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 filtering After data filtering
Clean Data(bp) Q20 (%) Q30 (%) N (%) GC (%) HQ Clean Data (bp) HQ Q20 (%) HQ Q30 (%) HQ N(%) HQ GC (%)
1423400136 1366681891(96.02%) 1276795857(89.7%) 4667(0.0%) 563350809(39.58%) 1396195302 1343251931(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
Mononucleotide 719
Dinucleotide 1094
Trinucleotide 2255
Tetranucleotide 1705
Pentanucleotide 714
Hexanucleotide 291

SSR motif information statistics of M. digitata.

Figure 1

Figure 1

(A) The proportion of different motifs of microsatellites in M. digitata. (B) The production of difference motifs of total length of microsatellites in M. digitata. (C) Distribution of number of base repetitions of microsatellites repeat motifs in M. digitata.

Figure 2

Figure 2

The distribution of microsatellite motifs in M. digitata. (A–F) The distribution of microsatellite motifs in the mono-, di-, tri-, tetra-, penta-, and hexanucleotide motif, respectively. Only the top ten repeat motifs are shown in (D–F). The number after the column represents the number of repeat motifs.

Table 3

Locus Repeat motif Forward primer (5'-3') Reverse primer (5'-3') Product size (bp)
ZH10002 (TCA)7 CTGTCCGTGCAAAGAACAAC CAAAGTTGCCTGGAAGGAAG 222
ZH10127 (CGTCA)5 TCAAACCGATTCCTTTCCTG AAGCAGCTACCACGTTCCAC 220
ZH11510 (TA)8(GA)7 TAAAAAGGCGTGCTCACAGA TGTTAACAGCGAGGGTATTGG 254
ZH11934 (AAT)7 TTCTCTTAAATCGACAAAAAGAAGT CCAGTACCATGGGCAGTTTT 220
ZH12347 (GAA)6gcaga(AGG)6 AAAAGCAAAACAGGCACCAT AAAATCACAGATAGTCTGCAAGAAAA 223
ZH12502 (TCA)6 TCATCGTCGTCATCATCGTT TCGCCAAAATTCAAGGTAGG 246
ZH12756 (TTTA)4 GAGCAGTGAAGATGGCTTCC TTTGGGCTTGTGATTGTTCA 198
ZH12912 (GTGA)10 GGTTGTTCACTTTTTGCGGT CACTTCCAACGGACCTGTTT 197
ZH13293 (TCAAGT)4 AATTACCCCGGCTTCGTAGT GCTAGCTCTGTTTTCAGTTTCTTTTT 216
ZH13301 (GT)6 TTGATAACCAGTGGCAGGCT ACCTGTGGTGCGAGATTTTC 203
ZH13561 (TTG)7 TTTTGCGTCGGTATCAAAGA GCAATTTATTGGACACGCCT 225
ZH13573 (TC)6 TTCGCCTTCGAAATCTCATC CGAAAGGAGCCTGGTTAGAA 241
ZH14680 (AAAT)4 CTTGCATTTTTCCCTGCTGT TGCTGTCACATTTCAATGCC 270
ZH15503 (AG)7 CTCTAAAACCCGCAGACCAC CATGACGGCGCTCATACATA 257
ZH15709 (TGT)5 CTAGCACCTGCTATTTGCGG GCGAAGATCGTGGAAACAAA 266
ZH17044 (CAA)5 TGTCCTGGCCATGAACATTA TCGATTTTCGATTAAACCACC 250
ZH18332 (TATT)6 ACCACTTAGGCTTCTGCACG GGGGGAGAGAAAAATGTCGT 219
ZH18580 (CACG)6 CAACGAAACTCGACCCTCAT GCAGAAATGAAGATGCCACA 209
ZH20640 (T)15 AGGGCTGGGCTCTAGTGAAT AGTAGAAGGTGGCACACGGT 205
ZH21554 (AAGT)7 CCAATCGGGGCTACTATGAA CGTGCACGTTCTCACTAGTTTT 254
ZH21810 (AT)7 TGAATGCGAAATGCGAAGTA AGGCTTGAAGAGTACCCCGT 275

Twenty one pairs of microsatellites primer information.

Figure 3

Figure 3

Peak diagram of capillary electrophoresis detection of 21 pairs of primers of M. digitata.

