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Article

An RNA Sequencing Transcriptome Analysis and Development of EST-SSR Markers in Chinese Hawthorn through Illumina Sequencing

by
Suliya Ma
1,
Wenxuan Dong
2,
Tong Lyu
3 and
Yingmin Lyu
1,*
1
Beijing Key Laboratory of Ornamental Germplasm Innovation and Molecular Breeding, China National Engineering Research Center for Floriculture, College of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
2
College of Horticulture, Shenyang Agricultural University, Shenyang 110866, China
3
Plant Research Institute, Beijing Botanical Garden, Beijing 100094, China
*
Author to whom correspondence should be addressed.
Forests 2019, 10(2), 82; https://doi.org/10.3390/f10020082
Submission received: 6 December 2018 / Revised: 15 January 2019 / Accepted: 18 January 2019 / Published: 22 January 2019
(This article belongs to the Special Issue Genetic Diversity of Tree Species in Forest and Conservation Management)
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Abstract

:
Chinese hawthorn (Crataegus pinnatifida) is an important ornamental and economic horticultural plant. However, the lack of molecular markers has limited the development and utilization of hawthorn germplasm resources. Simple sequence repeats (SSRs) derived from expressed sequence tags (ESTs) allow precise and effective cultivar characterization and are routinely used for genetic diversity analysis. Thus, we first reported the development of polymorphic EST-SSR markers in C. pinnatifida with perfect repeats using Illumina RNA-Seq technique. In total, we investigated 14,364 unigenes, from which 5091 EST-SSR loci were mined. Di-nucleotides (2012, 39.52%) were the most abundant SSRs, followed by mono- (1989, 39.07%), and tri-nucleotides (1024, 20.11%). On the basis of these EST-SSRs, a total of 300 primer pairs were designed and used for polymorphism analysis in 70 accessions collected from different geographical regions of China. Of 239 (79.67%) pairs of primer-generated amplification products, 163 (54.33%) pairs of primers showed polymorphism. Finally, 33 primers with high polymorphism were selected for genetic diversity analysis and tested on 70 individuals with low-cost fluorescence-labeled M13 primers using capillary electrophoresis genotyping platform. A total of 108 alleles were amplified by 33 SSR markers, with the number of alleles (Na) ranging from 2 to 14 per locus (mean: 4.939), and the effective number of alleles (Ne) ranging from 1.258 to 3.214 (mean: 2.221). The mean values of gene diversity (He), observed heterozygosity (Ho), and polymorphism information content (PIC) were 0.524 (range 0.205–0.689), 0.709 (range 0.132–1.000), and 0.450 (range 0.184–0.642), respectively. Furthermore, the dendrogram constructed based on the EST-SSR separated the cultivars into two main clusters. In sum, our study was the first comprehensive study on the development and analysis of a large set of SSR markers in hawthorn. The results suggested that the use of NGS techniques for SSR development represented a powerful tool for genetic studies. Additionally, fluorescence-labeled M13 markers proved to be a valuable method for genotyping. All of these EST-SSR markers have agronomic potential and constitute a scientific basis for future studies on the identification, classification, and innovation of hawthorn germplasms.

