Rapid Evolution of the MH Class I Locus Results in Different Allelic Compositions in Recently Diverged Populations of Atlantic Salmon

S. Consuegra*,1,2, H.-J. Megens{dagger},2, H. Schaschl*,3, K. Leon{dagger}, R. J. M. Stet{dagger},4 and W. C. Jordan*

* Institute of Zoology, Zoological Society of London, Regent's Park, London, United Kingdom; and {dagger} Cell Biology and Immunology Group, Department of Animal Sciences, Wageningen University, Wageningen, The Netherlands

Correspondence: E-mail: sonia.consuegra{at}st-andrews.ac.uk.


    Abstract
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
We compared major histocompatibility class I allelic diversity in two currently reproductively isolated Atlantic salmon (Salmo salar) populations (Irish and Norwegian) with a common postglacial origin in order to test for among-population differences in allelic composition and patterns of recombination and point mutation. We also examined the evidence for adaptive molecular divergence at this locus by analyzing the rate of amino acid replacement in relation to a neutral expectation. Contrary to our prediction, and in contrast to the situation for other genetic markers, the two populations have almost nonoverlapping sets of major histocompatibility class I alleles. Although there is a strong signal of point mutation that predates population divergence, recent recombination, acting in similar, but not identical, ways in both populations appears to be a significant force in creating new alleles. Moreover, selection acting on peptide-binding residues seems to favor new recombinant alleles and is likely to be responsible for the rapid divergence between populations.

Key Words: MHC • recombination • positive selection • Atlantic salmon • Salmo salar


    Introduction
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
The genes of the major histocompatibility complex (MHC) are some of the most variable protein-coding loci detected in vertebrates and among the most suitable candidate genes currently available to study processes of adaptive evolution due to their well-established role within the immune system (Hedrick 1994). MHC genes encode cell-surface molecules that bind small self-peptides or peptides from non–self-proteins derived from infectious pathogens within the cell and present them on the cell surface to T cells. Recognition of the MHC–non-self-peptide complex by a specific T cell receptor initiates either a cellular or humoral immune response depending on the classes of MHC molecule and T cell involved. Pathogen-driven balancing selection, either through overdominance or negative frequency–dependent selection or both, is thought to be the main evolutionary force that promotes and maintains the extensive variability in these genes (Edwards and Hedrick 1998; Jeffery and Bangham 2000; Hedrick 2002; Penn, Damjanovich, and Potts 2002). However, sexual selection (Jordan and Bruford 1998; Landry et al. 2001) and neutral forces such as genetic drift and gene flow may also have a role (Landry and Bernatchez 2001).

Several molecular mechanisms are thought to be involved in producing high levels of polymorphism at MHC loci. New alleles may arise through point mutations, and selective pressures then act on the substitution rate of such point mutations causing higher frequencies of nonsynonymous than synonymous replacements at those amino acid residues responsible for peptide binding (the peptide-binding residues [PBRs]) (Garrigan and Hedrick 2001). However, the rate of point mutation in MHC genes does not appear to be high enough to account for the high variability levels often observed, and other mechanisms such as recombination and gene conversion may play an important role (Martinsohn et al. 1999). In particular, recombination may create new MHC alleles in isolated populations even over short time periods, as is the case of the HLA-B alleles of the South American tribal Amerindians founded between 10,000–40,000 years B.P. (Belich et al. 1992; Watkins et al. 1992).

In contrast to other vertebrates in which they are tightly linked, the class I and class II major histocompatibility (MH) genes in teleosts are located in different linkage groups, an apparently derived condition that allows independent evolution of these loci (Ohta et al. 2000). As the class I and class II genes do not form a complex, they are known as MH genes in teleosts (Stet et al. 2002). Salmonid MH class I and class II genes are highly polymorphic, with many alleles differing in the composite patterns of amino acid substitutions (Shum et al. 2001; Stet et al. 2002). Grimholt et al. (1993) isolated the first class I sequences from salmon cDNA, and a single MH class I locus (Sasa-UBA) was found to be expressed (Grimholt et al. 2002). Class I alleles are thought to represent ancestral lineages that predate the separation of Onchorynchus and Salmo genera and have accumulated high levels of variability (Miller and Withler 1998; Shum et al. 2001; Grimholt et al. 2002). Evidence for recombination has been found in intron 2, which separates the exons encoding the {alpha}1 and {alpha}2 domains that contain the PBRs in class I genes, resulting in exon-domain shuffling (Shum et al. 2001). This mechanism of creating new alleles appears more common in salmonids than in primates, probably due to the greater length of the salmonid intron 2 (Shum et al. 2001; Grimholt et al. 2002).

