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Original Article • Braz. j. oceanogr. 67 • 2019 • https://doi.org/10.1590/S1679-87592019025106711 linkcopy

Open-access Genetic diversity of eight wild populations of Pampus argenteus along the coast of China inferred from fifteen polymorphic microsatellite markers

AuthorshipSCIMAGO INSTITUTIONS RANKINGS

    Abstract

    Pampus argenteus (Perciformes: Stromateidae) is widely distributed along the coast of China, Indian Ocean, Arabian Gulf and North Sea. Due to overfishing and environmental degradation, its resources reduced year after year. Thus, new management strategies are urgently needed for the sustainable growth and utilization of this species. Characterization of the genetic variation of this fish species is essential for conserving the genetic resource and restraining the population decline. Therefore, it is necessary to have a clear understanding of the intraspecific genetic diversity and population structure of the species. In this study, we assess the genetic diversity and population structure of P. argenteus by using microsatellites. We genotyped 240 P. argenteus individuals from eight wild populations collected from Shidao (SD), Lianyungang (LYG), Lvsi (LS), Zhoushan (ZS), Dongtou (DT), Xiapu (XP), Haikou (HK), and Beibuwan (BBW) along the coast of China using fifteen polymorphic microsatellites. A total of 139 alleles were determined at 15 loci across the eight populations, and a relatively high level of genetic diversity was observed, with observed heterozygosity (Ho) and expected heterozygosity (He) ranging from 0.100 to 1.000, and from 0.669 to 0.934 per locus-location combination, respectively. LS had the highest average allele (number of alleles, A=15.200), and HK the lowest (A=13.000). Hos of P. argenteus are less than Hes, indicating lack of heterozygote within populations. Analysis of molecular variance (AMOVA) showed that most variation (95.66%) occurred within populations, suggesting that this is the main source of total variance. This study will provide useful information for conservation and sustainable exploitation of this important fishery resource.

    Descriptors:
    Pampus argenteus; microsatellite; genetic diversity; population structure

    INTRODUCTION

    Pampus argenteus (Perciformes: Stromateidae) is an economically important benthopelagic fish, which is widely distributed along the coast of China, Indian Ocean, Arabian Gulf and North Sea (Davis and Wheeler, 1985; Liu et al., 2002). Due to overfishing and environmental degradation, its numbers reduced year after year (Jin et al., 2005; Zhang et al., 2007). Thus, new management strategies are urgently needed for the sustainable growth and utilization of this species. For effective fishery management, we need a mass of biological and evolutionary data. Characterization of the genetic variation of this fish species is essential for conserving the genetic resource and restraining the population decline. Therefore, it is necessary to have a clear understanding of the intraspecific genetic diversity and population structure of the species.

    Genetic diversity and population structure have vital importance in understanding and managing populations (Palumbi, 2003; Muths et al., 2009). So far, several studies on genetic diversity and population structure of P. argenteus have been reported (Xu et al., 2008; Peng et al., 2010; Meng et al., 2009; Wu et al., 2012; Zhao et al., 2011a). The markers used were mitochondrial DNA COI, D-loop, RAPD, and isozyme. No studies based on microsatellites of P. argenteus is yet available.

    Microsatellites or simple sequence repeats (SSR) are short (1-6 bp) repetitive DNA sequences that are highly abundant and almost evenly distributed in genomes. They are generally neutral, highly polymorphic, co-dominant and easily scored with PCR (Goldstein and Schlotterer, 1999). As a more variable marker than RFLP and RAPD, microsatellites have been widely used for population genetics, genome mapping, ecology, and evolution of animals including fishery species for several decades (Goldstein and Schlotterer, 1999; Herwerden et al., 1999; Liu and Cordes, 2004; Vargas-Caro et al., 2017; Kiper et al., 2018).

    Population structure is an important factor that should be taken into account by the appropriate regulatory authorities when considering sustainable applications of fish. The aim of this research was to examine the genetic diversity and population structure of P. argenteus along the coast of China to provide useful genetic information for the management of this species. In the present study, we used fifteen polymorphic microsatellite markers to assess the genetic diversity and population structure of eight wild populations of P. argenteus along the coast of China. This study provide useful information for better understanding the evolution potential and population structure of P. argenteus, and is good for the protection and reasonable utilization of this species.

