doi:10.1111/j.1420-9101.2010.01999.x
J. A. ANMARKRUD, A. JOHNSEN, L. BACHMANN & J. T. LIFJELD
National Centre for Biosystematics, Natural History Museum, University of Oslo, Blindern, Oslo, Norway
ancestral polymorphism; balancing selection;
Luscinia luscinia ;
Luscinia svecica ; major histocompatibility complex.
The genes of the major histocompatibility complex (MHC) are important model genes for understanding selective forces in evolution. Here, we document, using a cloning and sequencing approach, high polymorphism at the exon 2 of the MHC class II B (MHCIIB) genes in the bluethroat ( Luscinia svecica ); a minimum of 61 unique alleles were detected in 20 individuals, and at least 11 functional loci. In addition, several pseudogenes were revealed. The specimens originated from three different bluethroat subspecies ( azuricollis, cyanecula and svecica ), and we also analysed four specimens of the closely related thrush nightingale ( L .
luscinia ) for comparison. Phylogenetic analyses of the functional alleles revealed 258 equally parsimonious trees with poor statistical support for the majority of nodes. The distribution of the sequences in the trees point to an ancestral origin of the polymorphism in MHC class II B genes, a portion of which predated the phylogenetic split between the bluethroat and the thrush nightingale. Strong signatures of balancing selection were uncovered for the codons coding for the peptide-binding residues of the functional MHCIIB exon 2 alleles. Our results highlight the importance of duplication and recombination events for shaping passerine MHC and give insights in the evolutionary dynamics of MHC variation among closely related taxa.
Understanding how variation in functional genes arises and is maintained is a central issue in evolutionary genetics. Much attention has recently been drawn towards the evolution of the major histocompatibility complex (MHC) genes. MHC genes code for glycoproteins that launch foreign pathogenic substances to the cell surface and present them for T-lymphocytes, which in turn trigger an immune response. MHC genes are extraordinarily polymorphic (Robinson et al.
, 2003), presumably because strong diversifying selection pressures are acting on these genes (Hughes & Yeager, 1998).
Accordingly, these genes are interesting, not only for functional studies but also for studies of evolutionary
Correspondence: Jarl A. Anmarkrud, National Centre for Biosystematics,
Natural History Museum, University of Oslo, P.O Box 1172 Blindern,
NO-0318 Oslo, Norway.
Tel.: +47 22 85 16 22; fax: +47 22 85 18 37; e-mail: j.a.anmarkrud@nhm.uio.no
processes underlying genetic diversity and differentiation
(reviewed in Piertney & Oliver, 2005; Sommer, 2005).
MHC class I genes encode proteins that are expressed in approximately all somatic cells. They are associated with intracellular infections (e.g. viruses and cancer infected cells) and present viral peptides to CD8
+ receptors in cytotoxic T cells (Kaufmann, 1988; Rammensee et al.
, 1993). Class II proteins are presented on the surface of specialized antigen-presenting cells (such as macrophages, B cells and dentritic cells), presenting extracellular pathogenic substances to CD4
+ receptors in T-helper cells (Villadangos, 2001; Dengjel et al.
, 2005). Hence, parasite resistance is intimately connected to MHC class II genes.
Most of what is known about the avian MHC genes has been uncovered from studies of the chicken ( Gallus gallus ) genome. The chicken MHC has a relatively simple organization, also referred to as ‘‘the minimal essential
MHC’’ (Kaufman et al.
, 1995). Such minimal essential
MHC has also been described in a parrotlet, and one single MHC class II B (MHCIIB) locus is suggested to be
1206
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MHC class II diversity in bluethroats 1207 the ancient state for birds (Hughes et al.
, 2008). However, the minimal essential MHC is not a good representative for all avian lineages, and for the order Passeriformes an extraordinarily complex organization of MHC genes is known (reviewed in Westerdahl, 2007). Compared to the chicken minimal essential MHC, passerine MHC genes are larger and have more duplicated loci (Edwards et al.
,
1998; Hess & Edwards, 2002; Westerdahl et al.
, 2004).
Furthermore, contrasting the chicken MHC genes, pseudogenes have frequently been reported in passerine
MHC genes (e.g. Edwards et al.
, 1998, 2000; Westerdahl et al.
, 1999; Hess et al.
, 2000; Gasper et al.
, 2001;
Bonneaud et al.
, 2004; Miller & Lambert, 2004; Aguilar et al.
, 2006). Accordingly, the diversity of avian MHC complexity may reflect distinct evolutionary lineages.
Because of co-evolution between parasites and hosts,
MHC genes are expected to show signatures of adaptation to the pathogen spectrum (Hill, 1991; Hill et al.
