Isolation and characterization of microsatellite markers for an endemic tree in East Asia, Quercus variabilis (Fagaceae).

PREMISE OF THE STUDY: Microsatellite markers of Quercus variabilis (Fagaceae) were isolated for population genetic and landscape genetic studies. METHODS AND
RESULTS: Roche 454 pyrosequencing combined with the magnetic bead enrichment protocol were used to isolate microsatellite markers for Q. variabilis. A total of 2121 microsatellites were identified from 63,851 individual sequence reads. One hundred microsatellite loci were selected to test primer amplification efficiency among 24 individuals from two wild populations. Among the 100 tested markers, 34 primer pairs were successfully amplified. Of these, 14 yielded polymorphic amplification products, whereas the remaining 20 loci were monomorphic. The number of alleles for polymorphic loci ranged from two to six, and the observed heterozygosity ranged from 0.042 to 0.750.
CONCLUSIONS: These microsatellite loci will provide useful tools for further population genetic and landscape genetic studies on Q. variabilis.

Quercus variabilis Blume (Fagaceae) is a tree native to East Asia found mainly in warm-temperate and subtropical regions (Chen et al., 2012). Previous studies on this species have focused on its physiology and ecology (Lei et al., 2013; Du et al., 2014). To date, only a few studies have reported on the genetic diversity and population structure of this species using isozyme, chloroplast DNA, and microsatellite markers (Zhou et al., 2003; Xu et al., 2004; Chen et al., 2012). However, the polymorphic isozyme loci are limited in number and the chloroplast DNA is maternally inherited, which limits the comprehensive understanding of its genetic diversity and population structure. Xu et al. (2004) developed 16 microsatellite loci for Q. variabilis using primer pairs from three other Quercus L. species; however, these loci do not meet the requirement of high-density markers in landscape genetics (Hall and Beissinger, 2014). Thus, developing a greater number of microsatellite loci for this species is necessary for population genetic and landscape genetic studies of Q. variabilis to progress. Microsatellite markers, or simple sequence repeats (SSRs), are widely used in population genetic and landscape genetic studies, providing useful information to plant natural resources management and promoting future long-term strategies (Ohtsuki et al., 2014; Lei et al., 2015). Next-generation sequencing allows for rapid development of a large number of SSR markers (Huang et al., 2014; Kumar et al., 2014). Here, we report the development of microsatellite markers for Q. variabilis using Roche 454 pyrosequencing combined with a magnetic bead enrichment protocol.

