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Identifying Mutations by High Resolution Melting in a TILLING Population of Rice

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Summary

In this article, we present the protocol that is described as high-resolution melting analysis (HRM)-based Target Induced Local Lesions In Genomes (TILLING). This method utilizes fluorescence changes during the melting of the DNA duplex and is suitable for high-throughput screening of both insertion/deletion (Indel) and single base substition (SBS).

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Li, S., Yu, Y. P., Liu, S. M., Fu, H. W., Huang, J. Z., Shu, Q. Y., Tan, Y. Y. Identifying Mutations by High Resolution Melting in a TILLING Population of Rice. J. Vis. Exp. (151), e59960, doi:10.3791/59960 (2019).

Abstract

Target Induced Local Lesions In Genomes (TILLING) is a strategy of reverse genetics for the high-throughput screening of induced mutations. However, the TILLING system has less applicability for insertion/deletion (Indel) detection and traditional TILLING needs more complex steps, like CEL I nuclease digestion and gel electrophoresis. To improve the throughput and selection efficiency, and to make the screening of both Indels and single base substitions (SBSs) possible, a new high-resolution melting (HRM)-based TILLING system is developed. Here, we present a detailed HRM-TILLING protocol and show its application in mutation screening. This method can analyze the mutations of PCR amplicons by measuring the denaturation of double-stranded DNA at high temperatures. HRM analysis is directly performed post-PCR without additional processing. Moreover, a simple, safe and fast (SSF) DNA extraction method is integrated with HRM-TILLING to identify both Indels and SBSs. Its simplicity, robustness and high throughput make it potentially useful for mutation scanning in rice and other crops.

Introduction

Mutants are important genetic resources for plant functional genomics research and breeding of new varieties. A forward genetics approach (i.e. from mutant selection to gene cloning or variety development) used to be the main and sole method for the use of induced mutations about 20 years ago. The development of a novel reverse genetics method, TILLING (Targeting Induced Local Lesions In Genomes) by McCallum et al.1 opened a new paradigm and it has since been applied in a great number of animal and plant species2. TILLING is particularly useful for breeding traits that are technically difficult or costly to be determined (e.g., disease resistance, mineral content).

TILLING was initially developed for screening point mutations induced by chemical mutagens (e.g., EMS1,3). It includes the following steps: the establishment of a TILLING population(s); DNA preparation and pooling of individual plants; PCR amplification of target DNA fragment; heteroduplexes formation by denaturation and annealing of PCR amplicons and cleavage by CEL I nuclease; and identification of mutant individuals and their specific molecular lesions3,4. However, this method is still relatively complex, time consuming, and low-throughput. To make it more efficient and with higher throughput, many modified TILLING methods have been developed, such as deletion TILLING (De-TILLING) (Table 1)1,3,5,6,7,8,9,10,11,12.

HRM curve analysis, which is based on fluorescence changes during the melting of the DNA duplex, is a simple, cost-effective, and high-throughput method for mutation screening and genotyping13. HRM has already been widely used in plant research including HRM based TILLING (HRM-TILLING) for screening SBS mutations induced by EMS mutagenesis14. Here, we presented detailed HRM-TILLING protocols for screening of mutations (both Indel and SBS) induced by gamma (γ) rays in rice.