1. Introduction

The plants from genus Crataegus L., commonly known as hawthorn, a member of the Rosaceae family, have a wide distribution in the temperate regions of the northern hemisphere in Europe, Asia, and North America [1]. Hawthorn has considerable economic, ecological, and ornamental value, making it an important horticultural crop worldwide [2,3]. Therefore, hawthorn has a wide spectrum of uses. Some species are cultivated for their edible fruits, which have abundant contents of flavonoids, procyanidins, and vitamin C [4]. Some species with significant pharmaceutical value are well documented and widely used in traditional Chinese medicine [5,6]. Meanwhile, others are cultivated as landscape plants for ornamental purposes, such as Crataegus laevigata ‘Paul’s Scarlet’. Moreover, they are of great importance in nature and important for wildlife, as the fruit is also consumed by birds and mammals, and the dense branches offer good nesting sites for birds [2,3].
As one of the origin and cultivation centers of hawthorn, China has more than 1000 years of history of collecting and cultivating hawthorns [7]. Although a total of 18 species and 6 variations have been identified and confirmed [8], valuable hawthorn cultivars mainly originated from four species: Crataegus pinnatifida Bunge, Crataegus hupehensis Sarg., Crataegus scabrifolia (Franch.) Rehder, and Crataegus bretschnederi. The leading cultivated species by far is C. pinnatifida, and its variation Crataegus pinnatifida Bge. var. major N.E.Br., which is indigenous to northern China, large fruited (6–17 g), and represented by more than 100 cultivars [9,10,11]. The cultivar classification system of C. pinnatifida was based on the color of fruit and divided into red fruit cultivar group, orange fruit cultivar group, and yellow fruit cultivar group [8]. However, the genomic and molecular research studies on hawthorn have lagged behind those on other horticultural crops, such as apples (Malus × domestica) [12], pears (Pyrus sp.) [13], Ussurian pears (Pyrus ussuriensis) [14], and cherries (Prunus sp.) [15], which hindered the development and utilization of hawthorn. The coming research efforts should be focused on these.
Currently, molecular characterization is a favored way to quantify variation within germplasms. A number of efforts have been made to assess hawthorn genetic resources, mainly using random amplified polymorphism DNA (RAPD) [10,16,17,18,19,20,21,22,23] and inter-simple sequence repeats (ISSR) [10,16,24,25,26,27,28]. Moreover, sequence related amplified polymorphism (SRAP) [29] markers, inter-retrotransposon amplified polymorphism (IRAP) [26], and start codon targeted (SCoT) polymorphism [26,28] have been developed and used successfully for investigation of genetic characterization of hawthorns. Among dozens of markers, microsatellites, also known as simple sequence repeats (SSRs) or short tandem repeats (STRs), have gained considerable popularity in genetic diversity studies because of its codominant inheritance, reproducibility, relative abundance, extensive genome coverage, chromosome specific location, and high throughput genotyping [30].
Traditionally, there are two approaches to develop SSR primers: testing known SSR primers already developed for related species, or constructing a genomic library and developing SSRs from next generation sequencing (NGS) technologies [31]. Dickinson et al. [32] used the former method to select 23 microsatellite loci from Malus × domestica [33] for preliminary primer testing in Crataegus section Douglasianae. In total, 9 of these 23 loci proved to be variable in a larger sample of Crataegus [34,35]. Lo et al. [35] selected 13 SSR markers from Malus × domestica, which were shown by Liebhard et al. [33] to be transferable to Crataegus. These SSR markers, together with chloroplast microsatellites, were used to compare population structure and genetic variability in two closely related taxa (Crataegus douglasii Lindl. and Crataegus suksdorfii (Sarg.) Kruschke). Based on the results of Lo et al. [35], 13 SSR markers were tested for population genetic and structure analysis of Tunisian Azarole (Crataegus azarolus L. var. aronia (Willd.) Batt.) by Khiari et al. [36]. Brown et al. [2] firstly applied SSR markers to examine patterns of genetic diversity in Crataegus monogyna Jacq., which is a key component of hedgerows. All eight SSR markers were originally developed from Malus × domestica [33], and seven of them had previously been used by Lo et al. [35]. According to Emami et al. [28], 11 SSR markers, also selected form the report of Lo et al. [35], used to assess the genetic variation that exists in the 6 hawthorn species in Iran. Likewise, Betancourt-Olvera et al. [3] used 7 SSR markers (from Lo et al. [35]) to access the biodiversity of tejocote (hawthorn) in Mexico. As for Chinese hawthorn species, Zhang et al. [37] assessed the interspecific genetic relationship using 10 apple SSR primers. Therefore, all these markers were designed from related species, that is, apple (Malus × domestica), and tested in Crataegus sp. with positive results in previous research. They have been successfully used to distinguish between species and detect the genetic diversity and structure of hawthorn germplasms. However, not all primers developed in Malus are transferable to Crataegus (Liebhard et al. [33]), and it is too costly to test all the primers [35]. Only a very limited number of primers can be developed by this method, and some new methods should be explored as an alternative.
As high-throughput technology and NGS methods have been developed, it has paved the way for the large-scale discovery of genetic markers at reduced prices and in less time than the traditional methods (develop from related species) and Sanger sequencing, or the “first-generation sequencing”, which are both no longer utilized for SSR development [31]. De novo transcriptome sequencing (RNA-Seq) as a simple, straightforward, and reliable approach has been applied for SSR development in many species, even in non-model plant populations with limited background genetic information [38]. For RNA-Seq, four technologies (Roche 454, Illumina, Helicos BioSciences and Life Technologies) are generally used, among which Illumina is the most widely utilized NGS platform [39,40,41].
Dai et al. [11] reported the first application of Illumina-based RNA-Seq technology for transcriptome studies in soft and hard endocarp hawthorns (C. pinnatifida) in 2013. Yang et al. [42] used RNA-seq analysis identified numerous candidate genes involved in the hawthorn (C. pinnatifida) biosynthesis of polyphenolic compounds. Xu et al. [43] used RNA-Seq analysis to study soft and hard flesh textures in hawthorn (C. pinnatifida) fruits. All these research studies aided the studies on the development of expressed sequence tag SSRs (EST-SSRs), which are located in the flanking coding region and identified from transcribed RNA sequence, while the genomic SSRs (g-SSRs) are identified from random genomic sequences [44]. In contrast to g-SSRs, EST-SSRs are more likely to be conserved across species, resulting in high levels of transferability [38,45].
Once the EST-SSRs have been produced, genotyping can begin. SSR locus can be identified by agarose gel (AGE) or polyacrylamide gel electrophoresis (PAGE). However, both of them are laborious and time-consuming. Fortunately, a powerful alternative method has been developed to facilitate genotyped polymerase chain reaction (PCR) multiplexing by capillary electrophoresis (CE), such as the fluorescent-labeled M13 primer method proposed by Oetting et al. [46] and enhanced by Schuelke et al. [47]. In this method, the reaction is performed as a multiplexed PCR with three primers—a sequence-specific forward primer with universal primer M13 (TGTAAAACGACGGCCAGT) tail at its 5′ end, a sequence-specific reverse primer, and the fluorescent-labeled universal primer M13. Fluorescent-labeled M13-SSR markers have advantages of high-throughput and high accuracy, which have been successfully used in peanut (Arachis hypogaea L.) [48], wheat (Triticum aestivum L.) [49], Ussurian pear (Pyrus ussuriensis Maxim.) [14], Chinese bayberry (Myrica rubra Siebold & Zucc.) [50], and cranberry (Vaccinium macrocarpon Aiton) [51].
So far, no previous reports about EST-SSR markers are available in this genus. Also, no report has yet been published on genetic diversity assessment of cultivars of C. pinnatifida and other hawthorn species present in China using molecular markers. The deficiencies in the study on molecular variation in cultivated hawthorn are somewhat surprising, since it is an efficient tool for genetic analysis.
Thus, the present study is part of an effort to (1) further explore the benefit of RNA-Seq for SSR development in combination with M13-labeled SSR primers, (2) detect the polymorphic SSR markers for Chinese hawthorn cultivars, (3) assess the genetic diversity among selected Chinese hawthorn cultivars using EST-SSRs, and (4) provide baseline information for identification, classification, and utilization of the germplasm resources of the hawthorn.