,
1991). Such adaptation would result from contemporary divergent selection pressures acting within a comparatively short timeframe. Accordingly, a geographical structure in the MHC polymorphism may arise. However, local adaptation at MHC genes is not the general pattern in nonmodel vertebrates (reviewed in Bernatchez &
Landry, 2003). Only recently, intraspecific geographical variation of MHC genes with evidence of local adaptation has been documented in birds; MHC class II B, the great snipe ( Gallingo media ) and the lesser kestrel ( Falco neumanni ) and MHC class I, house sparrow ( Passer domesticus ) (Ekblom et al.
, 2007; Alcaide et al.
, 2008;
Loiseau et al.
, 2009).
An alternative, but not mutually exclusive, evolutionary model explaining the extensive MHC diversity uncovered in passerines is the ancestral polymorphism model (Klein, 1987). This model is also referred to as shared or trans-species polymorphism (Klein et al.
, 1998).
The model assumes that polymorphism has an ancestral origin and thus predates speciation or intraspecific differentiation processes (Klein et al.
, 1998). In contrast to the local adaptation, which is operating within a relatively short time frame, the ancestral polymorphism is the retention of allelic lineages over evolutionary time.
Accordingly, closely related species or subspecies, or otherwise differentiated populations of the same species should share similar, or even the same, MHC alleles regardless of the actual local selection through parasites.
This is considered common in MHC alleles as related species and subspecies often carry similar MHC alleles
(e.g. Arden & Klein, 1982). Even though ancestral polymorphism may be less evident in MHC genes in birds when compared to other vertebrates (Edwards et al.
,
1999), signatures of ancestral polymorphism have been revealed in several nonpasserines (e.g. Alcaide et al.
,
2007; Burri et al.
, 2008; Kikkawa et al.
, 2009) and passerines (e.g. Vincek et al.
, 1997; Bonneaud et al.
, 2004).
The bluethroat ( Luscinia svecica ) is breeding in Europe,
Asia and Alaska, and geographically differentiated into about ten subspecies (Cramp, 1988; Dickinson,
2003). The subspecies are generally recognized by a distinct throat coloration of males in their breeding plumage (Johnsen et al.
, 2006). Because the bluethroat is distributed over a relatively large geographical area, it offers a model for studying local adaptation and ⁄ or ancestral polymorphism of MHCIIB. In the present study we analysed polymorphism at MHCIIB exon 2 in three recognized subspecies from Norway (nominate subspecies; Linnaeus, 1758), the Czech Republic
(ssp.
cyanecula ; Meisner, 1804) and Spain (ssp.
azuricollis ; Mayaud, 1958), respectively. The subspecies have different breeding habitats, migration routes and wintering areas, and they were found to be genetically differentiated at 11 microsatellite markers (Johnsen et al.
, 2006) indicating restricted gene flow between them.
The main aims of this study were three-fold. First, we wanted to comprehensively describe the polymorphism at exon 2 of the MHCIIB genes in the bluethroat, a species where immune function seems to play a role in the mating system (Johnsen et al.
, 2000; Fossøy et al.
,
2008). We sequenced genomic DNA using a cloning and direct sequencing approach. We also sequenced cDNAderived clones from one individual to estimate the number of transcribed MHCIIB genes. Second, by reconstructing phylogenetic relationship of the MHCIIB exon 2 sequences from the bluethroat and the congeneric thrush nightingale ( L. luscinia ), we address the extent of ancestral polymorphism in these species. Third, to test for signatures of balancing selection, we compared substitution rates between specific residues involved in antigen recognition and MHC residues without any known binding affinities to foreign antigens and performed a Bayesian approximation for detecting spatial distribution of balancing selection signatures across the MHC fragment.
Samples and DNA isolation
Blood samples of bluethroats were collected from breeding adult birds (except for one juvenile) from three localities representing three subspecies: svecica (Øvre
Heimdalen, Norway), cyanecula (Trˇebonˇ, Czech Republic) and azuricollis (Valduerna, Spain). Blood samples of thrush nightingales were collected at Jomfruland
(Norway). Blood samples (up to 25 l L) were suspended in 1 mL of Queen’s lysis buffer (Seutin et al.
, 1991) and stored at 4 C. All samples are deposited in the DNA ⁄ tissue collection of the Natural History Museum,
University of Oslo. For further details about specimens and sampling sites, see Appendix S1. DNA was extracted from blood samples using the E.Z.N.A. Blood DNA Kit
(Omega Bio-Tek, Norcross, Georgia, USA), following the manufacturer’s protocol.
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1208 J . A . A N M A R K R U D E T A L .
PCR amplification of MHC class II B exon 2
Based on passerine MHCIIB exon 2 sequences retrieved from GenBank, the degenerated primers F1 (5 ¢ -GAGT
GTYVCTTCATTAACGGCAC-3 ¢ ) and R1 (5 ¢ -CKCGTAGTT
GTGCCGGCA-3 ¢ ) were designed. These primers target a
213-bp fragment corresponding to nucleotide position
27-239 in the chicken MHC class II (B-LBII) exon 2 locus
(Zoorob et al.
