METHODS article

Front. Mar. Sci., 16 June 2023

Sec. Coral Reef Research

Volume 10 - 2023 | https://doi.org/10.3389/fmars.2023.1019419

Microsatellite markers for Monitipora digitata designed using restriction-site associated DNA sequencing

  • Institute of Marine Ecology, Hainan Academy of Ocean and Fisheries Sciences, Haikou, China

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Abstract

Montipora digitata is a species belonging to the Acroporidae. In the Indo-Pacific region, M. digitata is widely distributed and is the dominant species of scleractinian coral in the South China Sea, however, there are currently no molecular markers suitable for assessing the species genetic diversity. Here, restriction-site associated DNA sequencing (RAD-seq) was used to isolate and characterize polymorphic microsatellite loci. A total of 317,361 RAD-tags were obtained using RAD-seq, including 6,778 microsatellite loci. Primer pairs for 106 loci were ordered and twenty-one polymorphic loci, that amplified reliably were identified. The number of alleles per locus were 2-7, observed heterozygosity was 0.111-0.556 with an average value of 0.285, and expected heterozygosity was 0.105- 0.802, with an average value of 0.536. Before Bonferroni correction 13 loci deviated significantly from the expectations of Hardy-Weinberg equilibrium (P < 0.05), after correction, two microsatellite loci deviated significantly (P < 0.0002). The polymorphic information content (PIC) ranged from 0.100-0.778, with 12 loci highly polymorphic (PIC > 0.5), six moderately polymorphic (0.25 < PIC < 0.5), and three loci with low polymorphism (PIC < 0.25). The microsatellite loci developed here will be effective tools for conservation genetic research on M. digitata.

Introduction

Owing to the impact of global climate change and human activities, for example, sea surface temperature increases, China’s coral reef ecosystem has declined rapidly, and coastal coral reefs cover has been reduced by at least 80% in the past 30 years (Hughes et al., 2012; Hughes et al., 2017). Scleractinian corals are an important component of coral reef ecosystems and studying the genetic structure and connectivity of scleractinian corals is important for the protection and restoration of coral reef ecosystems. However, there are only a few studies on genetic diversity of scleractinian corals in the South China Sea (Wu et al., 2021) and microsatellite markers have been developed for some scleractinian corals such as Porites lutea (Hou, 2018; Li et al., 2020), Pocillopora damicornis (Luo et al., 2020), Platygyra acuta (Yang, 2013), Galaxea fascicularis (Su, 2017), distributed in the South China Sea. Previous studies have increased our understanding of scleractinian corals in the South China Sea, for example in P. lutea, it was found that there was genetic differentiation between Hainan Island and Xisha (Hou, 2018), and that seasonal differences in surface temperature at different latitudes might be driving genetic differentiation (Luo et al., 2022). However, there are many species of scleractinian coral in the South China Sea, and the understanding of the genetic diversity and genetic structure across scleractinian coral in the South China Sea is limited.

Montipora digitata (Dana, 1846) belongs to the family Acroporidae and is widely distributed in the South China Sea (Gu et al., 2017; Zhou et al., 2017). In recent years, field investigations in the South China Sea have found that M. digitata has replaced P. lutea as the dominant species in some areas, such as Dazhou Island of Wanning (Zhou et al., 2017). However, there are no molecular markers available for M. digitata, which makes up a growing proportion of South China Sea scleractinian corals.

Genetic work with scleractinian corals is difficult because they have symbiotic relationships with zooxanthellae. Because a large number of zooxanthellae live within the gastrodermal cells of the coral (Gleason and Wellington, 1993; Douglas, 2003). DNA extracted directly from coral tissue, will contain a large amount of zooxanthellae DNA. At present, the most commonly used method to separate the zooxanthellae from the coral hosts is to treat live coral at high temperatures, inducing the endosymbionts to leave the host, a process also known as bleaching (Li et al., 2020; Luo et al., 2020). Batch separation requires multiple sites and equipment that is not easy to operate and would not be an efficient step in preparing DNA for population genetic analysis. However, heat-induced bleaching can be performed using a small number of individual corals, and combined with bioinformatics methods, residual zooxanthellae DNA can be removed to obtain microsatellite markers of coral hosts. (Li et al., 2020; Luo et al., 2020). Restriction site-associated DNA sequencing (RAD-seq) greatly reduces the cost of genome sequencing and is not limited to the reference genome (Li et al., 2021; He et al., 2022).

In this study, M. digitata was bleached at high temperatures and RAD-seq was conducted to screen the coral for host-specific microsatellite. The new polymorphic microsatellite markers provide effective tools for obtaining genetic data useful for conservation.

