Skip to main content
Applications in Plant Sciences logoLink to Applications in Plant Sciences
. 2018 Jun 24;6(6):e01161. doi: 10.1002/aps3.1161

Identification and characterization of microsatellites in Aconitum reclinatum (Ranunculaceae), a rare species endemic to North America

Juan Zhou 1, Wanzhen Liu 1, Hanghui Kong 2,3,, Wei Gong 1,
PMCID: PMC6025813  PMID: 30131903

Abstract

Premise of the Study

Aconitum reclinatum is the only representative species of Aconitum subg. Lycoctonum in North America, with restricted ranges and endangered populations. Polymorphic microsatellite markers were developed for A. reclinatum for further investigation of genetic diversity and population structure.

Methods and Results

Using Illumina HiSeq technology, we sequenced a genomic library for identification of simple sequence repeat markers. A total of 12 polymorphic primer pairs were developed and tested on 66 individuals from four populations in North America. The number of alleles ranged from one to seven per locus with an average of 3.48. Levels of observed and expected heterozygosity varied from 0 to 1.000 and 0 to 0.736, respectively, at population level. Three primer pairs were successfully amplified in three of four closely related species.

Conclusions

The microsatellites isolated in this study will be useful in further research on the genetic diversity and conservation genetics of A. reclinatum populations in North America.

Keywords: Aconitum reclinatum, endangered species, herbal medicine, microsatellites, next‐generation sequencing, Ranunculaceae


Aconitum reclinatum A. Gray, also known as white monkshood, is a perennial herb belonging to Aconitum L. subg. Lycoctonum (DC.) Peterm. in Ranunculaceae. Aconitum subg. Lycoctonum is composed of species distributed throughout Eurasia and North America (Jabbour and Renner, 2012). Aconitum reclinatum is the only representative species of this subgenus in North America, where it has a restricted geographic range. The largest existing populations of A. reclinatum are known to be located in North Carolina in the eastern United States (Hardin, 1964; Brink, 1982). This species is reportedly disturbed and threatened by logging activities and drainage of its habitat (NatureServe, 2007). To date, the genetic diversity and population genetic structure are still unclear for A. reclinatum populations.

Although some species in the genus Aconitum are highly toxic because of aconite alkaloids, it is popularly used as a traditional herbal medicine in Asia (Liang et al., 2017). Previous research has been conducted to reveal the genetic variation among some populations in Aconitum, using traditional molecular markers including amplified fragment length polymorphism (AFLP), inter‐simple sequence repeat (ISSR), and random‐amplified polymorphic DNA (RAPD) markers (Cole and Kuchenreuther, 2001; Meng et al., 2014; Zhao et al., 2015). Most recently, microsatellites have been developed and characterized for some Aconitum species based on next‐generation sequencing (He et al., 2015; Ge et al., 2016). However, those microsatellite markers cannot be amplified in A. reclinatum successfully due to low interspecific transferability. Therefore, we developed microsatellite markers specific for A. reclinatum for further application in the investigation of genetic diversity and population structure. A total of 12 polymorphic microsatellites were isolated and characterized, which will provide information to interpret the fine population structure of this rare and threatened species.

METHODS AND RESULTS

Tender leaves were collected from A. reclinatum and dried instantly in silica gel. A total of 66 individuals and four populations were sampled. Total genomic DNA was extracted from the dried leaves of A. reclinatum using a modified cetyltrimethylammonium bromide (CTAB) method (Doyle and Doyle, 1987). The DNA concentration was quantified using a NanoDrop spectrophotometer (Thermo Fisher Scientific, Carlsbad, California, USA), and a final DNA concentration of >30 ng/μL was used subsequently.

We used one individual of A. reclinatum collected in Three Top Mountain, North Carolina, USA, to construct a genomic DNA library with 360‐bp inserts. The genomic DNA library was sequenced using an Illumina HiSeq platform at Majorbio (Shanghai, China). Because the original sequencing data have some low‐quality reads, we filtered the original data in order to make the subsequent assembly more accurate. After removing the adapter sequences, we excluded those reads that showed no AGCT sequence at the 5′ end, contained high N content (≥10%), or presented short length (<25 bp) after quality trimming. Finally, we only retained reads with high quality scores; 93.4% of the reads were called accurately when using a threshold of Q20. Using SOAPdenovo version 2.04 splicing software (Luo et al., 2012), the assembly of multiple k‐mer parameters was performed on the optimized sequencer, and gaps in the assembly were then locally closed and bases were corrected using GapCloser within SOAPdenovo. A total of 2,464,714 scaffolds were generated. Using MISA (Thiel et al., 2003), a total of 197,407 simple sequence repeat (SSR) loci were detected from the 2,464,714 scaffolds. Primer pairs for the 105 SSR loci with the longest dinucleotide repeats were designed with Primer3 (Rozen and Skaletsky, 1999).