To date, most studies of MH gene functionality and variation in Atlantic salmon (Salmo salar) have focused on farmed fish (Grimholt et al. 1993, 2002; Shum et al. 2001; Stet et al. 2002) because they often have advantages such as known pedigree and the ability to manipulate families for segregation studies. However, comparative studies of natural populations can provide further insight into the manner in which environmental and demographic factors can contribute to the origin and maintenance of MHC variation (Garrigan and Hedrick 2001). In particular, the study of reproductively isolated populations with a common postglacial origin can help elucidate the mechanisms creating and maintaining MH diversity. Under an assumption of selective neutrality, populations founded from a common source should share a common pool of alleles with frequency distributions for each population shaped by forces such as genetic drift and gene flow. On the other hand, population-specific selective forces acting on common alleles could either increase divergence between populations or homogenize them through stabilizing selection, counterbalancing the effects of genetic drift (Koskinen, Haugen, and Primmer 2002).

Atlantic salmon are widely distributed along the Atlantic coasts of Europe and eastern North America. In Europe, the present-day distribution of the species (from the Iberian Peninsula to Russia) was established upon retreat of the ice sheets after the last glacial maximum (Bernatchez and Wilson 1998). Routes of recolonization from glacial refugia can be traced using neutral molecular markers as founder effects and genetic drift have probably been the dominant forces in shaping the patterns of genetic variability among present-day populations (Bernatchez and Wilson 1998). Evidence suggests that northern European rivers remained glaciated until ~15,000 years B.P. (Hewitt 1999) and that Atlantic salmon recolonized them from southern refugia (Verspoor et al. 1999; Nilsson et al. 2001; Consuegra et al. 2002). Natal homing, an important characteristic of the Atlantic salmon life cycle, makes populations prone to reproductive isolation and may promote local adaptation (Taylor 1991). Northern European Atlantic salmon populations therefore provide unique material to study the effect of natural selection over a short period of time (~15,000–20,000 years) using nonneutral genetic markers.

Here, we compare MH class I (Sasa-UBA) allelic diversity of two isolated Atlantic salmon populations with a common postglacial origin to test the null hypotheses that (1) there is no difference in allelic composition between them (as expected in populations derived from the same glacial refugium) and (2) they share a common pattern of recombination and point mutation (under a null assumption that the populations are subject to similar selective pressures).


    Materials and Methods
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
Samples
Juveniles were sampled from four west coast Irish rivers with natural populations of Atlantic salmon: Owenmore, Owenduff, Burrishole, and Carrowinskey. Similar samples from Norwegian populations were obtained from four rivers: Os, Aargardselva, Gaula, and Ims. Samples of white muscle were stored in 95% ethanol, while anterior kidney tissue was stored in RNAlater buffer (Qiagen, Ltd., West Sussex, UK) for subsequent extraction of RNA.

cDNA Isolation, Amplification, and Sequencing
Total RNA was extracted from anterior kidney tissue of 19 Irish and 34 Norwegian individuals using the Purescript RNA Isolation kit from GENTRA (Gentra Systems, Minneapolis, Minn.) or with the SV total RNA isolation kit from PROMEGA (Promega, Madison, Wis.). A total of 11 µl of the final purified RNA from the Irish samples was digested with DNAse I and was used to synthesize first-strand cDNA using the First-Strand cDNA Synthesis kit (Amersham Pharmacia Biotech UK Limited, Bucks, UK). First-strand cDNAs were used as templates for polymerase chain reaction (PCR) amplification of the Atlantic salmon ß-actin locus to check for possible genomic DNA contamination (the presence of an intron between the primers results in products of different size in genomic and cDNA) with the primers Act_fwd 5'-ATGGAAGATGAAATCGCCGC-3' and Act_rev 5'-TGCCAGATCTTCTCCATGTCG-3', and the resulting products were run in a 1.5% agarose gel. Samples that gave a band of the correct size (~200 bp), with no evidence of genomic DNA contamination (a product of ~450 bp), were then used for amplification of the MH class I locus. Reverse transcription (RT)–PCR was performed in the Norwegian samples from the total purified RNA with the Superscript one-step RT-PCR kit (Invitrogen, Paisley, UK).