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Protocol

1. Preparations

  1. Development of γ-rays mutagenized populations
    1. Treat about 20,000 dried rice seeds (with moisture content of ca. 14%) of a japonica rice line (e.g., DS552) with 137Cs gamma rays at 100 Gy (1 Gy/min) in a γ irradiation facility (e.g., gamma cell).
      NOTE: Seeds used for treatment should have a high viability (e.g., with a germination rate >85%). The irradiation dose for indica rice could be increased to 150 Gy.
    2. Sow the irradiated seeds after germination on a seedling bed and transplant seedlings individually to a paddy field and grow into the M1 population.
      NOTE: Direct seeding of M1 plants sparsely could also be applied to save labor cost. Prevent outcrossing of M1 plants with other rice varieties using physical or biological isolation means.
    3. Bulk-harvest M2 seeds from the M1 plants, with 1-2 seeds from each M1 panic, to form an M2 population.
      NOTE: In practice and for simplicity, harvest all seeds of M1 plants and after fully mixing, a portion is sampled to form an M2 population.
    4. Soak about 5,000 M2 seeds in water for 24 (indica rice)-36 (japonica rice) h at room temperature. Then let the seeds to germinate at 37 °C for 2 days on moist filter paper in Petri dishes.
      NOTE: More M2 seeds can be germinated for analysis to increase the probability of identifying mutants.
    5. Place the germinated seeds to seeding panels with small holes and grow them hydroponically for 3-4 weeks in a culture solution modified from Yoshida et al.15 in a glasshouse with a 12 h photoperiod [daytime (30±2) °C and night (24±2) °C].
  2. Sampling of leaf tissues: Cut one disk (Φ ~2 mm) from the fully extended leaf of each seeding at the same position using a hole puncher.
  3. Preparation of DNA extraction solutions
    1. Buffer A: Add 2 mL of 5 M NaOH and 10 mL of 20% Tween 20 to make the final volume of 50 mL. Freshly prepare buffer A before DNA extraction.
    2. Buffer B: Add 20 mL of 1 M Tris-HCl (pH 8.0) and 80 μL of 0.5 M EDTA to make a final volume of 100 mL.
  4. PCR primers
    1. Primers for HRM analysis: Design primers for amplification of the target sequence using software (e.g., Primer Premier5) and synthesize by a commercial company.
      NOTE: Because HRM is less applicable to the analysis of long fragments, and hence, the amplicons should be less than 400 bp. Fragments with too high (>75%) or too low (<25%) GC content are also not good for HRM analysis.
    2. Primers for quality control: Use the 24 SSR markers distributed on the 12 rice chromosomes from Peng et al.16.

2. DNA Extraction

  1. Place 4 leaf discs into each well of a 96-well PCR plate, add buffer A solution (50 mL/well). Freeze the plate in a -80 °C freezer for 10 min.
  2. Defreeze the plate at room temperature, and then incubate at 95 °C for 10 min.
  3. Add 50 μL of buffer B to each well and mix well by vortexing.
  4. Centrifuge the plate for 1 min at 1,500 x g. The supernatant is ready for PCR.

3. PCR Amplification

  1. PCR Optimization
    1. Add the following reagents to each PCR well: 1 μL of DNA (the supernatant in step 2.4), 5 μL of 2x Master Mix (containing 2x PCR buffer, 4 mmol/L MgCl2, 0.4 mmol/L 2'-deoxyribonucleoside triphosphates (dNTPs), and 50 U/mL Taq DNA polymerase, 0.2 μL each of 10 μmol/L primers, and make a final volume up to 10 μL using nuclease free water.
    2. Use a gradient-capable thermal block to determine the optimal annealing temperature for each target fragment by using the following PCR program: 5 min at 94 °C, followed by 40 cycles of 30 s at 94 °C, 30 s at 52-62 °C (gradient temperature), and 30 s at 72 °C, with a final extension at 72 °C for 8 min and a hold at 16 °C.
    3. Examine amplicons on 1% agarose gels for determination of the optimal annealing temperature.
      NOTE: An optimal annealing temperature should enable specific amplification of the target fragment, without nonspecific amplification and primer dimerization.
  2. PCR for HRM analysis
    NOTE: HRM compatible plates and DNA of M2 seedlings extracted as described in step 2.4 are used for PCR.
    1. Perform PCRs in a final volume of 10 μL, with 1 μL of DNA (supernatant), 5 μL of 2x Master Mix, 0.2 μL each of 10 μmol/L primers, and 1 μL of 10x fluorescence dye. In each plate include one wild type (WT) parented sample and one negative (without DNA) control.
    2. Add a drop of mineral oil to each well to prevent evaporation.
    3. Seal the plate with adhesive film and centrifuge at 1,000 x g for 1 min.
    4. Run the PCR using the optimized annealing temperature.
      NOTE: In a few cases, fluorescence dye may affect PCR amplification, hence the dye is added after completion of PCR. In such cases, the dye is incorporated into DNA strands by post-PCR denaturing and annealing.