2. Materials and Methods

2.1. Plant Material and DNA Extraction

A collection of 70 Chinese hawthorn cultivars (C. pinnatifida) was obtained from the National Hawthorn Germplasm Repository of China (Shenyang, China) and Institute of Forestry and Pomology, Beijing Academy of Agricultural and Forestry Sciences (Beijing, China), as summarized in Supplementary Table S1. These cultivars were chosen because they were diploid (2n = 34) and representative samples currently used in China. Fresh and young leaves were dried on silica gel and then ground into fine powder with liquid nitrogen. The powders were preserved at −80 °C refrigerator. Genomic DNA of each cultivar was extracted from 30 mg of leaf powders using DNAsecure Plant kit (DP320, Tiangen Biotech Co., Beijing, China) according to the manufacturer’s instructions with minor modifications. The DNA quality and quantity were evaluated on agarose gel (1.0%) and Quawell Q5000 (Quawell Technology Inc., San Joes, CA, USA). All DNA samples were subsequently diluted to 30 ng/μL, which was the working concentration for PCR, and stored at −20 °C for further use.

2.2. EST-SSR Mining from RNA-Seq and Primer Design

In the previous research, an RNA-Seq experiment was conducted using Illumina HiSeq 2500 by Xu and Dong [43]. The accession number was PRJNA339788. Based on these sequencing data, EST-SSRs were detected and mined among the unigenes with length >1000 bp using the MIcroSAtellite (MISA; http://pgrc.ipk-gatersleben.de). Seven types of microsatellites were investigated. The parameters were set as follows: the SSRs were considered to contain mono-, di-, tri-, tetra-, penta- and hexa-nucleotides with minimum repeat numbers of 10, 6, 5, 5, 5, and 5, respectively. As for the compound SSRs, there were at most 100 bases between two SSRs. Unigenes with a sequence of more than 150 bp before and after the SSR region were used for primer design by Primer v3.0.

2.3. Primer Selection and PCR Amplification

The criteria for primer selection referred to Du [52]. A total of 300 primers were selected according to the conditions of the base repeat type (di-nucleotide was preferred), the annealing temperature of 50–60 °C, with an optimum of 55 °C, and similar content of GC% in a range of 40%–60%. The primer size was set from 18 to 24 bp, with an optimum of 20 bp, and the expected PCR product size of the primers from 100 to 300 bp [53]. All primers performed in PCR reactions were synthesized by Sangon Biotech Co., Shanghai, China.
The PCR reaction system was performed in 20 μL volumes containing 2 μL of template DNA, 10 μL of 2× Taq PCR MasterMix (Aidlab Biotechnologies Co., Beijing, China), 1 μL of 10 μM each primer, and 6μL double distilled water. The following PCR program included an initial step at 95 °C for 3 min followed by 35 cycles of 95 °C for 30 s, the appropriate annealing temperature for 30 s, and 40 s at 72 °C, and a final extension for 5 min at 72 °C. Finally, the products were examined on 1% agarose gel. In addition, DL500 DNA marker (3590A, Takara Biotech Co., Beijing, China) was used to determine the sizes of the PCR products.
After PCR testing, primer pairs, which amplified distinct, reproducible, and strong bands with expected size, were selected for polymorphic screening using 8% non-denaturing PAGE in 1 × TBE buffer and then detected by silver staining. The pBR322 DNA/Mspl marker (MD206, Tiangen Biotech Co., Beijing, China) was used to identify alleles.

