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. 2016 Jan 7;21(1):67. doi: 10.3390/molecules21010067

Cross-Species, Amplifiable EST-SSR Markers for Amentotaxus Species Obtained by Next-Generation Sequencing

Chiuan-Yu Li 1,2,†, Tzen-Yuh Chiang 3,†, Yu-Chung Chiang 4,†, Hsin-Mei Hsu 5, Xue-Jun Ge 6, Chi-Chun Huang 7, Chaur-Tzuhn Chen 5, Kuo-Hsiang Hung 2,*
Editor: Derek J McPhee
PMCID: PMC6273106  PMID: 26751439

Abstract

Amentotaxus, a genus of Taxaceae, is an ancient lineage with six relic and endangered species. Four Amentotaxus species, namely A. argotaenia, A. formosana, A. yunnanensis, and A. poilanei, are considered a species complex because of their morphological similarities. Small populations of these species are allopatrically distributed in Asian forests. However, only a few codominant markers have been developed and applied to study population genetic structure of these endangered species. In this study, we developed and characterized polymorphic expressed sequence tag-simple sequence repeats (EST-SSRs) from the transcriptome of A. formosana. We identified 4955 putative EST-SSRs from 68,281 unigenes as potential molecular markers. Twenty-six EST-SSRs were selected for estimating polymorphism and transferability among Amentotaxus species, of which 23 EST-SSRs were polymorphic within Amentotaxus species. Among these, the number of alleles ranged from 1–4, the polymorphism information content ranged from 0.000–0.692, and the observed and expected heterozygosity were 0.000–1.000 and 0.080–0.740, respectively. Population genetic structure analyses confirmed that A. argotaenia and A. formosana were separate species and A. yunnanensis and A. poilanei were the same species. These novel EST-SSRs can facilitate further population genetic structure research of Amentotaxus species.

Keywords: Amentotaxus, endangered species, EST-SSR markers, genetic diversity, transcriptome

1. Introduction

Amentotaxus (Taxaceae) represents an ancient evolutionary plant lineage and includes six relic species [1]. Currently, Amentotaxus species are locally distributed in Taiwan, from southwest China to Assam in the eastern Himalayas, and in southern Vietnam [2]. Amentotaxus argotaenia (Hance) Pilg., A. formosana H. L. Li, A. poilanei (Ferré & Rouane) D. K. Ferguson, and A. yunnanensis H. L. Li are considered a species complex; yet, the presence of intermediate morphotypes [3] poses some taxonomic challenges. While A. argotaenia and A. yunnanensis have been reported to have 2n = 36 chromosomes, the number of chromosomes in A. formosana and A. poilanei remains unknown [4]. There is little information about Amentotaxus genome size; only the nuclear DNA content of A. yunnanensis has been estimated as 2C = 60.40 [5]. In ecologic terms, these four species have a small population size and are allopatrically distributed in subtropical Asian forests. According to the conservation status assigned by the International Union for the Conservation of Nature [6], A. argotaenia is near threatened (NT), while A. formosana, A. yunnanensis, and A. poilanei are vulnerable (VU). Human overexploitation and development of the lumber industry have resulted in habitat loss and a continuous population decline of these four species. Therefore, governments must develop conservation strategies for providing a sustainable environment for the endangered Amentotaxus species. Population genetic structure studies have used inter-simple sequence repeat (ISSR), organellar DNA, and genomic SSR analyses to evaluate low genetic diversity within species and considerable genetic differentiation among populations or species [7,8]. Molecular markers are now widely employed for assessing genetic patterns or population genetic structure, and are vital for the management of threatened and endangered species. Such genetic information can facilitate the development of more efficient conservation strategies [9].

Expressed sequence tag-simple sequence repeats (EST-SSRs) differ from, and have several advantages over traditional genomic SSR markers, such as lower development cost and higher transferability across related species [10]. Moreover, the flanking sequences of EST-SSRs are located in well-conserved regions of transcribed genes from phylogenetically related species, permitting a high level of transferability [11,12,13]. EST-SSRs have been extensively used for quantifying genetic diversity and population genetic structure of plants [14]. While the use of microsatellites as species-specific markers is expensive, its cost effectiveness has benefited from the development of next-generation sequencing techniques. These enable the straightforward detection and characterization of SSR loci through highly parallel sequencing processes. Random sequencing approaches for identifying microsatellites are rapid and effective and can identify numerous useful and polymorphic microsatellites in unstudied or understudied species [15,16,17].

Although phylogeographic patterns of Amentotaxus species have already been assessed by ISSR, organellar DNA, and genomic SSR marker analyses [7,8], EST-SSR markers developed in this study provide an additional and distinct solution for investigating population genetic structure of Amentotaxus species. We applied a next-generation sequencing method for developing SSR markers from the transcriptome of A. formosana. Our objectives were as follows: (1) assess the frequency distribution of microsatellite motifs in the transcriptome of A. formosana; (2) examine the transferability of EST-SSR markers to three other Amentotaxus species; and (3) estimate the extent of genetic diversity and differentiation among Amentotaxus species using the developed EST-SSR markers. The ensuing genetic information can help develop conservation strategies for these threatened taxa.

