High-resolution melting analysis for genotyping

Coral reefs are one of the most diverse and productive ecosystems on the planet, comparable to tropical rain forests (Connell 1978). This is largely caused by the symbiosis between scleractinian corals and dinoflagellates from the genus Symbiodinium (Freudenthal 1962). Symbiodinium is genetically classified into nine clades, designated with the letters A–I, with more than 50 types recognized (Coffroth & Santos 2005; Pochon & Gates 2010). Originally, it was thought that these dinoflagellates belonged to one, pandemic species, Symbiodinium microadriaticum (Taylor 1974). However, the introduction of molecular techniques opened a new avenue of research and gave light to the great existing diversity (Baker 2003; Coffroth & Santos 2005). Restriction fragment length polymorphism (RFLP) of PCR-amplified small subunit ribosomal RNA (SSU rRNA) was the first technique used (Rowan & Powers 1991). Further advances in the molecular identification of Symbiodinium include the use of denaturing gradient gel electrophoresis (DGGE) (LaJeunesse & Trench 2000), single-stranded conformation polymorphism (SSCP) (van Oppen et al. 2001), cloning (e.g. Apprill & Gates 2007), direct sequencing (e.g. Sampayo et al. 2009), real-time PCR (qPCR) (Ulstrup & van Oppen 2003; Mieog et al. 2007), and microsatellites (Santos & Coffroth 2003; Pettay & LaJeunesse 2007, 2009; Carlon & Lippe´ 2008; Andras et al. 2009; Bay et al. 2009). Ribosomal DNA (rDNA) is the molecular region that has been most widely used. Particular attention has given to the internal transcribed spacer, region 2, ITS2 rDNA, which has thus far been considered the most informative molecular region for identifying Symbiodinium types (e.g. LaJeunesse 2001). Given its variability and sensitivity, the majority of recent studies perform one of two post-PCR techniques: cloning or DGGE (Apprill & Gates 2007; Sampayo et al. 2009). The former technique has been criticized, because it may overestimate Symbiodinium diversity (Thornhill et al. 2007). DGGE, on the other hand, has been an alternative and successful approach used in the studies of microbial diversity, ecology and evolution (Muyzer 1999) and has been widely used to identify the genetic diversity of Symbiodinium (LaJeunesse & Trench 2000; LaJeunesse 2001, 2002). In DGGE, different banding profiles are obtained because of differences in the melting temperature, Tm, of double-stranded DNA (dsDNA) caused by a denaturant solvent, e.g. formamide and urea. However, this technique also possesses its limitations. It is time-consuming as it requires ~5 h for preparing the gel, between 10 and 15 h to run the electrophoresis, and ~1.5 h postelectrophoresis per gel. It is extremely labour-intensive with multiple steps including gel casting, visualization, excising bands, precipitation and re-amplification. Moreover, expertise is also required in several steps during DGGE analyses, such as skills in excising bands on the gel and, most importantly, in interpreting and comparing banding profiles from other studies ⁄ laboratories. In brief, while DGGE has been an effective technique in identifying Symbiodinium diversity, the mastering and, therefore, the use of this technique is an art.

Here, we use high-resolution melting (HRM) analysis as an alternative technique to rapidly and accurately genotype monotypic Symbiodinium populations. HRM is similar to DGGE in detecting mutations based on the melting properties of dsDNA. Different melting profiles are obtained from the transition of dsDNA to more denatured, single-stranded DNA (ssDNA) as a result of a gradual temperature increase after PCR amplification (Reed et al. 2007). All the processes of PCR amplification followed by HRM take place in the same tube during a real-time run in <2 h. The recent development of HRM can be attributed to the generation of new dyes designed for this technique and the technological improvements in real-time PCR instruments (Wittwer et al. 2003; Herrmann et al. 2007). HRM is considered the simplest method for genotyping and detecting mutations, because it is performed immediately after qPCR (Montgomery et al. 2007; Reed et al. 2007; Vossen et al. 2009). Previous studies have shown HRM as a more effective option compared to methods such as denaturing high-performance liquid chromatography (dHPLC), temperature gradient capillary electrophoresis (TGCE), mass spectroscopy and DGGE (Reed et al. 2007; Vossen et al. 2009). HRM has been used mainly for screening mutations linked to human diseases (e.g. Millat et al. 2009; Pineda-A´ lvarez et al. 2010) and for genotyping bacteria (e.g. Odell et al. 2005; Cheng et al. 2006; Stephens et al. 2008). To date, only one study has used HRM in wildlife populations. It demonstrated the ease and sensitivity of HRM for genetic studies of swordfish populations (Smith et al. 2010). To evaluate the potential of using HRM to genotype Symbiodinium strains based on the internal transcribed spacer, region 2, ribosomal DNA (ITS2 rDNA), HRM-based genotyping was compared to DGGE profiles, where gel bands were extracted and sequenced.

