MHC

Polymorphisms sometimes occur in host molecules that directly bind to parasites. For example, the MHC class I and II alleles are the most polymorphic of all human genes. The three main class I loci for presenting peptides, designated A, B, and C, currently have 175, 349, and 90 alleles described, respectively.

The class II molecules have separate designations for individual components of each molecule. The highly polymorphic components tend to be in the β₁ chains that contact bound peptides (Marsh et al. 2000). The β₁ chains for the DR, DQ, and DP class II molecules currently have 246, 44, and 86 alleles described, respectively. The IMGT/HLA online database lists recent allelic counts for both class I and class II loci, as described in Robinson et al. (2000; see http://www.ebi.ac.uk/imgt/hla/).

The MHC molecules bind to parasite peptides and present those peptides to T cells. Differences in MHC genotype cause significant variation between hosts in their ability to bind particular antigenic variants (Yewdell and Bennink 1999). Many studies have shown associations between MHC genotype and disease susceptibility (Hill 1998).

The MHC molecules also shape the TCR repertoire. As T cells mature in the thymus, they bind to MHC molecules presenting self-antigens. Those TCRs that bind too strongly cause the associated T cells to die. Those TCRs that bind too weakly fail to provide sufficiently strong reinforcing signals, again causing the associated T cells to die. Less than 1% of T cells pass these checks (Marsh et al. 2000). Thus, the particular MHC alleles of each individual strongly influence the naive TCR repertoire.

Variant naive repertoires lead to different TCR clones being stimulated in different individuals when challenged by the same epitope (Maryanski et al. 1996, 1999). Because helper T cells influence antibody response and other aspects of immune regulation, the variable TCR repertoire may have additional consequences beyond CTL variability.

Proteolysis of antigens and transport of peptides determine the peptides available for MHC binding. Strong challenge by a particular parasite could lead to selection favoring or disfavoring specific patterns of proteolysis. However, I am not aware of any evidence for polymorphism in proteolytic enzymes. The peptide transporter, TAP, is polymorphic: the two subunits, TAP1 and TAP2, have six and four sequences listed in the IMGT/HLA database (Robinson et al. 2000). So far, no functional differences between alleles have been found (Marsh et al. 2000).

TCR Germline

Different TCR germline loci somatically recombine and mutate to generate the DNA that codes for the variable, antigen-binding part of the TCR (Janeway et al. 1999). These generative mechanisms allow each individual to produce a huge variety of TCR binding specificities.

The intensity of direct selection on germline polymorphisms may be rather weak because specific recognition of antigens depends primarily on somatic mechanisms to create variability. However, the germline alleles do set the initial conditions on which somatic processes build, so it is certainly possible that germline polymorphisms influence individual tendencies to react to particular antigens.

The limited data currently available indicate that some germline polymorphisms exist for the TCR (e.g., Reyburn et al. 1993; Hauser 1995; Moffatt et al. 1997; Moody et al. 1998; Sim et al. 1998; see http://imgt.cines.fr). One interesting study found an interaction between a human germline polymorphism in a subunit of the TCR (VA8.1) and an MHC class II polymorphism (HLA-DRB1) (Moffatt et al. 1997). The authors analyzed two variants of the VA8.1 allele and the six most common HLA-DRB1 alleles. Individuals with enhanced allergic response to a dust mite antigen tended to have one of the two VA8.1 variants combined with the HLA-DRB1*1501 allele.

Moffatt et al. (1997) measured allergic response by the titer of IgE antibodies, which stimulate allergic symptoms (Janeway et al. 1999). Most likely, the TCR and MHC class II polymorphisms influence IgE via helper T cells—TCR binding to antigens presented by MHC class II stimulates helper T cells, which in turn play a role in antibody stimulation. Thus, specific recognition by the TCR and MHC can affect specific recognition by antibodies (Shirai et al. 1999).

Su et al. (1999) compared different TCR germline alleles across several vertebrate species in a phylogenetic analysis. Differences between species do not directly influence antigenic variation in parasites unless the parasites infect different species. But phylogenetic comparisons may illuminate the forces that shape TCR germline variability within species.