Materials and methods

Coral samples for RAD-seq were collected from Luhuitou of Hainan Island (18.2167136, 109.4840218). The depths of the collection points were 2-10 m. A piece of live coral, approximately 5 cm long, was transported in seawater to the laboratory. After recovery in the indoor ocean simulation system, the coral was placed in a 43 cm3 tank for heat bleaching treatment. After bleaching, it was frozen in liquid nitrogen and stored at -80 °C until DNA extraction. Tissues from nine corals were sampled from a population in Yinyu (16.58074097, 111.7079768) in the Xisha Islands and tissues from two individuals were sampled from Shiyu (16.54108719, 111.7526088) and Langhuajiao (16.46873192, 111.5773425) in the Xisha Islands, respectively. One tissue sample was collected from each reef, the interval between each reef was at least 2 m. Each tissue sample was approximately 2 cm in length and stored in absolute ethanol. After being transported back to the laboratory, tissues were it was stored in a refrigerator at -80 °C. Six individuals, including all individuals from Shiyu and Langhuajiao, and two randomly selected individuals from Yinyu were used for microsatellite discovery via RAD-seq and initial polymorphism screening.

Reduced-representation genome sequencing (RRGS) and microsatellite primer design

Artificial bleached coral tissue from Luhuitou was used for RAD-seq. RAD-seq-library generation and sequencing were completed in Genedenovo (Guangzhou, China). The CTAB method (Doyle and Doyle, 1987) was used to extract genomic DNA from each tissue sample and DNA quantity and quality were assessed using a NanoDrop 2000 (Thermo Fisher Scientific, Waltham, MA) and a Qubit (Thermo Fisher Scientific, Waltham, MA), as well as gel electrophoresis. Genomic DNA was digested using a restriction endonuclease (EcoRI) and P1 adapters with a unique 4-8 bp barcode sequence, were then ligated to DNA fragments using T4 ligase (NEB, Ipswich, MA, USA). Then DNA fragments were sheared randomly using a Branson Sonicator (model SX 30, Branson Ultrasonics, Danbury, CT, USA). The sheared DNA was purified, eluted and separated, and 300-700 bp corresponding DNA fragments were taken for purification by gel electrophoresis. Then, selected DNA fragments end were repaired, and dATP overhangs were added. Illumina sequencing adapters were added using NEBNext® ULtra™DNA Library Prep Kit (NEB, USA), and PCR amplification and enrichment were performed. Finally, AMPure XP (Beckman Coulter, Brea, CA, USA) was used to purify the PCR products. Agilent 2100 biological analyzer (Agilent, Santa Clara, CA) was used to detect the sequencing library, and real-time PCR was used to quantify the library. Sequencing was carried out on NovaSeq 6000 sequencer using PE 150 sequencing strategy. Raw reads were processed to get high quality reads using fastp v. 0.18.0 (Chen et al., 2018) according to three stringent filtering standards: 1) remove reads where the proportion of N greater than 10%; 2) remove reads where the quality value of Q ater accounts for more than 50% of the whole read; and 3) remove reads aligned to the barcode adapter. Read1 were clustered using stack v. 1.46 (Catchen et al., 2011). Read2 were clustered according to the clustering result of read1, and then spliced. After splicing, the stack sequence with read1 and the conting sequence with read2 were aligned to the Symbiodiniaceae genome (Symbiodinium microadriaticum, Gonziodinium microadriaticuBreviolum minutum, Shoguchi et al., 2013; Beedessee et al., 2015; Shoguchi et al., 2015; Symbiodinium kawagutii, Lin et al., 2015) and the Symbiodiniaceae sequences removed. After filtering, the stack and conting sequences were spliced to construct RAD-tags to be used as reference sequences.

All reference sequences were searched using MISA software (http://pgrc.ipk-gatersleben.de/misa/) for microsatellite loci. The minimum repeat number of each motif was set as15, six, and five times for mono-, di-, and trinucleotide motifs, respectively; and four times for tetra-, penta-, and hexanucleotide motifs. When designing primers, adjacent microsatellite sequences that were separated by less than 100 bp were regarded as single microsatellite loci. Based on the flanking sequences at both ends of the microsatellites, primers for microsatellites were designed using Primer 3 v2.3.6 (http://primer3.sourceforge.net) under default settings, with the size of PCR products ranging from 100 to 300 bp. The universal FAM tail (GAAGGTGACCAAGTTCATGCT; Chao et al., 2022; Fan et al., 2023) was added to the 5’ end of forward primers (Wuhan Tianyi Huayu Gene Technology Co., Ltd.). PCR amplification was performed in a total reaction volume of 25 μL that included 12.5μL 2 ×PCR Master Mix, 0.2μL forward primer (10μmol/L), 0.6 μL reverse primer (10μmol/L), 0.4μL FAM labeled primer (10μmol/L), 100ng template DNA, and finally supplemented with ddH2O. Amplification was performed according to the following procedure: one cycle at 95 °C for 2min for initial denaturation, 30 cycles of: denaturation at 95 °C for 20sec, annealing at 50-55 °C for 20sec, and extension at 72 °C for 20sec, eight cycles of: denaturation at 95 °C for 20sec, annealing at 53 °C for 20sec, and extension at 72 °C for 30sec, and lastly a final extension at 72 °C for 5min.

