for blackberry.

EST-SSR markers associated with expressed genes are useful to study functional diversity in populations. The putative function of 8 EST-SSR markers showing high PIC values were determined by using BLAST against the non-redundant GenBank database at the National Center for Biotechnology Information (NCBI) (Table 4). The 8 EST sequences showed significant similarities to known genes including E3 ubiquitin-protein ligase, chlorophyll a-b binding protein, zeta-carotene desaturase protein, allergen Pru av1-like, xyloglucan endotransglucosylase/hydrolase 8, and RING-H2 finger protein. The most homologies were from Fragaria vesca.

Genetic distance and cluster analysis

Genetic distance matrix (GDM) obtained from 27 polymorphic loci were shown in Table 5. The scale used for the genetic distance runs from 0 (meaning no genetic difference) to 0.898 with an average of 0.268. BSA-119 (No. 7), BSA-114 (No. 8), and BSB-127 (No. 10) exhibited no genetic difference among genotypes. Original cultivar (Hybrid boysenberry) and BSA-036 were the most distantly related.

Based on the simple-matching coefficient between boysenberry genotype, a cluster analysis was carried out and a dendrogram was generated with 27 polymorphic loci via the average linkage method for genetic relationships (Fig. 1). According to cluster analysis of the SSR markers, boysenberry genotypes could be divided into a gamma-ray-treated group (BSA-036, BSA-065, BSA-078, BSB-032, BSA-101, BSB-127, BSA-119 and BSA-144) and a non-treatment group (boysenberry and hybrid boysenberry), divided at the genetic distance of 0.485. However, a gamma-ray-treated group was not classified according to the dosage of gamma rays in the dendrogram.

DISCUSSION

In berry crops, mutation breeding has already been used to introduce many useful traits affecting fruit quality, phytochemicals, disease resistance, resistance to low temperature, and the presence or absence of thorns (Basaran and Kepenek 2011; Ryu et al. 2016). In this study, the ‘BS_Hybrid’ genotype, obtained from crosses between the thornless blackberry (R. fruticosus L. ‘V3’) and boysenberry, has unfavorable fruit characteristics such as malformed fruit, small size, and lower sugar content. However, the mutant genotypes, obtained from gamma-ray mutations with ‘BS_Hybrid’, showed improved agronomic characteristics such as higher fruit yields, sugar contents, and antioxidant components when compared with the original parent (Ryu et al. 2017). These mutant genotypes can be directly released as new cultivars.

Molecular markers were useful tools for the evaluation of genetic diversity, marker-assisted breeding, DNA fingerprinting, and cultivar identification in crops. However, there are many challenges in developing molecular markers in Rubus species for which genomic information is not available. Therefore, previous studies have analyzed the genetic diversity of boysenberry and blackberry using AFLP markers (Ipek et al. 2009; Ryu et al. 2014). While no sequence information is needed, the AFLP marker system is not as easy to perform as SSR markers, which show highly reproducible, co-dominant, multi-allelic, PCR-based, and medium throughput to assay (Dossett et al. 2012; Stafne et al. 2005). In particular, SSR markers developed for one plant species can be transferred between related species (Varshney et al. 2005). The high level of transferability of SSR markers could reduce the costs of marker development in boysenberry. In this study, a total of 103 SSR markers previously developed from EST and genomic libraries in blackberry and red raspberry showed 100% transferability to 10 boysenberry genotypes. The high level of transferability of SSR markers among three Rubus species means that they have a close genetic relationship with each other (Ipek et al. 2009; Lee et al. 2015). Similarly, high rates of transferability of SSR markers were reported in pine (94.2% between Pinus taeda and Pinus radiata), buckwheat (97.1% between Fagopyrum esculentum and Fagopyrum homotropicum), and onion (93.5% between Allium fistulosum and Allium altaicum) (Araki et al. 2010; Chagné et al. 2004; Ma et al. 2009).

