Legume Research

  • Chief EditorJ. S. Sandhu

  • Print ISSN 0250-5371

  • Online ISSN 0976-0571

  • NAAS Rating 6.80

  • SJR 0.391

  • Impact Factor 0.8 (2024)

Frequency :
Monthly (January, February, March, April, May, June, July, August, September, October, November and December)
Indexing Services :
BIOSIS Preview, ISI Citation Index, Biological Abstracts, Elsevier (Scopus and Embase), AGRICOLA, Google Scholar, CrossRef, CAB Abstracting Journals, Chemical Abstracts, Indian Science Abstracts, EBSCO Indexing Services, Index Copernicus
Submit

Research Article

Legume Research, volume 43 issue 1 (february 2020) : 8-17

DNA Fingerprinting of Vegetable Soybean Cultivar ‘Zhexian No.9’ Using 101 New Developed HRM-Based SNP Markers

X.J. Fu1, J.X. Pei1, Y.T. Zheng1, D.D. Guo1, Q.H. Yang1, H.X. Jin1, D.H. Zhu1, D.K. Dong1, S.C. Xu1,*
1Hangzhou National Sub-center of Soybean Improvement, Institute of Crop and Nuclear Technology Utilization, Zhejiang Academy of Agricultural Sciences. Hangzhou, Zhejiang 310021, P.R. China.

  • Submitted10-10-2018|

  • Accepted29-06-2019|

  • First Online 09-11-2019|

  • doi 10.18805/LR-456

Cite article:- Fu X.J., Pei J.X., Zheng Y.T., Guo D.D., Yang Q.H., Jin H.X., Zhu D.H., Dong D.K., Xu S.C. (2019). DNA Fingerprinting of Vegetable Soybean Cultivar ‘Zhexian No.9’ Using 101 New Developed HRM-Based SNP Markers . Legume Research. 43(1): 8-17. doi: 10.18805/LR-456.

ABSTRACT

Single nucleotide polymorphisms (SNPs) have been proved to be powerful markers in genetic analysis due to their high abundance and polymorphism in plant genomes. The recently developed high-resolution melting (HRM) analysis method provides a novel, quick, and close-tube PCR approach to analyze SNP variations. In present study, 101 HRM-based SNP markers from 20 soybean chromosomes were developed for genotyping vegetable soybean cultivar ‘Zhexian No.9’ with ‘Williams 82’ as reference. 33.7% of these markers were polymorphic between ‘Zhexian No.9’ and ‘Williams 82’. Polymorphic markers were found on 85% (17 of 20) of the soybean chromosomes when comparing ‘Zhexian No.9’ and ‘Williams 82’. Finally, an array of 101 in-sequence nucleotide letters was generated as the first precise SNP fingerprint of ‘Zhexian No.9’. The described marker-developing methodology could be used in other crops with known genomic information. 

INTRODUCTION

The vegetable soybean [Glycine max (L.) Merr.] is a kind of soybean that is harvested during R6-R7 growth stage when seeds fill at about 80-90% of their total weight (Young et al., 2000). The excellent appearance, balanced nutrition, delicious taste, pollution-free and high value characteristics make it popular worldwide. However, the relative shorter breeding history and narrower genetic basis limited the breeding program and variety identification of vegetable soybean. DNA fingerprinting technologies are widely used in assessment of genetic diversity, germplasm identification and variety protection (Korir et al., 2013). Due to the complex technical details, heavy workload, large quantities of DNA samples needed and relative lower sensitivity, the first-generation molecular markers, such as RFLP, were barely used nowadays (Powell et al., 1996). SSR and ISSR were representatives of the second-generation molecular markers. They are more abundant, polymorphic and easier to achieve than the first-generation markers and are still used in plant breeding researches (Dong et al., 2014; Singh et al., 2016). However, along with the development of SNP (single nucleotide polymorphism) markers and the detection technologies, the third-generation molecular markers show overwhelming advantages than all of the previously mentioned markers. SNP markers have been proved to be the most stable, abundant, easy to be high-throughput analyzed and most promising molecular marker (Gupta et al., 2001; Hayashi et al., 2004), which were used in germplasm identification and breeding programs (Wu et al., 2014; Wu et al., 2018).
 
