Next Article in Journal
Attenuation of Combined Nickel(II) Oxide and Manganese(II, III) Oxide Nanoparticles’ Adverse Effects with a Complex of Bioprotectors
Previous Article in Journal
The Role of HMGB1 Signaling Pathway in the Development and Progression of Hepatocellular Carcinoma: A Review
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Identification and Evaluation of 21 Novel Microsatellite Markers from the Autumnal Moth (Epirrita autumnata) (Lepidoptera: Geometridae)

1
Norwegian Institute for Bioeconomy Research (NIBIO), Norwegian Institute for Bioeconomy Research, Svanhovd, 9925 Svanvik, Norway
2
Department of Arctic and Marine Biology, University of Tromsø, 9294 Tromsø, Norway
3
Norwegian Institute for Nature Research (NINA), 9296 Tromsø, Norway
*
Authors to whom correspondence should be addressed.
Int. J. Mol. Sci. 2015, 16(9), 22541-22554; https://doi.org/10.3390/ijms160922541
Submission received: 20 August 2015 / Revised: 9 September 2015 / Accepted: 11 September 2015 / Published: 17 September 2015
(This article belongs to the Section Biochemistry)

Abstract

:
The autumnal moth (Epirrita autumnata) is a cyclically outbreaking forest Lepidoptera with circumpolar distribution and substantial impact on Northern ecosystems. We have isolated 21 microsatellites from the species to facilitate population genetic studies of population cycles, outbreaks, and crashes. First, PCR primers and PCR conditions were developed to amplify 19 trinucleotide loci and two tetranucleotide loci in six multiplex PCR approaches and then analyzed for species specificity, sensitivity and precision. Twelve of the loci showed simple tandem repeat array structures while nine loci showed imperfect repeat structures, and repeat numbers varied in our material between six and 15. The application in population genetics for all the 21 microsatellites were further validated in 48 autumnal moths sampled from Northern Norway, and allelic variation was detected in 19 loci. The detected numbers of alleles per locus ranged from two to 13, and the observed and expected heterozygosities varied from 0.04 to 0.69 and 0.04 to 0.79, respectively. Evidence for linkage disequilibrium was found for six loci as well as indication of one null allele. We find that these novel microsatellites and their multiplex-PCR assays are suitable for further research on fine- and large-scale population-genetic studies of Epirrita autumnata.

Graphical Abstract

1. Introduction

The autumnal moth (Epirrita autumnata) is a forest pest insect with cyclic outbreak dynamics, widespread across the northern hemisphere. In the northern-boreal birch forests of Fennoscandia, outbreaks by autumnal moth and other defoliating geometrid moths, in particular winter moth (Operopthera brumata) but locally also the ecologically similar and recently established scarce umber moth (Agriopis aurantiaria), may have severe and large-scale impacts on both tree and understory layers [1,2,3,4]. This includes local or regional defoliation of mountain birch (Betula pubescens ssp. tortuosa) and sometimes forest death following multiyear defoliation [5]. The impact of the outbreaks may cascade through other food web compartments [4] and, occasionally, extend into neighboring tundra ecosystems [6]. These geometrid moth species are a model system in population ecology, which is partly due to the pronounced spatial population synchrony and decadal population cycles [7,8,9], but also due to the rapid outbreak range shifts shown by these three species during recent decades due to climate warming [10,11,12]. However, so far, no microsatellites have been isolated and characterized from any of these species.
Development of microsatellite DNA markers for identification and application of lepidopteran species is difficult, associated to high similarity in flanking regions between different microsatellites within the same species [13,14,15] and/or the lack of conserved flanking regions leading to unrepeatable banding patterns [16]. In addition, the sequences flanking the microsatellites have been shown to have a high incidence of single nucleotide polymorphisms and indels [17]. These properties can result in a deficit of heterozygotes due to the presence of null alleles [14]. Nevertheless, microsatellites can be used for lepidopterans with proper attention being paid to these issues [17,18,19,20]. The application of next generation sequencing of enriched genomic libraries has previously been shown to be favorable for development of short tandem repeats (STR) in Lepidoptera, as suggested in [18] and shown in several other studies [19,21,22].
Thus, we have here applied Sanger sequencing of enriched genomic libraries to identify novel Epirrita autumnata (E. autumnata) microsatellites. Furthermore, we have developed multiplex-PCR assays for 21 of those loci. We have also performed tests for species specificity, measurements of sensitivity and precision for all the 21 microsatellites, and evaluated these assays in a Northern European E. autumnata population. With the species specificity test we tested for cross-amplification with the two sympatric outbreak species winter moth and scarce umber moth, as a common set of microsatellites for all three species would greatly facilitate comparative population genetic studies of population cycles, outbreaks, crashes and climate driven range shifts.

