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Molecules 2014, 19(2), 1939-1955; doi:10.3390/molecules19021939

Article
Use of EST-SSR Markers for Evaluating Genetic Diversity and Fingerprinting Celery (Apium graveolens L.) Cultivars
Nan Fu , Ping-Yong Wang , Xiao-Dan Liu and Huo-lin Shen *
College of Agronomy and Biotechnology, China Agricultural University, No.2 Yuanmingyuan Xi Lu, Haidian District, Beijing 100193, China; E-Mails: funan@cau.edu.cn (N.F.); wpy0320fn@cau.edu.cn (P.-Y.W.); liuxiaodan4875527@126.com (X.-D.L.)
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed; E-Mail: SHL1606@cau.edu.cn; Tel./Fax: +86-010-6273-2831.
Received: 23 December 2013; in revised form: 5 February 2014 / Accepted: 7 February 2014 /
Published: 10 February 2014

Abstract

: Celery (Apium graveolens L.) is one of the most economically important vegetables worldwide, but genetic and genomic resources supporting celery molecular breeding are quite limited, thus few studies on celery have been conducted so far. In this study we made use of simple sequence repeat (SSR) markers generated from previous celery transcriptome sequencing and attempted to detect the genetic diversity and relationships of commonly used celery accessions and explore the efficiency of the primers used for cultivars identification. Analysis of molecular variance (AMOVA) of Apium graveolens L. var. dulce showed that approximately 43% of genetic diversity was within accessions, 45% among accessions, and 22% among horticultural types. The neighbor-joining tree generated by unweighted pair group method with arithmetic mean (UPGMA), and population structure analysis, as well as principal components analysis (PCA), separated the cultivars into clusters corresponding to the geographical areas where they originated. Genetic distance analysis suggested that genetic variation within Apium graveolens was quite limited. Genotypic diversity showed any combinations of 55 genic SSRs were able to distinguish the genotypes of all 30 accessions.
Keywords:
celery; EST-SSR; genetic diversity; fingerprint; genotypic diversity

1. Introduction

Celery (Apium graveolens L.) is a biennial species from the family of Apiaceae with 2n = 2x = 22 chromosomes. It originated from the Mediterranean basin and several cultivated types are grown worldwide for consumption. Besides the wild (Apium chilanse) species and celeriac (Apium graveolens L. var. rapaceum) species, both coming from Western countries, common celery (Apium graveolens L. var. dulce) cultivars are generally classification based on their origin as celery (cultivars introduced from Western countries), local celery (Chinese celery) and the middle type (hybrids of celery and local celery).

Several types of biochemical and molecular markers have been applied for celery genotyping, such as isozymes [1], restriction fragment length polymorphism (RFLP) [2], random amplified polymorphic DNA (RAPD) [3,4], amplified fragment length polymorphism (AFLP) [5,6], sequence-related amplified polymorphism (SRAP) [7], expressed sequence tag based SSR (EST-SSR) [8,9]. Genotyping with molecular markers is used for identification of cultivars [10,11], cultivar fingerprinting [12,13], detection of genetic variation and genetic diversity [14,15,16], construction of linkage maps [17,18,19], mapping genes of interest, and for marker assisted selection (MAS) [20,21,22,23]. These researches are frequently carried out with SSR markers for their co-dominant and multi-allelic nature, which makes them more informative than dominant-types of markers. However, development of SSR markers is costly and time consuming and therefore research on celery was quite limited. Next-generation transcriptome sequencing provides an efficient means to develop SSR markers and it has been applied to many organisms [24,25,26]. In our previous work, we developed a set of EST-SSR markers [27] through transcriptome sequencing.

The objectives of the present work were to: (1) test marker polymorphism on a set of celery cultivars; (2) assess the genetic variation existing in the materials used; (3) detect the genetic diversity and population structure of these materials and (4) explore the efficiency of the primers used for cultivar identification.

2. Results and Discussion

A list of the samples investigated in this study is given in Table 1. This set of accessions comprised 28 common cultivars, one celeriac and one wild species. The 28 common cultivars can be further divided into 16 celery accessions, nine local celery accessions and three middle type accessions.

Table Table 1. List of the 30 accessions genotyped with genic SSR markers.

