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Aquaculture International

, Volume 26, Issue 6, pp 1365–1376 | Cite as

Validation of 12 species-specific, tetrasomic microsatellite loci from the Russian sturgeon, Acipenser gueldenstaedtii, for genetic broodstock management

  • K. KohlmannEmail author
  • P. Kersten
  • J. Geßner
  • O. Eroglu
  • S. Firidin
  • M. Ciorpac
  • E. Taflan
  • R. Suciu
Open Access
Article
  • 673 Downloads

Abstract

The Russian sturgeon, Acipenser gueldenstaedtii, is a critically endangered fish species. Hatcheries are operated in several countries within its natural range to produce stocking material for release into the wild and also for aquaculture purposes (caviar and meat production). An appropriate genetic broodstock management (plan or strategy) is required to avoid negative effects, e.g., admixture and hybridization of genetically differing stocks or loss of genetic variability due to inbreeding and genetic drift. Therefore, 11 tetrasomic microsatellite loci were newly isolated from the Russian sturgeon genome and arranged together with an already known locus into four multiplex PCR sets. These microsatellites were used to characterize three groups of hatchery juveniles from Germany (aquaculture production), Turkey, and Romania (production of stocking material) as well as a group of wild-caught adults from the Danube River, Romania. Based on the variability within groups, measured by the mean number of alleles per locus and expected heterozygosity, and the differentiation between groups, measured by Nei’s GST and genetic distance D, the ability of the 12 loci to detect unwanted reductions in genetic variability within hatchery juveniles and to differentiate between groups could be demonstrated. This set of loci can also be used to identify those pairs of spawners that transmit the highest possible genetic variability to the next generation.

Keywords

Acipenser gueldenstaedtii Broodstock Genetic diversity Hatchery Microsatellite Ploidy level Russian sturgeon 

Introduction

The Russian sturgeon (Acipenser gueldenstaedtii Brandt & Ratzeburg, 1833) is a species of the family Acipenseridae native to the Caspian, Black, and Azov Sea basins. However, aquaculture has resulted in intentional and accidental introductions throughout Europe (Gesner et al. 2010). Originally, the species had freshwater as well as anadromous populations. Freshwater populations existed in the Danube and Volga Rivers—but both are extinct now (Gesner et al. 2010). Also, the anadromous spawning populations in the Azov Sea were lost and only introduced (stocked) individuals remained. Currently, native anadromous spawning populations are only known from the Caspian and Black Sea tributaries; but even these populations have undergone major declines resulting in no confirmed natural reproduction of the Danube River populations over the last 5 years (Suciu, unpubl. data). Consequently, the Russian sturgeon was classified as “critically endangered” in the IUCN Red List of Threatened Species (Gesner et al. 2010). Gesner et al. (2010) also listed the major threats to the species: loss of spawning sites due to dam construction (e.g., 70% of spawning grounds in the Caspian Sea basin since the 1950s), poaching and illegal fishing, which appears to be increasing, and high levels of pollution (from oil and industrial waste), in both the Black and Caspian Sea basins. Genetic pollution is also a potential threat as stocks are moved to different locations (e.g., Caspian Sea stocks moved to the Azov Sea). To compensate the dramatic losses in individual numbers, restocking measures are ongoing. However, stocks are continuing to decline.

With the ever increasing importance of artificial hatchery reproduction both for aquaculture purposes and the production of stocking material, an appropriate broodstock management has to be implemented. From the genetic point of view, the final goal must be to preserve the original genetic structures and variabilities to a degree that avoids (i) the disruption of local adaptations caused by the admixture and hybridization of genetically differing stocks, and (ii) inbreeding, which may result from the small number of breeders usually propagated in hatcheries, in particular if the species is rare. On the other hand, the genetic management should allow genetic change (i.e., natural selection) from pressures that occur in the wild (after release of the stocked fish), but not from pressures in the hatchery. This goal is related to the concept of integrated hatchery programs and conservation hatcheries as defined by Trushenski et al. (2015). To meet these requirements, genetic markers are urgently needed—and have to be developed if not already available—to implement breeding plans for the ex situ measures and for stocking.