BCR Germline

The variable portion of the B cell receptor (BCR) develops by somatic recombination and mutation similar to the processes that generate variable TCRs. Antibodies are secreted forms of the BCR. I found only one report of a major germline polymorphism in the alleles that make up the variable components of the BCR. The same polymorphic alleles at a single BCR germline locus occur in both rabbits and snowshoe hares, suggesting that this polymorphism was inherited from a common ancestor and maintained for a long time in each species (Su and Nei 1999).

Hauser (1995) suggested that somatic hypermutation (affinity maturation) of the BCR provides a buffer between the germline and the matured BCR specific for particular antigens. The TCR has limited somatic mutation after the initial genetic recombinations, perhaps exposing germline TCRs to more intense selective pressures than BCRs. However, lack of observed variability in germline BCR genes may simply reflect limited study.

Match to Variant Cellular Receptors

Major deletions of cellular receptor genes can interfere with parasites that depend on those receptors for binding or entry into cells. For example, the human CCR5 gene encodes a coreceptor required for HIV-1 to enter macrophages. A 32-bp deletion of this gene occurs at a frequency of 0.1 in European populations. This deletion prevents the virus from entering macrophages (Martinson et al. 1997; O'Brien and Dean 1997; Smith et al. 1997).

Hill (1998) reviews cases in which variations in the hosts' vitamin D and other cellular receptors are associated with susceptibility to various diseases. It is not clear whether minor variants of cellular receptors occur sufficiently frequently to favor matching variation of parasites for attachment to those receptors.

Examples of Quantitative Variability

Stimulation of naive CD4⁺ helper T cells leads to proliferation of either T_H1 or T_H2 helper T cells. T_H1 response typically stimulates CTL proliferation, whereas T_H2 response typically stimulates antibody production.

Several studies have found genetic variation among hosts in the regulation of T_H1 versus T_H2 response. In mice, the actions of multiple genetic loci combine to determine regulation of T_H1 versus T_H2 against Leishmania infections (Coffman and Beebe 1998; Power 2000). Mice that develop a T_H1 response control infection because Leishmania can be cleared by CTLs. By contrast, those mice that develop a T_H2 response fail to clear infection because Leishmania cannot be controlled by a dominant antibody response.

In pigs, polygenic control has been observed for several traits including antibody response, with an important contribution of non-MHC loci; proliferative and cytokine responses of mononuclear blood lymphocytes, such as T cells, B cells, and natural killer (NK) cells; T cell–mediated inflammatory response to innocuous antigens (delayed-type hypersensitivity); and the total number and relative proportions of the various kinds of blood-borne immune cells (reviewed by Edfors-Lilja et al. 1998). High heritabilities have been estimated for several of these traits.

Studies of other organisms have also found polygenic control of quantitative immune responses outside the MHC region (Biozzi et al. 1982). Linkage studies of mice have begun to map locations of genes that influence quantitative variability in components of immunity (Puel et al. 1995; Wu et al. 1996). Many studies of humans report nucleotide polymorphisms in promoters of cytokines and other immune regulatory loci (Daser et al. 1996; Agarwal et al. 2000; Terry et al. 2000). Some human polymorphisms are associated with differential response to particular diseases (Hill 1998; Foster et al. 2000).

Interleukin 6 (IL6) Promoter Polymorphism

IL6 plays a central role in the regulation of immunity (Janeway et al. 1999). It stimulates hepatic acute phase response to infection, induction of fever, differentiation and activation of macrophages and T cells, growth of B cells, and many other functions (Terry et al. 2000). Many cell types release IL6 in response to infection or irritating stimuli.

The rate of gene expression regulates plasma levels of IL6 because this cytokine is cleared rapidly from circulation. Various transcription factors and steroid hormones interact with the promoter region of this gene to produce synergistic combinations of positive and negative stimuli for transcription (Terry et al. 2000).

Terry et al. (2000) sequenced the promoter region of IL6 for 442 haplotypes from 221 humans. They found polymorphisms at three nucleotide sites and a variable-length AT run. These promoter polymorphisms influenced expression level in a nonadditive way—a single nucleotide change may have been associated with higher or lower levels of expression depending on other variable sites in the haplotype. In addition, the IL6 polymorphisms influenced expression in different ways in response to different stimulatory signals and when in different cell types.

The three single nucleotide polymorphisms are separated by 25 and 398 nucleotides. The GGG combination occurs in 54% of haplotypes, AGC in 40%, and GCG in 5%.

