New polymorphic microsatellite loci for the zebra mussel Dreissena polymorpha (Pallas, 1771), a common bioindicator.
Microsatellites (Genetics) (Properties)
Mussels (Genetic aspects)
Population genetics (Research)
|Publication:||Name: Journal of Shellfish Research Publisher: National Shellfisheries Association, Inc. Audience: Academic Format: Magazine/Journal Subject: Biological sciences; Zoology and wildlife conservation Copyright: COPYRIGHT 2011 National Shellfisheries Association, Inc. ISSN: 0730-8000|
|Issue:||Date: April, 2011 Source Volume: 30 Source Issue: 1|
|Topic:||Event Code: 310 Science & research|
|Product:||Product Code: 4951110 Ocean & Coastal Water Pollution NAICS Code: 562 Waste Management and Remediation Services|
|Geographic:||Geographic Scope: Germany Geographic Code: 4EUGE Germany|
ABSTRACT To investigate the influence of environmental pollution on
the population genetics of Dreissena polymorpha, we developed five new
polymorphic microsatellite loci for the zebra mussel. This mussel is
widely distributed and is a common bioindicator in the field of
ecotoxicology. The amplification of the microsatellite loci was tested
on a single population of 24 individuals. In this population, the number
of alleles per locus ranged from 3-14, and the observed heterozygosity
ranged from 0.545-0.909 (mean, 0.772). All loci followed Hardy-Weinberg
expectations, suggesting no evidence for null alleles. There was no
significant genetic linkage disequilibrium, neither between the
aforementioned loci nor between aforementioned loci and five previously
published microsatellites for this species. All loci are valuable to
investigate the influence of anthropogenic stressors on the bioindicator
Dreissena polymorpha, and therefore on freshwater ecosystems in general.
KEY WORDS: microsatellite, Dreissena polymorpha, population genetics, ecotoxicology
Evolutionary toxicology investigates population genetic effects caused by environmental contamination (Bickham et al. 2000). Toxicant inputs of increasing industry, agriculture, and fast-growing cities have severely altered freshwater ecosystems. These fast-changing environments increase demands on the adaptability of animals. Genetic diversity enables species to adapt to changing environments. Environmental stressors are expected to reduce genetic diversity by causing mortalities, so that a recent reduction in genetic diversity is indicative of a deteriorating environmental condition. Thus, the amount of genetic diversity can be applied as a biomarker and provides essential information at two levels. First, a high genetic diversity ensures the sustainability of a population; second, it indicates the integrity of a whole ecosystem (Bagley et al. 2002).
Approaches used to measure genetic diversity are selected based upon the desired level of allelic diversity (Brown et al. 2000). Microsatellites underlie high mutation rates that generate the high level of allelic diversity necessary for the study of processes acting on fine-scale ecological questions (Selkoe & Toonen 2006). Because of their high variability and codominant inheritance, microsatellites are adequate markers to detect fast changes in genetic diversity within and among populations that are caused by environmental contamination (Dimsoski & Toth 2000).
The zebra mussel Dreissena polymorpha is widely distributed in Europe and North America (Minchin et al. 2002) and has been used as a model organism for freshwater mussels in several studies (Pain et al. 2005, Griebeler & Seitz 2007, Hidde 2008). D. polymorpha is a common bioindicator for passive as well as active biomonitoring of freshwater ecosystems (Bocherding & Jantz 1997, Sues et al. 1997, Roditi et al. 2000, Klobucar et al. 2003, Bervoets et al. 2005). Preliminary studies have shown the high genetic variability of zebra mussel populations (Astanei et al. 2005, Muller et al. 2002). It is therefore a promising approach to analyze the genetic diversity of the zebra mussel as an independent indicator of environmental conditions to assess the health of an ecosystem.
For the zebra mussel, five highly polymorphic microsatellites (loci A6, B6, B8, B9, and C5) have been described by Naish and Boulding (2001), and two of them (loci B8 and B9) have been modified by Astanei et al. (2005). Population genetic studies on D. polymorpha have shown high polymorphism of the published markers (Astanei et al. 2005) and, thus, their high power to resolve the genetic structure of populations (Muller et al. 2002). However, one locus (B8) turned out to be under selective pressure (Thomas & Seitz, unpublished data). The number of four microsatellites is critical to providing sufficient genetic variability to analyze the response of the population genetic structure to anthropogenic stressors. As a result of their selective neutrality, each microsatellite is an independent sample of the genome (Kalinowski 2002). Adding microsatellite loci to a study will increase genomewide sampling, and consequently boost the power of statistical analyses (Selkoe & Toonen 2006). We, therefore, developed additional microsatellite loci to establish a neutral marker set that allows a robust analysis of the genetics of zebra mussel populations.
In this article, the isolation and characterization of five new microsatellite markers, developed to investigate the influence of environmental pollution on the model organism and common bioindicator D. polymorpha, will be depicted.
A genomic library enriched for microsatellites was developed from the pooled DNA of four individuals of D. polymorpha. We isolated the DNA from the posterior adductor muscle by liquid nitrogen extraction and by using the High Pure PCR Template Preparation Kit (Roche Diagnostics GmbH, Mannheim, Germany).