Based on the number of chromosomes (250 ± 8) (Vasil’ev 1985; Vlasenko et al. 1989; both cited in Vasil’ev et al. 2010) and the allele banding patterns of microsatellite loci (Havelka et al. 2013; Rajkov et al. 2014), the Russian sturgeon is considered as an octaploid species at the evolutionary scale, which indicates the maximum ploidy level achieved, and a tetraploid one at the recent scale, which indicates the current functional ploidy level (Rajkov et al. 2014). Rajkov et al. (2014) could identify only one out of 20 microsatellite loci (= 5%) examined as disomic. Among the eight loci newly isolated from Russian sturgeon in their study, three loci (Ag09, Ag28, and Ag49) expressed the tetrasomic pattern expected for a tetraploid species, four loci (Ag01, Ag12, Ag14, and Ag22) displayed higher than tetrasomic patterns, and one locus (Ag18) was the disomic one mentioned above. This variation in microsatellite loci expression patterns of Russian sturgeon shows that the process of functional genome reduction is still ongoing in species with ~ 250 chromosomes (Ludwig et al. 2001).

Microsatellite loci are popular and efficient DNA markers widely applied in aquaculture genetics (Liu and Cordes 2004). However, they have limitations in the light of the present development of sequencing technologies (Putman and Carbone 2014) that are underlined by their utilization in polyploid organisms (Dufresne et al. 2014; Meirmans et al. 2018). Although microsatellites might be displaced by single nucleotide polymorphisms (SNPs) for sturgeon conservation in future (Ogden et al. 2013), their suitability for the management of a captive broodstock of another tetraploid sturgeon species, the critically endangered Adriatic sturgeon Acipenser naccarii, could clearly be demonstrated (Congiu et al. 2011).

A search in NCBI GenBank (accession date: 13 July 2017) for DNA sequence records of the species Acipenser gueldenstaedtii revealed 423 entries. However, there were no other microsatellite loci deposited than the eight described in Rajkov et al. (2014). Therefore, the aim of the present study was doubled: (i) to examine the variability of the three tetrasomic loci described by Rajkov et al. (2014) in a larger number of individuals and populations, and (ii) to isolate additional microsatellites from the Russian sturgeon genome to obtain a sufficient number of markers for improved broodstock management and the characterization of population structures.

Material and methods

Development of new microsatellite loci from the Russian sturgeon genome

Fin clips were collected from 20 juvenile Russian sturgeons at the “Rhönforelle” fish farm in Gersfeld, Germany, to isolate total genomic DNA using the DNeasy Blood & Tissue Kit (QIAGEN) according to manufacturer’s protocols. A pool of ten DNA isolates was sent to GenoScreen, Lille, France (www.genoscreen.fr), where 1 μg of the pooled DNA was used for the development of microsatellite libraries through 454 GS-FLX Titanium pyrosequencing as described in Malausa et al. (2011). The bioinformatics program QDD (Meglécz et al. 2010) was used to analyze sequences. QDD performs all steps from raw sequences until obtaining PCR primers: removing adapters/vectors, detecting microsatellites, detecting redundancy and possible mobile element association, selecting sequences with target microsatellites, and designing primers by using the BLAST (Altschul et al. 1990), Clustal W (Larkin et al. 2007), and Primer3 (Rozen and Skaletsky 2000) programs. Among 5752 sequences containing a microsatellite motif, 198 bioinformatically validated primer pairs were designed.

All sequences with validated primer pairs were ranked according to motif type (penta- > tetra- > tri- > di-nucleotide repeats), number of repeats (the higher the better), and PCR product size (> 100 bp) considering only sequences with perfect repeats. From this list, the 60 top-ranking primer pairs were selected for the identification of suitable microsatellites (consistent amplification, ease to score, sufficient variability). Amplification protocols were developed for future use of PCR multiplex kits (QIAGEN) and a peqSTAR 96X Universal Gradient thermocycler (Peqlab). PCR primers were multiplexed (grouped) by the software MultiPLX, version 2.1 (Kaplinski et al. 2005). Three different dye labels (BMN-6, Cyanine 5, and DY-751) were assigned to forward primers. Genotyping of microsatellite loci was performed on an eight-capillary sequencer CEQ 8000 (Beckman Coulter) using the Fragment Analysis module of the GenomeLab™ GeXP Genetic Analysis System, version 10.2 (Beckman Coulter).

The microsatellite variability was initially examined in the 20 juveniles from the German hatchery (broodstock and progenies kept for aquaculture purposes) but later on extended to 20 juveniles from the Romanian hatchery of Isaccea (F1 half-sibs of wild spawners used for restocking into the Danube River), 22 juveniles from a Turkish hatchery (F1 progeny of wild females and hatchery males used for enhancement of natural resources) and 11 wild-caught adults from the Romanian part of the Danube River. Due to the scarcity of disomic loci in Russian sturgeon, the focus of the study was to identify suitable tetrasomic loci showing consistent amplification and sufficient polymorphism.