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Table 1. List of the 30 accessions genotyped with genic SSR markers.
CodeVarietyTypeSpecies
C1XuebaiqincaiLocal celeryApium graveolens L. var. dulce
C3JinhuangqincaiLocal celeryApium graveolens L. var. dulce
C5JincuifuqinLocal celeryApium graveolens L. var. dulce
C6HuangxinqinLocal celeryApium graveolens L. var. dulce
C8ShanghaichunqinLocal celeryApium graveolens L. var. dulce
C13TieganqingLocal celeryApium graveolens L. var. dulce
C29WuqiangshiganqincaiLocal celeryApium graveolens L. var. dulce
C37ShixinqinLocal celeryApium graveolens L. var. dulce
C53DuolunshiganqincaiLocal celeryApium graveolens L. var. dulce
C111LinoCeleryApium graveolens L. var. dulce
C67JiazhouwangCeleryApium graveolens L. var. dulce
C87Ventura CeleryApium graveolens L. var. dulce
C114HuanghouCeleryApium graveolens L. var. dulce
C62Introduced from USA(fertile)CeleryApium graveolens L. var. dulce
C63Introduced from USA(sterile )CeleryApium graveolens L. var. dulce
C65-CeleryApium graveolens L. var. dulce
C68JiahuangCeleryApium graveolens L. var. dulce
C74Dore Golden SpartanCeleryApium graveolens L. var. dulce
C79KangnaierCeleryApium graveolens L. var. dulce
C83BailixiqinCeleryApium graveolens L. var. dulce
C89TU52-75CeleryApium graveolens L. var. dulce
C97KaifengbolicuiCeleryApium graveolens L. var. dulce
C118GuiheCeleryApium graveolens L. var. dulce
C121QianfangCeleryApium graveolens L. var. dulce
C123Ventura (yellow mutant)CeleryApium graveolens L. var. dulce
C58MajiagouqincaiMiddle typeApium graveolens L. var. dulce
C99JinnanhuangxinqinMiddle typeApium graveolens L. var. dulce
C159JinnanshiqinMiddle typeApium graveolens L. var. dulce
C163-CeleriacApium graveolens L. var. rapaceum
C130-WildApium chilanse

2.1. Marker Development

In the previous work [27] we mined approximately 3,000 SSRs using the MISA software. Among the SSRs, mononucleotide motifs were discarded since it was difficult to distinguish genuine mononucleotide repeats from those generated by polyadenylation products, base mismatches or sequencing errors. In present research, we randomly selected 140 SSRs for polymorphism analysis (Table 2).

Table Table 2. SSR markers used in this study.

