- Research article
- Open Access
Evaluating the maintenance of disease-associated variation at the blood group-related gene B4galnt2 in house mice
BMC Evolutionary Biology volume 17, Article number: 187 (2017)
B4galnt2 is a blood group-related glycosyltransferase that displays cis-regulatory variation for its tissue-specific expression patterns in house mice. The wild type allele, found e.g. in the C57BL/6 J strain, directs intestinal expression of B4galnt2, which is the pattern observed among vertebrates, including humans. An alternative allele class found in the RIIIS/J strain and other mice instead drives expression in blood vessels, which leads to a phenotype similar to type 1 von Willebrand disease (VWD), a common human bleeding disorder. We previously showed that alternative B4galnt2 alleles are subject to long-term balancing selection in mice and that variation in B4galnt2 expression influences host-microbe interactions in the intestine. This suggests that the costs of prolonged bleeding in RIIIS/J allele-bearing mice might be outweighed by benefits associated with resistance against gastrointestinal pathogens. However, the conditions under which such trade-offs could lead to the long-term maintenance of disease-associated variation at B4galnt2 are unclear.
To explore the persistence of B4galnt2 alleles in wild populations of house mice, we combined B4galnt2 haplotype frequency data together with a mathematical model based on an evolutionary game framework with a modified Wright-Fisher process. In particular, given the potential for a heterozygote advantage as a possible explanation for balancing selection, we focused on heterozygous mice, which express B4galnt2 in both blood vessels and the gastrointestinal tract. We show that B4galnt2 displays an interesting spatial allelic distribution in Western Europe, likely due to the recent action of natural selection. Moreover, we found that the genotype frequencies observed in nature can be produced by pathogen-driven selection when both heterozygotes and RIIIS/J homozygotes are protected against infection and the fitness cost of bleeding is roughly half that of infection.
By comparing the results of our models to the patterns of polymorphism at B4galnt2 in natural populations, we are able to recognize the long-term maintenance of the RIIIS/J allele through host-pathogen interactions as a viable hypothesis. Further, our models identify that a putative dominant-, yet unknown protective function of the RIIIS/J allele appears to be more likely than a protective loss of intestinal B4galnt2 expression in RIIIS/J homozygotes.
Von Willebrand disease (VWD) is a common human bleeding disorder characterized by a defect of coagulation caused either by low plasma levels of von Willebrand factor (VWF) or a dysfunctional VWF. In a mouse model of VWD – the laboratory strain RIIIS/J – the disease is caused by a cis-regulatory mutation at the B4galnt2 gene, a blood group related glycosyltransferase [1, 2]. This mutation switches the usual expression pattern of B4galnt2 in the gastrointestinal (GI) epithelium, as observed in the wild type strain C57BL/6 J and other vertebrates , to the vascular endothelium in the RIIIS/J strain. Vascular expression of B4galnt2 leads to aberrant glycosylation of VWF, resulting in its accelerated clearance from circulation. Accordingly, RIIIS/J mice have up to twenty times lower plasma levels of VWF than C57BL/6 J mice .
Despite the expected fitness cost of prolonged bleeding times for wild animals, the RIIIS/J allele is found in high frequencies in various wild populations of house mice and their relatives [4, 5]. Furthermore these populations show signs of long term balancing selection maintaining both C57BL/6 J and RIIIS/J allele classes for at least 2.8 Ma. Further, in a previous survey of Mus musculus domesticus populations, a partial selective sweep revealed a recent increase in RIIIS/J allele frequency in a population from Southern France, while the allele was absent from a German population . This suggests that selective force(s) operating on B4galnt2 alleles in Western Europe may differ according to space and/or time.
Genome-wide scans for balancing selection in the human genome [6,7,8] identified a moderate number of genomic regions, but nearly all of them are involved in immunity lato sensu, supporting the hypothesis that B4galnt2 could be involved in host-pathogen interactions, as shown for other blood-group related genes [9, 10]. Laboratory experiments show that the absence of B4galnt2-associated GalNac residues on the GI mucosa results in an altered resident microbiota , and that this modified GI microbiota confers lower susceptibility to a model of Salmonella typhimurium infection . On the other hand, bacteria such as Staphylococcus aureus are known to use VWF to invade the host and escape the immune system [13, 14]. Although experimental evidence is lacking, S. aureus’s ability to utilize VWF could be compromised in RIIIS/J allele-bearing mice due to the low plasma levels of VWF, and hence lead to protection against this pathogen. Thus, potential benefits of the RIIIS/J allele could reside either in the gain of vascular expression and/or in the loss of GI expression in mice homozygous for the RIIIS/J allele, which would be associated with resistance against systemic and/or intestinal pathogens, respectively.
Under the above hypothesis, heterozygous mice are of particular interest, as they express B4galnt2 in both blood vessels and the GI tract (i.e. the two allele classes influence on tissue-specific expression patterns is co-dominant) , potentially incurring both the cost of bleeding and pathogen susceptibility. It is known that heterozygous mice have the same bleeding phenotype as RIIIS/J homozygotes (i.e. the RIIIS/J allele’s effect on VWF is dominant) [2, 4], although we have little information concerning the susceptibility of heterozygous mice to pathogens in the wild. Indeed, heterozygous mice could have the same level of protection as the RIIIS/J homozygotes (e.g. in the case of S. aureus using VWF directly to infect), whereas on the other hand they could display similar susceptibility to gut pathogens as the C57BL/6 J homozygotes (e.g. when a gut pathogen utilizes B4galnt2 specific GalNac residues in the mucosa). Finally, heterozygous mice could have an intermediate phenotype compared to both homozygotes in terms of resistance or susceptibility to pathogens.
In this study, we set out to determine the conditions under which the trade-off between prolonged bleeding times and pathogen susceptibility leads to the maintenance of the RIIIS/J allele, and in addition extended a previous geographic survey of B4galnt2 allele frequencies  to characterize spatial selection across Western Europe in more detail. Accordingly, we modeled the interaction between host and pathogen using an evolutionary game with a Wright-Fisher process . Since mice are diploid sexual organisms, we modified the Wright-Fisher “asexual” random process to include diploid reproduction. Pathogens were modeled as an environmental variable, being either present or absent, with the possibility for the environment to change regularly from one state to another. Alternatively, we also relax this assumption and model the pathogens such that their population depends on frequencies of susceptible hosts. Although simplified, the model provides a method to disentangle the effects of genotypic costs and environmental variability on the host population. Moreover, the environmental model resembles a “trench warfare” dynamic (i.e. advances and retreats of resistance allele frequency due to costs in the absence of a pathogen [16, 17], which we might expect in the context of balancing selection acting on resistance/susceptibility alleles as may be the case at B4galnt2 . To identify the model parameters that best explain the natural population dynamics, we compared the simulated populations to the observed wild populations. We found that the genotype frequencies observed in nature were best explained by a model where heterozygotes are protected against infection with a pathogen in a frequency-dependent manner, and the cost of bleeding being half that of infection.
First, in order to further characterize the intriguing geographic pattern of RIIIS/J allele frequency observed by Johnsen et al. , we typed B4galnt2 allele classes using the same diagnostic PCR fragment in a set of eight wild population collections spread across France and Germany . These populations represent six new locations, in addition to a resampling of the two locations previously analyzed by Johnsen et al. . This reveals an intriguing pattern of distribution of the RIIIS/J allele: it is nearly absent in the north and east of France and in Germany, but it is consistently >30% in three local populations in the south and west of France (Fig. 1a).
As we previously attributed differences in RIIIS/J allele frequency between two of these locations to a recent, partial selective sweep , we evaluated whether this pattern holds in this broader dataset. We thus typed 12 microsatellite loci linked to B4galnt2 and resolved their haplotypic phase with respect to the RIIIS/J and C57BL/6 J alleles as previously described . This reveals a near identical pattern (Fig. 2), whereby the expected heterozygosity of the two loci located closest to the cis-regulatory mutation of B4galnt2 (−30 kb and 0 kb) is very low on the RIIIS/J background, while high and close to the genome average (as determined by 18 unlinked microsatellites)  on the C57BL/6 J background.