2. Results and Discussion

2.1. Frequency Distribution of Various SSRs in the Transcriptome

The assembled unigenes from the transcriptome of A. formosana were screened for the presence of EST-SSRs, using the SSR Locator software. In total, 4955 putative EST-SSRs were identified from 68,281 unigenes (≥300 bp) with perfect di-, tri-, tetra-, penta-, and hexanucleotide motifs. Dinucleotide repeats constituted the largest group of repeat motifs, accounting for more than half of the total EST-SSR content (62.54%), followed by tri- (31.95%), hexa- (2.52%), tetra- (1.82%), and pentanucleotide (1.17%) repeats (Figure 1). Di- and trinucleotide repeats formed a large proportion of EST-SSRs, whereas the remaining repeats constituted 5.51% of EST-SSRs.

Figure 1.

Figure 1

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Frequency distributions of various Amentotaxus formosana EST-SSRs with different numbers of repeats.

The number of EST-SSR repeats ranged widely from 5–15, 5–12, 5–9, 5–11, and 5–7 in di-, tri-, tetra-, penta-, and hexanucleotides, respectively. EST-SSR frequencies of dinucleotide repeats decreased stepwise with an increase in motif length. Among various SSRs, AT/AT accounted for the highest proportion (42.63%) of total dinucleotide repeats, followed by AG/CT (38.24%), AC/GT (18.94%), and CG/CG (0.19%) (Figure 2a). Among trinucleotide repeats, AAG/CTT was the most abundant (23.50%), followed by AGG/CCT (16.49%); while AAC/GTT, ACC/GGT, ACG/CGT, ACT/AGT, and CCG/CGG, accounted for <10% each (Figure 2b). By contrast, Ranade et al. [18] indicated the most abundant motif among angiosperms and gymnosperms was AG/CT. AT/AT is the most common repeat motif in other gymnosperms, such as Pinus massoniana [19], P. dabeshanensis [20], P. densiflora [21], and Cryptomeria japonica [22].

Figure 2.

Figure 2

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Number of various (a) di- and (b) trinucleotide repeat motifs in Amentotaxus formosana.

2.2. EST-SSR Marker Polymorphism and Cross-Species Transferability

Of the EST-SSR markers identified in the transcriptome of A. formosana, we selected 26 primer pairs to assess polymorphisms and transferability across Amentotaxus species (Table 1 and Table S1). All 26 primer pairs were successfully amplified to match the expected size using an initial repetitive DNA fragment. Twenty-three primer pairs were polymorphic among species. Amen24, Amen25, and Amen42 primer pairs were monomorphic in all four Amentotaxus species. The transferability of EST-SSR markers across Amentotaxus species was assessed by screening for the selected 26 EST-SSR markers. All 26 markers were transferable between Amentotaxus species. In total, 23 of the 26 EST-SSR markers were used to further analyze polymorphism information content (PIC), Hardy-Weinberg equilibrium (HWE), and genetic diversity of each Amentotaxus species. PIC values of the 23 polymorphic primer pairs ranged from 0.000 to 0.623 (mean = 0.169 ± 0.145) in A. formosana. PIC values were higher in A. argotaenia (mean = 0.328 ± 0.087) than in A. poilanei (mean = 0.291 ± 0.135) and A. yunnanensis (mean = 0.291 ± 0.184) (Table 1). In this study, transferability of polymorphic markers across Amentotaxus species was 100%. Furthermore, a high transferability of genomic SSRs was detected across Amentotaxus species [23,24]. The cross-amplification ratio varied among plants. Transferability was higher across Amentotaxus than Taxus [25] and Pinus [26] species, where it was 80% and 60%–80%, respectively. These results confirmed that the high cross-species transferability of the microsatellite markers developed in A. formosana can be used in related Amentotaxus species in order to assess genetic diversity and population genetic structure. The 23 polymorphic EST-SSR markers demonstrated low (PIC < 0.25) and moderate (0.5 > PIC > 0.25) polymorphisms in A. formosana and the other Amentotaxus species, respectively. A low EST-SSR polymorphism was also detected in P. dabeshanensis [20]. However, polymorphism of EST-SSRs was lower than that of genomic SSRs reported by Ko [24], indicating the occurrence of a typical low to medium polymorphism in the current study. Indeed, PIC values were 0.640, 0.614, 0.250, and 0.300 in A. argotaenia, A. formosana, A. poilanei, and A. yunnanensis, respectively [24]. The lower level of polymorphism of EST-SSRs compared to genomic SSRs may result from a higher level of conservation among expressed genes [27].

Table 1.

Characterization of the selected 26 EST-SSRs from four Amentotaxus species.