    MATERIALS AND METHODS

    Sampling and DNA extraction

    From June to August, 2013, a total of 240 wild P. argenteus individuals, i.e., 30 individuals of each population, were collected from eight locations along the coast of China (Figure 1). The eight locations are Shidao (SD) of Shandong Province, Lianyungang (LYG) and Lvsi (LS) of Jiangsu Province, Zhoushan (ZS) and Dongtou (DT) of Zhejiang Province, Xiapu (XP) of Fujian Province, Haikou (HK) of Hainan Province, and Beibuwan (BBW) of Guangxi Zhuang Autonomous Region, respectively. The vouchers of the sampled population were deposited in the herbarium of East China Sea Fisheries Research Institute. All individuals were transferred to the laboratory in dry ice and then stored at -80℃ before DNA extraction.

    Figure 1
    Map of the P. argenteus sampled locations (indicated by circles). The abbreviations correspond to sampled location names, as indicated in text.

    Total genomic DNA was extracted from muscle tissue of P. argenteus using the standard proteinase K/phenol/chloroform procedure (Sambrook et al., 1989). The quality of the extracted DNA was checked using 0.8% agarose gel electrophoresis, then stored at -20°C for PCR amplification.

    Microsatellite genotyping

    Twenty-six pairs of novel polymorphic microsatellite markers for P. argenteus were isolated using combined biotin capture method. Fifteen of them, i.e., YC79, YC90, YC140, YC275, YC339, YC353-1, YC353-2, YC459, YC687, YC705, YC731, YC742, YC754, YC764, and YC792 (Table 1), were used for genotyping all the 240 P. argenteus individuals. Polymerase chain reaction (PCR) amplification was performed in a 15 µL reaction volume containing approximately 10-50 ng of genomic DNA, 0.6 µM of each primer, 7.5 µL 2 ×Taq PCR MasterMix (TIANGEN), with the following thermal cycling conditions: an initial denaturation at 94°C for 3 min, followed by 35 cycles of denaturing at 94°C for 30 s, annealing at primer-specific annealing temperature (Ta, see Table 1) for 30 s, elongation at 72°C for 30 s, followed by a final extension at 72°C for 7 min. The final PCR products were separated by electrophoresis in denaturing 6% polyacrylamide gels and visualized by silver staining. The allele sizes were estimated with a 10 bp DNA ladder (Invitrogen) as reference.

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    Table 1
    Characterization of fifteen microsatellite loci used in this study.

    Statistical analysis

    Measurements of genetic diversity

    The number of alleles (A), number of effective alleles (Ae), observed heterozygosity (Ho), expected heterozygosity (He), and the inbreeding coefficient (FIS) for each locus from each population were obtained using GenAlEx 6.0 (Peakall and Smouse, 2006). The diversity level of each genetic locus was evaluated with polymorphic information content (PIC) using the CERVUS version 3.0.3 (Marshall et al., 1998). GENEPOP on the Web (http://genepop.curtin.edu.au/, Raymond and Rousset, 1995) was used to check the deviations from Hardy-Weinberg equilibrium (HWE) and Linkage Disequilibrium (LD) of each locus within each site. HWE and LD tests were performed using the Markov Chain method (10,000 dememorization steps, 100 batches, 5000 iterations).

    Measurements of population differentiation

    To study population structure, population-level pairwise FST was analyzed using a permutation with 10,000 replicates. Analysis of molecular variance (AMOVA) were calculated using software ARLEQUIN 3.1 (Excoffier et al., 2005). To examine the genetic relationships among populations, the matrix of Nei's genetic distance of pairwise locations (Nei, 1978) was calculated using the GenAlEx 6.0 (Peakall and Smouse, 2006). Then an unweighted pair-group mean analysis (UPGMA) tree was constructed based on Nei's genetic distance matrices using MEGA version 5.0 software (Tamura et al., 2011). In order to provide a visual representation of population subdivision, Principal Component Analysis (PCA) was performed in GenAlEx 6.0 (Peakall and Smouse, 2006).

    Demographical Bottleneck

    We detect recent population declines by computing the heterozygosity excess statistic using the computer program BOTTLENECK with two methods (Cornuet and Luikart, 1996). The first method basing on the principle of heterozygosity excess was executed under two different mutation models: two-phase mutation model (TPM) and stepwise mutation model (SMM), where 95% and 90% single-step mutations, and 5% and 10% multiple steps mutations with 1,000 simulation iterations were set as recommended by Piry et al. (1999). SMM was used because microsatellite loci appear to evolve under a mutation model that is more similar to the SMM than the infinite allele model (IAM) (Valdes et al., 1993; Shriver et al., 1993). The second method, mode-shift test (Luikart and Cornuet, 1998), was used to detect a potential bottlenecked population using an L-shaped distribution of allele frequency as mutation-drift equilibrium.