Due to the extremely high level of nucleotide divergence observed between the RIIIS/J and C57BL/6 J alleles, it is possible, however, that the two microsatellites displaying very low heterozygosity on the RIIIS/J background could have experienced e.g. one or more mutation interrupting their repeats and thus changing their mutation rate. Thus, we also performed direct Sanger sequencing of these individuals, which revealed no evidence of interruption. Rather, these two dinucleotide loci display a repeat number within the range of the alleles found on the C57BL/6 J background, but with very few alleles (two and four alleles at each locus, respectively). Thus, the pattern of a drastic, local reduction of microsatellite variability near the cis-regulatory mutation on the background of the RIIIS/J allele is most consistent with a partial selective sweep.
A second alternative is that the above-mentioned geographic pattern of allele frequency distribution could also be related to underlying population structure. Indeed, different waves of migration led to the colonization of Western Europe by house mice [19,20,21]: one coming from the east through modern day Turkey and Greece, and another from the south through North Africa and Spain. These migration routes led to the distinct maternal lineages present in Northern Europe and the Mediterranean basin [22, 23]. To test whether the distribution of B4galnt2 alleles might be explained by population structure, we compared the observed allele frequencies to the previously established distribution of the mitochondrial D-loop haplogroups (Fig. 1b) and the genetic clusters identified by 18 nuclear microsatellite markers (Fig. 1c) . This reveals little to no correspondence, e.g. some local populations dominated by the same mitochondrial haplogroup and/or genetic cluster display contrasting RIIIS/J frequencies, and on the other hand, local populations with similar RIIIS/J frequencies display contrasting haplogroups and/or genetic clusters. Thus, the observed pattern of RIIIS/J allele frequency appears to have little to do with underlying population structure.
Taken together, these results confirm and extend those of Johnsen et al. : a partial selective sweep visible through the B4galnt2-linked microsatellites indicates that the RIIIS/J allele recently rose in frequency in Southwestern France, most likely due to the action of strong natural selection.
Although a constant environment is very unlikely in nature, the study of this limiting case allows us to test the behavior of our model. We chose to vary the costs of bleeding and infection from 0 to 1, incremented by steps of 0.2.
In a constant environment with no pathogen (Fig. 3), the cost of infection ci is irrelevant, and only the cost of bleeding cb influences the outcome of the simulation. Thus, the three investigated values of the infection cost for the heterozygotes, ch (equivalent to the dominance coefficient of a resistance phenotype in heterozygotes, see “Host” section in the Methods), lead to the same results, as ch = ci = 0. When cb = 0, we have a neutral state (i.e. all individuals have the same fitness) leading to ~50% heterozygotes and ~25% of each homozygote. When cb > 0, as expected the CC individuals make up the majority of the population, representing over 80% of the individuals, whereas the RR individuals are very close to 0 and the heterozygotes remain in low frequency (<20%). These proportions depend on the value of cb. Indeed, when cb increases, the selection strength increases, particularly on the heterozygotes, leading to a deviation from Hardy-Weinberg equilibrium (HWE) and an excess of the favored homozygotes -- the CC individuals.
In a constant environment with a pathogen, the value of ch has a great influence on the population frequencies, as it is dependent on the cost of infection. With ch = 0 (Fig. 3), heterozygotes have the same fitness as the RR individuals, and the neutral state is reached whenever cb = ci. Consistently, when cb > ci, the CC individuals make up the majority of the population and when ci > cb, the RR individuals and heterozygotes represent the majority. Interestingly, when the difference in costs becomes too high, the selection becomes so strong that the population deviates from HWE with an excess of homozygotes. However, this is only true when cb > ci but not when cb < ci, indicating the asymmetry of the system that translates to a stronger effect of cb compared to that of ci.
When ch > 0 (Fig. 3) the heterozygotes have a lower fitness than both homozygotes, resulting in the population being mostly composed of the favored homozygotes (RR individuals when cb < ci and CC individuals when ci < cb). Notably, the neutral state is reached only for the two extreme cases were all individuals have the same fitness (cb = ci = 0 and cb = ci = 1) and not for every cb = ci as in the previous model. This can be explained by the strong selection acting on the heterozygotes when ch > 0, as they bear the dual cost of bleeding and infection. This leads to a deviation from HWE and an excess of homozygotes. As previously observed, this deviation is also present when the difference in costs becomes too strong, but this time for both ci > cb and ci < cb. However, due to the asymmetry of the system when ch = ci/2, the difference in costs must be stronger for ci > cb to lead to a deviation from HWE than for cb > ci. For ch = ci, the system becomes symmetrical: ci and cb have the same effect on the selection strength, leading to a deviation from HWE for the same difference in costs when ci > cb as when cb > ci.
To approach the trench warfare dynamics that may be relevant for the putative host-pathogen interactions involving B4galnt2, we modeled the pathogen as an exogenous variable, being either present or absent from the environment. This property of the environment was regularly alternated according to host generations: the environment switches from pathogenic to non-pathogenic and back every S host generations. We investigated a broad range of switching frequencies: every 1, 10, 50, 100, 500, 1000 and 5000 host generations. For all values of ch, the two rapid switching frequencies (1 & 10) show similar results, as do the intermediate (50 & 100) and slow ones (500 onwards), thus, we display the results for 1, 50 and 500.
First, we observe for ch = 0 (Fig. 4a) that the parameter space is divided in two distinct regions where a given genotype is favored, as in the case for the constant pathogenic environment described previously. These two regions are separated by a boundary line where all genotypes coexist. In the constant pathogenic environment described previously, this boundary line represented the neutral state, where all genotypes have the same fitness, and the population is composed of ~50% heterozygotes and ~25% of each homozygotes. In the case of a changing environment however, this boundary line is different, as it does not represent a neutral state, but is still characterized by the coexistence of the three genotypes. Interestingly, the boundary lies around cb = ci/2 for rapid switching, but approaches the cb = ci line for slower frequencies. Of note, this boundary region is important as it regularly occurs in the subsequent analyses and is in most cases characterized by the coexistence of all three genotypes, with only one- or a combination of two genotypes being favored above- or below the boundary region, respectively. In the case of rapid switching (Fig. 4a), the results are qualitatively very similar to the constant pathogenic environment: the boundary line approach a neutral state, since we have ~50% heterozygotes and ~25% of each homozygote; above this line, the population is composed of mostly CC individuals, and below this line, the RR individuals and heterozygotes represent the majority. The position of the line is however not the same. Indeed, the average payoff of the CC individuals in this model is ci/2, as half of the time these individuals bear no cost, and the other half they bear the cost of infection ci. This pushes the boundary line to cb = ci/2 rather than cb = ci as in the constant pathogenic environment. We observe, like in the constant pathogenic environment, a deviation from HWE for cb> > ci/2. In this rapid model, the heterozygotes can be seen as an allelic pool that helps the system maintain both alleles in the population, and ensure the transition between the two homozygous states. For slower frequencies of environmental (pathogenic) change (Fig. 4a), the delay between switches is long enough for the alleles to fix, and for each period the system reaches the characteristics of the corresponding constant environment, therefore bringing the boundary line back towards cb = ci, similar to the constant pathogenic environment. Moreover, under these conditions heterozygotes are no longer needed to maintain both alleles in the population, as the selection is strong enough to recover the alleles from very low frequencies. It appears that the heterozygotes even suffer from stronger selective pressure than the homozygotes, as we observe a deviation from HWE with an excess of homozygotes already with low fitness costs. Moreover, the asymmetry of the system is different compared to the constant environment. Indeed, in a non-changing environment, the influence of cb compared to ci on the selection strength is stronger, leading to deviations from HWE for smaller differences in costs when ci < cb than when cb < ci. However, in this fluctuating environment a certain difference in costs is needed above the boundary line to lead to a deviation from HWE, as for the constant environment, but below the boundary it seems that only the value of cb is important.
