GENETIC DIFFERENTIATION BETWEEN WEST AND EAST EUROPEAN KARYOTYPIC GROUPS OF SOREX ARANEUS (SORICOMORPHA) IN POLAND

Magdalena Moska¹, Heliodor Wierzbicki¹, Tomasz Strzała¹, Anna Mucha¹, Anna Jonkisz^2
1 Department of Genetics, Wrocław University of Environmental and Life Sciences, Poland
² Institute of Molecular Techniques, Medical University, Wrocław, Poland

ABSTRACT

A total of 177 shrews belonging to the Western (n=102, the Drnholec chromosome race) and Eastern (n=75, the Łęgucki Młyn and the Popielno chromosome races) European Karyotypic Groups were studied. The shrews were collected from 17 sites in Poland to analyze genetic differentiation between the groups. Seven polymorphic autosomal microsatellites (L9, L14, L33, L45, L67, L68, L97) and a Y-linked microsatellite (L8Y) were used for the analysis. Statistically significant genetic differentiation between the analyzed groups, based on F_ST (0.021, P<0.001), was additionally confirmed by the AMOVA test and pertained to both classes of markers used in the  analyses, i.e. inherited from both parents (autosomal microsatellites) and inherited  only from the father (Y-linked locus), (0.027 and 0.071, respectively, P<0.001). The results of STRUCTURE and AMOVA indicate that the two groups are genetically distant. Since, the populations sampled were located beyond the contact zone of WEKG and EEKG, the inference on their formation and survival in the separate glacial refugia needs further studies to avoid false conclusions.

Key words: common shrew, expansion, genetic differentiation, glacial refugia, karyotypic groups.

INTRODUCTION

The current geographical distribution of genes, populations and species reflects historical relationships between them. The advent of new molecular tools marked the beginning of a dynamic development of phylogeographic studies, which in turn created the possibility of verifying the available historical data on many species. What seems to be a particularly interesting subject are refugia, where various animal and plant species survived the last glaciation, and the “paths” of postglacial expansion of those species into originally unfavorable areas. There is a number of unanswered questions and controversy regarding the number and location of glacial refugia that contributed to modern populations.

Paleontological data suggest that most of the animal and plant species populating Europe during the Last Glacial Maximum (LGM) approx. 18 000–23 000 years ago survived it in isolated refugia [31]. In Europe, three southern peninsulas have traditionally been recognized as glacial refugia: Iberia, Italy and the Balkans [6, 21, 39]. However, recent phylogeographic studies suggest that apart from the southern refugia, there were probably many other, smaller ones situated at higher latitudes, including Western and Central Europe. Due to their geographical location, they are called “northern refugia” [29, 42, 43, 49]. The most compelling evidence supporting the existence of northern refugia comes from the vicinity of the Carpathians (mountain chain in Central Europe) [26, 29, 45, 49].

The common shrew Sorex araneus is a species with very interesting postglacial history. This Palearctic mammal presents an enormous chromosomal variability. Thus, within the species’ range, there are over 70 chromosomal races [48]. These races have been divided into five karyotypic groups: the West European Group (WEKG), the East European Group (EEKG), the North European Group (NEKG) and two Siberian groups [47]. Due to karyotypic differences between the groups and the fact that their ranges do not overlap, it was initially assumedthat the ancestors of those groups survived the last glaciation in separate refugia [17, 34, 40].

Poland is inhabitedby shrews representing two karyotypic groups: WEKG and EEKG. Their contact zone extends from the North to the South-East [15, 37, 41, 46]. Findings from karyotypic analyses indicate a separate history of WEKG and EEKG, both during the last glaciation and during postglacial expansion of the two groups [7, 34, 35]. After deglaciation, a secondary contact between the groups established.