A region of ~550 bp of the cDNA was amplified with a 50:50 mix of the following primers (Grimholt et al. 2002) for forward priming: Lead2S: 5'-CTGGGAATAGGCCTTCTACAT-3' and Lead4S:-5'-AGCCCTACATTCTTCATCTGC-3' and the reverse primers UBA3_rev: 5'-CTGTCGCGTGGCAGGTCACTG-3' and UBAex3R: 5'-TGTCCTIATCAGAGTGCTCTTCC-3'. This region spans from exon 1 to exon 4 of the class I locus (Grimholt et al. 2002), including the entire {alpha}1 and {alpha}2 domains (exons 2 and 3). The PCR and RT-PCR products were purified and cloned into the pCR2.1 plasmid vector (TA-cloning kit, Invitrogen). Both strands of at least five clones per individual were sequenced using M13 forward and reverse or SPS6 and T7 primers with the ABI Prism BigDye Terminator Cycle Sequencing Kit diluted with Better Buffer (Microzone Corporation, Ontario, Canada) following the manufacturer's protocol and sequences were resolved on an ABI Prism 377 automated sequencer.

Sequence Analysis
Only sequences represented by at least two clones from independent PCRs were considered in subsequent analyses. Sequences were aligned with Sequencher (Gene Codes Corp., Ann Arbor, Mich.) software and BioEdit v. 5.0.9 (using the ClustalW program included in the package). DNAML from PHYLIP 3.6 package (Felsenstein 1989) was used to estimate maximum likelihood trees used in both spatial phylogenetic variation (SPV) and positive selection analyses. In the following analyses, sequences from each population were analyzed independently in order to compare the patterns of recombination and selection between populations. As in previous studies (Grimholt et al. 2002), alleles were defined on the basis of deduced amino acid sequences, not on nucleotide sequence.

Recombination Analysis
The likelihood-based method of Grassly and Holmes (1997) for detection of phylogenetically anomalous regions or SPV was implemented using the program Plato (Partial Likelihoods Assessed Through Optimization) (Grassly and Holmes 1997). SPV may arise either as a result of selection or of conversion-recombination. In this method a likelihood for each site is calculated on the basis of the overall (or global) maximum likelihood phylogeny, and a "sliding window" technique is used to identify the region with the lowest likelihood score value for each window size. The window size is varied from a minimum of 5 bp up to half the sequence length. The standardized normal deviate (Z) is used to test the statistical significance of each of the lowest likelihood regions against a null normal distribution generated by simulation (100 replicates). A value of Z ≥ 3 is taken to be equivalent to P ≤ 0.05 adjusted for multiple tests (Grassly and Holmes 1997). The analysis was based on the overall maximum likelihood phylogeny under the Jukes Cantor substitution model (Jukes and Cantor 1969) as that model best fitted the data according to the analysis performed with MODELTEST v1.06 (Posada and Crandall 1998). DnaSP software (J. Rozas and R. Rozas 1999) was used to estimate the minimum number of recombination events (Rm) in the history of the sample using the four-gamete test (Hudson and Kaplan 1985). Estimates of the 95% confidence interval for Rm and the probability of obtaining a lower value than the observed value were obtained by coalescent simulations (1,000 replicates). The regions potentially involved in recombination identified by DnaSP were compared with regions of SPV detected with PLATO.

The program Ldhat (McVean, Awadalla, and Fearnhead 2002) was used to asses the importance of recombination relative to point mutation in the patterns of genetic variation of both Irish and Norwegian samples. This program uses a composite likelihood method to estimate the population recombination rates (4Ner) and implements a likelihood permutation test for recombination based on the loss of the interchangeable character of the sites when recombination occurs (McVean, Awadalla, and Fearnhead 2002). It also compares the result with other three permutation tests based in the decay of linkage disequilibrium with distance and in the sum of distances between pairs of sites. Based on the estimates of the recombination rate ({rho}) and mutation rate per site ({theta}), we calculated the ratio {rho}/{theta} as an indicator of the relative likelihood of a nucleotide being involved in recombination relative to mutation (McVean, Awadalla, and Fearnhead 2002). We repeated the analysis removing sites that were identified as positively selected.

Detecting Positive Selection
Potential PBRs were identified in the aligned sequences according to Grimholt et al. (1993). Distances based on synonymous and nonsynonymous substitutions for the PBR and non-PBR sites were calculated for each population using the distance of Nei and Gojobori (1986) with the Jukes and Cantor (1969) correction for multiple substitutions. Standard errors were calculated by 1,000 bootstrapping replicates. The Z-test (Nei and Kumar 2000) implemented in MEGA 2.1 (Kumar et al. 2001) was used for testing neutrality estimating the statistical significance of the differences between the synonymous and nonsynonymous distances in each one of the two domains ({alpha}1 and {alpha}2).

To detect positive selection at single amino acid sites, we used the maximum likelihood method (Yang 2000) implemented in CODEML of the PAML 3.14 package (Yang 1997) and the parsimony method (Suzuki and Gojobori 1999) with the modification of Su (2000) implemented in the SGI software (Su 2000).