Data analysis

The difficulty of analyzing codominant tetrasomic microsatellite loci of tetraploid species is to identify the true genotypes for partial heterozygotes. For instance, the genotype ABBC produces the microsatellite phenotype ABC, and cannot be reliably differentiated from genotypes AABC or ABCC. Jenneckens et al. (2001) suggested to estimate the number of allele copies on the basis of peak heights and areas in the electropherograms as exemplified by the microsatellite locus LS-39 in the Siberian sturgeon Acipenser baerii. However, this approach seems not to be fully reliable: Congiu et al. (2011) found that the four peaks of a complete heterozygous genotype did not always show the expected same height in Adriatic sturgeon, and the same discrepancy was observed in the present study of Russian sturgeon (data not shown). Instead, Congiu et al. (2011) proposed a band sharing approach for which microsatellite data were considered as presence/absence of bands, disregarding the number of alleles present in each individual. Another and probably more convenient solution of the problem is provided by software specifically designed for the analyses of (allo-)tetraploid microsatellite loci: TETRASAT (Markwith et al. 2006), TETRA (Liao et al. 2008), and ATETRA (Van Puyvelde et al. 2010). Van Puyvelde et al. (2010) compared the three programs and concluded that TETRASAT and ATETRA have a comparable precision but are both more precise than TETRA. Since ATETRA is able to calculate more genetic variables and can handle an infinite number of partial heterozygotes, this program was chosen for the present study.

ATETRA was used with default parameters for Monte-Carlo simulations to calculate (i) the Hardy-Weinberg expected heterozygosity or Nei’s genetic diversity HE (Nei 1987) as well as values corrected for sample size HE,c, (ii) the populational differentiation or Nei’s GST (Nei 1973), and (iii) Nei’s genetic distance D (Nei 1972, 1978). The matrix of pairwise genetic distances between the four Russian sturgeon groups was then used to construct a neighbor-joining tree (Saitou and Nei 1987) with MEGA5 (Tamura et al. 2011).

Data availability

The DNA sequences of the 11 newly isolated Agu microsatellite loci were deposited in NCBI GenBank (for accession numbers see Table 1).
Table 1

Characteristics of the 12 polymorphic, tetrasomic microsatellite loci tested in a total number of 73 Russian sturgeons

PCR multiplex set (annealing temperature)

Locus name/GenBank accession no.

Forward primer dye label

Primer concentration (μM)

Primer sequence (5′-3′)

Repeat type

Total number of alleles

Allele size range (bp)

1 (63 °C)

Agu56/MG956817

BMN-6

0.200

F: TGATTGGTAATAATGCCTGCC

R: TGTCACAGGGGACAGATTCA

TC

5

148–158

Agu51/MG956815

DY-751

0.180

F: TACATCCACAGCACCTTCCA

R: CCAGAGCTGGCTGTATGTGA

CT

5

141–159

Agu38/MG956812

BMN-6

0.175

F: ACTGGGGTTGAAGGACAGTG

R: TCCGTCTCATGTCCAAGGTA

GA

4

108–114

2 (63 °C)

Agu54/MG956816

Cyanine 5

0.055

F: GGAGCCAGTATCCCCTCAAT

R: CTTCGCACGCAACTTAACAA

TG

5

188–214

Agu36/MG956810

DY-751

0.195

F: GCAAACTGGGCTAGAACCTG

R: TCCCCTCTCCTCTTCTCCTC

AG

11

104–128

Agu37/MG956811

BMN-6

0.195

F: ACATGGTAGCAAAATCCCAA

R: CAGCAAGCTTAGATGCATGG

CT

5

128–136

3 (63 °C)

Ag49a/HG931711

Cyanine 5

0.075

F: TGTTATCTGCTCTGATATTGATTCG

R: CGTTTTAAAGTTTGAACGGCA

TTC

7

198–219

Agu15/MG956808

DY-751

0.500

F: CGCAGCACAACTGCACAT

R: CTGCCAGTGCGGTATCTACA

AGG

13

120–165

Agu59/MG956818

BMN-6

0.170

F: TCGAAACCATAAGCGTTGAA

R: TGACACTTGTGAAGCAACAGC

TG

5

210–280

4 (57 °C)