Several methods were reported in the detection of SNP markers, including SSCP (Kozlowski and Krzyzosiak, 2001), DGGE (Guldberg et al., 1993), CAPS (Li et al., 2012), DNA-chip (Singh et al., 2015), DHPLC (Wolford et al., 2000; Colasuonno et al., 2016), mass spectrometric detection (Park et al., 2017) and HRM (high-resolution melting) (Villano et al., 2016; Kim et al., 2016). The recently developed HRM analysis method provided us a novel, quick and close-tube PCR approach to analyze SNPs (Nguyen et al., 2012). Typically, 96 or 384 DNA samples could be in-tube genotyped in 3 hrs with no need of probe synthesis or PCR product sequencing. This technology has been successfully used in genotyping, mutation scanning, as well as in methylation studies of plants (Montgomery et al., 2007; De Koeyer et al., 2010; Distefano et al., 2013). It is estimated that one SNP will exist in every 273bp of soybean genome (Zhoy et al., 2003). Development of HRM based SNP markers will greatly facilitate the further use of the SNP markers in soybean. In present study, 101 HRM-based SNP markers that evenly distributed on the 20 soybean chromosomes were developed and successfully used in the genotyping of vegetable soybean ‘Zhexian No.9’.

MATERIALS AND METHODS

Vegetable soybean variety ‘Zhexian No.9’ was used in present study. Total genomic DNA was extracted from young trifoliate leaves using cetyl trimethylammonium bromide method as described by Permingeat et al., (1998). The genomic DNA was diluted to 25 ng•uL-1 and stored at -20°C for subsequent analysis.
 
The latest genomic sequence information (Glyma.Wm82.a2, Gmax2.0) were downloaded from the Soybase database (https://www.soybase.org/) (Table 1). One SNP marker in roughly every 10 Mb was chosen from the ‘Williams 82’ physical map using the Genome Browser tool in Soybase. Online primer design tool Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/) was used with a set of parameters to design the eligible primers listed in Table 2. All primers were synthesized by Tsingke Biological Technology Co., Ltd. (Hangzhou, China), then diluted to 10μmol•L-1 and stored at -20°C for further use.
 
A series of preliminary tests was conducted to decide the concentrations of template DNA and primers. The optimal PCR mix (20μL) included 2μL (50ng) genomic DNA, 4μL 5x EvaGreen Realtime PCR Mix (GeneSolution Co., Ltd.), 0.4μL (10μmol•L-1) each of the forward and the reverse primer, and 13.2μL deionized H2O. For each primer pair, three parallel repeats were deployed on the 96-well plates for both ‘Williams 82’ and ‘Zhexian No.9’.
 
All PCR reactions were run in a Roche LightCycler® 480II system. “SYBR Green I/HRM dye” was chosen as detection format. Four steps, i.e. the pre-incubation at 95°C for 15 min, the 45 regular amplification cycles, the high-resolution melting procedure, and the final product cool down step were included in the PCR program. The amplification cycle composed of a denaturation step of 95°C for 15 sec, an annealing step of 60°C for 20 sec and a elongation step of 72°C for 20 sec with data collection. For the high-resolution melting procedure, the PCR products were firstly denatured at 95°C for 1 min, then cooled down to 40°C for 1 min, heated to 65°C for 1 sec and then slowly heated to 95°C at the ramp rate of 0.02°C/sec. The fluorescence signals were continuously collected at the rate of 25 acquisitions per degrees Celsius when proceeding the heating from 65°C to 95°C. PCR products were cool down to 40°C before shutting down the system. The HRM data was analyzed using the LightCycler® 480 software release 1.5.0. Grouping results were compared with the melting curves of ‘Zhexian No.9’ and ‘Williams 82’.
 