Symbiodinium cultures of clades A–E were obtained from Dr Scott Santos (Auburn University, USA; webpage: http://www.auburn.edu/~santosr/phplabware.htm) and transferred to new f ⁄ 2 media (Sigma) on a monthly basis. They were maintained at a constant temperature of 25 "C and exposed to a photoperiod 12-h:12-h light:dark and a light intensity of !80 photons ⁄m2 per second. Each culture was used for DNA extraction, and for the majority of the cultures, two replicates were used, for a total of 42 samples (Table 1). DNA extractions were carried out using DNeasy# Plant Mini kit (Qiagen) with a slight modification: additional cellular lysis was performed manually with the aid of a pestle. From there, the protocol was followed according to the manufacturer’s guidelines. The ITS2 nuclear ribosomal region was amplified using the forward primer ‘ITSintfor2’ 5¢-GAA TTG CAG AAC TCC GTG-3¢ (Invitrogen) and the reverse primer ‘ITS2CLAMP’ 5¢-CGC CCG CCG CGC CCC GCG CCC GTC CCG CCG CCC CCG CCC GGG ATC CAT ATG CTT AAG TTC AGC GGG T-3¢ (Invitrogen) (LaJeunesse and Trench 2000). The underlined portion corresponds to the GC-clamp. PCR amplification had the following conditions: 1 lL of template at a concentration between 3 and 10 ng ⁄ lL, 10 lL of GoTaq Green Master Mix 1· (2· Green GoTaq reaction buffer, 400 lM each dNTP, 3.0 mM MgCl2, GoTaq DNA polymerase, Promega), 0.25 lM forward primer, 0.75 lM reverse primer, and added Milli-Q water for a final volume of 20 lL per reaction. The touchdown protocol consisted of an initial denaturing step at 92 "C for 3 min, 21 cycles at 92 "C for 30 s, 62 "C for 40 s, and 72 "C for 30 s, decreasing each cycle 0.5 "C, followed by 15 cycles with a 52 "C annealing step and a final extension at 72 "C for 10 min. DGGE was carried out using a gradient of 45–80% using 8% acrylamide and 100% acrylamide, which consisted of 7 M urea and 40% deionized formamide. The electrophoresis was run for 14 h at a constant voltage of 100 V at 60 "C. Bands were excised, precipitated with EtOH and re-amplified. The re-amplification was carried out with 1.0 lL of template, 10 lL of GoTaq Green Master Mix 1· (Promega), 0.25 lM forward primer, 0.25 lM reverse primer (ITS2Rev, without clamp; Coleman et al. 1994), brought to a final volume of 20 lL with Milli-q water, and annealing temperature at 52 "C. PCR products were sent to the DNA Analysis Facility at Yale University for preparation and sequencing. Sequences were verified and edited with CodonCode Aligner (CodonCode Corp.), and their identities were determined using BLAST to search NCBI’s GenBank. To further analyse nucleotide differences within the same clade, sequences were aligned using the program CLC Sequence Viewer v6.3 (CLC bio). Final edited sequences are available in GenBank (accession numbers HQ317737– HQ317756).

Summary

Table 1 summarizes and compares the results from both gel electrophoresis and HRM of all the cultures tested.

Culture information

All the cultures used where pretty consistent and with similar geographic location.

HRM

The HRM results all have a very high percentage confidence.

Data Comparison

Even though the two processes were accurate, the HRM was more accurate and quicker so it is the preferred process.