It is not clear what processes maintain this polymorphism. I consider a few possibilities in the remainder of this section and in the following sections.

Each haplotype could be associated with a particular variant of the coding region for IL6, thereby linking the pattern of gene expression to different properties of the cytokine. However, Terry et al. (2000) sequenced the coding region from twenty individuals and found no polymorphisms. Thus, positive interactions and linkage between promoters and coding regions seem unlikely in this case.

Alternatively, polymorphisms that affect phenotype are often maintained by a balance between the rate at which deleterious mutation adds variability and the rate at which selection can remove deleterious mutants. Mutation-selection balance probably explains a significant portion of the total quantitative genetic variability observed in populations (Barton and Turelli 1987).

Mutation-selection balance usually matches a high-frequency allele maintained by selection against a distribution of low-frequency mutant variants. Natural selection culls those lower-fitness variants, but mutation maintains a constant flow of new variants. For the IL6 polymorphism, mutation-selection balance cannot explain the similar frequencies of the two dominant haplotypes, because neither haplotype is a rare mutational variant of the other. Mutation-selection balance probably does explain the two rare trinucleotide haplotypes at frequencies of less than 1% observed by Terry et al. (2000).

Daser et al. (1996) and Mitchison (1997) suggested that heterozygosity in cytokine promoters enhances flexibility of the immune response. Enhanced flexibility could occur in heterozygotes by increasing the range of inputs that control the response kinetics of the cytokine.

Heterozygote advantage could explain the observed frequencies of the two common haplotypes if fitnesses are ordered as GGG/AGC > GGG/GGG > AGC/AGC, in which the heterozygote is more fit than either homozygote. However, by this scenario of heterozygote advantage, many individuals would carry lower-fitness homozygote genotypes. Given the three haplotype frequencies listed above, the expected frequency of homozygotes would be the sum of the squared haplotype frequencies, or 45%.

The frequency of heterozygotes would be increased by a larger number of promoter haplotypes. But such diversity would mean that any individual carried two randomly chosen haplotype patterns of regulatory control among the possible haplotypes. It is difficult to image how complementarity between diverse regulatory haplotypes would occur.

MHC Class II Promoter Polymorphism

Several studies describe promoter polymorphisms of the class II loci (Louis et al. 1993; Reichstetter et al. 1994; Guardiola et al. 1996; Cowell et al. 1998; Mitchison et al. 2000). These regulatory polymorphisms can affect the relative expression of the class II alleles in different cell types. For example, Louis et al. (1994) and Vincent et al. (1997) showed that different class II alleles at the HLA-DRB locus are expressed at different levels. Variable transcription rates were associated with nucleotide polymorphisms in the promoters of these alleles.

The class II molecules present antigens to the helper T cells. Those helper T cells tend to differentiate into type 1 or type 2 (T_H1 versus T_H2) responses. The T_H1 versus T_H2 split influences the balance between several immune effector functions, particularly CTLs favored by T_H1 and antibodies favored by T_H2.

Cowell et al. (1998) and Mitchison et al. (2000) have argued that levels of MHC class II expression, under the control of regulatory polymorphisms, influence the tendency for T_H1 versus T_H2 response. Higher class II expression appears to trigger a cascade leading toward T_H1 helper T cells, whereas lower class II expression favors development of T_H2 helper T cells. Thus, variable regulatory genotypes can influence important aspects of the hosts' immune responses.

Cowell et al. (1998) propose two processes by which heterozygosity in MHC class II regulatory regions might be favored. First, they suggest that in a heterozygote "one pattern of expression could favor differentiation of T_H1 cells while another could favor T_H2's, so that the two in combination together would generate a balanced immune response."

Heterozygote advantage imposes a severe cost, because a binary trait such as high or low expression causes at least 50% of the population to be homozygous and therefore at a disadvantage. Tissue-specific expression or intermediate expression does not require heterozygosity with the associated cost of frequent, disadvantaged homozygotes.

Cowell et al. (1998) propose a second explanation in which promoters are advantageously linked to particular MHC alleles. They argue that "the T_H1-favoring [pattern of expression] might become associated with exons involved in resistance to infections best dealt with by T_H1 cells, and the T_H2-favoring pattern with resistance to infections needing T_H2's."

