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 OJE  Vol.8 No.2 , February 2018
Novel Microsatellite Markers for Conservation of Australian Native Samadera bidwillii
Abstract: Microsatellite markers were developed for Samadera bidwillii, a nationally listed vulnerable shrub or small tree, to enable investigation of its genetic structure and diversity within and among populations from its known distribution throughout coastal areas mainly in fragmented and disturbed lands from Mackay to Gympie, Queensland, Australia. The loci were tested for cross-amplification in related Samadera species. Ten polymorphic microsatellite markers were isolated and characterised from an enriched library of S. bidwillii, which exhibited di- and trinucleotide repeats. The mean number of alleles per locus ranged from 1.3 to 2.5 and mean expected and observed heterozygosities ranged from 0.06 to 0.33 and from 0.03 to 0.26, respectively in five populations. All loci successfully amplified in six other closely associated Samadera species also reported from Australia. Developed loci can be used in genetic diversity, population structure and gene flow studies with an emphasis on the conservation of S. bidwillii and related species.

1. Introduction

Samadera bidwillii (Simaroubaceae, a family known for medicinal compound quassinoids) is a native shrub listed as “vulnerable” (Australian EPBC Act 1999 and Queensland Nature Conservation (Wildlife) Regulation 2006) [1] . The species is restricted to Queensland and known in approximately 40 locations distributed from Mackay to Gympie [2] . The species occurrence has been extended by a coal mine within the Callide Ranges near Biloela in Central Queensland [3] . However, continued loss of its range is occurring due to insect damage, fire, weed invasion, grazing and land clearing. These factors might have contributed to the reduction of genetic diversity level within populations leading towards potential risk of becoming endangered species. Information on partitions of genetic variation within and among populations is a pioneer for its conservation. To support the development of a conservation plan, understanding of the genetic variation in the species, its structure and gene flow patterns at the population level is vital. This genetic variation can be determined by differences in the number of small repeated DNA segment copies called microsatellites. Microsatellites are codominant, highly polymorphic and hyper-variable, robust and highly informative tools to assess inter-population comparisons. Earlier, microsatellite markers were developed in Australian threatened species [4] [5] and other Simaroubaceae genera [6] [7] [8] but not for the Samadera genus. The aim is to develop new microsatellite markers, which can be useful to find genetic variation at the population level in S. bidwillii but are also helpful in cross-amplification in other Samadera species that are also threatened.

2. Materials and Methods

Total genomic DNA was extracted using DNeasy Plant Mini-kit (Qiagen, Germany) from silica gel dried leaves of reference sample collected from an adult shrub sampled in a natural population located in the narrow strip of road reserve in Nikenbah, Queensland. To check the patterns of genetic variation, DNA was extracted from leaves of 30 adults each collected from five substantial populations of S. bidwillii (Figure 1). For cross amplification check, DNA was extracted from herbarium specimens except for Samadera sp. St Mary samples, which were collected from a natural stand found within St Mary State Forest (Figure 1).

The reference DNA elute sequenced at the Australian Genome Research Facility (AGRF, Brisbane), using GS-FLX Titanium KLR70 chemistry (454 Roche Applied Science, Germany) to build an enriched library. The sequences were trimmed to suitable length and filtered for redundancy and multiple copies in the sequence dataset using CLC Genomics Workbench 6.0 software (CLC Bio, Denmark). Microsatellites having a minimum of five repeats for di-nucleotides to hexanucleotides were identified using QDD v2.1 pipeline [9] . The possible primer combinations were viewed in an MS Excel (Microsoft Corp., Seattle) output file.

