Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Research Notes

Open Access

Development and characterization of 10 microsatellite markers in the Cape horseshoe bat, Rhinolophus capensis (Chiroptera, Rhinolophidae) and cross-amplification in southern African Rhinolophus species

  • Nicolas Nesi1Email author,
  • David S. Jacobs1,
  • Kevin Feldheim2 and
  • Jacqueline M. Bishop1
BMC Research Notes20158:477

https://doi.org/10.1186/s13104-015-1465-5

Received: 15 June 2015

Accepted: 21 September 2015

Published: 26 September 2015

Abstract

Background

The Cape horseshoe bat, Rhinolophus capensis, is endemic to the Cape region of South Africa. Coalescent analysis of mitochondrial DNA sequence data suggests extensive historical gene flow between populations despite strong geographic variation of their echolocation call phenotype. Nevertheless the fine-scale genetic structure and evolutionary ecology of R. capensis remains poorly understood. Here we describe the development of 10 novel polymorphic microsatellite loci to investigate of the dispersal ecology of R. capensis and to facilitate taxonomic studies of Rhinolophus species in southern Africa.

Findings

We report 10 microsatellite primer pairs that consistently amplify scorable and polymorphic loci across 12 African rhinolophid species. Initial analysis of two populations of R. capensis from South Africa revealed moderate to high levels of allelic variation with 4–14 alleles per locus and observed heterozygosities of 0.450–0.900. No evidence of linkage disequilibrium was observed and eight of the loci showed no departure from Hardy–Weinberg equilibrium. Cross-species utility of these markers revealed consistently amplifiable polymorphic loci in eleven additional rhinolophid species.

Conclusions

The cross-amplification success of the microsatellites developed here provides a cost-effective set of population genetic marker for the study of rhinolophid evolutionary ecology and conservation in southern Africa.

Keywords

Rhinolophus capensis MicrosatellitesCross-amplificationGenetic connectivityRhinolophidae

Findings

The Cape horseshoe bat (Rhinolophus capensis) has a predominantly coastal distribution encompassing several major biomes in South Africa [1]. Despite evidence for extensive historical gene flow the species is characterised by geographically structured variation in echolocation frequency which appears to be closely coupled to local environmental conditions [2]. Such phenotypic variation is evident in a number of rhinolophids (1; DSJ, unpublished data). Here we develop and test a suite of microsatellite markers (1) to test hypotheses of adaptive evolution in R. capensis and (2) to better understand population genetic structuring and the recent evolutionary history of African rhinolophids.

Twenty-six species of horseshoe bats have been described in sub-Saharan Africa with 11 occurring in the southern African countries of Botswana, Lesotho, Mozambique, Namibia, South Africa, Swaziland, and Zimbabwe [1]. The taxonomy and true number of species of African Rhinolophus nevertheless remains unresolved because of the highly convergent morphology observed across taxa. Currently species delimitation is based on slight variations in morphological measurements and echolocation call frequency [1, 3] but there is much ambiguity in these characters as a result of extensive intraspecific phenotypic variation and the true diversity of species is likely to be underestimated when using these methods alone. The recent inclusion of genetic data has uncovered a number of species complexes in the African rhinolophids (e.g. R. hildebrandtii and R. darlingi) [4, 5]. The use of an integrative approach combining molecular techniques and morphological analyses has enabled the recognition and description of several new species and will undoubtedly contribute to the improved taxonomic resolution of African rhinolophid diversity.

Microsatellite loci are characterised by high polymorphism and co-dominance which make them ideal genetic markers for use in population genetic studies. These molecular markers have also been widely used to study demographic and ecological processes within species and to resolve taxonomic problems among recently diverged lineages [6]. Their value in such studies is further enhanced by the fact that microsatellite markers primarily developed for a specific taxon can also be used on closely related species, making them a cost effective tool for taxonomic, population and conservation genetic studies.