2.4. Fluorescent-Labeled M13-SSR Markers and Capillary Electrophoresis

From these effective primers that amplified bands with the expected sizes and high polymorphism, new forward primers were designed with an M13 tail on the 5′ end. The multiplexed PCR reactions consist of three primers, among which the M13-labeled universal primer was alternatively labeled with the following four fluorescent dyes: FAM (blue), HEX (green), TAMRA (yellow), and ROX (red). All the primers were synthesized from Sangon Biotech (Shanghai, China).
The 33 EST-SSRs were amplified in individual reactions in 20 μL, 10 μl of 2× Taq PCR MasterMix (Aidlab Biotechnologies Co., Beijing, China), 2 μL of template DNA, 0.2 μL M13-tailed forward primer, 0.2 μL the reverse primer, 0.4 μL fluorescently labeled M13 primer, and 7.2 μL double distilled water. The PCR was performed with 5 min denaturation at 95 °C, followed by 20 cycles of denaturing at 95 °C for 30 s, annealing for 30 s at 56 °C, elongation at 72 °C for 30 s, 15 cycles at 95 °C for 30 s, 53 °C (the annealing temperature of the fluorescent-labeled M13 primer) for 30 s, 72 °C for 30 s, and an extra elongation at 72 °C for 5 min.
Finally, the separation and identification of alleles by CE was detected on an ABI PRISM 3730XL DNA Analyzer (Applied Biosystems, Foster, CA, USA). The amplified PCR products were separated and recorded automatically as individual GeneScan files. Sizes and peaks were calibrated automatically against the ROX-500 size standards.

2.5. Data Analysis

The original data from ABI platform, with a FSATA file format, were analyzed by Gene Mapper v3.2 (Applied Biosystems, Foster, CA, USA). Then, the peak feature and fragment size of the corresponding peaks were obtained and the fragment size was transferred onto alleles and formatted as an Excle file. Aberrant peaks were not scored. MicroSatellite tools (MS tools) were used to obtain the resulting data matrix and the computation of genetic diversity and polymorphism information content (PIC). The genetic diversity was evaluated by the number of alleles (Na), the number of effective alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), and Shannon’s information index (I) using GenAlEx v6.4.

2.6. Clustering Analysis

Using Power Marker v3.25 software, a depiction of the Q-clustering analysis of the 70 selected hawthorn cultivars based on the polymorphic alleles was constructed using the Unweighted Pairgroup Method, with Arithmetic Means (UPGMA) based on Nei’s genetic distance. Eventually, the dendrograms were generated and edited by Fig Tree v1.4.3.

3. Results

3.1. Frequency and Distribution of EST-SSRs

In total, 72,837 unigenes were obtained from Illumina sequencing. Among the unigenes, 14,364 (19.72%) unigenes have lengths of more than 1000 bp. 5091 EST-SSR loci were mined from the 14,364 unigenes, distributed in 4011 sequences. In 863 sequences, there was more than one EST-SSR. Among the identified EST-SSRs, 273 (5.36%) were present in compound formation, while others were of perfect one-repeat type (Table 1). Among the 5091 potential EST-SSRs, six types of motifs were identified; mono-nucleotide (1989, 39.07%) and di-nucleotide (2012, 39.52%) had the highest frequencies, followed by tri-nucleotide repeats (1024, 20.11%), tetra-nucleotide repeats (46, 0.90%), penta-nucleotide repeats (9, 0.18%), and hexa-nucleotide repeats (11, 0.22%). The number and percentage for six EST-SSR types is shown in Figure 1. The density of each type of EST-SSR is shown in Figure 2. These EST-SSRs served as the basis for further marker development.

3.2. Development, Screening, and Polymorphic Validation of EST-SSRs

A total of 3781 EST-SSR primers were designed and 300 primers were selected for validation. In the initial screen of EST-SSR primers, they were validated by agarose gel with a subset of eight accessions, which showed significant difference in appearance (Supplementary Table S2). The screening results of percentage for 300 EST-SSR primer pairs is shown in Figure 3. A total of 239 (79.67%) pairs of primers generated amplification products, while the remaining 61 (20.33%) pairs of primers failed to detect PCR amplification products at multiple annealing temperatures. For the primers that amplified successfully, 206 (68.67%) pairs of primers were obtained with clear and well-sized amplified products. While the remaining 33 (11.00%) pairs of primers amplified the PCR, product band size was greater than the expected product size, which could be due to the introns (Supplementary Figure S1). Then EST-SSR primers were selected for further screening based on their polymorphism, using PAGE with a larger subset of twelve accessions. Of the 206 pairs of EST-SSR primers capable of amplifying the expected product size, 163 (54.33%) pairs of primers showed polymorphism, while the remaining 43 (14.33%) pairs of primers were monomorphic (Supplementary Figure S1). Finally, 33 primers with high polymorphism were selected and used for genetic diversity analysis (Table 2).