The available data do not provide a clear test of synergism between particular promoters and class II alleles. The most interesting information comes from sequences of the promoter regions linked to different class II HLA-DRB1 alleles. Louis et al. (1993) found that divergence of promoters followed the phylogenetic history for the linked structural alleles.

Continuous divergence of promoters as a function of phylogenetic distance suggests drifting changes constrained by the balance between mutational input and selection to maintain functional integrity. There may also be a tendency for compensatory nucleotide changes, in which one slightly deleterious substitution is compensated by a second substitution at a different site (Hartl and Taubes 1996; Burch and Chao 1999).

Phylogenetic divergence does not rule out Cowell et al.'s (1998) hypothesis of functional synergism between promoter and allele. Common phylogeny provides the background for all evolutionary divergence, but other processes can occur. For example, functionally synergistic associations may exist between nucleotides in promoter and structural regions that cannot be explained by common phylogeny.

Benefits and Costs of Cytokine Expression

Increased expression of the immune response can have both benefits and costs. On the positive side, a more intense immune response may clear infections more rapidly. On the negative side, immune effectors can often be harsh medicine, causing collateral damage to host tissues.

Promoter polymorphisms may be maintained by the balance between effective clearance and tissue damage. For example, the human IL1 promoter polymorphism affects expression of the pro-inflammatory cytokine IL1β. Increased expression of this cytokine plays an important role in stimulating the inflammatory immune response against the widespread gastric pathogen Helicobacter pylori (Jung et al. 1997). Host genotypes with stronger IL1β responses probably clear the infection more effectively, but also suffer greater gastric tissue damage and a higher risk of gastric cancer (El-Omar et al. 2000).

A different trade-off occurs between high and low expression of IL10 during HIV-1 infection (Shin et al. 2000). Humans homozygous for a more active promoter variant of IL10 had a significantly delayed onset of AIDS relative to hosts either heterozygous or homozygous for a less active promoter. IL10 inhibits macrophage proliferation (Kollmann et al. 1996; Schols and De Clercq 1996), possibly reducing the number of activated macrophages available for viral replication. In this case, down-regulation of an immune effector, the macrophages, appears to reduce viral spread. Against other pathogens that do not replicate in macrophages, high IL10 expression and reduced macrophage proliferation may favor the pathogen.

Major Polymorphisms versus Rare Variants

The examples of variable regulatory control all have major polymorphisms in promoters, with two or more alternative haplotypes at significant frequencies. Initial screening would naturally turn up major polymorphisms rather than rare variants, which would be harder to detect. But each regulatory element inevitably has some rare variants in the population. Mutation provides a constant influx of such variants; natural selection culls those variants slowly in proportion to their negative effects on fitness.

The balance of mutation and selection almost certainly creates quantitative variability in every aspect of immune regulation. Each individual likely has several rare mutants spread across different regulatory steps, causing variable quantitative genetic profiles for the thresholds to trigger responses and the intensities of responses.

The balance of mutation and selection sets the amount of quantitative variability in each regulatory component. The influx of quantitative variability depends on how mutations translate into quantitative effects on regulation. The culling of variation depends on the intensity of natural selection acting on the particular regulatory step. Steps that affect fitness relatively weakly will accumulate relatively more variation, until a balance of mutation and selection occurs. Steps that affect fitness strongly will accumulate relatively less variation. In each case, the variation in fitness will be roughly the same.

The major polymorphisms likely arise by processes in addition to mutation-selection balance. In those cases, various trade-offs between immune control of parasites and collateral damage probably balance the fitnesses of different variants. The collateral damage may be inflammatory or other negative effects of a hyperimmune response, as in the gastric tissue damage promoted by IL6. Or more rarely, the damage may arise from reducing the proliferation of immune cells that normally control pathogens but also can be the target of parasitic attack. This latter trade-off appears to occur in IL10 control of macrophages in the context of HIV-1 infection.

Regulatory variability may sometimes alter immunodominance because cytokines modulate positive and negative stimulation of T and B cell clones. Badovinac et al. (2000) provide a hint of how regulatory cytokines influence immunodominance of a CTL response in mice. In their study, normal levels of interferon-γ were associated with about a 5-fold ratio of immunodominant to subdominant T cell clones for two Listeria monocytogenes epitopes. By contrast, reduced interferon-γ was associated with roughly equivalent clonal expansion of CTLs specific for these two epitopes.