, 1990).
The PCR was performed in a 15 l L mixture consisting of PCR buffer, 250 l
M
0.5
l
M dNTP (ABgene, Epsom, UK), of each primer, 0.3 U Phusion
TM
High-Fidelity
DNA polymerase (Finnzymes, Espoo, Finland) and approximately 50 ng template DNA. The following thermal cycling profile was applied: 98 C for 30 s, followed by 30 cycles of 8 s at 98 C, 15 s at 53 C and 20 s at
72 C and a final extension of 7 min at 72 C. Relatively long elongation times are recommended to reduce incomplete elongation, which may create sequence chimeras (Meyerhans et al.
, 1990; Judo et al.
, 1998;
Lenz & Becker, 2008). A twenty-second elongation step is, according to the manufacturer’s recommendations, sufficient elongation time to amplify fragments approximately three-fold to six-fold the size of the MHCIIB exon
2 fragments. As a control, 3 l L of the PCR products was visualized on 1.2% standard agarose gels. PCR products were NH
4
Ac ⁄ EtOH precipitated, recovered in 10 l L sterile water, and subjected to downstream cloning reactions.
RNA isolation and reverse transcription
Total RNA was isolated from about 100 l L fresh blood from a bluethroat individual from Heimdalen, Norway using TRIzol LS Reagent (Invitrogen, Carlsbad,
California, USA) according to the supplier’s protocol.
Approximately 1 l g total RNA was treated with DNase I
(Sigma-Aldrich, St Louis, MO, USA) before being reverse transcribed with the iScript
TM cDNA synthesis kit (BioRad, Hercules, CA, USA). The cDNA was subsequently used as template for PCR amplification with primers F1 and R1, as described earlier. A parallel
DNase-treated RNA sample served as a control for DNA contamination. The whole sample was loaded on a
1.2% agarose gel and the amplified MHC fragment was cut out with a sterile scalpel and purified with the
Nucleospin Extract II gel extraction Kit (Macherey-
Nagel, Du¨ren, Germany) and subsequently cloned as described in the following section.
Cloning of PCR products and sequencing
All cloning attempts were performed using the Zero
Blunt TOPO PCR Cloning Kit (Invitrogen) and transformed into Escherichia coli DH5 a chemically competent cells as recommended by the supplier (Invitrogen). Transformed cells were grown on LB agar with kanamycin
(100 l g mL
) 1
) as selection marker. Positive colonies were picked with a sterile toothpick, diluted in 6 l L dH
2
O and used directly as DNA template for PCR. The clones were amplified and purified as described for genomic DNA except for the use of standard M13-primers and an additional 96 C step for 20 s.
All clones were sequenced in both directions following the protocol for the BigDye Terminator v3.1 Cycle
Sequencing Kit (Applied Biosystems, Foster City, CA,
USA) and purified with ethanol ⁄ EDTA ⁄ sodium acetate, before they were run on an ABI 3100 ⁄ 3130xl
Genetic Analyser (Applied Biosystems). The sequences were assembled in ContigExpress, a component of the software package Vector NTI Advance 10.3 (Invitrogen).
Bryja et al.
(2005) revealed, based on a clone and single stranded conformational polymorphism (SSCP) comparison, that almost 25% of the clones contained artefacts in a MHC class II survey of water voles,
Arvicola terrestris . We approached the problem of false alleles by discarding alleles observed only once and that differed by not more than 1.5% (equivalent to £ 3 point substitutions) from a putative sister allele. We also sequenced directly the cDNA amplicon to ensure correct assignment of polymorphic nucleotide positions for this individual. Recombination because of jumping PCR or during bacterial replication is indistinguishable from genomic recombination. In this respect, we used a conservative approximation and discarded all alleles that achieved significant within-individual recombination score when screening for recombination signals.
Amplified PCR products of correct size, with no stop codons, that harboured conserved MHCIIB exon 2 motifs were considered functional MHCIIB exon 2 alleles. Other amplified PCR products were regarded as pseudogenes.
Sequence analyses and statistical methods
Nucleotide sequences were aligned using BioEdit (Hall,
1999). Number of alleles and nucleotide diversity ( p ) were calculated using DnaSP 4.10 (Rozas et al.
, 2003).
Several studies have emphasized the determination of the specific MHC residues involved in antigen recognition (She et al.
, 1991; Brown et al.
, 1993; Tong et al.
,
2006). Here, we analysed our data according to the assignment of peptide-binding region (PBR) by Tong et al.