A. argotaenia A. formosana A. yunnanensis A. poilanei
Locus A Ho He PIC HWE p-value A Ho He PIC HWE p-value A Ho He PIC HWE p-value A Ho He PIC HWE p-value
Amen03 2.000 0.000 0.153 0.141 0.001 1.000 0.000 0.000 0.000 - 3.000 0.182 0.517 0.422 0.057 2.000 0.286 0.245 0.215 0.659
Amen04 2.000 0.167 0.500 0.375 0.021 1.000 0.000 0.000 0.000 - 3.000 0.417 0.344 0.307 0.842 2.000 0.429 0.337 0.280 0.471
Amen07 2.000 0.250 0.219 0.195 0.621 1.000 0.000 0.000 0.000 - 2.000 0.167 0.153 0.141 0.753 1.000 0.000 0.000 0.000 -
Amen10 2.000 0.167 0.375 0.305 0.054 1.000 0.000 0.000 0.000 - 2.000 0.100 0.375 0.305 0.020 2.000 0.833 0.486 0.368 0.080
Amen13 2.000 0.909 0.496 0.373 0.006 4.000 0.667 0.543 0.480 0.003 4.000 0.917 0.740 0.692 0.000 2.000 1.000 0.500 0.375 0.008
Amen15 3.000 0.500 0.406 0.371 0.721 1.000 0.000 0.000 0.000 - 2.000 0.500 0.375 0.305 0.248 3.000 0.286 0.255 0.240 0.978
Amen18 2.000 0.083 0.080 0.077 0.880 2.000 0.167 0.375 0.305 0.174 1.000 0.000 0.000 0.000 - 2.000 0.143 0.133 0.124 0.839
Amen19 3.000 0.333 0.500 0.449 0.000 2.000 0.000 0.219 0.195 0.005 1.000 0.000 0.000 0.000 - 1.000 0.000 0.000 0.000 -
Amen20 2.000 0.417 0.413 0.328 0.977 2.000 0.000 0.153 0.141 0.001 1.000 0.000 0.000 0.000 - 3.000 0.000 0.571 0.501 0.003
Amen24 1.000 0.000 0.000 0.000 - 1.000 0.000 0.000 0.000 - 1.000 0.000 0.000 0.000 - 1.000 0.000 0.000 0.000 -
Amen25 1.000 0.000 0.000 0.000 - 1.000 0.000 0.000 0.000 - 1.000 0.000 0.000 0.000 - 1.000 0.000 0.000 0.000 -
Amen26 2.000 0.333 0.278 0.239 0.488 2.000 0.083 0.080 0.077 0.880 3.000 0.583 0.434 0.369 0.565 3.000 0.857 0.541 0.453 0.268
Amen27 2.000 0.000 0.500 0.375 0.001 2.000 0.000 0.278 0.239 0.001 1.000 0.000 0.000 0.000 - 3.000 0.286 0.653 0.580 0.066
Amen30 4.000 0.750 0.670 0.606 0.562 2.000 0.000 0.494 0.372 0.003 4.000 0.500 0.618 0.562 0.174 3.000 0.429 0.449 0.406 0.497
Amen35 2.000 0.000 0.444 0.346 0.001 2.000 0.000 0.165 0.152 0.001 3.000 0.250 0.226 0.212 0.970 1.000 0.000 0.000 0.000 -
Amen38 2.000 0.091 0.351 0.290 0.014 2.000 0.000 0.298 0.253 0.001 1.000 0.000 0.000 0.000 - 3.000 0.429 0.357 0.325 0.914
Amen40 2.000 0.636 0.434 0.340 0.122 1.000 0.000 0.000 0.000 - 3.000 0.250 0.538 0.432 0.133 3.000 0.429 0.500 0.427 0.808
Amen41 3.000 0.667 0.486 0.424 0.392 1.000 0.000 0.000 0.000 - 4.000 0.714 0.541 0.502 0.904 2.000 0.857 0.490 0.370 0.047
Amen42 1.000 0.000 0.000 0.000 - 1.000 0.000 0.000 0.000 - 1.000 0.000 0.000 0.000 - 1.000 0.000 0.000 0.000 -
Amen43 4.000 0.500 0.535 0.498 0.075 2.000 0.100 0.255 0.222 0.055 3.000 0.545 0.533 0.432 0.838 3.000 0.714 0.602 0.523 0.214
Amen44 2.000 0.167 0.278 0.239 0.166 2.000 0.000 0.486 0.368 0.001 4.000 0.667 0.517 0.482 0.809 2.000 0.571 0.408 0.325 0.290
Amen46 2.000 0.083 0.469 0.359 0.004 1.000 0.000 0.000 0.000 - 2.000 0.182 0.165 0.152 0.740 2.000 0.429 0.337 0.280 0.471
Amen47 2.000 0.364 0.397 0.318 0.782 1.000 0.000 0.000 0.000 - 4.000 0.200 0.575 0.526 0.034 2.000 0.286 0.245 0.215 0.659
Amen48 2.000 0.000 0.500 0.375 0.001 2.000 0.200 0.180 0.164 0.725 1.000 0.000 0.000 0.000 - 2.000 0.000 0.490 0.370 0.008
Amen50 3.000 0.000 0.406 0.371 0.001 2.000 0.000 0.375 0.305 0.046 3.000 0.000 0.594 0.511 0.001 2.000 0.000 0.375 0.305 0.046
Amen52 2.000 0.000 0.165 0.152 0.001 4.000 0.000 0.678 0.623 0.000 2.000 0.000 0.444 0.346 0.001 1.000 0.000 0.000 0.000 -
Mean a 2.348 ± 0.198 0.279 ± 0.057 0.394 ± 0.029 0.328 ± 0.087 - 1.783 ± 0.177 0.053 ± 0.030 0.1993 ± 0.044 0.1693 ± 0.145 - 2.478 ± 0.234 0.2683 ± 0.058 0.3343 ± 0.051 0.291 ± 0.184 - 2.174 ± 0.149 0.359 ± 0.067 0.347 ± 0.043 0.291 ± 0.135 -
Mean b 2.167 ± 0.090 0.250 ± 0.064 0.370 ± 0.028 0.317 ± 0.070 - 1.778 ± 0.222 0.053 ± 0.038 0.175 ± 0.051 0.152 ± 0.145 - 2.556 ± 0.258 0.258 ± 0.067 0.342 ± 0.057 0.300 ± 0.177 - 2.000 ± 0.162 0.348 ± 0.083 0.343 ± 0.051 0.281 ± 0.140 -
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a for 23 polymorphic EST-SSRs; b for 18 polymorphic and neutral EST-SSRs; HWE: Hardy-Weinberg equilibrium.

