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Insights into the Complex Associations Between MHC Class II DRB Polymorphism and Multiple Gastrointestinal Parasite Infestations in the Striped Mouse

Insights into the Complex Associations Between MHC Class II DRB Polymorphism and Multiple Gastrointestinal Parasite Infestations in the Striped Mouse

  • Götz Froeschke, 
  • Simone Sommer
PLOS
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Abstract

Differences in host susceptibility to different parasite types are largely based on the degree of matching between immune genes and parasite antigens. Specifically the variable genes of the major histocompatibility complex (MHC) play a major role in the defence of parasites. However, underlying genetic mechanisms in wild populations are still not well understood because there is a lack of studies which deal with multiple parasite infections and their competition within. To gain insights into these complex associations, we implemented the full record of gastrointestinal nematodes from 439 genotyped individuals of the striped mouse, Rhabdomys pumilio. We used two different multivariate approaches to test for associations between MHC class II DRB genotype and multiple nematodes with regard to the main pathogen-driven selection hypotheses maintaining MHC diversity and parasite species-specific co-evolutionary effects. The former includes investigations of a ‘heterozygote advantage’, or its specific form a ‘divergent-allele advantage’ caused by highly dissimilar alleles as well as possible effects of specific MHC-alleles selected by a ‘rare allele advantage’ ( = negativefrequency-dependent selection’). A combination of generalized linear mixed models (GLMMs) and co-inertia (COIA) analyses made it possible to consider multiple parasite species despite the risk of type I errors on the population and on the individual level. We could not find any evidence for a ‘heterozygote’ advantage but support for ‘divergent-allele’ advantage and infection intensity. In addition, both approaches demonstrated high concordance of positive as well as negative associations between specific MHC alleles and certain parasite species. Furthermore, certain MHC alleles were associated with more than one parasite species, suggesting a many-to-many gene-parasite co-evolution. The most frequent allele Rhpu-DRB*38 revealed a pleiotropic effect, involving three nematode species. Our study demonstrates the co-existence of specialist and generalist MHC alleles in terms of parasite detection which may be an important feature in the maintenance of MHC polymorphism.

Introduction

More than 50% of the known species on this planet are parasites or pathogens of some form [1]. Helminths represent the most prevalent macroparasite group of endoparasites [2] and especially gastrointestinal nematodes can have a large impact on human and animal health [3], [4]. It is known that intestinal worm infections generally cause a strong host immune response e.g. [5][7].

Although genetic control of worm burden is likely to be polygenic and it is acknowledged that the immune response is also regulated by interleukin receptor genes [8], [9] recent studies have emphasized the importance of immune genes of the major histocompatibility complex (MHC). The highly polymorphic MHC genes control the immunological self/non-self recognition. MHC molecules bind foreign peptides on the cell surface and present them to T-cells, which then trigger the appropriate immune response [10]. Many studies give evidence that high MHC polymorphism is maintained by pathogen-driven selection either due to the effects of specific MHC-alleles (‘rare allele advantage hypothesis’ or ‘frequency-dependent selection’, [11]) or an advantage of heterozygote individuals (‘heterozygote advantage’, [12]). The ‘heterozygote advantage hypothesis’ presumes that heterozygotes have a higher fitness than homozygotes due to their ability to recognize a wider variety of antigens derived from multiple pathogens. Thus the potential advantage for individuals carrying more than one allele may maintain high numbers of different alleles in populations. Within the heterozygotic genotypes, a more mechanistic explanation suggests that specifically those individuals possessing highly dissimilar MHC alleles potentially bind an even broader range of antigenic peptides then heterozygotes with less dissimilar alleles which may confer a broader immune competence in the case of varying or multiple infestations (‘divergent allele advantage hypothesis’, [12][15]). However, despite a tremendous effort identifying the relative importance of these mechanisms, the complex dynamics of parasite-host-interactions still remain elusive [16], [17].

In the wild, most animals, including humans, are simultaneously infected with more than one parasite [18][20]. Positive and negative associations can occur between parasites mirroring interactions which might cause substantial effects on the parasite load [21], [20]. Especially, macroparasites such as intestinal helminths contain many antigens to which immune responses can be generated. It is estimated that intestinal helminths possess 7,000 to 20,000 protein-encoding genes and even if only the bindings of surface proteins are considered, hosts have many immune targets [22]. Each MHC glycoprotein receptor can bind several hundred different peptides if they have certain sequence characteristics in common [23], [24]. However, despite the high diversity of helminths, immune responses of mammalian hosts seem to vary only relatively little. Generally, binding of helminth antigens to MHC glyoproteins induce a typical CD4+ T helper cell type 2 (Th2) cytokine response [25][27]. Therefore it has been suggested that the host immune system has only a limited ability to distinguish among different nematode parasites [25] contradicting the assumption that host pathogen co-evolutionary processes drive genetic diversity. It is argued that classic one-to-one gene parasite co-evolution models do not allow maintenance of diversity because one would expect the fixation of only the latest host allele or parasite strain [28]. By contrast, the existence of a many-to-many gene-parasite co-evolution is expected [29]. If so, multiple specialist and generalist MHC alleles in terms of pathogen detection should co-exist.

So far many MHC studies have presented associations between particular MHC alleles/haplotypes and resistance or susceptibility to single parasite infections (reviewed in [30], [16], e.g. [31]). However, in order to gain deeper insights into the intricate immune reactions and the co-evolutionary processes, it is essential to take into account the interactions between specific MHC alleles within the host species and distinct parasite species within the whole corresponding parasite community. Only then potential antagonistic effects, as well as possible interplays mediated by each parasite species can be detected [32]. Furthermore, it allows the examination of whether specific immune gene variations are associated with multiple parasite species. Up to today only few studies have explored the particular selection pressure exerted by each parasite species in multiple-infected animals ([32], [33], [9]). Hosts with widespread geographical distributions tend to harbour more parasite species than hosts with restricted geographical ranges [34]. To investigate the specificity of associations between MHC alleles/genotypes and different parasite species we therefore chose the striped mouse, Rhabdomys pumilio, as our focus species. This rodent is widely distributed in southern Africa and abundant in rural as well as urban areas [35]. Previous studies conducted in R. pumilio have already shown evidence for both historical and contemporary balancing as well as directional selection acting on the MHC DRB locus due to parasite pressure [36] (Froeschke and Sommer, unpublished data). These associations are considered as the most direct evidence that parasites work as selective agents to MHC genotypes [37].

The present study was designed to gain a deeper understanding of the genetic bases of host-pathogen co-evolutionary interactions in a multiple-infected host. For this we had the full record of gastrointestinal nematode species recovered from each genetically analysed host-individual available across the entire geographic range of the species, reaching from the Cape region in South Africa up to Northern Namibia [38]. Our specific aim was to investigate possible associations between the MHC class II DRB gene constitution and multiple nematodes a) on the population and b) on the individual level to gain insight into the underlying selection mechanisms and parasite species-specific co-evolutionary effects. For this we applied two multivariate approaches, which allowed us to confound for random factors and at the same time to minimize the impact of type I statistical errors due to multiple testing. We hypothesize that specialist and generalist MHC alleles in terms of pathogen detection are able to co-exist and thus add to the maintenance of MHC polymorphism in the wild.

This is one of the first large-scale-studies on possible correlations between MHC DRB Class II genes and parasites in wild small mammal populations taking into account the whole spectrum of gastrointestinal nematode species. Therefore the results contribute to a deeper understanding of the still poorly understood co-evolutionary dynamics of parasite-host-interactions.

Results

Genetic diversity and pathogen-driven selection analyses on the population level

All the results from our microsatellite and MHC genetic diversity analyses can be found in Table 1. Mean microsatellite D2 values varied a lot between populations with the lowest value of 196.18 in population 1 and the highest mean of 524.00 in the population 4. The MHC allelic richness varied between 29.18 at population 1 and 44.86 in population 2. Every population except for the population 4 had a significant observed heterozygosity deficit compared to the expected one. Population 4 showed the lowest null allele frequency. In contrast, the population 3 had the biggest heterozygosity deficit and at the same time the largest proposed null allele frequency (0.176).

We investigated the effects of population genetic diversity on the overall nematode load and all results are listed in Table 2. Neither neutral genetic nor MHC diversity showed significant effects on the nematode load. No support for a heterozygote advantage (nematode prevalence: P = 0.46; nematode infection intensity: P = 0.99) or divergent-allele advantage (amino acid distance (AAdist): nematode prevalence: P = 0.97; nematode infection intensity: P = 0.55) or association with MHC allelic richness (nematode prevalence: P = 0.95; nematode infection intensity: P = 0.28) could be detected on the population level.

