GLADS: A gel-less approach for detection of STMS markers in wheat and rice
Autoři:
Gautam Vishwakarma aff001; Ravi Prakash Sanyal aff002; Ajay Saini aff002; Parmeshwar Kumar Sahu aff004; Ravi Raj Singh Patel aff004; Deepak Sharma aff004; Ratan Tiwari aff005; Bikram Kishore Das aff001
Působiště autorů:
Nuclear Agriculture and Biotechnology Division, Bhabha Atomic Research Centre, Trombay, Mumbai, Maharashtra, India
aff001; Homi Bhabha National Institute, Anushaktinagar, Trombay, Mumbai, Maharashtra, India
aff002; Molecular Biology Division, Bhabha Atomic Research Centre, Trombay, Mumbai, Maharashtra, India
aff003; Department of Genetics and Plant Breeding, Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh, India
aff004; ICAR - Indian Institute of Wheat and Barley Research, Karnal, Haryana, India
aff005
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0224572
Souhrn
Sequence tagged microsatellite site (STMS) are useful PCR based DNA markers. Wide genome coverage, high polymorphic index and co-dominant nature make STMS a preferred choice for marker assisted selection (MAS), genetic diversity analysis, linkage mapping, seed genetic purity analysis etc. Routine STMS analysis involving low-throughput, laborious and time-consuming polyacrylamide/agarose gels often limit their full utility in crop breeding experiments that involve large populations. Therefore, convenient, gel-less marker detection methods are highly desirable for STMS markers. The present study demonstrated the utility of SYBR Green dye based melt-profiling as a simple and convenient gel-less approach for detection of STMS markers (referred to as GLADS) in bread wheat and rice. The method involves use of SYBR Green dye during PCR amplification (or post-PCR) of STMS markers followed by generation of a melt-profile using controlled temperature ramp rate. The STMS amplicons yielded characteristic melt-profiles with differences in melting temperature (Tm) and profile shape. These characteristic features enabled melt-profile based detection and differentiation of STMS markers/alleles in a gel-less manner. The melt-profile approach allowed assessment of the specificity of the PCR assay unlike the end-point signal detection assays. The method also allowed multiplexing of two STMS markers with non-overlapping melt-profiles. In principle, the approach can be effectively used in any crop for STMS marker analysis. This SYBR Green melt-profiling based GLADS approach offers a convenient, low-cost (20–51%) and time-saving alternative for STMS marker detection that can reduce dependence on gel-based detection, and exposure to toxic chemicals.
Klíčová slova:
Wheat – Rice – Polymerase chain reaction – Crops – India – Gel electrophoresis – Plant breeding – Crop genetics
Zdroje
1. Appels R, Lagudah ES. Manipulation of chromosomal segments from wild wheat for the improvement of bread wheat. Funct Plant Biol. 1990;17: 253–266.
2. Calderini DF, Slafer GA. Has yield stability changed with genetic improvement of wheat yield? Euphytica. 1999;107: 51–59.
3. Fleury D, Delannay X, Langridge P. Quantitative Trait Loci and Breeding. In: John Wiley & Sons, Ltd, editor. eLS. Chichester, UK: John Wiley & Sons, Ltd; 2012. http://doi.wiley.com/10.1002/9780470015902.a0023712.
4. Kumar LS. DNA markers in plant improvement: an overview. Biotechnol Adv. 1999;17: 143–182. 14538138
5. Kalendar R, Schulman AH. IRAP and REMAP for retrotransposon-based genotyping and fingerprinting. Nat Protoc. 2006;1: 2478–2484. doi: 10.1038/nprot.2006.377 17406494
6. Semagn K, Babu R, Hearne S, Olsen M. Single nucleotide polymorphism genotyping using Kompetitive Allele Specific PCR (KASP): overview of the technology and its application in crop improvement. Mol Breed. 2014;33: 1–14. doi: 10.1007/s11032-013-9917-x
7. Appleby N, Edwards D, Batley J. New Technologies for Ultra-High Throughput Genotyping in Plants. In: Gustafson JP, Langridge P, Somers DJ, editors. Plant Genomics. Totowa, NJ: Humana Press; 2009. pp. 19–39. http://link.springer.com/10.1007/978-1-59745-427-8_2.
8. Mienie CMS, Pretorius AE. Application of marker-assisted selection for ahFAD2A and ahFAD2B genes governing the high-oleic acid trait in South African groundnut cultivars (Arachis hypogaea L.). Afr J Biotechnol. 2013;12: 4283.