Agu34/MG956809

BMN-6

0.150

F: CGCAGCATGTGAAGGTTTTA

R: CTCACAAACTTTGGATGCCT

GA

6

116–130

Agu46/MG956814

DY-751

0.160

F: TGTACTGGAGTGTTTTTGAAAAGG

R: CATTTGTTGGATACATTCAGCTTT

TC

5

103–113

Agu41/MG956813

BMN-6

0.180

F: AAGACAAACAGTGGCCCAAC

R: CAATGGCAGGTGCTACTGAA

AG

14

178–218

aLocus name, Genbank accession number, primer sequences, and repeat type information: supplementary Table S1 of Rajkov et al. (2014)

Results and discussion

Out of the three tetrasomic microsatellites chosen from Rajkov et al. (2014) for testing two loci displayed pentasomy (Ag09 in two out of 40 samples, and Ag28 in two out of eight samples) and were therefore discarded from further use. Only one locus (Ag49) displayed the expected tetrasomic pattern consistently and was included in further analyses. Out of the 60 new Russian sturgeon PCR primer pairs chosen for testing, 11 amplified microsatellite loci matched our selection criteria, in particular consistent amplification, tetrasomic pattern, and sufficient variability. These 11 loci were combined with locus Ag49 into four multiplex PCR sets (Table 1). Optimized PCR reaction mixes consisted of 5.0 μl of master mix and 1.5 μl Q-solution (QIAGEN), 1.0 μl DNA isolate, primers with concentrations as stated in Table 1, and PCR-grade water up to a final volume of 10.0 μl. The PCR program based on QIAGEN recommendations included an initial denaturation at 95 °C for 15 min followed by 30 cycles of denaturation at 94 °C for 30 s, annealing at 57 °C (multiplex set 4), or 63 °C (multiplex sets 1 to 3) for 90 s, and extension at 72 °C for 60 s. A final extension at 60 °C lasted for 30 min.

The observed total number of alleles per locus ranged from 4 to 14 (Table 1) with an average of 7.08. The hatchery juveniles possessed a mean number of alleles per locus ranging from 3.67 to 4.25; the wild-caught adults were more variable with a mean number of 5.67 (Table 2). However, the differences in mean number of alleles per locus were only significant between the wild-caught adults and the hatchery juveniles from Romania (P = 0.02; two-sided t test) and Turkey (P = 0.04; two-sided t test), respectively. Mean expected heterozygosities HE were also lower in the hatchery juveniles, ranging from 0.519 to 0.648, than in the wild-caught adults with a value of 0.686 (Table 2). A significant difference was only found between the two Romanian groups (P = 0.02; two-sided t test). The correction for sample size resulted in only slight changes; again only the difference between the two Romanian groups was statistically significant (P = 0.02; two-sided t test).
Table 2

Variability of the 12 tetrasomic microsatellite loci in the four Russian sturgeon groups (A number of alleles, HE expected heterozygosity, HE,c expected heterozygosity, corrected for sample size)