PCR products with fluorescence labels were separated on an ABI 3730XL, and GeneMarker 3.0 used to identify genotypes. MicroChecker v.2.2.3 (Van Oosterhout et al., 2004) was used to check for genotyping errors and null alleles. The observed number of alleles (NA), effective number of alleles (NE), observed heterozygosity (HO), expected heterozygosity (HI), Shannon information index (I), inbreeding coefficient within populations (FIS), and Hardy-Weinberg equilibrium (HWE) were calculated using GenAlEx 6.5 (Peakall and Smouse, 2006). Linkage disequilibrium (LD) between each pair of loci was calculated using ARLEQUIN v3.5 (Excoffier and Lischer, 2010). Polymorphism information content (PIC) was calculated using PIC_CALC V. 0.6 (Germplasm Resources and Engineering Breeding Office, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences).

Results

After filtering, a total of 1,396,195,302 bp of high-quality data were generated by the Illumina NovaSeq6000 NGS platform (Table 1). After excluding the data of Symbiodiniaceae, a total of 317,361 reference contigs for analysis were obtained using RAD-seq on the genome of M. digitata. The longest contigs was 2209 bp, shortest contigs was 157 bp, and average sequence length was 311 bp.

Table 1

Before data filteringAfter data filtering
Clean Data(bp)Q20 (%)Q30 (%)N (%)GC (%)HQ Clean Data (bp)HQ Q20 (%)HQ Q30 (%)HQ N(%)HQ GC (%)
14234001361366681891(96.02%)1276795857(89.7%)4667(0.0%)563350809(39.58%)13961953021343251931(96.21%)1255475986(89.92%)4457(0.0%)551312001(39.48%)

Summary of genomic sequences generated by RAD-seq.

Clean Data: total base number of offline data; HQ Clean Data: total number of high-quality data bases after filtering; Q20 (%): the number of bases with the quality value of sequenced bases reaching the level of Q20 (sequencing error rate of 1%) and the percentage in RawData (or CleanData); Q30 (%): number of bases with the quality value of sequenced bases reaching the level of Q30 (sequencing error rate of 0.1%) and the percentage in Raw Data (or Clean Data); N (%): the number of N-base in single-end read and its percentage in Raw Data (or Clean Data); GC (%): percentage of sequence base GC before (after) filtration.

A total of 6,778 microsatellite loci were identified from the M. digitata data. Among them, 2,255 were trinucleotides, accounting for 33.27% of the total number of microsatellite loci, 1,705 were tetranucleotides, accounting for 25.15% of the total number. Dinucleotides, mononucleotides, pentanucleotides, and hexanucleotides had 1094, 719, 714, and 291 microsatellite loci, respectively, accounting for 16.14, 10.61, 10.53, and 4.29% of the total number, respectively (Table 2, Figure 1). The distribution of microsatellite repeat motifs in M. digitata is shown in Figure 2. Among the mononucleotides, the dominant motif was A/T, with 695, this accounted for 10.25% of the total number of microsatellite loci. In dinucleotides, the dominant motif was AT/AT, with 446, accounting for 6.58% of the total. Among the trinucleotides, the dominant repeat unit was AAT/ATT, with 712, accounting for 10.50% of the total. Among the tetranucleotides, AAAT/ATTT was the dominant motif, with 375, accounting for 5.53% of the total. Among the pentanucleotides, AAAGG/CCTTT was the dominant motif, with 93, and 1.37% of the total number of microsatellite loci. Among the hexanucleotides, AACCCT/AGGGTT was the dominant motif, with 38, accounting for 0.56% of the total number of microsatellite loci. One hundred and six microsatellite primers with PCR products of approximately 200 bp were randomly selected, and polymorphism was assessed for each locus using six individuals. Thirty-seven pairs of primers could be amplified clearly and were polymorphic. Finally, 21 highly polymorphic loci were selected for genetic analysis using nine tissue from Yinu (Table 3, Figure 3). The presence of null alleles at a nine loci (Table 4). The number of alleles per locus ranged from 2–7, Ho was 0.111-0.556, and HE was 0.111-0.802. Twelve loci were highly polymorphic (PIC > 0.5), six were moderately polymorphic (0.25 < PIC < 0.5), and three were less polymorphic (PIC < 0.25). Before Bonferroni correction 13 loci deviated significantly from the expectations of Hardy-Weinberg equilibrium (P < 0.05), after correction, two microsatellite loci deviated significantly (P < 0.0002). The linkage disequilibrium analysis of 21 SSR marker loci showed there were 18 pairs of paired points with significant linkage disequilibrium out of 210 total comparisons (8.57%, Table 5). See Table 6 for full summary of genetic diversity.

Table 2

Total number of sequences examined:317361
Total size of examined sequences (bp):98730250
Total number of identified SSRs:6778
Number of SSR containing sequences:6235
Number of sequences containing more than 1 SSR:457
Number of SSRs present in compound formation:470
Mononucleotide719
Dinucleotide1094
Trinucleotide2255
Tetranucleotide1705
Pentanucleotide714
Hexanucleotide291

SSR motif information statistics of M. digitata.