2.3. Genetic Diversity and Population Genetic Structure Analyses

Based on 23 polymorphic EST-SSRs, we assessed standard genetic diversity parameters, such as the number of alleles per locus (A), and observed (Ho) and expected (He) levels of heterozygosity, for each of the four taxa (Table 1). Overall, the number of alleles per locus ranged from 1–4 in A. formosana (mean = 1.783 ± 0.177), A. argotaenia (mean = 2.348 ± 0.198), and A. yunnanensis (mean = 2.478 ± 0.234), and from 1–3 in A. poilanei (mean = 2.174 ± 0.149). All 23 EST-SSRs were polymorphic in A. argotaenia; while 14, 17, and 19 EST-SSRs were polymorphic in A. formosana, A. yunnanensis and A. poilanei, respectively. Ho and He levels ranged between 0.000–0.667 and 0.000–0.678, respectively, in A. formosana; 0.000–0.909 and 0.080–0.670 in A. argotaenia; 0.000–0.917 and 0.000–0.740 in A. yunnanensis; and 0.000–1.000 and 0.000–0.653 in A. poilanei. The HWE test showed significant deviations for 11 and 10 EST-SSRs in A. argotaenia, and A. formosana, respectively; and 5 EST-SSRs in A. yunnanensis and A. poilanei. These could result from a deficiency of heterozygosity among Amentotaxus species.

The possibility of EST-SSR markers being located within functional genes, implies that the neutrality of EST-SSR markers observed in this study should not be assumed a priori. Non-neutral loci may bias population genetic structure analysis. Before proceeding any further, we used the LOSITAN software to identify possible outlier loci for all 23 polymorphic markers. Five outlier loci were identified (Figure 3): Amen15, Amen18, Amen30, Amen38, and Amen43. Because previous studies revealed that certain SSR markers were non-neutral, it is essential to conduct a neutrality test before applying SSR data to population genetic structure analysis [28,29,30]. Finally, we selected 18 polymorphic and neutral EST-SSR markers, with which to assess genetic diversity and population genetic structure among the four Amentotaxus species.

Figure 3.

Figure 3

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Assessment of outlier EST-SSR loci using LOSITAN software, which evaluated the expected distribution of the Wright inbreeding coefficient (Fst) and expected heterozygosity (He) by applying an island model of migration along with neutral markers. The five loci, Amen15, Amen18, Amen30, Amen38, and Amen43, were identified as outliers. Dots in the red and yellow areas represent candidate loci for positive or balanced selection.

The number of alleles per locus for the 18 polymorphic and neutral EST-SSRs was highest in A. yunnanensis (mean = 2.556 ± 0.258) and lowest in A. formosana (mean = 1.778 ± 0.222). In addition, the level of observed heterozygosity was highest in A. poilanei (mean= 0.348 ± 0.083) and lowest in A. formosana (mean = 0.053 ± 0.038). The levels of observed heterozygosity in all species were significantly lower than expected heterozygosity levels, and lower than those observed in other conifers [31,32], suggesting a deficiency of heterozygosity in Amentotaxus species. Generally, widespread species exhibit higher levels of genetic diversity than narrowly distributed species. The patterns of genetic variation are attributed to numerous evolutionary factors, such as founder effects, bottlenecks, and gene flows. Therefore, current population size may not be a reliable indicator for determining genetic diversity levels [33,34,35,36]. Nevertheless, Ge et al. [8] reported high levels of genetic diversity in A. poilanei and A. yunnanensis with small population sizes. Our results agree with other studies reporting high genetic diversity levels in a few threatened and endangered species, with a small population size and a narrow distribution [37,38,39].

We also examined population genetic structure at a finer resolution using STRUCTURE software [40]. Here, ΔK values computed for all classes indicated a strong signal for K = 3 (ΔK = 2.754). The proportions of each individual in each population were assigned to three clusters. For K = 3, the four Amentotaxus species were divided into three clusters, with A. yunnanensis and A. poilanei clustering together. Several individuals displayed an intermixed composition, which could be attributed to a historical gene flow between species (Figure 4). Furthermore, pairwise comparisons indicated significant genetic differentiation (Fst) between species (Fst = 0.134–0.315; p < 0.05). Consistent with STRUCTURE analysis results, A. formosana exhibited high genetic differentiation levels (Fst = 0.242–0.315), compared to the closely related A. yunnanensis and A. poilanei (Fst = 0.134) species, because of geographical isolation. Similar trends were observed by organellar DNA, genomic SSR, and ISSR fingerprinting analysis [7,8], confirming that both, A. argotaenia and A. formosana, could be considered as separate species, and A. yunnanensis and A. poilanei as the same species.

Figure 4.

Figure 4

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Genetic composition of Amentotaxus species in three clusters (K = 3) were detected through structure analyses by using the highest ΔK value.