High-quality DNA for HRM was obtained using the QIAamp DNA Stool kit (Qiagen) from fresh cultures. Precipitation was carried out using 100% cold ethanol (200- proof Molecular Grade, Fisher) and 3 M sodium acetate to eliminate PCR inhibitors. HRM assays were performed using the Rotor-Gene 6000 (Qiagen) with the provided Rotor-Gene Q Series Software v1.7 (Qiagen). PCR was carried out using the Type-It HRM PCR kit (Qiagen), with 1 lL of 1 ng ⁄ lL template, 1· HRM PCR Master Mix (2· HRM PCR Master Mix containing HotStartTaq Plus DNA polymerase, Type-it HRM PCR buffer (with EvaGreen dye), Q-solution, dNTPs; Qiagen), 0.7 lM of each primer (ITSintfor2 and ITS2rev), and adjusted with RNase-free water to a final volume of 10 lL. To check for precision, three technical replicates were performed per sample. The conditions of the qPCR consisted of an initial denaturing cycle at 95 "C for 5 min, 45 cycles at 95 "C for 10 s, 55 "C for 30 s and 72 "C for 10 s, acquiring dye (Eva- Green) emission at 530 nm for the 72 "C. HRM consisted of a temperature ramp between 78 and 90 "C, rising by 0.1 "C⁄ 2 s. Samples with the best amplification were used as reference controls for each genotype (Table 1). The rest of the samples were assigned to a genotype based on the percentage of confidence (Table 1). To calculate the percentage of confidence, an error is obtained from the square of the difference between the fluorescence of each reading of the sample and the reference genotype. This value is then added across all fluorescence readings and then incorporated in an algorithm executed by the Rotor- Gene Q Series Software v1.7 (Qiagen). The manufacturer technical support (Qiagen) recommends using a value above 85%. The accuracy of genotype detection by HRM was compared on the same samples genotyped using the DGGE technique.

Twenty cultured Symbiodinium isolates (!two replicates per culture) were successfully genotyped with PCR-DGGE (Table 1, Fig. S1, Supporting Information), confirming the previous identification carried out by Dr Scott Santos (Auburn University, USA) using 18S nrDNA and 23S cp-rDNA on the same cultures. Additionally, some of the cultures were previously genotyped using ITS2-DGGE, which also matches with the identification carried out in this study (Thornhill et al. 2007). Similarly, with the use of HRM analysis, we were able to detect and correctly identify the genotypes of all cultures with a percentage of confidence >90%, when compared to DGGE genotypes, except one genotype >84% (Fig. 1a, Table 1). The melting profiles of each clade were clearly distinctive. Moreover, different melting profiles were obtained within Symbiodinium clades (Fig. 1b). These differences were attributed to variation in the nucleotide composition. For example, Symbiodinium types A3 and A4 have 50 bp different when comparing their sequences from DGGE bands, while the difference between types D1a and D1 was only 2 bp. This distinction was clearly seen in their melting profiles (Fig. 1a).

All species

On the first chart (Fig. 1a), one can see there is a negative correlation between temperature and fluorescence. As temperature increases, fluorescence decreases, for all the species tested.

clade A

This graph (Fig.1b) shows the relationship between temperature and fluorescence in the clade A. Strain A1 peaks at a temperature of around 83˚C.

clade B

This graph (Fig.1c) also shows the relationship between temperature and fluorescence, but this one is for clade B. This graph ranges from positive to negative on the fluorescence, and there are negative and positive peaks at temperatures between 85˚C and 86˚C.

To understand potential limitations of the HRM technique, three tests were conducted. The first test determined whether template concentration affected the accuracy of genotyping by HRM. For this test, two trials were carried out at low- and high-template concentration. The low-template concentration trial used 29 samples at two final concentrations in the PCR, 1 and 10 ng ⁄ lL, with three technical replicates per sample. The high-template concentration trial used two samples with the best quality from Symbiodinium types B1 and B2 and increased the concentration considerably, 15, 30 and 50 ng ⁄ lL (the maximum recommended by technical guidelines from Qiagen). For each genotype, one of the samples was used as the reference genotype, while the other was treated as an ‘unknown.’ Two technical replicates were carried out per sample. The low-template concentration trial resulted in a shift of !3 Ct values, as expected, but this did not affect the correct genotyping by HRM (data not shown). Similarly, the high-template concentration trial resulted in a shift of the Ct values, but again genotyping was not affected (Fig. S2, Supporting Information). This was not surprising, as DNA concentration weakly affects the Tm of the template (Montgomery et al. 2007). However, when using the upper-limit concentration, e.g. 50 ng ⁄ lL, the correct genotype was identified with a lower percentage of confidence (Fig. S2, Supporting Information).