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Table 2. Effects of genetic diversity on nematode load in seven populations of R. pumilio.

https://doi.org/10.1371/journal.pone.0031820.t002

Pathogen-driven selection analyses on the individual level

(i) Generalized linear mixed models (GLMM).

The two separate generalized linear mixed models (GLMMs), which included either prevalence or infection intensity (based on faecal egg counts, FEC) as response variables from all nematode species combined and MHC genotypes (homozygote, heterozygote) as well as multilocus heterozygosity (MLH) as predictors did not reveal any support for a ‘heterozygosity advantage’. There was no support for the hypothesis that MHC heterozygous individuals are less infected than homozygotes (prevalence: ß ± SE = −0.189±0.269, t = −0.701, P = 0.484). The same applied to MLH (ß ± SE = 0.796±0.611, t = 1.304, P = 0.193). Also the restriction to the five most common nematodes (Syphacia obvelata, Heligmonina spira, Neoheligmonella capensis, Trichuris muris and Aspiculuris tetraptera) did not reveal any evidence for a heterozygote advantage (all P>0.12).

However, we found some indication for ‘divergent-allele advantage’. MHC AADist was a significant explanatory predictor for the overall infection intensities of all nematodes together (ß ± SE = 1.651±0.726, t = 3.310, P = 0.024), for the Nippostrongylinae (ß ± SE = 1.690±0.750, t = 2.253, P = 0.025), and for Aspiculuris tetraptera (ß ± SE = 4.785±2.094, t = 2.285, P = 0.023). The neutral D2 did not show any effects (ß ± SE = >0.001±>0.001, t = 0.605, P = 0.546).

Furthermore our GLMMs revealed relationships between specific MHC alleles and prevalence as well as infection intensity (FEC) of the five most prevalent nematodes recorded from R. pumilio. Seventeen of the 37 alleles had specific effects either in terms of positive or negative associations towards parasite loads (Table 3, Fig. 1). Whereas no associations between MHC alleles and prevalence of the most abundant nematode Syphacia obvelata could be found, the alleles Rhpu-DRB*35, *38, *47 and *76 showed a significant effect with an increased infection intensity. Furthermore, positive associations of the two nematode species from the subfamily Nippostrongylinae and the alleles Rhpu-DRB*44 and *55 could be revealed for both prevalence and infection intensity. Altogether five alleles were connected with an increased burden of these nematodes. As for Trichuris muris, mice which carried the allele Rhpu-DRB*38 were significantly less infected than animals without it while the occurrence of alleles Rhpu-DRB*42, and *44 was associated with an increased probability of a higher prevalence and infection intensity. Additionally alleles Rhpu-DRB*49 and *87 were associated with an elevated infection intensity. The genetic predictors, alleles Rhpu-DRB*21, *27, *36 and 41* were significantly associated to the status of infection in Aspiculuris tetraptera. Allele *21 was associated with a reduced while the other three alleles were significantly related to a higher prevalence and/or infection intensity.

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Figure 1. Frequency of the Rhpu-DRB alleles.

Frequency of the Rhpu-DRB alleles (observed in ≥5 individuals). X marks alleles which were detected as associated with a specific nematode species, resulting from generalized linear mixed models (GLMM) and/or co-inertia (COIA) analysis.

https://doi.org/10.1371/journal.pone.0031820.g001

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Table 3. Effects of the most abundant Rhpu-DRB* alleles on nematode prevalence and infection intensity.

https://doi.org/10.1371/journal.pone.0031820.t003

(ii) Co-inertia analysis (COIA).

The first two axes of the correspondence analysis, which is based on the genetic table, explained 7.2% of the variance in the data (3.6% F1, 3.6% F2) (factor map not shown) while the axes of the principal component model (PCA), which is based on the FEC table, explained 24.0% of it (12.8% F1, 11.2% F2) (Figure S1). The PCA revealed that the nematode Syphacia obvelata was located in the opposite direction to Aspiculuris tetraptera and Nematode C. Nematodes A–E are based on egg morphotypes and could not be identified to species level.

As for the COIA, the first two axes accounted for 44.5% (27.4% F1, 17.1% F2) of the variance shared between genetic and nematode infection intensity matrices. We found no significant overall relationship between the two matrices (Rv-coefficient = 0.060, simulated p = 0.410) but still the co-inertia factor map pointed to associations of certain parasites with the presence of specific MHC alleles (Fig. 2 A, B). The alleles Rhpu-DRB *76 and *35 were located in the opposite direction to the alleles Rhpu-DRB *36 and *28 on the F2 axes and therefore have antagonist effects. Specifically alleles Rhpu-DRB *76 and *36 structured the data at F2. Of note is allele Rhpu-DRB *76, which showed a strong association with S. obvelata and allele Rhpu-DRB *36 which was positively associated with A. tetraptera. On the F1 axes mainly the alleles Rhpu-DRB *44, *51, *27, *49, *87 and *42 discriminated the data. Alleles Rhpu-DRB *44, *87, *51 and *55 showed positive associations with the Nippostrongylinae and alleles Rhpu-DRB *42, *49 and *27 with Trichuris muris.

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Figure 2. Projection of results of A parasitological and B genetic co-intertia analysis.

Projection of results of (A) parasitological and (B) genetic co-inertia analysis from Rhabdomys pumilio (n = 432). Variables located in a common direction are positively associated whereas those located in the opposite direction are considered as negatively associated. Variables located close to the centre do not structure the data and are not labelled to improve clarity. Nematode A–E = based on egg morphotypes.

https://doi.org/10.1371/journal.pone.0031820.g002

The two approaches revealed that 16 out of the 37 MHC alleles (GLMMs: 14; COIA: 11) have effects on the nematode burden (Fig. 1). Nine of the eleven associations (82%) identified by COIA were concordant to those identified by GLMMs.

Discussion

We applied multivariate approaches to take into account the whole spectrum of gastrointestinal nematode species to advance our understanding of the underlying pathogen-driven selection mechanisms and parasite species-specific co-evolutionary effects on the population and on the individual level using a widely distributed African rodent, R. pumilio, as an example.

In our study, no significant effect of neutral microsatellite (MLH and D2) nor functional MHC genetic diversity and parasite load could be detected on the population level. These results imply that the used microsatellite markers are not affected by parasite infestation, which underlines their neutrality. Yet it is important to remark that seven microsatellite loci only partially describe genome-wide variation [39], [40]. Ongoing studies using next generation sequencing technologies should be based on many more markers randomly scattered throughout the genome to investigate the role of overall genomic variability on parasite resistance.

The lack of associations between MHC genetic diversity and parasite load on the population level in our study adds to the mixed results of former studies. Studies have indicated that if hosts and pathogens share a long-term coevolutionary history, selection through diverse pathogens cause high MHC polymorphism in a species or population, whereas low MHC polymorphism indicates the presence of relaxed pathogenic selection pressure [28] [41] [42].

However, in a contrasting unbalanced situation, i.e. after a recent loss of genetic diversity through, for instance, fragmentation effects, species with low MHC diversity could have lost resistance alleles or other important parts of its adaptive evolutionary potential. This would facilitate an easy spread of pathogens throughout the population, because most individuals share the same resistance genotype [43]. The later scenario has been supported by a recent study conducted by Meyer-Lucht and Sommer [44] which revealed positive associations between MHC allelic richness and nematode load in eight populations of the yellow necked mouse (Apodemus flavicollis). MHC heterozygosity in this study had no influence. In our case, maybe diverse interactions of parasites and potential specialist and generalist MHC alleles in terms of different pathogen detection are able to co-exist on the individual level and thus obscure the conformity on the population level.

