Analysis on Genetic Diversity of 40 Flowering Cherry Cultivars and Construction of Molecular ID Based on SSR Markers ()
1. Introduction
Flowering cherry refers mainly to ornamental plants in the subgenus Cerasus, which belongs to the genus Prunus of the family Rosaceae [1] . They are extremely important woody flowering plants in early spring owing to their beautiful blossoms, high ornamental characteristics and wide adaptability and play a key role in urban and rural landscaping. The subgenus Cerasus consisting of more than 150 species worldwide distributes mainly in the subtropical, warm temperate, and temperate regions of the northern hemisphere [2] . China is one of the centres of origin and diversity for subg. Cerasus, with 48 species and 10 varieties, accounting for nearly one-third of subg. Cerasus species [3] [4] . There are more than 300 cultivars in worldwide [5] , about 70 cultivars are grown in China [6] .
In order to identify and preserve flowering cherry germplasm resources, it is necessary to understand their genetic relationships and population structures and develop a molecular ID code system. Amplification with simple sequence repeats (SSR) molecular markers is one of the most widely used methods for studying genetic diversity and population structure [7] . Over the past two decades, many studies have been conducted on flowering cherry cultivars using SSR markers. Tsuda et al. assessed the genetic structures of 12 natural populations of C. jamasakura with 10 nuclear SSR markers [8] . Yueliang Lv et al. analysed the populations of 13 P. campanulata cultivars, also using SSR [9] . Qiong Zhang used 24 SSR primers to analyse 96 members of the subg. Cerasus [10] . Shuri Kato used SSR markers to study the origin of Japanese flowering cherry [11] , and Tao Fu identified 11 wild flowering cherries [12] . Jie Chen analysed the genetic diversity and structure of P. serrulata and Pei Wu examined the genetic characteristics of flowering cherries based on SSR molecular markers [13] [14] . However, there has been little research on the genetic structures of flowering cherry cultivars, or the development of a molecular ID code system. Capillary electrophoresis technology based on Next-Generation Sequencing (NGS) technology is currently being used to detect fragments amplified using SSR markers, provides results that are more accurate, sensitive, and efficient than conventional methods, and is better suited to the analysis of large numbers of samples. This approach has also been used to construct DNA fingerprints, or molecular ID code systems [15] . These methods have been applied widely to crops, fruits, ornamental plants, and other species.
In this study, we assessed the genetic structures and diversity of 40 flowering cherry cultivars and developed a molecular ID code system using SSR fluorescent-labeled capillary electrophoresis technology. This will lay a foundation for germplasm resources protection, cultivar identification.
2. Materials and Methods
2.1. Plant Materials
All the cultivars used in this study could be classified into five taxonomic groups [3] , hereafter referred to as Populations (Table 1). And they were taken from the Anshan nursery at the Wuhan Institute of Landscape Architecture, 3 - 5 young leaves of each cultivar were collected at random from three individuals on May 2021. All samples were enclosed in plastic bags and stored at –80˚C in the laboratory prior to DNA extraction.
2.2. DNA Extraction and PCR Amplification
Genomic DNA were extracted using the Rapid Plant Genome DNA Isolation Kit (B518231, Sangon Biotech, Shanghai, China) according to the production instruction. The DNA concentration was measured with a NanoDrop 2000 and then was stored at –20˚C. We collected 38 SSR primers from some studies on Subgenes Cerasus. These primers were shown in Supplementary Table S1. These primers were labeled with fluorescent dyes (FAM, HEX, or TAM). The amplified products were analyzed by polyacrylamide gel electrophoresis, and after gel imaging, selecting polymorphic primers according to the bands quality.
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Table 1. List of plant materials used in the study.
PCR was performed using 25 µL of reaction solution, the detail as follow: 0.5 µL 10 mM dNTP, 2.5 μL Taq buffer, 0.5 μL of each primer at 20 μmol·L−1, 1 μL genomic DNA, 2.5 μL 25 mM MgCl2, 1.0 U Taq DNA polymerase, and 17.8 µL double-distilled H2O. Amplification was carried with the following parameters: an initial denaturation at 95˚C for 3 min, ten cycles of denaturation at 95˚C for 30 s, annealing at 60˚C for 35 s, and extension at 72˚C for 30 s. This was followed by 20 cycles of denaturation at 95˚C for 30 s, annealing at 60˚C for 30 s, and extension at 72˚C for 30 s, with a final extension at 72˚C for 6 min. The PCR products were detected with ABI 3730xl DNA Analyser (Applied Biosystems, USA).
2.3. Data Processing
2.3.1. Cluster and Non-Metric Multidimensional (NMDS) Analysis
Cluster analysis based on Nei’ coefficient using UPGMA was performed with MVSP ver. 3.2. NMDS analysis was conducted using the package vegan (2.5.6) in R [16] .