Figure 1

Figure 2

Table 3

LocusRepeat motifForward primer (5'-3')Reverse primer (5'-3')Product size (bp)
ZH10002(TCA)7CTGTCCGTGCAAAGAACAACCAAAGTTGCCTGGAAGGAAG222
ZH10127(CGTCA)5TCAAACCGATTCCTTTCCTGAAGCAGCTACCACGTTCCAC220
ZH11510(TA)8(GA)7TAAAAAGGCGTGCTCACAGATGTTAACAGCGAGGGTATTGG254
ZH11934(AAT)7TTCTCTTAAATCGACAAAAAGAAGTCCAGTACCATGGGCAGTTTT220
ZH12347(GAA)6gcaga(AGG)6AAAAGCAAAACAGGCACCATAAAATCACAGATAGTCTGCAAGAAAA223
ZH12502(TCA)6TCATCGTCGTCATCATCGTTTCGCCAAAATTCAAGGTAGG246
ZH12756(TTTA)4GAGCAGTGAAGATGGCTTCCTTTGGGCTTGTGATTGTTCA198
ZH12912(GTGA)10GGTTGTTCACTTTTTGCGGTCACTTCCAACGGACCTGTTT197
ZH13293(TCAAGT)4AATTACCCCGGCTTCGTAGTGCTAGCTCTGTTTTCAGTTTCTTTTT216
ZH13301(GT)6TTGATAACCAGTGGCAGGCTACCTGTGGTGCGAGATTTTC203
ZH13561(TTG)7TTTTGCGTCGGTATCAAAGAGCAATTTATTGGACACGCCT225
ZH13573(TC)6TTCGCCTTCGAAATCTCATCCGAAAGGAGCCTGGTTAGAA241
ZH14680(AAAT)4CTTGCATTTTTCCCTGCTGTTGCTGTCACATTTCAATGCC270
ZH15503(AG)7CTCTAAAACCCGCAGACCACCATGACGGCGCTCATACATA257
ZH15709(TGT)5CTAGCACCTGCTATTTGCGGGCGAAGATCGTGGAAACAAA266
ZH17044(CAA)5TGTCCTGGCCATGAACATTATCGATTTTCGATTAAACCACC250
ZH18332(TATT)6ACCACTTAGGCTTCTGCACGGGGGGAGAGAAAAATGTCGT219
ZH18580(CACG)6CAACGAAACTCGACCCTCATGCAGAAATGAAGATGCCACA209
ZH20640(T)15AGGGCTGGGCTCTAGTGAATAGTAGAAGGTGGCACACGGT205
ZH21554(AAGT)7CCAATCGGGGCTACTATGAACGTGCACGTTCTCACTAGTTTT254
ZH21810(AT)7TGAATGCGAAATGCGAAGTAAGGCTTGAAGAGTACCCCGT275

Twenty one pairs of microsatellites primer information.

Figure 3

Table 4

LocusNull PresentOosterhoutChakrabortyBrookfield 1Brookfield 2
ZH10002yes0.3370.6210.2470.247
ZH10127no0.1390.1820.1200.120
ZH11510no0.1280.1350.1000.100
ZH11934no0.1500.2000.1330.133
ZH12347no0.1540.1740.1230.311
ZH12502yes0.3690.6760.2940.294
ZH12756yes0.3370.6440.2650.265
ZH12912yes0.2210.2870.1990.199
ZH13293no-0.333-0.161-0.1100.000
ZH13301no-0.057-0.029-0.0060.000
ZH13561yes0.2430.3530.2150.215
ZH13573yes0.2510.3830.2370.237
ZH14680no-0.057-0.029-0.0060.000
ZH15503no-0.184-0.091-0.0440.000
ZH15709no0.0930.1220.0650.065
ZH17044yes0.3650.6920.3100.310
ZH18332no0.1560.1710.1280.128
ZH18580no0.0920.0580.0420.042
ZH20640no0.1550.2000.1110.111
ZH21554yes0.2190.3030.1790.179
ZH21810yes0.3370.6440.2650.265

Results of null alleles at 21 microsatellite loci.