So far, evidence for heterozygote advantage has rarely been found in wild populations (but see [36], [45], [33]) which might be due to the fact that most studies were restricted to single parasite species. However, the advantages of being able to bind to multiple parasite epitopes may only be detected when multiple parasite-mediated immune insults are prevalent [46], [47]. In our study, this short-coming was ruled out as we had the full record of all gastrointestinal nematode species recovered from each genetically analysed individual and included both complete data sets in our GLMM models to test for a ‘heterozygote advantage’ on the population as well as on the individual level. It is known that host gender as well as seasonal changes can have significant effects of parasite infection patterns (e.g. [48][50]). By using the individual multivariate GLMM approach we were able to focus on ‘pure’ parasite-driven selection mechanisms because our models allowed us to include confounding factors which could obscure the detection of MHC effects. Therefore sex, trapping season and ‘population’ (which is a synonym for trapping site and a surrogate for the geographical position of each animal) were included as random factors. In our current study based on 424 individuals we found neither support for the ‘heterozygote advantage hypothesis’ on the population nor on the individual level. This suggests that heterozygote effects are not likely to play a key role in the maintenance of polymorphism in the MHC of the investigated R. pumilio populations. Our current results were in contrast to the observations of a previous study which we conducted on 58 individuals of R. pumilio located in the Southern Kalahari [36] where also a lower number of MHC alleles with diverging allele frequencies were observed. Based on recovered egg morphotypes, also the parasite fauna found in mice of the Southern Kalahari was very different (Froeschke & Sommer, unpublished data) compared to the gastrointestinal helminth fauna of the mice identified in this study, which were trapped much further west of the Kalahari. A much higher allelic diversity, which is usually observed in mammals like in our present study, increases the number of potential MHC genotypes and therefore diminishes the chance of detecting homozygotes. Therefore a test for ‘heterozygote advantage’ may require a much larger sample size, which becomes practically unfeasible [33]. This implies the difficulties of drawing general conclusions on the importance of MHC heterozygosity on parasitic helminth loads. But again, more in-depth investigations of the role and relative frequency of general and specialist MHC alleles in parasite resistance may provide an advanced picture on the relative importance of the different pathogen-driven MHC selection hypotheses.

Furthermore the possibility of MHC null alleles might obscure our results. As MHC sequences could be amplified from all individuals under study and on the basis of at least two independent PCR and SSCP assays as well as forward and backward sequences, respectively, the obstacle of null alleles is improbable but cannot be ruled out.

However, we found some support for the ‘divergent allele advantage’ hypothesis as proposed by Wakeland et al. [15]. The average genetic distance of MHC alleles on the amino acid level within an individual was positively correlated with the overall infection intensities of the total nematode burden. This was significantly influenced by the two Nippostrongylinae species and Aspiculuris tetraptera. Naturally it makes sense to give more importance to MHC allele pairs that differ by many amino acids than to those that differ by only a few. Also Lenz et al. [51] found support for the ‘divergent allele advantage’ hypothesis in the MHC IIB alleles of the Borneo Long-tailed giant rat (Leopoldamys sabanus).

In order to investigate the effects of specific MHC alleles in parasite resistance we used two separate multivariate analysis approaches (GLMM, COIA) as we wanted to take into account a) possible important co-founding factors and b) to control and discuss possible type I errors associated with multiple comparisons. GLMMs are increasingly used to model multivariate nested data, temporal and spatial correlation structures in count data or binomial data [52]. The more explanatory variables in the model, the higher the risk of collinearity, which makes it necessary to test predictors in separate models. In our study we fitted a GLMM for the five most abundant nematode species which amounted to more than 82% of all helminth infections [38], thus increasing the risk of type I errors due to multiple testing. COIA provides a less detailed but more holistic vision of possible associations between MHC constitutions and parasite load because it does not confound for random factors as the GLMMs. Apart from that it is robust to correlation between variables, can be used with all types of variables [53] and has recently been successfully applied to similar MHC-parasitological datasets ([54], [32], [9]).

Both multivariate analyses revealed significant relationships between specific MHC alleles and parasite load on the individual level. The majority of these alleles were positively associated with a high parasite load. The missing overall significant relationship in COIA between genetic and parasitological matrices is probably caused by the co-founding factors as they were sex, population and year which could not be included here, unlike in the GLMMs. Specifically nine MHC alleles showed positive associations to certain parasite species in our GLMM analysis, which also pointed to the same direction in our COIA (Fig. 1). We found 11 alleles in our GLMMs (Table 2 B), which were significantly associated with intensity in only one model. Due to multiple testing one should apply a more stringent significance level but maybe valuable information would get lost. We believe that a second multivariate approach can add reassurance to cases like for example allele Rhpu 35. After applying a stringent significance level (like e.g. Bonferroni) it would not be considered anymore as associated to the infection intensity of S. obvelata. The COIA nevertheless confirms its positive association and therefore it should be regarded as one of the more ‘specialized’ MHC alleles. Only allele Rhpu 41 might be considered as a statistical artifact and has to be treated with extra caution because it only occurs in one GLMM, its p-value cannot withstand a Bonferroni correction and also our COIA does not show any possible association. In general, our two applied different statistical methods show high concordance in the results in terms of alleles which featured specific associations with parasite burden (Fig. 1).

‘Disadvantageous’ MHC alleles, positively associated with infection, have also already been detected in several other studies (e.g. [36], [54][58]. This is usually interpreted as a support for the ‘rare allele advantage’ or ‘frequency-dependent selection’ [11] hypothesis proposing a co-evolutionary arms race between the pathogen and the host with dynamic and reciprocal cycling of the frequency of specific ‘protective’ MHC alleles and certain parasites. Hughes and Nei [46] claim that under natural conditions there is no good biological reason to believe that a previously favoured allele will become rare again, given the fact that it can bind several hundred peptides. But recent studies emphasize the importance of antagonistic effects of MHC alleles in pathogen resistance which might explain on one hand their persistance but on the other hand also strong frequency shifts in a population. Loiseau et al. [58] found in their study on malaria infections in house sparrows (Passer domesticus) that an MHC class I allele was associated with an increased risk in being infected with Plasmodium, but at the same time connected to a severe reduction in the risk to harbour a Haemoproteus strain. Those antagonistic effects for different parasite species may contribute to the maintenance of alleles in populations, which appear to be disadvantageous at first. We also found support for this conclusion in our study: GLMMs showed that the overall most abundant allele, Rhpu-DRB*38 was associated with elevated infestation rates of S. obvelata and Nippostrongylinae. At the same time its occurrence was negatively correlated to the burden of T. muris. Up to now, such an antagonistic, pleiotropic effect of an allele has rarely been shown in a natural animal host-parasite system before. A comparison of the amino acid sequences revealed that allele Rhpu-DRB *38 differs from all other alleles which showed associations by having a glutamatic acid residue at the antigen binding site (ABS) position 53 and a leucine residual directly neighbouring at position 54. The other alleles carried valine, alanine, arginine or glutamine at position 53 and valine at position 54 instead. Position 53 is a positively selected site in R. pumilio and also an antigen-binding site in humans [59]. Therefore it can be regarded as functionally important. Substitutions in the antigen binding site of an MHC molecule might e.g. reduce or support the binding of specific antigens to the MHC molecule which in turn might influence the whole immune response (summarized by [60], [61]). Further pleiotropic effects were revealed in the principal component analysis as well as the co-inertia analysis. Both showed oppositions between S. obvelata and A. tetraptera which co-occur in the same habitats and thus host specimen [38]. Often different helminth species in the same host specimen cause competition for nutrients and space [62], [63]. However, in R. pumilio and in studies conducted with laboratory mice and wild populations of Mus musculus, S. obvelata was recovered from the cecum while A. tetraptera was found in the small intestines [38], [64]. Therefore we attribute the discovered opposition in R. pumilio to an antagonist role of MHC allele-specific resistance and susceptibility to both nematodes.

More and more studies suggest that multiple parasites are required to drive MHC polymorphism [47], [28], [29] and a many-to-many instead of a one-to-one gene-parasite co-evolution is proposed (see discussion in [29]). Tellier and Brown [65] applied a simplified model and concluded that stable polymorphism is most likely to be detected in systems with strongly polycyclic diseases with high autoinfection. Our study supports this assumption of a many-to-many gene-parasite co-evolution because GLMMs revealed that the alleles Rhpu-DRB *27, *38 and *44 showed associations with more than one nematode species. At the same time each nematode species was associated with more than one MHC allele. An elevated infection intensity of Syphacia obvelata, the most prevalent nematode in our study, was explained by positive associations with the alleles Rhpu-DRB*35, *38, *47 and *76. Allele *38 is the most frequent one in our study (overall frequency: 7.74%, Fig. 1). Also the parasite load of other nematode species, such as Heligmonina spira, Neoheligmonella capensis, Aspiculuris tetraptera as well as the whipworm Trichuris muris showed positive as well as negative associations with different MHC alleles. These observations were also supported by the COIA. Also the co-inertia factor map presented specific alleles with positive associations to more than one parasite species (like e.g. Rhpu-DRB*44 with T. muris and the Nippostrongylinae) and therefore going along with a proposed many-to-many gene-parasite co-evolution.