Screening for polymorphism of SSR markers rendered the 11 polymorphic markers (10.7%) in boysenberry genotypes. This rate is low compared to the 18% to 30% polymorphisms between the two blackberry genotypes using SSR markers derived from Rubus and Fragaria L. (Castro et al. 2013; Stafne et al. 2005). The lower rate of polymorphism may be the result of the eight boysenberry genotypes developed from one BS_Hybrid through gamma-ray mutation techniques, as well as BS_Hybrid derived from the cross with the BS_PI. However, the blackberry-derived EST-SSR markers showed the 26.3% polymorphisms that had similar rates to the previous two studies of Castro et al. (2013) and Stafne et al. (2005).

The EST-SSR markers representing transcribed genes can be used for assaying functional diversity in accessions as well as yielding a higher rate of polymorphism (Varshney et al. 2005). Nine polymorphic SSR markers were developed from a blackberry leaf and raspberry root and fruit EST library (Graham et al. 2004; Lewers et al. 2008; Woodhead et al. 2008). The putative functions of two EST-SSR markers (RH_MEa0007aB01 and RH_MEa14bF07) showing the highest PIC value (0.580) were similar with Fragaria vesca subsp. vesca E3 ubiquitin-protein ligase and chlorophyll a-b binding protein, respectively (Table 4). Interestingly, the putative function of RH_MEa15aD04 marker was similar to zeta-carotene desaturase protein essential for carotenoid biosynthesis (Nisar et al. 2015). Avendaño-Vázquez et al. (2014) reported that the mutation of zeta-carotene desaturase caused severe alterations in leaf morphology. However, any abnormal leaf morphology was not detected within our boysenberry genotypes. In addition, the ERubLR_SQ01_N03 marker showing the putative function of major allergen Pru av1 (Prunus avium) can be used as a potential diagnostic marker for severe allergic reactions to boysenberry genotypes (Scheurer et al. 1997).

In the clustering analysis, the eight gamma-ray mutagen-treated genotypes and the original genotypes (BS_PI and BS_Hybrid) were divided into independent groups. Ryu et al. (2014) reported that blackberry mutants could be divided into gamma-ray and MNU-treated groups and crossbred (R. fruticosus × R. parvifolius) lines by AFLP markers. Also, individuals of Cymbidium spp. were divided into control and electron beam treatment groups using both ISSR and RAPD markers (Ryu et al. 2013). These results suggest that radiation could induce significant genetic variability in boysenberry genotypes. The GDM among boysenberry genotypes ranged from 0 to 0.898 with an average 0.268, which was similar to 11 cultivars of boysenberry and blackberry spanning from 0 to 0.85 by AFLP analysis (Ipek et al. 2009).

Analysis of genetic diversity and relationships may be helpful for QTL mapping and for gene cloning studies. This study also provides 11 polymorphic SSR markers among the boysenberry genotypes that would be useful for identification of cultivars. Therefore, these results may contribute to the selection of parental lines for both traditional and mutational breeding of boysenberry.

ACKNOWLEDGEMENTS

This study was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through the Agri-Bio Industry Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA), Republic of Korea (315025-03-1-HD060).

Supplementary Information
Figures
Fig. 1. Phylogenetic tree based on boysenberry genotypes of 11 polymorphic SSR markers.
Tables

List of 10 boysenberry genotypes evaluated with 103 SSR markers (Ryu et al. 2017).

No.LineOriginTreatmentStem Spiny
1BS_PIBoysenberry from JapanSpiny
2BS_HybridCross breedingBlackberry(V3) × BS_PIThornless
3BSA-036Hybrid BoysenberryGamma-ray 20 GyThornless
4BSA-065Hybrid BoysenberryGamma-ray 20 GyThornless
5BSA-078Hybrid BoysenberryGamma-ray 20 GyThornless
6BSA-101Hybrid BoysenberryGamma-ray 20 GyThornless
7BSA-119Hybrid BoysenberryGamma-ray 20 GyThornless
8BSA-144Hybrid BoysenberryGamma-ray 20 GyThornless
9BSB-032Hybrid BoysenberryGamma-ray 40 GyThornless
10BSB-127Hybrid BoysenberryGamma-ray 40 GyThornless

Amplification profiles for 103 SSR markers tested in 10 boysenberry genotypes.