Click here to display table

Table 2. SSR markers used in this study.
Primer NameForward PrimerReverse Primer
Fn1GCGCTTGGTGTATCTCCACTAGTGCGTCGAAATATCGCTT
Fn2GCTTCCGCTGTGTATTTTGAGGGAAGAAACTGCAACTTGG
Fn3TGAGCTCCACCAACTGACACGCATGAGCAGTTCCAAGACA
Fn4AATTTACCGCTCTTAGCCTCGATAGGCAGAATTTGCGACGA
Fn5TGAAACCCAAGATCACCCATTCATATTGACAGGCAACCGA
Fn6CCAATCTGGGACTGTGTAAGCTTCCTGGAGGTGAAGGACTG
Fn7TGGTGTTGCAGTGTGAATCCACCGAAGCATCCTTGAACAG
Fn8TGGTGTTGCAGTGTGAATCCACCGAAGCATCCTTGAACAG
Fn9CATAGGCTAACGCAGCTTCCAGTACTCCTTCAGCCGACGA
Fn10CAGGAGGCTGCAATAACACAGAGTCGCCGGAATATCAAGA
Fn11CACACAGACGACTGCTGCTTACCATGCATGCTCAACTGAT
Fn12CACACAGACGACTGCTGCTTACCATGCATGCTCAACTGAT
Fn13CGATCAGGGTACTTGGCAATTTTCTATATCCGTCTCATTTCTGTT
Fn14AACCCTAGCTGCCTCTCTCCCCATGCCACGAATAGCATTA
Fn15TGTGTTCTCGCATCTCCAACCCAATCTCAACATCGCACAG
Fn16GTTGGTCAATGCTGCTTCCTTGTGCCAGGGATACCTTCTC
Fn17TCACTCACTCCCTTGAGCCTTGAATCAACACCGTCCATTG
Fn18CAACCTGAACATCGTTGGTGTCAACTTGATCTCACGGCAG
Fn19CTCATACGGTCCAGATCGGTATGTCCTGGTGAAGGAGGTG
Fn20GAGATTGCGATAATGGTGGCCGCATCACATCACTTAACGG
Fn21CTGCTCTGAAAGGCTCTGCTACAGCTGACATCCTTACCGC
Fn22TTCACTTGTTCAGCGAGACGCCTAACCCTAGCTCGTCGTG
Fn23TCCCATCTCCAATTCCAATCTTCCTTGCAAGACCATAGGC
Fn24CATGTCACTGTCGAAGCACCTGACAATTGCCATTCTCCTG
Fn25GCCTGAGCATCATAAGAGCCTATTCACCTTCGTATCCGGC
Fn26TGTTCCATTATGTGTTGCAGTGGCGAGATGATGTCAGAACGA
Fn27ACCATGTCCACCACCTTCTCGCTGGTTATGGTGGTGCTG
Fn28ATCGCCAACACCTTCTCAAGAAGGGTGATTCTGATGGTGC
Fn29CATATCCAGCACCTCCACCTTCCAATGGCAACTCACAGAG
Fn30CTCATTCTTCTTCTTGGCCGTGCTGAAACGCTACCTCCTT
Fn31ACATGGAATCTTTCACCTTCACATGGCCTAGGAGGAGACAA
Fn32TTCCTGTCCAGCAGTATCCCGAATTGAGGGGTGAAAAAGC
Fn33AAATGAGGTGGTGGTGGAAGCAATGGGTATGGAATCAGGC
Fn34AGTACGGTGTCTACGACGGGCCTCCACCATGATTACCACC
Fn35CATACTTCTTTGGGGGCTCAACACAAACTTTCGGCCAGAT
Fn36AAGGTCAAGGTCCTGTGGTGGGTTTAGGCCTCCAATAGCC
Fn37ACAGTACGTGTCTCCCCCTGAACAACCCTATGATGGCTGC
Fn38GTTTGAGCCTCCGCTTACAGTGCCAGTGACACTCTTCACC
Fn39GTGACGAAGGAATTGACCGTATTTGTTGTCGGGTTCCAAA
Fn40CTGGCACTTGTACGAAACCATATGGGCTGTTGATGACAGG
Fn41TTCAACCCAGACTTCAACCCGCAGCCTTCAAATCCAGTTC
Fn42CCCAGCCCTATCAATCTTCACCCCTGCCAAGTCTGTTAAT
Fn43GAGACAGAGACCATGGGGAACGGTTTCGGTTTCGATTTTA
Fn44TCTTGTCCATTAAAAATGTACCCATGCGCATAATGAAAGGATCA
Fn45AGCAGCACAACAACACTTGGTTAGGGTCTCTTGTCGAGGC
Fn46GCAAGTTACCACCCCAGAAACCTTTTTCAAAAGCTTCCCC
Fn47AATTGGCCAGAGCAGAGAAATCCTTTATCCCTGACCATGC
Fn48AATGAGGTTGTTTTTCCCCCGTAAAGGCCCAAACTCCTCC
Fn49ACAACAATTTCAGGGCCAAGTCTTGATATCGGCTTCCTGG
Fn50ACATTTGTTGCTAGGGTGGCGCACGAATAGCCGTCCTAAA
Fn51TCCAATCTCCGGAGCAATACGCGGTGGACGAGTAAAAGAG
Fn52AAACCACCAAACAAGGTGCTTGAAGTGGAGGAGCAGGAGT
Fn53ATTCCCAGATGGCTGCATAGAATTCCAGCAAGCTCAAGGA
Fn54CCCTCTCCCTATCTTCCTCGTGAGATTGACTCGGTTGCTG
Fn55AAAGAAGAAACGGGGATGCTAACAGCAAGCAGTTCAGTAGTCA
Fn56CTACACCGCCAATTCAACCTTAAGCATACACCCCCTCACC
Fn57TAATGGTGGAAAGAAAGGCGGGCATACCACTCATTTGGCT
Fn58CCTAGGCGAACTCTCCTCCTGGGAATGATCCTCCTTCCAT
Fn59AGAGGGATAAAATCCCGGTGTGGAAATGCAAAAGAAGCAA
Fn60CCACCACCACAACTACAACGAAGCCGAGTAATGCTGGAAA
Fn61AGGGTTCGTCGGTTGTAGTGTCCCCATGTCTATTCTTCGC
Fn62TCTCGACGAGTTTTCACCTGGGTCTCTTTGGTGCCATTGT
Fn63TCAATTAGATCCAGACGCCCTCTTCTGCCTCCTCTTCGAG
Fn64CTGTTTTCTCCCTCGTCTGCCCCCATCTCTGCTATAGCTCC
Fn65CTTTCTGGGTAAGAAGGCCCCCAACCCACAACGTCTTACC
Fn66TCATTGGACAAACAGGACGACTGTTTGGCGCTCAATTTTT
Fn67TACATTTGTGGGATTGGGGTCCGCCAAAACATTGACAGTA
Fn68TCAGCTAAGCCACCCTGATTGTTGCTGCTGGAGAAAGGAG
Fn69TCCATGAATCTTTCAAGCCCTCCAAAGTCCAATCCCATTT
Fn70TAACTGAGTCGGTTGGGTCCCCTCTCTTTTACCAGCCAAGC