But we must keep in mind that though the MHC is of immense importance in parasite defence, also other genes are involved in the immune response cascade. A recent study by Schwensow et al. [9] showed for instance complex associations of the expression levels of TGF-ß, IL-10, IL-4 and IL-2 with species may depress the host's intestinal immune response, which may cause advantages for other parasites and their life traits (reviewed in [21], [66]). Furthermore, certain parasite are suspected to be immune suppressors [67]. Only one key parasite species might be sufficient to create the overall positive host immuno-mediated association structure if other species are affected by its immunosuppressive capacity [68].

To conclude, our large-scale study showed that certain MHC alleles were associated with more than one parasite species and vice versa. Specialist and generalist MHC alleles in terms of different pathogen detection are able to co-exist, thus favouring a many-to-many gene-parasite co-evolutionary prospect. Pleiotropic effects and further complex interactions must be considered when dealing with multi-infected host species in the wild and parasites as a driver for MHC class II DRB polymorphism.

We propose for future studies, that it is not only important to draw the attention on MHC gene diversity but also on the allele specificity to gain a more accurate picture. For the future it will be mandatory to characterize parasites antigens to a similar extent as their hosts immune genes to fully understand the process of host-parasite co-evolution.

Materials and Methods

Study sites and sample collection

The study has been conducted in accordance with the recommendations for care and use of animals approved by Ministry of Environment and Tourism, Namibia (permit no 853/2004 and 1065/2006), the Northern Cape Nature Conservation Service, South Africa (permit no 0592/04 and 0133/06) and the Cape Nature Department of the Western Cape, South Africa (permit no 001-202-00021 and AAA004-00029-0035).

We captured 470 individuals at seven different sites, each considered as a population of the striped mouse (Rhabdomys pumilio), across a large geographic range, reaching from the Cape Floristic Region in the south of South Africa up to the north of Namibia. A more detailed description about the site-specific meteorological variables can be found in Froeschke et al. [38]. Samples were taken twice, between November 2004–March 2005 and then again June–August 2006. Animals were marked with ear tags but no recaptures were observed between the capture sessions. We used standardized grid systems, consisting of Sherman traps 15 m apart from one another. Traps were baited with a peanut butter-, apple- and oats mixture. To avoid overheating of trapped animals we only opened traps from dusk until dawn. Throughout our study, only one mouse was caught per trap allowing us to allocate faeces collected from the trap to a single individual from which also a tissue sample from the ear was taken for genetic investigations. All samples were stored in 70% ethanol for later parasitological and genetical analyses. In order to limit age effects on parasite load only adult animals (n = 439) weighing ≥32 g [69] were considered for further studies. During the second capture session a subsample of 161 adult individuals were euthanized with isoflurane (Forene®, Abbott GmbH, Germany) and the gastrointestinal tracts (stomach, small intestine and caecum) were dissected out and stored in 70% ethanol [38].

Parasite Screening

We took advantage of previously published nematode data for this study and a detailed description of parasite screening and distribution can be found in Froeschke et al. [38] (also see Table S1). Briefly, to measure the gastrointestinal nematode prevalence and infection intensity of the 439 individuals, we applied faecal egg counts (FEC, number of eggs per gram faeces) using a McMaster floatation technique modified by Meyer-Lucht and Sommer [56]. A standardised volume of 200 mg faeces was used per animal. To recover and identify adult worms we screened all 161 dissected gastrointestinal tracts and used published species descriptions (a list can be supplied by the authors), scanning electron microscopy and personal communications with leading experts (see acknowledgements) in the affiliated field. A comparison of the worms that were recorded in the gastrointestinal tract and faecal material revealed that all of the highly abundant egg morphotypes could be linked to the adult nematodes found in the gastrointestinal tract [38]. Only the eggs from the two nematodes Heligmonina spira and Neoheligmonella capensis could not be distinguished because of their similar shape and size and therefore they were grouped together to their subfamily Nippostrongylinae.

Molecular Techniques

The molecular techniques have been described previously [36] and all nucleotide sequences from the MHC DRB exon II region can be found at GenBank (Accession numbers AY928313, AY928314, AY928318–AY928320, AY928324–AY928327, AY928329, GU332030–GU332268). In short, DNA was extracted from ear tissue using the DNeasy Tissue Kit (Qiagen, Hilden, Germany) and we used the primers JS1 and JS2, which amplify a 171-bp fragment [70] of MHC class II DRB exon2. The investigated region contains all the alleged functionally important antigen binding sites, derived from humans (ABS; [58]), and evidence for positive selection has been already shown in Froeschke and Sommer [36]. We applied the single stranded conformation polymorphism (SSCP) method to identify allelic diversity (following the manufacturer's protocol,; ETC Elektrophoresetechnik, Kirchentellinsfurt, Germany). SSCP bands were subsequently cut out of the gel and re-amplified. No more than two alleles per individual could be detected, suggesting that only one MHC locus was amplified. Cycle sequencing was performed with an Applied Biosystems automated sequencer model 3130, using a dye terminator sequencing kit (Applied Biosystems, Forster City, CA). Forward and backward sequences from each newly discovered allele were thereby taken from two separate PCR and SSCP assays, respectively, to confirm their allelic make-up. Sequences were edited and aligned with the software MEGA 4 [71]. Furthermore all individuals were genotyped at seven microsatellite markers. Details on the microsatellites, heterozygosity and null allele analyses are presented elsewhere (Froeschke and Sommer, in review).

Genetic diversity analysis

To measure the overall genetic neutral diversity per population we used MLH (multilocus heterozygosity, [72] and mean microsatellite D2 (difference in repeat units, averaged over all loci, [73].

MHC genetic diversity was described using the observed heterozygosity and mean- and individual amino acid distance (AADist) as well as the allelic richness. Observed and expected heterozygosity for the MHC per population was calculated with the software Arlequin 3.0 [74]. AADist was calculated with the software MEGA 4 [71]. Because the observed number of alleles in a sample is highly dependent on the number of sampled individuals, we calculated the allelic richness corrected for different sample sizes by using a rarefaction index implemented in FSTAT [75]. Thereby, the expected number of alleles in each population is calculated for the number of individuals present in the smallest population. Frequency of null alleles was estimated with the algorithm presented in Dempster et al. [76] implemented in the program FREENA [77].

Pathogen-driven selection analyses on the population level

To investigate associations between multiple nematode infestation and the gene constitutions considering both type of markers on the population level we used multivariate generalized linear mixed models (GLMM). Models were fitted for overall nematode prevalence and overall mean nematode infection intensity. The models for prevalence were calculated using a binomial error distribution and logit link function. In the models for mean infection intensity we applied Gaussian error distribution with an identity link function [44].

Due to the small number of seven populations and to avoid collinearity the five predictors of genetic diversity (microsatellite MLH, microsatellite D2, MHC heterozygosity, MHC AAdist and MHC allelic richness) were included in separate but otherwise identical models. Capture season [year] was added as a further predictor in each model. Because each trapping site and thus each population was characterized by a site-specific precipitation pattern, which had a big influence on the parasite load [38] we included ‘population’ as a random factor in our models.

Pathogen-driven selection analyses on the individual level

In order to test for possible associations and interactions between MHC alleles and specific nematodes as well as finding support for parasite-driven selection mechanisms on the individual level, we applied two different approaches and used (i) GLMM and (ii) co-inertia analysis (COIA). In all cases, the error structure of the parasite response variables was not normally distributed. To avoid collinearity in our GLMMs, which investigate the association between the individual genetic constitution and parasite load, we tested all predictors for correlations and, if necessary, included them in separate, otherwise identical models.

To test for ‘heterozygote advantage’ and ‘divergent allele advantage’ we took (a) the prevalence (presence/absence of a parasite) and (b) the infection intensity (FEC) data of all parasites species together, as well as of the five most abundant nematodes Syphacia obvelata, Heligmonina spira and Neoheligmonella capensis (combined to subfamily Nippostrongylinae), Trichuris muris and Aspiculuris tetraptera separately, as response variables. These five parasite species amounted to more than 82% of all helminth infections [38]. Heterozygote host individuals and animals with a higher allele divergence should be able to recognize a broader spectrum of parasites and thus potential lower prevalence and FEC rates would be interpreted as an advantage. The models for prevalence were calculated using a binomial error distribution and logit link function. In the models for infection intensity we applied a quasipoisson error distribution with a loglink function, which accounts for overdispersion in these data [78]. As predictors for ‘heterozygote advantage’ we included the MHC genotype (homo- or heterozygote) and microsatellite MLH for each individual as fixed factors in our model as well as sex and capture season [year]. To consider extra sources of variation in variances through the influences of different populations and accordingly geographical position of each individual, we added ‘population’ as a random factor.