Table 4

Locus Null Present Oosterhout Chakraborty Brookfield 1 Brookfield 2
ZH10002 yes 0.337 0.621 0.247 0.247
ZH10127 no 0.139 0.182 0.120 0.120
ZH11510 no 0.128 0.135 0.100 0.100
ZH11934 no 0.150 0.200 0.133 0.133
ZH12347 no 0.154 0.174 0.123 0.311
ZH12502 yes 0.369 0.676 0.294 0.294
ZH12756 yes 0.337 0.644 0.265 0.265
ZH12912 yes 0.221 0.287 0.199 0.199
ZH13293 no -0.333 -0.161 -0.110 0.000
ZH13301 no -0.057 -0.029 -0.006 0.000
ZH13561 yes 0.243 0.353 0.215 0.215
ZH13573 yes 0.251 0.383 0.237 0.237
ZH14680 no -0.057 -0.029 -0.006 0.000
ZH15503 no -0.184 -0.091 -0.044 0.000
ZH15709 no 0.093 0.122 0.065 0.065
ZH17044 yes 0.365 0.692 0.310 0.310
ZH18332 no 0.156 0.171 0.128 0.128
ZH18580 no 0.092 0.058 0.042 0.042
ZH20640 no 0.155 0.200 0.111 0.111
ZH21554 yes 0.219 0.303 0.179 0.179
ZH21810 yes 0.337 0.644 0.265 0.265

Results of null alleles at 21 microsatellite loci.

