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Research Article

Development and characterization of twenty-eight polymorphic microsatellite for rice field eel (Monopterus albus) using RAD tag sequencing

Received: January 01, 2018
Accepted: February 14, 2018
Published: February 22, 2018
Genet.Mol.Res. 17(1): gmr16039880
DOI: 10.4238/gmr16039880

Abstract

Monopterus albus is a special economic aquatic animal in China. Most of cultured M. albus are wild species captured from different areas, and this makes the wild resources of M. albus are under great threat. Microsatellite markers can help us understand the genetic resources of M. albus in different areas. In this study, using RAD-seq (restriction site associated DNA sequencing) protocol, a total of 9,897 microsatellites were identified in the genome with average frequency of 195 microsatellites per megabase of genomic sequences. Among these SSRs, the dinucleotide repeat motif was the most abundant type representing 61.77 % of the total microsatellite loci, followed by pentanucleotides (13.91 %), trinucleotides (12.08 %), tetranucleotides (9.30 %) and hexanucleotides (2.94 %). A total of 100 SSR primers were designed for PCR amplification. The polymorphism analysis showed that 28 primer pairs could successfully amplify the polymorphic fragments and the number of alleles per polymorphic locus ranged from 2 to 13, with an average of 5.70 alleles/locus. The values of observed and expected heterozygosity’s ranged from 0.0333 to 0.8000 and 0.0333 to 0.8887, respectively. This microsatellite locus will be useful to understand the structure, genetic diversity, and genetic difference of this species.

Introduction

The swamp eel (Monopterus albus), belongs to Order Synbranchiformes, Family Synbranchidae, and is widely distributed in the muddy ponds, swamps, canals, and rice fields (Chew SF, et al. 2015). Because of its great growth performance and rich nutrient content, swamp eel has become a commercially important farmed species in China (Chen D, et al. 2016). Due to the difficulties in artificial breeding, all cultured M. albus come from wild species, and this makes the wild resources of M. albus are under great threat. Proper conservation management of this species is of interest to ensure the sustainable exploitation. Knowledge of the population genetic structure and genetic resources condition is vital for conservation endeavors of this species. Microsatellites are very suitable markers for such studies because of their high frequency of distribution (Tóth G, 2000) co-dominance, reproducibility, and high level of polymorphism (Wang S, 2009). Recently, some microsatellites have been isolated for M. albus (Li WT, 2007). However, additional loci will facilitate high-resolution analyses such as pedigree reconstruction and assignment test.

The traditional development of microsatellite markers, based on probe hybridization, is time-consuming and expensive (An HS, 2012; Ekblom R, 2011). Current next generation sequencing (NGS) technologies can generate a large number of sequences and provide a great convenient for the acquisition of genetic markers [8]. Especially the “Restriction-site Associated DNA” (RAD) method, using the Illumina platform, it has been proved an efficient and cost-effective method for SSRs discovery (Castoe TA, et al. 2012)

In this study, the de novo RAD-seq of M. albus were performed to discovery microsatellite markers. Furthermore, we developed 28 polymorphic microsatellite markers which could provide an effective tool for population genetic structure and conservation genetics researches of this species.

Material and Methods

DNA extraction

Sample of M. albus were collected from wild population from Jiangxi Province, China (n=30). Genomic DNA was extracted from muscle tissue, using the traditional phenol chloroform method. Extracted DNA were dissolved in ddH2O and assessed by loading on a 1.5% agarose gel, stored at -20°C.

RAD-seq sequencing and contigs assembly

The RAD library was constructed at Meiji Inc (China) according to the protocol described by Etter et al. Briefly, genomic DNA was digested with PstI, then the P1 adapter (an adapter contains a matching sticky-end, Illumina sequencing priming sites, and the “barcoding” a nucleotide 4 or 5 bp long for sample identification) was ligated to the products of the restriction reaction. The reactions were then pooled, randomly sheared to a mean size of 500 bp, the DNA in the rage 300-800 bp was selected following electrophoresis. After end repair and the addition of 3’-adenine overhangs, DNA was then ligated to a second adapter (P2). PCR amplification was performed using P1-and P2-adapter primes, enriching for RAD tags that contain both adapters, and preparing them to be hybridized to HiSeq 2000 sequencer. Illumina raw data were processed using Stacks process_radtags. pl script (Catchen J, 2013), and then the high quality first and second reads were retained for de novo assembly using Velvet (Namiki T, 2012).