For ch = ci/2 (Fig. 4b), the selection on the heterozygotes is stronger than on the homozygotes, as already observed under the constant environment. This leads to the “disappearance” of heterozygotes on the boundary line when cb and ci increase and an excess of homozygotes. The position of the line is however similar to that of the ch = 0 model: it lies around cb = ci/2 for the rapid environmental changes and approaches cb = ci for slowly fluctuating environments. For the rapidly switching environment (Fig. 4b), when cb > ci/2 the CC individuals make up the majority of the population, as observed for ch = 0. When cb < ci/2 however, the population is composed of mostly RR individuals alone and not in conjunction with the heterozygotes as for ch = 0. This is due to the lower fitness of the heterozygotes. A similar pattern is observed for the intermediate environment (Fig. 4b), but the selection appears to be stronger for below the boundary, as we observe an excess of homozygotes. For the slowly switching environment (Fig. 4b), we still observe a majority of CC individuals above the boundary, but below the line the RR individuals do not take over and rather coexist with the CC individuals. The heterozygotes, however, are still in very low frequency due to their low fitness, leading to an excess of homozygotes. This is again due to the asymmetry of the system: the cost of bleeding is always present but the cost of infection is present only half of the time, leading to a stronger selective pressure from the bleeding phenotype than from infection.
For ch = ci (Fig. 4c), the trend is similar to that of ch = ci/2, but the selective pressure is stronger on the heterozygotes than with ch = ci/2, leading to deviations from HWE with lower values of cb and ci. Interestingly, we observe that the boundary line is no longer characterized by a linear cb = ci/2 relationship, but rather takes an exponential distribution. This might be due to the non-additive dual cost of the heterozygous mice.
Similarity to natural populations
One important goal of constructing our model is to compare its results to the pattern of B4galnt2 allele frequencies observed among wild populations of mouse species belonging to the genus Mus, in order to understand the selective forces maintaining disease-associated variation at this locus. Accordingly, we evaluated a broad collection of populations (summarized in Table 1) from the current- and two previous studies [4, 5]: Johnsen et al.  and the current study provide data from a total 10 M. m. domesticus populations from Europe, Africa and North America, whereas Linnenbrink et al.  added an ancestral M. m. domesticus population (Iran) and data from other house mouse subspecies and their relatives, including M. M. musculus (Kazakhstan), M. M. castaneus (India) and M. spretus (Spain).
The study by Linnenbrink et al.  revealed that greater allelic and functional diversity is present at B4galnt2 than that previously observed in derived M. m. domesticus populations. Indeed, the M. m. domesticus population from Iran and M. spretus population from Spain both display a modified RIIIS/J allele, which appears to turn off gastrointestinal expression of B4galnt2 without turning on vascular expression. Interestingly, the frequency of this modified RIIIS/J allele class is higher than in any of the derived M. m. domesticus populations, which is consistent with it having the potential to be beneficial against infections without incurring the cost of prolonged bleeding times. Further, the M. M. musculus population from Kazakhstan contains yet another allele class at low frequency, termed “CRK”, which appears to be a recombinant allele driving expression in both the GI tract and blood vessels. For simplicity, however, we did not consider the Kazakh population containing this low frequency CRK class in the analysis. The M. M. castaneus population from India was also excluded, as no functional data on B4galnt2 expression patterns is available for this population/subspecies. Thus, we ultimately grouped the included populations into three categories, summarized in Table 1:
Populations where the RIIIS/J allele is either absent or its frequency is very low, which are assumed to be in a non-pathogenic environment. These include five M. m. domesticus populations across Germany and Northeastern France, one of which (Cologne-Bonn) was sampled twice;
Populations displaying intermediate RIIIS/J allele frequencies, which are assumed to be in a pathogenic environment. These include one M. m. domesticus population from North America, one M. m. domesticus population from Africa, and three M. m. domesticus populations from Southwestern France, one of which (Massif Central) was sampled twice;
Populations with a modified RIIIS/J allele that are assumed to carry no bleeding cost and likewise assumed to be in a pathogenic environment. These include one M. m. domesticus population from Iran and one M. spretus population from Spain.
To evaluate which model and parameters best explain the observations in natural populations, we estimated the similarity between the simulated and observed genotype frequencies (see Methods).
First, we observe for the populations assumed to be in a non-pathogenic environment (population group “A”) (Fig. 5a) that the simulations from the constant, non-pathogenic environment match well with the observed populations whenever cb > 0 and for every value of ch. Notably, when cb is very strong (>0.2) the population deviates from HWE. Further, the simulations from the constant pathogenic environment and the changing environments all match well with the observed data above the boundary line, for every value of ch. This might be explained by the asymmetry of the model, which generally favors CC individuals, yielding all simulations above the boundary to closely match the observed group A populations. Notably, the majority of the simulated population deviate from HWE, since only a small cost window above the boundary line is in equilibrium for the constant pathogenic environment and the rapidly switching one, when ch < ci. For the slowly switching environments, only small values of cb leave the population in equilibrium.
For the populations assumed to be in a pathogenic environment (population group “B”) (Fig. 5b), we observe that the constant non-pathogenic environment does not explain the observed data very well, only in the case where cb = 0, which is likely unrealistic. For the constant pathogenic- and the switching environment, we observe that in general the best match to the real populations is at the boundary line, with the populations simulated in a rapidly changing environment for ch = 0, reaching the highest similarity to the observed populations. Notably, only the constant pathogenic- and rapidly switching environments with ch = 0 maintains HWE while providing relatively high similarity to the observed populations.
Finally, for the populations without bleeding phenotype (population group “C”) (Fig. 5c), we observe that the constant non-pathogenic environment is unlikely to explain the observed data: for cb > 0 the similarity is between 40 and 50%, although it reaches 70% for cb = 0. The constant pathogenic- and switching environment best explain the data below the boundary line, and it seems, as for the pathogenic populations, that these environments, are more likely to fit the real populations with ch = 0 than with ch > 0, as their genotype frequencies are very close to the observed ones (>90% similarity). The intermediate environment (S = 50) with ch = 0 and the slowly switching environment (S = 500) with all ch values both fit the populations well for null or very low values of cb, which is also consistent with our hypothesis that these mice carry no cost of bleeding. Notably, only the slowly switching environment produces high similarity with an excess of homozygotes, whereas the other environments produce high similarity while maintaining HWE.
Since all studied populations bear similar B4galnt2 alleles, with the exception of the modified RIIIS/J allele found in group C, it is reasonable to assume that the populations will bear largely the same fitness costs. This applies to the C57BL/6 J allele class in all three groups regarding the cost of infection, ci. Similarly, we can consider population groups A and B to carry the same cost of bleeding, cb > 0, whereas this is expected to be zero for group C. These assumptions allow us to further identify combinations of costs of bleeding and infection that might take place in nature, by comparing the similarity values across the three groups. First, group B can be seen as the “limiting factor” since they are approached by the simulated populations only at the boundary line in a rapidly changing environment and with ch = 0. This reduces the space of possible cost parameters to cb = ci/2, excluding the special case of cb = ci = 0. Population groups A and C are however never approached by the simulated populations at the boundary, but always above or below this line, respectively. This suggests that group A is in a constant environment without a relevant pathogen. There are however multiple possibilities for group C: assuming that cb is indeed zero in these populations, a constant environment, a rapidly changing environment, and an intermediate frequency environment are all capable of producing the observed genotype frequencies. This is limited to small non-zero values of ci in the constant environment, but is true for all ci > 0 for the switching environments.