2.3.2. Genetic Diversity and Differentiation
GenAIEx 6.502 was used to assess the genetic diversity parameters per locus and population [17] [18] . We also conducted analysis of molecular variance (AMOVA, 1000 permutations) among the populations with the same software. We also analysed the following parameters: number of alleles (Na), number of effective alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), number of migrants (Nm), Shannon’s information index (I), coefficient of genetic differentiation (FST), Fixation Index (F), and inbreeding coefficient within individuals (FIS).
2.3.3. Population Structure Analysis
Population structure was analysed in Structure (version 2.3.4) software, Parameter settings were as follows: burn-in 10,000 iterations. Clustering number from 2 to 10, repeat times 15. The results were submitted to the online program Structure Harvester [19] . The optimal K value was calculated with the method developed by Evanno [20] .
2.3.4. Construction of Molecular ID Code System
Coding was done according to the amplified fragment size. The detailed steps are as follows: 1) the amplified fragments were ordered by size; 2) codes consisted of combination of two Arabic numerals (i.e., 01~99), Missing values were represented by 00. If the value exceed 99, it was encoded using two English letters (i.e., aa - zz); and 3) the selected SSR primers were arranged in a fixed order. Molecular ID codes were then produced by sequentially combining all codes from the amplified fragments for each cultivar.
3. Results
3.1. Selecting the Polymorphic Primers
By literatures, 38 pairs of SSR primers of flowering cherry or similar species were collected, and then 8 representative samples from the tested plant materials were used to PCR amplify for polymorphic primers selection. By gel imaging, the stable amplification, clear bands 13 polymorphism were pick out from 38 primers, The detail as Figure 1, these primers information also shown in Supplementary Table S1.
3.2. Cluster Analysis
As Figure 2 showing, the genetic similarities range from 0.87 to 0.20, with a mean of 0.494. These cultivars could be divided into four groups at the genetic similarity value of 0.26; the groups were designated A, B, C, and D. Group A was consisted of seven cultivars, with genetic similarities between 0.87 and 0.47. Five of the seven cultivars belonged to P. subhirtella, and the other two belonged to P. yedoensis “Somei-yoshino” and P. pseudocerasus “Introsa”. There were21 cultivars in Group B, including 18 cultivars of P. serrulata and three of P. jamasakura. The genetic similarity valuein Group B ranged from 0.86 - 0.36, and were higher than Group A. This group was characterised by highly diverse flower colours, inflorescences, petal numbers, and flower types. Prunus serrulata “Sekiyama” and P. serrulata “Hongye” were found to be closely related, with the similarity value of 0.86. Group C comprised nine cultivars with genetic similarities ranging from 0.55 - 0.34; this group was mainly composed of cultivars and hybrid progenies of P. campanulata. Group D included P. conradinae, P. cerasoides var. rubea, and P. pseudocerasus “Keio-zakura”.
3.3. Non-Metric Multidimensional (NMDS) Analysis
The stress value in the NMDS analysis was 0.22, indicating a poor fit according to Kruskal J B’s standard of division stress value [21] . In Figure 3(a), The linear fit was poor (R2 = 0.775) but the non-metric fit was high (R2 = 0.95), suggesting that non-metric analyses were appropriate. In Figure 3(b), the sizes of the sample points with bubbles were representative of the fitness value; larger bubbles indicated poorer fit. Figure 3(c) showed the clustering of the cultivars in the two-dimensional space of nmds1 and nmds2. As the cluster analysis, the NMDS
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Figure 1. Partial screen of SSR primer. M = DNA Marker; 1 - 4 = PTCR1; 5 - 8 = CPSCT029; 9 - 12 = BPPCT005; 13 - 16 = BPPCT0037.
analysis indicated that the cultivars separated into four groups when K = 4. Group 1 included three accessions: P. conradinaes (10), P. cerasoides var. Rubea (1), and P. pseudocerasus “Keio-zakura” (23). Since these belong to three different species in Subgen. Cerasus, their spatial distributions were scattered in Figure 3(c). Group 2 included nine cultivars of bell flower cherry (P. campanulata). The Groups 3 and 4 cultivars were native to Japan and belonged to P.serrulata and P. subhirtella, respectively, indicating a narrow genetic background.
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Figure 2. Cluster of 40 flowering cherry cultivars based on Nei & Li’s Coefficient.
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Figure 3. NMDS and clusters analysis of 40 flowering cherry cultivars.