For the maximum likelihood method we used different codon-based models that allow for variable selection among sites as recommended by Yang et al. (2000). We compared the scenario where nonsynonymous mutations are neutral or deleterious (models M1 and M7, respectively) with models that allow for positive selection including an additional category for advantageous substitutions (M2, M3, M8). Six different models that allow for different intensity of selection among sites were tested. M0 assumes a constant substitution rate ({omega}) for all sites. Three of the models assume a discrete distribution of the {omega} among sites: M1 (neutral) assumes two categories of sites conserved ({omega} = 0) and neutral ({omega} = 1); M2 (selection) includes an additional category of sites with {omega} estimated from the data; M3 (discrete) assumes a discrete distribution of K different {omega} ratios. Two additional models assume a continuous distribution for heterogeneous {omega} ratios among sites: M7 (beta) that assumes a beta distribution and does not allow for positively selected sites and M8 (beta and {omega}) that accounts for positively selected sites ({omega} > 1). Nested models can be compared in pairs using the likelihood ratio test (LRT): twice the log-likelihood difference is compared with a {chi}2 distribution with degrees of freedom equal to the difference in the number of parameters between both models. In this way, the more general models M2 and M3 can be tested against M1 and M8 against M7. Nonnested models (M3 against M8) were compared using the Akaike information criterion (AIC; Akaike 1974): AIC = –2(estimated log likelihood of the model) + 2(number of free parameters of the model). A model that minimizes the value of AIC was considered the most appropriate model. A Bayesian approach implemented in CODEML was used to identify residues under positive selection in each of the domains ({alpha}1 and {alpha}2) separately. Following Yang et al. (2000) we repeated the analysis allowing for different initial values of {omega} (0.4 and 3 and 4), and only the results with the highest likelihood values were taken into account.

We also used Suzuki and Gojobori's (1999) parsimony-based method for identifying positively selected sites with a modification that allows the input of different tree topologies (Su 2000). For this analysis we used the same maximum likelihood tree as for the maximum likelihood analysis of selection. The SGI software reconstructs the ancestral sequences using a maximum parsimony approach and estimates the average numbers of synonymous and nonsynonymous sites for each codon through the phylogenetic tree in order to compute the number of synonymous and nonsynonymous changes and test for neutrality at each codon site. The numbers of synonymous and nonsynonymous changes are used to calculate the binomial probability of obtaining the observed numbers of changes for each codon site and the significance level is set at 5%. Positive selection is considered to occur if the number of nonsynonymous changes is significantly larger than that of synonymous changes.

We used a variability metric (V) (Reche and Reinherz 2003) similar to the Shannon entropy index (Shannon 1948) to identify variable amino acid residues that may be involved in immune recognition (Stewart et al. 1997). The result of this analysis can help independently identify polymorphic residues (V > 1) potentially involved in peptide contact (potential PBRs). This method, along with the Bayesian method implemented in CODEML and the maximum parsimony method in SGI, allows the detection of potential PBRs without making any a priori assumption about the position of such sites.


    Results
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
DNA Sequence Analyses
From analysis of cDNA it was evident that all individuals examined expressed only one dominant class I locus (Sasa-UBA) as no more than two sequences were cloned from any single fish. In total, 21 alleles were present in the 19 Irish individuals analyzed with 13 alleles in 34 Norwegian individuals (fig. 1). Only two Irish alleles were identical to the alleles described in Norwegian Atlantic salmon populations (Sasa-UBA*0601; Grimholt et al. 2002 and Sasa-UBA*0602; this study).




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FIG. 1.— Alignment of MHC class I amino acid sequences. Dots indicate identity, and gaps introduced to maximize alignment are indicated by dashes. Sequences in bold correspond to Irish populations, and sequences in italics are shared between Norwegian and Irish populations. Squares indicate sites involved in recombination identified by DnaSP, and regions of SPV are underlined. Nucleotide sequences were deposited in the GenBank under accession numbers AY62572AY62598.

 
Nucleotide diversity was distributed evenly over the length of the sequences and was similar within (Ireland {pi} = 0.204; Norway {pi} = 0.218) and between groups (K = 0.211). The proportion of polymorphic sites was 55.9% among Irish sequences and 56.3% among Norwegian sequences. The average number of nucleotide differences between groups (108.06) was similar to the average number of nucleotide differences within groups (Irish = 104.56, Norwegian = 111.96).