Pathogen as frequency
The models we investigated so far are important to understand the behavior of the system, approach trench warfare dynamics and model seasonal changes, but another key biological aspect is the reaction of a pathogen to the changes in host genotype frequencies. Thus, to address this aspect we modified our model to let the pathogen population vary according to the host population. For this, we express the pathogen as the proportion of susceptible individuals in the host population. Interestingly, this model does not lead to a trench-warfare dynamic, but quickly reaches an equilibrium that is maintained over 10,000 host generations.
First, we observe that the average population (Fig. 6) is relatively similar to the fast and intermediate environment (S = 1; S = 50). The selection strength appears however weaker, as more populations remain in HWE compared to the switching environments. For ch = 0, the boundary line lies around cb = ci/2, as for the rapidly switching environment. Above this line the population is composed of mostly CC individuals, whereas below the line RR individuals and heterozygotes share the majority. As for the rapidly switching environment, when cb becomes too high compared to ci, the population deviates from HWE with an excess of homozygotes. For ch = ci/2 however, the boundary appears to move towards cb = ci. CC individuals represent the majority above this line, whereas RR individuals predominate below the line, as the selective pressures on the heterozygotes are stronger when ch > 0. In contrast to the switching environment, where the boundary shows a deviation from HWE, we observe deviations from HWE in this model only above this line, suggesting that the selective pressures might be weaker in this model. For ch = ci, the results are very similar to ch = ci/2. However, with the selection strength being stronger on the heterozygotes, we see more deviations from HWE and a higher proportion of homozygotes above and below the boundary, as we previously observed for switching environments.
Given the resemblance of the frequency-dependent model to the switching environments in terms of genotype frequencies, we might expect similar results concerning the fit to the real populations. Indeed, we observe (Fig. 7) that the populations assumed to be in a non-pathogenic environment, group A, are best approximated when the costs are above the boundary line, regardless of the value of ch. For the populations assumed to be in a pathogenic environment, group B, the model fits best around the boundary in general, and in particular for ch = 0. For the populations with no bleeding phenotype, group C, the models fit best below the boundary: for the special case of cb = 0 with ch = 0, and in a narrow space just below it with ch > 0. As for the switching environment, comparing the results across populations enables us to infer the best overall model. Again, groups B and C are only compatible with ch = 0. Group C is best approximated by the model for any ci > 0.2, whereas group B is best approximated for cb = ci/2 with ci > 0.2. Group A is however not compatible with the two other population categories, as they are approximated by any cb > ci/2 for ch = 0, suggesting once again that they are associated with a non-pathogenic environment.
In conclusion, although conceptually very different from the exogenously changing environment, the frequency-dependent model leads to similar results as the rapidly changing environment. In both models it appears that the model with ch = 0 is more plausible, with costs within the non-zero range of cb = ci/2.
Hardy-Weinberg based process
Our modified Wright-Fisher based process represents a very good approximation of the real populations when considering genotype frequencies. In addition, we developed an alternative process based on HWE that calculates the expected number of individuals from each genotype based on the weighted fitness, which also represents a computationally faster model, as there is only one calculation and few random steps per generation.
Interestingly, this model leads to remarkably similar results to those obtained from the Wright-Fisher based process. The genotype frequencies are indeed very similar for both population dynamics model (constant environment: Additional file 1: Figure S1; switching environments: Additional file 2: Figure S2, frequency-dependent environment: Additional file 3: Figure S3) and consequently, the comparison to the natural populations is also very similar to the Wright-Fisher based process (constant & switching environment: Additional file 4: Figure S4, frequency-dependent environment: Additional file 5: Figure S5).
This model however represents a stronger selection regime, as the number of random steps is highly reduced compared to the Wright-Fisher based process. This translates into heterozygote frequencies being lower in the HWE-based process than in the random process for otherwise equal model parameters (cb, ci, ch, S), which consequently leads to an excess of homozygotes.
Effect of mutations/migration
The results presented thus far were produced using a mutation rate μ = 0.005, which in our system can also be viewed as a proxy for migration, as the two B4galnt2 alleles considered are highly divergent including numerous SNPs and indels, which renders direct mutation from one functional allele class to the other unlikely. Since this mutation/migration rate could significantly impact the results, we investigated its effects using the HWE-based process.
For the constant environments (Fig. 8), we observe a relatively similar pattern as with mutation. In a constant non-pathogenic environment, we observe a neutral state for cb = 0, and CC individuals make up the majority of the population when cb > 0. For constant pathogenic environments, CC individuals predominate when cb > ci for every value of ch, as already observed in the model with mutations. When cb < ci and for every value of ch, RR individuals predominate, in contrast to the model with mutation, which leads to both the RR individuals and heterozygotes taking over the population for ch = 0. In general, the model without mutation represents a very strong selection regime, as heterozygotes are nearly absent from every model, except for the neutral states, where all genotypes have equal fitness (cb = 0 for constant non-pathogenic environment, cb = ci for constant pathogenic environment when ch = 0, cb = ci = 0 and cb = ci = 1 for constant pathogenic environment when ch > 0).
Although the starting environment has little influence on the long-term dynamics in the models with mutation, it has potentially strong consequences in the models without mutation. Therefore, it is necessary to distinguish the models that started in a pathogenic environment from those that started in a non-pathogenic environment for the exogenously changing environments.
For the rapidly switching environment (Fig. 9a), the starting environment has little influence on the results. These are however different from the models with mutation. Although the boundary line is at the same position, the heterozygotes are mostly in much lower frequency: they are limited to a narrow region around cb = ci/2 for ch = 0, and nearly absent for ch > 0 (except for the neutral state cb = ci = 0), leaving the population to be comprised of mostly homozygotes.
For the intermediate environment (Fig. 9b), the starting environment has limited influence on the model with ch = 0, but a strong influence on the models with ch > 0. For the ch = 0, the boundary line is at cb = ci/2, as for the model with mutation, but like for the constant and rapid environment, the heterozygotes are very low in frequency except on the boundary. For ch = ci/2, the simulations beginning in a non-pathogenic environment are relatively similar to those with mutation, but again the heterozygotes are nearly absent. For the simulations beginning with a pathogenic environment, the results are very similar to those from the constant pathogenic environment. Finally, for ch = ci, both starting environments look very similar to the corresponding constant environment.
For the slowly switching environment (Fig. 9c), the starting environment strongly influences all models. For all values of ch, the models beginning in a non-pathogenic environment resemble the constant non-pathogenic environment, with the CC individuals representing the majority of the population for nearly all cb > 0. For the simulations beginning in the pathogenic environment however, only models with ch > 0 resemble the constant pathogenic environment. The model with ch = 0 on the other hand differs, as the RR individuals make up the majority of the population only when cb < ci/2, while both homozygotes are present when ci/2 < cb < ci. This can be explained by the presence of heterozygote individuals. When the population begins in a pathogenic environment for ch = 0, both RR individuals and heterozygotes are favored over CC individuals, leading to an initial phase where both RR individuals and heterozygotes are in high frequency in the population. The presence of heterozygotes allows the reappearance of CC individuals when the environment switches, and they subsequently become favored over both other genotypes. When ch > 0 heterozygotes disappear quickly from the population as they have a lower fitness than both homozygotes. Thus, when the environment changes, the population is composed of only one homozygote genotype, and due to the absence of mutation the other genotypes are unable to return, hence the resemblance to the constant environment.
In contrast to the switching environments, the frequency-dependent environment without mutation (Fig. 10) is quite similar to the one with mutation. However, more populations deviate from HWE, and heterozygotes are in lower frequencies compared to the model with mutation.
In conclusion, mutation/migration appear to be dispensable to the maintenance of all three genotypes in the population only in the rapid and intermediate environments, both with ch = 0 or in a frequency-dependent environment. Moreover, the starting environment, which has a negligible effect on the population frequencies when mutation is allowed, seems to dictate the genotype frequencies for intermediate and slowly switching environments in the absence of mutation.