3.4. Genetic Diversity and Differentiation
A total of 167 alleles were obtained from the 40 flowering cherry cultivars using the 13 SSR primers, with an average of 12.8 alleles per locus in Table 2. Among the locuses, the observed heterozygosity (Ho) and expected heterozygosity (He) ranged from 0.161 - 0.825 and 0.338 - 0.853, with means of 0.458 and 0.670, respectively. He was higher than Ho. The mean Shannon’s information index (I) and number of effective alleles (Ne) were 1.91 and 5.74, respectively. The number of migrants (Nm) ranged from 0.016 to 0.763, with a mean of 1.576. The fixation index (F) ranged from 0.712 to 2.631, with a mean of 0.318. Random breeding would produce F values close to zero, whereas larger positive values were indicative of inbreeding. The inbreeding coefficients within the sub-populations (FST) ranged from 0.087 to 0.252, with a mean of 0.157.
The population-level genetic diversity parameters were shown in Table 3. Among the populations, Na and Ne were 3.923 - 11.232 and 2.700 - 4.814, with means of 6.062 and 4.326, respectively. Ho ranged from 0.410 to 0.453, and Hefrom 0.564 to 0.770, with means 0.458 and 0.670, respectively. Ho was higher than He in all populations. I value ranged from 1.146 to 1.924, with a mean of 1.417. Population 3, which originated from P. serrulata, had higher values for Ho, He, and I compared to other populations, indicating that this population had higher levels of genetic diversity and differentiation. The F values ranged from 0.228 to 0.397, with a mean of 0.321. F was expected to be close to zero in situations of random mating, or under Hardy-Weinberg equilibrium.
The FST value among the five populations was 0.036, suggesting that inter-population variability only accounted for 3.6% of the genetic variation in the samples, whereas the other 96.4% of the variation occurred within the populations. Thus, intra-population variation was the major variation in the samples. The AMOVA assessed variation both populations and individuals (Table 4); the results indicated that variation between populations accounted for 4%, the39% variation came from individuals and 57% (p < 0.05) came from intra-individuals. The results showed that the genetic variation in the 40 cultivars were mainly attributable to variation within individuals; In contrast, the genetic differentiation between the populations was very low.
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Table 2. Genetic diversity parameters of 13 SSR locuses.
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Table 3. Genetic diversity parameters for 5 populations.
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Table 4. AMOVA of 40 flowering cherry cultivars.
3.5. Genetic Structure Analysis
A genetic structure analysis provided detailed information on the level of genome admixture between the populations. The results from Structure and Structure Harvester indicated that the ΔK was optimal at K = 4 (Figure 4). It followed that the optimal number of subpopulations was 4; that was to say, all of the cultivars could be divided into 4 subgroups, we referred to as Population 1-4. As Figure 5, the proportions of the 4 subpopulations were 0.148, 0.214, 0.452, and 0.186, respectively. The He values were 0.6635, 0.8553, 0.7589, and 0.6172,
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Figure 4. Diagnostic plots of L(K ) and ΔK from the STRUCTURE analysis 40 flowering cherry cultivars.
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Figure 5. Population genetic structure of 40 flowering cherry cultivars at K = 3, 4, 5.
and the FST values were 0.1660, 0.0015, 0.1543, and 0.2898, respectively. The results essentially reflected the phylogenetic relationships. Q value ≥ 0.6 indicated relatively pure lines, and Q values < 0.6 indicated admixture. In this study, the Q values for 38 samples were ≥0.6, showing that these lines were relatively pure. Only two individuals were Q values < 0.6, indicating that the two samples were admixed, had a highly diverse genetic composition or were of mixed origin (Figure 5).
3.6. Construction of Molecular ID Code System
The size of the fragments amplified using the 13 SSR primers ranged from 111 to 298 bp. These SSR primers PBBCT34, EMPAS02B, EMPA026, PSCHGMS1, and EMPA027 were found to be highly polymorphic and suitable for classifying ten cultivars at least; the PBBCT34, EMPAS02B, and EMPA026primers could classify 21, 26, and 30 cultivars, respectively. All samples could be classified adequately using a combination of these three primers. To allow for the addition of cultivars in the future, we integrated one additional primerEMPA027, into molecular ID code system (Supplementary Table S2). As an example, the Japanese flowering cherrycultivar P. × yedoensis “Somei-yoshino”, the sizes of the fragments obtained from the PBBCT34, EMPAS02B, EMPA026, and EMPA027 primer sets were 208/218 bp, 166/18 2bp, 206/212 bp, and 162/162 bp, respectively. According to Table 5, the corresponding fragment codes were 54/59, 33/41, 53/56, and 31/31, respectively. These were combined sequentially into a 16-bit string, which comprised the cultivar’s molecular ID code. In the above example, the final molecular ID code was 5459334153563131. The molecular ID code of 40 flowering cherry cultivars were shown in Table 5.