However, recent genetic analyses based mainly on mtDNA variability in Sorex araneus, point to a different scenario of the glacial and postglacial history of the species. Studies on the variability within the mitochondrial cytochrome b gene conducted by Ratkiewicz et al. [37] and Ratkiewicz [36] show that there is weak genetic differentiation between chromosomal races within WEKG and EEKG, and indicate their recent formation, most likely in a common refugium. Based on their analyses of microsatellite loci and mtDNA, also Jadwiszczak et al. [24] concluded that WEKG and EEKG expanded fairly recently from a single refugium. Ratkiewicz et al. [37] and Ratkiewicz [36], as well as Jadwiszczak et al. [24] analyzed individuals from the populations located in or near the contact zone between the two karyotypic groups.

The aim of our research was to analyze genetic differentiation between WEKG and EEKG using the material from populations located beyond the contact zone of the groups and to give better insight into the hypotheses concerning their common origin.

MATERIALS AND METHODS

Sampling and molecular methods
Shrews were collected from 17 sites in Poland (Fig. 1). A total of 177 animals were included in the microsatellite analysis, 102 from the WEKG (the Drnholec chromosome race) and 75 from the EEKG (the Łęgucki Młyn and the Popielno chromosome races) (Tab. 1).

The end of the tail from each individual was preserved in 75% ethanol. Seven polymorphic autosomal microsatellites (L9, L14, L33, L45, L67, L68, L97) and a Y-linked microsatellite (L8Y) were used for the analysis. The microsatellite loci were amplified under the conditions described by Wyttenbach et al. [51] (loci L9 and L45), Balloux et al. [5]  (loci L14, L33, L67, L68, L97), and Balloux et al. [4] (locus L8Y). To improve the estimation of the amplification product size, one primer of each primer pair was labeled with a fluorescent dye (FAM or JOE) on the 5’-end, which allowed all analyses with the use of an ABI 3100 Avant automated sequencer (Applied Biosystems).

Fig. 1. Map of the locations of the populations sampled. Bold line indicates position of the contact zone between EEKG and WEKG karyotypic groups.

Table 1. Localities of trapping sites and number of individuals (n) of two karyotypic groups used in the study (number of males is given in brackets).

Karyotypic group	Chromosome race	Sample locality	Coordinates	n
WEKG	Drnholec	Kletno (K)	50°14’N, 16°51’E	15 (8)
		Jodłów (J)	50°10’N, 16°46’E	27 (16)
		Nowa Morawa (NM)	50°14’N, 16°54’E	12 (7)
		Kamienica (KA)	50°14’N, 16°53’E	10 (7)
		Międzygórze I (MI)	50°14’N, 16°47’E	7 (5)
		Międzygórze II (MII)	50°13’N, 16°47’E	7 (2)
		Międzygórze III (MIII)	50°13’N, 16°46’E	12 (10)
		Międzygórze IV (MIV)	50°13’N, 16°47’E	12 (11)
Total				102 (66)
EEKG	Łęgucki Młyn	Prawdowo (P)	53°48’N, 21°32’E	10 (5)
	Popielno	Lisunie-Kulinowo (LM)	53°46’N, 21°33’E	15 (8)
		Lipowo (L)	53°47’N, 21°25’E	3 (2)
		Nowy Most (NM)	53°44’N, 21°31’E	7 (3)
		Krutyń I (KI)	53°42’N, 21°26’E	10
		Krutyń II (KII)	53°41’N, 21°26’E	4 (3)
		Zielony Lasek (ZL)	53°40’N, 21°27’E	11 (3)
		Zakręt (Z)	53°39’N, 21°26’E	13 (8)
		Karwica Mazurska (KM)	53°37’N, 21°27’E	2
Total				75 (32)

Statistical analyses
For the two karyotypic groups (WEKG and EEKG), the number of alleles (na), and the observed (H_O) and expected (H_E) heterozygosity were computed for all loci, using ARLEQUIN v. 3.1 [11].

To analyze null allele frequency the maximum likelihood approach implemented in ML-NullFreq was used [27].

Inbreeding coefficient (F_IS) was calculated per locus and over all loci for both the groups separately. Fixation index (F_ST) was estimated per locus and over all microsatellite loci. F_IS and F_ST values were tested for significance with permutations. The tests were performed using 10 000 permutations of alleles within samples for F_IS and 10 000 permutations of genotypes within samples for F_ST (ARLEQUIN v. 3.1) [11, 50].

