Advertisement

Journal of Zhejiang University-SCIENCE B

, Volume 19, Issue 8, pp 620–629 | Cite as

High-resolution melting-based TILLING of γ ray-induced mutations in rice

  • Shan Li
  • Song-mei Liu
  • Hao-wei Fu
  • Jian-zhong Huang
  • Qing-yao Shu
Article
  • 16 Downloads

Abstract

Targeting Induced Local Lesions IN Genomes (TILLING) is a reverse genetics strategy for the high-throughput screening of induced mutations. γ radiation, which often induces both insertion/deletion (Indel) and point mutations, has been widely used in mutation induction and crop breeding. The present study aimed to develop a simple, high-throughput TILLING system for screening γ ray-induced mutations using high-resolution melting (HRM) analysis. Pooled rice (Oryza sativa) samples mixed at a 1:7 ratio of Indel mutant to wild-type DNA could be distinguished from the wild-type controls by HRM analysis. Thus, an HRM-TILLING system that analyzes pooled samples of four M2 plants is recommended for screening γ ray-induced mutants in rice. For demonstration, a γ ray-mutagenized M2 rice population (n=4560) was screened for mutations in two genes, OsLCT1 and SPDT, using this HRM-TILLING system. Mutations including one single nucleotide substitution (G→A) and one single nucleotide insertion (A) were identified in OsLCT1, and one trinucleotide (TTC) deletion was identified in SPDT. These mutants can be used in rice breeding and genetic studies, and the findings are of importance for the application of γ ray mutagenesis to the breeding of rice and other seed crops.

Key words

Mutation screening High-resolution melting (HRM) analysis Targeting Induced Local Lesions IN Genomes (TILLING) Mutant Indel γ ray Rice 

基于高分辨率熔解曲线技术的水稻伽玛射线诱发突变的TILLIN体系

中文概要

目的

建立适用于筛选伽马射线诱发突变的、基于高分辨率熔解曲线(high-resolution melting,HRM)技术的高通量定向诱导基因组局部突变技术 (Targeting Induced Local Lesions IN Genomes, TILLING)体系。

创新点

建立起了基于HRM技术、适用于伽玛射线诱发的小片段插入/缺失突变的高通量TILLING体系(HRM-TILLING)。

方法

通过不同野生型/突变型比例混池DNA的HRM分析,确定HRM检测不同类型插入/缺失突变的能力,确定M2植株突变检测的适宜混池比例,并用一个伽玛诱变M2 群体(n=4560)筛选OsLCT1SPDT两个基因的突变体,确定实际效果。