SSR identification and primer design

All assembly contigs were used to identify SSR motifs by MISA (Beier S, 2017). The search criteria were that the minimum repetitions of di-, tri-, tetra-, penta-, and hexa-nucleotides were 6, 5, 4, 3 and 3, respectively. Mononucleotide repeats were excluded. Using primer premier 5.0 software (Premier Biosoft International), 100 pairs of primers were then designed flanking the repeat regions of interest and the target amplicon size was in the range of 100-250 bp. All the primer pairs were subject to the temperature gradient PCR system for annealing temperature optimization. Only the primers which could successfully amplify the target fragments were used for the next polymorphism analysis.

Microsatellite polymorphism verification and data analysis

30 wild individuals were used to test the polymorphic. Each amplification was performed in a 15-μL final volume containing 1.5 μL of 10 × PCR buffer, 1.2 μL of dNTP (10 mM), 0.3 U of Taq DNA polymerase, 0.2 μM of each primer, 50 ng of genomic DNA. The PCR parameters were 94°C for 5 min (initial denaturation), followed by 30 cycles with 94°C for 30 s, annealing temperature 30 s, 72°C for 60 s and an additional extension at 72°C for 10 min. Polymorphic amplification products were separated on 12% non-denaturing polyacrylamide gel and stained with ethidium bromide. After establishing the AB matrix, the software Genepop v1.32 (Rousset F, 2008). was applied to estimate microsatellite alleles, observed heterozygosities, HWE and expected heterozygosities. Polymorphic information content (PIC) was obtained by PIC_CALC software. Micro-Checker was used to test for the presence of null alleles (Oosterhout CV, 2004).

Results and Discussion

RAD-seq sequencing and contigs assembly

Compared with the traditional enrichment library, RAD-seq technology is more convenient and less time consuming in SSRs identification (Miller MR, 2007). In this study, the sequencing procedure generated 18.53 million raw reads, and after filtering using process_radtags. pl script, 18.23 million clean paired reads were retained (Table 1). These clean reads were assembled into 249,278 contigs (105-852 bp) using Velvet. The average contigs length was 204 bp, and most contigs was 101-300 bp (96.7%) (Figure 1). Raw data have been made available through the Sequence Read Archive (SRA) repository at NCBI (SRR5980345).

Illumina sequencing information M. albus
Raw paired Reads (million) 18.53
Clear paired Read (million) 18.23
Number of contigs 249,278
Contigs length range (bp, min-max) 105-852
Average contigs length (bp) 204
Total contigs length of SSR analyzed (bp, million) 50.87
Number of contigs containing SSR 9,321
Number of SSR 9,897
SSR/Mb 195

Table 1: Summary statistics of the RAD-seq sequencing via Illumina.

Development-characterization-Contigs-length

Figure 1: Contigs length distribution of RAD-seq.

Characterization of SSRs

A total of 9,897 microsatellites were identified with average frequency of 195 microsatellites per megabase of genomic sequences (Table 1), which was little higher than that documented in swamp eel whole genome (179/Mb) (Li Z, 2017), but low than that in Clarias batrachus (247/Mb) (Srivastava S, 2016), This difference may be due to the sizes of the databases and bioinformatics software tools.

Among these SSRs, the most abundant repeat motif were dinucleotides (61.77%), followed by pentanucleotides (13.91%), trinucleotides (12.08%), tetranucleotides (9.30%) and hexanucleotides (2.94%) (Figure 2A). Similar results were found in many other creatures, such as Buhalus Bubalis (Qi WH, 2015), Panulirus guttatus (Truelove N, 2016) Clarias batrachus (Srivastava S, 2016) and Scophthalmus maximus (Pardo BG, 2007) in which the dinucleotides were the most abundant repeat motif.

Similar to other fishes (Zhang J, 2014; Kim KS, 2016) in dinucleotides repeat motif, AC/GT motifs (76.00%) were most abundant and GC/CG (0.15%) repeats were extremely rare in M. albus (Figure 2B). Among the trinucleotides, AAT/ATT (24.08%) was most abundant motifs, followed by AGG/CCT (18.39%), AAC/GTT (13.29%) and AGC/GCT (13.29%), whereas CGG/CCG (0.42%) motifs were the least (Figure 2C). Three most frequent repeats in tetranucleotide were AAAT/ATTT (21.52%), AAAC/GTTT (14.46%) and ATGG/CCAT (13.91%) (Figure 2D). Among pentanucleotide repeats, the top three types of repeat motifs are AAATC/GATTT (15.25%), AAAAC/GTTTT (12.27%) and AAAAT/ATTTT (8.93%) (Figure 2E). In hexanucleotides, GATTCT/AGAATC (10.65%), AAAAAT/ATTTTT (9.62%) and GGGTTA/TAACCC (4.46%) were the most abundant repeats (Figure 2F). The abundance of A-rich repeat motifs in genome SSRs indicated its significant evolution role in fish species (Li Z, 2017).