To estimate gene flow between localities, conventional F-statistics were used [44]. In pair-wise group comparisons both F_ST and R_ST were estimated. R_ST, which is an F_ST-analogue assuming a stepwise mutation model (SMM) [28], is thought to reflect the mutation pattern of microsatellites more accurately.

Two approaches were used do asses the level of genetic differentiation between WEKG and EEKG. In the first approach the sampled populations were divided a priori based on their geographic location. The genetic structure of both the groups was then calculated using the standard analysis of molecular variance (AMOVA) based on F-statistics and R-statistics (implemented in ARLEQUIN v. 3.1). A hierarchical analysis of variance was conducted to partition variance into covariance components due to differences among karyotypic groups, among populations within groups, and within individuals. Covariance components were then used to calculate fixation indices [10, 12]. The extent of genetic differentiation within each sex separately was also examined using an AMOVA (ARLEQUIN) and allelic frequencies.

In the second approach the population genetic structure was analyzed with a Bayesian approach implemented in Structure v2.3.3 [13, 14, 33]. We used correlated allele frequencies with an admixture model, varying K from 1–17 and performing ten replicates for each K with 100 000 burn-ins and 500 000 replicates. To analyze the Structure results, we combined the K method [9] with standard prediction of K based on plotted mean ln probability of K (L(K)). Both plots (Delta K and L(K)) were generated using Structure Harvester [8]. We used the clumpp program [25] to estimate pair-wise similarities between runs (results with over 95% similarity were considered as identical) and visualized the most probable clustering with the distruct software [38].

RESULTS

Genetic variability of loci and heterozygote deficit within populations
One hundred and seventy one alleles were found for seven autosomal microsatellite loci. The most variable locus (L9) had 38 alleles, whereas fifteen alleles were detected at the least variable loci (L67 and L68) (Tab. 2). Out of all alleles detected at autosomal microsatellite loci, 136 were found in EEKG and 154 in WEKG.

No null and race-diagnostic alleles were detected.

Twenty one alleles were found among the ninety eight males screened for the L8Y locus. Out of L8Y alleles four were specific to EEKG and five to WEKG.

Observed heterozygosity (H_O) per locus was similar for both karyotypic groups and varied from 0.55 to 0.87 in EEKG, 0.74 over all loci, and from 0.49 to 0.91 in WEKG, 0.73 over all loci. Expected heterozygosity (H_E) fluctuated from 0.73 to 0.94 with an average value of 0.87 in EEKG and from 0.81 to 0.96 with an average value of 0.90 in WEKG. (Tab. 2).

Inbreeding coefficient values (F_IS) for each locus, varied from 0.01 (L68) to 0.28 (L45) in EEKG and from 0.06 (L67, L68) to 0.39 (L45) in WEKG (Tab. 2). Results from the permutation procedure indicated that in both groups the F_IS values were significantly different from zero for three loci: L33, L45 and L97. Additionally,F_IS for locus L14 in WEKGwas significantly different from zero (Tab. 2).

Table 2. Number of alleles (na), observed (HO) and expected (HE) heterozygosity, inbreeding coefficient (FIS), FST and RST estimated between both karyotypic groups.

Locus	Total	EEKG		WEKG		FIS		FST	RST
	na	HO	HE	HO	HE	EEKG	WEKG
L9	38	0.86	0.94	0.91	0.96	0.08	0.05	0.007***	-0.01085
L14	19	0.81	0.89	0.80	0.91	0.07	0.12***	0.020***	-0.00370
L33	30	0.68	0.93	0.69	0.93	0.27***	0.25***	0.021***	0.01312
L45	18	0.55	0.78	0.49	0.81	0.28***	0.39***	0.012	-0.01554
L67	15	0.67	0.73	0.80	0.85	0.08	0.06	0.066***	0.15622***
L68	15	0.87	0.88	0.83	0.89	0.01	0.06	0.013**	0.06488***
L97	36	0.79	0.94	0.62	0.94	0.15***	0.34***	0.009**	0.01405
All loci	171	0.74	0.87	0.73	0.90	0.13***	0.17***	0.021***	-0.00508
L8Y	21	–	0.93	–	0.91	–	–	0.127***	0.213

Genetic structure of the studied samples
FST value over all loci in both karyotypic groups was low albeitsignificant (F_ST= 0.021, P<0.001; Tab. 2). Locus-specific F_ST values were significant for all autosomal microsatellite loci, except for L45, and varied from 0.007 (L9) to 0.066 (L67). For L8Y, F_ST value was also significant between EEKG and WEKG (F_ST = 0.079, P<0.000; Tab. 2).