METHODS AND RESULTS

Fresh leaves from 24 individuals of Q. variabilis were collected in two wild populations in Henan and Shandong provinces (Song Mountain: 34°28′08″N, 113°05′07″E; Meng Mountain: 35°29′34″N, 117°47′58″E) during May 2012. The samples were stored in silica gel until genomic DNA extraction and genotyping. Vouchers were deposited at the herbarium of Henan Agricultural University (Herbarium accession no. HHAU-2101-2112 [Song Mountain population] and HHAU-2113-2124 [Meng Mountain population]). Genomic DNA was extracted from approximately 30 mg of dried leaf tissue using a Tiangen Biotech (Beijing, China) DNA extraction kit (Plant no. DP305) following the manufacturer’s protocol. A shotgun library was prepared by shearing 1 μg of genomic DNA using the DNA Library Preparation Kit (Roche Applied Science, Indianapolis, Indiana, USA) following the GS FLX+ library preparation protocol. The shotgun library was further enriched by eight 5′-biotinylated probes, namely, (AG)10, (AC)10, (AAC)8, (ACG)8, (AAG)8, (AGG)8, (ACAT)6, and (ATCT)6, and eventually sequenced on a Roche 454 GS FLX platform. A 1/16th run was performed using this pyrosequencing system. MISA software (Thiel et al., 2003) was used to search for microsatellites, while Primer3 software (Rozen and Skaletsky, 1999) was used to design primer pairs. Primer design parameters were set as follows: primer size = 18–27 nucleotides with 20 as optimum; primer melting temperature (Tm) = 55–65°C with 60°C as optimum; primer GC% = 40–60%; product size = 100–500 bp with 200 bp as optimum; and CG clamp = 3. PCRs were performed with a single microsatellite primer pair in a 30-μL reaction mixture containing 30 ng of genomic DNA, 0.2 mmol/L of each dNTP, 0.3 μmol/L of each primer, 3 μL 10× polymerization buffer, and 1 unit of Taq polymerase (Tiangen Biotech). PCR reactions were performed in a PTC-200 Thermal Cycler (MJ Research, Watertown, Massachusetts, USA) as follows: an initial denaturation at 95°C for 5 min; followed by 35 cycles at 94°C for 40 s, locus-specific annealing temperature (Table 1) for 45 s, and 72°C for 50 s; and a final extension at 72°C for 5 min. The PCR products were resolved on 8% native polyacrylamide gel and visualized by silver staining. The band size was determined in comparison with a 50-bp DNA ladder (TaKaRa Biotechnology Co., Dalian, Liaoning, China). Analyses of the number of alleles per locus (A), observed heterozygosity (Ho), expected heterozygosity (He), Hardy–Weinberg equilibrium (HWE), and linkage disequilibrium (LD) were performed using GENEPOP version 4.2 (Rousset, 2008). Analysis of the inbreeding coefficients (FIS) and its significance testing were performed using FSTAT version 2.9.3.2 (Goudet, 1995).

Table 1.

Primer sequences and characterization for 34 microsatellite loci isolated from Quercus variabilis.