The study of polymorphism at B4galnt2 in house mice provides an interesting opportunity to elucidate the selective forces leading to the maintenance of disease-associated variation in nature. Previous studies revealed the action of long-term balancing selection on the one hand , in addition to dynamics on more recent timescales in the present study. Systematic review of signatures of balancing selection in the human genome identified genes involved in immunity lato sensu [6,7,8], indicating that host-pathogen interactions could be among the forces maintaining allelic diversity at B4galnt2. In this study, we set out to better understand the nature of potential trade-offs between resistance against pathogens and susceptibility to prolonged bleeding times surrounding variation at B4galnt2.
First, by extending our previous geographic survey of B4galnt2 alleles across Western Europe from two-  to eight locations , we discovered an intriguing pattern of B4galnt2 allele distribution, with northern populations being nearly devoid of the RIIIS/J allele in contrast to southwestern populations, which display intermediate RIIIS/J allele frequencies. Comparing this distribution of B4galnt2 allele frequencies to population structure based on either mtDNA haplotypes or unlinked nuclear microsatellite markers  reveals little- to no correspondence. In contrast, we confirmed the pattern of a partial selective sweep of the RIIIS/J allele  in two additional populations, indicating that the high frequency of the RIIIS/J allele in the Southwestern populations is most consistent with the action of recent natural selection.
To further investigate host-pathogen interactions as a possible driver of selection at B4galnt2, we developed a mathematical model derived from the classical Wright-Fisher process, to which we added random mating. This was necessary as existing mathematical models, including the Wright-Fisher process are applicable to haploid hosts. Moreover, heterozygous individuals are of particular interest in this study system, as they express B4galnt2 both in the blood vessels and the gastrointestinal tract, leaving them with a potential dual cost of prolonged bleeding and infection susceptibility. As our model focuses on the mechanisms maintaining host genotypes in a population, we treated the pathogen as an environmental probability p that can be constant (p = 1 - all susceptible hosts are infected; p = 0 - no host is infected), fluctuating (switching between 0 and 1 with frequency S expressed in host generations) or dependent on the proportion of susceptible individuals in the host population. We ran a very broad set of simulations, aiming to maximize the possible parameter combinations to fully understand the model and its behavior, and ultimately determine which parameters can lead to population frequencies similar to those observed in the wild.
Importantly, for nearly all combinations of parameters studied, we observe a similar dichotomy between regions of parameter space where a given genotype(s) is favored. The boundary between these regions can be observed at cb = ci/2 or cb = ci (depending on the values of ch and S), and is characterized by the coexistence of all three genotypes. The model is however asymmetric, as above the boundary, CC individuals always make up the majority of the population, while below the line RR individuals predominate, either alone or in conjunction with heterozygotes (depending on the values of ch and S).
Interestingly, the two different population dynamic models used, a Wright-Fisher process with random mating or a HWE-based process, lead to remarkably similar results. The average genotype frequencies are similar in both models, with the differences being characterized by the variation around the mean being much larger in the random process, which contains 3 N random steps per generation, while the HWE-based process contains only a few. This result is quite valuable as it allows the use of the HWE-based model to quickly screen the parameter space to identify interesting parameter combinations, which can subsequently be run under the random process, which is much slower to compute due to its speed being proportional to population size.
Not surprisingly, the mutation rate can have a strong influence on the model, although the extent of this influence greatly depends on the environmental model considered. First, for constant-, frequency-dependent- or rapidly switching environments, the effect of mutation is rather weak. Indeed, for constant environments the direction of selection does not change over time, leading to the near fixation of a favored genotype, as the small number of unfavoured individuals produced through mutation is quickly removed by selection. In contrast, for rapidly switching environments the favored individuals do not have the time to take over the population before the environment changes, leading to the maintenance of all three genotypes. In this case, the switch from one homozygote to the other when the environment changes is ensured partly through the mating of heterozygotes and partly through mutation, which explains the limited influence of the latter on the population frequencies. For frequency-dependent environments, the system quickly reaches an equilibrium, which stems from the fitness of the different genotypes with little input from the mutation rate. Second, for slowly fluctuating environments the absence of mutation drastically influences the population. Indeed, when the environment changes infrequently, the favored genotype has sufficient time to take over, such that when the environment changes, only mutation can rescue the other genotypes in the population, leading to completely different population dynamics with- or without mutation. With mutation the system is reversible, and the genotypes can alternate according to the environment. Without mutation the population becomes rapidly fixed for one homozygous genotype and can no longer change, even when the environment switches between pathogenic and non-pathogenic states.
In the context of long-term balancing selection at B4galnt2, we use the mutation rate as a proxy for migration, since the high divergence between B4galnt2 haplotypes and likely complex nature of the regulatory sequences separating them  make it unlikely that one allele would directly mutate to another. Moreover, M. m. domesticus populations in Western Europe display very little population structure on a continental scale , thus, models with migration are clearly more realistic.
Lastly, we compared the results of our models to the current and previous surveys [4, 5, 18] of DNA sequence polymorphism at B4galnt2, which we grouped in three categories: populations with very low RIIIS/J allele frequency, which are suspected to be in a non-pathogenic environment (group A); populations with intermediate RIIIS/J allele frequencies, which are suspected to be in a pathogenic environment (group B); and finally populations with high frequencies of a modified RIIIS/J allele that does not carry a cost of bleeding, which are suspected to be in a pathogenic environment (group C). For group B, the best fitting models are those with a rapidly switching environment and the frequency-dependent environment, each with costs of bleeding and infection on the boundary line (cb = ci/2), excluding the special case of cb = ci = 0. For group C, the best fitting models are those with rapid- and intermediate switching environments and the frequency-dependent environment, for costs below the boundary (cb < ci/2) and particularly for cb = 0, which corresponds to prior knowledge of the expression phenotype for the modified RIIIS/J allele. For group A, any environment is suitable to explain the observed data, for any costs above the boundary.
With the exception of group C harboring a modified RIIIS/J allele class, the remaining populations studied share related alleles and their corresponding expression patterns, suggesting that they may have similar costs of bleeding and infection. For the three population groups to be compatible, the costs must follow a cb = ci/2 relationship, excluding the cb = ci = 0 special case; group A must be in a constant non-pathogenic environment; groups B & C must be in a rapidly changing environment or frequency-dependent environment; and finally group C must have cb = 0. It is notable that the model predictions perfectly match our hypotheses for groups A and C. Moreover, the predictions for group B (a frequency-dependent or rapidly switching environment) represent two biologically relevant models, the first one taking the response of the pathogen into account, while the second may reflect seasonal changes.
Interestingly, the better fitting model is that in which heterozygotes and RIIIS/J homozygotes are protected against bacterial infections (ch = 0). Although our previous analysis of both commensal gut bacterial communities  and an experimental model of infectious gastroenteritis (S. typhimurium; ) suggest a potential benefit of the removal of B4galnt2 expression in the GI tract, we note that S. typhimurium is not a naturally occurring mouse gut pathogen and requires antibiotic pre-treatment in order to cause intestinal pathology. On the other hand, although it played a comparatively smaller role, blood vessel expression driven by the RIIIS/J allele does appear to provide a small degree of protection in the S. typhimurium model, which might be associated with increased mucus thickness . This indicates that the potential benefit of vascular B4galnt2 expression does not reside solely in the blood vessels -- as could be the case with e.g. systemic infection with Staphylococcus -- but also in the gastrointestinal tract, where it seems to have a protective effect against S. typhimurium colonization .
Although our results appear to be in agreement with previous research, they raise new questions regarding the potential mechanism(s) of protection against pathogens involving B4galnt2. Indeed, if the heterozygotes experience the same degree of protection against infection as RIIIS/J homozygotes (ch = 0), it implies that the benefit lies in the vascular expression of B4galnt2 and not in the absence of gastrointestinal expression. However, group C carries a modified RIIIS/J allele that turns off GI expression without turning the vascular expression on . Consequently the RIIIS/J allele would presumably lack the protection provided by vessel expression. This suggests that the RIIIS/J allele might have another, yet unknown function(s) that leads to protection against pathogens. Although further functional characterization of group C alleles is needed, another consideration given their presence in multiple species of the Mus genus (an ancestral M. m. domesticus population from Iran and M. spretus) is that the group C allele class is either ancestral or experienced compensatory evolutionary changes to remove the deleterious effect of blood vessel expression.