3. Experimental Section

3.1. Plant Material and DNA Extraction

Twenty-four plant samples of A. formosana were collected from Chinshuiying (120°46′ E, 22°16′ N), Taiwan. In addition, we sampled 24 individuals of A. argotaenia from Suining, Hunan Province (110°10′ E, 26°29′ N, N = 12), and Datian, Fujian Province (117°48′ E, 25°40′ N, N = 12), China; 24 individuals of A. yunnanensis from Malipo (104°58′ E, 23°16′ N, N = 12) and Xichou (104°13′ E, 23°16′ N, N = 12), Yunnan Province, China; and seven individuals of A. poilanei from Lao Va Chai Municipality (105°04′ E, 23°06′ N), Vietnam. Samples for DNA extraction were dried in silica gel. Total genomic DNA was extracted using cetyltrimethylammonium bromide [41].

3.2. cDNA Preparation, Illumina Sequencing, and de Novo Assembly

Trizol reagent (Life Technologies, Carlsbad, CA, USA) was used for extracting total RNA from the leaves of whole young juveniles of A. formosana previously frozen in liquid nitrogen and ground into a powder. Total extracted RNA was purified using the RNeasy Mini RNA kit (QIAGEN, Hilden, Germany) according to the manufacturer’s instructions. mRNA was isolated from purified total RNA using Dynabeads (Life Technologies). The purity and quality of mRNA were verified. A cDNA library was constructed using the SMART™ cDNA library construction kit (Clontech, Mountain View, CA, USA). The synthesized cDNA was subjected to end-repair and phosphorylation, in which the repaired cDNA fragments were 3′-adenylated using Klenow 3′ to 5′ exopolymerase, and then ligated with an adapter using T4 DNA ligase. Finally, the cDNA library was constructed using a 200 bp insertion fragment excised from the gel. PCR primer pairs PE 1.0 and 2.0 (Illumina Inc., San Diego, CA, USA) were used for amplifying the cDNA fragments. The cDNA library was sequenced using the Illumina HiSeq™ 2000 (Illumina Inc.).

Quality trimming was performed to filter out poor quality or ambiguous data from raw sequencing reads. The reads from >10% of bases gave a poor quality score (Q < 20), and ambiguous sequences with an excess of N nucleotides were removed. We then applied SOAPdenovo software (http://soap.genomics.org.cn/soapdenovo.html) for a de novo assembly of the transcriptome using default settings, except for the K-mer value (default K = 29) [42]. To this end, we applied de Bruijn graphs to assemble the reads assigned to each gap and construct contigs without ambiguous bases. Paired-end libraries and connecting overlapping contigs were employed to build the scaffolds. Paired-end reads were used to fill in gaps and generate larger scaffolds, which were defined as unigenes. Finally, we used overlapping unigenes to assemble a large and continuous DNA sequence and screen for EST-SSR markers.

3.3. Detection of EST-SSR Markers and Primer Design

SSR Locator software [43] was applied to screen for SSR regions within 68,281 unigenes (>300 bp). Parameters were adjusted to identify the perfect di-, tri-, tetra-, penta-, and hexanucleotide motifs with a minimum of five and a maximum of 15 repeats. Next, 26 microsatellites were selected to determine transferability across species. We assumed that tri- or hexanucleotide motifs might possess a higher level of polymorphism than the other motifs. Therefore, most of the selected 26 microsatellites contained tri- or hexanucleotide motifs. EST-SSR primers were designed using Primer3 software [44], and primer sequences of the identified microsatellites are listed in Table S1.

3.4. DNA Amplification and Genotyping

The 26 selected microsatellites were examined for transferability and polymorphism among species. Microsatellites were subjected to PCR amplification in a 25-µL volume containing 10 ng genomic DNA, 0.2 mM dNTP, 2 mM MgCl2, and 5 pmol of each primer. Forward primers used for the PCR reaction were fluorescently labeled. PCR conditions were as follows: 3 min at 94 °C, 40 cycles of 30 s at 94 °C, 30 s at a primer-specific annealing temperature (Table S1), 30 s at 72 °C, and a final extension step of 5 min at 72 °C. PCR products were separated using an ABI 3100 automated sequencer, and fragment size was assessed using genemapper Version 3.7 software (Applied Biosystems, Foster City, CA, USA).

3.5. Data Analysis

After assessing transferability across species, standard genetic diversity parameters, A, Ho, and He, were calculated for polymorphic microsatellites using GenAlEx Version 6.5 software [45]. In addition, we conducted PIC and HWE tests on PowerMarker Version 3.25 [46] and GENEPOP Version 3.4 software [47]. A biased genetic diversity analysis may have resulted because of non-neutral loci. A pairwise comparison of the genetic differentiation (Fst) outlier test was performed using LOSITAN software [48,49] in order to identify candidate non-neutral SSR loci, which might have biased the genetic diversity analysis. The identified neutral SSR loci were used for further genetic diversity and population genetic analyses of each species. Fst among species was performed using GenAlEx Version 6.5 software [45]. Genetic composition of Amentotaxus species was examined using STRUCTURE Version 2.3.3 software [40,50,51]. structure applies a Bayesian method for inferring K values, without using prior information of individual sampling locations. Stability of the results was estimated by executing 10 separate runs with K = 1–5. Each run was pursued for 1,000,000 Markov chain Monte Carlo interactions with an initial burn-in of 100,000 and an ancestry model that allowed for admixture [52]. The most favorable grouping number (K) was determined by calculating ΔK [53] using STRUCTURE HARVESTER Version 0.6.8 software [54].