Afterwards we applied a similar model as described above with the covariates AADist and D2 as fixed predictors to investigate a possible ‘divergent allele advantage’.

To examine possible association between specific MHC alleles (‘rare allele advantage’) and parasite species, we continued with the five most abundant nematode species. Again the models for prevalence were calculated using a binomial error distribution and logit link function, and for the models for infection intensity we applied a quasipoisson error distribution with a loglink function. As predictors we included the presence/absence of the 37 alleles observed in more than five individuals (Fig. 1) as fixed factors as well as sex and capture season [year]. ‘Population’ from each individual was included again as a random factor.

For our GLMMs we had the required data (individual parasite, MHC and microsatellite data, sex) of 424 individuals available. Models were validated by the examination of the plots of residuals against fitted values and checked for heteroscedasticity and outliers. Predictors with extreme standard errors (<100 000, p = 1) were excluded from the specific model [78]. They were caused by the fact that some of the scarce nematode species and alleles did not co-occur in all populations. All models were conducted in R [79] and implemented the function glmmPQL [80], using the package MASS [81] and NLME [82].

For the second approach to estimate possible positive or negative associations between specific MHC alleles and helminth burden we performed a co-inertia analysis (COIA) with the complete dataset. Here we pooled our data based on the assumption that individual allele-parasite co-evolution should hold across populations [83].

A COIA is a multivariate method for coupling two tables with the only constraint that the sites are weighted in the same way for each table [53]. This method has been used in the past mainly for ecological data and only recently applied for genetic – parasite interaction studies [32], [83], [9], [84]. First we conducted a correspondence analysis (COA) with the genetic presence/absence table of the 37 MHC alleles included in the analysis. As a second step we performed a principal component analysis (PCA) with the parasitological matrix, including the infection intensity (FEC) of eleven of thirteen previously recovered gastrointestinal nematodes [38]. The nematodes Streptopharagus sudanensis and Trichostrongylus probulurus were excluded from the analysis since we only had FEC data from one and two individuals available, respectively. We then performed a co-inertia (COIA) analysis to link the COA and PCA analysed matrices and assessed associations between MHC alleles and nematode infection intensity visually on factorial maps. The significance of correlation between genetic and parasitological matrices was measured with the Rv-coefficient. Significance between both matrixes was assessed with a permutation test, which compares 1000 randomly generated data sets with the real data set. COA, PCA and COIA analyses were performed with the ade4TkGUI package. All statistical tests were conducted in R [79].

Supporting Information

Figure S1.

Projection of results of parasitological principal component analysis. Projection of results of parasitological principal component analysis from Rhabdomys pumilio (n = 432). Variables located in a common direction are positively associated whereas those located in the opposite direction are considered as negatively associated. Variables located close to the centre do not structure the data and are not labelled to improve clarity. Nematode A–E = based on egg morphotypes.

https://doi.org/10.1371/journal.pone.0031820.s001

(TIF)

Table S1.

Nematode load per population. Nematode infestation rate [%], mean species richness, abundance [log no. of worms] and mean infection intensity [logEPG] per population ± S.E.

https://doi.org/10.1371/journal.pone.0031820.s002

(DOC)

Acknowledgments

The study has been conducted in accordance with the Ministry of Environment and Tourism, Namibia (permit no 853/2004 and 1065/2006), the Northern Cape Nature Conservation Service, South Africa (permit no 0592/04 and 0133/06) and the Cape Nature Department of the Western Cape, South Africa (permit no 001-202-00021 and AAA004-00029-0035). We declare that all aspects of the study comply with the current law of the country in which they were performed. We are grateful to K. Junker and J. Boomker from the Department of Veterinary Tropical Diseases, Pretoria University and R. Bray from the Natural History Museum, London for helping with helminth identifications. Further we would like to thank A. Schmidt for assistance in the lab, and R. Harf, S. Matthee, N. Schwensow and Y. Meyer-Lucht for their tremendous support in various ways. Three anonymous referees provided very helpful comments on a former version of the manuscript.

Author Contributions

Conceived and designed the experiments: GF SS. Performed the experiments: GF. Analyzed the data: GF SS. Contributed reagents/materials/analysis tools: GF SS. Wrote the paper: GF SS.