(2006). The PBR- and non-PBR motifs were manually edited using GeneTool 2.0 (BioTools Inc,
Edmonton, Canada).
There are several ways to test for selection on the MHC alleles. The parameter x is widely used, estimated as the ratio of nonsynonymous (d
N
) to synonymous (d
S
) substitutions. The overall d
N
⁄ d
S
-ratio was calculated in
MEGA4 (Tamura et al.
, 2007) using the method by Nei
& Gojobori (1986) with Jukes Cantor corrections and
1000 bootstrap replicates, and tested against the null
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MHC class II diversity in bluethroats 1209 hypothesis of strict neutrality (d
N
= d
S
) with a Z -test (Nei
& Gojobori, 1986). Standard selection tests (Tajima’s D,
Fu & Li’s F* and Fu & Li’s D*) were calculated using
DnaSP 4.10 (Rozas et al.
, 2003).
To estimate spatial positive selection and recombination rates along exon 2, we used the program omegaMap v.0.5 (Wilson & McVean, 2006). This program utilizes a
Bayesian population genetics approximation for co-estimating the selection parameter ( x ) and the recombination rate ( q ). The analyses were implemented using an objective set of priors (as we had no preassumptions for the different parameters). Default values were used for l
(synonymous transversion rate) and j (transition to transversion ratio) with improper inverse distribution.
Two independent MCMC chains were run for 500 000 iterations, of which the 50 000 first were discarded as burn-in. The selection parameter ( x ) and the recombination rate ( q ) were adjusted to follow inverse distribution ranging between 0.1 and 100 in the first MCMC chain and 0.0001 and 30 in the second MCMC chain. The independent model of variation was implemented for these parameters.
Recombination has been suggested to be an important factor in creating allelic diversity (Martinsohn et al.
, 1999; Richman et al.
, 2003) and is commonly recognized in analyses involving avian MHC genes
(Hunt et al.
, 1994; Wittzell et al.
, 1999a; Miller &
Lambert, 2004; Edwards & Dillon, 2005; Alcaide et al.
,
2008; Burri et al.
, 2008; Silva & Edwards, 2009).
Several methods and software applications are available to test for signatures of recombination. The algorithms in the various methods might respond differently to parameters such as levels of sequence divergence, amount of recombination and nucleotide substitution rates. Furthermore, the probability of creating false positives varies between methods. As a consequence, multiple methods should be applied when screening for recombination events. In the present study, we tested for recombination using the software RDP3 Beta 27
(Martin et al.
, 2005). The program was run with the
RDP (Martin & Rybicki, 2000), GENECONV (Padidam et al.
, 1999), BootScan (Salminen et al.
, 1995), MaxChi
(Smith, 1992), Chimaera (Posada & Crandall, 2001) and 3Seq (Boni et al.
, 2007) methods using default settings. Maximum P value was set to 0.05, and the
Bonferroni correction for multiple comparisons was applied. The ‘‘disentangle overlapping signals’’ option was set ‘‘on’’. If three or more of the applied methods uncovered the same recombination signal, the hit was considered significant.
The software TNT 1.1. (Goloboff et al.
, 2008) was used to infer the most parsimonious tree. The four tree searching algorithms sectorial, ratchet, drift and tree fusing were employed with default settings using random additional sequences with 10 000 replicates. Resampling was performed in TNT using standard bootstrap with
1000 replications and ‘‘new tech search’’.
MHCIIB exon 2 alleles in the bluethroat
MHCIIB exon 2 sequences were obtained from 20 bluethroat individuals, ten svecica , five cyanecula and five azuricollis . A total of 342 clones derived from
PCR products of bluethroat genomic template DNA were sequenced (Table 1).
Most bluethroat MHC sequences ( n = 261, Table 1) were considered MHCIIB exon 2 homologues to the functional chicken MHCIIB-
LBII genes, 121 of the sequences were considered pseudogenes.
Polymorphism of exon 2 alleles
Our analytical approach yielded sequences from multiple loci but an assignment of alleles to particular loci is impossible with our approach. Thus, standard nomenclature (Klein et al.
, 1990) cannot be used for the sequences presented here, and the 61 unique MHCIIB exon 2 alleles were named MhcLusv 01-61 (only the abbreviation Lusv will be used further in the text). These sequences are retrievable from GenBank as accession no.
FJ529861-FJ529913 and GQ403035-GQ403542 and are provided as Appendix S2. The dataset including all bluethroat MHCIIB exon 2 sequences that were considered functional revealed 100 variable sites ( S ), with a nucleotide diversity ( p ) of 0.152. Any two alleles differed on average by 25.95 (SD = 11.5) nucleotides.