4. Conclusions

A total of 4955 EST-SSR markers were revealed when screening the transcriptome of A. formosana. The AT/AT motif was the most common repeat unit in A. formosana and other gymnosperms, such as P. massoniana, P. dabeshanensis, P. densiflora, P. dabeshanensis, and C. japonica. The identified polymorphic and neutral EST-SSR markers were used to assess genetic diversity and differentiation among Amentotaxus species. Population genetic structure analysis indicated that A. argotaenia and A. formosana were separate species and that A. yunnanensis and A. poilanei were the same species. In summary, the Amentotaxus species exhibited a higher level of genetic differentiation, except between A. yunnanensis and A. poilanei. The low level of genetic diversity estimated by EST-SSR markers is possibly caused by past fragmentation and recent habitat degradation. EST-SSR markers developed in this study can potentially be used for assessing genetic diversity and population genetic structure among Amentotaxus species.

Acknowledgements

This study was supported by the Ministry of Science and Technology, Taiwan (NSC 98-2321-B020-002; NSC 100-2621-B-020-002).

Supplementary Materials

Click here for additional data file. (642.7KB, pdf)

Supplementary materials can be accessed at: http://www.mdpi.com/1420-3049/21/1/67/s1.

Author Contributions

L.C.Y., C.T.Y., C.Y.C., and H.K.H. designed the experiments. L.C.Y., C.T.Y., C.Y.C., G.X.J., C.C.T., and H.C.C collected plant materials. L.C.Y. and H.X.M. performed the SSR experiments. C.Y.C., C.T.Y., and H.K.H. analyzed the data. L.C.Y., C.Y.C., C.T.Y., and H.K.H. drafted the manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

Footnotes

Sample Availability: Samples are available from the authors.