Type of SSR markersPrimer pairs testedAmplification success (%)No. polymorphic markersRatio of polymorphic markers (%)References
Genomic SSR of blackberry410000Castro et al. (2013)
Genomic SSR of blackberry and red raspberry10100110Castillo et al. (2010)
Genomic SSR of red raspberry3910012.6Graham et al. (2004)
EST SSR of blackberry19100526.3Lewers et al. (2008)
EST SSR of red raspberry23100313.0Woodhead et al. (2008)
EST SSR of red raspberry8100112.5Graham et al. (2004)
Total (Mean)103(100)11(10.7)

Characteristics of 11 SSR markers showing polymorphisms in 10 boysenberry genotypes.

No.Marker nameType of SSRNaz)Ney)GDx)PICw)
1RH_MEa0007aB01EST SSR of blackberry2.0001.4580.6400.580
2RH_MEa12cE03EST SSR of blackberry2.0001.2200.3400.310
3RH_MEa14bF07EST SSR of blackberry2.0001.5840.6400.580
4RH_MEa15aD04EST SSR of blackberry2.0001.3950.5800.540
5RH_MEa13cF08EST SSR of blackberry2.0001.4900.1800.160
6ERubLR_SQ01_N03EST SSR of red raspberry2.0001.2200.1800.164
7ERubLR_SQ053_H01EST SSR of red raspberry1.5001.5000.4200.330
8ERubLR_SQ191_A05EST SSR of red raspberry2.0001.3450.6200.540
9RubfruitG7EST SSR of red raspberry2.0001.4900.1800.164
10Rubusr43aGenomic SSR of red raspberry2.0001.5710.3800.310
11RiM019Genomic SSR of blackberry and red raspberry1.5001.3620.3200.270
Mean1.9091.4210.4070.359

z)Na: Observed number of alleles,

y)Ne: Effective number of alleles,

x)GD: Gene diversity,

w)PIC: Polymorphism information content.


Putative function of EST-SSR markers showing polymorphisms in 10 boysenberry genotypes.

No.Marker nameGenBank accession no.HomologyE-value
1RH_MEa0007aB01FF683655Fragaria vesca subsp. vesca E3 ubiquitin-protein ligase SIS31e-155
2RH_MEa12cE03FF684289Fragaria vesca subsp. vesca uncharacterized LOC1013024681e-119
3RH_MEa14bF07FF684762Fragaria vesca subsp. vesca chlorophyll a-b binding protein of LHCII type 1-like0.0
4RH_MEa15aD04FF684949Fragaria x ananassa zeta-carotene desaturase protein (zds)0.0
5RH_MEa13cF08FF684532Fragaria vesca subsp. vesca ranBP2-type zinc finger protein At1g673252e-31
6ERubLR_SQ01_N03EX567290Fragaria vesca subsp. vesca major allergen Pru av 1-like (LOC101298594)3e-121
7ERubLR_SQ053_H01EX567274Rosa x bourboniana xyloglucan endotransglucosylase/hydrolase 8 (XTH8)1e-78
8ERubLR_SQ191_A05EX567282Prunus mume RING-H2 finger protein ATL20-like6e-40

Matrix of Nei (1978)’s genetic distances between boysenberry genotype.

Line Noz)12345678910
10.000
20.1600.000
30.8980.8980.000
40.4630.4630.5880.000
50.4060.4060.5230.2050.000
60.5880.5880.5880.2510.2050.000
70.5880.5880.4630.3510.1180.0770.000
80.5880.5880.4630.3510.1180.0770.0000.000
90.7310.7310.5880.4630.2050.1600.0770.0770.000
100.5880.5880.4630.3510.1180.0770.0000.0000.0770.000

z)Line No.: Line number is listed in Table 1.


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