    RESULTS

    Genetic diversity

    All the 15 microsatellite markers were well amplified in the eight populations of P. argenteus. Of the 120 HWE tests across all 15 loci, 38 were conformed to HWE (Table Sup) within each sample location after applying sequential Bonferroni corrections (minimum adjusted alpha = 0.00050) (Rice, 1989). In addition, significant genotypic LD for multiple comparisons of 15 loci within populations was detected in 18 out of 1680 tests (minimum adjusted alpha = 0.00015) (Rice, 1989) after sequential Bonferroni correction. Thus, we think that most microsatellite loci could be deemed as genetically independent for further analysis.

    All 15 loci were polymorphic in all of the eight studied populations of P. argenteus with high allelic diversity and heterozygosity (Table Sup.). A total of 139 alleles were detected at the 15 microsatellite loci across the eight populations. The number of private alleles for the eight populations are 9 (SD), 10 (LYG), 7 (LS), 6 (ZS), 7 (DT), 18 (XP), 18 (HK), and 23 (BBW), respectively. For A, LS was the highest population with A=15.200, while HK was the least variable one with A=13.000. For Ae, DT was the highest with Ae=10.222, while HK was the least with Ae=8.340.

    For the eight populations, the average polymorphism information content (PIC) ranged from 0.854 (HK) to 0.879 (LS) per location. The average inbreeding coefficient (FIS) ranged from -0.200 (YC459-XP) to 0.870 (YC687-HK) with an average value 0.363 per locus, and ranged between 0.290 (DT) and 0.426 (HK) for populations (Table Sup.), suggesting that there is high inbreeding in these eight P. argenteus populations. Observed and expected heterozygosity (Ho and He) ranged from 0.100 to 1.000 and from 0.669 to 0.934 per locus-location combination, while from 0.504 (HK) to 0.627 (DT) and from 0.867 (HK) to 0.889 (LS) per location, respectively (Table Sup.). Within sampling locations, the mean expected heterozygosities (He) were consistently higher than the observed one (Ho) across all loci, which revealed a deficit of heterozygosity among the samples.

    Population structure

    Significant genetic heterozygosity among the eight populations was indicated by AMOVA analysis. The results showed that 4.34% of total genetic variation came from among population variation, while the within population variation explained 95.66% of total variation. Genetic differentiation between populations was analyzed using FST. The overall FST value over all locations and loci was statistically different from zero (Fixation Index FST=0.0434, p<0.001) (Table 2). Pairwise FST comparing population pairs ranged from 0.022 (DT and ZS) to 0.074 (HK and XP) (Table 3). The highest genetic differentiation between populations using FST was between the populations HK and XP, while the lowest differentiation was between DT and ZS (Table 3). The results of FST indicated that there was low but statistically significant genetic differentiation among the populations.

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    Table 2
    AMOVA analysis of eight populations of P. argenteus.
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    Table 3
    Estimates of pairwise FST values (below diagonal) and Nei's Genetic Distance (above diagonal) between eight populations of P. argenteus detected by 15 microsatellites.

    UPGMA phylogenetic trees (Figure 2) were constructed on the basis of Nei's genetic distance matrix (Table 3). Eight populations were divided into two main clusters: HK and BBW populations formed Cluster I; ClusterⅡincludes the remaining six populations. Cluster II is consisted of three subgroups, i.e., subgroup 1 includes XP, subgroup 2 includes LS, and subgroup 3 includes two SD, LYG, ZS, and DT (Figure 3). Mantel Test showed that there are significant linear correlation between geographic distances and genetic distances with R2=0.8012 (data not shown), which could imply isolation by distance. Furthermore, the PCA analysis also showed the HK and BBW populations to be genetically distinct from the other six locations (Figure 3). These results are consistent with the FST and Nei's distance tests.

    Figure 2
    Unweighted Pair-group Method with Arithmetic Means tree (UPGMA) of eight populations of P. argenteus based on Nei's genetic distance.

    Figure 3
    Principal component analysis (PCA) of eight populations of P. argenteus.

    Demographic Bottleneck

    Analysis of recent population declines of P. argenteus was detected using the Wilcoxon signed-rank test in BOTTLENECK under two models of microsatellite evolution (SMM and TPM)(Table 4). Excepted TPM (90%) for SD and DT, the probability values of the bottleneck test all above 0.05 (Table 4), suggesting no significant excess of heterozygosity in most populations. It indicated that there was no genetic bottleneck in most of the eight populations due to mutation-drift equilibrium. In addition, the mode-shift test showed that all populations were in normal L-shaped pattern of the allele frequency distributions (Table 4), revealing the lack of population declines in the recent history of P. argenteus.

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    Table 4
    p-values of bottleneck tests for detecting the recent population declines of P. argenteus using stepwise mutation model (SMM), two phased mutation model (TPM), and mode shift indicator.