9. Tsilo TJ, Jin Y, Anderson JA. Diagnostic Microsatellite Markers for the Detection of Stem Rust Resistance Gene in Diverse Genetic Backgrounds of Wheat. Crop Sci. 2008;48: 253. doi: 10.2135/cropsci2007.04.0204
10. Randhawa HS, Mutti JS, Kidwell K, Morris CF, Chen X, Gill KS. Rapid and targeted introgression of genes into popular wheat cultivars using marker-assisted background selection. PLoS One. 2009;4: e5752. doi: 10.1371/journal.pone.0005752 19484121
11. Jaiswal S, Sheoran S, Arora V, Angadi UB, Iquebal MA, Raghav N, et al. Putative Microsatellite DNA Marker-Based Wheat Genomic Resource for Varietal Improvement and Management. Front Plant Sci. 2017;8. doi: 10.3389/fpls.2017.02009 29234333
12. Distefano G, Caruso M, La Malfa S, Gentile A, Wu S-B. High Resolution Melting Analysis Is a More Sensitive and Effective Alternative to Gel-Based Platforms in Analysis of SSR–An Example in Citrus. Niedz RP, editor. PLoS ONE. 2012;7: e44202. doi: 10.1371/journal.pone.0044202 22957003
13. Yan Z, Wu F, Luo K, Zhao Y, Yan Q, Zhang Y, et al. Cross-species transferability of EST-SSR markers developed from the transcriptome of Melilotus and their application to population genetics research. Sci Rep. 2017;7. doi: 10.1038/s41598-017-18049-8 29263338
14. Vieira MLC, Santini L, Diniz AL, de Munhoz CF. Microsatellite markers: what they mean and why they are so useful. Genet Mol Biol. 2016;39: 312–328. doi: 10.1590/1678-4685-GMB-2016-0027 27561112
15. Kuleung C, Baenziger PS, Dweikat I. Transferability of SSR markers among wheat, rye, and triticale. Theor Appl Genet. 2004;108: 1147–1150. doi: 10.1007/s00122-003-1532-5 15067402
16. Varshney RK, Sigmund R, Börner A, Korzun V, Stein N, Sorrells ME, et al. Interspecific transferability and comparative mapping of barley EST-SSR markers in wheat, rye and rice. Plant Sci. 2005;168: 195–202. doi: 10.1016/j.plantsci.2004.08.001
17. Collard BC., Mackill DJ. Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos Trans R Soc B Biol Sci. 2008;363: 557–572. doi: 10.1098/rstb.2007.2170 17715053
18. Gupta PK, Rustgi S, Mir RR. Array-based high-throughput DNA markers for crop improvement. Heredity. 2008;101: 5–18. doi: 10.1038/hdy.2008.35 18461083
19. Vishwakarma G, Saini A, Das BK, Bhagwat SG, Jawali N. Rapid and convenient gel-free screening of SCAR markers in wheat using SYBR green-based melt-profiling. Plant Breed. 2016;135: 643–653. doi: 10.1111/pbr.12415
20. Taheri S, Lee Abdullah T, Yusop M, Hanafi M, Sahebi M, Azizi P, et al. Mining and development of novel ssr markers using next generation sequencing (Ngs) data in plants. Molecules. 2018;23: 399. doi: 10.3390/molecules23020399 29438290
21. Cheng J, Zhao Z, Li B, Qin C, Wu Z, Trejo-Saavedra DL, et al. A comprehensive characterization of simple sequence repeats in pepper genomes provides valuable resources for marker development in Capsicum. Sci Rep. 2016;6. doi: 10.1038/srep18919 26739748
22. Varshney R, Graner A, Sorrells M. Genomics-assisted breeding for crop improvement. Trends Plant Sci. 2005;10: 621–630. doi: 10.1016/j.tplants.2005.10.004 16290213
23. Ma W, Zhang W, Gale KR. Multiplex-PCR typing of high molecular weight glutenin alleles in wheat. Euphytica. 2003;134: 51–60.