Fn71TCAACACTCAATTTAAACACCCATGATAACGATCGTGACGGAA
Fn72CATCAACACTCGAAATCGAAACAAGATGCTTGTTATCCTTGCT
Fn73GGATCGGAGGAAGGAAGAAAGGAGGTGGAGGAGGAGAGTT
Fn74CCCCCAAACAATAAGTATCCCTTGGAACTTTTTGTGTCCATTG
Fn75GCCAGCAGTGTCCCATATTTGCCCGGAAAAATAACAATCA
Fn76TTCTACCACTTTCCTTGAATCCCAGGAGCAGTCTCGATTTCC
Fn77GGTGTATTTGAATATTAACACCTTTCGAGAGATGGTGGTCTTGTGGG
Fn78ACAAGCCCCCTCTACTTTGGATGTTGCCAGTTCAGGTTCC
Fn79TGGGACCCATTTCTTGATTTAAAATTGCTCCGATTTGTGC
Fn80CCAGGTAAGCCCAGTCTCAATTTTTCTCAATTAAAACTTGCTCATTT
Fn81AATCCTTGAACTAACCGGGGCTCTTCGCCACCAGATTCAT
Fn82GGACGCCCAAGAGAGGTAGTAGTGGTCTCGACATTTTCCC
Fn83CCACACCTTGATCGTTGAGATTGCTTCTTCCGGCTCTTTA
Fn84GTTACTTGACGGCACCGTTTATCAGTTCTTCATCCGTGGG
Fn85TCACCCTCTCATCCACATCAGCAGTGGGTGGATCTAGGAA
Fn86TCAAATGGACGACGAATCAATGCAATGATTTATCCCCCTC
Fn87CACACACAGGACACACATATTTCGTAAAGCCGTCTTGGACGAG
Fn88CGGCATCTTCTCTTCCTCACTGTTTGGATCTTTTCTGTTTTCA
Fn89CAGAAGCGGCTCCTTCTCTACCCATTTGAGCTTCACCACT
Fn90CTAAACGACGCCGTTACCATGCTTCTCTCCGCCTTGTATG
Fn91GGCATACATCGGACGCTAATTTGACCCTTTATCTCAATACACACA
Fn92CCCTCTCTCTCCCTCCTGTCACGATTAGCCATTGGTGAGC
Fn93TGTGTGCTGATTTGAAACCCACCGACACTCCACCTTCATC
Fn94CACCTCTGCTTTCACGGAACGTCCAAGAGTGGTCCTCACC
Fn95ATGGTAACACCACCCTGGAAGCTTCAACCAGGCAAAGACT
Fn96TACTTACACCCCTCCCTCCCTGCAGCACAAGGGATTCATA
Fn97AAGAGCGATCAAGAACAGGGTCCCATCTCTCTCCCTCGTA
Fn98TGCGAACAATACAGTCCCAACAGATCCAAACACAGAATTAGCA
Fn99GAAGAAAGAGGAGAAGGCCGTCTCGAAACCACCCATCTTC
Fn100GCGATCCCTAATCAATCCAACTTTGAGAGTTACGACGGGC
Fn101TCAATGGTGTAGAACCAGAACAACCCAGATGCTTAAAAGAACCA
Fn102ACAGGAGGCACTGGTCTCACCATTAAAATCCCACAAAAACTTCA
Fn103TCCCATTCCATTTCAACCTCAGAGGTGTGGGGAGATTGTG
Fn104GCGGGGACACTCCACTACTTTGATCATCAGCAGACTGGC
Fn105CAAAAATTTAACCCCATACCCACATGTACGGACGTTGTGGA
Fn106GCTTTGCACACACACACACACTTTCCCTCGACCTCATCCT
Fn107GATTTTTCCGATGCAGCACTGGCATGCACCAAACGTTATC
Fn108GAGGAGGCTGTTACGTGGAGTCCCTTTTCTCACTCCATTCC
Fn109GTAGAAGGCCTGCAGATGCTGTCTTTGCTTCTCCTCACCG
Fn110GCACCAGCAAGAGGAGACTTTTGTTGCTTGCCAGTGAAAC
Fn111AAGCGAGTAGCTGAAAGGCACACTACCACCTCCGATTGCT
Fn112CCAAGCTTCGACCATTGTTTTTGTACATCGGTGAAACGGA
Fn113AGCAGAAAGGCGTTCCACTAGTTGAGCCCTTCCTGCATAA
Fn114CGCCCTCTTCCTTTATCTCCCACTTAGGTTTACCGCTGCC
Fn115CACGTTTGGTGACATTCCAGACAATTATCTCCTTCCGCCC
Fn116TCCTCTCCTCACCAAACCACCACAACCCTTCAACATCACG
Fn117GTGGTTGGTGGGGATCATAGGCCCAAAGTTCTTCCAAACA
Fn118CAATCATATTAATCATCCCCAAAGAGTTGGTCTGCAGGAGGAG
Fn119TAAGATGCATGAGGCACGACGACTTTGATGCGCACTTTCA
Fn120TGATTTGTGCACCAAAAGGAGGAGAGTCGACCGATTCAGA
Fn121AACTCAGCAACCGGAATCACATACGTAGACGCATCGGAGG
Fn122GCACACAATAAGCCTCCCATCACATGCTACAAAACAGGCG
Fn123CCACTGGACATTTCTTGGCTTTTACAAGCCCCAACAGAGC
Fn124CTGGAACCGGAGTAGGTGAAAACAGCCTTTACCCTTCATCA
Fn125ATCTGCCTGTAGCCGAACAGCTCTTAGTTGGCGCTGCTCT
Fn126ATAATTTGCCCAACGCTCTGCTCTCTTGAAAAACACGGGC
Fn127CAACACAAACACCAAAACCCTCGTGCCTCATTGGGTTCTAT
Fn128GTTGTACTTGGTGCGGAGGTCAAAATTCCAAAAGCCCAAA
Fn129TCTTTCGATTTGGATTTGGGTAGAGCTCTCGGCCTCTTCA
Fn130TTGGTGCCATTGTTGTTGTTAACGCCTTTCTTCCCAATTT
Fn131CACCGCGATTCTTCTCTCTCCGACATCGTCTCTCTCCCTC
Fn132TTCTTTTTCTGTTCCGCCCCCGCCGTTAGAGACAAACTC
Fn133ATTGAAACCCCACCACTGAAAACGGCCAGAAAAAGCTGTA
Fn134TGGTTGGGGGAGAATTGTAATGAGTTTGCCACAACTGACA
Fn135TCCCGATAACAAGAGAGAGACTTGGAGATGAACAAGGGAAGG
Fn136AGTCCTCAGTTCTCCTGGCACAGAATGGTGATGCTGATGG
Fn137CCAGGACATACATACGTTCTCAAGACGGACTTAGCCCCCTTAT
Fn138TAGCTGCGGTTGATTCAGTGATTATCAGCGGAAGGCACAC
Fn139TGCACCACCAAAAACACCTAGAGGAGGGGTTGAGTGATGA
Fn140TCACCACCCCTAATTACCGAAGATAAACCGGGGAGCTTGT

