Int. J. Aquat. Biol. (2013) (1): 22-27 E-ISSN: 2322-5270; P-ISSN: 2383-0956 Journal homepage: www.ij-aquaticbiology.com © 2013 Iranian Society of Ichthyology Microsatellite variations and genetic structure of common carp (Cyprinus carpio) populations in Gomishan bay and Gorganroud River (Southeast of the Caspian Sea) Melika Ghelichpour*1, Ali Shabani1, Bahare Shabanpour1 1 Department of Fisheries, Faculty of Fisheries and Environment, Gorgan University of Agriculture Science and Natural Resources, Gorgan, Iran Article history: Received 3 March 2013 Accepted 11 April 2013 Available online 14 April 2013 Keywords: Population Gorganroud River Gomishan bay Microsatellite Genetic structure Abstract: Common carp (Cyprinus carpio) population has been declined in the Caspian Sea in the recent years, mainly due to human manipulation. This valuable species needs to be protected in the Caspian Sea. Considering the commercial value of common carp, its rehabilitation program has been established. In the present study, 8 microsatellite loci were used to assess genetic variation and population structure of common carp in Gomishan bay (GB) and Gorganroud River (GR). These two regions are the most important habitat of common carp. Mean actual (Na) and expected (Ne) alleles numbers were 15.12 and 11.35 for GB and GR, respectively. Mean observed (Ho) and expected (He) heterozygocity were 0.99 and 0.90 for GB and GR, respectively. Results, also, showed that all investigated loci were polymorphic. Twelve out of 16 tested locus×region combinations showed significant deviation from Hardy-Weinberg equilibrium (HWE) which could be mainly due to increase in He. FST index was found to be 0.011. Hence, AMOVA showed that observed variation was related to within population (99%) as well as between populations (1%). According to the results, it is suggested that studied populations have a great allelic richness and gene flow. Introduction Nowadays, many fishes are under threat due to destruction of their habitats, over-exploitation, pollution and introduction of predator and competitor species. Hence, they need to be protected via a restocking program (Millennium Ecosystem Assessment, 2005). Restocking programs are a managerial strategy, in which mature individuals are caught from wild and propagated under controlled conditions. Then, fries are released to their natural habitats and this process is repeated next years (Fiumera et al., 1999). Species ability to survive in the nature is determined by genetic variation that affects their ability to adapt environmental changes. Thus, genetic variation is necessary for the species survival and resistance (Bataillon et al., 1996). Genetic variation management needs the evaluation of genetic * Corresponding author: Melika Ghelichpour E-mail address: ml.ghelichpour@gmail.com structure and separation rate of targeted species stocks (Pujolar et al., 2009). Hence, permanent monitoring of genetic status of species that subjected to rehabilitation program is necessary for their conservation and management. In the recent decades, little molecular studies were conducted on aquatic organism in compare to terrestrial species (Shabani et al., 2006). A variety of molecular markers are used in population genetic studies, however, among them, the microsatellite markers are widely used. This is mainly due to high frequency in genome, Mendelian inheritance, being semi-dominant, small loci size, ease to determine genotype by polymerase chain reaction (PCR) and great polymorphism (Chen et al., 2008; Dewoody and Avise 2000). Also, this marker has been widely used in fisheries and aquaculture studies, where 23 Ghelichpour et al./ Int. J. Aquat. Biol. (2013) (1) 22-27 inter- and intra-population variation may be limit (Thai et al., 2007). Cyprinids are one of the important families of fishes, containing more than 2000 species (Kirpichnikov, 1972). Among cyprinids, the common carp (C. carpio), a native fish of Eurasia, is commercially valuable species that transferred to different regions of the world (Kohlman et al., 2003). This species, also, inhabits in the Caspian Sea and considered as an important food resource for local people. Although