24. Hernández P, Dorado G, Cabrera A, Laurie DA, Snape JW, Martín A. Rapid verification of wheat–introgressions by direct staining of SCAR, STS, and SSR amplicons. Genome. 2002;45: 198–203. doi: 10.1139/g01-087 11908662
25. Heid CA, Stevens J, Livak KJ, Williams PM. Real time quantitative PCR. Genome Res. 1996;6: 986–994. doi: 10.1101/gr.6.10.986 8908518
26. Dong C, Vincent K, Sharp P. Simultaneous mutation detection of three homoeologous genes in wheat by High Resolution Melting analysis and Mutation Surveyor®. BMC Plant Biol. 2009;9: 143. doi: 10.1186/1471-2229-9-143 19958559
27. Vemireddy LR, Archak S, Nagaraju J. Capillary Electrophoresis Is Essential for Microsatellite Marker Based Detection and Quantification of Adulteration of Basmati Rice (Oryza sativa). J Agric Food Chem. 2007;55: 8112–8117. doi: 10.1021/jf0714517 17867634
28. Agarwal M, Shrivastava N, Padh H. Advances in molecular marker techniques and their applications in plant sciences. Plant Cell Rep. 2008;27: 617–631. doi: 10.1007/s00299-008-0507-z 18246355
29. Schuelke M. An economic method for the fluorescent labeling of PCR fragments. Nat Biotechnol. 2000;18: 233–234. doi: 10.1038/72708 10657137
30. Gupta PK, Rustgi S, Sharma S, Singh R, Kumar N, Balyan HS. Transferable EST-SSR markers for the study of polymorphism and genetic diversity in bread wheat. Mol Genet Genomics. 2003;270: 315–323. doi: 10.1007/s00438-003-0921-4 14508680
31. Helguera M, Khan IA, Kolmer J, Lijavetzky D, Zhong-qi L, Dubcovsky J. PCR Assays for the Cluster of Rust Resistance Genes and Their Use to Develop Isogenic Hard Red Spring Wheat Lines. Crop Sci. 2003;43: 1839. doi: 10.2135/cropsci2003.1839
32. Simko I. High-Resolution DNA Melting Analysis in Plant Research. Trends Plant Sci. 2016;21: 528–537. doi: 10.1016/j.tplants.2016.01.004 26827247
33. Croxford AE, Rogers T, Caligari PDS, Wilkinson MJ. High-resolution melt analysis to identify and map sequence-tagged site anchor points onto linkage maps: a white lupin (Lupinus albus) map as an exemplar. New Phytol. 2008;180: 594–607. doi: 10.1111/j.1469-8137.2008.02588.x 18684160
34. Gachon C, Mingam A, Charrier B. Real-time PCR: what relevance to plant studies? J Exp Bot. 2004;55: 1445–1454. doi: 10.1093/jxb/erh181 15208338
35. Sánchez-Pérez R, Ballester J, Dicenta F, Arús P, Martínez-Gómez P. Comparison of SSR polymorphisms using automated capillary sequencers, and polyacrylamide and agarose gel electrophoresis: Implications for the assessment of genetic diversity and relatedness in almond. Sci Hortic. 2006;108: 310–316. doi: 10.1016/j.scienta.2006.02.004
36. Paux E, Faure S, Choulet F, Roger D, Gauthier V, Martinant J-P, et al. Insertion site-based polymorphism markers open new perspectives for genome saturation and marker-assisted selection in wheat. Plant Biotechnol J. 2010;8: 196–210. doi: 10.1111/j.1467-7652.2009.00477.x 20078842
37. Eswaran N, Bhagwat S, Jawali N. A simple method for isolation of DNA from plants suitable for long term storage and DNA marker analysis. BARC Newsl. 2004;249: 208–214.