4. Discussion
4.1. Genetic Relationships
Cluster analysis indicated that our results were generally consistent with morphological classifications [3] . Genetic variation between cultivars within the same lines was low, indicating that these lines had relatively narrow genetic backgrounds. This was attributable to seed production and cross breeding. Conversely, the genetic differences between lines were substantial, with the high diversity due to their wide distribution and numerous cultivars. These results were consistent with the previous findings [10] [13] [22] .
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Table 5. The molecular ID code of 40 flowering cherry cultivars.
NMDS analysis was used to assess the similarity of data between objects and illustrate the spatial relationships between them [3] . This method could sort, cluster and reduce dimensions. In this study, the close relationships of 40 samples were clearly and vividly illustrated in two-dimensional space. At K = 4, the cluster results were similar to UPGMA.
The results of the population structure analysis were consistent with those of the cluster analysis and the NMDS, with the 40 tested samples dividing into 4 groups. Each method had its own unique advantages; for instance, NMDS could reflect relationships between genetics and geographical distribution in two-dimensional space. However, for individuals with complex genetic backgrounds, the results of the different methods showed differences in clustering and population structure divisions, as demonstrated by the results of Haiwen Zhang [23] .
4.2. Analysis of Genetic Diversity and Structure
The genetic diversity among cultivars was found to be very high. Compared to similar species, the mean of Shannon’s information index (I = 1.417) was higher than those of P. mahaleb (I = 0.1720) [24] and P. serrulata (I = 0.939) [25] , but lower than that of P. pseudocerasus (I = 1.525) [26] . The high genetic diversity may be associated with out-crossing mating systems, wide geographic distributions, and large difference in climate and habitat conditions among the cultivars. The diversity of the cultivars originating from Prunus subg. Cerasus was also an important factor.
Population genetic structure analysis was an important method to insight into genetic relationships among cultivars, and SSR molecular marker technology could be used to determine the degree of genetic variation and differentiation at the population level [26] [27] [28] . Genetic differentiation was an important parameter of population genetic structure. According to Wright [29] , if FST < 0.25, genetic differentiation was low; in our study it was very low (FST = 0.036). The AMOVA indicated that only 4% of the genetic variation occurred among populations, whereas 39% occurred among individuals and 57% within individuals. Thus, genetic variation within the populations was the major variation. This was consistent with the results of Cai Yu Liang and Chen Jiao [26] [30] . Seed dispersal mechanisms had a significant effect on genetic differentiation among populations; because flowering cherries mainly relied on animal dispersal, the range of animal activities restricted dispersal [30] .
Nm affected the genetic differentiation of populations. Generally speaking, when Nm > 1, genetic drift within populations was impeded, preventing differentiation [31] . The Nm value in this study was 1.577. So, genetic variation among the populations was restricted, and most genetic variation was found within populations.
4.3. Construction of Molecular ID Code System
Three methods were typically used to construct plant molecular ID code systems. The first was to use RFAP, RAPD, or AFLP molecular marker technologies, and labeled them 1 or 0 based on the presence or absence of the DNA bands and then the molecular ID code was added to the binary string [32] [33] [34] in decimal or by converting it to decimal, generating a decimal molecular ID code [35] . The second method, developed in recent years, used a combination of SSR technology and capillary electrophoresis to measure the sizes of amplified fragments. The amplified fragments were then sorted and encoded, and several primers’ amplified fragment codes were combined to construct a molecular ID code [36] [37] . The third method was similar to the second method but produces a different type of ID code: the amplified fragments are encoded, and the resulting codes are combined in a sequence to form a molecular ID code [38] .
We used the third method to construct a molecular ID code. To improve the distinguishability of SSR primers, we used both amplified fragments to encode in each locus. Given the increasing number of cherry cultivars and breeds, it was important to allow for increasing in future. Thus, we added one additional primer in the construction of our molecular ID code system. In this study, we encoded 99 fragments of 102 to 298 bp. If this was not sufficient for the number of codes required, two English letters (aa~zz) could also be appended, allowing for an additional 676 fragments. Therefore, this system could meet future needs and also ensure the ongoing uniformity of the 16-bit code.
Author Contributions
Experimental design: C.R.N; investigation: H.B.S., X.G.X.; data analysis: X.Q.Z. and C.R.N.; funding acquisition: C.R.N.; project administration, W..S.X., C.R.N., and X.Q.Z.; writing—original draft, C.R.N. writing—review & editing, C.R.N., X.Q.Z. and W.S.X. All the co-authors have read and approved the submitted version of the manuscript. All authors have read and agreed to the published version of the manuscript.
Acknowledgements
We would like to acknowledge Hongling Kuang, Qinghua Wang who helped us with the sampling.
Supplementary
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Table S1. Details of the 13 SSR primers used for flowering cherry analysis.