References

  • 1.Farjon A. A Handbook of the World’s Conifers. Brill; Leiden, The Netherlands: 2010. [Google Scholar]
  • 2.Royer D.L., Hickey L.J., Wing S.L. Ecological conservatism in the “living fossil” Ginkgo. Paleobiology. 2003;29:84–104. doi: 10.1666/0094-8373(2003)029&#x0003c;0084:ECITLF&#x0003e;2.0.CO;2. [DOI] [Google Scholar]
  • 3.Zhou Q.X. Ph.D. Thesis. Kunming Institute of Botany: Kunming; China: 2001. Studies on Systematics of the Taxaceae. [Google Scholar]
  • 4.Zhou Q.X., Gu Z.J., Yue Z.S. Karyomorphology and relationships of Amentotaxus. Pilg. Acta Phytotaxon. Sin. 2000;38:522–527. [Google Scholar]
  • 5.Zonneveld B.J.M. Genome sizes of 172 species, covering 64 out of the 67 genera, range from 8 to 72 picogram. Nord. J. Bot. 2012;30:490–502. doi: 10.1111/j.1756-1051.2012.01516.x. [DOI] [Google Scholar]
  • 6.The IUCN Red List of Threatened Species. [(accessed on 2 November 2015)]. Version 2013.2. Available online: http://www.iucnredlist.org.
  • 7.Ge X.J., Zhou X.L., Li Z.C., Hsu T.W., Schaal B.A., Chiang T.Y. Genetic structure of the relict Amentotaxus argotaenia complex (Taxaceae) based on ISSR fingerprinting. J. Plant Res. 2005;118:415–422. doi: 10.1007/s10265-005-0235-1. [DOI] [PubMed] [Google Scholar]
  • 8.Ge X.J., Hung K.H., Ko Y.Z., Hsu T.W., Gong X., Chiang T.Y., Chiang Y.C. Genetic Divergence and Biogeographical Patterns in Amentotaxus argotaenia Species Complex. Plant. Mol. Biol. Rep. 2015;33:264–280. doi: 10.1007/s11105-014-0742-0. [DOI] [Google Scholar]
  • 9.Rao V.R., Hodgkin T. Genetic diversity and conservation and utilization of plant genetic resources. Plant. Cell. Tissue Organ Cult. 2002;68:1–19. [Google Scholar]
  • 10.Mian M.A.R., Saha M.C., Hopkins A.A., Wang Z. Use of tall fescue EST-SSR markers in phylogenetic analysis of cool-season forage grasses. Genome. 2005;48:637–647. doi: 10.1139/g05-029. [DOI] [PubMed] [Google Scholar]
  • 11.Uchiyama K., Fujii S., Ishizuka W., Goto S., Tsumura Y. Development of 32 EST-SSR markers for Abies firma (Pinaceae) and their transferability to related species. Appl. Plant. Sci. 2013;1:1200464. doi: 10.3732/apps.1200464. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Chung J.W., Kim T.S., Suresh S., Lee S.Y., Cho G.T. Development of 65 Novel Polymorphic cDNA-SSR Markers in Common Vetch (Vicia sativa subsp. sativa) Using Next Generation Sequencing. Molecules. 2013;18:8376–8392. doi: 10.3390/molecules18078376. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Sato M., Hasegawa Y., Mishima K., Takata K. Isolation and characterization of 22 EST-SSR markers for the genus Thujopsis. (Cupressaceae) Appl. Plant. Sci. 2015;3:1400101. doi: 10.3732/apps.1400101. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Fageria M.S., Rajora O.P. Effects of harvesting of increasing intensities on genetic diversity and population structure of white spruce. Evol. Appl. 2013;6:778–794. doi: 10.1111/eva.12064. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Castoe T.A., Poole A.W., Gu W., de Koning A.P.J., Daza J.M., Smith E.N., Pollock D.D. Rapid identification of thousands of copperhead snake (Agkistrodon contortrix) microsatellite loci from modest amounts of 454 shotgun genome sequence. Mol. Ecol. Resour. 2010;10:341–347. doi: 10.1111/j.1755-0998.2009.02750.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Castoe T.A., Poole A.W., de Koning A.P.J., Jones K.L., Tomback D.F., Oyler-McCance S.J., Fike J.A., Lance S.L., Streicher J.W., Smith E.N., et al. Rapid Microsatellite Identification from Illumina Paired-End Genomic Sequencing in Two Birds and a Snake. PLoS ONE. 2015;10:0030953. doi: 10.1371/journal.pone.0136465. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Zalapa J.E., Cuevas H., Zhu H., Steffan S., Senalik D., Zeldin E., McCown B., Harbut R., Simon P. Using next-generation sequencing approaches to isolate simple sequence repeat (SSR) loci in the plant sciences. Am. J. Bot. 2012;99:193–208. doi: 10.3732/ajb.1100394. [DOI] [PubMed] [Google Scholar]
  • 18.Ranade S.S., Lin Y.C., Van de Peer Y., García-Gil M. Comparative in silico analysis of EST-SSRs in angiosperm and gymnosperm tree genera. BMC Plant. Biol. 2014;14:220. doi: 10.1186/s12870-014-0220-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Liu G., Ji K. Designing Pinus massoniana SSR Primers from Pinus. EST Sequences. Mol. Plant. Breed. 2009;7:833–838. [Google Scholar]
  • 20.Xiang X., Zhang Z., Wang Z., Zhang X., Wu G. Transcriptome sequencing and development of EST-SSR markers in Pinus dabeshanensis, an endangered conifer endemic to China. Mol. Breed. 2015;35:158. doi: 10.1007/s11032-015-0351-0. [DOI] [Google Scholar]
  • 21.Liu L., Zhang S., Lian C. De Novo Transcriptome Sequencing Analysis of cDNA Library and Large-Scale Unigene Assembly in Japanese Red Pine (Pinus densiflora) Int. J. Mole Sci. 2015;16:29047–29059. doi: 10.3390/ijms161226139. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Ueno S., Moriguchi Y., Uchiyama K., Ujino-Ihara T., Futamura N., Sakurai T., Shinohara K., Tsumura Y. A second generation framework for the analysis of microsatellites in expressed sequence tags and the development of EST-SSR markers for a conifer, Cryptomeria japonica. BMC Genom. 2012;13 doi: 10.1186/1471-2164-13-136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Ho C.S., Chen C.T., Ko Y.Z., Ge X.J., Hung K.H., Hsu T.W., Chiang Y.C. Isolation and characterization of 15 microsatellite loci in four endangered Amentotaxus species (Taxaceae) Am. J. Bot. 2012;99:e157–e159. doi: 10.3732/ajb.1100452. [DOI] [PubMed] [Google Scholar]
  • 24.Kuo Y.Z. Master’s Thesis. National Sun Yat-sen University; Kaohsiung, Taiwan: 2012. The Study of Populations Genetics and Conservation in Amentotaxus argotaenia Complex. [Google Scholar]
  • 25.Yang J.B., Li H.T., Li D.Z., Liu J., Gao L.M. Isolation and characterization of microsatellite markers in the endangered species Taxus wallichiana using the FIASCO method. HortScience. 2009;44:2043–2045. [Google Scholar]
  • 26.Guan L., Suharyanto, Shiraishi S. Isolation and characterization of tetranucleotide microsatellite loci in Pinus massoniana (Pinaceae) Am. J. Bot. 2011;98:e216–e217. doi: 10.3732/ajb.1100076. [DOI] [PubMed] [Google Scholar]
  • 27.Scott K.D., Eggler P., Seaton G., Rossetto M., Ablett E.M., Lee L.S., Henry R.J. Analysis of SSRs derived from grape ESTs. Theor. Appl. Genet. 2000;100:723–726. doi: 10.1007/s001220051344. [DOI] [Google Scholar]