References

  1. 1. Brooks DR, Hoberg EP (2006) Systematics and emerging infectious diseases: From Management to solution. J Parasitol 92: 426–429.DR BrooksEP Hoberg2006Systematics and emerging infectious diseases: From Management to solution.J Parasitol92426429
  2. 2. Weil ZM, Martin LB II, Nelson RJ (2006) Interactions among immune, endocrine, and behavioural response to infection. In: Morand S, Krasnov BR, Poulin R, editors. Micromammals and Macroparasites. Tokyo: Springer-Verlag. pp. 443–473.ZM WeilLB Martin IIRJ Nelson2006Interactions among immune, endocrine, and behavioural response to infection.S. MorandBR KrasnovR. PoulinMicromammals and MacroparasitesTokyoSpringer-Verlag443473
  3. 3. Stear MJ, Bairden K, Duncan JL, Holmes PH, McKellar QA, et al. (1997) How hosts control worms. Nature 389: 27.MJ StearK. BairdenJL DuncanPH HolmesQA McKellar1997How hosts control worms.Nature38927
  4. 4. Mas-Coma S, Valero MA, Bargues MD (2008) Effects of climate change on animal and zoonotic helminthiases. In: de La Rocque S, editor. Climate change: impact on epidemiology and control of animal disease. Rev Sci Tech Oie. pp. 443–452.S. Mas-ComaMA ValeroMD Bargues2008Effects of climate change on animal and zoonotic helminthiases.S. de La RocqueClimate change: impact on epidemiology and control of animal diseaseRev Sci Tech Oie443452
  5. 5. Behnke JM, Wakelin D (1973) The survival of Trichuris muris in wild populations of its natural hosts. Parasitol 67: 157–164.JM BehnkeD. Wakelin1973The survival of Trichuris muris in wild populations of its natural hosts.Parasitol67157164
  6. 6. Behnke JM, Gilbert FS, Abu-Madi MA, Lewis JW (2005) Do the helminth parasites of wood mice interact? J Anim Ecol 74: 982–993.JM BehnkeFS GilbertMA Abu-MadiJW Lewis2005Do the helminth parasites of wood mice interact?J Anim Ecol74982993
  7. 7. Michels C, Nieuwenhuizen N, Brombacher F (2006) Infection with Syphacia obvelata (Pinworm) induces protective Th2 immune responses and influences Ovalbumin-induced allergic reactions. Infect Immun 74: 5926–5932.C. MichelsN. NieuwenhuizenF. Brombacher2006Infection with Syphacia obvelata (Pinworm) induces protective Th2 immune responses and influences Ovalbumin-induced allergic reactions.Infect Immun7459265932
  8. 8. Fumagalli M, Pozzoli U, Cagliani R, Comi GP, Riva S, et al. (2009) Parasites represent a major selective force for interleukin genes and shape the genetic predisposition to autoimmune conditions. J Exp Med 206: 1395–1408.M. FumagalliU. PozzoliR. CaglianiGP ComiS. Riva2009Parasites represent a major selective force for interleukin genes and shape the genetic predisposition to autoimmune conditions.J Exp Med20613951408
  9. 9. Schwensow N, Axtner J, Sommer S (2010) Are associations of immune gene expression, body condition and parasite burden detectable in nature? A case study in an endemic rodent from the Brazilian Atlantic Forest. Infect Genet Evol 11: 22–30.N. SchwensowJ. AxtnerS. Sommer2010Are associations of immune gene expression, body condition and parasite burden detectable in nature? A case study in an endemic rodent from the Brazilian Atlantic Forest.Infect Genet Evol112230
  10. 10. Klein J (1986) Natural history of the major histocompatibility complex. New York: Wiley & Sons. J. Klein1986Natural history of the major histocompatibility complexNew YorkWiley & Sons
  11. 11. Clarke B, Kirby DR (1966) Maintenance of histocompatibility polymorphisms. Nature 211: 999–1000.B. ClarkeDR Kirby1966Maintenance of histocompatibility polymorphisms.Nature2119991000
  12. 12. Doherty PC, Zinkernagel RM (1975) Enhanced immunological surveillance in mice heterozygous at the H-2 gene complex. Nature 256: 50–52.PC DohertyRM Zinkernagel1975Enhanced immunological surveillance in mice heterozygous at the H-2 gene complex.Nature2565052
  13. 13. Hughes AL, Nei M (1988) Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection. Nature 335: 167–170.AL HughesM. Nei1988Pattern of nucleotide substitution at major histocompatibility complex class I loci reveals overdominant selection.Nature335167170
  14. 14. Hughes AL, Nei M (1989) Nucleotide substitution at major histocompatibility complex class II loci: Evidence for overdominant selection. Proc Natl Acad Sci USA 86: 948–962.AL HughesM. Nei1989Nucleotide substitution at major histocompatibility complex class II loci: Evidence for overdominant selection.Proc Natl Acad Sci USA86948962
  15. 15. Wakeland EK, Boehme S, She JX, Lu C-C, McIndoe RA, et al. (1990) Ancestral polymorphisms of MHC class-II genes – divergent allele advantage. Immunol Res 9: 115–122.EK WakelandS. BoehmeJX SheC-C LuRA McIndoe1990Ancestral polymorphisms of MHC class-II genes – divergent allele advantage.Immunol Res9115122
  16. 16. Sommer S (2005) The importance of immune gene variability (MHC) in evolutionary ecology and conservation. Front Zool 2: 16.S. Sommer2005The importance of immune gene variability (MHC) in evolutionary ecology and conservation.Front Zool216
  17. 17. Spurgin LG, Richardson DS (2010) How pathogens drive genetic diversity: MHC, mechanisms and misunderstandings. Proc R Soc Lond B Biol Sci 277: 979–988.LG SpurginDS Richardson2010How pathogens drive genetic diversity: MHC, mechanisms and misunderstandings.Proc R Soc Lond B Biol Sci277979988
  18. 18. Lello J, Boag B, Fenton A, Stevenson IR, Hudson PJ (2004) Competition and mutualism among the gut helminths of a mammalian host. Nature 428: 840–844.J. LelloB. BoagA. FentonIR StevensonPJ Hudson2004Competition and mutualism among the gut helminths of a mammalian host.Nature428840844
  19. 19. Ezenwa VO, Etienne RS, Luikart G, Beja-Pereira A, Jolles AE (2010) Hidden consequences of living in a wormy world: nematode induced immune suppression facilitates tuberculosis invasion in African buffalo. American Nat 176: 613–624.VO EzenwaRS EtienneG. LuikartA. Beja-PereiraAE Jolles2010Hidden consequences of living in a wormy world: nematode induced immune suppression facilitates tuberculosis invasion in African buffalo.American Nat176613624
  20. 20. Telfer S, Lambin X, Birtles R, Beldomenico P, Burthe S, et al. (2010) Species interactions in a parasite community drive infection risk in a wildlife population. Sci 330: 243–246.S. TelferX. LambinR. BirtlesP. BeldomenicoS. Burthe2010Species interactions in a parasite community drive infection risk in a wildlife population.Sci330243246
  21. 21. Behnke JM (2008) Structure in parasite component communities in wild rodents: predictability, stability, associations and interactions … or pure randomness? Parasitol 135: 751–766.JM Behnke2008Structure in parasite component communities in wild rodents: predictability, stability, associations and interactions … or pure randomness?Parasitol135751766
  22. 22. Pearce EJ, Tarloton RL (2002) Overview of the parasitic pathogens. In: Kaufmann SHE, Sher A, Ahmed R, editors. Immunology of infectious diseases. ASM Press, Washington. pp. 39–52.EJ PearceRL Tarloton2002Overview of the parasitic pathogens.SHE KaufmannA. SherR. AhmedImmunology of infectious diseasesASM Press, Washington3952
  23. 23. Falk K, Rötschke O, Stevanović S, Jung G, Rammensee H-G (1991) Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules. Nature 351: 290–296.K. FalkO. RötschkeS. StevanovićG. JungH-G Rammensee1991Allele-specific motifs revealed by sequencing of self-peptides eluted from MHC molecules.Nature351290296
  24. 24. Altuvia Y, Margalit H (2004) A structure-based approach for prediction of MHC-binding peptides. Methods 34: 454–459.Y. AltuviaH. Margalit2004A structure-based approach for prediction of MHC-binding peptides.Methods34454459
  25. 25. Finkelman FD, Shea-Donohue T, Mooris SC, Gildea L, Strait R, et al. (2004) Interleukin-4- and interleukin-13- mediated host protection against intestinal nematode parasites. Imunol Rev 201: 139–155.FD FinkelmanT. Shea-DonohueSC MoorisL. GildeaR. Strait2004Interleukin-4- and interleukin-13- mediated host protection against intestinal nematode parasites.Imunol Rev201139155
  26. 26. Perrigoue JG, Marshall FA, Artis D (2008) On the hunt for helminths: innate immune cells in the recognition and response to helminth parasites. Cell Microbiol 10: 1757–1764.JG PerrigoueFA MarshallD. Artis2008On the hunt for helminths: innate immune cells in the recognition and response to helminth parasites.Cell Microbiol1017571764
  27. 27. Allen JE, Maizels RM (2011) Diversity and dialogue in immunity to helminths. Nat Rev Immunol 11: 375–388.JE AllenRM Maizels2011Diversity and dialogue in immunity to helminths.Nat Rev Immunol11375388
  28. 28. Wegner KM, Reusch TBH, Kalbe M (2003) Multiple parasites are driving major histocompatibility complex polymorphism in the wild. J Evol Biol 16: 224–232.KM WegnerTBH ReuschM. Kalbe2003Multiple parasites are driving major histocompatibility complex polymorphism in the wild.J Evol Biol16224232