Although only two of the sequences from Irish and Norwegian populations were identical, several Irish and Norwegian alleles were identical in either the {alpha}1 or {alpha}2 domain but differed in the rest of the sequence (fig. 1). In total, the 40 sequences described were formed by 27 {alpha}1 and 27 {alpha}2 unique domains. The Irish and Norwegian class I sequences are composed of 13 and 10 unique {alpha}1 sequences and 16 and 15 unique {alpha}2 sequences, respectively. This distribution suggests an important role of recombination between the {alpha}1 and {alpha}2 domains in the formation of novel alleles in both populations.

Patterns of Recombination
There was significant evidence of recombination in both Irish and Norwegian groups of alleles using a combination of a likelihood permutation test and three other permutation tests (table 1). The estimated population recombination rate ({rho} = 4Ner) in the Norwegian alleles was {rho} = 25 and in the Irish alleles was {rho} = 10. The estimates of the mutation rate per site were similar in both Irish (0.219) and Norwegian (0.207) groups of alleles (table 1). Watterson estimates of the amount of mutation per population ({theta} = 4Neµ) exceeded the recombination rates per population (table 1), suggesting a larger accumulation of mutations than of new recombinants. The exclusion of Sasa-UBA*1001 and Sasa-UBA*0901 from the Norwegian sequences did not change the estimated mutation rate, although it lowered the recombination rate ({rho} = 14), suggesting an origin by recombination for these two sequences. Excluding Sasa-UBA*2001 from the Irish sequences, however, did not change the estimated recombination rate, although it lowered the estimated mutation rate from {theta} = 85 to {theta} = 23, suggesting an origin through point mutation for this sequence.


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Table 1 Results of the Analysis of Recombination and Point Mutation in Irish and Norwegian MH Class I Sequences

 
The recombination tests performed with the DnaSP software estimated a minimum of 48 and 33 recombination events (Rm) in the Norwegian and Irish sequences, respectively. The majority of sites involved in recombination are common to both groups of sequences (fig. 1). The exclusion of Sasa-UBA*1001 and Sasa-UBA*0901 from the analysis resulted in a decrease of the estimated number of recombination events for the Norwegian populations to 39. Excluding Sasa-UBA*2001 from the Irish sequences in the analysis only lowered the number of estimated recombination events by one (to 32). The 95% confidence intervals estimated by coalescent simulations were overlapping between populations with estimated population mean Rms that were not significantly different (Ireland, mean Rm = 13.8 [95% confidence intervals 8, 23]: Norway, mean Rm = 15.8 [95% confidence intervals 7, 25]). Areas of SPV were similar in both groups of sequences (fig. 1), with larger regions identified in the {alpha}1 domain (10–35 bp) compared with the {alpha}2 domain (5–13 bp).

We also observed significant evidence of recombination after repeating the maximum likelihood recombination test without sites identified as positively selected (see below) (P < 0.001). After removing selected sites the estimated recombination rate in the populations ({rho} = 23 and 10 for Norway and Ireland, respectively) remained lower than the mutation rates ({theta} = 64.7 and 68.7). The number of recombination events detected by DnaSP after excluding selected sites was 34 in the Norwegian populations and 23 in the Irish populations.

Patterns of Positive Selection
Evidence of positive selection in the potential PBRs identified according to Grimholt et al. (1993) was found in the {alpha}1 domain in both Irish and Norwegian groups of sequences (table 2). Tests for positive selection gave nonsignificant results for the non-PBR sites in both domains and also for the potential PBR sites of the {alpha}2 domain in both populations.


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Table 2 Average Nonsynonymous (dN) and Synonymous (dS) Distances Between MHC Class I Alleles Found in the Irish (IRL) and Norwegian (NRW) Populations of Atlantic Salmon Analyzed and Probability (P) of dN = dS

 
Maximum likelihood models that allowed positive selection fitted the data significantly better than those that assumed only neutral or deleterious mutations (table 3). The results were similar using maximum likelihood trees based on the {alpha}1 and {alpha}2 domains separately or on the whole sequence, so here we present only the results of both domains combined. The {omega} estimates of the model M0 (an average over all sites in the protein) indicated that purifying selection dominated the evolution of both MH domains ({alpha}1: {omega} = 0.290/0.340; {alpha}2: {omega} = 0.880/0.974; table 4). An LRT test indicated that model M2 that allows for positive selection, fitted the data better than model M1 that only considers conserved and neutral sites (table 3). Estimates using the M2 model suggested that 4% of the sites in the {alpha}1 domain were under positive selection in the Irish sequences ({omega} = 12.4) and 12% in the Norwegian sequences ({omega} = 3.44), while 15% and 17% of the sites of the {alpha}2 domain were under selection in the Irish and Norwegian sequences, respectively (Irish {omega} = 9.39; Norwegian {omega} = 11.34). The model M3, that assumes three site classes, fitted the data significantly better than all the previous models (table 3). The results of model M3 suggest that 4% of the sites in the {alpha}1 domain are under strong positive selection in the Irish sequences ({omega} = 6.0) and 2.7% of the sites in the Norwegian sequences are under moderate selection ({omega} = 1.12). According to this model ~13% of the sites in the {alpha}2 domain are under strong positive selection in both populations ({omega} > 9).