Development-characterization-Proportion-distribution

Figure 2: Distribution of repeat types in the M. albus. The proportion of five different nucleotide repeat types to total SSR (A). Proportion distribution of selected motifs of dinucleotide repeats (B), trinucleotides repeats (C), tetranucleotides (D), pentanucleotides (E) and hexanucleotides (F).

Development of polymorphic microsatellite markers

A total of 100 SSR primers were designed for PCR amplification and the results showed that 28 primer pairs could successfully amplify the polymorphic fragments. The polymorphic percentage was 28%, which was lower than Ichthyoelephas longirostris (60%) (Landinez-Garcia RM, 2016) Lateolabrax japonicus (42%) (Li BB, 2016) and Geophagus brasiliensishigh (36%) (Ferreira DG, 2013), but higher than Clarias batrachus (17%) [18], hybrid giant tiger grouper (20%) (Huang YS, 2015) and Girella elevate (22%) (McWilliam RA, 2016).

Across 30 individuals surveyed, the number of alleles at these informative loci varied from 2 (SSRSZ78, SSRSZ92, SSRSZ98) to 13 (SSRSZ97), with an average of 5.70 alleles/locus (Table 2).

Locus Accession no. Primer sequences (5'-3') Repeat motif Ta (℃) Allele size (bp) Na Ho He PIC P
SSRSZ2 MF773693 F:TGTCAGTGAGCAGAGAGCAGGC
R:GATAAGGGTTAGGTTAGGCAGGTAG
(AC)11 59.5 135~180 9 0.7812 0.7981 0.7569 0.44
SSRSZ10 MF773694 F:CTGCTGCTACTTAATAAGACTTTCG
R:TTTCAGACTACACCATATGCCTCAC
(CA)12 65.3 182~215 10 0.4333 0.7429 0.6899 0.21
SSRSZ14 MF773695 F:CTCTCTAATCACCTCACCGTCAAAC
R:CTACCTTACTTCCGTTCCTTCATTC
(TC)8 59 130~150 3 0.1000 0.3678 0.3310 <0.05
SSRSZ15 MF773696 F:ATGTGTTTTCTGAGCATCTGTGGAC
R:TCTGTAAGTCTGTGCAAGGTTCCTG
(TG)15 67 185~205 7 0.5185 0.7065 0.6465 0.57
SSRSZ16 MF773697 F:ACAAACTTAACCGCAGAAATCTCAG
R:GGAACAGACATACTGATGGAGAGAC
(AC)12 65.3 184~195 8 0.5122 0.7823 0.7371 0.18
SSRSZ26 MF773698 F:CTTTGACCTTAGAGAGTAAATGCTG
R:TCTGGTACATTAAGCTCACAAGTTC
(TTTG)4 58.3 188~205 5 0.1500 0.2291 0.2138 0.20
SSRSZ33 MF773699 F:CCTTTGCTGAGTGTGGTTTTGAATG
R:ATGTGACAAACTGATGTGAGGATGC
(TG)9 63.2 164~200 3 0.0333 0.3678 0.3310 <0.05
SSRSZ38 MF773700 F:CTTTTCACTACCGGATGAGAGCTTG   
R:ATTCAGCCTGGTACAGTGTAAGTGC
(GT)14 56.6 160~173 6 0.6000 0.7706 0.7186 0.09
SSRSZ44 MF773701 F:GTGGCTGAAGTCTCTACTCCTCTGC
R:CGTCTTAACGTGAAACTGCCCATAC
(CTG)11 60.9 130~150 5 0.0345 0.5632 0.4925 <0.05
SSRSZ45 MF773702 F:CTCAGGTTTTGATAGAAAGGTGCAG
R:GCTTCATATACATTCACTCGTTGCG
(TG)16 60.9 145~190 8 0.5667 0.7616 0.7222 0.07
SSRSZ54 MF773703 F:GAGGCAGTTCAGAAGCAGAGTGGAG
R:AAACGCTGCCACCAACACCTAC
(AGC)7 63 150~153 3 0.1000 0.4367 0.3706 <0.05
SSRSZ57 MF773704 F:CAAACACTGTAAAGCAATGGAGC
R:GCGAAAATAGGGTAGACGACAC
(CA)10 61.8 171~190 4 0.1000 0.3418 0.3054 <0.05
SSRSZ58 MF773705 F:CTGAAACACATCATGCCAAACAAC
R:GTGCCATTGCTACGAGGGTTTC
(GT)9 59.6 170~205 4 0.5357 0.6617 0.5828 <0.05
SSRSZ62 MF773706 F:CTTCTGAGACCTTGTCCTGCTG
R:GGTGGACGCAACAACTTCTACTG
(GT)8 63.7 130~175 7 0.4667 0.5944 0.5569 0.41
SSRSZ65 MF773707 F:AGAGGACAAGAGGTATGGGACAAGC
R:TCCGCCCTCCTGCCTTTTTC