Individual 10 from Heimdalen, Norway, revealed a particularly high number of 10 different MHCIIB exon 2 alleles, and was thus expected to be heterozygous at most
MHCIIB loci. This individual was selected for screening the cDNA to approximate the number of functional
MHCIIB exon 2 loci in the bluethroat. Forty clones of cDNA-derived PCR products uncovered 20 different
MHCIIB exon 2 alleles (GenBank accession no.
FJ529898, FJ529900, FJ529902, FJ529904, FJ529913,
FJ529870-FJ529884). Eight of these alleles matched alleles revealed from the genomic DNA sequences.
Combining the sequences obtained from both genomic
DNA and cDNA, a total of 22 MHC alleles were detected for individual 10 from Heimdalen, Norway. Assuming all loci being heterozygous, a minimum of 11 bluethroat
MHCIIB exon 2 loci must exist.
We examined MHCIIB exon 2 alleles in four thrush nightingale ( L .
luscinia ) individuals from Jomfruland
(Norway). The 82 clones that were sequenced revealed
15 MHCIIB exon 2 alleles, 12 were unique ( MhcLulu 01-12;
GenBank accession no.
FJ529849-FJ529860).
The remaining 67 (82%) clones harboured nonfunctional
MHCIIB elements with frame-shift mutations, and were considered pseudogenes. An alignment of the thrush nightingale sequences is provided as Appendix S3. The thrush nightingale pseudogenes are provided as Appendix S4.
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1210 J . A . A N M A R K R U D E T A L .
Table 1 Number of sequenced clones, functional MHCIIB alleles and pseudogenes revealed for all bluethroat and all thrush nightingale individuals.
Species ⁄ subspecies
L. s. svecica
Sum svecica
L. s. azuricollis
Sum azuricollis
L. s. cyanecula
Sum cyanecula
Total bluethroats
L. luscinia
Individual
9
10 (gDNA)
10 (cDNA)
7
8
5
6
3
4
1
2
3
4
1
2
3
4
1
2
5
3
4
1
2
5
Sequenced clones
16
14
76
382
12
11
33
26
82
76
18
17
11
15
8
17
18
9
10
8
15
7
16
21
44
14
46
40
230
18
Clones containing functional MHC alleles
5
4
4
2
15
37
15
10
8
15
13
61
221 ⁄ 261*
7
10
8
10
6
11
5
21
7
10
6
6
8
37
40
123 ⁄ 163*
8
Number of different MHC alleles
5
3
4
2
12
14
6
4
4
6
4
13
51 ⁄ 61*
4
5
3
4
4
10
20
34 ⁄ 44*
5
4
7
5
4
3
8
4
4
Number of clones with pseudogene sequences
15
121
1
1
28
22
8
9
67
39
3
7
3
10
8
9
2
0
5
2
0
16
23
1
5
0
67
10
6
9
Total thrush nightingales
MHC, major histocompatibility complex.
*First number displays genomic DNA-derived alleles. Second number displays total amount of alleles including the cDNA sequences.
Phylogenetic analysis
Phylogenetic relationships were assessed for the functional MHCIIB exon 2 alleles (61 bluethroat alleles and 12 thrush nightingale alleles). The maximum parsimony analysis yielded 258 most parsimonious trees. A majorityrule (50%) consensus tree is shown in Fig. 1. The Lusv sequences may be clustered into eight clades (Fig. 1), though only three of the clades possessed high bootstrap values. MHCIIB exon 2 sequences from all the three subspecies are represented in clade 1, 2 and 6.
L .
s .
svecica and L .
s .
azuricollis alleles were represented in clade 4, and
L .
s .
svecica and L .
s .
cyanecula alleles were represented in clade 7. Although the Lulu sequences clustered into two species-specific clades (clade 5 and clade 8), some Lulu alleles were distributed within clade 2 and clade 4.
Only a few alleles were shared between the bluethroat subspecies, i.e. two alleles were shared by all three subspecies ( Lusv 01 and Lusv 13), five alleles were shared by svecica and cyanecula ( Lusv 03, Lusv 20, Lusv 25, Lusv 31 and Lusv 33), and one allele was shared by svecica and azuricollis ( Lusv 07). It must be emphasized that the sampling of alleles in the three subspecies is presumably rather incomplete, which prevents meaningful analyses of allelic differentiation and local adaptation.
Selection and recombination
Traditional selection statistics for detecting footprints of selection on the functional MHCIIB exon 2 alleles did not uncover any selection patterns statistically different from neutral expectations (Tajima’s D =
)
0.36, P > 0.1, Fu &
Li’s D* = 0.91, P > 0.1, Fu & Li’s F* = 0.43, P > 0.1).