Locus

Parameter

German hatchery

Romanian hatchery

Turkish hatchery

Romanian wild-caught

Agu56

A

3

4

1

5

H E

0.626

0.675

0.000

0.570

H E,c

0.634

0.684

0.000

0.583

Agu51

A

3

2

3

3

H E

0.429

0.496

0.488

0.507

H E,c

0.434

0.502

0.494

0.519

Agu38

A

3

2

3

4

H E

0.494

0.095

0.515

0.644

H E,c

0.500

0.096

0.521

0.659

Agu54

A

4

3

4

4

H E

0.746

0.542

0.733

0.669

H E,c

0.755

0.549

0.742

0.685

Agu36

A

6

4

4

7

H E

0.754

0.603

0.394

0.768

H E,c

0.764

0.611

0.399

0.786

Agu37

A

5

4

4

5

H E

0.755

0.380

0.566

0.698

H E,c

0.764

0.385

0.572

0.715

Ag49

A

3

6

6

7

H E

0.649

0.798

0.778

0.763

H E,c

0.657

0.808

0.787

0.780

Agu15

A

7

7

5

8

H E

0.700

0.773

0.600

0.771

H E,c

0.709

0.783

0.607

0.789

Agu59

A

2

2

2

5

H E

0.476

0.495

0.495

0.631

H E,c

0.483

0.501

0.501

0.646

Agu34

A

5

3

4

4

H E

0.745

0.512

0.664

0.676

H E,c

0.754

0.519

0.672

0.692

Agu46

A

4

2

4

5

H E

0.647

0.179

0.697

0.685

H E,c

0.655

0.181

0.705

0.701

Agu41

A

6

5

7

11

H E

0.758

0.674

0.830

0.852

H E,c

0.767

0.682

0.840

0.872

Mean values

A

4.25

3.67

3.92

5.67

H E

0.648

0.519

0.563

0.686

H E,c

0.656

0.525

0.570

0.702

Pairwise population differentiation was lowest between German hatchery juveniles and Romanian wild-caught adults (GST = 0.033) and highest between Romanian and Turkish hatchery juveniles (GST = 0.143) (Table 3). Accordingly, the lowest genetic distance was observed between German hatchery juveniles and Romanian wild-caught adults (D = 0.150) and the highest between Romanian and Turkish hatchery juveniles (D = 0.491) (Table 3). The neighbor-joining algorithm does not simply join the two groups displaying the lowest genetic distance (i.e., German hatchery juveniles and Romanian wild-caught adults) but also takes into consideration the genetic distances to all other groups. Hence, on the neighbor-joining tree (Fig. 1) the German and Turkish hatchery juveniles (D = 0.263) were placed together into one group separated from a Romanian group consisting of the hatchery juveniles and wild-caught adults (D = 0.202).
Table 3

Populational differentiation GST (Nei 1973) (above diagonal) and genetic distance D (Nei 1972, 1978) (below diagonal) between the four groups of Russian sturgeon

 

German hatchery

Romanian hatchery

Turkish hatchery

Romanian wild-caught

German hatchery

 

0.113

0.074

0.033

Romanian hatchery

0.412

 

0.143

0.066

Turkish hatchery

0.263

0.491

 

0.062

Romanian wild-caught

0.150

0.202

0.220

 
Fig. 1

Neighbor-joining tree of the four Russian sturgeon groups based on genetic distance D (Nei 1972, 1978)

Comparable studies on Russian sturgeon or other tetraploid sturgeon species are still relatively rare. Most of them dealt either with the isolation and characterization of new microsatellite loci or their use as nuclear markers for sturgeon species and hybrid identification and to examine genome duplication events (Table 4). Comparisons of general microsatellite variability might be biased by ploidy level and sample size. However, if only tetrasomic loci were considered then the ranges of numbers of alleles per locus are rather similar across different loci and sturgeon species (Table 4): Russian sturgeon—4-14 (present study), 6–16 (Ludwig et al. 2001), 7–19 (Havelka et al. 2013); Adriatic sturgeon—8-16 (Congiu et al. 2011); lake sturgeon—8-10 (McQuown et al. 2003); Siberian sturgeon—11-18 (Barmintseva and Mugue 2017). Heterozygosity estimates are only available for disomic loci of the lake sturgeon (Table 4). Nevertheless, they are also at similar levels as the expected heterozygosities calculated in the present study. The magnitude of genetic population differentiation strongly depends on the population status (wild or farmed) as well as the geographical scale of sampling (i.e., a stronger differentiation can be expected if the whole distribution range is covered compared to sampling within a single river, lake, or sea basin). Therefore, a comparison with available population genetic studies on lake sturgeon (McQuown et al. 2003; Welsh et al. 2008; Wozney et al. 2011) and Siberian sturgeon (Barmintseva and Mugue 2017) would be suspect and was not attempted.
Table 4

Use of microsatellite loci in tetraploid sturgeon species (n.d. no data reported)

Sturgeon species

Sample size

Origin of microsatellite loci

Number of loci (allelic pattern)

Number of alleles per locus

Heterozygosity (HO observed, HE expected)

Purpose of the study

Reference

Russian sturgeon (A. gueldenstaedtii)

99

Lake sturgeon (A. fulvescens)

5 (4 tetrasomic, 1 octosomic)

6–16 (tetrasomic loci); 28 (octosomic locus)

n.d.

Examination of genome duplications in sturgeons

Ludwig et al. (2001)

 

16

Lake sturgeon (A. fulvescens), Atlantic sturgeon (A. oxyrinchus), and Adriatic sturgeon (A. naccarii)

8 (some tetrasomic)

1–8

n.d.

Nuclear markers for detection of Danube sturgeons hybridization

Dudu et al. (2011)

 

35

Lake sturgeon (A. fulvescens), Atlantic sturgeon (A. oxyrinchus), white sturgeon (A. transmontanus), and shovelnose sturgeon (Scaphirhynchus platorynchus)

11 (7 tetrasomic, 4 octosomic)

7–19 (tetrasomic loci); 19–26 (octosomic loci)

n.d.

Examination of genome duplications in sturgeons

Havelka et al. (2013)

Adriatic sturgeon (A. naccarii)

20

Adriatic sturgeon (A. naccarii)

7 (higher than disomic)

3–19

n.d.