To screen the SSR primers, we conducted PCR amplification using one individual from each of the four populations. PCR amplification was performed in a total volume of 25 μL, containing 12 ng of template DNA, 1.25 units of Taq DNA polymerase, 0.8 μM of forward and reverse primer, 0.2 μM of dNTP, 2 mM of MgCl2, 2.5 μL of 10× PCR buffer, and 14.25 μL of sterilized double‐distilled water. Thermocycling conditions were 94°C (5 min); followed by 35 cycles of denaturation at 94°C (40 s), annealing at 58°C (45 s), and extension at 72°C (50 s); and a final extension of 72°C (10 min). A total of 105 primers were designed for A. reclinatum. The PCR products of 17 primer pairs produced the expected size (Table 1). The remaining 88 primer pairs presented poor amplification. To test the polymorphism level of the 17 primer pairs, the forward primers were labeled using the fluorescent dye FAM. The amplification products were sent to Invitrogen (Shanghai, China) for genotyping. Sequencing Analysis 5.2 (Applied Biosystems, Carlsbad, California, USA) was used to measure the size of the PCR products in the ABI 3730 DNA sequencer (Applied Biosystems). Among a total of 17 primer pairs, five were monomorphic and 12 produced polymorphic sites.

Table 1.

Characteristics of 12 polymorphic and five monomorphic microsatellite loci developed for Aconitum reclinatum

Locus Primer sequences (5′–3′) Repeat motif Allele size range (bp) T a (°C) GenBank accession no.
AR01a F: TTAGACTTACACGGCCCAGG (TG)10 416–424 60.1 SRR6476459
R: GTTCCGGGCTTCTCATAACA
AR02a F: GCTGAACTTGCCATTGTTGA (CA)13 364–374 59.8 SRR6476460
R: TTCAGCCCTCAGGTCAGTCT
AR03a F: ATGAATGCAAAGTCCCTTGG (AG)10 397–411 59.9 SRR6476457
R: GAAGGAGTGCGGTTGATGAT
AR04a F: TGCTGCTTTCAGGAACAATG (TC)10 288–292 60.0 SRR6476458
R: AGGAGGACATTGGTGAATCG
AR05a F: GCTGACAGAGCCATGCTGTA (GT)11 428–438 60.2 SRR6476455
R: ATGGTATTCCCATGCTCAGG
AR06a F: CGATCTGACTAGGCCCACAT (AG)14 398–428 60.1 SRR6476456
R: GGAGAGGGTGGGAATTAGGA
AR07a F: AACACCCTAGAATCCCCCAC (TG)11 351–367 60.1 SRR6476453
R: CGACACACACCGAGTGACAT
AR08a F: ACCCATCATACCAATTCCGA (AC)10 319–325 60.0 SRR6476454
R: TCACATTGGGAATCAAAGCA
AR09a F: CGAGCCATTTCACTTGTGTG (CA)13 285–317 60.3 SRR6476451
R: AGGAGCGAATGTGAGTTGCT
AR10a F: GAAGGGTATTTTCTCCCCCA (CA)11 292–298 60.1 SRR6476452
R: ATCCACAGGGACAAACTTGC
AR11a F: ACCAACTCAGGCATTTGGTC (GA)10 268–282 60.0 SRR6476466
R: CTCCTCCAATCCCATCAGAA
AR12a F: ACCGTTTGATCTTGGCAATC (GA)13 258–276 60.0 SRR6476467
R: TCCTACCCTTGCATCTTTGG
AR13b F: ACCTAACCGAATTGGCTCCT (TC)9 287 60.3 SRR6476464
R: GATGTGCATCCCACAATCAA
AR14b F: TGTTTATACAAGCACCGCGA (GA)9 247 60.0 SRR6476465
R: AGTACGGACCCTTGATCGTG
AR15b F: GGAAAGGGATGAGTCGATGA (GT)10 286 59.9 SRR6476462
R: ACACACACGATTCGGGTACA
AR16b F: CATCCCACAGACATGAATGC (CT)9 360 60.0 SRR6476463
R: TGCAAATCACTAGTGCCGAG
AR17b F: GCTGCATTTGGAAATAGGGA (TC)12 342 59.4 SRR6476461
R: CCTTCAAACCCAACTCAACC

T a = annealing temperature.

a

Tested for polymorphism.

b

Monomorphic markers.