Table 5

LocusLocusP–ValueLocusLocusP–ValueLocusLocusP–Value
ZH10002ZH101270.00013ZH12347ZH146800.46803ZH12347ZH185800.00827
ZH10002ZH115100.01033ZH12502ZH146800.74762ZH12502ZH185800.00477
ZH10127ZH115100.00299ZH12756ZH146800.59985ZH12756ZH185800.0007
ZH10002ZH119340.00013ZH12912ZH146800.55004ZH12912ZH185800.02808
ZH10127ZH119340.00013ZH13293ZH146800.24813ZH13293ZH185800.09874
ZH11510ZH119340.00211ZH13301ZH146800.73153ZH13301ZH185800.74762
ZH10002ZH123470.00348ZH13561ZH146800.5092ZH13561ZH185800.01411
ZH10127ZH123470.00063ZH13573ZH146800.29231ZH13573ZH185800.02964
ZH11510ZH123470.02951ZH10002ZH155030.01739ZH14680ZH185800.74762
ZH11934ZH123470.00096ZH10127ZH155030.01923ZH15503ZH185800.00718
ZH10002ZH125020.00083ZH11510ZH155030.03396ZH15709ZH185800.27994
ZH10127ZH125020.00041ZH11934ZH155030.01923ZH17044ZH185800.00048
ZH11510ZH125020.01568ZH12347ZH155030.09522ZH18332ZH185800.02765
ZH11934ZH125020.00046ZH12502ZH155030.04807ZH10002ZH206400.00325
ZH12347ZH125020.00773ZH12756ZH155030.01923ZH10127ZH206400.00303
ZH10002ZH127560.00013ZH12912ZH155030.06061ZH11510ZH206400.00642
ZH10127ZH127560.00008ZH13293ZH155030.14028ZH11934ZH206400.00098
ZH11510ZH127560.00155ZH13301ZH155030.53963ZH12347ZH206400.00709
ZH11934ZH127560.00013ZH13561ZH155030.00718ZH12502ZH206400.00357
ZH12347ZH127560.00168ZH13573ZH155030.0169ZH12756ZH206400.0014
ZH12502ZH127560.00008ZH14680ZH155030.53963ZH12912ZH206400.01745
ZH10002ZH129120.02676ZH10002ZH157090.29042ZH13293ZH206400.01327
ZH10127ZH129120.01479ZH10127ZH157090.16135ZH13301ZH206400.80831
ZH11510ZH129120.06496ZH11510ZH157090.21714ZH13561ZH206400.17313
ZH11934ZH129120.00601ZH11934ZH157090.15175ZH13573ZH206400.13199
ZH12347ZH129120.02471ZH12347ZH157090.18919ZH14680ZH206400.22931
ZH12502ZH129120.01361ZH12502ZH157090.11498ZH15503ZH206400.02908
ZH12756ZH129120.00766ZH12756ZH157090.11827ZH15709ZH206400.47306
ZH10002ZH132930.08334ZH12912ZH157090.16089ZH17044ZH206400.00117
ZH10127ZH132930.03827ZH13293ZH157090.50313ZH18332ZH206400.00525
ZH11510ZH132930.25831ZH13301ZH157090.19934ZH18580ZH206400.00132
ZH11934ZH132930.08313ZH13561ZH157090.02814ZH10002ZH215540.00083
ZH12347ZH132930.0375ZH13573ZH157090.02155ZH10127ZH215540.00053
ZH12502ZH132930.05999ZH14680ZH157090.04205ZH11510ZH215540.01177
ZH12756ZH132930.04707ZH15503ZH157090.33708ZH11934ZH215540.00024
ZH12912ZH132930.18552ZH10002ZH170440.00013ZH12347ZH215540.00143
ZH10002ZH133010.65703ZH10127ZH170440.00013ZH12502ZH215540.00401
ZH10127ZH133010.39869ZH11510ZH170440.00398ZH12756ZH215540.00064
ZH11510ZH133010.27472ZH11934ZH170440.00013ZH12912ZH215540.03147
ZH11934ZH133010.62779ZH12347ZH170440.00087ZH13293ZH215540.05562
ZH12347ZH133010.6057ZH12502ZH170440.00048ZH13301ZH215540.69499
ZH12502ZH133010.09628ZH12756ZH170440.0001ZH13561ZH215540.02948
ZH12756ZH133010.04205ZH12912ZH170440.00536ZH13573ZH215540.03377
ZH12912ZH133010.55004ZH13293ZH170440.07532ZH14680ZH215540.09628
ZH13293ZH133010.41194ZH13301ZH170440.4855ZH15503ZH215540.04807
ZH10002ZH135610.03502ZH13561ZH170440.00105ZH15709ZH215540.0502
ZH10127ZH135610.03292ZH13573ZH170440.00431ZH17044ZH215540.00035
ZH11510ZH135610.036ZH14680ZH170440.4855ZH18332ZH215540.00825
ZH11934ZH135610.03775ZH15503ZH170440.01923ZH18580ZH215540.0003
ZH12347ZH135610.0256ZH15709ZH170440.06352ZH20640ZH215540.00119
ZH12502ZH135610.00404ZH10002ZH183320.00718ZH10002ZH218100.00013
ZH12756ZH135610.00772ZH10127ZH183320.00533ZH10127ZH218100.00008
ZH12912ZH135610.01208ZH11510ZH183320.12313ZH11510ZH218100.00155
ZH13293ZH135610.20554ZH11934ZH183320.00477ZH11934ZH218100.00013
ZH13301ZH135610.17538ZH12347ZH183320.00879ZH12347ZH218100.00168
ZH10002ZH135730.09412ZH12502ZH183320.03709ZH12502ZH218100.00008
ZH10127ZH135730.07951ZH12756ZH183320.00663ZH12756ZH218100.00001
ZH11510ZH135730.02726ZH12912ZH183320.18372ZH12912ZH218100.00766
ZH11934ZH135730.02836ZH13293ZH183320.09161ZH13293ZH218100.04707
ZH12347ZH135730.03055ZH13301ZH183320.80371ZH13301ZH218100.04205
ZH12502ZH135730.0193ZH13561ZH183320.02702ZH13561ZH218100.00772
ZH12756ZH135730.02561ZH13573ZH183320.02089ZH13573ZH218100.02561
ZH12912ZH135730.01363ZH14680ZH183320.27472ZH14680ZH218100.59985
ZH13293ZH135730.33354ZH15503ZH183320.11112ZH15503ZH218100.01923
ZH13301ZH135730.29231ZH15709ZH183320.06901ZH15709ZH218100.11827
ZH13561ZH135730.00004ZH17044ZH183320.00054ZH17044ZH218100.0001
ZH10002ZH146800.65703ZH10002ZH185800.00076ZH18332ZH218100.00663
ZH10127ZH146800.39869ZH10127ZH185800.00069ZH18580ZH218100.0007
ZH11510ZH146800.42182ZH11510ZH185800.01033ZH20640ZH218100.0014
ZH11934ZH146800.31389ZH11934ZH185800.00039ZH21554ZH218100.00064