the common carp inhabits in all parts of southern part of the Caspian Sea and inter to its tributaries for reproduction, its population has been declined because of over-fishing and degradation of their spawning ground. Therefore it needs to be conserved (Abdoli and Naderi, 2008). Currently, stock rehabilitation program of common carp is conducted by releasing artificially propagated fries to the Caspian Sea. Unfortunately, despite the commercial importance and huge market demand for this species, there is no comprehensive information about its population structure in different regions of the Caspian Sea. The available data are related to common carp population genetic in southern Caspian Sea, using mtDNA (Yousefian and Laloei, 2011). Therefore, in the present study, common carp population structure and genetic variation were studied in Gorganroud River (GR) and Gomishan bay (GB) (southeast Caspian) using 8 microsatellite loci. Materials and Methods Sampling: A total of 30 specimens were sampled from each study area. Two grams of each fish caudal fin was sampled and preserved in 90% ethanol, separately. Nuclear DNA was extracted according to Hillis et al., (1996). In this protocol, tissue samples were digested by K-proteinase in buffer [100 mM of acidic tris, 10 mM EDTA, 250 mM NaCl and 1% sodium dodecyl sulfate (SDS): pH=8] over 12h at 55 ˚C. The resulted product was purified using phenol-chloroform. DNA was precipitated by adding Table 1. Characteristics and sequence of the primers used in microsatellite analyses in common carp. Locus Allele number Allele size Allele size Sequence Temperature (˚C) MFW2 22 200-228 200-228 F: CACACCGGGCTACTGCAGAG R: GTGCAGTGCAGGCAGTTTGC 64 MFW7 22 160-272 160-272 F: TACTTTGCTCAGGACGGATGC R: ATCACCTGCACATGGCCACTC 62 MFW13 17 188-272 188-272 F: ATGATGAGAACATTGTTTACAG R: TGAGAGAACAATGTGGATGAC 56 MFW16 18 128-204 128-204 F: GTCCATTGTGTCAAGATAGAG R: TCTTCATTTCAGGCTGCAAAG 57 MFW17 25 208-316 208-316 F: CTCAACTACAGAGAAATTTCATC R: GAAATGGTACATGACCTCAAG 57 MFW20 19 208-304 208-304 F: CAGTGAGACGATTACCTTGG R: GTGAGCAGCCCACATTGAAC 60 MFW26 16 108-172 108-172 F: CCCTGAGATAGAAACCACTG R: CACCATGCTTGGATGCAAAAG 60 CypG24 14 112-168 112-168 F: CTGCCGCATCAGAGATAAACACTT R: TGGCGGTAAGGGTAGACCAC 58 Table 2. Cycle number, time (min) and temperature (˚C) used in PCR. Cycle number Stage Time Temperature 1 Denaturation 5 94 Denaturation 30 94 5 Annealing 30 5 ˚C above the annealing temperature (marked by*) Extension 30 72 Denaturation 30 94 32 Annealing 30 56-64* Extension 30 72 1 Final extension 10 72 Locus Allele number Allele size Allele size Sequence Temperature (˚C) 24 Ghelichpour et al./ Int. J. Aquat. Biol. (2013) (1) 22-27 cold ethanol (90%) and centrifugation. Then extracted DNA were dissolved in deionized water and kept at –20 ˚C. Quality and quantity of extracted DNA was determined by agarose gel (1%) and biophotometer. Microsatellite analyses: Eight microsatellite loci MFW2, MFW7, MFW13, MFW16, MFW17, MFW20, MFW26 and CypG24 were chosen based on the previous studies (Crooijmans et al., 1997; Baerwald, 2004) (Table 1). PCR amplification was carried out in 0.2 ml PCR tubes with an Eppendorf thermal cycler (Table 2). Fifteen μl PCR reactions contained, 0.5 U Taq DNA Polymerase (Fermentas), 1×PCR buffer, 0.2 mMdNTP mix, 1.5 mM MgCl2, 1 μM of each primer set, and about 100 ng template DNA. PCR temperature cycles were as follows: a pre- denaturation at 95 ◦C for 3 minute; followed by 35 cycles of denaturation at 95 ◦C for 30 s, annealing for 30 s and extension at 72 ◦C for 30 s and a final extension at 72 ◦C for 3 min. PCR products were separated on 10% polyacrylamid gels stained with silver nitrate (Rajora et al., 2000). A 50 bp molecular weight marker (Fermentas) was used as the molecular weight standard. Microsatellite allele lengths were estimated using Gel-Pro Analyzer 3.9 software (Gene, USA). Data analyses: Na, Ne, Ho and He were estimated by Arlequin 2.0 software (Schneider et al., 2000). The reason of deviation from HWE and its significance was determined by FSTAT software. Deviation distribution, genetic distance, genetic identity (Nei, 1978), deviation from HWE and FST index (AMOVA) were determined by