2.2. Marker Informative Analysis of Accessions

When the 140 developed SSR markers were used for genotyping the set of 30 accessions, 23 markers had no clear bands or were amplified only in very few accessions. These markers were excluded from further analyses, reducing the number of good quality markers to 117 (83.57%). Among those good quality markers, 54 were monomorphic and 63 (53.85%) were polymorphic on the 30 accessions. The successful amplification rate and polymorphism rate were similar to those of our previous work (81.25% and 59.57%, respectively) [27], though the set of accessions previously genotyped with EST-SSRs was not identical to the current set of accessions.

The number of loci per SSR ranged from two to five, with a mean value of 2.71, which was similar to that of previous studies on celery using EST-SSRs (2.68) [27], but lower than the number of ISSR markers reported by Qing-Kuo (5.05) [9]. This indicated that primer sequences designed from SSR flanking regions were highly conserved and SSR markers were more specific than ISSR markers. Polymorphism information content (PIC) values ranged from 0.06 to 0.67, with an average of 0.33. The largest group of markers (27.42%) was in a range from 0.4 to 0.5, followed by the group with PIC values ranging from 0 to 0.1 (Figure 1). The second largest group was made up of markers polymorphic between the wild and cultivated species, but monomorphic within cultivated species.

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Figure 1. Distribution of polymorphism information content (PIC) values of the SSR markers used for genotyping the 30 studied varieties.

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Figure 1. Distribution of polymorphism information content (PIC) values of the SSR markers used for genotyping the 30 studied varieties.
Molecules 19 01939 g001 1024

The number of loci per SSR and the PIC values in our study were low. Generally, it was believed that these indexes for genomic-derived SSRs were significantly higher than EST-SSRs, as indicated by the reports on flax [28], wheat [29], levant cotton [30], sunflower [31] and sugar beet [32]. The lower polymorphism of EST-SSR markers than genomic SSRs was likely due to the conserved nature of genome coding regions [33]. However, it has been reported in some other studies on sorghum [34] and apple [35] that EST-SSR markers have greater discriminating power than the genomic SSRs. The higher average number of alleles per EST-SSR marker reported may be primarily attributed to the difference of species used or the selection of multiple-locus SSRs or compound SSRs, since we usually believe that single-locus SSRs provided less polymorphism. In addition, genotypes may also influence the number of alleles detected at each SSR locus.