In this study microsatellite markers were isolated following an enrichment protocol developed by Glenn and Schable [7]. To construct the library we used genomic DNA (gDNA) from both R. capensis and a closely related taxa Dent’s horseshoe bat, R. denti. Total DNA was extracted using a DNeasy Blood and Tissue Kit (Qiagen) from biopsy wing punch samples (3 mm) collected from the wing or tail membrane. To increase the amount of enriched fragments, a ‘recovery’ PCR was performed after the initial round of enrichment. Reactions were performed in a final volume of 25 μL containing 1X PCR buffer (10 mM Tris–HCl, 50 mM KCl, pH 8.3), 1.5 mM MgCl2, 0.16 mM of each dNTP, 10 × BSA, 0.5 μM of the Super SNX24 forward primer, 1U Taq DNA polymerase, and approximately 25 ng enriched gDNA fragments. Thermal cycling, performed in an MJ Research DYAD, was performed as follows: 95 °C for 2 min followed by 25 cycles of 95 °C for 20 s, 60 °C for 20 s, and 72 °C for 90 s, and a final elongation step of 72 °C for 30 min. Subsequent PCR fragments were cloned using the TOPO-TA Cloning kit (Invitrogen) following the manufacturer’s protocol. DNA sequencing was performed using the BigDye® Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems) and run on an ABI 3730 DNA Analyzer. From the resulting libraries we identified 50 R. capensis and 55 R. denti clones that contained repetitive elements with sufficient flanking regions for primer development. A total of 55 primer pairs were designed from the positive clones both libraries using the web-based program Primer3Plus [8].

For the initial PCR trials, amplification of 21 and 15 microsatellite loci from the R. capensis and R. denti libraries, respectively, were tested using eight individuals of each species. PCR was carried out in a final volume of 20 µL containing ~25 ng of DNA template, 0.5 µM of forward and reverse primers, 1X PCR buffer, 1.5 mM MgCl2, 0.2 mM of each dNTP and 5 U/µL Kapa Taq DNA polymerase (Kapa Biosystems). PCR amplification was performed under the following conditions: initial denaturation at 95 °C for 5 min, 35 cycles of 95 °C for 45 s, annealing for 45 s at 48–60 °C (gradient tests) and 72 °C for 45 s, followed by a final extension of 72 °C for 10 min. To confirm amplification and identify unambiguous loci PCR products were first visualised on a 3 % agarose gel under UV. Of the initial 36 loci tested, seven loci isolated from R. capensis and three loci from R. denti consistently amplified scorable loci in our target study species. These loci were then used to determine levels of polymorphism using a sample of 40 R. capensis individuals. All loci were then tested for cross-species amplification and polymorphism in 11 additional rhinolophids distributed in southern and central Africa using 2–5 individuals from across their respective ranges (geographic localities indicated in the Additional file 1). The 5′ end of each forward primer was labelled with a fluorescent dye (6-FAM, NED, PET and VIC) for visualisation of the PCR products. For the initial characterisation of loci amplification for each locus was performed individually using 40 R. capensis individuals from two populations (see Table 1 for PCR conditions). PCR products were then pooled for each individual in two different multiplex panels (Table 1).
Table 1

Characterisation and PCR conditions for 10 microsatellite loci in the Cape horseshoe bat, Rhinolophus capensis

Locus

Repeat motif

Primer sequences (5′–3′)

Dye

Temp. annealing (°C)

Panel

Expected product size (bp)

Observed size range (bp)

Population

n

NA

NE

HO

HE

PHW

PIC

GenBank accession number

RCA1

(ATAG)10

F: TGG GAA ATC TCC AAA CCT TCC

VIC

55

1

560

554–582

Table farm

19

8

4.57

0.737

0.781

0.092

0.789

KP842268

  

R: GGG CTG CCT CAA CAT AAT ACC

     

Heidehof

20

6

4.87

0.700

0.795

0.288

  

RCA2

(AG)32

F: GTG AAC CTA CTT GCT TGA CTC ACC

6-FAM

54

1

519

492–538

Table farm

14

12

6.87

0.786

0.855

0.030

0.903

KP842262

  

R: TGG CCA GTT GAA GAC AGT CTC C

     

Heidehof

17

14

9.63

0.529

0.896

0.0001*

  

RCA3

(TAGA)14

F: AGC AGC CTC CAT AAT AAC ATA AGC

VIC

50

2

420

398–418

Table farm

20

5

3.37

0.700

0.704

0.756

0.708

KP842266

  

R: AAC TCT TAT CCT AAT CTC ACC TCC

     

Heidehof

20

6

4.08

0.750

0.755

0.657

  