2. Results and Discussion

Genomic screening of 192 contigs from E. autumnata yielded in total 90 sequences that contained an STR, and 40 STRs were selected for the development of primer sets after elimination of the dinucleotide motifs and deletion of regions where the microsatellite was too close to an end of the sequence. The 40 primer sets were tested using DNA from four moth samples to insure amplification of an appropriate amplicon. Of these, 27 were selected for detailed genotype analysis and tested on 12 field collected larvae samples as well as on 15 cultivated larvae samples. Among these 27 microsatellites, 21 showed unambiguous genotype patterns, and gave successful amplification.
The 21 microsatellites were then successfully organized into six PCR multiplex panels, making genotyping faster and cost effective, which is important if a large number of samples have to be analyzed for population genetic studies. The multiplex panels had also easily readable chromatograms with very low stutter peaks. Figure 1 shows the results obtained using these six multiplex PCRs, while primers and combination of markers used in each multiplex reaction are given in Table 1.
Amplifications for Operophtera brumata and Agriopis aurantiaria were unsuccessful, indicating species specificity of the developed primers to E. autumnata, and thus showing their applicability to comparative population genetic studies of these sympatric outbreak species. Development of species-specific primers for the two other species is necessary.
Figure 1. Chromatograms from capillary electrophoresis (ABI 3130xl) showing multiplex PCR reactions I–VI (see also Table 1) for the 21 microsatellites developed for E. autumnata. The peak height (Relative fluorescence units (RFU)) is indicated on the Y-axis, and fragment length (base-pairs) is indicated on the X-axis. The names of the microsatellites markers are indicated below the peaks.
Figure 1. Chromatograms from capillary electrophoresis (ABI 3130xl) showing multiplex PCR reactions I–VI (see also Table 1) for the 21 microsatellites developed for E. autumnata. The peak height (Relative fluorescence units (RFU)) is indicated on the Y-axis, and fragment length (base-pairs) is indicated on the X-axis. The names of the microsatellites markers are indicated below the peaks.
Ijms 16 22541 g001aIjms 16 22541 g001b
Table 1. Twenty-one microsatellite loci arranged into six multiplex PCR panels for E. autumnata.
Table 1. Twenty-one microsatellite loci arranged into six multiplex PCR panels for E. autumnata.
Multiplex PanelLocusPrimer Sequences (5ʹ–3ʹ) aRepeat MotifSize Range (bp) bPrimer Conc., DyeGenBank Accession Number
IA021F: CCTAAGAGGGAGGCCCATGTTGA86–952 µM, FAMKT428619
R: CAGCTTGGTTCGTTAGCAAGG
A019F: GCGTTGGCGCATCTGTAAATCAT146–1733 µM, FAMKT428620
R: CGCCACAGAGGTCGTCAAA
A022F: CTGCGTGCTAAAACCTACGGACAT141–1471 µM, VICKT428621
R: CAGCAGTGGACTTCTTCTGGC
A016F: AGACCTACACCTGAGTGCATCTTAGTTCAT135–1413 µM, NEDKT428622
R: CCATCCCAGGTGTGGTGATT
IID005F: CGGTGGTTGCTATGGGTGTTCTT142–1632 µM, FAMKT428623
R: TTGCATTCTATGTGGGAGGCT
A015F: AATTGTATGCCACCGCTGCTCAT301–3252 µM, FAMKT428624
R: TCCGTCTGCCAAGTGTA
A139F: ACCTGCGATTACCAATCCGAGAT140–1491 µM, NEDKT428625
R: TTCCGTGGTTCTTCTTCATCAAC
IIIB115F: TTATAGGTGTCGGTTAAACACTTTAAAAACACT141–1482 µM, VICKT428626
R: GGTTAAGGCTGCGCTAAAGCT
A107F: TAGGGCCAGCAGTGGACTTCATG147–2012 µM, NEDKT428627
R: GTTTCTTTGGATGTCCCTTGGCCTTTA
IVA124F: TGAATCGGTGCTCCAATAGGATAA115–1243 µM, NEDKT428628
R: GTCTCTGTTGACCCCAGGGA
A113F: AGACCTCGTCCAACAGTGGGGAT144–1651 µM, FAMKT428629
R: AACATTGGACGATCTTATCGCC
D014F: TCGTTTTCATCTATTATTAGTTTAGGATTCACAAT140–1562 µM, NEDKT428630
R: TTGTTGCACGCCTTAAATGG
B008F: TGCATTGTAATAGGACCTTCATATTTTTACAG173–1785 µM, PETKT428631
R: TTATAGGATCACTTTGTTGTCCGTCT
VA114F: TGTCGAGCTCTACAAAAACTGCACAT1182 µM, FAMKT428632
R: AATTGGGCCTCAGGTTTCTGT
A101F: GAAGCCGCGCTGTTTCTTAAGTT1511 µM, VICKT428633
R: GAGAGGTCGTCGAAACACCCT
A011F: CTAGACCGGAGGCAAACCAATGA153–1590.5 µM, NEDKT428634
R: CAAAATGACGGTTTGAGCGA
A137F: GATCCAGGATCTGAAGCGGACAT153–1741 µM, FAMKT428635
R: AAGACCGTTCGTCATGGCAT
VIA111F: GGCGGAGGTCTTTTCTAGCAGATG122–1372 µM, FAMKT428636
R: AACAAGTTTGGGTTGCAAAAGTTT
A130F: AACACACTCGAGGGTCCCAACAT173–2273 µM, FAMKT428637