To guarantee the accuracy of the HRM based SNP analysis, some of the results were verified by Sanger sequencing randomly. The PCR products of both ‘Williams 82’ and ‘Zhexian No.9’ were sequenced at Tsingke Biological Technology Co., Ltd. (Hangzhou, China). The sequences were compared with the public database to confirm the consistency or difference at the SNP locus.

RESULTS AND DISCUSSION

SNP loci selected in present study
 
The genetic background of vegetable soybean is narrow due to excessive usage of limited elite germplasms in breeding (Dong et al., 2014). It is difficult in germplasm identification and breeding selection by first and second-generation markers, including SSR, ISSR and RFLP markers, because of their low polymorphism (Zhang et al., 2013). With the fast development of high-throughput sequencing, SNPs are more suitable for faster and cheaper genotyping compared to other markers (Han et al., 2012). In present study, a total of 101 SNPs, including 6 for Gm01 and Gm18, 4 for Gm11 and 5 for each of the other chromosomes, was chosen and successfully converted into HRM based SNP markers (Table 1 and Table 2, Fig 1). Due to the complexity of soybean genome and the higher requirements for primer design, usually more than one SNP in a selected chromosome region were tested before the final generation of the optimal primer pairs. Taking into account the actual size differences of each chromosomes, the physical intervals between SNP markers varied from 7,164,318 bp to 11,196,952 bp on each chromosome with an overall average of 9,927,838 bp. The expected amplicons ranged from 60 bp to 100 bp with an average of 81.7 bp in length (Table 2). The probe, microarray, Sanger sequencing, the next generation sequencing (NGS) and so on. High-resolution melting curve (HRM) analysis has been proved a fast, accurate and inexpensive method to analyze SNPs (Liew et al., 2004; Lopez et al., 2008). HRM-based SNP genotyping method was broadly used to analyze germplasm diversity of plants, including Capsicum (Jeong et al., 2010), Medicago sativa (Han et al., 2012), Prunus avium (Ganopoulos et al., 2013) and Taihangia rupestris (Li et al., 2018). In present study, HRM analysis was also used for genotyping SNPs in vegetable soybean germplasms. Theoretically, there could be three kinds of genotypes for a HRM-based SNP analysis, i.e., allele I, allele II and heterozygous. However, only two scenarios were discovered in this study due to the highly genomic homozygosity of these two soybean varieties (Fig 2).
 

Table 1: Genomic information of ‘Williams 82’ and the numbers of selected SNPs on each chromosome.


 

Table 2: SNP loci and primer information for the HRM-based SNP analysis.


 

Fig 1: Distribution of HRM-based SNP markers developed in this study.


 
In scenarios I, no difference was found between ‘Williams 82’ and ‘Zhexian No.9’ at the analyzed SNP locus, as shown by Melting Peaks (Fig 2A), Normalized Melting Curves (Fig 2B) and Normalized and Temp-Shifted Difference Plots (Fig 2C). Thus, the genotype of ‘Zhexian No.9’ could be deduced to be the same as that of the reference variety ‘Williams 82’ (Fig 2D). In scenarios II, two obvious groups of plots were observed in the Melting Peaks (Fig 2E), Normalized Melting Curves (Fig 2F), as well as in Normalized and Temp-Shifted Difference Plots (Fig 2G). It was significant different between ‘Williams 82’ and ‘Zhexian No.9’ at the analyzed SNP locus (Fig 2H). In consideration of the known SNP alleles in the public database, the genotype of ‘Zhexian No.9’ should be the alternative one from that of the ‘Williams 82’.
 