4. HRM Scanning and Mutation Confirmation

  1. Remove the adhesive film from plate and insert the plate into an HRM machine.
  2. Select New Run from the file menu or press the Run button at the top of the screen.
  3. Specify the starting and ending temperatures for the melt from 55 °C to 95 °C.
    NOTE: After the first run, the range of melt temperature can be determined for a particular fragment; hence, the melt temperature can be adjusted for subsequent analysis of the same fragment to save time.
  4. Select samples for high resolution melting analysis, exclude samples similar to the negative control.
  5. Normalize the melting curves to have the same beginning and ending fluorescence. Visually confirm that the Lower Min and Lower Max temperature cursors are in a region of the curves.
  6. Keep the ∆F (difference of fluorescence) level at the default setting of 0.05.
  7. Select the Common versus Variant from the Standards selection list and choose the Normal sensitivity.
  8. Use the WT as the control; samples with a ∆F value of ≥0.05 from WT are considered to contain mutant plant.
  9. Identification and confirmation of mutant plants.
    1. Identify the four plants, of which each mutant pool was made.
    2. Extract DNA from each of these plants using a CTAB method according to Allen et al.17 with some modifications.
      NOTE: DNase-free RNase and NaAc are not used when extracting DNA using the CTAB method described by Allen et al.17, as the DNA quality is good enough for further PCR amplification. Adjust the DNA to a final concentration of ~25 ng/μL after quantification using a spectrophotometer.
    3. Amplify the target fragment using PCR primers and program the same as for HRM analysis.
    4. Identify specific molecular lesions by Sanger sequencing of the amplicons.
      NOTE: Once the completion of PCR amplification, send the amplicons to a company for Sanger sequencing. The molecular lesion can be identified by comparing the sequences between the M2 plant and the WT.

5. Quality Control of Selected Mutants with Molecular Markers

  1. Perform PCR for the 24 SSR markers in a final volume of 10 μL with 25 ng of genomic DNA (extracted using the CTAB protocol), 5 µL of 2x master mix, 0.2 µL of each of 10 μM SSR primers.
  2. Run PCR using the following program: 5 min at 94 °C, followed by 30 cycles of 30 s at 94 °C, 30 s at 55 °C and 30 s at 72 °C, with a final extension at 72 °C for 7 min.
  3. Separate the amplicons on 8% polyacrylamide gels and reveal polymorphism of amplified fragments by silver staining18.
  4. Compare the SSR haplotypes of selected variants and the WT.
    NOTE: Induced mutants often have SSR haplotypes identical to their WT, if more than one SSR markers are different between a variant and the WT, the variant is high likely to be a genetic contaminant (e.g., a mixture or outcrossed plant) rather than an induced mutant18.

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Representative Results

HRM Scanning and Analysis

In total, 1,140 pooled DNA samples from 4,560 M2 seedlings were produced and subjected to PCR amplification. Two fragments with the size of 195 bp and 259 bp were amplified for OsLCT1 and SPDT, respectively (Table 2). Most samples had melting curves not significantly different from the WT (ΔF < 0.05). HRM curves significantly different from the WT (ΔF > 0.05) were grouped with color(s) different from the WT by the software (Figure 1).

Mutation Confirmation and Frequency

A DNA sample from an individual seedling was used for amplification and the respective fragment was sequenced. The mutation type and position on the genes could be confirmed by the sequencing chromatograms (Figure 2). Three mutations including two Indels and one SBS were identified from 4,560 M2 seedlings (Table 2). It was found that all three mutant parts were heterozygous at the mutation site (Figure 2).

The mutation frequency here referred to as the frequency of mutation occurred in the genome in a TILLING population. Based on the formula below, it was found that the mutation frequency amounted to about 1/690 kb.

Equation 1

Figure 1
Figure 1: HRM-TILLING of M2 Seedlings for Mutations in OsLCT1 (A, B) or SPDT (C) Genes. The wild type was chosen as reference for the development of fluorescence difference curves. The mutants showed significantly different HRM curves with ΔFs > 0.05 at temperatures of 90.0-92.0 °C and 81.5-83.5 °C, respectively. This figure has been modified from Li et al.19. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Sequencing Chromatograms of Targeted Fragments of Mutants. The rectangular box indicates a heterozygous site with a mutation of G→A at 4304 bp of OsLCT1 (A); one single nucleotide A insertion at the 4240 bp of OsLCT1 is indicated by an arrow (B); and a TTC trinucleotide deletion at the position of 5948-5950 bp of SPDT is indicated by a black line (C). This figure has been modified from Li et al.19. Please click here to view a larger version of this figure.

Abbreviation Full name Advantages or applicability Disadvantages Reference
Traditional TILLING Targeting Induced Local Lesions In Genomes For screening point mutations Time and cost consuming McCallum et al., 2000; Till et al., 2003
Eco-TILLING Ecotype-TILLING For screening mutations in natural populations Costly and low sensitivity Comai et al., 2004
De-TILLING Deletion TILLING For large deletion screening Depends strongly on good PCR system Rogers et al., 2009
iTILLING Individualized TILLING Identification of mutations using personalized TIILLING populations Not permanent population Bush and Krysan, 2010
DHPLC-TILLING Denaturing high-performance liquid chromatography -based TILLING Time saving Low throughput Colasuonno et al., 2016
Seq-TILLING TILLING by sequencing High throughput, Non-enzymatic system Relatively costly and higher false positive Tsai et al., 2011; Kumar et al., 2017
HRM-TILLING High resolution melting-TILLING High throughput and time saving; Non-enzymatic system Limit of PCR fragment length Dong et al., 2009; Gady et al., 2009

Table 1: The advantages and disadvantages of various TILLING approaches.