Table 5

Locus Locus P–Value Locus Locus P–Value Locus Locus P–Value
ZH10002 ZH10127 0.00013 ZH12347 ZH14680 0.46803 ZH12347 ZH18580 0.00827
ZH10002 ZH11510 0.01033 ZH12502 ZH14680 0.74762 ZH12502 ZH18580 0.00477
ZH10127 ZH11510 0.00299 ZH12756 ZH14680 0.59985 ZH12756 ZH18580 0.0007
ZH10002 ZH11934 0.00013 ZH12912 ZH14680 0.55004 ZH12912 ZH18580 0.02808
ZH10127 ZH11934 0.00013 ZH13293 ZH14680 0.24813 ZH13293 ZH18580 0.09874
ZH11510 ZH11934 0.00211 ZH13301 ZH14680 0.73153 ZH13301 ZH18580 0.74762
ZH10002 ZH12347 0.00348 ZH13561 ZH14680 0.5092 ZH13561 ZH18580 0.01411
ZH10127 ZH12347 0.00063 ZH13573 ZH14680 0.29231 ZH13573 ZH18580 0.02964
ZH11510 ZH12347 0.02951 ZH10002 ZH15503 0.01739 ZH14680 ZH18580 0.74762
ZH11934 ZH12347 0.00096 ZH10127 ZH15503 0.01923 ZH15503 ZH18580 0.00718
ZH10002 ZH12502 0.00083 ZH11510 ZH15503 0.03396 ZH15709 ZH18580 0.27994
ZH10127 ZH12502 0.00041 ZH11934 ZH15503 0.01923 ZH17044 ZH18580 0.00048
ZH11510 ZH12502 0.01568 ZH12347 ZH15503 0.09522 ZH18332 ZH18580 0.02765
ZH11934 ZH12502 0.00046 ZH12502 ZH15503 0.04807 ZH10002 ZH20640 0.00325
ZH12347 ZH12502 0.00773 ZH12756 ZH15503 0.01923 ZH10127 ZH20640 0.00303
ZH10002 ZH12756 0.00013 ZH12912 ZH15503 0.06061 ZH11510 ZH20640 0.00642
ZH10127 ZH12756 0.00008 ZH13293 ZH15503 0.14028 ZH11934 ZH20640 0.00098
ZH11510 ZH12756 0.00155 ZH13301 ZH15503 0.53963 ZH12347 ZH20640 0.00709
ZH11934 ZH12756 0.00013 ZH13561 ZH15503 0.00718 ZH12502 ZH20640 0.00357
ZH12347 ZH12756 0.00168 ZH13573 ZH15503 0.0169 ZH12756 ZH20640 0.0014
ZH12502 ZH12756 0.00008 ZH14680 ZH15503 0.53963 ZH12912 ZH20640 0.01745
ZH10002 ZH12912 0.02676 ZH10002 ZH15709 0.29042 ZH13293 ZH20640 0.01327
ZH10127 ZH12912 0.01479 ZH10127 ZH15709 0.16135 ZH13301 ZH20640 0.80831
ZH11510 ZH12912 0.06496 ZH11510 ZH15709 0.21714 ZH13561 ZH20640 0.17313
ZH11934 ZH12912 0.00601 ZH11934 ZH15709 0.15175 ZH13573 ZH20640 0.13199
ZH12347 ZH12912 0.02471 ZH12347 ZH15709 0.18919 ZH14680 ZH20640 0.22931
ZH12502 ZH12912 0.01361 ZH12502 ZH15709 0.11498 ZH15503 ZH20640 0.02908
ZH12756 ZH12912 0.00766 ZH12756 ZH15709 0.11827 ZH15709 ZH20640 0.47306
ZH10002 ZH13293 0.08334 ZH12912 ZH15709 0.16089 ZH17044 ZH20640 0.00117
ZH10127 ZH13293 0.03827 ZH13293 ZH15709 0.50313 ZH18332 ZH20640 0.00525
ZH11510 ZH13293 0.25831 ZH13301 ZH15709 0.19934 ZH18580 ZH20640 0.00132
ZH11934 ZH13293 0.08313 ZH13561 ZH15709 0.02814 ZH10002 ZH21554 0.00083
ZH12347 ZH13293 0.0375 ZH13573 ZH15709 0.02155 ZH10127 ZH21554 0.00053
ZH12502 ZH13293 0.05999 ZH14680 ZH15709 0.04205 ZH11510 ZH21554 0.01177
ZH12756 ZH13293 0.04707 ZH15503 ZH15709 0.33708 ZH11934 ZH21554 0.00024
ZH12912 ZH13293 0.18552 ZH10002 ZH17044 0.00013 ZH12347 ZH21554 0.00143
ZH10002 ZH13301 0.65703 ZH10127 ZH17044 0.00013 ZH12502 ZH21554 0.00401
ZH10127 ZH13301 0.39869 ZH11510 ZH17044 0.00398 ZH12756 ZH21554 0.00064
ZH11510 ZH13301 0.27472 ZH11934 ZH17044 0.00013 ZH12912 ZH21554 0.03147
ZH11934 ZH13301 0.62779 ZH12347 ZH17044 0.00087 ZH13293 ZH21554 0.05562
ZH12347 ZH13301 0.6057 ZH12502 ZH17044 0.00048 ZH13301 ZH21554 0.69499
ZH12502 ZH13301 0.09628 ZH12756 ZH17044 0.0001 ZH13561 ZH21554 0.02948
ZH12756 ZH13301 0.04205 ZH12912 ZH17044 0.00536 ZH13573 ZH21554 0.03377
ZH12912 ZH13301 0.55004 ZH13293 ZH17044 0.07532 ZH14680 ZH21554 0.09628
ZH13293 ZH13301 0.41194 ZH13301 ZH17044 0.4855 ZH15503 ZH21554 0.04807
ZH10002 ZH13561 0.03502 ZH13561 ZH17044 0.00105 ZH15709 ZH21554 0.0502
ZH10127 ZH13561 0.03292 ZH13573 ZH17044 0.00431 ZH17044 ZH21554 0.00035
ZH11510 ZH13561 0.036 ZH14680 ZH17044 0.4855 ZH18332 ZH21554 0.00825
ZH11934 ZH13561 0.03775 ZH15503 ZH17044 0.01923 ZH18580 ZH21554 0.0003
ZH12347 ZH13561 0.0256 ZH15709 ZH17044 0.06352 ZH20640 ZH21554 0.00119
ZH12502 ZH13561 0.00404 ZH10002 ZH18332 0.00718 ZH10002 ZH21810 0.00013
ZH12756 ZH13561 0.00772 ZH10127 ZH18332 0.00533 ZH10127 ZH21810 0.00008
ZH12912 ZH13561 0.01208 ZH11510 ZH18332 0.12313 ZH11510 ZH21810 0.00155
ZH13293 ZH13561 0.20554 ZH11934 ZH18332 0.00477 ZH11934 ZH21810 0.00013
ZH13301 ZH13561 0.17538 ZH12347 ZH18332 0.00879 ZH12347 ZH21810 0.00168
ZH10002 ZH13573 0.09412 ZH12502 ZH18332 0.03709 ZH12502 ZH21810 0.00008
ZH10127 ZH13573 0.07951 ZH12756 ZH18332 0.00663 ZH12756 ZH21810 0.00001
ZH11510 ZH13573 0.02726 ZH12912 ZH18332 0.18372 ZH12912 ZH21810 0.00766
ZH11934 ZH13573 0.02836 ZH13293 ZH18332 0.09161 ZH13293 ZH21810 0.04707
ZH12347 ZH13573 0.03055 ZH13301 ZH18332 0.80371 ZH13301 ZH21810 0.04205
ZH12502 ZH13573 0.0193 ZH13561 ZH18332 0.02702 ZH13561 ZH21810 0.00772
ZH12756 ZH13573 0.02561 ZH13573 ZH18332 0.02089 ZH13573 ZH21810 0.02561
ZH12912 ZH13573 0.01363 ZH14680 ZH18332 0.27472 ZH14680 ZH21810 0.59985
ZH13293 ZH13573 0.33354 ZH15503 ZH18332 0.11112 ZH15503 ZH21810 0.01923
ZH13301 ZH13573 0.29231 ZH15709 ZH18332 0.06901 ZH15709 ZH21810 0.11827
ZH13561 ZH13573 0.00004 ZH17044 ZH18332 0.00054 ZH17044 ZH21810 0.0001
ZH10002 ZH14680 0.65703 ZH10002 ZH18580 0.00076 ZH18332 ZH21810 0.00663
ZH10127 ZH14680 0.39869 ZH10127 ZH18580 0.00069 ZH18580 ZH21810 0.0007
ZH11510 ZH14680 0.42182 ZH11510 ZH18580 0.01033 ZH20640 ZH21810 0.0014
ZH11934 ZH14680 0.31389 ZH11934 ZH18580 0.00039 ZH21554 ZH21810 0.00064

