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12 April 2017 Development and Characterization of EST-SSR Markers in Stipa breviflora (Poaceae)
Jing Ren, Zhi-Zhen Su, Zhen-Hua Dang, Yu Ding, Pei-Xuan Wang, Jian-Ming Niu
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Stipa breviflora Griseb. (Poaceae) is one of the dominant species covering the desert steppe region of the eastern Eurasian grassland. In China, it is widely distributed in the southern Mongolia Plateau, western Ordos Plateau, northwestern Loess Plateau, Tibet Plateau, and Xinjiang Province (Zhang et al., 2009). Stipa breviflora, which is characterized by drought resistance, grazing tolerance, fine palatability, and early spring growth, serves as an indispensable forage resource for herbivores in dryland areas. Moreover, it plays an important role in both conserving soil and water and preventing desertification (Zhang et al., 2010). Stipa breviflora grassland, however, has been severely degraded by global warming and anthropogenic disturbances. Despite the ecological significance of S. breviflora, we know little about its population genetics and evolutionary biology using current technologies. The only study that has evaluated the genetic diversity of this species used RAPD markers (Zhang et al., 2012). Microsatellite (simple sequence repeat [SSR]) markers, which can be developed using genomes or transcriptomes, are powerful tools for examining population genetic diversity. SSRs are often presumed to be neutral, but can be subject to both positive and negative selection for a variety of reasons. Although we reported preliminary results on SSR development of S. breviflora (Zhao et al., 2016), a systematic and improved study is also required.


Transcriptome sequencing of eight S. breviflora individuals, collected from a wide geographic range, was conducted using Illumina HiSeq 4000 (Illumina, San Diego, California, USA). Approximately six million 150-bp reads of each individual were obtained and de novo assembled into 178,901 unigenes (>300 bp) using Trinity (Grabherr et al., 2011), with mean length of 1235 bp. SSRs were detected using MicroSAtellite Identification Tool (MISA; Thiel et al., 2003), with the criteria of 12, six, five, five, four, and four repeat units for mono-, di-, tri-, tetra-, penta-, and hexanucleotide motifs, respectively. The criteria were determined by the settings for minimum number of repeats according to the MISA software instructions. We adjusted the repeat numbers of pentanucleotide and hexanucleotide motifs on the basis of our search results for a higher number of SSR loci. A total of 29,817 SSRs were identified, with trinucleotide repeats (16,785, 62.76%) being the most common, followed by dinucleotide (7489, 28.00%), mononucleotide (3071, 10.30%), hexanucleotide (958, 3.58%), pentanucleotide (923, 3.45%), and tetranucleotide (591, 2.21%) repeats.

By comparing the transcriptome sequences, we obtained 1954 unigenes likely to contain polymorphic SSR loci. Then 81 loci demonstrating significant length variation among data for eight transcriptomes were selected. Primer3 (Untergasser et al., 2012) was used to design primer pairs with lengths of 18–21 bp amplifying product sizes ranging from 90–250 bp. We initially screened 81 primer pairs using 16 S. breviflora individuals; 63 of these loci were successfully amplified after PCR optimization. The polymorphism of these loci was tested using 24 samples from eight populations of S. breviflora (three individuals per population). Finally, we obtained 21 polymorphic expressed sequence tag-SSR (EST-SSR) markers, which were deposited into GenBank (Table 1). Polymorphism of these loci was assessed using 96 individuals from eight populations (12 individuals per population). Stipa grandis P. A. Smirn., S. krylovii Roshev., S. bungeana Trin., S. aliena Keng, S. gobica Roshev., and S. purpurea Griseb. (five individuals of each population) were used to test the cross-species amplification of polymorphic markers in S. breviflora (Appendix 1). Owing to lack of plant specimens, voucher specimens of these species could not be provided.

Genomic DNA was extracted from frozen leaf tissues using the Plant Genomic DNA Extraction Kit (Tiangen Biotech, Beijing, China). PCR amplification was performed in a reaction mixture (25 µL) containing 1 µL of template DNA (30–40 ng/µL), 0.5 µL (10 pM) of each primer, 12.5 µL of Premix Taq (TaKaRa Biotechnology Co., Dalian, Liaoning Province, China), and 10.5 µL of ddH2O. Conditions for PCR amplification were as follows: 4 min at 94°C; 35 cycles of 30 s at 94°C, 30 s at a primer-specific annealing temperature (Table 1), 30 s at 72°C; and a final extension step at 72°C for 10 min. PCR products were first detected by 1.5% agarose gel electrophoresis to check for successful amplification. Forward primers for the 21 successfully amplified polymorphic loci were labeled with one of three different fluorescent dyes (FAM, HEX, or TAMRA) and used for amplifications with the same protocol. The labeled PCR products were analyzed on an ABI 3730 DNA Analyzer with a GeneScan 500 LIZ Size Standard (Applied Biosystems, Beijing, China). Allele sizes were called using GeneMarker version 2.6.0 (SoftGenetics, State College, Pennsylvania, USA). Number of alleles per locus, observed heterozygosity, and expected heterozygosity were calculated using GenAlEx version 6.5 (Peakall and Smouse, 2012). GENEPOP version 4.42 (Rousset, 2008) was used to measure the departure from Hardy–Weinberg equilibrium and linkage disequilibrium, and MICROCHECKER version 2.2.3 (van Oosterhout et al., 2004) was used to check the possibility of null alleles.

Table 1.

Characteristics of 21 microsatellite primers developed for Stipa breviflora.