Locus	Primer sequences (5′–3′)	Repeat motif	Ta (°C)	Allele size range (bp)	A	GenBank accession no.
Qv020	F: CATCGTCGAAGTCGAAGTCA	(AAG)6	50	199–205	3	KP854060
	R: CTTCGATTTCACACGCTCAA
Qv024	F: GGTTATCTCATTGCTCCTGAAAA	(ATT)5	52	124–130	2	KP854064
	R: GTTGCCAACTCATCATCCCT
Qv025	F: CCTGAGAGAGGGGTGTGAGA	(AG)6TGAT(GA)8	52	180	1	KP854065
	R: TCCTCCCCACAGACAAACTC
Qv026	F: TCCATTGCCACACAACTCAT	(GA)15	52	180–210	6	KP854066
	R: CCTCTAAGCCCCTCTCTCTCA
Qv028	F: TAATTTGAGGCATGCACAGC	(GA)7	48	245–255	3	KP854068
	R: GTGCCAAAATTGGGTTGAGT
Qv030	F: CCCATTACCCGATCTTGCTA	(CT)6	48	390	1	KP854070
	R: CTCACCGTCACAACAACCAC
Qv033	F: CAAAACTTTTTGTTGCTGTGAA	(AC)7ATG(CA)6	48	229	1	KP854073
	R: GCCCAAACAAACATTCCATC
Qv034	F: TAAGCCTGGATCCTCTGTGG	(GAA)6(GAT)5	48	249–267	2	KP854074
	R: CCTTTGGTGCTCTTGCTAGG
Qv035	F: TCACAAACACAAAGAACACTCTCA	(AC)6	54	116	1	KP854075
	R: TGGATCTGACGTGGCATTTA
Qv036	F: GCCCTGGTAACGGGGTATTA	(ATAA)5	50	395	1	KP854076
	R: CTTTTGAGGCCGAGAACAAG
Qv040	F: TCCCCCTTACAAAAGGAATG	(AC)8	50	400	1	KP854080
	R: TCACAAGTGTTTACATGCGCT
Qv041	F: CGCTGGCCTTAACAGTGATT	(AG)6	52	263–265	2	KP854081
	R: GGCCACTGACAATGAGGAAT
Qv042	F: AGGCTCTGATACCATGTGGG	(AG)8	50	314	1	KP854082
	R: GAAGCCATGTCCTTCAGCTC
Qv048	F: AAAGGGGAAGAAAACCCTGA	(AGA)9	48	189–195	2	KP854088
	R: CTGTTTTCCTCTTCACCCCA
Qv049	F: AGAAACCCACAGCCACAAAC	(CCT)5	48	255–264	2	KP854089
	R: ATTCCAGCATGTTCCAAAGC
Qv056	F: CGGACCTTCCCATTACAGAA	(AG)7	54	276	1	KP854096
	R: TGCGTGCTAGTAGGTTTGGA
Qv060	F: GGCCTAGGAAGCTCTAGCTCA	(GAA)5	52	354–363	2	KP854100
	R: GGCCTCTCTGGATGATGGTA
Qv062	F: TCAACAGCCAACAGACAAGC	(GA)6	48	308	1	KP854102
	R: GATGATTCTAATCTCACATCTCGG
Qv063	F: TGGGAAAAATTAGTTTTGGGG	(CAG)5	48	103	1	KP854103
	R: GATCCTGCTGGTGAGGATGT
Qv070	F: TTATGAACTAAGGGCCCACG	(AG)8	48	380	1	KP854110
	R: CTGAGGGTGCACTGTTCAGA
Qv071	F: TAAAATGCCTGGAGTGGACC	(ATG)6	54	368	1	KP854111
	R: AGCAGCCACCTCAGTAGGAA
Qv075	F: TAGTCATCCTCGTTTTGGGC	(TAG)9	50	325–348	3	KP854115
	R: CACCTTGAAGGAGACAAGGG
Qv076	F: GAGGACCCAAATTTGAAGCA	(GA)6	54	335	1	KP854116
	R: TGGATTCACCGATTCACTCA
Qv078	F: AGGACCAAGGGTAGCTGGTT	(GT)7	48	345	1	KP854118
	R: GGGTGACTGCACACGTTATG
Qv081	F: ATTTGGTTTCAGCTCCCCTT	(TTC)14	50	320	1	KP854121
	R: GCAATAGGCAAAGGATTGAGA
Qv083	F: GGGATTGAACCTCAACCTCA	(GAT)6	48	165–174	2	KP854123
	R: TTTGCATTTACTCCCTTGCC
Qv084	F: CAATGTACTCGGGTTGGCTT	(AT)8	48	173	1	KP854124
	R: CCTCCAGATGGTTGCAAGTT
Qv089	F: GGGTGAGATTGAAAAGCCAA	(AAG)7(AGG)5	48	217–229	4	KP854129
	R: GCTTCCTCTTCCGCTTACCT
Qv092	F: GAAATGTAAGGTGTATTGTAAAAATGG	(TA)9	48	336–344	3	KP854132
	R: TCTTGACCCTATCGGACGAC
Qv093	F: TCGTGCGAAATGAGACTTTG	(TG)7	48	195	1	KP854133
	R: CGTGTTGCTAAAATATCGGTCA
Qv094	F: AAGGCCGAAAGAGGAAGAAC	(AG)14…(GA)10	48	248–256	4	KP854134
	R: TCCTCATTTGTCACTCCCAA
Qv095	F: GCATTTTGTCAAAGGAATAGCC	(ACA)16	52	110	1	KP854135
	R: AGATTGCCAAAGGGGAGATT
Qv096	F: AAGCACACGAATCCTCCAAA	(AC)8	52	131	1	KP854136
	R: GATTTGGCCATCACAAAGGT
Qv097	F: TGTTTTGCTTGGTTGGTTGA	(AG)16	54	223	1	KP854137
	R: TCTTCTATGCTCACGGGCTT

Note: A = number of alleles; Ta = annealing temperature.