Negative value of R_ST estimated for all autosomal loci in both karyotypic groups indicates lack of genetic differentiation between the groups (R_ST= -0.00508; Tab. 2). Locus-specific R_ST values were significant only for loci L67 and L68 (Tab. 2). High but not statistically significantvalue of R_ST (0.213) was estimated for locus L8Y (Tab. 2).

AMOVA analyses, based on F_ST (Tab. 3), revealed significant genetic differentiation among EEKG and WEKG for both autosomal microsatellite loci (F_CT=0.0278, P<0.0002) and for the Y-linked locus (F_CT= 0.0714, P<0.0001). Significant genetic structuring was also detected between populations within groups (F_SC=0.0179, P<0.0001 for autosomal microsatellites, and F_SC=0.0597, P<0.0025 for the Y-linked microsatellite) and within populations (F_ST=0.0453, P<0.0000 for autosomal microsatellites, and F_ST=0.1268, P<0.0000 for the Y-linked microsatellite).

Analysis of molecular variance carried out using R-statistics (Tab. 4) revealed significant genetic differentiation only between EEKG and WEKG (F_CT=0.2474, P<0.015) for the Y-linked microsatellite. We did not find significant genetic differentiation among populations within groups and within populations for both, autosomal and Y-linked microsatellites.

AMOVA analyses based on autosomal microsatellites, for both sexes separately, indicated that female populations had  slightly higher F_ST than male samples (0.024 and 0.019, respectively). In both cases, the values were significantly higher than zero (P<0.000) .

Table 3. Results from analysis of molecular variance of seven autosomal loci and of Y-linked locus. Analyses were carried out using conventional F-statistics.

Source of variation	Autosomal microsatellites				Y-linked microsatellite
	Variance components	Percentage of variation	P (more extreme value)	Fixation indices	Variance components	Percentage of variation	P (more extreme value)	Fixation indices
Among karyotypic groups	0.04939	2.79	0.0002	FCT = 0.02787	0.03553	7.14	0.0001	FCT = 0.07136
Among populations within groups	0.03090	1.74	0.0001	FSC = 0.01793	0.02762	5.55	0.0025	FSC = 0.05974
Within populations	1.69189	95.47	0.0000	FST = 0.04530	0.43470	87.32	0.0000	FST = 0.12684

Table 4. Results from analysis of molecular variance of seven autosomal loci and of Y-linked locus. Analyses were carried out using R-statistics.

Source of variation	Autosomal microsatellites				Y-linked microsatellite
	Variance components	Percentage of variation	P (more extreme value)	Fixation indices	Variance components	Percentage of variation	P (more extreme value)	Fixation indices
Among karyotypic groups	5.74227	0.75	0.09624	FCT = 0.00753	79.91209	24.74	0.00149	FCT = 0.24744
Among populations within groups	-4.69267	-0.62	0.77970	FSC = -0.00620	-11.19078	-3.47	0.81168	FSC = -0.04605
Within populations	761.89750	99.86	0.06740	FST= 0.00138	254.22996	78.72	0.08366	FST = 0.21279

The STRUCTURE analysis gavea strong support for the two genetic units as the most probable genetic structure of the analyzed population< please prophase this sentence using corresponding papers. The Delta K plot presented the highest peak at K=2 (Fig. 2a) which indicates two genetically distinct groups of individuals as genetic structure. These results were confirmed by L(K) plot (Fig. 2b) which showed asymptotic inflection and the lowest standard deviation for K=2.

Fig. 2. STRUCTURE results indicating the most probable number of genetic units (K) based on ten summarized replicates. a) Delta K presents rate of change between successive values of K. b) Plot of the estimated mean of the ln probability of K [mean ln Pr(K)] with standard deviation.