Isolation and characterization of microsatellites

Zane et al. (2002)

 

42

Adriatic sturgeon (A. naccarii), lake sturgeon (A. fulvescens), Atlantic sturgeon (A. oxyrinchus), and shovelnose sturgeon (Scaphirhynchus platorynchus)

24 (12 tetrasomic, 4 disomic, 7 with max. 3 alleles, 1 with max. 7 alleles)

2–8 (disomic loci); 8–16 (tetrasomic loci)

n.d.

Managing polyploidy in ex situ conservation genetics

Congiu et al. (2011)

 

20

Adriatic sturgeon (A. naccarii)

24 from genomic library, 33 from transcriptomic library (some higher than tetrasomic)

2–7; 2–16

n.d.; n.d.

Isolation and characterization of microsatellites from the genome and transcriptome

Boscari et al. (2015)

Lake sturgeon (A. fulvescens)

210 (15–40) from 7 locations

Lake sturgeon (A. fulvescens), Atlantic sturgeon (A. oxyrinchus), and shovelnose sturgeon (Scaphirhynchus platorynchus)

7 (4 disomic, 3 presumptive tetrasomic)

5–12 (disomic loci); 8–10 (presumptive tetrasomic loci)

HO = 0.46–0.66 (based on the 4 disomic loci)

Genetic comparison of populations

McQuown et al. (2003)

 

943

(1–136)

from 27 locations

Lake sturgeon (A. fulvescens), Atlantic sturgeon (A. oxyrinchus), and shovelnose sturgeon (Scaphirhynchus platorynchus)

12 (all disomic)

2–12

HE = 0.46–0.63

Genetic assessment of population structure in the Great Lakes

Welsh et al. (2008)

 

316

(13–66) from 10 locations

Lake sturgeon (A. fulvescens)

14 (all disomic)

2–12

HO = 0.514–0.563; HE = 0.522–0.664

Genetic assessment of population structure in the Ottawa River

Wozney et al. (2011)

Siberian sturgeon (A. baerii)

151 (20–48) from 5 locations

Lake sturgeon (A. fulvescens), Atlantic sturgeon (A. oxyrinchus), and Adriatic sturgeon (A. naccarii)

5 (all tetrasomic)

11–18

n.d.

Natural genetic polymorphism and phylogeography

Barmintseva and Mugue (2017)

Conclusion

Although the sample size of the four Russian sturgeon groups examined in the present study was relatively small, the 12 tetrasomic microsatellite loci showed a level of polymorphism that allowed to differentiate between groups and to detect unwanted reductions in genetic variability within hatchery juveniles. The genotypic data obtained with this set of microsatellites can therefore be used to improve the genetic management of captive broodstocks reared for aquaculture purposes (caviar and meat production) or restoration programs (production of stocking material). In particular, these microsatellites can be applied to identify those combinations of spawners that transmit the highest possible genetic variability to the next generation. The process of selecting such suitable spawners can be aided by the software Genassemblage (Kaczmarczyk 2015).

Moreover, the neighbor-joining tree indicates that both the German and Turkish hatchery Russian sturgeons may originate most probably from the Caspian Sea basin, while the Romanian ones are from the Black Sea/Danube River population. Because of its implications for Russian sturgeon supportive stocking programs in the Black Sea basin, this assumption urgently requires more detailed investigations, in particular by including Russian sturgeon samples from the Caspian Sea basin.

Notes

Acknowledgements

We thank Mr. Peter Groß, fish farm “Rhönforelle”, Gersfeld, Germany for generously providing access to the offspring of the ex situ stocks for sampling. Marian Paraschiv, Ștefan Honț and Marian Iani of the DDNI Sturgeon Research Group collected most of the tissue samples from the Danube River in Romania.

Funding information

This project was sponsored by the COFASP (Cooperation in Fisheries, Aquaculture and Seafood Processing) ERA-NET partners, which has received funding from the European Union’s Seventh Framework Programme for Research, Technological Development and Demonstration under grant agreement no. 321553. Financial support was provided by the German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE), grant number 2814ERA01G; by the Republic of Turkey, Ministry of Food, Agriculture and Livestock, project number TAGEM/HAYSUD/2015/A11/P01/5; and by the Romanian Executive Agency for Higher Education, Research, Development and Innovation Funding (UEFISCDI), contract number 26/2015.

Compliance with ethical standards

All sturgeon samples were collected in conformity with national legislations.