Unlabelled forward and reverse primer pairs for at least 60 microsatellite loci were targeted for design with suitable flanking regions using Primer 3 web 4.0.0 [10] [11] . Selection criteria such as product size within 100 to 400 bp, optimal primer size 20 bp, melting temperature 59˚C, GC content 50%, and zero GC clamp were left default. Primer lengths less than 18 bp and more than 23 bp were eliminated from selections. Optimisation of PCR conditions on a Mastercycler® gradient thermocycler (Eppendorf, Germany) was performed for each locus using four randomly selected genomic DNA samples of S. bidwillii. The reaction

Figure 1. Location map of Samadera populations and voucher specimen used in this study. Geographic locations for the study populations: C1―Callide; H1―Hervey Bay; R1―River Heads, Queensland; Y3―Young State Forest, Tinana; M1―Mt Bauple. All populations are located in Queensland, Australia.

mix contained 1 µL genomic DNA, 1 x reaction buffer, 0.2 mM of each dNTPs, 1.5 mM MgCl2, 0.4 µM of each primer, 250 ng BSA and 0.25 U of Taq F1 DNA polymerase (Fisher Biotec, Australia) in a total volume of 12.5 µL. The reaction buffer was made up with 67 mM Tris-HCl with pH 8.8, 16.6 mM (NH4)2SO4, 0.45% Triton X-100 and 0.2 mg/mL gelatin. A touchdown gradient program was set with an initial 3 min of denaturation at 95˚C; 14 cycles at 94˚C for 30 s, annealing at 60˚C for 30 s and extension at 72˚C for 30 s (example of 60˚C - 53˚C), and further 21 cycles at 94˚C for 30 s, annealing at 53˚C for 30 s and extension at 72˚C for 30 s. The final extension was for 10 min at 72˚C followed by incubation at 15˚C. A temperature gradient decrease of 0.5˚C in each PCR cycle was imposed for first 14 cycles.

The amplified PCR product was then visualised under UV on 1.5% agarose 0.6 × TBE gel (140 volts; 40 min) containing 0.1 µL/mL EtBr and sized with 100 bp DNA ladder (Fisher Biotec, Australia). Suitable primers with single and clear band patterns were identified at suitable annealing temperature and tested for potential polymorphism on 16 randomly selected DNA samples across the populations. The reaction was performed using the touchdown cycles with the same PCR conditions by setting primers annealing at 55˚C for 30 s in last 21 cycles. Subsets of potentially polymorphic primers were identified from images that showed the combination of repeatable clear bands within the expected size range by running PCR mix on 3% agarose 0.6 × TBE gel electrophoresis (140 volts; 110 min) (Figure 2).

The forward primer of each pair was labelled with one of the FAM, VIC, NED or PET fluorescent dyes to enable multiplexed reaction mixes for fragment analysis of all DNA samples [12] . For that, each mix was made up with 1 µL of 50 times diluted PCR product, 0.5 µL 600 LIZ Size Standard v2.0 (Applied Biosystems, USA) and 8.5 µL Hi-Di™ Formamide (Applied Biosystems, USA). These master mixed samples were denatured for three minutes at 95˚C on heat plate followed by quick cooled in ice before analysed using POP 7™ polymer (Applied

Figure 2. Primer bands patterns at different temperature gradients visualised under UV lights on 1.5% agarose gel. Primer SB01 to SB16, L = 100 bp ladder.

Biosystems, USA) under standard conditions in 8 capillaries AB 3500 Genetic Analyser (Applied Biosystems, USA).

Fragment sizes of the alleles were determined relative to the internal size standard using GeneMarker v.2.4.0 software (SoftGenetics, USA). Scoring was double-checked manually for consistency and accuracy. MICRO-CHECKER v2.2.3 [13] was used to check for evidence of extreme shuttering, large allele dropout and null alleles based on 1000 bootstraps and 95% confidence interval.

Parameters used to assess genetic diversity included a number of alleles per locus (A), observed (HO) and expected heterozygosity (HE) and multilocus matches analysis (unique MLGs) were calculated using the GenAlEx 6.5 software package [14] . A contingency Chi-squared test was performed for each locus to determine if each population site deviated significantly from expectations under the Hardy-Weinberg Equilibrium (HWE) using GenAlEx 6.5. In addition, six other Samadera species from Queensland and New South Wales were also tested for cross-amplification with the same-screened loci.