38. Röder MS, Korzun V, Wendehake K, Plaschke J, Tixier M-H, Leroy P, et al. A microsatellite map of wheat. Genetics. 1998;149: 2007–2023. 9691054
39. McCouch SR. Development and Mapping of 2240 New SSR Markers for Rice (Oryza sativa L.). DNA Res. 2002;9: 199–207. doi: 10.1093/dnares/9.6.199 12597276
40. Pallavi J, Singh A, Rao IU, KV P. Identification, validation of a SSR marker and marker assisted selection for the goat grass derived seedling resistance gene lr28 in wheat. J Plant Pathol Microbiol. 2015;6. doi: 10.4172/2157-7471.1000277
41. Ashkani S, Rafii MY, Rusli I, Sariah M, Abdullah SNA, Abdul Rahim H, et al. SSRs for marker-assisted selection for blast resistance in rice (Oryza sativa L.). Plant Mol Biol Report. 2012;30: 79–86. doi: 10.1007/s11105-011-0315-4
42. Hayden MJ, Kuchel H, Chalmers KJ. Sequence tagged microsatellites for the Xgwm533 locus provide new diagnostic markers to select for the presence of stem rust resistance gene Sr2 in bread wheat (Triticum aestivum L.). Theor Appl Genet. 2004;109: 1641–1647. doi: 10.1007/s00122-004-1787-5 15340687
43. Zhou W-C, Kolb FL, Bai G-H, Domier LL, Boze LK, Smith NJ. Validation of a major QTL for scab resistance with SSR markers and use of marker-assisted selection in wheat. Plant Breed. 2003;122: 40–46. doi: 10.1046/j.1439-0523.2003.00802.x
44. Ma W, Zhang W, Gale KR. Multiplex-PCR typing of high molecular weight glutenin alleles in wheat. Euphytica. 2003;134: 51–60. doi: 10.1023/A:1026191918704
45. Bagge M, Lübberstedt T. Functional markers in wheat: technical and economic aspects. Mol Breed. 2008;22: 319–328. doi: 10.1007/s11032-008-9190-6
46. Monis PT, Giglio S, Saint CP. Comparison of SYTO9 and SYBR Green I for real-time polymerase chain reaction and investigation of the effect of dye concentration on amplification and DNA melting curve analysis. Anal Biochem. 2005;340: 24–34. doi: 10.1016/j.ab.2005.01.046 15802126
47. Gibson NJ. The use of real-time PCR methods in DNA sequence variation analysis. Clin Chim Acta. 2006;363: 32–47. doi: 10.1016/j.cccn.2005.06.022 16182268
48. Singh BD, Singh AK. Marker-assisted plant breeding: principles and practices. New Delhi: Springer; 2015.
49. Li J, Xiong C, He X, Lu Z, Zhang X, Chen X, et al. Using SSR-HRM to Identify Closely Related Species in Herbal Medicine Products: A Case Study on Licorice. Front Pharmacol. 2018;9. doi: 10.3389/fphar.2018.00407 29740326
50. Xanthopoulou A, Ganopoulos I, Koubouris G, Tsaftaris A, Sergendani C, Kalivas A, et al. Microsatellite high-resolution melting (SSR-HRM) analysis for genotyping and molecular characterization of an Olea europaea germplasm collection. Plant Genet Resour. 2014;12: 273–277. doi: 10.1017/S147926211400001X
51. An Jianyu, Yin Mengqi, Zhang Qin, Gong Dongting, Jia Xiaowen, Guan Yajing, et al. Genome Survey Sequencing of Luffa Cylindrica L. and Microsatellite High Resolution Melting (SSR-HRM) Analysis for Genetic Relationship of Luffa Genotypes. Int J Mol Sci. 2017;18: 1942. doi: 10.3390/ijms18091942 28891982
52. Ganopoulos I, Argiriou A, Tsaftaris A. Microsatellite high resolution melting (SSR-HRM) analysis for authenticity testing of protected designation of origin (PDO) sweet cherry products. Food Control. 2011;22: 532–541. doi: 10.1016/j.foodcont.2010.09.040
53. Ganopoulos I, Argiriou A, Tsaftaris A. Adulterations in Basmati rice detected quantitatively by combined use of microsatellite and fragrance typing with High Resolution Melting (HRM) analysis. Food Chem. 2011;129: 652–659. doi: 10.1016/j.foodchem.2011.04.109 30634282
54. Somers DJ, Isaac P, Edwards K. A high-density microsatellite consensus map for bread wheat (Triticum aestivum L.). Theor Appl Genet. 2004;109: 1105–1114. doi: 10.1007/s00122-004-1740-7 15490101
55. Periyannan S, Moore J, Ayliffe M, Bansal U, Wang X, Huang L, et al. The Gene Sr33, an Ortholog of Barley Mla Genes, Encodes Resistance to Wheat Stem Rust Race Ug99. Science. 2013;341: 786–788. doi: 10.1126/science.1239028 23811228
56. Das BK, Saini A, Bhagwat SG, Jawali N. Development of SCAR markers for identification of stem rust resistance gene Sr31 in the homozygous or heterozygous condition in bread wheat. Plant Breed. 2006;125: 544–549. doi: 10.1111/j.1439-0523.2006.01282.x
57. Das B, Saini A, Bhagwat S, Jawali N. Marker assisted selection for stem rust resistance gene Sr24 in Indian wheat genotypes: validation of a SCAR marker. J Genet Breed. 2006;60: 189–196.