R: GTTCTAGGGCCAGCAGTGGA
A135F: TCCTCCAACTCTTTCCGTGGCAT160–1871 µM, PETKT428638
R: TTATGGGTGAGGCTTCGTCC
A024F: TCGTCTGTAGATATCAACTGCTGGACAT145–1991 µM, NEDKT428639
R: GTGGACGTAAGCAGGCTGGT
a F forward, R reverse; b Allele size range in base pairs observed in 48 individuals of E. autumnata (see Table 4); and Conc., Concentration.
We tested a concentration series of 20, 10, 1, 0.5, 0.2, 0.1, 0.05, 0.04, 0.03, 0.02 and 0.01 ng of template DNA in the six multiplex reactions. All markers successfully typed with signals above the lower peak height threshold of 200 RFU with template DNA in the range 20–1.0 ng. The sample material in this work had high DNA-concentration, using fresh larval head material for extraction, and the protocol were optimized to extract approximately 20 ng of DNA. This indicates a relatively low sensitivity of the assays, and for studies with other material with degraded DNA, like decades-old dried museum specimens, a new optimization is likely needed. The extraction protocol presented here should be ideal for most population genetic applications where field collected larvae or adults are used as source material.
We also tested the within-run precision using ten independent amplifications and subsequent runs of one tissue-sample of E. autumnata (Table 2). These results show that the standard deviations (S.D.) from allele length measurements of all the 21 loci tested were between 0.03 and 0.1 bp. The electrophoretic separation is, thus, not affected by origin of the template (Table 2).
DNA sequencing was performed on the largest and the smallest alleles in each marker found so far in the material. The sequencing revealed that all size variation observed between the two alleles selected from the same loci could be explained by variation in repeat numbers in the tandem repeat arrays (Table 3). Nineteen loci were tandem arrays of trinucleotide repeats while two had a repeat array of tetranucletide repeats (D014 and B008). Twelve of the loci showed simple tandem repeat array structures while nine loci showed complex repeat structures, often common in microsatellites in insects [23].
Table 2. Measurements of precision for 21 STRs from E. autumnata.
Table 2. Measurements of precision for 21 STRs from E. autumnata.
LocusAllele/Genotype aMean bS.D. (bp) c
A021 allele A9292.240.07
A021 allele B9595.490.1
A019 allele A135135.360.08
A019 allele B138138.470.08
A022 allele A145145.920.08
A022 allele B188188.560.1
A016 allele A144144.200.1
A016 allele B147148.620.09
D005 allele A148148.430.07
D005 allele B154154.990.03
A015 allele A301301.080.07
A015 allele B310309.280.07
A139 allele A143143.530.07
B115 allele A141141.700.06
A107 allele A147146.450.09
A107 allele B201201.980.05
A124 allele A121121.440.06
A124 allele B124124.240.07
A113 allele A147147.060.08
D014 allele A152152.470.07
B008 allele A181181.210.06
A114 allele A118118.480.06
A101 allele A151150.680.07
A011 allele A159158.890.08
A137 allele A165165.680.09
A137 allele B171171.550.08
A111 allele A125125.360.07
A111 allele B131131.640.07
A130 allele A173174.160.07
A130 allele B227227.310.08
A135 allele A160159.920.08
A024 allele A154154.100.06
A024 allele B172172.090.09
a Genotype nomenclature is based on PCR fragment sizes.; b Mean value allele sizes when measured with POP7 on ABI3730; and c SD from within-run measurement of 10 run per sample.
Table 3. DNA sequencing of tandem repeat structure of 21 microsatellites from E. autumnata.
Table 3. DNA sequencing of tandem repeat structure of 21 microsatellites from E. autumnata.
LocusAllele/Genotype aNumber RepeatsRepeat Structure
A021 allele A926 R(TGA)6
A021 allele B957 R(TGA)7
A019 allele A146(CAT)n(AAT)(CAT)n *
A022 allele A141 *5 R(CAT)5
A022 allele B147 *6 R(CAT)6
A016 allele A1386 R(CAT)6
D005 allele A1487 R(CTT)7
D005 allele B1549 R(CTT)9
A015 allele A3015 R(CAT)5
A015 allele B3226 R(CAT)6
A139 allele A1407 R(GAT)7
A139 allele B1438 R(GAT)8
B115 allele A1417 R(ACT)1(TT)(ACT)1(ACA)(ACT)5
B115 allele B1448 R(ACT)1(TT)(ACT)1(ACA)(ACT)6
A107 allele A147
A107 allele B20114 R(ATG)6(AGGCTG)(ATG)3(ACG)(ATG)3(CTG)(ATG)2
A124 allele A1217 R(TAA)7
A124 allele B1248 R(TAA)8
A113 allele A1479 R(GAT)9