3.3. Genetic Diversity Analysis

All of the 33 EST-SSR markers amplified successfully across all 70 accessions and showed high polymorphism by CE (Supplementary Figure S2). A total of 108 alleles were detected with these markers, with an average of 3.272 alleles per locus. As shown in Table 2, the number of alleles (Na) ranged from 2 (Pr59, Pr117, Pr237, Pr244, and Pr255) to 14 (Pr171), with an average of 4.939; and the effective number of alleles (Ne) varied from 1.258 (Pr117) to 3.214 (Pr171), with a mean value of 2.221. The Shannon’s information index (I) varied between 0.359 (Pr117) and 1.571 (Pr171). The observed heterozygosity (Ho) ranged from 0.132 (Pr174) to 1.000 (Pr235 and Pr237). The gene diversity (expected heterozygosity, He) ranged from 0.205 (Pr117) to 0.689 (Pr171), and the lowest and highest values of polymorphism information content (PIC) were 0.184 for Pr117 and 0.642 for Pr171, with an average value of 0.450. Generally, markers with fewer number of alleles (Na) had lower He values; for instance, Pr117 with only 2 alleles displayed the lowest He value, while the Pr171 with 14 alleles displayed the highest He value.

3.4. Cluster Analysis Using EST-SSR Markers

According to the UPGMA dendrogram (Figure 4), 70 individual cultivars of Chinese hawthorn were divided into two clusters. Group I contained cultivar 36, which was the only soft-seed germplasm, while the remaining accessions with hard-seeds were all clustered in Group II. From the results of the cluster analysis, we continued dividing Group II into two subgroups, roughly according to the color of fruits. It can be seen that the cultivars of 16, 53, and 62 with yellow color fruits, as well as the cultivars of 2, 3, 6, 44, 57, 67, and so forth with orange color fruits, were clustered together and separated from the germplasm with red color fruits. The molecular data and dendrogram generated by EST-SSR marker were in good agreement with the traditional cultivar classification system in China, which is mostly according to the color of the fruit.

4. Discussion

Currently, phylogenetic relationship and genetic diversity analysis among hawthorns based on molecular markers are in progress. In the study of Wu et al. [54], chloroplast DNA PCR-RFLP (Restriction Fragment Length Polymorphism) was employed to investigate the phylogenetic diversity of 8 Crataegus species in China. C. brettschnederi was once regarded as a variety or subspecies of C. pinnatifida by Dai [9], but the data obtained in Wu’s study indicated that it was not a variation of C. pinnatifida but a new species. Another important result was that interspecies of the genus Crataegus had higher chloroplast DNA variations, but no visual bands were detected in different genotypes of C. pinnatifida. Thus, the investigated chloroplast DNA intergenic regions may, however, only be suitable for the separation of Crataegus at section and series levels but not on species level [55]. Therefore, we did not use this method in our study.
RAPD (Random Amplification Polymorphic DNA) markers used to be widely used for genetic studies in the past, especially in natural populations. For instance, Ferrazzini et al. [17] investigated the amount and distribution of genetic variation within and among six populations of the one-seed hawthorn (C. monogyna) located in Italy using RAPD markers. Rajeb et al. [18] assessed the genetic diversity of nine wild Tunisian C. azarolus var. aronia populations from different bioclimates using RAPD markers. These Crataegus populations maintained a low level of genetic diversity, as observed in above research studies. In Iran, Erfani-moghadam et al. [22] investigated the genetic variability among four species of Crataegus using morphological traits and RAPD markers. The results showed a relatively high genetic diversity. Besides, Yilmaz et al. [19] used RAPD markers to study 17 hawthorn genotypes in Turkey. Serce et al. [20] characterized 15 Crataegus accessions sampled from Turkey, which showed that molecular data generated by RAPD and morphological data were in good agreement. Dai et al. [10] used RAPD and ISSR markers to determine the genetic relationship in 8 species from China. The results of similarity coefficient indicated a high level of genetic diversity and the RAPD-based tree showed a better cluster than the ISSR-based tree. Although RAPD markers have been widely used to study the molecular relatedness of Crataegus species, they have a number of limitations, such as poor reproducibility, homology, and dominance, which may lead to an underestimation of the level of genetic diversity [36]. Fewer studies with these methods have been reported in recent years.
For cultivated plants, SSR markers are becoming extensively employed in studies of genetic diversity, population structure, cultivar identification, DNA fingerprints, quantitative trait loci (QTL) mapping, and molecular assisted selection (MAS) [40]. Therefore, it is enormously useful to develop SSR markers from hawthorn transcriptomes, which proved to be cost-effective and species specific.
However, there have been very few reports on the transcriptome sequencing of hawthorn, and until now, the EST sequences available for hawthorn have been very limited. For the entire Crataegus genus, only 29 EST sequences have been deposited in the National Center of Biotechnology Information (NCBI) EST database, which cannot be used for EST-SSR marker development. As to the RNA-Seq data used in the current study, more than 23 million reads were generated and assembled into 72,837 unigenes, with an N50 of 1656 bp, which is greater than the results of previous studies (Table 3) [11,42]. These indicated that transcriptome data were efficiently assembled and are appropriate for transcriptome analysis and marker development. In this study, we have identified a total of 5091 potential EST-SSRs from 14,364 unigenes, revealing the abundance of EST-SSRs for hawthorn. Here, di- and mono- nucleotide repeats were found to be the most abundant repeats in hawthorn, which was consistent with previous studies. As shown in Table 3 and Table 4, Dai [11] conducted a de novo assembly of the fruit transcriptome of C. pinnatifida and EST-SSRs were generated from 3174 unigenes, which represents about 29.5% (3174/10,744) of the analyzed unigenes. EST-SSRs with mono-, di-, tri-, tetra-, penta-, and hexa-nucleotide repeats composed about 31.25%, 39.51%, 21.33%, 0.91%, 0.13%, and 0.13%, respectively. Di- and mono- nucleotide repeats were shown to be the most frequent repeats. Yang [42] used de novo mRNA-Sequence analysis and obtained 83,817 transcripts, and 10,472 EST-SSRs were detected from 9180 (10.95%) transcripts. In these EST-SSRs, di-nucleotide (64.05%) repeat motifs had the highest frequency, followed by tri-nucleotide (29.77%), hexa-nucleotide repeats (3.17%), tetra-nucleotide repeats (1.52%), and penta-nucleotide repeats (1.49%). In our study, both di-nucleotide and tri-nucleotide SSR markers were developed. Additionally, some researchers were concerned that tri-nucleotide SSR markers derived from transcriptomes might be biased towards coding regions, and therefore could be under different selective pressures than SSRs from non-coding regions. Especially in some downstream analysis, where SSRs were assumed to be neutral, tri-nucleotide repeats developed from ESTs should be excluded to avoid potential coding regions [56]. Even if the tri-nucleotide SSRs are developed and used, they could be analyzed separately or tested for selection, which could possibly reveal important insights.
A significant number of high-quality EST-SSRs were generated, which will allow a better understanding of the genetic diversity and facilitate the application in breeding programs. Traditional cultivar identification and classification depended on morphological characters like leaf blade, fruit color, fruit size, and so on, but the accuracy was often affected by environmental factors. The molecular markers developed from our study were efficient alternatives to morphological identification, especially for hybrids, which will lay a foundation for DNA fingerprinting and hawthorn breeding in the future. In this study, 33 newly developed EST-SSR markers were used to evaluate the genetic diversity of 70 hawthorn cultivars of C. pinnatifida. The genetic diversity estimated by EST-SSRs was based on the values of Na, Ne, I, Ho, He, and PIC. When the PIC value was higher than 0.500, the locus was regarded to be of high polymorphism [28,57]. Thus, in our study, 14 SSR primers were highly polymorphic. The average value of Na, Ne, He, Ho, I, and PIC were 4.939, 2.221, 0.924, 0.709, 0.524, and 0.450, respectively. These values (except Na) were lower than those seen in previous studies on other hawthorn species [28,36]. This could be because the expresses sequences, from which EST-SSR are derived, are highly conserved. Notably, the genetic diversity is much higher among different species than among different cultivars from one species. Nevertheless, in this study, we report an efficient protocol for the development of EST-SSR markers of Chinese hawthorn cultivars from RNA-Sequence. Nowadays, NGS technologies are constantly evolving. Third generation platforms are also currently available, including SMRT (Single -molecule real time) sequencing, single-molecule nanopore DNA sequencing, and others. They are being considered an efficient and viable alternative for developing SSR markers [40,58].