The second and third tests addressed real-case scenarios of mixed Symbiodinium populations. As previously shown, natural biological variation exists between the coral and its symbiont, where more than one Symbiodinium clade or type could be hosted by a single colony (Baker 1999). A problem in these cases would be to call for a particular (single) genotype using HRM knowing that more than one Symbiodinium strain is present in the sample. The second test was carried out in a pairwise fashion by combining DNA from Symbiodinium types of the same clade. For this test, three clades were available: clade A with types A3 and A4, clade B with types B1, B2 and B3, and clade D with types D1a and D1. The only exception of a pairwise combination was clade B, where one mixture included all three types, B1, B2 and B3. A 25 lL reaction was assembled with equal concentrations of each template, and with the same HRM conditions and protocol as previously described. Each pairwise combination had two technical replicates. We found that the melting profiles of combined samples were different from the original reference genotypes used in the mixtures. Fluorescence difference plots clearly show the distinct profiles obtained for the mixtures (Fig. 1b–d, grey lines represent combinations). For the third test, pairwise combinations between all clades (A–E), including types within each clade, were performed. DNA of each Symbiodinium type was mixed in the PCR with DNA of other Symbiodinium type (e.g. A3 + B1, A3 + B2, A3 + B3, A3 + C3, and so on). PCR conditions and protocol were kept constant, and two technical replicates were used. The melting profiles obtained for the pairwise combinations were always intermediate between the two reference genotypes been mixed (Fig. S3, grey lines represent combinations, Supporting Information). Because combined samples display different melting profiles, false-positives (type-1 error) are not expected, and therefore, there is no concern of overestimating the abundance of a particular strain of Symbiodinium within a complex sample.

In this study, we showed that high-resolution DNA melting analysis (HRM) allows precise genotyping of Symbiodinium strains. There are several advantages of HRM, making it an attractive technique. HRM is a closedtube technique that reduces cross-contamination and does not require the handling of hazardous materials, such as acrylamide, formamide and ethidium bromide. It is time-effective, requiring <2 h per run, which facilitates rapid turnover. This technique is sensitive, simple, nondestructive and of low cost. The software used reduces subjectivity by the researcher during the genotyping process. Moreover, instruments like the Rotor-Gene 6000 (Qiagen) are of suitable size to be brought to the field for genotyping during the sampling process. However, it is also important to understand its limitations. While currently all real-time PCR instruments are designed to detect the fluorescence of melting curves, not all of them are designed for HRM analysis (Herrmann et al. 2007). Though initial investment of acquiring the equipment could be high, the cost of the technique is up to ten times less than traditional screening, making it cost-effective in the long run (e.g. Cheng et al. 2006). One limitation of HRM analysis is that genotyping is restricted to the collection of available reference genotypes. The more Symbiodinium strains genotyped by the scientific community, the more power HRM will gain. Another limitation of HRM is that it is constrained to the detection of monotypic Symbiodinium populations. In our case, this limitation was proven when performing the pairwise combinations of Symbiodinium strains. Therefore, in cases where genotypes cannot be identified, these could be the result of either a lack of an appropriate reference genotype at the time of the analysis and ⁄ or the presence of a mixture of strains. Nonetheless, the conservative estimation of Symbiodinium diversity when using HRM contrasts with other methods that could overestimate abundance, such as solely cloning or DGGE. Cloning increases the estimation of diversity when assuming that intragenomic variation correlates with symbiont types (Thornhill et al. 2007). DGGE could potentially increase Symbiodinium diversity if PCR artefacts, especially heteroduplexes, or intragenomic variation, e.g. pseudogenes, are not correctly detected (Thornhill et al. 2007). Overall, HRM is an excellent alternative to easily, rapidly and accurately genotype monotypic populations of Symbiodinium. Moreover, this is of great advantage for field coral reef ecologists and physiologists as no expertise in advanced molecular techniques is required.

ACKNOWLEDGEMENTS

We thank Dr Scott Santos from Auburn University for kindly providing all the Symbiodinium cultures, Ms Amy Smith at the Technical Service from Qiagen for helping in troubleshooting problems with the HRM runs, Ms Genevieve Calixte (undergraduate student) for her assistance in the laboratory and graduate students from IMaGeS lab Mr Anthony Bellantuono and Ms Pei-Ciao Tang for reviewing previous drafts of this manuscript. This research was funded by a NSF-OCE grant (0851123) awarded to MRL. This project was also supported in part by funds from a PADI Foundation grant awarded in 2009 to CGC.