3. Results and Discussion

DNA extracted from leaf samples of S. bidwillii using the DNeasy® Plant Mini-kit resulted in a right quantity of DNA. Australian herbarium specimens assigned to S. bidwillii were reported to contain alkaloids and tannins, and these chemicals are known to interfere with DNA extraction in woody plants [15] . Thus, fast, reliable and high-quality DNA extraction methods are needed for S. bidwillii leaves. The DNeasy® Plant Mini-kit provided high-throughput isolation of high-quality DNA by removing impurities such as proteins, polysaccharides and enzyme inhibitors from S. bidwillii leaf samples [16] .

The genomic DNA enriched library resulted in 508 sequences with a read length ranging from 150 to 529 (mean 372) nucleotides and 7488 possible primer combinations matching with quality criteria implemented in QDD pipeline. Next-generation sequencing was adopted as it is able to provide large volumes of data within a short time, making it superior to other technologies such as Sanger sequencing [17] . The result was an integration of microsatellite detection and a redundancy check performed to avoid amplification of multiple PCR products [18] . A total of 516 SSR regions were identified, having 364 di-, 133 tri-, 11 tetra- and 8 hexanucleotide repeats (with a minimum of five subunits for all repeats). According to Reference [19] , there is no strict definition of the number of repeats required and even small repeats number could detect polymorphisms [20] . According to Reference [21] , SSR-flanking regions also display microsatellite polymorphism, therefore right size DNA sequence with clear flanking regions from any repeated bases also equally crucial in selecting primers [20] . DNA sequences without microsatellite markers were discarded from this process.

Of the carefully designed 60 microsatellite loci, 57 were found simple repeats and three were compound repeats. All loci ranged from 5 - 12 subunits with a product size from 102 to 332 bp. Subsets of potentially polymorphic primers were verified from images that showed a combination of repeated clear bands within the expected size range (obtained during optimisation of PCR conditions) for each locus obtained from four randomly selected genomic DNA samples.

Based on the variations recorded within 16 random DNA samples of S. bidwillii, 18 loci were selected for further testing (Table 1). The other 42 loci were discarded with no amplification or polymorphism was detected by gel electrophoresis after sequencing. Thus, the efficiency of simple sequence repeats development from the DNA sequencing data was 3.5% obtaining potential loci. Among the 18 loci, 10 carried a di-nucleotide motif and 8 carried a tri-nucleotide motif.

The 18 microsatellite loci were further tested using 150 S. bidwillii samples collected from five populations (each containing 30 samples). Of 18 microsatellite loci, a total of eight loci were detected with null alleles, or with inconsistent scorable bands. According to Reference [22] , null alleles can cause deviation from HWE, causing substantial bias in the population genetic analysis. Hence, the eight microsatellite loci were discarded from further testing (Table 1).

Ten polymorphic microsatellite loci consisting of a total of 28 scorable alleles with firm and precise peaks were scored for the five population samples. In all loci, the number of alleles per locus was found to range from one to four, with the highest in locus SB37 (Table 2). The average mean number of alleles per locus (A) ranged from 1.3 to 2.5. HO varied from 0 to 0.53 and HE from 0 to 0.65, which was found to be comparatively higher than HO across all loci. Based on the locus-wise observed and expected heterozygosity (HE), SB37 has the maximum heterozygosity (0.53 and 0.65, respectively). Similar results have been recorded for S. amara (Simaroubaceae) [23] and Davidsonia jerseyana (an Australian endangered tree species) [24] . The deficiency of heterozygotes could be due to self-incompatibility or clonality in S. bidwillii observed during periodic field visits. There were a total of 69 unique genotypes recorded in multilocus matches analysis, ranged from one (C1) to 30 (H1). This variation may be due to phenotypic plasticity or mutation in S. bidwillii populations, where considerable differences in plant height, stem diameter leaf size and flower colour have been observed during field trips to collect leaves for this study.

A contingency Chi-squared test was performed for each locus to determine whether each population site deviated significantly from expectations under HWE in GenAlEx 6.5. The results revealed significant departures for the loci SB34 and SB40 (p < 0.05) (Table 2). Furthermore, six other Samadera species from Australia were also tested with 10 loci (Table 3).