    DISCUSSION

    In this study, we examined the genetic diversity and population structure of eight wild populations of P. argenteus using 15 highly polymorphic microsatellites. We observed low but significant genetic differences in the spatial distribution of P. argenteus along the coast of China. The results suggested a high level of population genetic diversity of P. argenteus (A:13.000-15.200, Ho:0.504-0.627, He: 0.867-0.889, and PIC:0.854-0.879), which is in line with the previous studies that showed a high level of genetic diversity in P. argenteus using RAPD, AFLP and mtDNA (Meng et al., 2009; Zhao et al., 2011a; Peng et al., 2009a; Wu et al., 2012). Genetic diversity is not only the consequence of evolution, but also represented the evolution potential of animals. In order to be fit for survival, animals accumulated more genetic diversity to ensure their increase of the fitness for many kinds of environmental pressures (Liu et al., 2010). P. argenteus may use a relative high genetic diversity to adopt its rigorous habitats during its evolution.

    The amount of effective alleles (Ae) was obviously less than that of the observed alleles (A) (Table Sup.), indicating that some alleles were lost between the populations studied in this study. The amount of alleles is very important to maintain populations because it provides the necessary spectrum of genotypes for adaptive response to changing environments (Thai et al., 2007). The loss of genetic diversity will prevent future improvements via selection of the species to a certain extent. The most likely explanation for the loss of alleles may be that the wild population was under stress from overfishing and deterioration in the environment resulting in less recruitment (Alam and Islam, 2005). In order to prevent the alleles from losing and to conserve the genetic resource, we could strengthen the conservation and supervision of wild resources and forbid overfishing in the natural waters.

    Generally, marine species are capable of migration over long distances, showing little or no genetic differentiation in geographic scales because of long distance pelagic dispersal potential during planktonic egg, larval, or adult history stages coupled with an absence of physical barriers to movement between ocean basins or adjacent continental margins (Hewitt, 2000). The small but highly significant values of pairwise FST in the present study indicated that the P. argenteus have genetic differentiation among populations, and more than 95.66% of the total genetic variation was among individuals within populations (Table 2). UPGMA and PCA analysis indicated that the population of eight P. argenteus can divided into two major groups: one group including populations of P. argenteus in the South China Sea (SCS) and the other group consisting populations of P. argenteus in the Yellow Sea (YS) and the East China Sea (ECS). Earlier studies using RAPD and mtDNA (Meng et al., 2009; Peng et al., 2009) did support the idea that the populations from the South China Sea are genetically different from other populations. In all, these results revealed that there are different isolation patterns between two major groups and significant population differentiation throughout the examined range of sampled locations. Populations of P. argenteus seem to show high levels of gene flow between ECS and YS groups. A complex system of surface currents is likely the main factor influencing gene flow. The China Coastal Current and Yellow Sea Warm Current, including outflow of water from the Yellow Sea to the East China Sea along the China coast and inflow from the East China Sea to the Yellow Sea along the west coast of Korea (Li et al., 2000), respectively, could contribute to mixing of YS and ECS groups. The gene flow between populations from the South China Sea and other populations may be limited by some factors such as oceanographic characteristics and life history.

    Zhao et al. (2011b) indicates that LYG and ZS samples have close relationship. However, our study results show that the normalized Euclidean distance between ZS and DT samples was shorter than all other pairwise distances. In general, morphological differences of P. argenteus among Chinese coastal waters can be discerned by multivariate morphometrics methods. Our study with microsatellite as marker showed significant genetic differentiation in P. argenteus, which were to some extend different from the results based on mitochondrial DNA gene sequences (Peng et al., 2009b; Wu et al., 2012). The reasons leading to this divergence may include intrinsic variability of the population, sample size, spatial replication, and the number and characteristics of the marker loci used (Curley and Gillings, 2009). More samples should be collected from all over Chinese coastal waters including Bohai Sea, Yellow Sea, East China Sea and South China Sea to allow for a more comprehensive picture of genetic variation of this species.

    ACKNOWLEDGEMENTS

    This work was supported by Central Public-interest Scientific Institution Basal Research Fund, ECSFR, CAFS (No.2012M09) and Science and Technology Commission of Shanghai Municipality (Shanghai Natural Science Foundation No. 19ZR1470100).

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    Supplementary Table

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    Table Sup
    Statistics for genetic variation at 15 microsatellite loci in eight populations of P. argenteus.

    Edited by

    • Editor: June Ferraz Dias

    Publication Dates

    • Publication in this collection
      02 Dec 2019
    • Date of issue
      2019

    History

    • Received
      29 Jan 2019
    • Accepted
      02 Oct 2019
    Creative Common - by 4.0
    This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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