Notably, the potential pathogen-driven selection acting on B4galnt2 in wild mice appears to be similar to the well-documented malaria-driven selection acting on the beta globin gene (HBB) in humans [26, 27], where one allele confers resistance to a pathogen but carries a cost. In the case of B4galnt2, the RIIIS/J allele confers resistance to an unknown pathogen(s), while carrying a fitness cost due to prolonged bleeding; for HBB, the HbS allele confers resistance to malaria (Plasmodium falciparum) while producing the costly sickle cell phenotype [26, 27]. Where the systems diverge is in the genetic features of the genes. The HBB is a case of overdominance (heterozygote advantage), as heterozygotes have a higher fitness than either homozygote due to increased protection against malaria with a less severe sickling phenotype. Selection operating at B4galnt2 may be an example of either dominance and/or underdominance, depending on the model considered: when ch = 0, the resistance allele is dominant, and heterozygotes are as protected against infections as the RIIIS/J homozygotes; when ch > 0 however, the resistance allele is co-dominant and heterozygotes are less protected that the RR individuals, and their resulting fitness is less than that of both homozygotes (due to the double cost of infections and bleeding disorder). Interestingly, in the case of the well-studied major histocompatibility complex (MHC) genes, it is often assumed that heterozygote advantage is the leading selective force maintaining the genetic diversity, as dominance alone cannot . However, most studies that set out to determine the nature of selective forces acting on MHC genes failed to identify overdominance, but rather observed signs of rare-allele advantage (or negative frequency-dependence) and/or fluctuating selection, which is modeled here via the exogenous switching of pathogen presence. This indicates that our model is not only valid for the special case of B4galnt2, but also for other pathogen-interacting genes.
In conclusion, by comparing the results of our models to the patterns of polymorphism at B4galnt2 in natural populations and considering the still limited functional information available for this gene, we are able to recognize the long-term maintenance of the RIIIS/J allele through host-pathogens interactions as a viable hypothesis if its fitness costs due to prolonged bleeding time are roughly half those of being susceptible to a given pathogen. Further, our models identify that a putative dominant-, yet unknown protective function of the RIIIS/J allele appears to be more likely than a protective loss of GI expression in RIIIS/J homozygotes, which may help guide future experiments. Lastly, our model developed here may be used for numerous other biological scenarios, as it does not depend on explicit assumptions regarding a given gene or phenotype, but could be applied to any other diploid model where two co-dominant alleles are maintained by fluctuating selection.
The Mus musculus domesticus DNA samples used in this study were derived from previous trapping campaigns . Mus musculus domesticus is not a protected species. Permits for catching them were not required at the time they were caught. Individuals were caught on the properties of private landowners, with their oral permission to enter the property and catch mice. Mice were trapped in live traps by experienced personnel. Water-rich food was added in the trap. Straw was placed on the traps for mice to use as nest material, thus providing them with an adequate temperature, and reducing their stress-level. After trapping, mice were sacrificed by CO2 inhalation directly in the trap to avoid their handling by the experimenter, thus reducing the human-caused stress to its practical minimum. All procedures were conducted in accordance with German animal welfare law (Tierschutzgesetz) and FELASA guidelines.
We genotyped the B4galnt2 locus by sequencing a previously developed diagnostic PCR product following the procedure described in Johnsen et al. . Sequences were edited in Seqman (included in DNASTAR, Inc., Madison, Wisc.) and aligned to the homologous sequences from RIIIS/J (GenBank EF372924) and C57BL/6 J (NCBI build 36) using the ClustalW algorithm  included in MEGA 4.0.2 .
We further typed 12 microsatellite loci located around B4galnt2 cis-regulatory mutation as described previously  (Additional file 6). The alleles were called with GENEIOUS 7.0 (Biomatters Ltd) and the haplotypic phase was reconstructed with PHASE 2.1 . The algorithm was run 5 times with 10,000 iterations, a thinning interval of 100 and a burn-in of 10.000, and the best output was chosen based on the “goodness of fit”. Microsatellite gene diversity estimates were calculated using GenoDive 2.0 . The two microsatellite loci with highly reduced diversity - located at −30 kb and 0 kb from B4galnt2 start position - were additionally sequenced using the same primer pairs and PCR conditions as for their typing; the sequencing was performed as for the B4galnt2 Fragment 5, and the sequences were analyzed in GENEIOUS 7.0 (Biomatters Ltd). Finally, the STRUCTURE analysis included was taken from the output of Linnenbrink et al. .
We modeled the interaction between mouse hosts and pathogens as an evolutionary game [33, 34]. Evolutionary game theory uses mathematical models assuming that a genotype with a high fitness (given by the payoff from the interaction) has a high probability to spread within a population . More precisely, we investigated whether the presence of a pathogen can lead to the maintenance of the two murine alleles of B4galnt2. In short, B4galnt2 is a glycosyltransferase expressed either in the gastrointestinal epithelium (C57BL/6 J allele) or in the vascular endothelium (RIIIS/J allele). Although the second allele causes prolonged bleeding times, most likely at a significant cost to wild mice, both alleles have been maintained by balancing selection for over 2.8 My. Our working hypothesis is that this maintenance may be due to a protective effect of the RIIIS/J allele against pathogen(s), where protection could result from the loss of gastro-intestinal expression and/or from the gain of vascular expression.
As our goal is to understand whether the presence of a pathogen can lead to the maintenance of the host alleles in wild populations, we modeled the pathogen as being present with prevalence p. If p = 1, the pathogen is overwhelmingly present and every susceptible host is infected; if p = 0, no host is infected; if 0 < p < 1, the burden of infection for the susceptible hosts is proportional to p. This method allows us to focus on the host population and avoid the many assumptions we should make if we were to model a dynamic and co-evolving pathogen population (e.g. generation time relative to host generation time, population size, transmission mode, transmission efficiency…).
Under the hypothesis of balancing selection due to a trade-off between prolonged bleeding time and pathogen resistance/tolerance, we expect a trench warfare dynamic: (i) the frequency of susceptible hosts increases in the absence of the pathogen due to the cost of resistance, (ii) as the number of susceptible hosts increases, the pathogen population grows, favoring the resistant hosts, (iii) as the number of resistant hosts increases, the pathogen population declines, favoring the susceptible hosts, and the cycle continues [16, 17]. To approximate this phenomenon, we let the environment vary between a state where no pathogen is present (p = 0) and a state where pathogens are overwhelming (p = 1). This environmental switch (S) is based on the host generations so that the environment changes every S host generations. Varying S allows us to investigate different rates of evolution.
Alternatively, we approximated p with the proportion of susceptible hosts present in the population. This may represent a more “natural” model, as we do not externally force the switch from a pathogenic to non-pathogenic environment. This approximation is thereafter referred to as “frequency dependent environment”.
Considering the host population and our focus on the gene B4galnt2, we have two allelic states: R represents the vascular endothelium expression allele (RIIIS/J) and C represents the gastrointestinal epithelium expression allele (C57BL/6 J). These alleles can be combined into three possible genotypes - RR, RC and CC - and determine the payoff of an individual. RR individuals carry a cost of bleeding cb. CC individuals carry a cost of infection ci in a pathogenic environment and no cost in a pathogen-free environment. RC individuals present an interesting case as they express B4galnt2 in both tissues, potentially carrying both costs. We previously demonstrated that heterozygous mice display the same bleeding phenotype as the homozygous RR individuals ; hence, they carry the same cost of bleeding cb. However, we have no evidence that the intestinal phenotype of the heterozygotes is equivalent to that of CC individuals, so we defined a separate infection cost for the heterozygotes ch, which is equivalent to the dominance coefficient, h, of the pathogen resistance phenotype, and was explored for three values corresponding to different phenotypes. The first is ch = 0, whereby heterozygotes, like the RR individuals, are not infected by the pathogen. This corresponds to the hypothesis of protection through the gain of vascular expression, i.e. the resistance conferred by the R allele is dominant. The second is ch = ci, where heterozygotes are infected to the same degree as the CC individuals. This corresponds to the hypothesis of protection through the loss of gastrointestinal expression, i.e. the resistance allele carried by the R allele is recessive. Finally ch = ci/2 represents a state where heterozygotes carry an intermediate resistance phenotype to that of both homozygotes, i.e. the resistance conferred by the R allele is co-dominant.