Satisfactory amplifications were achieved except for Samadera sp. from the Moonee Creek samples (which were found difficult to score for locus SB40). The number of alleles ranged from one to five, indicating that this set of loci could be used in population genetic studies involving species interrelation and delineation for associated Samadera species. Earlier studies proved successful as cross-species amplification of microsatellite markers among closely related taxa [25] [26] [27] ,

Table 1. Characteristics of eighteen primer pairs developed for Samadera bidwillii.

TA = annealing temperature. *Polymorphic loci that were used in the further study, while other loci presented in the table were not. a,b,cIndicates loci combination group that were mixed in the same capillary electrophoresis run.

Table 2. Patterns of variation observed across five populations of Samadera bidwillii.

n = sample size; A = number of alleles; HO = observed heterozygosity; HE = expected heterozygosity. *P < 0.05, **P < 0.01 and ***P < 0.001 indicate a significant deviation from Hardy-Weinberg equilibrium expectation.

Table 3. Cross-amplification of microsatellite loci in other Australian congeneric species.

n = sample size; A = number of alleles; NA = Nonspecific Amplification. Species sample/voucher information: Samadera sp. St Mary, St Mary State Forest, Samadera sp. Tozer Range (AQ 727812, AQ 749148); Samadera sp. Dam Creek (AQ 490886, AQ 672751, AQ 565375, AQ 772859, AQ 558744, AQ 533148); Samadera sp. Mt Goonaneman (AQ 768, AQ 622748, AQ 650561, AQ 668777); Samadera sp. Mt Nardi (CANB 567803, CANB 597565, CANB 597560, CANB 542646, CBG 9003549, CBG 8900669-2); Samadera sp. Moonee Creek (CANB 509611, CANB 537288). Voucher specimen code with AQ: Brisbane herbarium; CANB & CBG: Canberra herbarium.

and these could save time and cost for laboratory cloning and microsatellite screening in characterising many species [28] .

4. Conclusion

Ten polymorphic microsatellite markers for the vulnerable species S. bidwillii were developed and characterised for the first time on record. The efficiency of simple sequence repeats development was found to be 3.5% of the total DNA sequencing data from the developed genomic library to obtain potential loci. The 454 sequencing approach offered a fast and straightforward alternative to traditional methods for microsatellite isolation in genetically unknown and narrowly distributed threatened species. The method has proved appropriate for the ten reported markers suitable for S. bidwillii population genetics study. Primer sequences of ten loci are available with their estimates of expected heterozygosity. The average number of alleles per locus ranged from 1.3 to 2.5 and expected heterozygosity varied from 0 to 0.65. The microsatellite markers that were developed are useful tools for measuring genetic variation, estimation of gene flow and characterisation of the spatial population genetic structure of S. bidwillii. Furthermore, these markers are also useful for devising reliable collection and conservation procedures for S. bidwillii and related species of the genus Samadera.

Acknowledgements

This research was funded by Anglo American (Callide) Coal Ltd and supported by Central Queensland University, The University of the Sunshine Coast, Queensland Herbarium and the Department of Environment and Heritage Protection.

Cite this paper: Naik, V. , Ashwath, N. , Lamont, R. and Shapcott, A. (2018) Novel Microsatellite Markers for Conservation of Australian Native Samadera bidwillii. Open Journal of Ecology, 8, 75-85. doi: 10.4236/oje.2018.82006.
References

[1]   Department of the Environment (2014) Samadera bidwillii in Species Profile and Threats Database. Department of the Environment, Canberra.
http://www.environment.gov.au.

[2]   QDNR (2001) Species Management Profiles for Plants, Species Management Manual, Vol. 3. Queensland Department of Natural Resources, Brisbane.
http://www.qld.gov.au.

[3]   Elsol, J.A. and Elsol, J. (2005) Report on the Rare Plant Species (Version April 2005). Report, Submitted to Anglo Coal (Callide Management) Pty Ltd., Brisbane.