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Table 3 Likelihood Ratio Tests (LTR) Comparing Models to Test for Evidence of Positive Selection in the Irish (IRL) and Norwegian (NRW) MHC Class I Alleles

 

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Table 4 Log-likelihood Values (L), Model Parameter Estimates and Positively Selected Sites Identified by a Bayesian Method Implemented in CODEML

 
An LRT test comparing two models that both assume a beta distribution of {omega} over sites (M7 and M8) indicated that M8 (that allows for selection) fitted the data better than M7 (that does not allow for selection). The estimates from M8 indicated that 3% of the sites in the {alpha}1 domain were under strong positive selection in the Irish sequences ({omega} = 7.4) and 2% in the Norwegian sequences ({omega} = 21.44), while in the {alpha}2 domain 15% of the sites were under strong diversifying selection in both populations, consistent with the results from the M3 model.

The results from the M8 model (with a smaller value of AIC: 2,795.04 and 3,929.46 for the M8 model against 2,796.46 and 3,933.48 for M3 model, for Irish and Norwegian sequences, respectively) were used to compare the sites identified as positively selected in the sequences from the posterior probabilities for both populations (fig. 2). Only two sites were identified with a strong signal of positive selection (at the 99% confidence level) in the {alpha}1 domain of the Irish sequences. The same sites (plus one more with a lower signal of selection) were also detected as positively selected in the Norwegian sequences (fig. 2a). The {alpha}2 domain shows a larger number of sites with a strong selective signal in both populations. There were no differences between populations in the distribution of the sites under diversifying selection in the {alpha}2 domain under the M8 model, although 16 additional sites were identified in the Norwegian population under M3. All the sites identified with M8 were included in those identified with M3.



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FIG. 2.— Posterior probabilities of site classes for sites along the MHC class I (a) {alpha}1 and (b) {alpha}2 domain under the random-sites model M8 (beta and {omega}). Ten equal-probability categories were used to approximate the beta distribution (Yang et al. 2000), so that the model has 11 categories. The location of the positively selected sites is indicated by the dotted line (95% level of significance). Polymorphic sites determined by their entropy values: gray arrows indicate sites with V > 1.0, and black arrows indicate sites with V > 2.0. The amino acid positions refer to Sasa-UBA*0101 for the Norwegian sequences and Sasa-UBA*1601 for Irish sequences.

 
The maximum parsimony method implemented in SGI only predicted one positively selected site in the {alpha}2 domain of the Norwegian sequences (42S, also predicted by the likelihood method), although some of the sites with the highest probabilities (≥0.9) coincided with sites identified by CODEML as positively selected (65G in {alpha}1 in the Irish sequences; 71L and 74T in {alpha}2 in the Norwegian sequences). Several sites were identified as subject to purifying selection in the {alpha}1 domain (7 and 13 in Irish and Norwegian sequences, respectively) and also in the {alpha}2 domain (8 and 6).

The analysis of sequence variability (V) based on the Shannon entropy index identified 17 and 28 highly variable sites (V > 1.5, Stewart et al. 1997) in the {alpha}1 domain of the Irish and Norwegian sequences, respectively, from which 4 and 9 in each population had values V > 2.0 (fig. 2). Fewer sites were identified in the {alpha}2 domain (9 for the Irish sequences and 10 for the Norwegian sequences from which 2 in each case had V > 2.0). Most variable sites in the {alpha}2 domain (7 in the Irish sequences and 9 in the Norwegians) coincided with positively selected sites, and in both sets of sequences the selected sites in the {alpha}1 domain had V > 1.5.

In summary, the results of the different analyses indicated that there is variable selective pressure across sites of the MH class I sequences analyzed. The patterns of distribution of positively selected sites were similar between both populations (fig. 2) and were generally consistent between methods of analysis, although a larger number of sites under selection were inferred under maximum likelihood models. All the regions of SPV identified by PLATO contained potential PBRs: (1) as defined by Grimholt et al. 1993, (2) deduced as positively selected by the Bayesian method in CODEML, and (3) highly variable with entropy values V > 1.5. Regions of SPV also overlapped with areas identified as involved in recombination events by DnaSP (fig. 1).