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment tool. J Mol Biol 215:403–410CrossRefGoogle Scholar
  2. Barmintseva AE, Mugue NS (2017) Natural genetic polymorphism and phylogeography of Siberian sturgeon Acipenser baerii Brandt, 1869. Russ J Genet 53:358–368CrossRefGoogle Scholar
  3. Boscari E, Vidotto M, Martini D, Papetti C, Ogden R, Congiu L (2015) Microsatellites from the genome and the transcriptome of the tetraploid Adriatic sturgeon, Acipenser naccarii (Bonaparte, 1836) and cross-species applicability to the diploid beluga sturgeon, Huso huso (Linnaeus, 1758). J Appl Ichthyol 31:977–983CrossRefGoogle Scholar
  4. Congiu L, Pujolar JM, Forlani A, Cenadelli S, Dupanloup I, Barbisan F, Galli A, Fontana F (2011) Managing polyploidy in ex situ conservation genetics: the case of the critically endangered Adriatic sturgeon (Acipenser naccarii). PLoS One 6(3):e18249CrossRefGoogle Scholar
  5. Dudu A, Suciu R, Paraschiv M, Georgescu SE, Costache M, Berrebi P (2011) Nuclear markers of Danube sturgeons hybridization. Int J Mol Sci 12:6796–6809CrossRefGoogle Scholar
  6. Dufresne F, Stift M, Vergilino R, Mable BK (2014) Recent progress and challenges in population genetics of polyploid organisms: an overview of current state-of-the-art molecular and statistical tools. Mol Ecol 23:40–69CrossRefGoogle Scholar
  7. Gesner J, Freyhof J, Kottelat M (2010) Acipenser gueldenstaedtii. In: The IUCN Red List of Threatened Species 2010: e.T232A13042340. doi:  https://doi.org/10.2305/IUCN.UK.2010-1.RLTS.T232A13042340.en
  8. Havelka M, Hulák M, Bailie DA, Prodöhl PA, Flajšhans M (2013) Extensive genome duplications in sturgeons: new evidence from microsatellite data. J Appl Ichthyol 29:704–708CrossRefGoogle Scholar
  9. Jenneckens I, Meyer JN, Hörstgen-Schwark G, May B, Debus L, Wedekind H, Ludwig A (2001) A fixed allele at microsatellite LS-39 is characteristic for the black caviar producer Acipenser stellatus. J Appl Ichthyol 17:39–42CrossRefGoogle Scholar
  10. Kaczmarczyk D (2015) Genassemblage software, a tool for management of genetic diversity in human-dependent populations. Conserv Genet Resour 7:49–51CrossRefGoogle Scholar
  11. Kaplinski L, Andreson R, Puurand T, Remm M (2005) MultiPLX: automatic grouping and evaluation of PCR primers. Bioinformatics 21:1701–1702CrossRefGoogle Scholar
  12. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23:2947–2948CrossRefGoogle Scholar
  13. Liao WJ, Zhu BR, Zeng YF, Zhang DY (2008) TETRA: an improved program for population genetic analysis of allotetraploid microsatellite data. Mol Ecol Resour 8:1260–1262CrossRefGoogle Scholar
  14. Liu ZJ, Cordes JF (2004) DNA marker technologies and their applications in aquaculture genetics. Aquaculture 238:1–37CrossRefGoogle Scholar
  15. Ludwig A, Belfiore NM, Pitra C, Svirsky V, Jeneckens I (2001) Genome duplication events and functional reduction of ploidy levels in sturgeon (Acipenser, Huso and Scaphirhynchus). Genetics 158:1203–1215PubMedPubMedCentralGoogle Scholar
  16. Malausa T, Gilles A, Meglécz E, Blanquart H, Duthoy S, Costedoat C, Dubut V, Pech N, Castagnone-Sereno P, Délye C, Feau N, Frey P, Gauthier P, Guillemaud T, Hazard L, Le Corre V, Lung-Escarmant B, Malé P-J, Ferreira S, Martin J-F (2011) High-throughput microsatellite isolation through 454 GS-FLX titanium pyrosequencing of enriched DNA libraries. Mol Ecol Resour 11:638–644CrossRefGoogle Scholar
  17. Markwith SH, Stewart DJ, Dyer JL (2006) TETRASAT: a program for the population analysis of allotetraploid microsatellite data. Mol Ecol Notes 6:586–589CrossRefGoogle Scholar
  18. McQuown E, Krueger CC, Kincaid HL, Gall GAE, May B (2003) Genetic comparison of lake sturgeon populations: differentiation based on allelic frequencies at seven microsatellite loci. J Great Lakes Res 29:3–13CrossRefGoogle Scholar