REFERENCES

1. J.P. Andras, N.L. Kirk, M.A. Coffroth, C.D. Harvell, Molecular Ecology Resources 9, 989–993 (2009).
2. A.M. Apprill, R.D. Gates, Molecular Ecology 16, 1127–1134 (2007).
3. A.C. Baker, The symbiosis ecology of reef-building corals. (PhD dissertation, University of Miami, Miami, FL, 1999) 120 pp.
4. A.C. Baker, Annual Review of Ecology, Evolution, and Systematics 34, 661–689 (2003).
5. L.K. Bay, E.J. Howells, M.J.H. van Oppen, Conservation Genetic Resources 1, 199–203 (2009).
6. D.B. Carlon, C. Lippe, Molecular Ecology Resources 8, 870–873 (2008).
7. J.C. Cheng, C.L. Huang, C.C. Lin et al. Clinical Chemistry 52, 1997–2004 (2006).
8. M.A. Coffroth, S.R. Santos, Protist 156, 19–34 (2005).
9. A.W. Coleman, A.S. Suarez, L.J. Goff, Journal of Phycology 30, 80–90 (1994).
10. J.H. Connell, Science 199, 1302–1310 (1978).
11. H.D. Freudenthal, Journal of Protozoology 9, 45–52 (1962).
12. M.G. Herrmann, J.D. Durtschi, C.T. Wittwer, K.V. Voelkerding, Clinical Chemical 53, 1544–1548 (2007).
13. T.C. LaJeunesse, Journal of Phycology 37, 866–880 (2001).
14. T.C. LaJeunesse, Marine Biology 141, 387–400 (2002).
15. T.C. LaJeunesse, R.K. Trench, Biological Bulletin 199, 126–134 (2000).
16. J.C. Mieog, M.J.H. van Oppen, N.E. Cantin, W.T. Stam, J.L. Olsen, Coral Reefs 26, 449–457 (2007).
17. G. Millat, V. Chanavat, C. Rodriguez-Lafrasse, R. Rousson, Clinical Biochemistry 42, 491–499 (2009).
18. J. Montgomery, C.T. Wittwer, R. Palais, L. Zhou, Nature 2, 59–66 (2007).
19. G. Muyzer, Current Opinion in Microbiology 2, 317–322 (1999).
20. I.D. Odell, J.L. Cloud, M. Seipp, C.T. Wittwer, American Journal Clinical Pathology 123, 96–101 (2005).
21. M.J.H. van Oppen, F.P. Palstra, A.M.T. Piquet, D.J. Miller, Proceedings of the Royal Society of London B 268, 1759–1767 (2001).
22. D.T. Pettay, T.C. LaJeunesse, Molecular Ecology Notes 7, 1271–1274 (2007).
23. D.T. Pettay, T.C. LaJeunesse, Molecular Ecology Resources 9, 1022–1025 (2009).
24. D. Pineda-Alvarez, C. Dubourg, V. David, E. Roessier, M. Muenke, America Journal of Medical Genetics Part C Seminars in Medical Genetics 154C, 93–101 (2010).
25. X. Pochon, R.D. Gates, Molecular Phylogenetics and Evolution 56, 492–497 (2010).
26. G.H. Reed, J.O. Kent, C.T. Wittwer, Pharmacogenomics 8, 597–608 (2007).
27. R. Rowan, D.A. Powers, Marine Ecology Progress Series 71, 65–73 (1991).
28. E.M. Sampayo, S. Dove, T.C. LaJeunesse, Molecular Ecology 18, 500–519 (2009).
29. S.R. Santos, M.A. Coffroth, Biological Bulletin 204, 10–20 (2003).
30. B.L. Smith, C.P. Lu, J.R. Alvarado Bremer, Molecular Ecology Resources 10, 193–196 (2010).
31. A.J. Stephens, J. Inman-Bamber, P.M. Giffard, F. Huygens, Clinical Chemistry 54, 432–436 (2008).
32. D.L. Taylor, In: Symbiosis in the Sea (ed. Vernberg WB), pp. 245–262. (University of South Carolina Press, South Carolina, 1974).
33. D.J. Thornhill, T.C. LaJeunesse, S.R. Santos, Molecular Ecology 16, 5326–5340 (2007).
34. K.E. Ulstrup, M.J.H. van Oppen, Molecular Ecology 12, 3477–3484 (2003).
35. R.H.A.M. Vossen, E. Aten, A. Roos, J.T. den Dunnen, Human Mutation 30, 860–866 (2009).
36. C.T. Wittwer, G.H. Reed, C.N. Gundry, J.G. Vandersteen, R.J. Pryor, Clinical Chemistry 49, 853–860 (2003).