statistical-geneticl software, GenAlex 6.5. Difference in Ho, He and allelic variation was determined by Wilcoxon’s test using statistical software, SPSS v. 16. To adjust significance level for repetitive tests, sequential Bonferroni correction was used (Rice, 1989). Results Results showed that all studied loci were polymorphic. A total of 139 alleles with average of 17.37 alleles were observed in each locus. MFW17 with 23 alleles and CypG24 with 12 alleles had the most and least alleles, respectively. Na and Ne were 15.12 and 11.35 for GR, while they were 16 and 11.78 for GB, respectively (Table 3). There was no significant difference in Na and Ne between two studied areas (P>0.05). Mean Ho was 0.99%, as Ho was found to be 1.00 in all locus exception of MFW20 in GR which was 0.96. Mean He was found to be 0.90 which highest and lowest values were observed in MFW20 (0.93) and CypG24 (0.82) in GR (Table 3). There was no significant difference in Ho and He values between the studied areas (P>0.05). Fifteen out of 16 tests (8 locus ×2 areas) showed a significant deviation from HWE (P<0.05), however, only 12 out of 16 tests showed the significant deviation from HWE when sequential Bonferroni correction was performed (P=0.005). FST index was found to be 0.011. Hence, AMOVA showed that observed variation was related to within Table 3. Na, Ne, Ho, He and pHW of tested loci in Gorganroud River and Gomishan bay. Area CypG24 MFW26 MFW20 MFW17 MFW16 MFW13 MFW7 MFW2 GR Na 9 15 18 17 14 14 16 18 Ne 5.64 12.74 14.38 10.96 10.66 10.31 13.17 12.95 Ho 1.00 1.00 0.96 1.00 1.00 1.00 1.00 1.00 He 0.823 0.922 0.930 0.909 0.906 0.903 0.924 0.923 pHW ** *** *** *** *** ns *** * GB Na 12 16 18 21 16 14 11 20 Ne 9.13 12.08 14.63 12.24 12.16 10.46 9.04 14.51 Ho 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 He 0.891 0.917 0.932 0.918 0.918 0.904 0.889 0.931 pHW *** *** ** *** *** *** *** *** GR=Gorganroud River. GB=Gomishan bay. pHW = Hardy-Weinberg probability test after sequential Bonferroni correction. Ns = not significant. Asterisks show significant difference: * P<0.05; ** P<0.01; *** P<0.001. 25 Ghelichpour et al./ Int. J. Aquat. Biol. (2013) (1) 22-27 population (99%) as well as between populations (1%) (Table 4). FST (0.006 - 0.020) and gene flow (Nm) (12.23-40.73) values for each tested locus are presented in Table 5. Results showed that genetic identity and genetic distance between the studied areas were 0.25 and 0.78, respectively (Table 6). Discussion Many fishes have more than one stock which fisheries managements should conserve their genetic variation via their sustainable exploitation. Thus, recognition of genetic information of different stocks is fundamental in fisheries managements (Waldman et al., 1999). Microsatellites markers have provided important information on genetic variation and factors affecting formation of population. These markers encompass a great polymorphism and inheritance (Crooijmans et al., 1997). Heterozygocity and allele number are of the important parameters in population genetic variation which determine the ability of organism to compete and survive in natural habitats (Hakansson and Jensen, 2005; Frankharn, 2008). In genetic variation studies, allelic richness is more worthy than heterozygocity. In fact, higher allelic richness shows higher effective population size and use of allelic richness is suitable for populations which are treated by selection or conservation programs (Diz and Persa, 2009). In the case of allele number, Ho and He, the present study is in line with the previous one on common carp with the similar primers (Ghelichpour et al., 2010). However, the result of this study showed higher rates than that of Crooijmans et al. (1997) on common carp using similar primers. Such contradictories might be related to difference in sample size or higher genetic variation in studied populations compared to the previous one. Crooijmans et al. (1997) stated that higher allele number and heterozygocity could be affected by sample size and studied area. Hence, based on the results, there was no significant difference in allele number and heterozygocity between GB and GR, in the present study. Twelve out of 16 