5. Conclusions

In the present study, we developed a large number of EST-SSR markers for hawthorn from transcriptome data. A total of 72,837 unigenes were generated, and 5091 EST-SSRs were identified. For these EST-SSRs, 3781 primer pairs were successfully designed. Of these primers, 300 were selected for further validation, and 163 primers were detected for polymorphism. Eventually, 33 EST-SSRs were selected to estimate the genetic diversity, with a total of 108 alleles detected, ranging from 2 to 14 per locus. A UPGMA cluster analysis was used to separate the Chinese hawthorn cultivars into two clusters. There is no doubt that these novel EST-SSR markers will be helpful for future research on cultivar identification, population structure, and QTL analysis for hawthorn. In addition, the analysis of genetic diversity is a prerequisite for its exploration and utilization.

Supplementary Materials

The following are available online at https://www.mdpi.com/1999-4907/10/2/82/s1, Supplementary Table S1. Details of 70 cultivars used in study, Supplementary Table S2. Details of 300 primer pairs selected for initial screening, Supplementary Figure S1. Initial screening of EST-SSR primers with 1% agarose gel and 8% non-denaturing PAGE, Supplementary Figure S2 Screening of EST-SSR primers with CE.

Author Contributions

S.M., W.D., T.L., and Y.L. conceived and designed the experiment. S.M. and T.L. performed the experiment, analyzed the data, and wrote the manuscript. W.D. and Y.L. revised the manuscript.

Funding

This work was funded by the State Forestry and Grass Administration of the People’s Republic of China, grant number was 2014-LY-020.