(AGGAG)8 63 130~170 10 0.7692 0.8531 0.8234 0.62
SSRSZ73 MF773708 F:CATCAAGCCACGAAAGTGTCAAAG
R:CTCGGGAATGAAACAGGATGTAAAC
(TG)10 62.6 113~168 7 0.6500 0.7782 0.7304 0.61
SSRSZ75 MF773709 F:GGATAGAGTTAGGTGGAAACAAATG
R:ATCCCTGGTGGAATCTTTGAC
(AAG)9 59.3 144~170 12 0.7179 0.9048 0.8831 0.15
SSRSZ78 MF773710 F:GCAAAAATCAGGGAGGAGAGAATG
R:AGCAAGGTTATGCCAGACCAGAGG
(ATTT)4 64.2 181~190 2 0.0250 0.0250 0.0244 1.00
SSRSZ81 MF773711 F:TACTAACAGAGGATGCCTTCACCAG
R:ATTGGTGTATTGCTGGGAAGTGGTC
(GT)7 62.3 140~175 3 0.2000 0.1873 0.1769 0.93
SSRSZ82 MF773712 F:TACCTGTTGCCACTGTGACCTG
R:GGATTTGATGGGTGTTTCTGTG
(ATGG)8 60.1 140~170 3 0.2000 0.3452 0.3129 <0.05
SSRSZ84 MF773713 F:GGCAGTCATCGCACACCAGGTATTC
R:AAGGAAGAGCAGGTCAAGAAGGTGG
(TGTCAG)3 61.5 166~175 3 0.2000 0.1859 0.1711 0.90
SSRSZ85 MF773714 F:GTGTGCGTGATACAGTGATTTGAAC
R:CGCTCTGACGCAGTGGTTTC
(TGA)6 62.9 146~153 4 0.1000 0.6627 0.5972 <0.05
SSRSZ87 MF773715 F:GTTCTCCATTGCTGCTGAAACACTG
R:GGTTCTCATTCACCCTGGCTACAC
(TACC)6 61.5 186~210 8 0.6829 0.7696 0.7241 0.64
SSRSZ89 MF773716 F:GATGAGTAGCCACCGACCAC
R:CTTACAGGGAGTTTCTTCTTCTACC
(CTTTT)3 59.4 130~135 3 0.8000 0.5757 0.4747 <0.05
SSRSZ92 MF773717 F:TCTTCTTTCCTCCTACCCGCCACTG
R:ATTTCCCTCTTCCTTCCCTTCCATC
(AAAAT)3 61.4 150~160 2 0.0333 0.0333 0.0323 1.00
SSRSZ96 MF773718 F:TACAGCAGGGTCAAGAGGAAAACAG
R:AACCAGCATCACATACCAGCCTCC
(TGC)7 63.8 180~200 5 0.0667 0.4689 0.4347 <0.05
SSRSZ97 MF773719 F:GGTGTTCAGCCCTTATTATTACTTG
R:CAGTGAAGTCAGGGAGAAATGCTAC
(TG)13 57.1 156~170 13 0.8718 0.8868 0.8640 0.08
SSRSZ98 MF773720 F:GCTTTGGGTGGTAATCACTGCTG
R:AAGATTGTTGTCACACAGCCAGGAG
(TCTA)4 63.8 145~160 2 0.3793 0.3128 0.2602 0.12
Mean   - - - - 5.70 0.3795 0.5397 0.4987  

Table 2: Characteristics of 28 microsatellite loci in Monopterus albus

Conclusion

The expected heterozygosity ranged from 0.0333 to 0.8887, while the observed heterozygosity varied from 0.0333 to 0.8000. The average value of PIC was 0.4987, indicating the high level of polymorphism of these loci. After Bonferroni correction in Genepop v1.32, ten loci showed significant deviation from HWE. The microsatellite markers characterized here would contribute to the investigation of the genetic diversity and population structure in this species.

Acknowledgment

This work was supported by grants from the National Natural Science Foundation of China (No. 31160530 and No. 31360634), the National Natural Science Foundation of Jiangxi Province (20114BAB214003), the Science and Technology Support Program of Jiangxi Province (20122BBF60074) and the Project of Education Department in Jiangxi Province (GJJ13289).

About the Authors

Corresponding Author

Lili Wei

College of Animal Science and Technology, Jiangxi Agricultural University, 330045, Nanchang, P.R. China

Email:
hbliliwei@163.com

Qiwang Zhong

College of Biological Science and Engineering, Jiangxi Agricultural University, Nanchang, P.R. China

Email:
zhongqw2000@163.com

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