Significance test of 21 microsatellite linkage disequilibrium.

Table 6

LocusNANEIHOHEFISPICp (HWE)
ZH100023.0001.9060.7870.1110.4750.7660.4040.025*
ZH101273.0002.7931.0610.4440.6420.3080.5680.010*
ZH115106.0003.6821.5230.5560.7280.2370.6950.024*
ZH119343.0003.0001.0990.4440.6670.3330.5930.019*
ZH123474.0003.4591.3050.5000.7110.2970.6580.133
ZH125024.0002.3481.0140.1110.5740.8060.5000.000**
ZH127563.0002.0510.8280.1110.5120.7830.4260.028*
ZH129127.0005.0631.7730.4440.8020.4460.7780.000**
ZH132932.0001.6700.5910.5560.401-0.3850.3210.249
ZH133012.0001.1170.2150.1110.105-0.0590.1000.860
ZH135614.0003.3061.2760.3330.6980.5220.6420.003*
ZH135735.0003.9511.4880.3330.7470.5540.7090.001*
ZH146802.0001.1170.2150.1110.105-0.0590.1000.860
ZH155032.0001.3850.4510.3330.278-0.2000.2390.549
ZH157093.0001.7420.7300.3330.4260.2170.3710.719
ZH170443.0002.5711.0110.1110.6110.8180.5360.008*
ZH183326.0004.6291.6480.5560.7840.2910.7530.015*
ZH185804.0002.6561.1680.5560.6230.1090.5790.005*
ZH206402.0002.0000.6930.3330.5000.3330.3750.317
ZH215544.0002.6561.1170.3330.6230.4650.5570.125
ZH218103.0002.0510.8280.1110.5120.7830.4260.028*
Mean3.3752.5230.9470.2850.5360.432

Characteristics of 21 newly developed polymorphic microsatellite markers in M. digitata.

NA, observed number of alleles; NE, effective number of alleles; HO, observation of heterozygosity; HE, expected heterozygosity; I, Shannon information index; PIC, polymorphism information content; FIS, inbreeding coefficient within populations; p(HWE), probability of Chi-square test for Hardy-Weinberg equilibrium; *, significant departure from expected Hardy-Weinberg equilibrium before Bonferroni correction (p < 0.05); **, significant departure from expected Hardy-Weinberg equilibrium after Bonferroni correction (p < 0.0002).

Discussion

In this study, 6,778 microsatellite loci were detected from RAD-seq data of M. digitata, with a distribution frequency of 2.14%. This distribution frequency was similar to that of Parus palustris (2.2%; Wan et al., 2016), Patinopecten yessoensis (1.4%; Ni et al., 2018), and Clematis (2.11%; Song et al., 2022) but was much lower than those of Datnioides pulcher (16.1%; Qu et al., 2019) and Pelteobagrus vachellii (20.52%; Wang et al., 2021). This indicates a significant difference in the abundance of microsatellites among different species. This result is consistent with the findings of Liu et al. (2021).

The microsatellite loci of M. digitata are dominated by trinucleotides, followed by tetranucleotides, which is consistent with results reported for other cnidarians. Ruiz-Ramos and Baums (2014) studied 11 species of cnidarians and found that the highest abundance of microsatellites in Anthozoa and Hydrozoa were trinucleotides and tetranucleotides. This is similar to the distribution of microsatellite loci in other invertebrates. Among the 33 animal species counted in this study (Table 7), the dominant microsatellite motif of most invertebrates is mainly mononucleotides (Tenebrio molitor, Zhu et al., 2013; Phenacoccus Solenopsis, Luo et al., 2014;Galeruca daurica; Zhang et al., 2016), dinucleotides (Exopalaemon carinicauda, Duan et al., 2016) or trinucleotides (Eucryptorrhynchus chinensis, Wu et al., 2016; Tetranychus dichromata, Wang et al., 2013). However, it is significantly different from that of vertebrates, which are dominated by mononucleotides and dinucleotides (Qi et al., 2015; Tang et al., 2022).