Acknowledgments

We would like to sincerely thank National Hawthorn Germplasm Repository of China (Shenyang, China) and Institute of Forestry and Pomology, Beijing Academy of Agricultural and Forestry Sciences (Beijing, China) for providing plant materials.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. General information of the percentage for six EST-SSR types.
Figure 1. General information of the percentage for six EST-SSR types.
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Figure 2. Details of the density of each type of EST-SSR.
Figure 2. Details of the density of each type of EST-SSR.
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Figure 3. Details of the screening results of percentage for EST-SSR primer pairs.
Figure 3. Details of the screening results of percentage for EST-SSR primer pairs.
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Figure 4. A UPGMA dendrogram of 70 hawthorn cultivars based on 33 EST-SSRs. The numbers on x-axis were genetic similarities represented by coefficient value.
Figure 4. A UPGMA dendrogram of 70 hawthorn cultivars based on 33 EST-SSRs. The numbers on x-axis were genetic similarities represented by coefficient value.
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Table
Table 1. Summary of RNA-Sequence for SSRs in Chinese hawthorn (C. pinnatifida).
Table 1. Summary of RNA-Sequence for SSRs in Chinese hawthorn (C. pinnatifida).
Parameters Used in ScreeningData Generated by MIcroSAtellite (MISA)
Total number of sequences examined14,364
Total size of examined sequences (bp)28,888,844
Total number of identified SSRs5091
Number of SSR-containing sequences4011
Number of sequences containing more than one SSR863
Number of SSRs present in compound formation273
Table
Table 2. Details of the 33 SSR primer pairs used and their summary of information.
Table 2. Details of the 33 SSR primer pairs used and their summary of information.
Primer PairsForward Sequence (5′-3′)
Reverse Sequence (5′-3′)		Motif RepeatProduct Size (bp)Tm (°C)NaNeIHoHePIC
Pr1AATATTTGACCCGCTGCAAG
TTCTGCAGGAAAAACCCATC		(AC)813053.532.9331.0870.9000.6590.585
Pr19ATCAGTTTCCATCCCTGTCG
TGGTGGACGTAACAGCACTC		(TA)620656.562.6321.1680.7970.6200.554
Pr26GCTGCTGCAGTAAGCAATGA
GCAACCCACAAAACTGGAAT		(CCT)610354.562.2710.9860.9130.5600.469
Pr29ATGAGGCTGACGAGAGAGGA
TTTGCAGAACCCAAAAGTCC		(AG)723555.541.4070.5850.2140.2890.272
Pr31TTACACTCGCCAGAACCCTC
CCGTCATGTTGAATCCTGTG		(CCG)518256.532.0100.7900.7290.5030.407
Pr56GACGAGAATAACTCGCCTCG
TGCACGACTTGCAGTACCTC		(TCG)519957.562.4541.1170.8240.5920.522
Pr59CTGCAAGATTGGAGGAGGAG
TCAAGGGGAGTGCTCTCAGT		(TGT)516657.522.0000.6930.8290.5000.375
Pr81AGGAAGAGGAGGAATAGCCG
GGATTCGCAGAGGATGTTGT		(GA)715756.542.1810.8730.7540.5420.441
Pr83TTCCTTCTCACGCAAAATCC
TGGGTTTTGGAAGCTTTGAG		(CT)611653.541.2800.4570.1940.2190.206
Pr100GGACACCTTCTTTGGCACTC
TGTGGGTTGTGTGTTTTGCT		(CT)917355.541.4790.5610.2500.3240.280
Pr110CGCCGTAAACAGAGAGAGGA
GTCGGAGAAAATGGTGTCGT		(CT)814456.042.5371.0710.8410.6060.533
Pr114TTCAACCTCCATCCATCCAT
CAAGCCTCATCAGAACACGA		(GCC)612354.052.3400.9760.8550.5730.480
Pr117CTCAATGCAGTGGGAACTCA
AGAGCTTGAGCAAGCAGAGG		(AG)626256.021.2580.3590.2320.2050.184
Pr125GATGTGGACGATTGAGTTGC
GCATGTAGCCCACAAGACAA		(TG)626654.531.5400.5820.4140.3510.298
Pr134GGGTTGGTGAAAGCCCTAAT
ATGCATACGCAGCAGTCTTG		(ATC)613056.042.6541.0540.8140.6230.548
Pr146AGAAGATGACGACCACGACC
TGCGATTCGAAACCCTAATC		(TTC)626954.542.3731.0080.7860.5790.518
Pr152CGCTTGGTTAACTGTGGAGC
TTGGACTCCACACATGCAAT		(AC)612756.042.1330.8800.7140.5310.444
Pr167AACACTTCCCTCACCGTCAC
TAGATTGCCAGGACCAGACC		(GTC)520957.062.0900.8780.7540.5210.425
Pr171CATACCCACTCAAGGGCTGT
GGCCTTTGACTCCAAATGAA		(CA)822656.0143.2141.5710.8570.6890.642
Pr174TCTTTCTCACCGAACCCATC
TCGTAATCGGCGAGAGAGTT		(CT)822754.541.7730.7850.1320.4360.389
Pr175TTGCACTTGTGGCTAGATCG
CAATTCATTGCACTTCGGTG		(AT)713353.593.0101.3830.8700.6680.616
Pr205ACCGTAGGAAGTCCAACACG