Our results suggest that next-generation sequencing technology combined with the magnetic bead enrichment protocol is a rapid and cost-effective means to identify large numbers of polymorphic microsatellite loci in Q. variabilis. By taking advantage of the Roche 454 sequencing platform, 63,851 individual sequence reads with an average length of 327 bp were obtained. A total of 2121 microsatellites were identified that contained enough flanking sequences for primer design. These sequences were deposited in GenBank (KP854041–KP856161). To test the amplification efficiency of these microsatellite loci, we randomly selected 100 loci to design primer pairs and perform PCRs. Sixty-six primer pairs were discarded for unsuccessful or nontarget amplification, whereas the remaining 34 primer pairs were tested for polymorphisms (Table 1). The 34 microsatellite sequences were compared with others in GenBank through BLAST searches. Qv040 and Qv089 matched significantly with the microsatellite CsCAT35 sequence (GenBank accession no. FJ868967) in Castanea sativa Mill. (Fagaceae) and REO_390 sequence (GenBank accession no. KJ207438) in Q. rubra L. (Fagaceae), respectively. Qv078 matched significantly with the mRNA sequence (GenBank accession no. XM_011077301) in Sesamum indicum L. (Pedaliaceae).

Fourteen of the 34 tested loci in 24 individuals from two Q. variabilis wild populations were polymorphic, with A ranging from two to six. He and Ho ranged from 0.042 to 0.721 and 0.042 to 0.750, respectively (Table 2). Locus Qv026 deviated significantly from HWE (P < 0.05) because of excessive homozygosity. FIS varied from −0.571 to 0.759 (Table 2), and no significant negative FIS values were observed. Three pairs of loci (Qv034 and Qv060, Qv034 and Qv041, Qv028 and Qv049) showed significant LD, indicating significant allelic associations between them. These new microsatellite loci will provide useful tools for further population genetic and landscape genetic studies on Q. variabilis.

Table 2.

Genetic diversity parameters for two populations of Quercus variabilis.

	Meng Mountain (N = 12)				Song Mountain (N = 12)
Locus	A	He	Ho	FIS	A	He	Ho	FIS
Qv020	2	0.489	0.750	−0.571	3	0.540	0.750	−0.414
Qv024	2	0.042	0.042	0.000	2	0.159	0.167	−0.048
Qv026	6	0.667	0.167 (0.000)*	0.759	4	0.721	0.417 (0.044)*	0.433
Qv028	3	0.540	0.750	−0.414	3	0.453	0.583	−0.305
Qv034	2	0.489	0.750	−0.571	2	0.224	0.250	−0.125
Qv041	2	0.518	0.583	−0.132	2	0.480	0.583	−0.228
Qv048	2	0.228	0.250	−0.100	2	0.159	0.167	−0.048
Qv049	2	0.159	0.167	−0.048	2	0.344	0.417	−0.222
Qv060	2	0.290	0.333	−0.158	2	0.083	0.083	0.000
Qv075	3	0.413	0.500	−0.224	2	0.290	0.333	−0.158
Qv083	2	0.159	0.167	−0.048	2	0.344	0.417	−0.222
Qv089	4	0.656	0.583	0.115	3	0.301	0.333	−0.114
Qv092	3	0.552	0.417	0.254	2	0.392	0.500	−0.297
Qv094	4	0.601	0.500	0.175	4	0.645	0.500	0.233

Note: A = number of alleles; FIS = analysis of the inbreeding coefficient; He = expected heterozygosity; Ho = observed heterozygosity.

Geographic coordinates of the localities: Meng Mountain: 35°29′34″N, 117°47′58″E; Song Mountain: 34°28′08″N, 113°05′07″E.

*Significant deviation from Hardy–Weinberg equilibrium.

CONCLUSIONS

In this study, we isolated 2121 microsatellite loci from Q. variabilis. One hundred microsatellite loci were randomly selected to design primer pairs and perform PCRs in two wild populations. Among these tested loci, 14 microsatellite markers were polymorphic. These new microsatellite loci increase the density of the microsatellite markers for this species, which will be helpful for further population genetic and landscape genetic studies on Q. variabilis.