Based on the polymorphic marker data, we made an analysis of the observed heterozygosity (Ho) and expected heterozygosity (He). The former varied from 0 to 0.73 (mean 0.13), while the latter varied from 0.07 to 0.68 (mean 0.33). The mean Ho and He values were similar to the previous results of 0.14 and 0.36, respectively [27]. The distribution of He values showed that 85.48% of the markers were in the range from 0 to 0.3, which was a very low heterozygosity (Figure 2). The fact that observed heterozygosity was lower than expected may be due to the small sample size or the results of inbreeding.

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Figure 2. Distribution of the estimate of genetic heterozygosity (He).

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Figure 2. Distribution of the estimate of genetic heterozygosity (He).
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2.3. Analysis of Molecular Variance (AMOVA)

AMOVA analysis indicated that approximately 35% of the genetic diversity was within individuals, 43% among individuals, and the remaining 22% among horticultural types (Table 3). This was consistent with the findings from other organisms like faba bean [36], grape [37], Haematococcus pluvialis [38], olive [39], apple [35] and lettuce [22] showing that considerable genetic diversity was partitioned within, rather than among populations. On the contrary, low levels of genetic diversity within populations and significant genetic differentiation among populations were detected in Omphalogramma souliei, barely and Chinese-grown pecan [40,41,42].

Table Table 3. Analysis of molecular variance (AMOVA).

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Table 3. Analysis of molecular variance (AMOVA).
Source of variationPercentage of variationp-value
Among horticultural types22%0.001
Among Individuals43%0.001
Within Individuals35%

We also calculated pairwise differentiation (Fst) for all pairs of horticultural types with at least two accessions per type (Table 4). The variation of the Fst values ranged from 0.086 to 0.261. Obviously, differentiation between celery and the other two types were significantly higher than that between local celery and the middle type.

Table Table 4. Pairwise differentiation (Fst) among horticultural types.

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Table 4. Pairwise differentiation (Fst) among horticultural types.
Horticultural typeLocal celeryCeleryMiddle type
Local celery-
Celery0.231-
Middle type0.0860.261-

2.4. PCA Analysis

The PCA results revealed that accessions of the same horticultural types clustered together. Accessions of local celery were well separated from those of celery. The middle type celery accessions were scattered among celery accessions and were closer to celery cluster than local celery cluster (Figure 3).

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Figure 3. Principal component analysis (PCA) of the 30 accessions genotyped with 62 polymorphic SSRs. Red, local celery; Green, celery; Blue, the middle type; Yellow, celeriac; Pink, the wild type.

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Figure 3. Principal component analysis (PCA) of the 30 accessions genotyped with 62 polymorphic SSRs. Red, local celery; Green, celery; Blue, the middle type; Yellow, celeriac; Pink, the wild type.
Molecules 19 01939 g003 1024

PCA analysis unambiguously separated the wild and the var. rapaceum (celeriac species) from var. dulce (cultivated celery). Compared to the wild species, var. rapaceum was closer to var. dulce, which may be due to the fact that the studied var. rapaceum belonged to cultivars. The observed distances between wild species, celeriac species and var. dulce accessions corresponded to the sexual compatibility of the two species with var. dulce accessions, which can be further proved by the fact that marker transfer rate was 100% in var. rapaceum, but only 54.84% in wild type. What’s more, both celeriac and wild species were the closest to the cluster of celery accessions, but the most distant from the cluster of local celery accessions. This result was supported by the differences in their origins.

2.5. Genetic Distance Analysis

The average Nei genetic distance for the 29 accessions of var. dulce and var. rapaceum was 0.34, with a range from 0.26 (C123) to 0.72 (C1). The largest genetic distance (0.72) was between C1 and C83, while the least genetic distance of 0.02 was found between C29 and C97. The average genetic distance of wild species was 2.42 with a range from 2.06 to 3.22, which was much larger than cultivated accessions (less than 0.7). Overall, seventy percent of the genetic distance between any two cultivars was no more than 0.4 and only thirty percent were larger than 0.4 (Figure 4). These results suggested that genetic variation within Apium graveolens was limited, while the wild species had wider genetic diversity and could serve as a valuable resource.