A113 allele B16515 R(GAT)15
D014 allele A140(CAAT)n(CAAC)(CAT)n *
D014 allele B142
B008 allele A1735 R(ACAG)2(ACAT)(ACAG)3
B008 allele B181
A114 allele A1186 R(CAT)6
A101 allele A1518 R(GTT)3(GT)(GTT)2(T)(GTT)1(GTA)(GTT)2
A011 allele A1596 R(TGA)3(TGT)(TGA)3
A137 allele A1657 R(CAT)7
A137 allele B1719R(CAT)9
A111 allele A12510 R(ATG)10
A111 allele B1347 R(ATG)7
A130 allele A1736 R(CAT)6
A130 allele B22719 R(CAT)7(CAGCCT)(CAT)6(CAGCCTCAC)(CAT)6
A135 allele A16010 R(CAT)10
A024 allele A1456 R(CAT)6
A024 allele B18717 RCTGAT(CAT)2(CACA)(CAT)15
– Sequencing not interpretable; * Structure repeat is taken from the DNA sequences done at Armalil; and a Nomenclature of alleles is based on PCR fragment size.
Allele size distribution was consistent for all STRs, except for A111, A130, B115 and D014. The irregular allele sizes observed in these STR markers are possibly caused by single base indels changing the expected sizes (see Table 3). This is also shown in other studies [17,24], and should be verified by sequencing the relevant alleles.
The six final multiplex PCR panels were applied to DNA samples from 48 E. autumnata from Northern Norway, and we found 98 alleles (Table 4) for all the 21 microsatellite loci, and allelic variation was detected in 19 of those, ranging from 2 to 13 (Table 4). Observed and expected heterozygosities ranged from 0.04 to 0.69 and 0.04 to 0.79, respectively.
Table 4. Basic statistics of 21 microsatellites loci developed for E. autumnata in a survey of 48 individuals from Northern Norway.
Table 4. Basic statistics of 21 microsatellites loci developed for E. autumnata in a survey of 48 individuals from Northern Norway.
LocusNAHOFISHEPIFNULLHWE p Values a
A02130.4380.12580.4950.320.02490.2676
A01930.553−0.14230.4790.32−0.04960.9699
A02230.4375 0.26540.58770.240.09180.0054 **
A01630.1667−0.06970.15430.72−0.01111.0000
D00570.6042 0.03470.61910.190.00910.6835
A01550.45830.32330.66800.170.13220.0001 **
A13930.2290.10090.2520.590.01780.2429
B11550.3540.27660.4830.340.09130.0420 *
A10780.5960.17960.7170.120.05710.0341*
A12440.479−0.08100.4390.39−0.02250.7396
A11370.1580.77390.6820.140.31270.0000 **
D01460.1060.41480.1790.680.06250.0026 *
B00820.080.63360.2110.640.10830.0194 *
A11410001.000No
A10110001.000No
A01120.041−0.01080.0410.92−0.00091.0000
A13750.6880.00610.6450.160.00340.5214
A11180.4040.45190.7260.120.17220.0000 **
A13070.5680.19750.6980.140.0550.0603
A13520.255−0.12310.2250.62−0.02461.0000
A024130.6140.23790.7940.060.08330.0014 *
NA: number of different alleles; HO: observed heterozygosity; FIS: inbreeding value; HE: expected heterozygosity; PI: probability of identity; FNULL: Null-alleles estimated with the Brookfiled1 method implemented in Micro-Checker (van Oosterhout et al., 2006 [25]); HWE: significance of departure from Hardy–Weinberg equilibrium; * <0.05, ** <0.01; and a Based on assay of 48 individuals from each locus.
For this survey, we observed a significant linkage disequilibrium (p < 0.05) for nine of the 210 pairwise comparisons between loci after sequential Bonferroni correction, particularly involving loci A015, A024, A111, A130, B115 and D014 (see Table S1).
Of the nine loci that deviated significantly from HWE, three loci (A111, A113 and B008) showed large heterozygote deficiencies. These three loci also exhibited overall significant excess of homozygotes with null allele frequency of 0.172, 0.313 and 0.108, respectively, possibly indicating the presence of null allele in this population. Simulations have shown that the bias induced by null alleles is negligible at frequencies below 0.2 [26], and therefore in this population only A113 showed null allele frequency that is not negligible. Excess of homozygotes can also be due to small sample size of 48 individuals, but heterozygote deficiencies and the presence of null allele are highly common in Lepidoptera [20,27,28]. Future population studies applying these loci in a broader sampling area will help clarify both this question and whether any of the observed deviations from linkage and HW also occur consistently in other populations, which may suggest that the respective loci should be excluded from the marker set.