Table 7

SpeciesBase number of dominant motifThe most common SSR motifs of six different repeat typesThe identification criteria minimum number of repeat timesReferences
Mono-Di-Tri-Tetra-Penta-Hexa-
Vertebrate
Ictalurus punctatusMono-AACAATAAATATAATTGACTA10,6,5,5,5,5Tang et al., 2022
Bagarius yarrelliMono-AACAATATAGAATCTAACCCT10,6,5,5,5,5Yang et al., 2021
Ctenopharyngodon idellaMono-AACAATAGATAATATAACCCT10,6,5,5,5,5Huang et al., 2022
Mugilogobius chulaeMono-AAGAGC12,6,5,5,4,4Cai et al., 2015
Takifugu rubripesMono-AACAGGACCTAGAGGTTAGGG10,6,5,5,5,5Xu et al., 2021a
T. flavidusMono-AACAGGAGGTAGAGGAACCCT10,6,5,5,5,5Xu et al., 2021a
T. bimaculatusMono-AACAGGACAGAGAGGTTAGGG10,6,5,5,5,5Xu et al., 2021a
Tetraodon nigroviridisMono-AACAGGATCTAAGATAACCCT10,6,5,5,5,5Xu et al., 2021a
Placocheilus cryptonemusMono-AACAGGAGATAGAGGAAAGAC10,6,5,5,5,5Ren and Ma, 2021
Pelteobagrus vachelliDi-AACAATAAATAATCTGGGTTA10,6,5,5,5 ,5Peng et al., 2022
Ageneiosus marmoratusDi-AATAATAAATAATATAAATGT10,6,5,5,5,5Su et al., 2021
Scatophagus argusDi-AACAGGAGATAGAGGAATCAG10,6,5,5,5 ,5Wang et al., 2020
Pelteobagrus fulvidracoDi-AACAATAAATAATCTAACCCT10,6,5,5,5 ,5Xu et al., 2020
Acanthogobius ommaturusTri-ATATTCATGAATTCTTCTGA-,6,4,4,4 ,4Song et al., 2020
Boa constrictorMono-AACAATAAATAAAATACATAT12,7,5,4,4,4Nie et al., 2017
Protobothrops mucrosquamatusMono-AACAATAAATAATAGACATAT12,7,5,4,4,4Nie et al., 2017
Arborophila rufipectusMono-AACAACAAACAAACAAGGGTT12,7,5,4,4,4Huang et al., 2015
Macaca fascicularisMono-AACAATAAATAAACAAAACAA12,7,5,4,4,4Tu et al., 2018
Ailuropoda melanoleucaMono-AACAATAAATAAACAAAACAA12,7,5,4,4,4Li et al., 2014
Ursus maritimusMono-AACAACAAATAAACAAAACAA12,7,5,4,4,4Li et al., 2014
Pantholops hodgsoniiMono-AACAGCAAATAACTGAAAGTG12,7,5,4,4,4Qi et al., 2016
Capra hircusMono-AACAGCAAATAACTGAAACAA12,7,5,4,4,4Qi et al., 2016
Invertebrate
Sepiella japonicaMono-AATAATAAAG12,6,5,5,4,4Sun et al., 2019
Eriocheir sinensisMono-AACAGGAAGGAACCTAAGAGG10,6,5,5,5,5Xu et al., 2021b
Phenacoccus solenopsisMono-AACAACAAAGAATCG12,6,5,5,4,4Luo et al., 2014
Anopheles sinensisMono-AACAGCAAATAACCTAACAGC10,6,5,5,5,5Wang et al., 2016
Ixodes scapularisMono-AATAATAAATAAATGACGCCG12,7,5,4,4,4Wang et al., 2013
Eucryptorrhynchus chinensisTri-AATTTAATAAAGGTT12,6,5,5,5,5Wu et al., 2016
Tomicus yunnanensisTri-AACAACAAAT12,6,5,5,4,4Yuan et al., 2014
Tetranychus urticaeTri-AACATCAAATAACCTAAGATG12,7,5,4,4,4Wang et al., 2013
Patinopecten yessoensisTri-ATATACAAAAAACCMinimum length of SSR motifs is 12Ni et al., 2018
Artemia franciscanaDi-ATAATAAATAATATAGAGCC-,5,5,5,5,5Jo et al., 2021
Apis mellifera ligusticaDi-ATAATAAAGAAAAG-,6,5,5,5,5Guo et al., 2018

Statistics of microsatellite characteristics of 33 published studies.

Mono-, Mononucleotide; Di-, Dinucleotide; Tri-, Trinucleotide; Tetra-, Tetranucleotide; Penta-, Pentanucleotide; Hexa-, Hexanucleotide.