2.6. Cluster and Population Structure Analysis

In order to see the relationship of the materials used in this study, a dendrogram was constructed from the pairwise distance matrices (Figure 5). UPGMA cluster analysis indicated that at the genetic distance of 0.38, cultivated and wild species were separated. The statistical analysis based on the allele frequencies separated most of the cultivars, both in the trees (Figure 5) and in the PCA (Figure 3), into two main clusters corresponding to the geographical areas where they originated. At the distance of 0.72, most local celery accessions formed a cluster and all celery formed a large cluster with three local varieties scattered in. What’s more, the three middle type accessions (C58, C99, and C159) were well clustered together.

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Figure 4. Distribution of genetic distance values obtained from pairwise comparisons of 29 cultivars using SSR marker data.

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Figure 4. Distribution of genetic distance values obtained from pairwise comparisons of 29 cultivars using SSR marker data.
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Figure 5. Dendrogram of 30 accessions based on SSR marker data generated from Nei’s genetic distance matrix by UPGMA in NTSYSpc 2.11a. LC: local celery; C: celery; M: middle type.

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Figure 5. Dendrogram of 30 accessions based on SSR marker data generated from Nei’s genetic distance matrix by UPGMA in NTSYSpc 2.11a. LC: local celery; C: celery; M: middle type.
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We also estimated the number of genetic clusters of 30 accessions using Structure software without specifying prior information concerning sample class and allowing for admixed individuals. In order to choose an appropriate value of K for modeling the data, we ran a series of independent runs of the data at a range of values of K from 1 to 7.

When K ranged from 2 to 7, the wild species were separated from the cultivated species and when K was larger than 3, the wild species stood alone. When K = 3, three populations were obtained (Figure 6).

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Figure 6. Bar plot of population structure estimates for 30 Apium varieties by SSR markers. Each accession is represented by a single vertical bar broken into three colored segments, with lengths proportional to Q of the three inferred populations (K = 3). The sum of Q values for each bar is 1. Classes of the materials are shown at the top.

Click here to enlarge figure

Figure 6. Bar plot of population structure estimates for 30 Apium varieties by SSR markers. Each accession is represented by a single vertical bar broken into three colored segments, with lengths proportional to Q of the three inferred populations (K = 3). The sum of Q values for each bar is 1. Classes of the materials are shown at the top.
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The first population mainly comprised local celery accessions. The second population contained all celery accessions, the celeriac accessions and three local celery varieties. The third population only included the wild species. This structure was identical with the obtained dendrogram and supported the accuracy of the clustering.

2.7. Genotypic Diversity

Genotypic diversity is defined as the probability that two individuals taken at random have different genotypes. This value is 0 if every individual is the same, and 1 if every individual is different. We used the Multilocus program to calculate the number of different genotypes and the genotypic diversity on the set of 30 accessions. On average five markers were needed to identify 50% of genotypes, 14 markers to identify 90% of genotypes, and 29 markers to identify 99% of genotypes (Figure 7). Our analysis showed that any combinations of 55 SSR markers were able to distinguish genotypes of all 30 accessions unambiguously. This was a relatively high number of markers that were needed for genotyping.

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Figure 7. Effect of the increasing number of SSR markers on the estimate of genotyping diversity. Circles indicate genotypic diversity of 50%, 90%, 99%, and 100%, respectively. The value of 100% was reached with 55 and more markers.

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Figure 7. Effect of the increasing number of SSR markers on the estimate of genotyping diversity. Circles indicate genotypic diversity of 50%, 90%, 99%, and 100%, respectively. The value of 100% was reached with 55 and more markers.
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For example, 32 SSR markers were sufficient to distinguish genotypes of all 36 lettuce accessions [22], only 17 SSR markers on average were required to identify 54 sugar beet hybrid varieties [43] and eight SSR markers were enough to distinguish 35 asparagus varieties [44]. In general, genetic similarity among accessions of the same type is high. So it is more difficult to distinguish closely related or less diverse materials. In this study, a total of 50 unique genotypes specific to some accessions were identified by different markers. The number of unique genotype indentified by one primer ranged from 1 to 4. Of these 50 unique genotypes, 23 (46%) exclusively presented in wild species, suggesting the low diversity of the materials used. Therefore more molecular markers were needed to distinguish these closely related materials with high genetic similarity. In addition, the polymorphism of the markers was another important factor affecting whether we can distinguish more genotypes or not. So it is a must to develop higher polymorphic markers to distinguish accessions more efficiently.