3. Materials and Methods

3.1. Sampling and Materials

For method development, PCR specificity and precision larval samples from a laboratory culture of E. autumnata, Operophtera brumata and Agriopis aurantiaria were used. The cultivated larvae originated from eggs laid by multiple females collected at Reinøya near Tromsø in Northern Norway. Thus, the cultivated larvae originated from different families within the same population. For the sensitivity test and the tests of the final set of 21 microsatellite markers (see below) we used 48 individual E. autumnata larvae collected at Storelva near Tromsø in Northern Norway. The 48 larvae were collected from birch trees along a linear transect with 12 individual sampling stations spaced at 200 m intervals. This was done to ensure that the larval samples were collected from a reasonably large area within the study site. Four larvae were collected from each station. Each larvae was stored individually in an Eppendorf tube and frozen at −18 °C until it was used in the DNA analysis.

3.2. Identification of Microsatellites Markers

A total of 40 adult moth samples of E. autumnata were selected and sent to Armalil Microsatellite Identification Service (www.geneticidentificationservices.com) for genome sequencing and construction of enriched genomic libraries. Here, four libraries, employing 16 capture motifs (6 TETRAs, 8 TRIs and 2 DIs), were produced using magnetic bead capture technology, and a total of 192 clones from these libraries were sequenced by Sanger sequencing using an ABI 3100 Genetic Analyzer (Applied Biosystems (ABI), Waltham, MA, USA) with a Big Dye Terminator V3.1 Cycle Sequencing Kit (ABI). Among these sequences, there were 90 microsatellite loci, and from these regions, with help of ABI Primer Express primer determining program, 40 were selected for the development of primer sets after elimination of the di-nucleotide motifs and deletion of regions where the microsatellite was too close to an end of the sequence. The 40 primer sets were tested using DNA from four moth samples to insure amplification of an appropriate amplicon. Of those tested, 27 were selected for detailed genotype analysis and tested on 16 moth samples using ABI3700 genetic analyzer.

3.3. DNA Extraction

DNA was extracted from head tissue of E. autumnata using Qiagen DNeasy Tissue kit (Qiagen, Hilden, Germany) following the manufactures’ instructions, except for the final step where we used 400 µL elution buffer to decrease the concentration of DNA. The yield of DNA was quantified using a NanoDrop 2000 (Thermo Scientific, Waltham, MA, USA).

3.4. Development of PCR Assays

PCR primers for the 27 loci were tested using OligoPerfect™ Designer (ABI), with the following criteria: (i) length of PCR product should be as short as possible and between 90 and 300 bp; (ii) flanking regions should not contain a mononucleotide stretch of more than five bases; (iii) annealing temperature were optimized to fall between 56 and 63 °C; and (iv) difference in temperature between forward and reverse primer should not exceed 2 °C.
Single PCRs were initially performed on 15 individuals for each of the 27 primer pairs, in a 10 µL containing 1× PCR Gold buffer (ABI), 200 µM dNTP (Eurogentec, Liège, Belgium), 1.5 mM MgCl2 (ABI), 0.2 µM of each primer (ABI), 1 U Amplitaq Gold DNA polymerase (ABI), 1× BSA (New England Biolabs (NEB), Ipswich, MA, USA) and 1 µL template.
DNA amplification was on an ABI 2720 for 10 min at 95 °C, 30 cycles of 30 s at 94 °C, 30 s at 56 °C, and 1 min at 72 °C, and ended with final extension for 45 min at 72 °C.
Multiplex-PCR development involved tests of different combinations of markers and primer concentration (details not shown). For the final analysis, the 21 microsatellites were split into four tetraplex (multiplex I, IV, V and VI), one triplex (multiplex II) and one diplex (multiplex III) in PCR-approach in 10 µL reaction volume using the following conditions: 5 µL 2× multiplex PCR master mix (Qiagen Multiplex kit), 0.05 µg/µL BSA (NEB) and adjusted primer set concentrations (Table 1).
PCR conditions for multiplex I–V were 10 min at 95 °C, 25 cycles of 30 s at 94 °C, 30 s at 58 °C, 1 min 72 °C and final extension for 45 min at 72 °C. PCR conditions for multiplex VI were 10 min at 95 °C, 25 cycles of 30 s at 94 °C, 30 s at 60 °C, 1 min 72 °C and final extension for 45 min at 72 °C.
PCR products (1 µL) were mixed with Genescan 500 LIZ (ABI) size standard (0.25 µL) and Hi-Di formamide (9.75 µL) following capillary electrophoresis on an ABI 3130xl Genetic Analyzer (ABI). The POP-7™ (ABI) Polymer was used as separation matrix and the sample injection time were set to 6–8 s/2 kV. PCR fragments were analyzed in GeneMapper 4.1 (ABI).
To check for possible contamination, negative controls were included for every seventh sample in all measurements in this study.