The dominant motifs of mononucleotides, dinucleotides, trinucleotides, and tetranucleotides in M. digitata are A\T, AT\AT, AAT\ATT, and AAAT\ATTT, respectively, similar to previous microsatellite distribution research results (Wang et al., 2013; Jo et al., 2021; Su et al., 2021). In mononucleotides, A\T is the dominant motif of most species (Wang et al., 2013; Luo et al., 2014; Qi et al., 2015; Wu et al., 2016; Liu et al., 2021; Su et al., 2021). Among dinucleotides, AC is the most common motif, however, AT is also common in invertebrates such as Ixodes scapularis (Wang et al., 2013), E.chinensis (Wu et al., 2016), P.yessoensis (Ni et al., 2018), and Artemia franciscana (Jo et al., 2021). AAT\ATT and AAAT\ATTT are also common trinucleotide and tetranucleotide motifs, respectively, such as those in A.franciscana (Jo et al., 2021), I.scapularis (Wang et al., 2013), Boa constrictor, and Protobothrops mucrosquamatus (Nie et al., 2017). This shows that dominant motifs of M. digitata are similar to what is seen in most species. Previous studies that used transcriptome data to develop microsatellite loci also found fewer GC motifs, presumably due to the methylation of cytosine in CpG sequences important for the regulation of transcription (Gonzalez-Ibeas et al., 2007; Xing et al., 2017; Liu et al., 2021).

Microsatellites are among the most commonly used molecular markers for genetic diversity analysis. However, traditional methods to develop microsatellite markers are tedious and have a low success rate. For example, to develop SSR markers using standard enrichment protocols requires the construction of microsatellite enrichment libraries, hybridization, and sequencing, which requires a large amount of experimental work expertise and high cost (Jia et al., 2013; Jia and Zhang, 2019). With the development of high-throughput sequencing, microsatellite marker development based on transcriptome and RRGS data have emerged. RRGS has been widely used as follows: specific-locus amplified fragment sequencing (SLAF-seq) and RAD-seq, of which RAD-seq is the more widely used. Compared with SLAF-seq, RAD-seq can obtain more markers, and the splicing of read2 may result in longer fragments, which is often used in the development of high-density and microsatellite markers (Wang et al., 2012; Sun et al., 2013; Wang et al., 2014; Andrews et al., 2016). In addition, transcriptome sequencing data are widely used to develop EST-SSR (He et al., 2020; Liu et al., 2021). EST-SSRs are derived from transcribed regions of genes, and compared with genome SSR markers, more conserved, but they may be used to identify alleles associated with significant traits (Chen et al., 2017; Karcι et al., 2020). But most EST-SSR markers are byproducts of stress experiments. However, during the development of microsatellite markers of reef-building corals, coral bleaching is induced by heating, which causes the symbiotic zooxanthellae in the coral to expel from the coral. After coral bleaching, RNA may be partially degraded, which is not conducive to transcriptome sequencing. Therefore, RAD-seq is an advantageous method for coral to develop microsatellite primers.

Conclusion

In this study, the large-scale development of SSR molecular markers of M. digitata was carried out through RAD-seq sequencing data, and the sequence characteristics and distribution rules of different motifs of coral SSR loci were analyzed and summarized. Twenty-one pairs of stable polymorphic primers were screened from nine randomly selected coral samples. The acquisition of these microsatellites has laid a foundation for the development of highly polymorphic microsatellite primers to study the genetic diversity, and population genetic structure of populations of M. digitata in the future. M. digitata is a non-model organism. This study further demonstrates that screening SSRs from high-throughput data is a fast and effective method for discovering SSRs in non-model organisms.

Statements

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: BioProject, PRJNA895921.

Author contributions

Investigation, JS, YL, SC, ZC, YW, JS, ZW and DW. Performed the experiments, JS, YL and YW. Writing-original draft preparation, JS and YL. Writing-review and editing, JS, YL, SC, ZC, YW, JS, ZW and DW. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Hainan provincial Natural Science Foundation of China (421RC1106), Department budget projects of Hainan provincial in 2022 (KYL-2022-12), the Ministry of Industry and Information Technology with the research project under Grant number [2019]357, and the Major Science and Technology Program of Hainan Province (Grant ZDKJ2019011).

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Summary

Keywords

South China Sea, scleractinian coral, polymorphic loci, genetic diversity, polymorphic information content

Citation

Jia S, Li Y, Chen S, Cai Z, Shen J, Wang Y, Wu Z and Wang D (2023) Microsatellite markers for Monitipora digitata designed using restriction-site associated DNA sequencing. Front. Mar. Sci. 10:1019419. doi: 10.3389/fmars.2023.1019419

Received

15 August 2022

Accepted

30 May 2023

Published

16 June 2023

Volume

10 - 2023

Edited by

David Seth Portnoy, Texas A&M University Corpus Christi, United States

Reviewed by

Guanpin Yang, Ocean University of China, China; Xianyun Ren, Yellow Sea Fisheries Research Institute (CAFS), China; Xuehe Lu, Suzhou University of Science and Technology, China

Updates

Copyright

*Correspondence: Shuwen Jia, ; Zhongjie Wu, ; Daoru Wang,

†These authors have contributed equally to this work

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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