Following these definitions, we can build the following payoff matrix, where the maximum payoff is 1, and to which the costs of the respective genotypes are withdrawn (similarly to the payoff used by Tellier et al. ):
In this payoff matrix, cb is the cost of bleeding, ci is the cost of infection for CC individuals, and ch is the cost of infection for heterozygotes. Finally, p is the pathogen prevalence, defined as 0 or 1 in the exogenously changing environment, or the proportion of susceptible hosts in the frequency dependent environment. Finally, we used an exponential fitness mapping, leading to the following fitness matrix:
Our model constrains the host population to a constant size N. We assume that each mouse transmits their strategy at a probability proportional to the fitness of the whole population. This corresponds to an evolutionary game in a Wright-Fisher process . However, since mice are diploid sexual organisms, we added additional steps to the typical haploid “asexual” Wright-Fisher process. First, we selected one individual based on fitness; second we randomly (no mate choice) selected another individual without replacement; third, given the genotypes of the parents, we drew one offspring at random from the set of possible offspring. This process is repeated N times, so that the population always consists of non-overlapping generations. As a result, this method contains 3 N random steps per generation.
Alternatively, we calculated the expected genotypes of the offspring population from the parent population weighted by its fitness, using Hardy-Weinberg equilibrium (HWE). First we calculated the weight W of each genotype according to their population frequencies P and their fitness f (1). Then we calculated the weighted allele frequencies A (2), and finally the offspring genotype frequencies O (3).
As this calculation creates non-integer values, the results were rounded to the next lower integer and the difference to N was adjusted by randomly adding/removing individuals of any genotype. Hence this method contains only few random steps per generation.
Finally, the new generation - obtained either by the HWE-based or the Wright-Fisher like process - is allowed to mutate with a probability μ taken from a Poisson distribution. In our case, this is a proxy for migration, as the two B4galnt2 alleles are highly divergent and thus unlikely to easily mutate from one state to the other.
Simulations (Additional file 9)
We ran every model with a local population size of 500 and a mutation rate of 0.005 over 10,000 generations. All simulations were started with a random population that consisted of roughly 1/3 of each genotype. We varied the genotype-specific costs cb, ch and ci, the environmental switch S, the starting environment and the definition of the pathogen. Each parameter combination was repeated 50 to 100 times. All the parameter combinations tested are summarized below:
Population Dynamics: Wright-Fisher process with random mating
ch = 0, ch = ci/2, ch = ci
cb and ci from 0 to 1 by 0.2 steps
Mutation rate: μ = 0.005
o Constant: 100 iterations for both environments
o Frequency-dependent: 100 iterations
o Switching environment:
Switch frequencies: 1, 10, 50, 100, 500, 1000, 5000
50 iterations starting with a pathogenic environment +50 starting with a non-pathogenic environment
Population Dynamics: HWE-based process
ch = 0, ch = ci/2, ch = ci
cb and ci from 0 to 1 by 0.05 steps
Mutation rate: μ = 0, μ = 0.005
o Constant: 100 iterations for both environments
o Frequency-dependent: 100 iterations
o Switching environment:
Switch frequencies: 1, 10, 50, 100, 250, 500, 750, 1000
100 iterations starting with a pathogenic environment +100 starting with a non-pathogenic environment
We present two aspects of the model results: first the average population frequencies are the frequencies of each genotype in the host population averaged across the 10,000 generations and across the 50 to 100 repetitions; second, the comparison to the real data consists of the average similarity (Si) between the simulated (F) and the observed (O) population frequencies for each genotype (RR for RIIIS/J homozygotes, CC for C57BL/6 J homozygotes and RC for heterozygotes):
- CC individuals:
Individuals homozygote for C57BL/6 J allele
- RC individuals:
Individuals heterozygotes for the RIIIS/J and C57BL/6 J allele
- RR individuals:
Individuals homozygote for the RIIIS/J allele
Von Willebrand disease
Von Willebrand factor
Mohlke KL, Nichols WC, Ginsburg D. The molecular basis of von Willebrand disease. Int J Clin Lab Res. 1999;29(1):1–7.
Mohlke KL, Purkayastha AA, Westrick RJ, Smith PL, Petryniak B, Lowe JB, Ginsburg D. Mvwf, a dominant modifier of murine von Willebrand factor, results from altered lineage-specific expression of a glycosyltransferase. Cell. 1999;96(1):111–20.
Stuckenholz C, Lu L, Thakur P, Kaminski N, Bahary N. FACS-assisted microarray profiling implicates novel genes and pathways in zebrafish gastrointestinal tract development. Gastroenterology. 2009;137(4):1321–32.
Johnsen JM, Teschke M, Pavlidis P, McGee BM, Tautz D, Ginsburg D, Baines JF. Selection on cis-regulatory variation at B4galnt2 and its influence on von Willebrand factor in house mice. Mol Biol Evol. 2009;26(3):567–78.
Linnenbrink M, Johnsen JM, Montero I, Brzezinski CR, Harr B, Baines JF. Long-term balancing selection at the blood group-related gene B4galnt2 in the genus Mus (Rodentia; Muridae). Mol Biol Evol. 2011;28(11):2999–3003.
Andrés AM. Balancing selection in the human genome. eLS. 2011;
Andres AM, Hubisz MJ, Indap A, Torgerson DG, Degenhardt JD, Boyko AR, Gutenkunst RN, White TJ, Green ED, Bustamante CD, et al. Targets of balancing selection in the human genome. Mol Biol Evol. 2009;26(12):2755–64.
Leffler EM, Gao ZY, Pfeifer S, Segurel L, Auton A, Venn O, Bowden R, Bontrop R, Wall JD, Sella G, et al. Multiple instances of ancient balancing selection shared between humans and chimpanzees. Science. 2013;339(6127):1578–82.
Fumagalli M, Cagliani R, Pozzoli U, Riva S, Comi GP, Menozzi G, Bresolin N, Sironi M. Widespread balancing selection and pathogen-driven selection at blood group antigen genes. Genome Res. 2009;19(2):199–212.
Segurel L, Gao Z, Przeworski M. Ancestry runs deeper than blood: the evolutionary history of ABO points to cryptic variation of functional importance. BioEssays. 2013;35(10):862–7.
Staubach F, Kunzel S, Baines AC, Yee A, McGee BM, Backhed F, Baines JF, Johnsen JM. Expression of the blood-group-related glycosyltransferase B4galnt2 influences the intestinal microbiota in mice. ISME J. 2012;6(7):1345–55.
Rausch P, Steck N, Suwandi A, Seidel JA, Kunzel S, Bhullar K, Basic M, Bleich A, Johnsen JM, Vallance BA, et al. Expression of the blood-group-related gene B4galnt2 alters susceptibility to salmonella infection. PLoS Pathog. 2015;11(7):e1005008.
McAdow M, Missiakas DM, Schneewind O. Staphylococcus Aureus secretes coagulase and von Willebrand factor binding protein to modify the coagulation cascade and establish host infections. J Innate Immun. 2012;4(2):141–8.
Thomer L, Schneewind O, Missiakas D. Multiple ligands of von Willebrand factor-binding protein (vWbp) promote Staphylococcus Aureus clot formation in human plasma. J Biol Chem. 2013;288(39):28283–92.