  29. 29. Goüy de Bellocq J, Charbonnel N, Morand S (2008) Coevolutionary relationship between helminth diversity and MHC class II polymorphism in rodents. J Evol Biol 21: 1144–1150.J. Goüy de BellocqN. CharbonnelS. Morand2008Coevolutionary relationship between helminth diversity and MHC class II polymorphism in rodents.J Evol Biol2111441150
  30. 30. Apanius V, Penn D, Slev PR, Ruff LR, Potts WK (1997) The nature of selection on the major histocompatibility complex. Crit Rev Immunol 17: 179–224.V. ApaniusD. PennPR SlevLR RuffWK Potts1997The nature of selection on the major histocompatibility complex.Crit Rev Immunol17179224
  31. 31. Dionne M, Miller KM, Dodson JJ, Bernatchez L (2009) MHC standing genetic variation and pathogen resistance in wild Atlantic salmon. Phil Trans R Soc B 364: 1555–1565.M. DionneKM MillerJJ DodsonL. Bernatchez2009MHC standing genetic variation and pathogen resistance in wild Atlantic salmon.Phil Trans R Soc B36415551565
  32. 32. Tollenaere C, Bryja J, Galan M, Cadet P, Deter J, et al. (2008) Multiple parasites mediate balancing selection at two MHC class II genes in the fossorial water vole: insights from multivariate analyses and population genetics. J Evol Biol 21: 1307–1320.C. TollenaereJ. BryjaM. GalanP. CadetJ. Deter2008Multiple parasites mediate balancing selection at two MHC class II genes in the fossorial water vole: insights from multivariate analyses and population genetics.J Evol Biol2113071320
  33. 33. Oliver MK, Telfer S, Piertney SB (2009) Major histocompatibility complex (MHC) heterozygote superiority to natural multi-parasite infections in the water vole (Arvicola terrestris). Proc R Soc Lond B Biol Sci 276: 1119–1128.MK OliverS. TelferSB Piertney2009Major histocompatibility complex (MHC) heterozygote superiority to natural multi-parasite infections in the water vole (Arvicola terrestris).Proc R Soc Lond B Biol Sci27611191128
  34. 34. Gregory RD (1990) Genetics, sex and exposure – the ecology of Heligmosomoides polygyrus (Nematoda) in the wood mouse. J Anim Ecol 59: 363–378.RD Gregory1990Genetics, sex and exposure – the ecology of Heligmosomoides polygyrus (Nematoda) in the wood mouse.J Anim Ecol59363378
  35. 35. De Graaff G (1981) The rodents of southern Africa. Durban: Butterworths. G. De Graaff1981The rodents of southern AfricaDurbanButterworths
  36. 36. Froeschke G, Sommer S (2005) MHC class II DRB variability and parasite load in the striped mouse (Rhabdomys pumilio) in the Southern Kalahari. Mol Biol Evol 22: 1254–1259.G. FroeschkeS. Sommer2005MHC class II DRB variability and parasite load in the striped mouse (Rhabdomys pumilio) in the Southern Kalahari.Mol Biol Evol2212541259
  37. 37. Wegner KM (2008) Historical and contemporary selection of teleost MHC genes: did we leave the past behind? J Fish Biol 73: 2110–2132.KM Wegner2008Historical and contemporary selection of teleost MHC genes: did we leave the past behind?J Fish Biol7321102132
  38. 38. Froeschke G, Harf R, Sommer S, Matthee S (2010) Effects of precipitation on parasite burden along a natural climatic gradient in southern Africa – implications for possible shifts in infestation patterns due to global changes. Oikos 119: 1029–1039.G. FroeschkeR. HarfS. SommerS. Matthee2010Effects of precipitation on parasite burden along a natural climatic gradient in southern Africa – implications for possible shifts in infestation patterns due to global changes.Oikos11910291039
  39. 39. Ellegren H (2004) Microsatellites: simple sequences with complex evolution. Nat Rev Genet 5: 435–445.H. Ellegren2004Microsatellites: simple sequences with complex evolution.Nat Rev Genet5435445
  40. 40. Väli Ü, Einarsson A, Waits L, Ellegren H (2008) To what extend do microsatellite markers reflect genome-wide genetic diversity in natural populations? Mol Ecol 17: 3808–3817.Ü. VäliA. EinarssonL. WaitsH. Ellegren2008To what extend do microsatellite markers reflect genome-wide genetic diversity in natural populations?Mol Ecol1738083817
  41. 41. Prugnolle F, Manica A, Charpentier M, Guégan JF, Guernier V, et al. (2005) Pathogen-Driven Seleciton and Worldwide HLA Class I Diversity. Curr Biol 15: 1022–1027.F. PrugnolleA. ManicaM. CharpentierJF GuéganV. Guernier2005Pathogen-Driven Seleciton and Worldwide HLA Class I Diversity.Curr Biol1510221027
  42. 42. Alcaide M (2010) On the relative roles of selection and genetic drift in shaping MHC variation. Mol Ecol 19: 3842–3844.M. Alcaide2010On the relative roles of selection and genetic drift in shaping MHC variation.Mol Ecol1938423844
  43. 43. Meyer-Lucht Y, Otten C, Püttker T, Pardini R, Metzger JP, et al. (2010) Variety matters: adaptive genetic diversity and parasite load in two mouse opossums from the Brazilian Atlantic Forest differing in their sensitivity to habitat fragmentation. Conserv Genet 11: 2001–2013.Y. Meyer-LuchtC. OttenT. PüttkerR. PardiniJP Metzger2010Variety matters: adaptive genetic diversity and parasite load in two mouse opossums from the Brazilian Atlantic Forest differing in their sensitivity to habitat fragmentation.Conserv Genet1120012013
  44. 44. Meyer-Lucht Y, Sommer S (2009) Number of MHC alleles is related to parasite loads in natural populations of yellow necked mice, Apodemus flavicollis. Evol Ecol Res 11: 1085–1097.Y. Meyer-LuchtS. Sommer2009Number of MHC alleles is related to parasite loads in natural populations of yellow necked mice, Apodemus flavicollis.Evol Ecol Res1110851097
  45. 45. Arkush KD (2002) Resistance to three pathogens in the endangered winter run Chinook salmon (Oncorhynchus tshawytscha): effects of inbreeding and major histocompatibility complex genotypes. J Fish Aquat Sci 59: 966–975.KD Arkush2002Resistance to three pathogens in the endangered winter run Chinook salmon (Oncorhynchus tshawytscha): effects of inbreeding and major histocompatibility complex genotypes.J Fish Aquat Sci59966975
  46. 46. Hughes AL, Nei M (1992) Models of host-parasite interaction and MHC polymorphism. Genet 132: 863–864.AL HughesM. Nei1992Models of host-parasite interaction and MHC polymorphism.Genet132863864
  47. 47. McClelland EE, Penn DJ, Potts WK (2003) Major histocompatibility complex heterozygote superiority during coinfection. Infect Immun 71: 2079–2086.EE McClellandDJ PennWK Potts2003Major histocompatibility complex heterozygote superiority during coinfection.Infect Immun7120792086
  48. 48. Wilson K, Bjørnstad ON, Dobson AP, Merler S, Poglayen G, et al. (2001) Heterogeneities in macroparasite infections: Patterns and processes. In: Hudson PJ, Rizzoli A, Grenfell BT, Heesterbeek H, Dobson AP, editors. The ecology of wildlife diseases. Oxford Univ Press, Oxford. pp. 6–44.K. WilsonON BjørnstadAP DobsonS. MerlerG. Poglayen2001Heterogeneities in macroparasite infections: Patterns and processes.PJ HudsonA. RizzoliBT GrenfellH. HeesterbeekAP DobsonThe ecology of wildlife diseasesOxford Univ Press, Oxford644
  49. 49. Skorping A, Jensen KH (2004) Disease dynamics: All caused by males? Trends Ecol Evol 19: 219–220.A. SkorpingKH Jensen2004Disease dynamics: All caused by males?Trends Ecol Evol19219220
  50. 50. Nwosu CO (2007) Prevalence and seasonal changes in the population of gastro-intestinal nematodes of small ruminants in the semi-arid zone of northeastern Nigeria. Vet Parasitol 144: 118–124.CO Nwosu2007Prevalence and seasonal changes in the population of gastro-intestinal nematodes of small ruminants in the semi-arid zone of northeastern Nigeria.Vet Parasitol144118124
  51. 51. Lenz TL, Wells K, Pfeiffer M, Sommer S (2009) Diverse MHC IIB allele repertoire increases parasite resistance and body condition in the Long-tailed giant rat (Leopoldamys sabanus). BMC Evol Biol 9: 269.TL LenzK. WellsM. PfeifferS. Sommer2009Diverse MHC IIB allele repertoire increases parasite resistance and body condition in the Long-tailed giant rat (Leopoldamys sabanus).BMC Evol Biol9269
  52. 52. Zuur AF, Leno EN, Walker N, Saveliev AA, Smith GM (2009) Mixed effects models and extensions in ecology with R. New York: Springer. AF ZuurEN LenoN. WalkerAA SavelievGM Smith2009Mixed effects models and extensions in ecology with RNew YorkSpringer
  53. 53. Dray S, Chessel D, Thioulouse J (2003) Co-inertia analysis and the linking of ecological data tables. Ecology 84: 3078–3089.S. DrayD. ChesselJ. Thioulouse2003Co-inertia analysis and the linking of ecological data tables.Ecology8430783089
  54. 54. Deter J, Bryja J, Chaval Y, Galan M, Henttonen H, et al. (2008) Association between the DQA MHC class II gene and Puumala virus infection in Myodes glareolus, the bank vole. Infect Gen Evol 4: 450–458.J. DeterJ. BryjaY. ChavalM. GalanH. Henttonen2008Association between the DQA MHC class II gene and Puumala virus infection in Myodes glareolus, the bank vole.Infect Gen Evol4450458