Genotypes appeared diploid, displaying at most two alleles per locus per individual. For each locus, the number of alleles per locus (A), observed and expected heterozygosity (H o and H e), polymorphism information content (PIC), coefficient of inbreeding (F IS), null allele frequency (r), and Hardy–Weinberg equilibrium were analyzed for the four populations. A, H o, H e, and Hardy–Weinberg equilibrium were estimated using GenAlEx 6.5 (Peakall and Smouse, 2012). The presence of null alleles was checked using MICRO‐CHECKER 2.2.3 (van Oosterhout et al., 2004). Linkage disequilibrium and F IS were estimated using GENEPOP software (Rousset, 2008). CERVUS 3.0.7 was used to calculate PIC and r (Kalinowski et al., 2007).

A varied from one to seven with an average of 3.48. Levels of H o and H e ranged from 0 to 1.000 and from 0 to 0.736, respectively, with averages of 0.619 and 0.470. PIC values ranged from 0 to 0.674 with an average of 0.417, and F IS values ranged from −1.000 to 1.000 with an average of −0.177. Null allele frequency values ranged from −0.3330 to 0.971 (Table 2).

Table 2.

Genetic characteristics of the 12 polymorphic microsatellites developed in Aconitum reclinatum.a

Locus US15 (N = 11) US17 (N = 20) US22 (N = 16) US27 (N = 19)
A H o H e b PIC r F IS A H o H e b PIC r F IS A H o H e b PIC r F IS A H o H e b PIC r F IS
AR01 4 0.909 0.616** 0.539 −0.235 −0.439 5 1.000 0.721*** 0.674 −0.175 −0.365 6 1.000 0.613*** 0.537 −0.260 −0.611 5 0.895 0.694*** 0.645 −0.180 −0.265
AR02 2 0.364 0.397ns 0.318 0.044 0.130 4 1.000 0.548*** 0.445 −0.304 −0.818 3 0.750 0.656*** 0.375 −0.333 −0.111 4 0.895 0.630*** 0.558 −0.219 −0.397
AR03 3 0.818 0.533ns 0.432 −0.213 −0.500 3 0.750 0.611*** 0.531 −0.129 −0.203 3 0.938 0.529** 0.421 −0.315 −0.758 3 1.000 0.547*** 0.445 −0.307 −0.819
AR04 3 0.364 0.512* 0.444 0.146 0.333 2 0.350 0.289ns 0.247 −0.094 −0.188 2 0.063 0.061ns 0.375 2 0.526 0.488ns 0.369 −0.038 −0.053
AR05 2 1.000 0.500*** 0.375 −0.333 −1.000 3 1.000 0.524*** 0.410 −0.320 −0.905 3 0.938 0.525** 0.421 −0.315 −0.772 6 1.000 0.658*** 0.597 −0.228 −0.500
AR06 6 0.909 0.711*** 0.665 −0.161 −0.235 3 0.250 0.466* 0.395 0.301 0.484 6 0.688 0.736** 0.586 −0.239 0.098 6 0.474 0.683*** 0.647 0.170 0.331
AR07 2 0.000 0.165*** 0.152 0.888 1.000 3 0.250 0.501*** 0.395 0.290 0.520 2 0.000 0.117*** 0.000 1.000 2 0.000 0.100*** 0.095 0.749 1.000
AR08 2 1.000 0.500*** 0.375 −0.333 −1.000 2 1.000 0.500*** 0.375 −0.333 −1.000 2 1.000 0.500*** 0.375 −0.333 −1.000 4 1.000 0.550*** 0.448 −0.302 −0.810
AR09 7 0.545 0.661** 0.635 0.106 0.221 3 0.700 0.516ns 0.463 −0.186 −0.333 2 0.063 0.061ns 0.375 5 0.474 0.591*** 0.559 0.113 0.225
AR10 3 0.091 0.169*** 0.163 0.451 0.500 1 0.000 0.000 0.000 3 0.063 0.174** 0.375 0.659 2 0.000 0.266*** 0.231 0.971 1.000
AR11 3 1.000 0.541* 0.436 −0.310 −0.833 6 1.000 0.613*** 0.536 −0.261 −0.617 3 1.000 0.529** 0.419 −0.316 −0.882 7 1.000 0.695*** 0.645 −0.201 −0.416
AR12 1 0.000 0.000 0.000 3 0.500 0.551* 0.461 0.030 0.118 5 0.563 0.531ns 0.640 −0.027 5 0.579 0.457ns 0.418 −0.152 −0.241