Fig 2: Two scenarios of the HRM based SNP genotyping in this study. A-D, results of the non-polymorphic (between ‘Williams 82’ and ‘Zhexian No.9’) marker ss715590226;


 
Verification of the HRM based SNP genotyping by sequencing
 
The accuracy of the HRM based SNP genotyping is the precondition for the subsequent application of this methodology. For this purpose, we sequenced the PCR products from 9 SNP markers randomly to verify the accuracy of HRM method. Consistencies between HRM based SNP genotyping and Sanger sequencing were observed for all those sequenced SNP loci, suggesting that the HRM based SNP analysis system established in this study is accurate and could be used in soybean SNP genotyping (Fig 3).
 

fig 3: Samples of the comparison between the normalized melting curves and the sequencing results.


 
SNP fingerprinting of soybean variety ‘Zhexian No.9’
 
All of those 101 evenly distributed HRM based SNP markers were subsequently used in the genotyping of vegetable soybean variety ‘Zhexian No.9’. Among these SNP markers, 33.7% (34 of 101) SNP loci were polymorphic between ‘Williams 82’ and ‘Zhexian No.9’. When the number of assessed germplasms increased to 32 (31 vegetable soybean varieties/inbred lines plus the reference variety ‘Williams 82’), the polymorphic rate soared to 61.4% (62/101, data not shown). The polymorphic rate of SNP markers were higher than that of SSR markers with 34.38% in vegetable soybean (Zhang et al., 2013), as well as 9.83% and 32.35% in grain soybean (Hisano et al., 2007; Li et al., 2010). All of these results indicated that the 101 new developed HRM-based SNP markers are suiTable for soybean DNA fingerprinting and the related researches. The methodology described in this study can also be used for reference in other crops with known genomic information.
 
Polymorphism between ‘Williams 82’ and ‘Zhexian No.9’ was observed on 85% (17 of 20) of the soybean chromosomes (Fig 4). Thus, the SNP fingerprint of ‘Zhexian No.9’ could be described as “TCAACT AGCCA CTGCT CCTAG ATGAC CGCGA TGGGG TGGGC CACCA TCACT TCTT CTGGT TACCA TACCG TGGCG AGGTC TAAAC GATCTC CTCTG CTACT” with letters listed in the order of located chromosomes and physical positions of each SNPs.

Fig 4: The precise fingerprint of vegetable soybean ‘Zhexian No.9’ using those 101 HRM based SNP markers developed in this study.

ACKNOWLEDGEMENT

This work was supported by grants from National Soybean Industrial Technology System (CARS-04-CES26), National Natural Science Foundation of China (31401400) and National key research and development program (2017YFD0101500).

REFERENCES


  1. Colasuonno, P., Incerti, O., Lozito, M.L., Simeone, R., Gadaleta, A., Blanco, A. (2016). DHPLC technology for high-throughput detection of mutations in a durum wheat TILLING population. Bmc Genetics, 17(1):43. 

  2. De Koeyer, D., Douglass, K., Murphy, A., Whitney, S., Nolan, L., Song, Y., De Jong, W. (2010). Application of high-resolution DNA melting for genotyping and variant scanning of diploid and autotetraploid potato. Molecular Breeding, 25(1):67-90. 

  3. Distefano, G., La Malfa, S., Gentile, A., Wu, S.B. (2013). EST-SNP genotyping of citrus species using high-resolution melting curve analysis. Tree Genetics and Genomes, 9(5):1271-1281. 

  4. Dong, D.K., Fu, X.J., Yuan, F.J., Chen, P.Y., Zhu, S.L., Li, B.Q., Yang, Q.H., Yu, X.M., Zhu, D.H. (2014). Genetic diversity and population structure of vegetable soybean (Glycine max (L.) Merr.) in China as revealed by SSR markers. Genetic Resources and Crop Evolution, 61(1):173-183. 

  5. Ganopoulos, I., Tsaballa, A., Xanthopoulou, A., Madesis, P., Tsaftaris, A. (2013). Sweet Cherry Cultivar Identification by High-Resolution-Melting (HRM) Analysis Using Gene-Based SNP Markers. Plant Molecular Biology Reporter, 31(3):763-768.