However, the Z -test revealed significant P values, implying positive selection in the PBR region but not in the non-PBR region (Fig. 2). The analysis of spatial signatures of positive selection along the targeted fragment of the MHCIIB exon 2 using omegaMap (Wilson & McVean,
2006) revealed varying extent of positive selection. High values of x and posterior probability of positive selection corresponded largely to the amino acids in the PBR
(Fig. 3). Overall average of x in the PBR residues was
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MHC class II diversity in bluethroats 1211
Clade 1
Clade 2
Clade 3
Clade 4
Clade 5
Clade 6
Clade 7
Clade 8
Fig. 1 Fifty Percent majority-rule tree of the functional MHCIIB exon 2 alleles. Bootstrap values < 50 are not displayed. Thrush nightingale alleles are visualized by grey letters. The Passer domesticus MHCIIB exon 2 allele Pado-DAB*01 was used as outgroup. cDNA alleles are illustrated by red rectangles, and other svecica alleles are illustrated by yellow rectangles.
Cyanecula alleles are illustrated by green rectangles, and azuricollis alleles are illustrated by blue rectangles.
3.077 (SE = 0.14) compared to 0.995 (SE = 0.02) for the non-PBR residues. The posterior probability of positive selection for the respective regions were 0.922
(SE = 0.01) and 0.22 (SE = 0.01).
Screening for recombination yielded one significant recombination signal for the MHCIIB exon 2 alleles, indicating that Lusv 53 probably originated from Lusv 06 and Lusv 25. Accordingly, the recombination signals where significant for the MaxChi ( P < 0.05), Chimaera
( P < 0.01) and 3Seq ( P < 0.01) analyses. The omegaMap analysis revealed an overall recombination rate ( q ) of 0.49
(SE = 0.01), which exceeds the mean mutation rate
( h = 0.27, lower and higher 95% posterior probability dense intervals were 0.20 and 0.36). Overall mean recombination rate ( q ) was 0.515 (SE = 0.03) in the PBR residues and 0.482 (SE = 0.02) in the non-PBR residues.
There was one significant recombination signal in the thrush nightingale sequences, i.e.
Lulu 4 achieved a
ª 2 0 1 0 T H E A U T H O R S .
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1212 J . A . A N M A R K R U D E T A L .
(a)
(b)
Fig. 2 Values for x (the ratio of nonsynonymous to synonymous substitutions) calculated for the non–peptide binding region (non-
PBR) and the peptide-binding region (PBR) of the MHCIIB exon 2 alleles.
Pvalues represent the probability to reject a hypothesis of strict neutrality in favour of the alternative hypothesis of dN > dS
(balancing selection) and were calculated in MEGA4 using a Z -test.
P -values < 0.01 are depicted by ** and nonsignificant P -values
(all > 0.43) are depicted by n.s.
significant hit of having Lulu 3 and Lulu 5 as parental alleles. The recombination signals were significant when applying the Bootscan ( P < 0.05), MaxChi ( P < 0.01),
Chimaera ( P < 0.01) and 3Seq ( P < 0.001) methods.
Bluethroat pseudogenes
Of all the sequenced MHCIIB exon 2 clones, 121 (31.7%)
( svecica : 29.1%, cyanecula : 19.7%, azuricollis ; 51.3%) sequences harboured stop codons or frame-shift mutations that disturbed their functionality. These pseudogenes could be divided into three groups based on sequence lengths.
Group 1 (10.2% of the sequences and four different alleles) consisted of a 172 bp fragment (excluding primers).
This group had a stop codon in the first position and contained an indel at position 89 when aligned with the functional Lusv alleles. Group 2 (17.5% of the sequences and one allele) consisted of a 158 bp fragment and group 3
(5.5% of the sequences and three different alleles) consisted of a 155 bp fragment. Both group 2 and 3 had an indel at position 73 when aligned with the functional
Lusv alleles, and sequences from both groups contained stop codons in residue no. 41. Group 3 also contained a gap in position 80–82 when compared to those of group 2. All groups of pseudogenes were represented in all three subspecies, except for group 3 which was not found in cyanecula . The relative frequency of obtained pseudogenes sequences seemed to differ among subspecies, being more than 50% in azuricollis 35% in svecica and less than 20% in cyanecula . The bluethroat pseudogene sequences have been submitted to GenBank as accession no. FJ409236-
Fig. 3 Variation in omega (a) and posterior probability of positive selection (b) along the MHCIIB exon 2. Amino acids expected to be involved in antigen recognition are highlighted with an arrow. Grey line indicates 95% highest posterior probability dense intervals.
FJ409243 and are provided as Appendix S4. Nucleotide diversity ( p ) differed considerably between the functional
Lusv ( p = 0.152) alleles and the pseudogenes [ p = 0.015
(group 1), p = 0.0 (group 2), p = 0.02 (group 3)].
We document substantial genetic diversity and polymorphism of the MHCIIB genes in the bluethroat. Based on cloned MHCIIB exon 2 amplicons from total genomic DNA and cDNA of svecica individual 10, a minimum of 11 functional MHCIIB loci must be present in the bluethroat genome. In addition, many pseudogenes were detected.