Fig. 3. Clustering result of Sorex araneus population for K=2

DISCUSSION

Based on the chromosomal differences observed between karyotypic groups of Sorex araneus, researchers initially thought that those groups survived the last glaciation in isolated, different refugia [17, 34, 40].

If that was true, the distinct chromosomal variability would have to be accompanied by genetic differentiation. However, a recent study on the differentiation between two karyotypic groups (EEKG and WEKG) in Poland, based mainly on mtDNA variation, did not show any molecular differences between these karyotypically distinct units. According to Ratkiewicz [36], this is because the two groups originated from one refugium. He suggestedthat the chromosomal races could have been formed during the postglacial expansion of the groups about 37 000–62 000 years ago. The hypothesis is also supported by the results obtained from the analysis of the observed and expected distributions of differences between pairs of compared sequences. These findings shed light on the evolutionary history of the species and point to a recent expansion of these two groups from one refugium [36]. However, the analysis of bi-parentally inherited markers may challenge scenarios proposed when using a single mtDNA marker [32].

Based on their studies, Jadwiszczak et al. [24] also concluded that WEKG and EEKG come from the same refugium. They conducted genetic analyses of 7 autosomal microsatellite sequences, Y-linked locus (L8Y) and a fragment of the mtDNA cytochrome b gene, which demonstrated the lack of significant genetic differentiation between the Drnholec and Białowieża chromosomal races representing the karyotypic groups discussed. The results of the analyses indicate genetic proximity of both the races, which as the authors suggest is a consequence of their recent expansion from one refugium.

In the light of the above research, our findings from the analyses of genetic differentiation between the populations representing EEKG and WEKG, based on polymorphism at microsatellite loci, seem to be interesting< in what sense?. The low albeit statistically significant genetic differentiation between the analyzed groups, based on F_ST (0.021, P<0.001), was additionally confirmed by the AMOVA test and pertained to both classes of markers used in the analyses, i.e. inherited from both parents (autosomal microsatellites) and inherited only from the father (Y-linked locus), (0.027 and 0.071, P<0.001). Similar findings were reported by Horn et al. [22], who also reported low and significant level of genetic differentiation between WEKG (the Drnholec chromosome race) and EEKG (the Białowieża chromosome race). Using 16 microsatellite markers they studied shrews sampled from five hybrid zones. The results of the STRUCTURE test were similar, indicating that the two population groups representing WEKG and EEKG are genetically distant (Fig. 2, Fig. 3).

It is noteworthy that AMOVA based on R_ST performed for locus L8Y showed high and statistically significant value of F_CT (0.247, P≤0.005), which is 2.5 times higher than F_CT estimated for autosomal loci. This indicates significant genetic differentiation between the studied karyotypic groups. Our findings are comparable to results previously reported by different authors who observed differences in the estimates of genetic structure based on the Y- microsatellite variation compared to the autosomal microsatellite [2, 4].

And so a question arises: why do we observe certain genetic divergence between the two population groups representing WEKG and EEKG?

(1) Different classes of genetic markers
As a phylogeographic tool, every genetic marker has its pros and cons. Although most frequently used, mtDNA is a marker that presents certain limitations. For example, it is inherited exclusively from mother and is prone to genetic bottleneck. Thanks to this marker, which is a single inheritance unit, i.e. corresponds to a marker from one locus, it is possible to reconstruct the history of only one genetic unit (gene or genome), which does not necessarily have to reflect the history of the analyzed species [18]. Moreover, the effective size of the uniparental marker population is lower than the effective size of the nuclear gene population. As a result, particular haplotypes can be eliminated from a population faster than autosomal alleles. Thus, uniparental markers may present only simplified history of a given population or incorrectly estimated genetic variability. Furthermore, the conclusions based on a single mtDNA marker may be changed when using autosomal microsatellites [32].Therefore, for accurate conclusions about the relationship between different genealogical lines, mtDNA analyses should be completed with analyses based on other types of markers, e.g. autosomal microsatellite loci inherited biparentally[18].