  6. Guldberg, P., Romano, V., Ceratto, N., Bosco, P., Ciuna, M., Indelicato, A., Mollica, F., et al. (1993). Mutational spectrum of phenylalanine hydroxylase deficiency in Sicily: implications for diagnosis of hyperphenylalaninemia in southern Europe. Human Molecular Genetics, 2(10):1703-1707. 

  7. Gupta, P.K., Roy, J.K. and Prasad, M. (2001). Single nucleotide polymorphisms: A new paradigm for molecular marker technology and DNA polymorphism detection with emphasis on their use in plants. Current Science, 80(4):524–535.

  8. Han, Y.H., Khu, D.M. and Monteros, M.J. (2012). High-resolution melting analysis for SNP genotyping and mapping in tetraploid alfalfa (Medicago sativa L.). Molecular Breeding, 29(2): 489- 501.

  9. Hayashi, K., Hashimoto, N., Daigen, M. and Ashikawa, I. (2004). Development of PCR-based SNP markers for rice blast resistance genes at the Piz locus. Theoretical and Applied Genetics, 108(7):1212-1220. 

  10. Hisano, H., Sato, S., Isobe, S., Sasamoto, S., Wada, T., Matsuno, A., Fujishiro, T., et al. (2007). Characterization of the soybean genome using EST-derived microsatellite markers. DNA Research, 14(6):271-281.

  11. Jeong, H.J., Jo, Y.D., Park, S.W. and Kang, B.C. (2010). Identification of Capsicum species using SNP markers based on high resolution melting analysis. Genome, 53(12):1029-1040.

  12. Kim, B., Kim, N., Kim, J.Y., Kim, B.S., Jung, H.J., Hwang, I., Noua, I.S., Sim, S.C. and Park, Y. (2016). Development of a high-    resolution melting marker for selecting Fusarium crown and root rot resistance in tomato. Genome, 59(3):173-183.

  13. Korir, N.K., Han, J., Shangguan, L.F., Wang, C., Kayesh, E., Zhang, Y.Y. and Fang, J.G. (2013). Plant variety and cultivar identification: advances and prospects. Critical Reviews in Biotechnology, 33(2):111-125. 

  14. Kozlowski, P. and Krzyzosiak, W.J. (2001). Combined SSCP/duplex analysis by capillary electrophoresis for more efficient mutation detection. Nucleic Acids Research, 29(14):e71. 

  15. Li, A.Q., Zhao, C.Z., Wang, X.J., Liu, Z.J., Zhang, L.F., Song, G.Q., Yin, J., Li, C.S., Xia, H., Bi, Y.P. (2010). Identification of SSR markers using soybean (Glycine max) ESTs from globular stageembryos. Electronic Journal of Biotechnology, 13(5):1-11.

  16. Li, D.D., Lewis, R.S., Jack, A.M., Dewey, R.E., Bowen, S.W. and Miller, R.D. (2012). Development of CAPS and dCAPS markers for CYP82E4, CYP82E5v2 and CYP82E10 gene mutants reducing nicotine to nornicotine conversion in tobacco. Molecular Breeding, 29(3):589-599. 

  17. Li, W.G., Liu, S., Jiang, S.T., Li, X.L. and Li, G. (2018). Development of 30 SNP markers for the endangered plant Taihangia rupestris based on transcriptome database and high resolution melting analysis. Conservation Genetics Resources, 10(4):775-778.

  18. Liew, M., Johnson, M., Graham, R., Meadows, C., Erali, M., Mao, R., Lyon, E., Wittwer, C. (2004). Fluorescent SNP genotyping by high-resolution melting analysis without probes. Clinical Chemistry, 50(11):2227-2227.