Gene name Gene Loci Gene function Primer sequence (5’-3’) Tm after optimization (°C) Product size (bp) Number of Mutations (Mutation type)
OsLCT1 LOC_Os06g38120 Low cadmium transporter LCT-F: CTCGATGTTAAGCATGCTCC LCT-R: AGAGTCAGGAACGCGGCTAC 61 195 2 (G-A transition, 1-bp insertion)
SPDT LOC_Os06g05160 SULTR-like phosphorus distribution transporter SPDT-F: TTCTCGGAGGAGGCTAAT SPDT-R: CCACGCATTCTGGTTACAT 52 259 1 (3-bp deletion)

Table 2: Information of Two Target Genes, PCR Amplification and Mutations Identified.

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Discussion

TILLING has proved to be a powerful reverse genetic tool for identifying induced mutations for gene functional analysis and crop breeding. For some traits not easily observed or determined, TILLING with high-throughput PCR-based mutation detection can be a useful method to obtain mutants for different genes. HRM-TILLING method has been used in EMS-mutagenized populations of tomato12, wheat11 and grapevine20 for mutation screening. In this paper, a simpler and more powerful HRM-TILLING was demonstrated, applicable for both Indel and SBS mutation screening.

To improve the efficiency, DNAs were extracted using the SSF method instead of the regular CTAB method, which is time- and labor-consuming and requires toxic chemical agents. Si et al.21 compared the quality of DNA extracted using SSF and CTAB method. Although it was found that DNA from SSF extraction had purity inferior to that of CTAB extraction, it could meet the requirements of PCR and for HRM analysis in rice. However, it should be noted that the DNA from SSF extraction could not be stored for a long time, hence it is not suitable for establishing a permanent mutant library.

In a previous study, pooled samples with 1/8 mutant DNA could be differentiated from wild-type by HRM analysis in both EMS and gamma mutagenized populations14,19. Considering M2 plants could be heterozygous for induced mutations, pooling of four M2 plants was recommended for detecting mutations by HRM-TILLING.

To obtain more mutants, it is also critical to develop mutagenized populations with a high mutation frequency. Gamma rays had significant effect on the growth of M1 plants. It has been reported that treatment with higher dose resulted in increased frequency of chlorophyll-deficient mutants and inferior performance of fertility, seed set and plant height in M2 plants18. Furthermore, it was known that tolerance to mutagens varied among different species. Therefore, the dose resulting in about 50% lethality (LD50) in M1 plants was considered as the optimum irradiation dose. However, Li et al. found different mutation doses (165, 246 and 389 Gy) resulted in similar mutation rates in the whole genome by resequencing, which implied a low dose could be applied to generate mutations22.

This paper showed the example of HRM-TILLING for mutation screening of gamma induced plants. The HRM-TILLING described in this paper should also be applicable for screening mutations in EMS-mutagenized populations.

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Disclosures

The authors declare that they have no conflict of interest.

Acknowledgments

This work was supported by the National Key Research and Development Program of China (No. 2016YFD0102103) and the National Natural Science Foundation of China (No.31701394).

Materials

Name Company Catalog Number Comments
2× Taq plus PCR Master Mix Tiangen, China KT201 PCR buffer, dNTP and polymerase for PCR amplification
96-well plate Bio-rad, America MSP-9651 Specific plate for PCR in HRM analysis
Mastercycler nexus Eppendorf, German 6333000073 PCR amplification
LightScanner Idaho Technology, USA LCSN-ASY-0011 For fluorescence sampling and processing
CALL-IT 2.0 Idaho Technology, USA For analysis of the fluorescence change
EvaGreen Biotium, USA 31000-T Fluorescence dye of HRM
Nanodrop 2000 Thermo Scientific, USA ND2000 For DNA quantification