    Discussion
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
Population Divergence at the MH Class I Locus
Evidence from neutral molecular markers suggests that Irish and Norwegian Atlantic salmon populations originate from a common Pleistocene glacial refugium, with the onset of divergence between these populations due to postglacial dispersal around 12,000–15,000 years ago (Verspoor et al. 1999; Nilsson et al. 2001; Consuegra et al. 2002). However, contrary to expectation on the basis of this relatively recent divergence, these populations had different allelic compositions at the MH class I locus, with only 2 out of 41 sequences in common. In contrast, Irish and Norwegian populations share the majority of mitochondrial DNA haplotypes (Verspoor et al. 1999; Nilsson et al. 2001) and alleles at allozyme (Bourke et al. 1997) and microsatellite (unpublished data) loci.

Collectively, the sequences displayed high levels of nucleotide diversity but did not show fixed substitutions between populations, suggesting that much of the observed polymorphism predates population divergence. Such a pattern of shared polymorphism is indicative of relatively ancient allelic lineages (Shum et al. 2001) possibly maintained by balancing selection (Garrigan and Hedrick 2003).

The presence of the Sasa-UBA*0601 and Sasa-UBA*0602 alleles in both populations may be a relict of their common origin with selective pressure(s) common to both populations maintaining the presence of these alleles. Alternatively, escape of fish of Norwegian origin from Atlantic salmon farms on the west coast of Ireland (Clifford et al. 1998) may have recently introduced these alleles into the Irish population, particularly through the contribution of mature parr to natural reproduction (Garant et al. 2003). A third possibility is that our sample could have included juveniles escaped from farms that were mistaken for wild fish. Regardless of the explanation for the presence of these two sequences in common, from our sampling it appears that the MH class I allelic compositions in Irish and Norwegian Atlantic salmon is almost entirely nonoverlapping.

Levels of Recombination
Although recombination/mutation ratios suggested that mutation has been the dominant force in shaping the present allelic diversity in both populations, recombination appears to play an important role in creating new alleles in these isolated populations. Congruent results from several forms of analyses provided strong evidence for recombination in the sequences from both Irish and Norwegian populations, with similar regions of sequence potentially involved in the recombination process in each population. As there were no fixed single nucleotide polymorphisms between populations, but little sharing of complete alleles, it may be that the Ldhat analysis is picking up a strong signal of relatively old mutation, which is masking the signature of recombination that has occurred after population divergence and is responsible for the nonoverlapping allele compositions.

Patterns of Selection
Our analyses revealed a complex pattern of selection across sites, with the best-fitting models of substitution allowing residues to evolve in neutral, conservative, and/or positively selected manners. Such a result is consistent with what is known of the interactions between residues within the MHC molecule, interactions between the MHC and peptide molecules, and interactions between the MHC-peptide complex and a suite of other cell surface receptors (a suite that is probably not yet completely defined) (Van den Berg, Yoder, and Litman 2004). While little is currently known about structure-function relationships in fish MH molecules, comparison with the relatively well-understood human and mouse systems allows some inferences to be drawn. Most sites appear to be conserved, regardless of the model of substitution used, possibly in order to maintain structural integrity of the {alpha} helices and ß sheets formed by both the {alpha}1 and {alpha}2 domains (Bjorkman et al. 1987a). However, conserved residues also appear to be necessary for nonspecific binding with T cell receptors (Bjorkman et al. 1987b; Garboczi et al. 1996; Garcia et al. 1996) and other antigen receptors (Van den Berg, Yoder, and Litman 2004).

Conventionally, PBRs have been accepted as generally under positive selection (Hedrick et al. 1991), and a high level of variability has been used as a criterion for identifying PBRs on occasion (Reche and Reinherz 2003). However, in this case when PBRs are deduced from the alignment with human HLA-A2 sequences (Grimholt et al. 1993) and tests of neutrality were performed, evidence for positive selection was found only in the {alpha}1 domain. This is in contrast with the results of the maximum likelihood test where evidence for positive selection was found in both domains. The maximum parsimony method, on the other hand, only presented evidence for positive selection in the {alpha}2 domain.