Imhof LA, Nowak MA. Evolutionary game dynamics in a Wright-fisher process. J Math Biol. 2006;52(5):667–81.
Stahl EA, Dwyer G, Mauricio R, Kreitman M, Bergelson J. Dynamics of disease resistance polymorphism at the Rpm1 locus of Arabidopsis. Nature. 1999;400(6745):667–71.
Woolhouse ME, Webster JP, Domingo E, Charlesworth B, Levin BR. Biological and biomedical implications of the co-evolution of pathogens and their hosts. Nat Genet. 2002;32(4):569–77.
Linnenbrink M, Wang J, Hardouin EA, Kunzel S, Metzler D, Baines JF. The role of biogeography in shaping diversity of the intestinal microbiota in house mice. Mol Ecol. 2013;22(7):1904–16.
Bonhomme F, Rivals E, Orth A, Grant GR, Jeffreys AJ, PRJ B. Species-wide distribution of highly polymorphic minisatellite markers suggests past and present genetic exchanges among house mouse subspecies. Genome Biology 2007;8:R80. https://doi.org/10.1186/gb-2007-8-5-r80.
Gabriel SI, Johannesdottir F, Jones EP, Searle JB. Colonization, mouse-style. BMC Biol. 2010;8
Harr B, Karakoc E, Neme R, Teschke M, Pfeifle C, Pezer Z, Babiker H, Linnenbrink M, Montero I, Scavetta R, et al. Genomic resources for wild populations of the house mouse, Mus Musculus and its close relative Mus Spretus. Sci Data. 2016;3:160075.
Bonhomme F, Orth A, Cucchi T, Rajabi-Maham H, Catalan J, Boursot P, Auffray JC, Britton-Davidian J. Genetic differentiation of the house mouse around the Mediterranean basin: matrilineal footprints of early and late colonization. Proc Biol Sci. 2011;278(1708):1034–43.
Jones EP, Johannesdottir F, Gunduz I, Richards MB, Searle JB. The expansion of the house mouse into north-western Europe. J Zool. 2011;283(4):257–68.
Johnsen JM, Levy GG, Westrick RJ, Tucker PK, Ginsburg D. The endothelial-specific regulatory mutation, Mvwf1, is a common mouse founder allele. Mamm Genome. 2008;19(1):32–40.
Salcedo T, Geraldes A, Nachman MW. Nucleotide variation in wild and inbred mice. Genetics. 2007;177(4):2277–91.
Kwiatkowski DP. How malaria has affected the human genome and what human genetics can teach us about malaria. Am J Hum Genet. 2005;77(2):171–92.
Williams TN, Obaro SK. Sickle cell disease and malaria morbidity: a tale with two tails. Trends Parasitol. 2011;27(7):315–20.
Spurgin LG, Richardson DS. How pathogens drive genetic diversity: MHC, mechanisms and misunderstandings. P R Soc B. 2010;277(1684):979–88.
Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994;22(22):4673–80.
Tamura K, Dudley J, Nei M, Kumar S. MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol. 2007;24(8):1596–9.
Stephens M, Smith NJ, Donnelly P. A new statistical method for haplotype reconstruction from population data. Am J Hum Genet. 2001;68(4):978–89.
Meirmans PG, Van Tienderen PH. GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol Ecol Notes. 2004;4(4):792–4.
Broom M, Rychtář J. Game-theoretical models in biology. Boca Raton: CRC Press, Taylor and Francis Group; 2013.
Hofbauer J, Schuster P, Sigmund K. Game dynamics in Mendelian populations. Biol Cybern. 1982;43(1):51–7.
Tellier A, Moreno-Gamez S, Stephan W. Speed of adaptation and genomic footprints of host-parasite coevolution under arms race and trench warfare dynamics. Evolution. 2014;68(8):2211–24.
Wickham H. Reshaping data with the reshape package. J Stat Softw. 2007;21(12):1–20.
Taiyun Wei VS: Corrplot: visualization of a correlation matrix. 2016.
We thank Silke Carstensen for excellent technical assistance and Aurélien Tellier for comments on the manuscript.
This work was supported by the International Max Planck Research School in Evolutionary Biology, the German Research Foundation (DFG) grants BA 2863/2–1, BA 2863/2–2 and the Excellence Cluster 306 “Inflammation at Interfaces” to J.F.B.
Availability of data and materials
B4galnt2 sequences are deposited at GenBank under the accession numbers: KY709331 - KY709452.
Microsatellite sequences from Sanger sequencing are available at GenBank, under the accession numbers: KY746338-KY746349.
The animals used in this study are Mus musculus domesticus, a species that is not protected. Permits for catching them were not required at the time they were caught. All procedures were conducted in accordance with German animal welfare law (Tierschutzgesetz) and FELASA guidelines.
Consent for publication
Arne Traulsen is an editor of BMC Evolutionary Biology in the section “Theories and models”. We declare no other competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Average genotype frequencies in the model with a constant environment. The frequencies are displayed according to the cost of bleeding (y axis) and of infection (x axis). For the non-pathogenic environment (left panel), all ch values are equivalent since ci = ch = 0 in all cases. For the pathogenic environment however, the different models of ch values are shown. The average genotype frequencies across 100 simulations using the HWE; process, each with 10,000 generations, are displayed. The frequencies are color-coded according to the legend on the right. Stars indicate an excess of homozygotes. (PDF 192 kb)
Average genotype frequencies in the model with a switching environment. The frequencies are displayed according to the frequency of environmental change expressed in host generations, the cost of bleeding (y axis) and of infection (x axis). The average genotype frequencies across 200 simulations using the HWE-process, each with 10,000 generations, are displayed for ch = 0 (A), ch = ci/2 (B) and ch = ci (C). The frequencies are color-coded according to the legend on the right. Stars indicate an excess of homozygotes. (PDF 469 kb)
Average genotype frequencies in the model with a frequency-dependent environment. The frequencies are displayed according to the value of ch, the cost of bleeding (y axis) and of infection (x axis). The average genotype frequencies across 100 simulations using the HWE-process, each with 10,000 generations, are displayed. The frequencies are color-coded according to the legend on the right. Stars indicate an excess of homozygotes. (PDF 152 kb)
Similarity of the populations simulated with the HWE-process to the natural populations. A) Similarity to populations from Group A, B) Similarity to populations from Group B, C) Similarity to populations from Group C. The similarity is displayed according to the value of ch, the cost of bleeding (y axis) and of infection (x axis), and the modeled environment (constant with or without pathogen, and switching between pathogenic and non pathogenic every 1, 50 or 500 host generations). The similarity is color-coded according to the legend on the right. Stars indicate an excess of homozygotes. Full similarity is achieved when all genotype frequencies coincide. (PDF 1079 kb)
Similarity of the natural populations to the populations simulated in the model with a frequency-dependent environment, using the HWE-process. The similarity is displayed according to the value of ch, the cost of bleeding (y axis) and of infection (x axis). The similarity is color-coded according to the legend on the right. Stars indicate an excess of homozygotes. (PDF 197 kb)
Microsatellite data. Repeat number for the 12 microsatellite markers linked to B4galnt2. (XLSX 62 kb)
Model. Python code of the model used in this study. (PY 47 kb)
Model ReadMe. Description of the model parameters and output formats to ease the use of the python code. (PDF 92 kb)
Simulated Average Populations. Text file containing the results of all simulations done in this study to allow for direct comparison with natural datasets without the need to rerun the program. (ZIP 10672 kb)
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Cite this article
Vallier, M., Abou Chakra, M., Hindersin, L. et al. Evaluating the maintenance of disease-associated variation at the blood group-related gene B4galnt2 in house mice. BMC Evol Biol 17, 187 (2017). https://doi.org/10.1186/s12862-017-1035-7
- Blood group
- Host-pathogen interaction
- Balancing selection
- Evolutionary game theory
- Wright-fisher process