[4]   Conroy, G., Lamont, R.W. and Ogbourne, S. (2013) Development of Microsatellite Markers in Fontainea picrosperma, Isolated Using 454 Pyrosequencing. 2013 University Research Conference: Communicate, Collaborate, Connect—Research on the Rise, Sunshine Coast, 1 July 2013.

[5]   Martins, E.M., Martinelli, G., Arbetman, M.P., Lamont, R.W., Simoes-Araujo, J.L., Powell, D., Ciampi-Guillardi, M., Baldauf, C., Quinet, A., Galisa, P. and Shapcott, A. (2014) Development and Characterization of Microsatellite Loci for Ocotea species (Lauraceae) Threatened with Extinction. Genetics and Molecular Research, 13, 5138-5142.
https://doi.org/10.4238/2014.July.7.6

[6]   Rodriguez, H., Geistlinger, J., Berlyn, G., Kahl, G. and Weising, K. (2000) Characterization of Novel Microsatellite Loci Isolated from the Tropical Dioecious Tree Simarouba amara. Molecular Ecology, 9, 498-500.
https://doi.org/10.1046/j.1365-294x.2000.00871-7.x

[7]   Dallas, J.F., Leitch, M.J.B. and Hulme, P.E. (2005) Microsatellites for Tree of Heaven (Ailanthus altissima). Molecular Ecology Notes, 5, 340-342.
https://doi.org/10.1111/j.1471-8286.2005.00920.x

[8]   Tnah, L.H., Lee, C.T., Lee, S.L., Ng, K.K.S., Ng, C.H. and Hwang, S.S. (2011) Microsatellite Markers of an Important Medicinal Plant, Eurycoma longifolia (Simaroubaceae), for DNA Profiling. American Journal of Botany, 98, e130-e132.
https://doi.org/10.3732/ajb.1000469

[9]   Meglecz, E., Costedoat, C., Dubut, V., Gilles, A., Malausa, T., Pech, N. and Martin, J.-F. (2010) QDD: A User-Friendly Program to Select Microsatellite Markers and Design Primers from Large Sequencing Projects. Bioinformatics, 26, 403-404.
https://doi.org/10.1093/bioinformatics/btp670

[10]   Koressaar, T. and Remm, M. (2007) Enhancements and Modifications of Primer Design Program Primer3. Bioinformatics, 23, 1289-1291.
https://doi.org/10.1093/bioinformatics/btm091

[11]   Untergasser, A., Cutcutache, I., Koressaar, T., Ye, J., Faircloth, B.C., Remm, M. and Rozen, S.G. (2012) Primer3—New Capabilities and Interfaces. Nucleic Acids Research, 40, e115.
https://doi.org/10.1093/nar/gks596

[12]   Schuelke, M. (2000) An Economic Method for the Fluorescent Labeling of PCR Fragments. Nature Biotechnology, 18, 233-234.
https://doi.org/10.1038/72708

[13]   Van Oosterhout, C., Hutchinson, W.F., Wills, D.P.M. and Shipley, P. (2004) MICRO-CHECKER: Software for Identifying and Correcting Genotyping Errors in Microsatellite Data. Molecular Ecology Notes, 4, 535-538.
https://doi.org/10.1111/j.1471-8286.2004.00684.x

[14]   Peakall, R. and Smouse, P.E. (2012) GENALEX 6.5: Genetic Analysis in Excel. Population Genetic Software for Teaching and Research—An Update. Bioinformatics, 28, 2537-2539.
https://doi.org/10.1093/bioinformatics/bts460

[15]   Gibbons, S., Craven, L., Dunlop, C., Gray, A.I., Hartley, T.G. and Waterman, P.G. (1997) The Secondary Metabolites of aff. Samadera SAC-2825: An Australian Simaroubaceae with Unusual Chemistry. Phytochemistry, 44, 1109-1114.
https://doi.org/10.1016/S0031-9422(96)00708-X

[16]   Sanchez-Hernandez, C. and Gaytan-Oyarzun, J.C. (2006) Two Mini-Preparation Protocols to DNA Extraction from Plants with High Polysaccharide and Secondary Metabolites. African Journal of Biotechnology, 5, 1864-1867.