  19. Lopez, C.M.R., Croxford, A.E. and Wilkinson, M.J. (2008). High-resolution melt analysis for SNP discovery, linkage mapping and analysis of DNA methylation. Comparative Biochemistry and Physiology A, 150(3):S49-S50.

  20. Montgomery, J., Wittwer, C.T., Palais, R. and Zhou, L.M. (2007). Simultaneous mutation scanning and genotyping by high-resolution DNA melting analysis. Nature Protocols, 2(1): 59- 66. 

  21. Nguyen, Q., Mckinney, J., Johnson, D.J., Roberts, K.A. and Hardy, W.R. (2012). STR melting curve analysis as a genetic screening tool for crime scene samples. Journal of Forensic Sciences, 57(4):887-899. 

  22. Park, J.H., Jang, H., Jung, Y.K., Jung, Y.L., Shin, I., Cho, D.Y. and Park, H.G. (2017). A mass spectrometry-based multiplex SNP genotyping by utilizing allele-specific ligation and strand displacement amplification. Biosensors and Bioelectronics, 91:122-127. 

  23. Permingeat, H.R., Romagnoli, M.V., Sesma, J.I. and Vallejos, R.H. (1998). A simple method for isolating DNA of high yield and quality from cotton (Gossypium hirsutum L.) leaves. Plant Molecular Biology Reporter, 16(1):89.

  24. Powell, W., Morgante, M., Andre, C., Hanafey, M., Vogel, J., Tingey, S. and Rafalski, A. (1996). The comparison of RFLP, RAPD, AFLP and SSR (microsatellite) markers for germplasm analysis. Molecular Breeding, 2(3):225-238. 

  25. Singh, N., Choudhury, D.R., Tiwari, G., Singh, A.K., Kumar, S., Srinivasan, K., Tyagi, R.K., Sharma, A.D., Singh, N.K. and Singh, R. (2016). Genetic diversity trend in Indian rice varieties: an analysis using SSR markers. BMC Genetics, 17(1):127. 

  26. Singh, N., Jayaswal, P.K., Panda, K., Mandal, P., Kumar, V., Singh, B., Mishra, S., et al. (2015). Single-copy gene based 50 K SNP chip for genetic studies and molecular breeding in rice. Scientific Reports, 5:11600. 

  27. Villano, C., Miraglia, V., Iorizzo, M., Aversano, A. and Carputo, D. (2016). Combined use of molecular markers and high-resolution melting (HRM) to assess chromosome dosage in potato hybrids. Journal of Heredity, 107(2):187-192. 

  28. Wolford, J.K., Blunt, D., Ballecer, C. and Prochazka, M. (2000). High-throughput SNP detection by using DNA pooling and denaturing high performance liquid chromatography (DHPLC). Human Genetics, 107(5):483-487. 

  29. Wu, X.H., Wang, B.G., Lu, Z.F., Wu, X.Y., Li, G.J. and Xu, P. (2014). Identification and Mapping of a Powdery Mildew Resistance Gene Vu-Pm1 in the Chinese Asparagus Bean Landrace Zn016. Legume Research, 37(1):32-36.

  30. Wu, X.Y., Wang, B.G., Wu, X.H., Lu, Z.F., Li, G.J. and Xu, P. (2018). SNP marker-based genetic mapping of rust resistance gene in the vegetable cowpea landrace ZN016. Legume Research, 41(2):222-225.

  31. Young, G., Mebrahtu, T. and Johnson, J. (2000). Acceptability of green soybeans as a vegetable entity. Plant Foods for Human Nutrition, 55(4):323-333. 

  32. Zhang, G.W., Xu, S.C., Mao, W.H., Hu, Q.Z. and Gong, Y.M. (2013). Determination of the genetic diversity of vegetable soybean Glycine max (L.) Merr. using EST-SSR markers. Journal of Zhejiang University-Science B, 14(4):279-288. 
Reviewed By

Download Article
In this Article
    Contact us
    Follow us

    Editorial Board

    View all (0)