  • 28.Li Y.C., Korol A.B., Fahima T., Nevo E. Microsatellites within genes: Structure; function; and evolution. Mol. Biol. Evol. 2004;21:991–1007. doi: 10.1093/molbev/msh073. [DOI] [PubMed] [Google Scholar]
  • 29.Ganopoulos I., Aravanopoulos F., Argiriou A., Tsaftaris A. Genome and population dynamics under selection and neutrality: An example of S-allele diversity in wild cherry (Prunus avium L.) Tree Genet. Genom. 2012;8:1181–1190. doi: 10.1007/s11295-012-0504-9. [DOI] [Google Scholar]
  • 30.Shen D.F., Bo W.H., Xu F., Wu R.L. Genetic diversity and population structure of the Tibetan poplar (Populus szechuanica var. tibetica) along an altitude gradient. BMC Genet. 2014;15 doi: 10.1186/1471-2156-15-S1-S11. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Moriguchi Y., Kang K.S., Lee K.Y., Lee S.W., Kim Y.Y. Genetic variation of Picea jezoensis populations in South Korea revealed by chloroplast, mitochondrial and nuclear DNA markers. J. Plant. Res. 2009;122:153–160. doi: 10.1007/s10265-008-0210-8. [DOI] [PubMed] [Google Scholar]
  • 32.Parchman T.L., Benkman C.W., Jenkins B., Buerkle C.A. Low levels of population genetic structure in Pinus. contorta (Pinaceae) across a geographic mosaic of co-evolution. Am. J. Bot. 2011;98:669–679. doi: 10.3732/ajb.1000378. [DOI] [PubMed] [Google Scholar]
  • 33.Dong Y.H., Chen J.M., Gituru R.W., Wang Q.F. Gene flow in populations of the endangered aquatic fern Ceratopteris pteridoides in China as revealed by ISSR markers. Aquat. Bot. 2007;87:69–74. doi: 10.1016/j.aquabot.2007.03.006. [DOI] [Google Scholar]
  • 34.Hamrick J.L., Godt M.J. Effects of life history traits on genetic diversity in plant species. Philos. Trans. R. Soc. Lond. Ser. B. 1996;351:1291–1298. doi: 10.1098/rstb.1996.0112. [DOI] [Google Scholar]
  • 35.Loveless M.D., Hamrick J.L. Ecological determinants of genetic structure of plant populations. Annu. Rev. Ecol. Syst. 1984;15:65–95. doi: 10.1146/annurev.es.15.110184.000433. [DOI] [Google Scholar]
  • 36.Bauert M.R., Kalin M., Baltisberger M., Edwards P.J. No genetic variation detected within isolated relic populations of Saxifraga. cernua in the Alps using RAPD markers. Mol. Ecol. 1998;7:1519–1527. doi: 10.1046/j.1365-294x.1998.00470.x. [DOI] [Google Scholar]
  • 37.Ci X.Q., Chen J.Q., Li Q.M., Li J. AFLP and ISSR analysis reveals high genetic variation and inter-population differentiation in fragmented populations of the endangered Litsea szemaois (Lauraceae) from Southwest China. Plant. Syst. Evol. 2008;273:237–246. doi: 10.1007/s00606-008-0012-4. [DOI] [Google Scholar]
  • 38.Gordon S.P., Sloop C.M., Davis H.G., Cushman J.H. Population genetic diversity and structure of two rare vernal pool grasses in central California. Conserv. Genet. 2012;13:117–130. doi: 10.1007/s10592-011-0269-y. [DOI] [Google Scholar]
  • 39.Zhao X.F., Ma Y.P., Sun W.B., Wen X., Milne R. High genetic diversity and low differentiation of Michelia coriacea (Magnoliaceae), a critically endangered endemic in southeast Yunnan, China. Int. Mol. Sci. 2012;13:4396–4411. doi: 10.3390/ijms13044396. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Pritchard J., Stephens M., Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–959. doi: 10.1093/genetics/155.2.945. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Doyle J.J., Doyle J.L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull. 1987;19:11–15. [Google Scholar]
  • 42.Li R., Fan W., Tian G., Zhu H., He L., Cai J., Huang Q., Cai Q., Li B., Bai Y., et al. The sequence and de novo assembly of the giant panda genome. Nature. 2010;463:311–317. doi: 10.1038/nature08696. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Da Maia L.C., Palmieri D.A., de Souza V.Q., Kopp M.M., de Carvalho F.I., Costa de Oliveira A. SSR locator: Tool for simple sequence repeat discovery integrated with primer design and pcr simulation. Int. J. Plant. Genom. 2008;2008 doi: 10.1155/2008/412696. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Untergrasser A., Cutcutache I., Koressaar T., Ye J., Faircloth B.C., Remm M., Rozen S.G. Primer3—New capabilities and interfaces. Nucleic Acids Res. 2012;40 doi: 10.1093/nar/gks596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Peakall R., Smouse P.E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics. 2012;28:2537–2539. doi: 10.1093/bioinformatics/bts460. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 46.Liu K., Muse S.V. PowerMarker: An integrated analysis environment for genetic marker analysis. Bioinformatics. 2005;21:2128–2129. doi: 10.1093/bioinformatics/bti282. [DOI] [PubMed] [Google Scholar]
  • 47.Raymond M., Rousset F. Genepop (version-1.2)—Population-Genetics Software for exact tests and ecumenicism. J. Hered. 1995;86:248–249. [Google Scholar]
  • 48.Antao T., Lopes A., Lopes R., Beja-Pereira A., Luikart G. LOSITAN: A workbench to detect molecular adaptation based on a Fst-outlier method. BMC Bioinform. 2008;9:323. doi: 10.1186/1471-2105-9-323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Beaumont M.A. Adaptation and speciation: What can FST tell us? Trends Ecol. Evol. 2005;20:435–440. doi: 10.1016/j.tree.2005.05.017. [DOI] [PubMed] [Google Scholar]
  • 50.Falush D., Stephens M., Pritchard J.K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics. 2003;164:1567–1587. doi: 10.1093/genetics/164.4.1567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Falush D., Stephens M., Pritchard J.K. Inference of population structure using multilocus genotype data: Dominant markers and null alleles. Mol. Ecol. Notes. 2007;7:574–578. doi: 10.1111/j.1471-8286.2007.01758.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 52.Hubisz M.J., Falush D., Stephens M., Pritchard J.K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 2009;9:1322–1332. doi: 10.1111/j.1755-0998.2009.02591.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 53.Evanno G., Regnaut S., Goudet J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 2005;14:2611–2620. doi: 10.1111/j.1365-294X.2005.02553.x. [DOI] [PubMed] [Google Scholar]
  • 54.Earl D.A., vonHoldt B.M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 2012;4:359–361. doi: 10.1007/s12686-011-9548-7. [DOI] [Google Scholar]

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