Badovinac et al.'s (2000) study shows that variations in nonspecific components of immune regulation may affect immunodominance. Immunodominance, in turn, affects the intensity of selection on particular pathogen epitopes. Thus, variations in immune regulation may influence patterns of antigenic variation.

1. Effects of antigenic variation on MHC diversity

Parasites may favor change in the frequencies of host MHC alleles. For example, the human class I MHC molecule B35 binds to common epitopes of Plasmodium falciparum's circumsporozoite protein (Gilbert et al. 1998). The B35 allele occurs in higher frequency in The Gambia, a region with endemic malaria, than in parts of the world with less severe mortality from malaria.

It would be interesting to know if variant epitopes influence the frequency of matching MHC alleles. For example, one epitope variant may be common in one location and another variant common in another location. Do those variants affect the local frequencies of MHC alleles in the host population? This question focuses attention on the kind of selection pressure parasites impose on MHC alleles.

Each MHC allele may have a qualitative relationship with each particular epitope, in that one amino acid substitution in the epitope can have a large effect on binding. But over the lifetime of an individual, each MHC type meets many potential epitopes from diverse parasites. Thus, MHC alleles vary quantitatively in the net benefit they provide by their different matches to the aggregate of potential epitopes. It may be rather rare for a single parasite to impose strong, sustained pressure on a particular MHC allele. Perhaps only major killers of young hosts can cause such strong selection. Mathematical models could clarify the nature of aggregate selection imposed on MHC alleles.

2. Effects of MHC diversity on antigenic variation

Does the distribution of MHC alleles in the host population shape the distribution of antigenic variants?

It would be interesting to compare parasites in two locations, each location with hosts that have different frequencies of MHC alleles. In principle, differing host MHC profiles could influence antigenic variation. But the pattern of selection may be complex. Each epitope could potentially interact with several MHC alleles. The net effect depends on the balance of fitness gains by an escape substitution against one MHC allele and the potential costs of that substitution in terms of functional performance and the possibility of creating enhanced binding to other MHC alleles.

It would also be interesting to compare parasites that attack only a single host species with those that attack multiple vertebrate species. The generalist parasites face variable selective pressures in the different hosts.

3. Regulatory variability of immune response

I described a few major polymorphisms in immune regulatory promoters. I also listed several hypotheses to explain those polymorphisms: linkage with synergistic coding regions, mutation-selection balance, and heterozygote advantage. These explanations lack empirical support, and the case of heterozygote advantage may also have logical flaws.

I reviewed two cases in which the costs and benefits of a more potent regulatory stimulus may favor polymorphism. In one example, host genotypes with stronger IL1β responses probably clear infection by Helicobacter pylori more effectively, but also suffer greater gastric tissue damage and a higher risk of gastric cancer.

In another example, humans with a more active promoter of IL10 had a significantly delayed onset of AIDS. IL10 inhibits macrophage proliferation, possibly reducing the number of activated macrophages available for HIV-1 replication. Against other pathogens that do not replicate in macrophages, reduced macrophage proliferation may favor the pathogen against the immune system.

Mathematical analysis could establish the necessary conditions to maintain polymorphism for controls of the immune response by trade-offs between high and low expression. Such models would clarify the kinds of experiments needed to understand these polymorphisms.

4. Effects of regulatory variability on antigenic diversity

Two possibilities come to mind. First, different patterns of immune regulation may affect immunodominance (Badovinac et al. 2000). Second, immune regulation may affect the intensity and duration of memory. Immunological memory shapes antigenic diversity because a parasite often cannot succeed in hosts previously infected by a similar antigenic profile.

5. Regulatory variability as model for quantitative variability

The widespread genetic variability of quantitative traits forms a classical unsolved puzzle of genetics. To solve this puzzle, one must understand the links between nucleotide variants, the regulatory control of trait development and expression, and fitness. The immune system is perhaps the most intensively studied complex regulatory system in biology. This chapter provided a glimpse of how it may be possible to link genetic variation to immune regulatory control and its fitness consequences. The studies done so far focus on major polymorphisms. But it may soon be possible to study rare variants and their association with regulatory variability and susceptibility to different pathogens. This may lead to progress in linking quantitative genetic variability and the evolution of regulatory control systems.