tests showed a significant deviation from HWE after use of sequential Bonferroni correction. This deviation is mainly due to increase in heterozygocity. As mentioned above, Ho was greater than He. Population combination or non- randomized mating (Liu et al., 2005) could cause deviation from HWE, in this study. Generally, a single factor could not cause deviation from HWE, however, combination of some factors such as artificial propagation and stock rehabilitation programs could participate in increase of heterozygocity and deviation from HWE. Environmental deterrents, life history and mating type could alter populations’ genetic structure (Tiedemann et al., 2000). In addition, stock rehabilitation, due to selective propagation, could affect genetic structure. AMOVA, as a statistical analyses, is a suitable means to determine population Table 4. AMOVA for FST. df SS MS Est. Var. % Between populations 1 4.73 4.73 0.023 1 Within populations 58 231.5 3.99 3.99 99  Table 5. FST and Nm for tested locus. MFW2 MFW7 MFW13 MFW16 MFW17 MFW20 MFW26 CypG24 FST 0.008 0.009 0.012 0.006 0.020 0.007 0.007 0.020 Nm 29.17 27.58 20.97 40.71 12.23 33.57 36.04 12.43 Table 6. Genetic identity (regular font) and genetic distance (bolded font) of common carp originated from Gorganroud River and Gomishan bay. Area Gorganroud River Gomishan bay Gorganroud River 0.25 Gomishan bay 0.78 26 Ghelichpour et al./ Int. J. Aquat. Biol. (2013) (1) 22-27 structure and between population variation (Grassi et al., 2004). Results of this study showed that observed variation was related to within population (99%) and between populations (1%) and were in line with the previous study on common carp populations which had 99% within population and 1% between populations’ variation in the southern Caspian (Ghelichpour et al., 2010). FST value (0.011), also, confirmed the little between populations’ variation. According to Wright (1978), FST range of 0–0.05 means small variation. Hence, genetic similarity and distance between the two studied areas were 0.78 and 0.25, respectively. According to value of genetic similarity in same- species populations (0.8-0.9) and in same-genus populations (0.35-0.85), it could be stated that the studied populations belong to same-genus (Thorpe, 1982). On the other hand, the present results showed a great gene flow between the studied areas, which could cause small variation in genetic structure of the areas. This high gene flow might be related to natural fish migration. Also, stock rehabilitation programs could involve in this great gene flow; as produced fries of present restocking program of common carp in the southern Caspian Sea were released into the sea, without considering their parents origin, it could cause great gene flow. The results of this study suggest that despite of enclosed system of the Caspian Sea and artificial propagation, genetic variation of common carp is considerably high. However, as stock rehabilitation programs are running yet, management programs should be performed to conserve the genetic variation to avoid the problems caused by inbreeding. References Abdoli A., Naderi M. (2009). Biodiversity of fishes of the southern basin of the Caspian Sea. Abzian scientific publication, Tehran, Iran. 237 p. Baerwald M.R., May B. (2004). Characterization of microsatellite loci for five members of the minnow family Cyprinidae found in the Sacramento-San Joaquin Delta and its tributaries. Molecular Ecology, 4: 385-390. Bataillon T.M., David J.L., Schoen D.J. (1996). Neutral genetic markers and conservation genetics: simulated germplasm collections. Genetics, 144: 409-417. Chen L., Li Q., Yang J. (2008). Microsatellite genetic variation in wild and hatchery populations of the sea cucumber (Apostichopus Japonicus Selenka) from northern China. Aquaculture Research, 39: 1541-1549. Crooijmans R.P.M.A., Bierbooms V.A.F., Komen J., Van der poal J.J., Groenen M.A.M. (1997). Microsatellite markers in common carp (Cyprinus carpio L.). Animal Genetics, 28: 129-134. Dewoody J.A., Avise J.C. (2000). Microsatellite variation in marine, freshwater and anadromous fishes compared with other animals. Journal of Fish Biology, 56: 461-473. Diz P.A., Presa