Genterprise Genomics GmbH (Mainz, Germany) restricted the pooled DNA with the HindIII restriction enzyme (Fermentas GmbH, St. Leon-Rot, Germany). This company ran the products on agarose gels and excised the restricted fragments that ranged from 1.2-2.5 kb and more than 2.5 kb. They then purified and cloned them into pUC 18 vector plasmids cut with the restriction enzyme SmaI. Afterward, the ligated plasmids were transformed into DH10B Escherichia coli host cells. Colonies were screened with the following radioactively labeled oligonucleotides: (AAT)n, (GGC)n, (AAG)n, (ATG)n, (AAC)n, (GATC)n, (GATA)n, and (TAAA)n. Finally, Genterprise Genomics GmbH (Mainz, Germany) isolated and purified the plasmid DNA from positive colonies.
In our laboratory, we amplified the inserts in both directions using the standard forward and reverse pUCI8 primers. Sequencing reactions were performed in a 10-[micro]L volume containing 6 [micro]L plasmid DNA, 2 [micro]L 5x sequencing buffer (Genterprise Genomics GmbH), 1 [micro]L primer (10 pmol/[micro]L), and 1 [micro]L Big Dye Terminator v3.1 Sequencing Standard Kit (Applied Biosystems Deutschland GmbH, Darmstadt, Germany). After purification, we ran the products on the capillary sequencer ABI 3130xl (Applied Biosystems Deutschland GmbH, Darmstadt, Germany). We checked the sequences visually for the presence of microsatellites consisting of 5 or more noninterrupted repeats, using the free software FinchTV 1.4.0 (Geospiza 2009). For these microsatellites, primers were designed using the free software PRIMER3PLUS (Untergasser et al. 2007). The 5'- ends of the forward primers were modified with an M13(-21) sequence tag (5'-ACGACGTTGTAAAACGAC-3') following Schuelke (2000).
For fragment analysis, we slightly modified the singlereaction nested PCR method described by Schuelke (2000). Thermocycling conditions consisted of a denaturation at 94[degrees]C for 5 min, 30 additional cycles of denaturation at 94[degrees]C for 30 sec, annealing at 45-65[degrees]C for 45 sec, and extension at 72[degrees]C for 45 sec; 8 additional cycles of denaturation at 94[degrees]C for 30 sec, annealing at 53[degrees]C for 45 sec, and extension at 72[degrees]C for 45 sec; and a final extension of 72[degrees]C for 15 min. The PCR was carried out in a 25 [micro]L volume using Ready-To-Go PCR Beads (GE Health Care Europe GmbH, Freiburg, Germany). In addition to the beads, the reaction contained 50-200 ng DNA, 2 pmol of the forward primer, 8 pmol of the reverse primer and the fluorescent-labeled M13 primer, 0.25-0.3 [micro]L Mg[Cl.sub.2] (50 mM), and sterile water up to a volume of 25 [micro]L. First, we optimized the PCR protocol, considering the annealing temperature in the range of 45-65[degrees]C and the concentration of Mg[Cl.sub.2], using the DNA of 4 individuals. Second, the polymorphism of the successfully optimized loci was tested on 24 individuals of 1 population (River Danube, near Budapest, Hungary). By this standard test, it was possible to prove that the microsatellite loci were variable enough to discriminate between individuals within a population. We estimated the observed ([H.sub.o]) and expected ([H.sub.e]) heterozygosity, and tested for HardyWeinberg equilibrium and linkage disequilibrium with GENEPOP version 1.2 program (Raymond & Rousset 1995) for all new loci. In addition, we proved new loci for pairwise linkage disequilibrium with the already published loci A6, B6, B8, B9, and C5 (Naish & Boulding 2001, Astanei et al. 2005).
After plasmid preparation, 192 positive colonies were sequenced forward and reverse. Among these we identified 28 colonies with repeat motifs that were not interrupted and had at least 5 repeats. The other 164 positive colonies were second copies of a specific locus or did not contain any repetitive sequence. Nine of the identified repeats were [(AAT).sub.n], 1 was [(AAG).sub.n], 5 were [(ATG).sub.n], 1 was [(TAAA).sub.n], 2 were [(TAG).sub.n], 1 was [(GT).sub.n], 3 were [(AT).sub.n], 2 were [(GA).sub.n], 2 were [(GTC).sub.n], 1 was [(AGC).sub.n], and 1 was [(AATAATT).sub.n]. Twenty-two primer pairs were designed, and PCR was carried out on those 22 loci. Eight of these loci were successfully amplified. They were tested on a single population of 24 individuals. Five of these loci displayed distinct peaks and showed allelic polymorphism among the tested individuals. Allele numbers of the 5 loci ranged from 3-14 (mean, 8.4). Observed and expected heterozygosity was between 0.545 and 0.909 (mean [H.sub.o], 0.772), as well as between 0.432 and 0.833 (mean [H.sub.e], 0.736), respectively. Table 1 provides the locus designations, repeat motifs, primer sequences, ranges of PCR product sizes, numbers of observed alleles, annealing temperatures, and observed and expected heterozygosities. All 5 loci conformed to Hardy-Weinberg expectations, suggesting no evidence for null alleles. None of these 5 loci was significantly linked to any other locus, including the 5 that had already been published.