Significance test of 21 microsatellite linkage disequilibrium.

Table 6

Locus N A N E I H O H E F IS PIC p (HWE)
ZH10002 3.000 1.906 0.787 0.111 0.475 0.766 0.404 0.025*
ZH10127 3.000 2.793 1.061 0.444 0.642 0.308 0.568 0.010*
ZH11510 6.000 3.682 1.523 0.556 0.728 0.237 0.695 0.024*
ZH11934 3.000 3.000 1.099 0.444 0.667 0.333 0.593 0.019*
ZH12347 4.000 3.459 1.305 0.500 0.711 0.297 0.658 0.133
ZH12502 4.000 2.348 1.014 0.111 0.574 0.806 0.500 0.000**
ZH12756 3.000 2.051 0.828 0.111 0.512 0.783 0.426 0.028*
ZH12912 7.000 5.063 1.773 0.444 0.802 0.446 0.778 0.000**
ZH13293 2.000 1.670 0.591 0.556 0.401 -0.385 0.321 0.249
ZH13301 2.000 1.117 0.215 0.111 0.105 -0.059 0.100 0.860
ZH13561 4.000 3.306 1.276 0.333 0.698 0.522 0.642 0.003*
ZH13573 5.000 3.951 1.488 0.333 0.747 0.554 0.709 0.001*
ZH14680 2.000 1.117 0.215 0.111 0.105 -0.059 0.100 0.860
ZH15503 2.000 1.385 0.451 0.333 0.278 -0.200 0.239 0.549
ZH15709 3.000 1.742 0.730 0.333 0.426 0.217 0.371 0.719
ZH17044 3.000 2.571 1.011 0.111 0.611 0.818 0.536 0.008*
ZH18332 6.000 4.629 1.648 0.556 0.784 0.291 0.753 0.015*
ZH18580 4.000 2.656 1.168 0.556 0.623 0.109 0.579 0.005*
ZH20640 2.000 2.000 0.693 0.333 0.500 0.333 0.375 0.317
ZH21554 4.000 2.656 1.117 0.333 0.623 0.465 0.557 0.125
ZH21810 3.000 2.051 0.828 0.111 0.512 0.783 0.426 0.028*
Mean 3.375 2.523 0.947 0.285 0.536 0.432