3. Experimental

3.1. Plant Materials and DNA Isolation

A set of 30 accessions (Table 1) was used to test polymorphism of the developed SSR markers. This set comprised 28 common cultivars, one celeriac and one wild species. All materials were grown at the experimental station of China Agricultural University (Beijing, China). Genomic DNA was extracted from celery tender leaves using a modified version of the cetyltrimethylammonium bromide (CTAB) method [45]. Quality of DNA was checked by electrophoresis in 1% agarose gel. The genomic DNA was diluted 10-fold for PCR analysis.

3.2. Development of Genic SSR Markers and Genotyping with Markers

The SSR markers were developed through celery transcripotme sequencing [27]. Primers were designed using Primer 3 [46] with default parameters and synthesized at Sangon Biotech Co., Ltd. (Shanghai, China). PCR amplifications were conducted in a final volume of 10 μL containing 3.5 μL 2× Taq PCR MasterMix (Beijing Biomed Co., Ltd., Beijing, China), 4.5 μL double distilled (dd) H2O, 0.5 μL of each primer (5 μM) and 1 μL of template (aprox. 20 ng/μL). PCR was performed as follows: denaturation at 94 °C for 5 min, followed by 38 cycles of 30 s at 94 °C, 30 s at Tm (annealing temperature), 1 min at 72 °C and a final step at 72 °C for 10 min. PCR products were firstly detected by agarose gel electrophoresis and the products possessing single band or only a few bands were subjected to 7% polyacrylamide gel to separate alleles. With regard to those had no bands or multiple bands, we optimized the PCR condition to get better products for separation of alleles. PCR products were mixed with a volume of loading buffer and then denatured at 95 °C for 10min before being loaded on the polyacrylamide gel.

3.3. Analysis of Marker Polymorphism and Genetic Heterozygosity

SSR alleles were scored manually starting from the smallest to the largest-sized bands. The presence or absence of each single fragment was coded as 1 or 0, respectively, and scored for a binary data matrix. Scored data from polymorphic loci were used to calculate the polymorphism information content (PIC) according to Equation (1):

[PIC = 1 − ∑pi2]
where pi is the frequency of ith allele for each locus [47]. Observed heterozygosity (Ho) and expected heterozygosity (He) were calculated using the Popgene software version 1.31 [48]. Ho represents the estimated proportion of observed heterozygotes at a given locus for co-dominant markers. He, estimated using the Levene algorithm [49], represents the estimated proportion of expected heterozygotes under random mating for co-dominant markers.

3.4. AMOVA and PCA Analysis

Analysis of molecular variance (AMOVA) [50] between all the pairs of horticultural types with at least two accessions, and principal components analysis (PCA) of all accessions were performed using GenAlEx 6.5 [51].

3.5. Genetic Diversity and Population Structure Analysis

A genetic similarity matrix was constructed and Nei’s genetic distance [52] was calculated for each pair of all accessions using the NTSYSpc 2.1 software [53]. Unweighted pair group method with arithmetic mean (UPGMA) cluster analysis was performed to develop a dendrogram. Population structure was analyzed using the free software package STRUCTURE 2.3.4 [54,55,56]. A model without prior population information was used to assign individuals to populations.

3.6. Identification of Genotypes

In order to see whether scoring more loci is likely to increase the genotypic diversity, or whether one has reached a plateau, we used the software MultiLocus ver. 1.3b [57] to estimate the number of different genotypes that can be identified in a set of 30 accessions with a gradually increasing number of markers. The program randomly sampled from 1 to m−1 loci from the dataset and calculated the number of different genotypes identified.

4. Conclusions

This was the first attempts at celery genetic and genotypic diversity analysis using SSR markers developed from transcriptome sequencing. The AMOVA analysis indicated that the largest part of genetic diversity was within populations, while genetic diversity found among populations was low. The geneetic distance of wild species was much larger than that of cultivated accessions, suggesting the wider genetic diversity of the wild species, while the diversity within cultivars was quite limited. PCA analysis revealed that accessions of the same horticultural types were well clustered together. The UPGMA dendrogram and population structure clearly separated wild species from cultivars, and further divided the cultivars into two clusters, corresponding to the geographical areas from where they originated. Genotypic diversity analysis suggested that 29 markers were needed to identify 99% of genotypes and any combinations of 55 SSR markers were able to distinguish genotypes of all 30 accessions. Given that the genetic similarity of commonly used accessions was high, we need to develop more and higher polymorphic markers to efficiently distinguish closely related varieties. This study would provide a common ground for celery accessions identification, breeding and protection of breeders’ rights.

Acknowledgments

This study was funded by Chinese Universities Scientific Fund (No. 2013QJ094). The authors thank all our lab mates for the field sampling and DNA extraction.

Conflicts of Interest

The authors declare no conflict of interest.

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  • Sample Availability: All samples are available from the authors.
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