The maximum parsimony method has been argued to be more robust to violations of the assumptions of the models and less prone to false positives than the maximum likelihood method (Suzuki and Nei 2001, 2004). The main difference between methods is in the way that positively selected sites are identified. The maximum likelihood method groups the codon sites into categories with different nonsynonymous-synonymous ({omega}) rate ratios and test whether the {omega} of the group is >1, while the maximum parsimony method examines each codon with a standard statistical approach (Suzuki and Nei 2004). Although maximum parsimony method may detect fewer false positives, it has lower statistical power for identifying positive selection and is less sensitive in detecting truly selected sites than the maximum likelihood method (Wong et al. 2004) unless a large number of sequences are analyzed (Suzuki and Nei 2001, 2004). In general, the maximum likelihood method seems to be most powerful in detecting positive selection and predicting positive selected sites when the analyses are repeated with several initial values and when the interpretation of the data is cautious (e.g., when the {omega} ratios are close to 1, the prediction is less accurate) (Wong et al. 2004). This method is particularly appropriate for detecting selection in MHC genes, where positive selection could be acting simultaneously in groups of codons (Suzuki and Nei 2004). In this case, only 1 of the 13 sites predicted by the maximum likelihood method was also predicted by maximum parsimony, although another 3 sites had a high (although nonsignificant) probability (≥0.9) and 4 more sites had a probability ≥0.8.

The results of the maximum likelihood analysis are also supported by the coincidence of the majority of the positively selected sites predicted by CODEML with the potential PBRs deduced by Grimholt et al. (1993) based on the alignment of a salmon UBA class I sequence with a human HLA-A2 sequence. Ten of fifteen predicted positively selected sites were deduced as possible PBR sites (65G and 79A in the {alpha}1 domain and 5N, 7W, 24E, 26W, 63Q, 66H, 67D, and 74T in the {alpha}2 domain).

Moreover, there was high congruence between sites of high entropy and predicted positively selected sites: of 15 positively selected sites, 12 had high entropy. Five sites were predicted as positive selected but not potential PBRs (17A, 34I, 41K, 42S, and 71L). In particular, the site 42S in the {alpha}2 domain was predicted as positively selected by three different methods (maximum likelihood, maximum parsimony, and high entropy), although it was not amongst the potential PBRs from Grimholt et al. (1993). Crystallographic models of MH molecules in fish are not available (Grimholt et al. 2002), and the structure and variability of the class I molecule (with insertions and deletions) make it difficult to align the Atlantic salmon sequences with the HLA sequences; this may be the origin of some of the discrepancies observed between predicted positively selected sites and deduced PBR sites.

Interplay Between Recombination and Selection
As methods for identifying recombination and positive selection-variability often identified the same sites, our results are consistent with a model of selection on recombination events (Ohta 1995) to produce divergence between populations in the set of MHC class I alleles each possesses. However, recombination and positive selection may produce similar signals in the pattern of polymorphism. For example, the analysis of SPV does not seek to distinguish between the two processes (Grassly and Holmes 1997). In particular, congruence of sites involved in recombination and under positive selection could be due to the effect of recombination on the maximum likelihood methods for identifying positive selection. These models of codon substitution are phylogeny based and do not account for the effects of recombination (Anisimova, Nielsen, and Yang 2003). However, when comparing the results of models M7 and M8, which appear to be relatively unaffected by recombination, with those of other models (M2, M3) that appear to be more sensitive to recombination, results were consistent. In general, Bayesian methods for identifying positively selected sites seem to be robust to recombination effects (Anisimova, Nielsen, and Yang 2002). Moreover, the results of the recombination analyses excluding positively selected sites indicate that recombination is playing an important role in generating allelic diversity as the signals of recombination persist after the exclusion of potential PBRs.


    Conclusion
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
In summary, in spite of their postglacial common origin, Irish and Norwegian salmon populations currently appear to have largely different pools of MH class I alleles. However, the evidence suggests a probable common origin for all the alleles. Although point mutation appears to be the main force creating new alleles, there is evidence for extensive recombination. Recombination events are most often observed to involve sites under positive selection, with similar but not identical patterns in both populations. Selection favoring new recombinant alleles is therefore likely to be responsible for the rapid divergence of the MH class I variation of these populations.


    Acknowledgements
 TOP
 Abstract
 Introduction
 Materials and Methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
We are grateful to Elvira De Eyto, Phil McGuinnity, and Kjetil Hindar for providing the samples on which this study is based. We also thank Pekka Pamilo and two anonymous reviewers for useful comments on an earlier version of the manuscript. Funding for this study was supplied by a contract from the European Commission (Salimpact: QLRT-2000-01185).


    Footnotes
 
1 Present address: Fish Muscle Research Group, Gatty Marine Laboratory, University of St Andrews, St Andrews, Fife, Scotland, United Kingdom. Back

2 S.C. and H.-J.M. equally contributed to this study. Back

3 Present address: Max-Planck-Institute of Limnology, Department of Evolutionary Ecology, Ploen, Germany. Back

4 Present address: Scottish Fish Immunology Research Centre, University of Aberdeen, Zoology Building, Aberdeen, Scotland, United Kingdom. Back

Pekka Pamilo, Associate Editor


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Accepted for publication January 25, 2005.