A = number of alleles; F IS = coefficient of inbreeding; H e = expected heterozygosity; H o = observed heterozygosity; HWE = Hardy–Weinberg equilibrium; N = number of individuals sampled; PIC = polymorphism information content; r = null allele frequency.

a

Locality and voucher information are provided in Appendix 1.

b

Deviations from HWE using χ2 tests: *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, ns = not significant.

We also tested interspecific transferability of these loci in four additional Aconitum taxa (A. angustius W. T. Wang, A. barbatum Pers., A. finetianum Hand.‐Mazz., and A. sinomontanum Nakai). Six or seven individuals were selected from each species and used for amplification (Table 3, Appendix 1). A total of six loci (AR01, AR02, AR07, AR08, AR11, AR12) exhibited successful amplifications in A. finetianum, five loci (AR01, AR02, AR08, AR11, AR12) in A. angustius, and four loci (AR02, AR03, AR08, AR09) in A. barbatum. No microsatellite loci were successfully amplified in A. sinomontanum.

Table 3.

Results of cross‐amplification testing showing allele size ranges of microsatellite loci isolated from Aconitum reclinatum and tested in four related taxa.a

Locus A. angustius (N = 6) A. finetianum (N = 7) A. sinomontanum (N = 6) A. barbatum (N = 7)
AR01 418–424 418–424
AR02 364–374 364–374 364–374
AR03 397–411
AR04
AR05
AR06
AR07 351–367
AR08 319–325 319–325 319–325
AR09 285–325
AR10
AR11 274–282 274–282
AR12 258–276 258–276

— = unsuccessful amplification; N = number of individuals sampled.

a

Locality and voucher information are provided in Appendix 1.

CONCLUSIONS

A total of 69 alleles were identified for 12 polymorphic loci in the four populations of A. reclinatum. These microsatellite loci will be valuable for further interpretation of the fine population structure and conservation strategies of this rare, threatened species.

DATA ACCESSIBILITY

The raw data has been deposited to the National Center for Biotechnology Information Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra/SRP129852); the GenBank accession numbers are provided in Table 1. The BioProject ID number is PRJNA427053, and the BioSample accession number is SAMN08218777.

ACKNOWLEDGMENTS

The authors thank Dr. Andrew Jenkins and Susan Lutz for their assistance in fieldwork. This research was supported by the National Natural Science Foundation of China (31470312, 31470319) and the Science and Technology Planning Project of Guangdong Province, China (2016A030303048, 2017A030303067).

APPENDIX 1. Voucher and locality information for Aconitum species used in this study.

Species Voucher specimen accession no.a Population location N Geographical coordinates
Aconitum reclinatum A. Gray US15 Mt. Jefferson, North Carolina, USA 11 36°24′N, 81°27′W
US17 Mt. Three Top, North Carolina, USA 20 36°25′N, 81°35′W
US22 Hightown, Virginia, USA 16 38°27′N, 79°42′W
US27 Mitchell County, North Carolina, USA 19 36°05′N, 82°09′W
A. angustius W. T. Wang LJP195 Shangcheng County, Henan, China 6 31°42′N, 115°31′E
A. barbatum Pers. ZY57 Huairou District, Beijing, China 7 40°57′N, 116°27′E
A. finetianum Hand.‐Mazz. ZY25 Mt. Junfu, Jiangxi, China 7 26°37′N, 115°19′E
A. sinomontanum Nakai ZY46 Kai County, Chongqing, China 6 31°39′N, 108°46′E

N = number of individuals sampled.

a

One voucher was collected for each sampled population. Herbarium vouchers are deposited in the Herbarium of South China Botanical Garden (IBSC).

Zhou, J. , Liu W., Kong H., and Gong W.. 2018. Identification and characterization of microsatellites in Aconitum reclinatum (Ranunculaceae), a rare species endemic to North America. Applications in Plant Sciences 6(6): e1161.

Contributor Information

Hanghui Kong, Email: konghh@scbg.ac.cn.

Wei Gong, Email: wgong@scau.edu.cn.