结论

以4 株M2植株混样,采用HRM可以有效检出突变。建立的基于HRM的TILLING体系适用于伽玛射线诱发突变的高通量筛选。

关键词

突变筛选 高分辨率熔解曲线(HRM) 定向诱导基因组局部突变技术(TILLING) 突变体 插入缺失 伽玛射线 水稻 

CLC number

Q319 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Acanda Y, Martínez Ó, Prado MJ, et al., 2014. EMS mutagenesis and qPCR-HRM prescreening for point mutations in an embryogenic cell suspension of grapevine. Plant Cell Rep, 33(3):471–481. https://doi.org/10.1007/s00299-013-1547-6 CrossRefPubMedGoogle Scholar
  2. Ahloowalia BS, Maluszynski M, Nichterlein K, 2004. Global impact of mutation-derived varieties. Euphytica, 135(2):187–204. https://doi.org/10.1023/B:EUPH.0000014914.85465.4f CrossRefGoogle Scholar
  3. Botticella E, Sestili F, Hernandez-Lopez A, et al., 2011. High resolution melting analysis for the detection of EMS induced mutations in wheat Sbella genes. BMC Plant Biol, 11:156. https://doi.org/10.1186/1471-2229-11-156 CrossRefPubMedPubMedCentralGoogle Scholar
  4. Bovina R, Brunazzi A, Gasparini G, et al., 2014. Development of a TILLING resource in durum wheat for reverse-and forward-genetic analyses. Crop Pasture Sci, 65(1):112–124. https://doi.org/10.1071/cp13226 Google Scholar
  5. Bush SM, Krysan PJ, 2010. ITILLING: a personalized approach to the identification of induced mutations in Arabidopsis. Plant Physiol, 154(1):25–35. https://doi.org/10.1104/pp.110.159897 CrossRefPubMedPubMedCentralGoogle Scholar
  6. Colasuonno P, Incerti O, Lozito ML, et al., 2016. DHPLC technology for high-throughput detection of mutations in a durum wheat TILLING population. BMC Genet, 17:43. https://doi.org/10.1186/s12863-016-0350-0 CrossRefPubMedPubMedCentralGoogle Scholar
  7. Colbert T, Till BJ, Tompa R, et al., 2001. High-throughput screening for induced point mutations. Plant Physiol, 126(2):480–484. https://doi.org/10.1104/pp.126.2.480 CrossRefPubMedPubMedCentralGoogle Scholar
  8. Cousins MM, Donnell D, Eshleman SH, 2013. Impact of mutation type and amplicon characteristics on genetic diversity measures generated using a high-resolution melting diversity assay. J Mol Diagn, 15(1):130–137. https://doi.org/10.1016/j.jmoldx.2012.08.008 CrossRefPubMedPubMedCentralGoogle Scholar
  9. Dong CM, Vincent K, Sharp P, 2009. Simultaneous mutation detection of three homoeologous genes in wheat by high resolution melting analysis and mutation surveyor®. BMC Plant Biol, 9:143. https://doi.org/10.1186/1471-2229-9-143 CrossRefPubMedPubMedCentralGoogle Scholar
  10. Fu HW, Li YF, Shu QY, 2008. A revisit of mutation induction by gamma rays in rice (Oryza sativa L.): implications of microsatellite markers for quality control. Mol Breed, 22(2):281–288. https://doi.org/10.1007/s11032-008-9173-7 CrossRefGoogle Scholar
  11. Gady ALF, Herman FWK, van de Wal MHBJ, et al., 2009. Implementation of two high through-put techniques in a novel application: detecting point mutations in large EMS mutated plant populations. Plant Methods, 5:13. https://doi.org/10.1186/1746-4811-5-13 CrossRefPubMedPubMedCentralGoogle Scholar
  12. Hofinger BJ, Jing HC, Hammond-Kosack KE, et al., 2009. High-resolution melting analysis of cDNA-derived PCR amplicons for rapid and cost-effective identification of novel alleles in barley. Theor Appl Genet, 119(5):851–865. https://doi.org/10.1007/s00122-009-1094-2 CrossRefPubMedGoogle Scholar
  13. Hwang JE, Jang DS, Lee KJ, et al., 2017. Identification of gamma ray irradiation-induced mutations in membrane transport genes in a rice population by TILLING. Genes Genet Syst, 91(5):245–256. https://doi.org/10.1266/ggs.15-00052 CrossRefPubMedGoogle Scholar
  14. Kumar APK, McKeown PC, Boualem A, et al., 2017. TILLING by sequencing (TbyS) for targeted genome mutagenesis in crops. Mol Breed, 37(2):14. https://doi.org/10.1007/s11032-017-0620-1 CrossRefGoogle Scholar
  15. Li S, Zheng YC, Cui HR, et al., 2016. Frequency and type of inheritable mutations induced by γ rays in rice as revealed by whole genome sequencing. J Zhejiang Univ-Sci B (Biomed & Biotechnol), 17(12):905–915. https://doi.org/10.1631/jzus.B1600125 CrossRefGoogle Scholar
  16. Lochlainn SÓ, Amoah S, Graham NS, et al., 2011. High resolution melt (HRM) analysis is an efficient tool to genotype EMS mutants in complex crop genomes. Plant Methods, 7:43. https://doi.org/10.1186/1746-4811-7-43 CrossRefPubMedPubMedCentralGoogle Scholar
  17. Lu HP, Zhang HL, Fu HW, et al., 2016. Identification and characterization of a novel lesion mimic mutant in rice. J Nucl Agric Sci, 30(6):1037–1044 (in Chinese). https://doi.org/10.11869/j.issn.100-8551.2016.06.1037 Google Scholar
  18. Mader E, Lukas B, Novak J, 2008. A strategy to setup codominant microsatellite analysis for high-resolutionmelting-curve-analysis (HRM). BMC Genet, 9:69. https://doi.org/10.1186/1471-2156-9-69 CrossRefPubMedPubMedCentralGoogle Scholar
  19. Matoulkova E, Michalova E, Vojtesek B, et al., 2012. The role of the 3' untranslated region in post-transcriptional regulation of protein expression in mammalian cells. RNA Biol, 9(5):563–576. https://doi.org/10.4161/rna.20231 CrossRefPubMedGoogle Scholar
  20. McCallum CM, Comai L, Greene EA, et al., 2000. Targeting induced local lesions in genomes (TILLING) for plant functional genomics. Plant Physiol, 123(2):439–442. https://doi.org/10.1104/pp.123.2.439 CrossRefPubMedPubMedCentralGoogle Scholar