3.5. Testing for PCR Specificity, Sensitivity and Precision

All 21 markers were tested for cross-species amplification against DNA samples from two other species, Operophtera brumata (n = 2) and Agriopis aurantiaria (n = 2). Extraction and PCR were performed as for E. autumnata. Sensitivity of the six multiplex reactions in the 21 STR approach was evaluated using three samples with different amount of template DNA ranging 20–0.1 ng. Measurements of within-run precision were performed in 10 independent amplifications and subsequent runs of a single sample of E. autumnata.

3.5.1. DNA Sequencing

The tandem repeat array and the immediate upstream and downstream sequences at each of the 21 loci were analyzed by DNA sequencing. PCR products amplified from E. autumnata were sequenced using the BigDye Terminator v3.1 Cycle Sequencing Kit (ABI) as recommended by the manufacturer. Forward and reverse PCR primers were used as sequencing primers in forward and reverse sequencing reactions, respectively (Table 1). Forward and reverse sequences from each sample were aligned in Sequencher 4.7 (Gene Codes Corporation, Ann Arbor, MI, USA). The allelic sequences from each locus were aligned and the sequence and size variation at each locus was determined by manual inspection. The sequence data from Armalil were used as a guidance of the suggested repeat structure.

3.5.2. Analysis of Data

Analysis of Hardy–Weinberg equilibrium (HWE), expected and observed heterozygosities, population structure (FIS) and test for linkage disequilibrium (LD) were computed with Genetix [29]. Bonferroni corrected significance levels were applied when testing HWE and LD. Micro-Checker ver. 2.2.3 [25] was used to analyze the causes of departures from HWE: real disequilibrium, null alleles or scoring errors.

4. Conclusions

We find that these newly developed microsatellites and their multiplex-PCR assays are robust, fast, precise and promising to facilitate further research on fine- and large-scale population genetic studies of E. autumnata.

Supplementary Materials

Supplementary materials can be found at https://www.mdpi.com/1422-0067/16/09/22541/s1.

Acknowledgments

We thank Norwegian Institute for Bioeconomy Research (NIBIO) for financial support and Benedicte Beddari for assistance in the laboratory.