  55. 55. Harf R, Sommer S (2005) Association between major histocompatibility complex class II DRB alleles and parasite load in the hairy-footed gerbil, Gerbillurus paeba, in the southern Kalahari. Mol Ecol 14: 85–91.R. HarfS. Sommer2005Association between major histocompatibility complex class II DRB alleles and parasite load in the hairy-footed gerbil, Gerbillurus paeba, in the southern Kalahari.Mol Ecol148591
  56. 56. Meyer-Lucht Y, Sommer S (2005) MHC diversity and the association to nematode parasitism in the yellow necked mouse (Apodemus flavicollis). Mol Ecol 14: 2233–2243.Y. Meyer-LuchtS. Sommer2005MHC diversity and the association to nematode parasitism in the yellow necked mouse (Apodemus flavicollis).Mol Ecol1422332243
  57. 57. Bonneaud C, Perez-Tris J, Federici P, Chastel O, Sorci G (2006) Major histocompatibility alleles associated with local resistance to malaria in passerine. Evolution 60: 383–389.C. BonneaudJ. Perez-TrisP. FedericiO. ChastelG. Sorci2006Major histocompatibility alleles associated with local resistance to malaria in passerine.Evolution60383389
  58. 58. Loiseau C, Zoorob R, Garnier S, Birard J, Federici P, et al. (2008) Antagonistic effects of a MHC class I allele on malaria-infected house sparrows. Ecol Lett 11: 258–265.C. LoiseauR. ZoorobS. GarnierJ. BirardP. Federici2008Antagonistic effects of a MHC class I allele on malaria-infected house sparrows.Ecol Lett11258265
  59. 59. Brown JH, Jardetzky S, Gorga JC, Stern LJ, Urban RG, et al. (1993) Three-dimensional structure of the human class II histocompatibility antigen HLA-DR1. Nature 364: 33–39.JH BrownS. JardetzkyJC GorgaLJ SternRG Urban1993Three-dimensional structure of the human class II histocompatibility antigen HLA-DR1.Nature3643339
  60. 60. Frank SA (2002) Immunology and the evolution of infectious disease. Princeton: Princeton University Press. SA Frank2002Immunology and the evolution of infectious diseasePrincetonPrinceton University Press
  61. 61. Summers K, McKeon S, Sellars J, Keusenkothen M, Morris J, et al. (2003) Parasitic exploitation as an engine of diversity. Biol Rev 78: 639–675.K. SummersS. McKeonJ. SellarsM. KeusenkothenJ. Morris2003Parasitic exploitation as an engine of diversity.Biol Rev78639675
  62. 62. Hayunga EG (1991) Morphological adaptations of intestinal helminths. Parasitol 77: 865–873.EG Hayunga1991Morphological adaptations of intestinal helminths.Parasitol77865873
  63. 63. Gonçalves L, Pinto RM, Vicente JJ, Noronha D, Gomes DC (1998) Helminth parasites of conventionally maintained laboratory mice - II. Inbred strains with an adaptation of the anal swab technique. Mem Inst Oswaldo Cruz 93: 121–126.L. GonçalvesRM PintoJJ VicenteD. NoronhaDC Gomes1998Helminth parasites of conventionally maintained laboratory mice - II. Inbred strains with an adaptation of the anal swab technique.Mem Inst Oswaldo Cruz93121126
  64. 64. Singleton GR (1985) Population dynamics of Mus musculus and its parasites in Mallee Wheatland in Victoria during and after a drought. Wildl Res 12: 437–445.GR Singleton1985Population dynamics of Mus musculus and its parasites in Mallee Wheatland in Victoria during and after a drought.Wildl Res12437445
  65. 65. Tellier A, Brown JKM (2007) Stability of genetic polymorphism in host-parasite interactions. Proc R Soc B 274: 809–817.A. TellierJKM Brown2007Stability of genetic polymorphism in host-parasite interactions.Proc R Soc B274809817
  66. 66. Behnke JM, Bajer A, Sinski E, Wakelin D (2001) Interactions involving intestinal nematodes of rodents: experimental and field studies. Parasitology 122: 39–49.JM BehnkeA. BajerE. SinskiD. Wakelin2001Interactions involving intestinal nematodes of rodents: experimental and field studies.Parasitology1223949
  67. 67. Axtner J, Sommer S (2011) Heligmosomoides polygyrus infection is associated with lower MHC class II gene expression in Apodemus flavicollis: indication for immune suppression? Infect Genet Evol 11(8): 2063–2071.J. AxtnerS. Sommer2011Heligmosomoides polygyrus infection is associated with lower MHC class II gene expression in Apodemus flavicollis: indication for immune suppression?Infect Genet Evol11820632071
  68. 68. Cattadori IM, Haukisalmi V, Henttonen H, Hudson PJ (2006) Transmission ecology and the structure of parasite communities in small mammals. In: Morand S, Krasnov BR, Poulin R, editors. Micromammals and Macroparasites. Tokyo: Springer-Verlag. pp. 349–369.IM CattadoriV. HaukisalmiH. HenttonenPJ Hudson2006Transmission ecology and the structure of parasite communities in small mammals.S. MorandBR KrasnovR. PoulinMicromammals and MacroparasitesTokyoSpringer-Verlag349369
  69. 69. Apps PE (2000) Smither's mammals of southern Africa – a field guide. Johannesburg: Southern Book Publishers. PE Apps2000Smither's mammals of southern Africa – a field guideJohannesburgSouthern Book Publishers
  70. 70. Schad J, Sommer S, Ganzhorn JU (2004) MHC variability of a small lemur in the littoral forest fragments of southeastern Madagascar. Conserv Genet 5: 199–209.J. SchadS. SommerJU Ganzhorn2004MHC variability of a small lemur in the littoral forest fragments of southeastern Madagascar.Conserv Genet5199209
  71. 71. Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: Molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol 24: 1596–1599.K. TamuraJ. DudleyM. NeiS. Kumar2007MEGA4: Molecular evolutionary genetics analysis (MEGA) software version 4.0.Mol Biol Evol2415961599
  72. 72. Coltman D, Pilkington J, Smith JA, Pemberton J (1999) Parasite-mediated selection against inbred soay sheep in a free-living, island population. Evolution 53: 1259–1267.D. ColtmanJ. PilkingtonJA SmithJ. Pemberton1999Parasite-mediated selection against inbred soay sheep in a free-living, island population.Evolution5312591267
  73. 73. Coulson TN, Pemberton JM, Albon SD, Beaumont M, Marshall TC, et al. (1998) Microsatellites reveal heterosis in red deer. Proc R Soc Lond B Biol Sci 265: 489–495.TN CoulsonJM PembertonSD AlbonM. BeaumontTC Marshall1998Microsatellites reveal heterosis in red deer.Proc R Soc Lond B Biol Sci265489495
  74. 74. Excoffier L, Laval G, Schneider S (2005) Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evol Bioinform Online 1: 47–50.L. ExcoffierG. LavalS. Schneider2005Arlequin ver. 3.0: An integrated software package for population genetics data analysis.Evol Bioinform Online14750
  75. 75. Goudet J (2001) J. Goudet2001FSTAT, a program to estimate and test gene diversities and fixation indices. Ver. 2.9.3. Available from: http://www.unil.ch/izea/softwares/fstat.html. FSTAT, a program to estimate and test gene diversities and fixation indices. Ver. 2.9.3. Available from: http://www.unil.ch/izea/softwares/fstat.html.
  76. 76. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc B 39: 1–38.AP DempsterNM LairdDB Rubin1977Maximum likelihood from incomplete data via the EM algorithm.J R Stat Soc B39138
  77. 77. Chapuis M-P, Estoup A (2007) Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol 24: 621–631.M-P ChapuisA. Estoup2007Microsatellite null alleles and estimation of population differentiation.Mol Biol Evol24621631
  78. 78. Crawley MJ (2007) The R Book, 1st edition. Oxford: Wiley Publishing. MJ Crawley2007The R Book, 1st editionOxfordWiley Publishing
  79. 79. R Development Core Team (2008) R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. R Development Core Team2008R: A language and environment for statistical computingVienna, AustriaR Foundation for Statistical Computing
  80. 80. Breslow NE, Clayton DG (1993) Approximate inference in generalized linear models. J Am Stat Assoc 88: 9–25.NE BreslowDG Clayton1993Approximate inference in generalized linear models.J Am Stat Assoc88925
  81. 81. Venables WN, Ripley BD (2002) Modern applied statistics with S. Statistics and computing. New York: Springer. WN VenablesBD Ripley2002Modern applied statistics with S. Statistics and computingNew YorkSpringer
  82. 82. Pinheiro JC, Bates DM (2002) Mixed-effects models in S and S-plus. Statistics and computing. New York: Springer. JC PinheiroDM Bates2002Mixed-effects models in S and S-plus. Statistics and computingNew YorkSpringer
  83. 83. Evans ML, Neff BD (2009) Major histocompatibility complex heterozygote advantage and widespread bacterial infections in populations of Chinook salmon (Oncorhynchus tshawytscha). Mol Ecol 18: 4716–4729.ML EvansBD Neff2009Major histocompatibility complex heterozygote advantage and widespread bacterial infections in populations of Chinook salmon (Oncorhynchus tshawytscha).Mol Ecol1847164729
  84. 84. Schwensow N, Dausmann K, Eberle M, Fietz J, Sommer S (2010) Functional associations of similar MHC alleles and shared parasite species in two sympatric lemurs. Infection, Genetics and Evolution 10: 662–668.N. SchwensowK. DausmannM. EberleJ. FietzS. Sommer2010Functional associations of similar MHC alleles and shared parasite species in two sympatric lemurs.Infection, Genetics and Evolution10662668