Even though the numbers of loci probably differs between bird species, the complex MHC organization in the bluethroat emphasizes the impact of gene duplications and recombination in the evolution of passerine MHC
(Westerdahl, 2007), and contrasts the ‘‘minimal essential
MHC’’ (Kaufman et al.
, 1995; Hughes et al.
, 2008).
ª 2 0 1 0 T H E A U T H O R S .
J . E V O L . B I O L .
2 3 ( 2 0 1 0 ) 1 2 0 6 – 1 2 1 7
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MHC class II diversity in bluethroats 1213
In individual 10, 20 unique clones were detected among 40 cDNA-derived clones and 10 unique clones were detected among 28 functional gDNA-derived clones (Table 1). Eight of the cDNA-derived clones were observed among the gDNA sequences. It is difficult, based on this information, to estimate the saturation of our screening approach. The assessment of how comprehensive our allele characterization actually is depends on several parameters that may cause biases in allele frequencies; e.g. PCR conditions, random selection of clones for analysis, lowly expressed alleles may escape detection when analysing cDNA. Given the limitations to our approach, the true number of MHCIIB loci in bluethroats is likely higher than 11. The high diversity of
MHCIIB genes found in the present study is not entirely surprising, as high levels of MHC polymorphism, based on studies using RFLP analysis, have been reported for other passerines as well (Wittzell et al.
, 1999b; Westerdahl et al.
, 2000; Freeman-Gallant et al.
, 2002; Richardson &
Westerdahl, 2003). In addition, Westerdahl et al.
(2004) reported a minimum of six MHC class I exon 3 loci in the great reed warbler ( Acrocephalus arundinaceus ) based on sequencing of cloned PCR products.
The ancestral polymorphism model may be reflected in two different patterns: (i) identical alleles may be shared between different species or (ii) similar alleles from different species may be preserved on the same phylogenetic lineage in a gene tree. Here, we have documented the latter, indicated by the shared clades between the bluethroat subspecies and the closely related thrush nightingale in the maximum parsimony tree (Fig. 1).
However, grouping of sequences into clades 1–5 received no reasonable bootstrap support (Fig. 1). Clades 6–8, received robust bootstrap support, and consisted of sequences from either the bluethroat or the thrush nightingale. Nevertheless, the overlap in allelic range among taxa (species and subspecies) is striking and suggests an ancestral origin of much of the detected polymorphism. Given the restricted sample size and potential limitations of our approach (i.e. PCR bias or allelic dropout), we cannot reasonably address possible local adaptation in allelic frequencies and the occurrence of population-specific alleles. Recently, both Alcaide et al.
(2008) and Ekblom et al.
(2007) found support for local adaptation in MHC genes in migratory birds with restricted gene flow. However, in these studies, the within-individual allelic variation was much lower, allowing the authors to determine population-specific allele frequencies; no individuals analysed by Ekblom et al.
(2007) yielded more than four alleles, and Alcaide et al.
(2008) used locus-specific primers. In our study, however, potential geographical structure and signatures of local adaptation may be masked by the high number of alleles when targeting multiple MHC loci.
Ancestral polymorphism in MHC alleles has commonly been reported, particularly within mammalian species
[e.g. rodents (Arden & Klein, 1982; Figueroa et al.
, 1988;
McConnell et al.
, 1988), primates (Lawlor et al.
, 1988;
Otting et al.
, 2002), ungulates (Van Den Bussche et al.
,
1999; Schaschl et al.
, 2006)]. However, Edwards et al.
(1999) speculated that trans-species evolution of MHC genes may be less common in closely related bird species than in other vertebrates. Yet, signatures of ancestral polymorphism have recently been uncovered in several avian lineages, both in MHC class II genes [raptors
(Alcaide et al.
, 2007), owls (Burri et al.
, 2008), passerines
(Vincek et al.
, 1997; Sato et al.
, 2001; Freeman-Gallant et al.
, 2002; Bonneaud et al.
, 2004; Jarvi et al.
, 2004), penguins (Kikkawa et al.
, 2009)] and in MHC class I genes
[passerines (Richardson & Westerdahl, 2003; Arnaiz-
Villena et al.
, 2007)]. The results provided here support the suggestion by Burri et al.
(2008) that ancestral polymorphism in MHC genes may be more prominent in avian lineages than previously assumed.
Gene flow between the different bluethroat subspecies may also explain the lack of genetic differentiation among MHCIIB exon 2 alleles. However, microsatellite analyses have revealed that the three subspecies are genetically differentiated (Johnsen et al.