Author Contributions

All authors contributed to conceive and plan the study. Siv Grethe Aarnes, Ida Fløystad, Julia Schregel, Hans Geir Eiken, and Snorre B. Hagen genotyped the samples, analysed the data and wrote the manuscript with input from all other authors. Jane Uhd Jepsen, Ole Petter Laksforsmo Vindstad, Rolf A. Ims collected the samples.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Kallio, P.; Lehtonen, J. On the plasmatic template system in Micrasterias morphogenesis. Ann. Acad. Sci. Fenn. Biol. 1973, 199, 1–6. [Google Scholar] [PubMed]
  2. Tenow, O.; Bylund, H. Recovery of a Betula pubescens forest in northern Sweden after severe defoliation by Epirrita autumnata. J. Veg. Sci. 2000, 11, 855–862. [Google Scholar] [CrossRef]
  3. Karlsen, S.R.; Jepsen, J.U.; Odland, A.; Ims, R.A.; Elvebakk, A. Outbreaks by canopy-feeding geometrid moth cause state-dependent shifts in understorey plant communities. Oecologia 2013, 173, 859–870. [Google Scholar] [CrossRef] [PubMed]
  4. Jepsen, J.U.; Biuw, M.; Ims, R.A.; Kapari, L.; Schott, T.; Vindstad, O.P.L.; Hagen, S.B. Ecosystem impacts of a range expanding forest defoliator at the forest-tundra ecotone. Ecosystems 2013, 16, 561–575. [Google Scholar] [CrossRef]
  5. Vindstad, O.P.L.; Schultze, S.; Jepsen, J.U.; Biuw, M.; Kapari, L.; Sverdrup-Thygeson, A.; Ims, R.A. Numerical responses of saproxylic beetles to rapid increases in dead wood availability following geometrid moth outbreaks in sub-arctic mountain birch forest. PLoS ONE 2014. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Post, E.; Forchhammer, M.C.; Bret-Harte, M.S.; Callaghan, T.V.; Christensen, T.R.; Elberling, B.; Fox, A.D.; Gilg, O.; Hik, D.S.; Hoye, T.T.; et al. Ecological dynamics across the arctic associated with recent climate change. Science 2009, 325, 1355–1358. [Google Scholar] [CrossRef]
  7. Hagen, S.B.; Jepsen, J.U.; Schott, T.; Ims, R.A. Spatially mismatched trophic dynamics: Cyclically outbreaking geometrids and their larval parasitoids. Biol. Lett. 2010, 6, 566–569. [Google Scholar] [CrossRef] [PubMed]
  8. Hagen, S.B.; Jepsen, J.U.; Yoccoz, N.G.; Ims, R.A. Anisotropic patterned population synchrony in climatic gradients indicates nonlinear climatic forcing. Proc. R. Soc. B Biol. Sci. 2008, 275, 1509–1515. [Google Scholar] [CrossRef] [PubMed]
  9. Ims, R.A.; Yoccoz, N.G.; Hagen, S.B. Do sub-Arctic winter moth populations in coastal birch forest exhibit spatially synchronous dynamics? J. Anim. Ecol. 2004, 73, 1129–1136. [Google Scholar] [CrossRef]
  10. Hagen, S.B.; Jepsen, J.U.; Ims, R.A.; Yoccoz, N.G. Shifting altitudinal distribution of outbreak zones of winter moth Operophtera brumata in sub-arctic birch forest: A response to recent climate warming? Ecography 2007, 30, 299–307. [Google Scholar] [CrossRef]
  11. Jepsen, J.U.; Kapari, L.; Hagen, S.B.; Schott, T.; Vindstad, O.P.L.; Nilssen, A.C.; Ims, R.A. Rapid northwards expansion of a forest insect pest attributed to spring phenology matching with sub-Arctic birch. Glob. Chang. Biol. 2011, 17, 2071–2083. [Google Scholar] [CrossRef]
  12. Jepsen, J.U.; Hagen, S.B.; Ims, R.A.; Yoccoz, N.G. Climate change and outbreaks of the geometrids Operophtera brumata and Epirrita autumnata in subarctic birch forest: Evidence of a recent outbreak range expansion. J. Anim. Ecol. 2008, 77, 257–264. [Google Scholar] [CrossRef] [PubMed]
  13. Meglecz, E.; Solignac, M. Microsatellite loci for Parnassius mnemosyne (Lepidoptera). Hereditas 1998, 128, 179–180. [Google Scholar] [CrossRef]
  14. Meglecz, E.; Petenian, F.; Danchin, E.; Coeur d’Acier, A.; Rasplus, J.Y.; Faure, E. High similarity between flanking regions of different microsatellites detected within each of two species of Lepidoptera: Parnassius apollo and Euphydryas aurinia. Mol. Ecol. 2004, 13, 1693–1700. [Google Scholar] [CrossRef] [PubMed]
  15. Zhang, D.X. Lepidopteran microsatellite DNA: Redundant but promising. Trends Ecol. Evol. 2004, 19, 507–509. [Google Scholar] [CrossRef] [PubMed]
  16. Sarhan, A. Isolation and characterization of five microsatellite loci in the Glanville fritillary butterfly (Melitaea cinxia). Mol. Ecol. Notes 2006, 6, 163–164. [Google Scholar] [CrossRef]
  17. Hundsdoerfer, A.K.; Sanetra, M.; Corbeil, D.; Stuckas, H. Eleven hawkmoth microsatellite loci of Canary Island Hyles tithymali (Lepidoptera). Conserv. Genet. Resour. 2010, 2, 241–244. [Google Scholar] [CrossRef]
  18. Sinama, M.; Dubut, V.; Costedoat, C.; Gilles, A.; Junker, M.; Malausa, T.; Martin, J.-F.; Neve, G.; Pech, N.; Schmitt, T.; et al. Challenges of microsatellite development in Lepidoptera: Euphydryas aurinia (Nymphalidae) as a case study. Eur. J. Entomol. 2011, 108, 261–266. [Google Scholar] [CrossRef]
  19. Massardo, D.; Roratto, P.A.; Vargas, H.A.; Kronforst, M.R.; Moreira, G.R.P. Development of a microsatellite library for the passion flower butterfly Dione moneta Hubner (Lepidoptera: Nymphalidae: Heliconiinae). Conserv. Genet. Resour. 2012, 4, 719–724. [Google Scholar] [CrossRef]
  20. Weibin, J.; Jianqing, Z.; Ling, Z.; Minghai, C.; Chao, S.; Weidong, Y. Isolation and characterization of microsatellite loci in Polytremis nascens (Lepidoptera: Hesperiidae) and their cross-amplification in related species. Appl. Entomol. Zool. 2014, 49, 177–181. [Google Scholar] [CrossRef]
  21. Mira, O.; Martinez, J.G.; Dawson, D.A.; Tinaut, A.; Sanchez-Prieto, C. Twenty new microsatellite loci for population structure and parentage studies of Parnassius apollo nevadensis (Lepidoptera; Papilionidae). J. Insect Conserv. 2014, 18, 771–779. [Google Scholar] [CrossRef]
  22. Zima, J., Jr.; Lestina, D.; Jansta, P.; Petru, V.; Tropek, R. Isolation and characterisation of microsatellite markers for Mylothris jacksoni knutsoni (Lepidoptera: Pieridae), an endemic butterfly of the Gulf of Guinea Highlands. Conserv. Genet. Resour. 2014, 6, 763–764. [Google Scholar] [CrossRef]
  23. Behura, S.K.; Severson, D.W. Motif mismatches in microsatellites: Insights from genome-wide investigation among 20 insect species. DNA Res. 2015, 22, 29–38. [Google Scholar] [CrossRef] [PubMed]
  24. Mende, M.B.; Stuckas, H.; Hundsdoerfer, A.K. Eight new microsatellite loci of the Western Palearctic Hyles euphorbiae complex (Lepidoptera, Sphingidae). Ann. Zool. Fenn. 2011, 48, 142–146. [Google Scholar] [CrossRef]
  25. Van Oosterhout, C.; Weetman, D.; Hutchinson, W.F. Estimation and adjustment of microsatellite null alleles in nonequilibrium populations. Mol. Ecol. Notes 2006, 6, 255–256. [Google Scholar] [CrossRef]
  26. Dakin, E.E.; Avise, J.C. Microsatellite null alleles in parentage analysis. Heredity 2004, 93, 504–509. [Google Scholar] [CrossRef] [PubMed]
  27. Kim, M.; Kim, H.; Kwon, D.H.; Lee, S. Isolation and characterization of microsatellite loci from Spodoptera exigua (Lepidoptera: Noctuidae). Appl. Entomol. Zool. 2012, 47, 149–152. [Google Scholar] [CrossRef]
  28. Sauné, L.; Abella, F.; Kerdelhué, C. Isolation, characterization and PCR multiplexing of 17 microsatellite loci in the pine processionary moth Thaumetopoea pityocampa (Lepidoptera, Notodontidae). Conserv. Genet. Resour. 2015, 7, 755–757. [Google Scholar] [CrossRef]
  29. GENETIX, version 4.05: Logiciel sous Windows pour la Génétique des Populations, Laboratoire Génome, Populations, Interactions, CNRS UMR 5171, Université de Montpellier II: Montpellier, France, 1996–2004.