RCA4

(TG)20

F: GTA TGA GTC CTT TGT CAG ATA GAG G

6-FAM

52

2

375

374–384

Table farm

19

6

3.48

0.632

0.713

0.008

0.685

KP842263

  

R: TGG TAA CAA TGC AGT GTA ATG G

     

Heidehof

20

5

3.68

0.850

0.729

0.667

  

RCA5

(TC)26

F: GCT CAT GCA TGG TAG GCA ATG G

VIC

57

1

311

288–312

Table farm

18

4

2.48

0.833

0.597

0.121

0.631

KP842265

  

R: AGA CAT TCA GGT CTC CAC ACT CC

     

Heidehof

18

7

3.17

0.667

0.685

0.506

  

RCA6

(GATA)12

F: GCA GAG CAG AAT GTC AAC ATC C

PET

52

2

258

238–262

Table farm

19

4

3.04

0.632

0.672

0.122

0.634

KP842264

  

R: AGT AGC CAT CCC TTT CAA TCC

     

Heidehof

20

5

3.21

0.850

0.689

0.572

  

RCA7

(ATAG)10

F: GCC TTC ATA GAG CTG ATA TGT TGG

NED

56

1

234

228–244

Table farm

20

5

3.90

0.900

0.744

0.359

0.690

KP842267

  

R: GAG CCT GCC ATT CTA CAA TCC

     

Heidehof

20

4

3.49

0.800

0.714

0.893

  

RDE1

(TCTA)11

F: TGT TAC ATC TTG GTC CCT TGA GG

NED

53

1

400

394–410

Table farm

20

5

3.82

0.650

0.739

0.710

0.685

KP842271

  

R: GCT GCA TAT GGG CTG AGA TAA AC

     

Heidehof

20

4

3.37

0.850

0.704

0.326

  

RDE2

(AC)25

F: TCA CTC ACT TCT CTC GAC TCC

PET

53

1

400

387–409

Table farm

18

7

3.10

0.500

0.677

0.106

0.642

KP842269

  

R: AGA CCA CAA CTC TTA ACT CTG C

     

Heidehof

20

7

2.87

0.450

0.653

0.006

  

RDE3

(TC)24

F: CCT TCT CAG TTA CTA TTA CCT CAC C

NED

53

2

336

332–388

Table farm

19

12

9.13

0.737

0.891

0.059

0.872

KP842270

  

R: TGC TCT AAC TCC TCA CAC ACC

     

Heidehof

20

11

5.51

0.750

0.840

0.514

  

Number of individuals genotyped (n), number of alleles observed per locus (NA), effective number of alleles (NE), observed (HO), and expected heterozygosity (HE), Probability of deviation from Hardy–Weinberg Equilibrium (PHW), Polymorphic information content (PIC)

* P values significant after Bonferroni correction at α = 0.05 (p < 0.0025)

For cross-species testing of the loci PCR amplification was carried out using a QIAGEN Multiplex PCR Kit on two multiplex panels containing primers labelled with different fluorescent dyes (seven and two loci); one locus (RCA3, T 50 °C) was amplified alone using the protocol described earlier. Multiplex PCR amplifications were performed in a final volume of 20 µL containing 10 µL of QIAGEN Multiplex PCR Master Mix, 2 µL of the 10× primer mix with each primer at a final concentration of 2 µM and 1 µL of DNA template. Thermal cycling, performed in an Applied Biosystems Veriti 96-Well Thermal Cycler, was performed as follows: 95 °C for 15 min; 35 cycles of 94 °C for 30 s, 57 °C for 90 s, 72 °C for 60 s and a final step of 60 °C for 30 min. PCR products separated by capillary electrophoresis on an AB3730 DNA analyser (Central Analytical Facilities, Stellenbosch, South Africa). Alleles were sized using an internal size standard (GeneScan600LIZ) and scored using the software GeneMarker 2.6.3 (SoftGenetics).

To characterise variation at these loci individuals of R. capensis were genotyped from two colonies representing the full geographic extent of the species (Table Farm: −33.28, 26.42; Heidehof: −34.62, 19.50). For each locus a number of parameters were calculated in GenAlEx 6.5 [9], including the number of alleles observed (NA), effective number of alleles (NE), observed and expected heterozygosities (HO and HE). Tests of Hardy–Weinberg equilibrium (PHW) and tests of linkage of disequilibrium (LD) between loci and populations were calculated in Genepop 4.3 [10]. MicroChecker 2.2.3 [11] was used to detect typing errors and null alleles. The polymorphism information content (PIC) for each locus was assessed with Cervus 3.0.7 [12].