, 2006), indicating very little gene flow between these populations. The considerable geographical distance between the breeding areas, the different migration routes and breeding habitats between the subspecies (Cramp, 1988; Ellegren &
Staav, 1990; Herna´ndez et al.
, 2003; Bermejo & De La
Puente, 2004; Arizaga et al.
, 2006), may also be forwarded in support of restricted current gene flow between the study populations.
The classical standard tests for selection (Tajima’s D, Fu
& Li’s D* and Fu & Li’s F*), except the Z -test, showed no deviation from neutral expectations. Given the level of variation and the short sequences, these tests are certainly not very powerful for our data set. We believe that there is strong support for signatures of balancing or diversifying selection for the codons coding for the amino acids involved in antigen recognition in the MHCIIB exon 2.
The amino acids in the PBR are the most important for immune function and accordingly of particular relevance in an evolutionary perspective. Yet, it is uncertain whether the observed selection can be attributed to overdominance. The Z -test revealed x values significantly higher than expected under neutral expectation in the
PBR, but not for the non-PBR, a pattern that is consistent with balancing selection (Hughes & Nei, 1988; Hughes &
Yeager, 1998). This pattern, with high x values in the PBR compared to the non-PBR, is in line with most other studies reporting selective signatures at MHC genes
(Bernatchez & Landry, 2003). Moreover, high x values and high posterior probability of selection were to a large degree confined to the PBR residues (Figs 2 and 3).
Nucleotide diversity ( p ) is expected to be high for loci undergoing balancing selection (Strand et al.
, 2007).
Interestingly, we observed that nucleotide diversity was considerably higher in the functional Lusv alleles than in
ª 2 0 1 0 T H E A U T H O R S .
J . E V O L . B I O L .
2 3 ( 2 0 1 0 ) 1 2 0 6 – 1 2 1 7
J O U R N A L C O M P I L A T I O N ª 2 0 1 0 E U R O P E A N S O C I E T Y F O R E V O L U T I O N A R Y B I O L O G Y

1214 J . A . A N M A R K R U D E T A L .
the presumed pseudogenes, which are probably not affected by non-neutral selection pressures.
The minimum of eleven different MHCIIB loci may have originated by gene duplications and evolved according to the concept of concerted evolution. One significant recombination signal was detected both within the bluethroat and the thrush nightingale sequences. Interestingly, a relatively uniform recombination rate ( q ) was observed between the PBR and the non-PBR residues.
This contrasts with recently published results by Silva &
Edwards (2009) who found a higher recombination rate in the PBR than in the non-PBR in the thin-billed prion
( Pachyptila belcheri ). For the bluethroat, we determined an overall recombination rate ( q ) of almost twice the mean value of mutation rate ( h ), indicating an accumulation of new recombinants relative to new mutations. Signatures of recombination have commonly been reported within avian lineages (Hunt et al.
, 1994; Wittzell et al.
, 1999a;
Miller & Lambert, 2004; Edwards & Dillon, 2005; Alcaide et al.
, 2008; Burri et al.
, 2008), and are expected to have a major impact on the evolution of avian MHC variation.
Given the minimum number of 11 MHCIIB loci in the bluethroat one can safely assume the importance of gene duplication and recombination events for shaping the avian MHC. However, our approach does not reveal the genomic organization of the different MHCIIB loci.
The relative frequency of obtained pseudogenes sequences differed considerably between species and subspecies, exceeding 50% in L. s. azuricollis and L. luscinia . The low number of individuals studied for some of the taxa does not allow assessing whether or not these differences are significant. Future studies need to address the issue more comprehensively. In any case, the data point to a relatively high number of MHCIIB pseudogenes in the genomes of the bluethroat and the thrush nightingale.
In conclusion, we document extensive MHCIIB exon 2 diversity in the bluethroat. This polymorphism is most likely of ancestral origin, indicated by the overlapping distribution of MHCIIB alleles between the bluethroat subspecies and the closely related thrush nightingale. The
MHCIIB exon 2 fragments have probably evolved under balancing selection acting in particular on the amino acids in the PBR.
We thank Bohumı´r Chutny´, Javier Garcia Fernandez,
Oddmund Kleven, Terje Laskemoen, Toril Lohne, Benito
F. Marcos and Va´clav Pavel for help with collecting samples. The study was funded by the Natural History
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Additional Supporting Information may be found in the online version of this article:
Appendix S1 Details about specimens and sampling locations.
Appendix S2 Alignment of the bluethroat Luscinia svecica MHC Class II B exon 2 alleles.
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MHC class II diversity in bluethroats 1217
Appendix S3 Alignment of the thrush nightingale
Luscinia luscinia MHCIIB exon 2 alleles.
Appendix S4 Alignment of the MHCIIB pseudogenes.
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Such materials are peer-reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.
Received 6 January 2009; revised 17 February 2010; accepted 22
February 2010
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