  21. Nawaz Z, Shu QY, 2014. Molecular nature of chemically and physically induced mutants in plants: a review. Plant Genet Res, 12(S1):S74–S78. https://doi.org/10.1017/S1479262114000318 CrossRefGoogle Scholar
  22. Nida H, Blum S, Zielinski D, et al., 2016. Highly efficient de novo mutant identification in a Sorghum bicolor TILLING population using the ComSeq approach. Plant J, 86(4):349–359. https://doi.org/10.1111/tpj.13161 CrossRefPubMedGoogle Scholar
  23. Reed GH, Kent JO, Wittwer CT, 2007. High-resolution DNA melting analysis for simple and efficient molecular diagnostics. Pharmacogenomics, 8(6):597–608. https://doi.org/10.2217/14622416.8.6.597 CrossRefPubMedGoogle Scholar
  24. Ririe KM, Rasmussen RP, Wittwer CT, 1997. Product differentiation by analysis of DNA melting curves during the polymerase chain reaction. Anal Biochem, 245(2):154–160. https://doi.org/10.1006/abio.1996.9916 CrossRefPubMedGoogle Scholar
  25. Rogers C, Wen JQ, Chen RJ, et al., 2009. Deletion-based reverse genetics in Medicago truncatula. Plant Physiol, 151(3):1077–1086. https://doi.org/10.1104/pp.109.142919 CrossRefPubMedPubMedCentralGoogle Scholar
  26. Sato Y, Shirasawa K, Takahashi Y, et al., 2006. Mutant selection from progeny of gamma-ray-irradiated rice by DNA heteroduplex cleavage using brassica petiole extract. Breed Sci, 56(2):179–183. https://doi.org/10.1270/jsbbs.56.179 CrossRefGoogle Scholar
  27. Shu QY, Forster BP, Nakagawa H, 2012. Plant Mutation Breeding and Biotechnology. CABI Publishing, Wallingford, UK, p.123–134. https://doi.org/10.1079/9781780640853.0000 CrossRefGoogle Scholar
  28. Simko I, 2016. High-resolution DNA melting analysis in plant research. Trends Plant Sci, 21(6):528–537. https://doi.org/10.1016/j.tplants.2016.01.004 CrossRefPubMedGoogle Scholar
  29. Taheri S, Lee Abdullah T, Jain SM, et al., 2017. TILLING, high-resolution melting (HRM), and next-generation sequencing (NGS) techniques in plant mutation breeding. Mol Breed, 37:40. https://doi.org/10.1007/s11032-017-0643-7 CrossRefGoogle Scholar
  30. Tan YY, Yu XM, Shu QY, et al., 2016. Development of an HRM-based, safe and high-throughput genotyping system for two low phytic acid mutations in soybean. Mol Breed, 36:101. https://doi.org/10.1007/s11032-016-0529-0 CrossRefGoogle Scholar
  31. Till BJ, Reynolds SH, Greene EA, et al., 2003. Large-scale discovery of induced point mutations with high-throughput TILLING. Genome Res, 13(3):524–530. https://doi.org/10.1101/gr.977903 CrossRefPubMedPubMedCentralGoogle Scholar
  32. Till BJ, Cooper J, Tai TH, et al., 2007. Discovery of chemically induced mutations in rice by TILLING. BMC Plant Biol, 7:19. https://doi.org/10.1186/1471-2229-7-19 CrossRefPubMedPubMedCentralGoogle Scholar
  33. Tsai H, Howell T, Nitcher R, et al., 2011. Discovery of rare mutations in populations: TILLING by sequencing. Plant Physiol, 156(3):1257–1268. https://doi.org/10.1104/pp.110.169748 CrossRefPubMedPubMedCentralGoogle Scholar
  34. Uraguchi S, Kamiya T, Sakamoto T, et al., 2011. Low-affinity cation transporter (OsLCT1) regulates cadmium transport into rice grains. Proc Natl Acad Sci USA, 108(52):20959–20964. https://doi.org/10.1073/pnas.1116531109 CrossRefPubMedGoogle Scholar
  35. Wang QZ, Fu HW, Huang JZ, et al., 2012. Generation and characterization of bentazon susceptible mutants of commercial male sterile lines and evaluation of their utility in hybrid rice production. Field Crop Res, 137:12–18. https://doi.org/10.1016/j.fcr.2012.09.001 CrossRefGoogle Scholar
  36. Wittwer CT, Reed GH, Gundry CN, et al., 2003. High-resolution genotyping by amplicon melting analysis using LCGreen. Clin Chem, 49(6):853–860. https://doi.org/10.1373/49.6.853 CrossRefPubMedGoogle Scholar
  37. Yamaji N, Takemoto Y, Miyaji T, et al., 2017. Reducing phosphorus accumulation in rice grains with an impaired transporter in the node. Nature, 541(7635):92–95. https://doi.org/10.1038/nature20610 CrossRefPubMedGoogle Scholar
  38. Yoshida S, Forno DA, Cock J, et al., 1976. Laboratory Manual for Physiological Studies of Rice. The International Rice Research Institute, Los Banos, Manila, Philippines.Google Scholar
  39. Zhang HL, Huang JZ, Chen XY, et al., 2014. Competitive amplification of differentially melting amplicons facilitates efficient genotyping of photoperiod-and temperaturesensitive genic male sterility in rice. Mol Breed, 34(4):1765–1776. https://doi.org/10.1007/s11032-014-0136-x CrossRefGoogle Scholar
  40. Zhao HJ, Liu QL, Ren XL, et al., 2008. Gene identification and allele-specific marker development for two allelic low phytic acid mutations in rice (Oryza sativa L.). Mol Breed, 22(4):603–612. https://doi.org/10.1007/s11032-008-9202-6 CrossRefGoogle Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Key Laboratory of Rice Biology, Institute of Crop SciencesZhejiang UniversityHangzhouChina
  2. 2.Hubei Collaborative Innovation Center for Grain IndustryJingzhouChina
  3. 3.Institute of Nuclear Agricultural SciencesZhejiang UniversityHangzhouChina
  4. 4.Jiaxing Academy of Agricultural SciencesJiaxingChina

Personalised recommendations