Table 2.

Results of initial screening of 21 polymorphic loci identified in eight populations of Stipa breviflora. a


Table 3.

Cross-species amplification results of 21 polymorphic EST-SSR loci developed for Stipa breviflora in six other Stipa species.a


Among these 21 polymorphic loci, the total number of alleles per locus ranged from two to 24, observed heterozygosity ranged from 0.083 to 0.958, and expected heterozygosity ranged from 0.396 to 0.738. Only locus SB21 with null alleles consistently departed from Hardy–Weinberg equilibrium in all populations, and we did not detect linkage disequilibrium between any loci (Table 2). Cross-species amplification of the 21 polymorphic markers was tested in six related species, namely S. grandis, S. krylovii, S. bungeana, S. aliena, S. gobica, and S. purpurea (five individuals per population). Fourteen (66.67%) of the 21 loci successfully amplified in all tested species; the remaining loci were amplified in some species (Table 3).


This is the first known report of 21 polymorphic EST-SSRs for S. breviflora. These SSRs will be used to evaluate impacts of isolation by distance and recent habitat fragmentation on the genetic diversity and structure of S. breviflora populations. These SSRs may also be used in investigations of genetic diversity of other Stipa L. species. Hodel et al. (2016) indicate that microsatellites generated from transcriptomes could likely be found in translated regions of the genome. The majority of loci favored in translated regions are trinucleotide repeats (Hodel et al., 2016). Markers occurring in or near coding regions are prone to selective pressures (Morgante et al., 2002), which casts doubt on the application of microsatellites in genetic diversity analysis. However, one study about Glycine Willd. and Oenothera L. demonstrates that many trinucleotide repeats linked closely to translated regions are not themselves within a translated region of a gene (Hodel et al., 2016); therefore, these loci are potentially useful. In our study, 21 SSRs were derived from transcriptome data, and a significant number of these loci (11/21) were or contained trinucleotide or hexanucleotide repeats. Therefore, we suggest that all 21 loci could be used to examine genetic diversity while neutrality is tested. Researchers investigating selection should use the 11 loci with trinucleotide repeats.


This study is supported by the National Natural Science Foundation of China (NSFC; no. 5141116).



Grabherr, M. G., B. J. Haas, M. Yassour, J. Z. Levin, D. A. Thompson, I. Amit, X. Adiconis, et al. 2011. Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology 29: 644–652. Google Scholar


Hodel, R. G. J., M. A. Gitzendanner, C. C. Germain-Aubrey, X. Liu, A. A. Crowl, M. Sun, J. B. Landis, et al. 2016. A new resource for the development of SSR markers: Millions of loci from a thousand plant transcriptomes. Applications in Plant Sciences 4: 1600024. Google Scholar


Morgante, M., M. Hanafey, and W. Powell. 2002. Microsatellites are preferentially associated with nonrepetitive DNA in plant genomes. Nature Genetics 30: 194–200. Google Scholar


Peakall, R., and P. E. Smouse. 2012. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research-An update. Bioinformatics (Oxford, England) 28: 2537–2539. Google Scholar


Rousset, F. 2008. GENEPOP'007: A complete re-implementation of the GENEPOP software for Windows and Linux. Molecular Ecology Resources 8: 103–106. Google Scholar


Thiel, T., W. Michalek, R. K. Varshney, and A. Graner. 2003. Exploiting EST databases for the development and characterization of genederived SSR-markers in barley (Hordeum vulgare L.). Theoretical and Applied Genetics 106: 411–422. Google Scholar


Untergasser, A., I. Cutcutache, T. Koressaar, J. Ye, B. C. Faircloth, M. Remm, and S. G. Rozen. 2012. Primer3–New capabilities and interfaces. Nucleic Acids Research 40: e115. Google Scholar


van Oosterhout, C., W. F. Hutchinson, D. P. M. Wills, and P. Shipley. 2004. MICRO-CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes 4: 535–538. Google Scholar


Zhang, Q., J. M. Niu, J. J. Dong, Y. Ding, F. L. Wang, J. Guo, and L. Q. Zhao. 2009. Floristic study of spermatophyte on Stipa breviflora steppe distributed in Inner Mongolia. Journal of Desert Research 29: 451–456. Google Scholar


Zhang, Q., J. M. Niu, Y. Ding, S. Kang, and J. J. Dong. 2010. Current research advances and future prospects of biology and ecology of Stipa breviflora. Chinese Journal of Grassland 32: 93–101. Google Scholar


Zhang, Q., J. M. Niu, S. B. Wu, and J. J. Dong. 2012. Impact of climatic factors on genetic diversity of Stipa breviflora populations in Inner Mongolia. Genetics and Molecular Research 11: 2081–2093. Google Scholar


Zhao, L., Z. H. Dang, Y. N. Zhao, Y. Zhang, J. M. Niu, J. Ren, and J. M. Zhou. 2016. Optimization of SSR-PCR reaction system and primers screening in Stipa breviflora. Journal of Inner Mongolia University 47: 58–64. Google Scholar


Appendix 1.

Location information for populations of Stipa breviflora and six other Stipa species used in this study.a

Jing Ren, Zhi-Zhen Su, Zhen-Hua Dang, Yu Ding, Pei-Xuan Wang, and Jian-Ming Niu "Development and Characterization of EST-SSR Markers in Stipa breviflora (Poaceae)," Applications in Plant Sciences 5(4), (12 April 2017).
Received: 17 December 2016; Accepted: 1 March 2017; Published: 12 April 2017

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