CCCTCAGTTCAAGGAATACTCG		(GAG)512557.542.5101.0160.8990.6020.523
Pr211TGTTGCTGCAATCTGCTTTC
CCACTTGGAGGCAGTAGTGA		(AAT)618356.043.0821.2280.9000.6760.615
Pr224AAACTTCATCGGTCCGTCAG
CTCTTCCTTGCTGCATCCTC		(AG)624256.0102.4561.2070.8360.5930.527
Pr226ACGAGAGCATGCAGGAGAAT
GCTTTCCGAACCTTCAACAG		(GGC)524256.042.0070.7550.8290.5020.390
Pr235GCAAACAATGGGCACACATA
GCTGTGCATGAGATGGAAGA		(GAG)516354.592.5631.1911.0000.6100.533
Pr237CGGGATCAAGCAATGAAGAT
CAGAGCCATGGTTCAGGAAT		(CCT)524254.522.0000.6931.0000.5000.375
Pr239ACCATGGAAGATTCGTGGAG
CCAATGAGCAAAAGCGTGTA		(CTT)519654.542.1960.9020.7290.5450.456
Pr244GCCGACTCTACTTGAAGGGA
TACGGATAAACCCAGCTTCG		(GTT)617356.021.9980.6930.6570.5000.375
Pr246GCAAGGGACTTGAAATTGGA
ATCCGATCGGTTCTGTGAAG		(GA)917454.5132.2591.3970.6090.5570.542
Pr251TCTTCCTTCTCCGCTCTCTG
CCCATTTCTGTCTGCTCCAT		(AG)922756.052.7711.1120.8380.6390.569
Pr255GCCGTATGGAGGGTCCTTAG
GCGGAGGTGATTGGAGACTA		(TC)617758.521.8450.6510.7100.4580.353
Pr295GCAATCGATGCATACAGCTC
ACAAGAGTGTCAAGGCCCAG		(AG)922556.032.0470.7720.7290.5120.401
Mean////4.9392.2210.9240.7090.5240.450
Standard Error////0.5150.0880.0490.0420.0220.118
Na: the number of alleles, Ne: the number of effective alleles, I: Shannon’s information index, Ho: observed heterozygosity, He: expected heterozygosity, and PIC: polymorphism information content.
Table
Table 3. General information of sequencing and assembly for three hawthorn transcriptomes.
Table 3. General information of sequencing and assembly for three hawthorn transcriptomes.
SourceDai et al., 2013 [11]Yang et al., 2015 [42]Xu et al., 2016 [43]
MaterialsC. pinnatifida H8C. pinnatifida S7C. pinnatifida “Zezhouhong”C. pinnatifida “Qiujinxing” and “Ruanroushanlihong3Hao”
Clean reads11,538,39514,659,62432,332,20723,183,612 *
Total bases (bp)2,140,589,5522,743,145,6726,530,452,3065,839,454,759 *
Number of >200 bp contigs460,119515,118/2,182,914
Mean length of contigs (bp)130131/77
Number of >200 bp transcripts54,66262,65383,817199,204
Mean length of transcripts (bp)7568469381056
N50 length of transcripts (bp)1237142116151656
Number of unigenes39,66341,723/72,837
Mean length of unigenes (bp)656703/696
SequencerIllumina HiSeq 2000Illumina HiSeq 2000Illumina HiSeq 2500
* Mean value of 8 samples.
Table
Table 4. General information of repeat type of EST-SSR motif for three hawthorn transcriptomes.
Table 4. General information of repeat type of EST-SSR motif for three hawthorn transcriptomes.
SourceDai et al., 2013 [11]Yang et al., 2015 [42]Our Study
Mono-nucleotide99231.25%//198939.07%
Di-nucleotide125439.51%670764.05%201239.52%
Tri-nucleotide67721.33%311829.77%102420.11%
Tetra-nucleotide290.91%1591.52%460.90%
Penta-nucleotide40.13%1561.49%90.18%
Hexa-nucleotide40.13%3323.17%110.22%
Number of compound SSRs2146.74%//2735.36%
Total number of identified SSRs3174 */10,472/5091 **/
Note: * the number repeat type of SSR were statistic total number of identified SSRs included compound SSRs was calculated separately, ** total number of identified SSRs not included the number of compound SSRs.

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Ma, S.; Dong, W.; Lyu, T.; Lyu, Y. An RNA Sequencing Transcriptome Analysis and Development of EST-SSR Markers in Chinese Hawthorn through Illumina Sequencing. Forests 2019, 10, 82. https://doi.org/10.3390/f10020082

AMA Style

Ma S, Dong W, Lyu T, Lyu Y. An RNA Sequencing Transcriptome Analysis and Development of EST-SSR Markers in Chinese Hawthorn through Illumina Sequencing. Forests. 2019; 10(2):82. https://doi.org/10.3390/f10020082

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Ma, Suliya, Wenxuan Dong, Tong Lyu, and Yingmin Lyu. 2019. "An RNA Sequencing Transcriptome Analysis and Development of EST-SSR Markers in Chinese Hawthorn through Illumina Sequencing" Forests 10, no. 2: 82. https://doi.org/10.3390/f10020082

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