58. Li W, Xi B, Yang W, Hawkins M, Schubart UK. Complex DNA melting profiles of small PCR products revealed using SYBR ®$ Green I. BioTechniques. 2003;35: 702–706. doi: 10.2144/03354bm07 14579734
59. Varga A, James D. Real-time RT-PCR and SYBR Green I melting curve analysis for the identification of Plum pox virus strains C, EA, and W: effect of amplicon size, melt rate, and dye translocation. J Virol Methods. 2006;132: 146–153. doi: 10.1016/j.jviromet.2005.10.004 16293321
60. Lipsky R, Mazzanti C, Rudolph J, Xu K, Vyas G, Bozak D, et al. DNA melting analysis for detection of single nucleotide polymorphisms. Clin Chem. 2001;47: 635–644. 11274012
61. Ririe KM, Rasmussen RP, Wittwer CT. Product Differentiation by Analysis of DNA Melting Curves during the Polymerase Chain Reaction. Anal Biochem. 1997;245: 154–160. doi: 10.1006/abio.1996.9916 9056205
62. Arikawa E, Sun Y, Wang J, Zhou Q, Ning B, Dial SL, et al. Cross-platform comparison of SYBR Green real-time PCR with TaqMan PCR, microarrays and other gene expression measurement technologies evaluated in the MicroArray Quality Control (MAQC) study. BMC Genomics. 2008;9: 328. doi: 10.1186/1471-2164-9-328 18620571
63. Zipper H. Investigations on DNA intercalation and surface binding by SYBR Green I, its structure determination and methodological implications. Nucleic Acids Res. 2004;32: e103–e103. doi: 10.1093/nar/gnh101 15249599
64. Meyer N, Lind V, Karlovsky P, Zahn M, Friedt W, Ordon F. Development of a real-time PCR method for the identification of wheat genotypes carrying different eyespot resistance genes. Plant Breed. 2011;130: 16–24. doi: 10.1111/j.1439-0523.2010.01808.x
65. Germer S, Higuchi R. Single-tube genotyping without oligonucleotide probes. Genome Res. 1999;9: 72–78. 9927486
66. Baris I, Etlik O, Koksal V, Ocak Z, Baris ST. SYBR green dye-based probe-free SNP genotyping: Introduction of T-Plex real-time PCR assay. Anal Biochem. 2013;441: 225–231. doi: 10.1016/j.ab.2013.07.007 23872005
67. Barkley NA, Chamberlin KDC, Wang ML, Pittman RN. Development of a real-time PCR genotyping assay to identify high oleic acid peanuts (Arachis hypogaea L.). Mol Breed. 2010;25: 541–548. doi: 10.1007/s11032-009-9338-z
68. Giménez MJ, Pistón F, Martín A, Atienza SG. Application of real-time PCR on the development of molecular markers and to evaluate critical aspects for olive oil authentication. Food Chem. 2010;118: 482–487. doi: 10.1016/j.foodchem.2009.05.012
69. Orsi I, Malatrasi M, Belfanti E, Gullì M, Marmiroli N. Determining resistance to Pseudomonas syringae in tomato, a comparison of different molecular markers. Mol Breed. 2012;30: 967–974. doi: 10.1007/s11032-011-9681-8
70. Huang Y, Yin X, Zhu C, Wang W, Grierson D, Xu C, et al. Standard Addition Quantitative Real-Time PCR (SAQPCR): A Novel Approach for Determination of Transgene Copy Number Avoiding PCR Efficiency Estimation. Alvarez ML, editor. PLoS ONE. 2013;8: e53489. doi: 10.1371/journal.pone.0053489 23308234
Článok vyšiel v časopise
PLOS One
2019 Číslo 11
- Metamizol jako analgetikum první volby: kdy, pro koho, jak a proč?
- Nejasný stín na plicích – kazuistika
- Masturbační chování žen v ČR − dotazníková studie
- Úspěšná resuscitativní thorakotomie v přednemocniční neodkladné péči
- Dlouhodobá recidiva a komplikace spojené s elektivní operací břišní kýly
Najčítanejšie v tomto čísle
- A daily diary study on maladaptive daydreaming, mind wandering, and sleep disturbances: Examining within-person and between-persons relations
- A 3’ UTR SNP rs885863, a cis-eQTL for the circadian gene VIPR2 and lincRNA 689, is associated with opioid addiction
- A substitution mutation in a conserved domain of mammalian acetate-dependent acetyl CoA synthetase 2 results in destabilized protein and impaired HIF-2 signaling
- Molecular validation of clinical Pantoea isolates identified by MALDI-TOF