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References

  1. McCallum, C. M., Comai, L., Green, E. A., Henikoff, S. Targeting induced local lesions IN genomes (TILLING) for plant functional genomics. Plant Physiology. 123, 439-442 (2000).
  2. Taheri, S., Abdullah, T. L., Jain, S. M., Sahebi, M., Azizi, P. TILLING, high-resolution melting (HRM), and next-generation sequencing (NGS) techniques in plant mutation breeding. Molecular Breeding. 37, (3), 40 (2017).
  3. Till, B. J., et al. Large-scale discovery of induced point mutations with high throughput TILLING. Genome Research. 13, (3), 524-530 (2003).
  4. Comai, L., Henikoff, S. TILLING: practical single nucleotide mutation discovery. The Plant Journal. 45, (4), 684-694 (2006).
  5. Comai, L., et al. Efficient discovery of DNA polymorphisms in natural populations by Ecotilling. The Plant Journal. 37, 778-786 (2004).
  6. Rogers, C., Wen, J., Chen, R., Oldroyd, G. Deletion-based reverse genetics in Medicagotruncatula. Plant Physiology. 151, (3), 1077 (2009).
  7. Bush, S. M., Krysan, P. J. ITILLING: a personalized approach to the identification of induced mutations in arabidopsis. Physiology. 154, (1), 25-35 (2010).
  8. Colasuonno, P., et al. DHPLC technology for high-throughput detection of mutations in a durum wheat TILLING population. BMC Genetics. 17, (1), 43 (2016).
  9. Tsai, H., et al. Discovery of rare mutations in populations: TILLING by sequencing. Plant Physiology. 156, 1257-1268 (2011).
  10. Kumar, A. P. K., et al. TILLING by Sequencing (TbyS) for targeted genome mutagenesis in crops. Molecular Breeding. 37, 14 (2017).
  11. 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 Biology. 9, 143 (2009).
  12. Gady, A. L., Herman, F. W., Wal, M. H. V. D., Loo, E. N. V., Visser, R. G. Implementation of two high through-put techniques in a novel application: detecting point mutations in large EMS mutated plant populations. Plant Methods. 5, (41), 6974-6977 (2009).
  13. Ririe, K. M., Rasmussen, R. P., Wittwer, C. T. Product differentiation by analysis of DNA melting curves during the polymerase chain reaction. Analytical Biochemistry. 245, 154-160 (1997).
  14. Lochlainn, S. O., et al. High resolution melt (HRM) analysis is an efficient tool to genotype EMS mutants in complex crop genomes. Plant Methods. 7, 43 (2011).
  15. Yoshida, S., Forno, D. A., Cock, J. H., Gomez, K. A. Laboratory manual for physiological rice. The International Rice Research Institute. Manila, the Philippines. (1976).
  16. Peng, S. T., Zhuang, J. Y., Yan, Q. C., Zheng, K. L. SSR markers selection and purity detection of major hybrid rice combinations and their parents in China. Chinese Journal of Rice Science. 17, 1-5 (2003).
  17. Allen, G. C., Flores-Vergara, M. A., Krasynanski, S., Kumar, S., Thompson, W. F. A modified protocol for rapid DNA isolation from plant tissues using cetyltrimethymmonium bromide. Nature. 1, (5), 2320-2325 (2006).
  18. Fu, H. W., Li, Y. F., Shu, Q. Y. A revisit of mutation induction by gamma rays in rice (Oryza sativa L.): implications of microsatellite markers for quality control. Molecular Breeding. 22, (2), 281-288 (2008).
  19. Li, S., Liu, S. M., Fu, H. W., Huang, J. Z., Shu, Q. Y. High-resolution melting-based tilling of γ ray-induced mutations in rice. Journal of Zhejiang University-Science B. 19, (8), 620-629 (2018).
  20. Acanda, Y., Óscar, M., Prado, M. J., González, M. V., Rey, M. EMS mutagenesis and qPCR-HRM prescreening for point mutations in an embryogenic cell suspension of grapevine. Cell Reports. 33, (3), 471-481 (2014).
  21. Si, H. J., Wang, Q., Liu, Y. Y., Huang, J. Z., Shu, Q. Y., Tan, Y. Y. Development and application of an HRM-based, safe and high-throughput genotyping system for photoperiod sensitive genic male sterility gene in rice. Journal of Nuclear Agricultural Sciences. 31, (11), 2081-2086 (2017).
  22. Li, S., Zheng, Y. C., Cui, H. R., Fu, H. W., Shu, Q. Y., Huang, J. Z. Frequency and type of inheritable mutations induced by γ rays in rice as revealed by whole genome sequencing. Journal of Zhejiang University-Science B. 17, (12), 905 (2016).

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