Share and Cite

MDPI and ACS Style

Aarnes, S.G.; Fløystad, I.; Schregel, J.; Vindstad, O.P.L.; Jepsen, J.U.; Eiken, H.G.; Ims, R.A.; Hagen, S.B. Identification and Evaluation of 21 Novel Microsatellite Markers from the Autumnal Moth (Epirrita autumnata) (Lepidoptera: Geometridae). Int. J. Mol. Sci. 2015, 16, 22541-22554. https://doi.org/10.3390/ijms160922541

AMA Style

Aarnes SG, Fløystad I, Schregel J, Vindstad OPL, Jepsen JU, Eiken HG, Ims RA, Hagen SB. Identification and Evaluation of 21 Novel Microsatellite Markers from the Autumnal Moth (Epirrita autumnata) (Lepidoptera: Geometridae). International Journal of Molecular Sciences. 2015; 16(9):22541-22554. https://doi.org/10.3390/ijms160922541

Chicago/Turabian Style

Aarnes, Siv Grethe, Ida Fløystad, Julia Schregel, Ole Petter Laksforsmo Vindstad, Jane Uhd Jepsen, Hans Geir Eiken, Rolf A. Ims, and Snorre B. Hagen. 2015. "Identification and Evaluation of 21 Novel Microsatellite Markers from the Autumnal Moth (Epirrita autumnata) (Lepidoptera: Geometridae)" International Journal of Molecular Sciences 16, no. 9: 22541-22554. https://doi.org/10.3390/ijms160922541

Article Metrics

Back to TopTop