  19. Meglécz E, Costedoat C, Dubut V, Gilles A, Malausa T, Pech N, Martin J-F (2010) QDD: a user-friendly program to select microsatellite markers and design primers from large sequencing projects. Bioinformatics 26:403–404CrossRefGoogle Scholar
  20. Meirmans PG, Liu S, van Tienderen PH (2018) The analysis of polyploid genetic data. J Hered 109:283–296CrossRefGoogle Scholar
  21. Nei M (1972) Genetic distance between populations. Am Nat 106:283–292CrossRefGoogle Scholar
  22. Nei M (1973) Analysis of gene diversity in subdivided populations. PNAS USA 70:3321–3323CrossRefGoogle Scholar
  23. Nei M (1978) Estimation of average heterozygosity and genetic distance from small number of individuals. Genetics 89:583–590PubMedPubMedCentralGoogle Scholar
  24. Nei M (1987) Molecular evolutionary genetics. Columbia University Press, New YorkGoogle Scholar
  25. Ogden R, Gharbi K, Mugue N, Martinsohn J, Senn H, Davey JW, Pourkazemi M, McEwing R, Eland C, Vidotto M, Sergeev A, Congiu L (2013) Sturgeon conservation genomics: SNP discovery and validation using RAD sequencing. Mol Ecol 22:3112–3123CrossRefGoogle Scholar
  26. Putman AI, Carbone I (2014) Challenges in analysis and interpretation of microsatellite data for population genetic studies. Ecol Evol 4:4399–4428PubMedPubMedCentralGoogle Scholar
  27. Rajkov J, Shao Z, Berrebi P (2014) Evolution of ploidy and functional diploidization in sturgeons: microsatellite analysis in 10 sturgeon species. J Hered 105:521–531CrossRefGoogle Scholar
  28. Rozen S, Skaletsky H (2000) Primer3 on the WWW for general users and for biologist programmers. Methods Mol Biol 132:365–386PubMedPubMedCentralGoogle Scholar
  29. Saitou N, Nei M (1987) The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol Biol Evol 4:406–425PubMedGoogle Scholar
  30. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28:2731–2739CrossRefGoogle Scholar
  31. Trushenski JT, Blankenship HL, Bowker JD, Flagg TA, Hesse JA, Leber KM, MacKinlay DD, Maynard DJ, Moffitt CM, Mudrak VA, Scribner KT, Stuewe SF, Sweka JA, Whelan GE, Young-Dubovsky C (2015) Introduction to a special section: hatcheries and management of aquatic resources (HaMAR) — considerations for use of hatcheries and hatchery-origin fish. N Am J Aquac 77:327–342CrossRefGoogle Scholar
  32. Van Puyvelde K, Van Geert A, Triest L (2010) ATETRA, a new software program to analyse tetraploid microsatellite data: comparison with TETRA and TETRASAT. Mol Ecol Resour 10:331–334CrossRefGoogle Scholar
  33. Vasil’ev VP (1985) Evolution karyology of fishes. Nauka, Moscow (in Russian)Google Scholar
  34. Vasil’ev VP, Vasil’eva ED, Shedko SV, Novomodny GV (2010) How many times has polyploidization occurred during acipenserid evolution? New data on the karyotypes of sturgeons (Acipenseridae, Actinopterygii) from the Russian far east. J Ichthyol 50:950–959CrossRefGoogle Scholar
  35. Vlasenko AD, Pavlov AV, Sokolov LI, Vasil’ev VP (1989) Acipenser gueldenstaedtii Brandt, 1833. In: Holčik J (ed) The freshwater fishes of Europe, v. 1 part 2. Aula, Wiesbaden, pp. 294–344Google Scholar
  36. Welsh A, Hill T, Quinlan H, Robinson C, May B (2008) Genetic assessment of lake sturgeon population structure in the Laurentian Great Lakes. N Am J Fish Manag 28:572–591CrossRefGoogle Scholar
  37. Wozney KM, Haxton TJ, Kjartanson S, Wilson CC (2011) Genetic assessment of lake sturgeon (Acipenser fulvescens) population structure in the Ottawa River. Environ Biol Fish 90:183–195CrossRefGoogle Scholar
  38. Zane L, Patarnello T, Ludwig A, Fontana F, Congiu L (2002) Isolation and characterization of microsatellites in the Adriatic sturgeon (Acipenser naccarii). Mol Ecol Notes 2:586–588CrossRefGoogle Scholar

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© The Author(s) 2018

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Leibniz-Institute of Freshwater Ecology and Inland FisheriesBerlinGermany
  2. 2.Central Fisheries Research InstituteTrabzonTurkey
  3. 3.Danube Delta National InstituteTulceaRomania

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