[17]   Metzker, M.L. (2010) Sequencing Technologies—The Next Generation. Nature Reviews Genetics, 11, 31-46.
https://doi.org/10.1038/nrg2626

[18]   Lepais, O. and Bacles, C.F.E. (2011) Comparison of Random and SSR-Enriched Shotgun Pyrosequencing for Microsatellite Discovery and Single Multiplex PCR Optimization in Acacia harpophylla F. Muell. Ex Benth. Molecular Ecology Resources, 11, 711-724.
https://doi.org/10.1111/j.1755-0998.2011.03002.x

[19]   Yu, K., Park, S.J. and Poysa, V. (1999) Abundance and Variation of Microsatellite DNA Sequences in Beans (Phaseolus and Vigna). Genome, 42, 27-34.
https://doi.org/10.1139/g98-100

[20]   Caixeta, E.T., Borém, A. and Kelly, J.D. (2005) Development of Microsatellite Markers Based on BAC Common Bean Clones. Crop Breeding and Applied Biotechnology, 5, 125-133.

[21]   Rallo, P., Dorado, G. and Martín, A. (2000) Development of Simple Sequence Repeats (SSRs) in Olive Tree (Olea europaea L.). Theoretical and Applied Genetics, 101, 984-989.
https://doi.org/10.1007/s001220051571

[22]   Kati, V., Le Corre, V., Michel, S., Jaffrelo, L., Poncet, C. and Délye, C. (2013) Isolation and Characterisation of 11 Polymorphic Microsatellite Markers in Papaver rhoeas L. (Corn Poppy), a Major Annual Plant Species from Cultivated Areas. International Journal of Molecular Sciences, 14, 470-479.
https://doi.org/10.3390/ijms14010470

[23]   Hardesty, B.D., Dick, C.W., Hamrick, J.L., Degen, B., Hubbell, S.P. and Bermingham, E. (2010) Geographic Influence on Genetic Structure in the Widespread Neotropical Tree Simarouba amara (Simaroubaceae). Tropical Plant Biology, 3, 28-39.
https://doi.org/10.1007/s12042-010-9044-3

[24]   Eliott, F.G., Connelly, C., Rossetto, M., Shepherd, M., Rice, N. and Henry, R.J. (2013) Novel Microsatellite Markers for the Endangered Australian Rainforest Tree Davidsonia jerseyana (Cunoniaceae) and Cross-Species Amplification in the Davidsonia Genus. Conservation Genetics Resources, 5, 161-164.
https://doi.org/10.1007/s12686-012-9758-7

[25]   Peakall, R., Gilmore, S., Keys, W., Morgante, M. and Rafalski, A. (1998) Cross-Species Amplification of Soybean (Glycine max) Simple Sequence Repeats (SSRs) within the Genus and Other Legume Genera: Implications for the Transferability of SSRs in Plants. Molecular Biology and Evolution, 15, 1275-1287.
https://doi.org/10.1093/oxfordjournals.molbev.a025856

[26]   Collevatti, R.G., Brondani, R.V. and Grattapaglia, D. (1999) Development and Characterization of Microsatellite Markers for Genetic Analysis of a Brazilian Endangered Tree Species Caryocar brasiliense. Heredity, 83, 748-756.
https://doi.org/10.1046/j.1365-2540.1999.00638.x

[27]   Ouinsavi, C., Sokpon, N., Bousquet, J., Newton, C.H. and Khasa, D.P. (2006) Novel Microsatellite DNA Markers for the Threatened African Endemic Tree Species, Milicia excelsa (Moraceae), and Cross-Species Amplification in Milicia regia. Molecular Ecology Notes, 6, 480-483.
https://doi.org/10.1111/j.1471-8286.2006.01281.x

[28]   Dayanandan, S., Bawa, K.S. and Kesseli, R. (1997) Conservation of Microsatellites among Tropical Tree (Leguminosae). American Journal of Botany, 84, 1658-1663.
https://doi.org/10.2307/2446463

 
 
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