LITERATURE CITED

  1. Brink, D. 1982. Tuberous Aconitum (Ranunculaceae) of the continental United States: Morphological variation, taxonomy and disjunction. Bulletin of the Torrey Botanical Club 109: 13–23. [Google Scholar]
  2. Cole, C. T. , and Kuchenreuther M. A.. 2001. Molecular markers reveal little genetic differentiation among Aconitum noveboracense and A. columbianum (Ranunculaceae) populations. American Journal of Botany 88: 337–347. [PubMed] [Google Scholar]
  3. Doyle, J. J. , and Doyle J. L.. 1987. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bulletin 19: 11–15. [Google Scholar]
  4. Ge, X. , Tian H., and Liao W.. 2016. Characterization of 19 microsatellite loci in the clonal monkshood Aconitum kusnezoffii (Ranunculaceae). Applications in Plant Sciences 4: 1500141. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Hardin, J. W. 1964. Variation in Aconitum of eastern United States. Brittonia 16: 80–94. [Google Scholar]
  6. He, J. , Zhang Z., Yang J., Wang H., and Meng J.. 2015. Isolation and characterization of 18 microsatellites for Aconitum vilmorinianum Kom. (Ranunculaceae) using next‐generation sequencing technology. Conservation Genetics Resources 7: 579–581. [Google Scholar]
  7. Jabbour, F. , and Renner S. S.. 2012. A phylogeny of Delphinieae (Ranunculaceae) shows that Aconitum is nested within Delphinium and that Late Miocene transitions to long life cycles in the Himalayas and Southwest China coincide with bursts in diversification. Molecular Phylogenetics and Evolution 62: 928–942. [DOI] [PubMed] [Google Scholar]
  8. Kalinowski, S. T. , Taper M. L., and Marshall T. C.. 2007. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular Ecology 16: 1099–1106. [DOI] [PubMed] [Google Scholar]
  9. Liang, X. , Lan C., Lei S., Fei W., Min H., He C., and Yin Z.. 2017. Diterpenoid alkaloids from the root of Aconitum sinchiangense W. T. Wang with their antitumor and antibacterial activities. Natural Product Research 31: 2016–2023. [DOI] [PubMed] [Google Scholar]
  10. Luo, R. , Liu B., Xie Y., Li Z., Huang W., Yuan J., He G., et al. 2012. SOAPdenovo2: An empirically improved memory‐efficient short‐read de novo assembler. GigaScience 1: 18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Meng, F. , Peng M., Wang R., Wang C., and Guan F. C.. 2014. Analysis of genetic diversity in Aconitum kongboense L. revealed by AFLP markers. Biochemical Systematics and Ecology 57: 388–394. [Google Scholar]
  12. NatureServe . 2007. NatureServe Explorer: An online encyclopedia of life, Version 6.2. Website http://www.natureserve.org/explorer [accessed 18 August 2017].
  13. 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Rousset, F. 2008. GENEPOP'007: A complete re‐implementation of the GENEPOP software for Windows and Linux. Molecular Ecology Resources 8: 103–106. [DOI] [PubMed] [Google Scholar]
  15. Rozen, S. , and Skaletsky H.. 1999. Primer3 on the WWW for general users and for biologist programmers In Misener S. and Krawetz S. A. [eds.], Methods in molecular biology, vol. 132: Bioinformatics: Methods and protocols, 365–386. Humana Press, Totowa, New Jersey, USA. [DOI] [PubMed] [Google Scholar]
  16. Thiel, T. , Michalek W., Varshney R. K., and Graner A.. 2003. Exploiting EST databases for the development and characterization of gene‐derived SSR‐markers in barley (Hordeum vulgare L.). Theoretical and Applied Genetics 106: 411–422. [DOI] [PubMed] [Google Scholar]
  17. van Oosterhout, C. , Hutchinson W. F., Willis D. P. M., and Shipley P. F.. 2004. MICRO‐CHECKER: Software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes 4: 535–538. [Google Scholar]
  18. Zhao, F. , Nie J., Chen M., and Wu G.. 2015. Assessment of genetic characteristics of Aconitum germplasms in Xinjiang Province (China) by RAPD and ISSR markers. Biotechnology and Biotechnological Equipment 29: 309–314. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The raw data has been deposited to the National Center for Biotechnology Information Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra/SRP129852); the GenBank accession numbers are provided in Table 1. The BioProject ID number is PRJNA427053, and the BioSample accession number is SAMN08218777.


Articles from Applications in Plant Sciences are provided here courtesy of Wiley

RESOURCES