Across the ten loci the number of alleles per locus, NA, ranged from 4 to 14, while the effective number of alleles per locus, NE, ranged from 2.48 to 9.63 in R. capensis (Table 1). The observed (HO) and expected (HE) heterozygosities ranged from 0.450 to 0.900 and 0.597 to 0.896, respectively. Deviation from Hardy–Weinberg equilibrium (HWE) was not found evident except for locus RCA2 (P < 0.05 after Bonferroni adjustment). Null alleles was found for two loci (Table Farm, RDE2 r = 0.122; Heidehof, RDE2, r = 0.172, RCA2, r = 0.201). All of the loci revealed moderate to high PIC values (0.631–0.904).

Cross-amplification tests of the 10 loci were successful on all the 11 species selected, and 98 % of loci were polymorphic (Table 2). The number of Rhinolophus species that showed polymorphism across the 10 loci ranged from 11 to 12 and the number of polymorphic loci within taxa ranged from 9 to 10.
Table 2

Cross amplification results in 11 additional Rhinolophus species from southern Africa

Taxa

Collection country

n

Loci tested

RCA1

RCA2

RCA3

RCA4

RCA5

RCA6

RCA7

RDE1

RDE2

RDE3

Rhinolophus blasii

Malawi, RSA, Zambia, Zimbabwe

5

2/5

5/5

5/5

6/5

3/5

7/5

4/5

6/5

6/5

6/5

 

500–546

488–500

402–454

364–388

200–304

254–270

214–236

376–418

379–401

298–344

Rhinolophus clivosus

RSA, Mozambique

5

5/5

6/5

4/5

6/5

8/5

5/5

5/5

5/5

5/5

8/5

 

534–566

484–524

406–422

366–376

272–324

270–286

236–256

424–454

373–387

330–398

Rhinolophus damarensis

Namibia, RSA

5

4/5

7/5

5/5

6/5

7/5

6/5

4/5

4/5

6/5

7/5

 

528–548

494–534

406–422

376–392

268–332

262–278

232–244

398–490

379–397

298–342

Rhinolophus darlingi

RSA, Zimbabwe

5

1/3

3/5

6/5

7/5

4/5

8/5

6/5

7/5

4/5

7/5

 

500

478–500

418–442

364–392

200–256

244–296

212–248

374–454

395–401

302–338

Rhinolophus denti

Botswana, Namibia, RSA

5

4/4

4/4

4/4

2/4

6/4

3/4

3/5

4/4

5/4

4/3

 

512–540

510–530

406–422

370–376

258–340

242–258

228–248

386–402

391–401

312–350

Rhinolophus fumigatus

Namibia, Zimbabwe

5

5/5

5/5

6/5

4/5

8/5

6/5

5/5

7/5

5/5

4/5

 

536–560

478–528

414–442

368–394

228–292

302–330

220–236

438–472

374–402

302–308

Rhinolophus hildebrandtii

RSA, Zimbabwe

5

3/5

2/5

3/5

4/5

8/5

6/5

5/5

4/5

3/5

2/5

 

544–552

474–480

414–432

370–376

248–284

258–296

226–240

380–398

385–391

308–312

Rhinolophus landeri

Zimbabwe

2

4/2

2/2

2/2

2/2

2/2

4/2

4/2

1/2

2/2

4/2

 

536–554

464–486

424–436

362–376

238–254

266–282

224–238

376

379–391

272–306

Rhinolophus mossambicus

Zambia, Zimbabwe

4

4/3

6/3

6/3

4/4

5/3

4/4

5/3

6/3

4/3

2/3

 

544–556

478–520

398–430

360–366

224–242

278–292

224–240

402–446

361–391

300–304

Rhinolophus simulator

Botswana, RSA, Zambia, Zimbabwe

5

5/5

8/5

6/5

4/5

4/5

3/5

5/5

5/5

7/5

7/5

 

542–574

488–528

398–430

370–384

294–304

250–258

228–256

386–402

381–409

318–352

Rhinolophus swinnyi

RSA

5

3/5

6/5

4/5

1/5

3/5

3/5

2/5

4/5

3/5

5/5

 

532–540

500–522

402–418

364

268–314

246–254

232–236

386–398

381–393

332–346

For each species on the top row: the number of alleles/genotypes scored. On the bottom row: allele size ranges

GPS locality for each individual are indicated in the Additional file 1

The 10 microsatellite markers reported here will be highly useful for a wide range of population and evolutionary genetic studies in the African Rhinolophidae. Microsatellite markers presented here, together with mitochondrial and additional nuclear markers, will provide valuable tools to resolve the systematic and taxonomic relationships of several species complexes of Rhinolophus.