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

Species Base number of dominant motif The most common SSR motifs of six different repeat types The identification criteria minimum number of repeat times References
Mono- Di- Tri- Tetra- Penta- Hexa-
Vertebrate
Ictalurus punctatus Mono- A AC AAT AAAT ATAAT TGACTA 10,6,5,5,5,5 Tang et al., 2022
Bagarius yarrelli Mono- A AC AAT ATAG AATCT AACCCT 10,6,5,5,5,5 Yang et al., 2021
Ctenopharyngodon idella Mono- A AC AAT AGAT AATAT AACCCT 10,6,5,5,5,5 Huang et al., 2022
Mugilogobius chulae Mono- A AG AGC 12,6,5,5,4,4 Cai et al., 2015
Takifugu rubripes Mono- A AC AGG ACCT AGAGG TTAGGG 10,6,5,5,5,5 Xu et al., 2021a
T. flavidus Mono- A AC AGG AGGT AGAGG AACCCT 10,6,5,5,5,5 Xu et al., 2021a
T. bimaculatus Mono- A AC AGG ACAG AGAGG TTAGGG 10,6,5,5,5,5 Xu et al., 2021a
Tetraodon nigroviridis Mono- A AC AGG ATCT AAGAT AACCCT 10,6,5,5,5,5 Xu et al., 2021a
Placocheilus cryptonemus Mono- A AC AGG AGAT AGAGG AAAGAC 10,6,5,5,5,5 Ren and Ma, 2021
Pelteobagrus vachelli Di- A AC AAT AAAT AATCT GGGTTA 10,6,5,5,5 ,5 Peng et al., 2022
Ageneiosus marmoratus Di- A AT AAT AAAT AATAT AAATGT 10,6,5,5,5,5 Su et al., 2021
Scatophagus argus Di- A AC AGG AGAT AGAGG AATCAG 10,6,5,5,5 ,5 Wang et al., 2020
Pelteobagrus fulvidraco Di- A AC AAT AAAT AATCT AACCCT 10,6,5,5,5 ,5 Xu et al., 2020
Acanthogobius ommaturus Tri- AT ATT CATG AATTC TTCTGA -,6,4,4,4 ,4 Song et al., 2020
Boa constrictor Mono- A AC AAT AAAT AAAAT ACATAT 12,7,5,4,4,4 Nie et al., 2017
Protobothrops mucrosquamatus Mono- A AC AAT AAAT AATAG ACATAT 12,7,5,4,4,4 Nie et al., 2017
Arborophila rufipectus Mono- A AC AAC AAAC AAACA AGGGTT 12,7,5,4,4,4 Huang et al., 2015
Macaca fascicularis Mono- A AC AAT AAAT AAACA AAACAA 12,7,5,4,4,4 Tu et al., 2018
Ailuropoda melanoleuca Mono- A AC AAT AAAT AAACA AAACAA 12,7,5,4,4,4 Li et al., 2014
Ursus maritimus Mono- A AC AAC AAAT AAACA AAACAA 12,7,5,4,4,4 Li et al., 2014
Pantholops hodgsonii Mono- A AC AGC AAAT AACTG AAAGTG 12,7,5,4,4,4 Qi et al., 2016
Capra hircus Mono- A AC AGC AAAT AACTG AAACAA 12,7,5,4,4,4 Qi et al., 2016
Invertebrate
Sepiella japonica Mono- A AT AAT AAAG 12,6,5,5,4,4 Sun et al., 2019
Eriocheir sinensis Mono- A AC AGG AAGG AACCT AAGAGG 10,6,5,5,5,5 Xu et al., 2021b
Phenacoccus solenopsis Mono- A AC AAC AAAG AATCG 12,6,5,5,4,4 Luo et al., 2014
Anopheles sinensis Mono- A AC AGC AAAT AACCT AACAGC 10,6,5,5,5,5 Wang et al., 2016
Ixodes scapularis Mono- A AT AAT AAAT AAATG ACGCCG 12,7,5,4,4,4 Wang et al., 2013
Eucryptorrhynchus chinensis Tri- A AT TTA ATAA AGGTT 12,6,5,5,5,5 Wu et al., 2016
Tomicus yunnanensis Tri- A AC AAC AAAT 12,6,5,5,4,4 Yuan et al., 2014
Tetranychus urticae Tri- A AC ATC AAAT AACCT AAGATG 12,7,5,4,4,4 Wang et al., 2013
Patinopecten yessoensis Tri- AT ATA CAAA AAACC Minimum length of SSR motifs is 12 Ni et al., 2018
Artemia franciscana Di- AT AAT AAAT AATAT AGAGCC -,5,5,5,5,5 Jo et al., 2021
Apis mellifera ligustica Di- AT AAT AAAG AAAAG -,6,5,5,5,5 Guo 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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