The results of genetic differentiation analyses for Sorex araneus in Scandinavia reflect the difficulty of reconstructing glacial and postglacial history of species using different genetic markers. Andersson et al. [2, 3] and Lundqvist et al. [30] compared chromosomal races representing two karyotypic groups, WEKG and NEKG, by means of mtDNA and autosomal loci. Microsatellite sequences confirmed the authors' hypothesis on two separate refugia (hypothesis they had formed earlier based on chromosomal analyses), even though they found low, albeit significant level of genetic structuring of populations in the hybrid zone between these groups (F_ST = 0.015, P<0.001), [2, 7, 16, 20]. On the other hand, lack of variability within mtDNA found in later studies suggests that the analyzed karyotypic groups survived the last glaciation in one refugium and re-colonized Scandinavia via two paths [3, 30]. However, the same pattern (i.e. two groups suggested by microsatellite markers and one group suggested by mtDNA) could arise after a secondary contact of two populations followed by a “mitochondrial capture” event [19].

(2) Locality of sampled populations
In their analyses of mtDNA polymorphism, Ratkiewicz et al. [37] used individuals from the populations located in North-Eastern and in Western Poland. Most of the populations were located along the contact zone between the two karyotypic groups.

Jadwiszczak et al. [24], on the other hand, performed their analyses on populations located in the hybrid zone between the Drnholec and Białowieża races, which is also a contact zone between WEKG and EEKG, located in South-Eastern Poland. This is the narrowest zone of all hybrid zones of the common shrew described so far. Therefore, the analyzed populations were separated by a very short geographic distance – 0.65 km  [23].

Populations analyzed in our study were located at two opposite ends of the country, in South-Western and North-Eastern Poland (Fig. 1). Thus, geographical distance separating the two populations could theoretically explain the existence of low albeit highly significant genetic differentiation between the analyzed groups if it was not for the fact that the population structure analysis we performed clearly demonstrates that the two karyotypic groups are distant (Fig. 3). However, according to Pritchard et al. (2000) in such a situation (two remote populations) the migration rates between the sampling locations are not high enough to make the population act as a single unstructured population. In the research studies referred to above, such situation did not occur, i.e. the admixture analysis performed using STRUCTURE did not reveal any distinct population structure within the different karyotypic groups [2, 24].

CONCLUSION

The number of refugia in which the ancestors of contemporary Polish populations of Sorex araneus survived the last glaciation remains unclear. The results presented here indicate that WEKG and EEKG are genetically distinct with respect to autosomal and Y-linked microsatellites. Although statistically significant, the level of differentiation between the two populations is relatively low, which may reflect recent divergence of the two groups. However, an understanding why the studied groups are distinct (historically, ecologically or because the gene frequency fluctuated randomly) needs further investigation.

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Accepted for print: 5.06.2014

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Magdalena Moska
Department of Genetics, Wrocław University of Environmental and Life Sciences, Poland
Kożuchowska 7
51-631 Wrocław
Poland
Phone +48 71 320 5921
email: magdalena.moska@up.wroc.pl

Heliodor Wierzbicki
Department of Genetics, Wrocław University of Environmental and Life Sciences, Poland
Kożuchowska 7
51-631 Wrocław
Poland
email: heliodor.wierzbicki@up.wroc.pl

Tomasz Strzała
Department of Genetics, Wrocław University of Environmental and Life Sciences, Poland
Kożuchowska 7
51-631 Wrocław
Poland
email: arrow00@poczta.fm

Anna Mucha
Department of Genetics, Wrocław University of Environmental and Life Sciences, Poland
Kożuchowska 7
51-631 Wrocław
Poland
email: anna.mucha@up.wroc.pl

Anna Jonkisz
Institute of Molecular Techniques, Medical University, Wrocław, Poland
M. Curie-Skłodowskiej 52
50-369 Wrocław
Poland
email: annaj@forensic.am.wroc.pl

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Responses to this article, comments are invited and should be submitted within three months of the publication of the article. If accepted for publication, they will be published in the chapter headed 'Discussions' and hyperlinked to the article.

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