P. (2009). The genetic diversity pattern of Mytilus alloprovincialis in Galician Rías (NW Iberian estuaries). Aquaculture, 287: 278-285. Fiumera A.C., Wu L., Parker P.G., Fuerst P.A. (1999). Effective population size in the captive breeding program of the Lake Victoria cichlid Paralabidochromis chilotes. Zoo biology, 18: 215-222. Frankham R. (2008). Genetic adaptation to captivity in species conservation programs. Molecular Ecology, 17: 325-333. Ghelichpour M., Shabani A., A.B. Shabanpour A. (2010). Genetic diversity of the two populations of Common carp (Cyprinus carpio) in Gharahsu and Anzali regions using eight microsatellite markers. Taxonomy and Biosystematics, 5: 39- 49. Grassi F., Imazio S., Gomarasca S., Citterio S., Aina R., Sgorbati S., Sala F., Patrignani G., Labra M. (2004). Population structure and genetic variation within Valeriana wallrothii Kreyer in relation to different ecological locations. Plant Science, 166: 1437-1441. 27 Ghelichpour et al./ Int. J. Aquat. Biol. (2013) (1) 22-27 Hakansson J., Jensen P. (2005). Behavioural and morphological variation between captive populations of red jungle fowl (Gallus gallus) – possible implications for conservation. Biological Conservation, 122: 431-439. Hillis D.M., Mable B.K., Larson A., Davis S.K., Zimmer E.A. (1996). Nucleic acids IV: sequencing and cloning. In: Molecular Systematics. Sinauer Associates, Sunderland, USA. Kirpichnikov V.S. (1972). Methods and effectiveness of ropsha carp breeding. Communication I. Breeding Amis, Original Forms and Cross System. Russian Journal of Genetics, 8: 65-72. Kohlmann, K., Gross R., Murakaeva A., Kersten P. (2003). Genetic variation and structure of common carp populations throughout the distribution range inferred from allozyme, microsatellite and mtDNA marker. Aquatic Living Resources, 16: 421-431. Liu Y., Chen S., Li J., Li B. (2005). Assessing the Genetic structure of three Japanese flounder (Paralichthys olivaceus) stocks by microsatellite markers. Aquaculture, 243: 103-111. Millennium Ecosystem Assessment (2005) Ecosystems and human well-being: current state and trends, Volume 1: Findings of the conditions and trends working group of the Millennium Ecosystem Assessment. Island Press, Washington, D.C. Nei M. (1978). Estimation of average heterozygosity and genetic distance from small number of individuals. Genetics, 89: 583-590. Pujolar J.M., De Leo G.A., Ciccotti E., Zane L. (2009). Genetic composition of Atlantic and Mediterranean recruits of European eel Anguilla anguilla based on EST‐linked microsatellite loci. Journal of Fish Biology, 74: 2034-2046. Rice W.R. (1989). Analyzing tables of statistical tests. Evolution, 43: 223-225. Schneider S., Roessli D., Excoffier L. (2000). Arlequin ver. 2.000. A software for population genetics data analysis. Genetics and Biometry Laboratory, University of Geneva, Switzerland. Shabani A., Pourkazemi M., Rezvani S. (2006). Study of mtDNA variation of stellate sturgeon (Acipenser stellatus) population from the North (Volga River) and South (Sefidrud River) Caspian Sea using RFLP analysis of PCR amplified ND5/6 gene regions. Journal of Agricultural Sciences and Natural Resources, 12: 195-204. Thai T.B., Christopher P.B., Christopher M.A. (2007). Genetic diversity of (Cyprinus carpio L.) in Vietnam using four microsatellite loci. Aquaculture, 269: 174-186. Thorpe J.P. (1982). The molecular clock hypothesis: biochemical evolution, genetic differentiation and systematic. Annual Review of Ecology and Systematics, 13: 139-168. Tiedemann R., Hardy O., Vekemans X., Milinkovitch M.C. (2000). Higher impact of female than male migration on population structure in large mammals. Molecular Ecology, 9: 1159-1163. Waldman J.R. (1999). The importance of comparative studies in stock analysis. Fisheries Research, 43: 237-246. Wright S. (1978). Evolution and the genetics of populations. 4: variability within and among natural populations. University of Chicago Press, Chicago. Yousefian M., Laloei F. (2011). Genetic variations and structure of common carp, (Cyprinus carpio) populations by use of biochemical, mitochondrial and microsatellite markers. Middle-East Journal of Scientific Research, 7: 339-345.