In this study we isolated five new microsatellite loci. With the five already published loci (Naish & Boulding 2001, Astanei et al. 2005), we now have a set of 10 polymorphic loci for D. polymorpha. One of these loci (B8) appeared to be not neutral to selection (Thomas & Seitz, unpublished data). We assume that the nine other markers are neutral, because they did not show any significant deviation from Hardy-Weinberg equilibrium. The relatively high expected heterozygosity (mean [H.sub.e], 0.736) of the new loci indicates their high degree of polymorphism. There was no significant genetic linkage disequilibrium, suggesting that all 9 loci are transmitted to offspring independently.
All the nine neutral microsatellite loci are appropriate to analyze the population genetic structure of D. polymorpha. As a result of their high polymorphism, these loci are suitable to resolve a fine-scale population structure and to detect recent changes in populations, such as changes in genetic diversity and population bottlenecks. Microsatellites that are neutral to selection and are not significantly linked to each other are independent samples of the genome, and the genetic patterns detected by microsatellite analysis reflect patterns of the whole genome (Kalinowski 2002). It is therefore reasonable to conclude that if we detect a high genetic diversity at neutral microsatellite markers in a population, then genetic diversity is probably high at genes that encode fitness-related characters, which contributes to the sustainability of the population in the long term. Likewise, by measuring a low genetic diversity at neutral markers, we are able to detect a potentially decreased genetic diversity at fitness loci (Bagley et al. 2002), which results in a loss of adaptability and a potentially decreased fitness of the population (Murdoch & Hebert 1994). In summary, estimating the biomarker genetic diversity by using these microsatellites will contribute to elucidating the condition and sustainability of zebra mussel populations.
As a result of their high polymorphism, the microsatellite loci are also applicable to detecting differences in genetic structure, even among closely adjacent populations. Comparing the genetic patterns of populations in front of and behind a pollution source can resolve the relationship between stressor exposure and genetic diversity (Murdoch & Hebert 1994). As we apply the genetic diversity of Dreissena populations as an independent indicator of environmental condition, this can help us to understand the consequences of anthropogenic stressors for freshwater ecosystems.
This established marker system for the bioindicator D. polymorpha is a powerful tool to investigate the influence of anthropogenic stressors on zebra mussel populations and on freshwater ecosystems in general. The markers are currently used in several projects, including studies on the population structure in the River Danube and the genetic consequences of pollution stress for zebra mussel populations.
We thank Dr. Eva Maria Griebeler and Dana Berens for their helpful comments on the manuscript. We thank Nina Fouts for improving the English text. This research was funded by the Johannes Gutenberg-University of Mainz. This paper is part of the PhD dissertation of Godila Thomas.
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GODILA THOMAS, * NASERA HAMMOUTI AND ALFRED SEITZ
Department of Ecology, Zoological Institute, University of Mainz, PO Box 3980, D-55099 Mainz, Germany
* Corresponding author. E-mail: firstname.lastname@example.org
TABLE 1. Five new microsatellite loci tested on 24 individuals of 1 population. Locus Repeat Motif Dpo16 [(AAT).sub.40] Dpo17 [(AAT).sub.30] Dpo19 [(TAAA).sub.12] Dpo115 [(TTC).sub.10][(ATC).sub.8] [(ATT).sub.11] Dpo119 [(ATG).sub.40] Size Range Locus Primer Sequence (5'-3') (bp) A Dpo16 F: M13(-18)TGTTTCACCCCATTAATGACAG 479-509 6 R:GTCCATTGTTGATGCCACATTA Dpo17 F: M13(-18)TCATACCGCCATTGATATGC 205-290 14 R:TGCGCTCGAATAAATGACAA Dpo19 F: M13(-18)TGGTTGATGCAGTGACCCTA 218-249 8 R:TGTCGCTTGATCCATGTTTT Dpo115 F: M13(-18)GATCCCCTATTACTGACATAATGC 450-461 3 R:GGAAGGTCATTTACCATACTATTGC Dpo119 F: M13(-18)GCATTCCATCAAAAACACAGAT 293-418 11 R:GATCAACACCAAAGTTCGTTTC GenBank Temperature Accession Locus ([degrees]C) [H.sub.o] [H.sub.e] No. Dpo16 64 0.739 0.82 JF318965 Dpo17 64 0.833 0.765 JF318961 Dpo19 59 0.833 0.829 JF318962 Dpo115 45 0.545 0.432 JF318963 Dpo119 58 0.909 0.833 JF318964 Locus designations, repeat motifs of the positive clones, sequences of forward primers (F) with M 13-tag and of the reverse primers (R), size ranges of detected alleles, numbers of alleles (A), annealing temperatures of the specific primers, observed heterozygosities (Ho), expected heterozygosities ([H.sub.e]), and the accession numbers in NCBI GenBank are provided.
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