Availability of the supporting data

The microsatellite sequences are available through the National Centre for Biotechnology Information (http://www.ncbi.nlm.nih.gov); GenBank accession numbers KP842262–KP842271.

Declarations

Authors’ contributions

JMB and NN were responsible for the design of the study and the interpretation of results. KF developed the clone libraries. NN performed the lab work and data analysis. NN and JMB wrote the manuscript. All authors read and approved the final manuscript.

Acknowledgements

We thank L. Odendaal for R. capensis and R. denti tissues samples. Microsatellite development was carried out in the Field Museum’s Pritzker Laboratory for Molecular Systematics and Evolution operated with support from the Pritzker Foundation. This research was funded by grants to DSJ from the South African Research Chair Initiative of the Department of Science and Technology and administered by the National Research Foundation of South Africa. NN is funded by aSouth African Research Chair Initiative Postdoctoral fellowship.

Compliance with ethical guidelines

Competing interests The authors declare that they have no competing interests.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Biological Sciences, University of Cape Town
(2)
Pritzker Laboratory for Molecular Systematics and Evolution, Field Museum of Natural History

References

  1. Monadjem A, Taylor PJ, Cotterill FPDW, Schoeman MC. Bats of Southern and Central Africa a biogeographic and taxonomic synthesis. Johannesburg: Wits University Press; 2010. p. 534.Google Scholar
  2. Odendaal LJ, Jacobs DS, Bishop JM. Sensory trait variation in an echolocating bat suggests roles for both selection and plasticity. BMC Evol Biol. 2014;14:60.PubMed CentralView ArticlePubMedGoogle Scholar
  3. Csorba G, Ujhelyi P, Thomas N. Horseshoe bats of the world (Chiroptera: Rhinolophidae). Alana Books; 2003. p. 160i.Google Scholar
  4. Taylor PJ, Stoffberg S, Monadjem A, Schoeman MC, Bayliss J, Cotterill FPD. Four new bat species (Rhinolophus hildebrandtii Complex) reflect Plio-Pleistocene divergence of dwarfs and giants across an afromontane archipelago. PLoS One. 2012;7:e41744.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Jacobs DS, Babiker H, Bastian A, Kearney T, van Eeden R, Bishop JM. Phenotypic convergence in genetically distinct lineages of a Rhinolophus species complex (mammalia, chiroptera). PLoS One. 2013;8:e82614.PubMed CentralView ArticlePubMedGoogle Scholar
  6. Selkoe KA, Toonen RJ. Microsatellites for ecologists: a practical guide to using and evaluating microsatellite markers. Ecol Lett. 2006;9:615–29.View ArticlePubMedGoogle Scholar
  7. Glenn TC, Schable NA. Isolating microsatellite DNA loci. Methods Enzymol. 2005;395:202–22.View ArticlePubMedGoogle Scholar
  8. Untergasser A, Nijveen H, Rao X, Bisseling T, Geurts R, Leunissen JAM. Primer3Plus, an enhanced web interface to Primer3. Nucleic Acids Res. 2007; 35 (Web Server issue):W71–4. http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi/.
  9. Peakall R, Smouse PE. GenALEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics. 2012;28:2537–9.PubMed CentralView ArticlePubMedGoogle Scholar
  10. Rousset F. GENEPOP’007: a complete re-implementation of the GENEPOP software for Windows and Linux. Mol Ecol Resour. 2008;8:103–6.View ArticlePubMedGoogle Scholar
  11. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P. Micro-Checker: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes. 2004;4:535–8.View ArticleGoogle Scholar
  12. Kalinowski ST, Taper ML, Marshall TC. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol. 2007;16:1099–106.View ArticlePubMedGoogle Scholar

Copyright

© Nesi et al. 2015

Advertisement