Molecular genetics and population studies of the KIR and HLA gene complexes

Division Of Basic Sciences - Nci

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Abstract

HLA-E is expressed at low levels by most cell types, and is loaded primarily with nonamer peptides, called VL9, derived from the signal peptides (SPs) of HLA-A, -B, -C, and -G. HLA-E is the ligand for the inhibitory CD94/NKG2A and activating CD94/NKG2C receptors expressed on subsets of NK and T cells. HLA-E-mediated inhibition dominates over activation because CD94/NKG2A is expressed on a much greater fraction of effector cells and has higher affinity for HLA-E than does CD94/NKG2C. Like KIR, CD94/NKG2A plays a role in NK cell education, which results in functional competence of cells expressing inhibitory receptors that bind self-HLA class I. Functionally competent CD94/NKG2A positive NK cells can eliminate target cells in the periphery that have downregulated levels of HLA-E on their cell surface. HLA class I SPs are moderately polymorphic, but not all allotypes can generate peptides suitable for HLA-E loading. We systematically characterized the degree to which all common HLA-A-, HL-B and -C derived SPs facilitate cell surface expression of HLA-E and recognition by CD94/NKG2. We found that among 16 common classical HLA class I SP variants, only 6 can be efficiently processed to generate epitopes that enable CD94/NKG2 engagement, which we term 'functional SPs'. The single functional HLA-B SP, known as HLA-B-21M, induced high HLA-E expression, but conferred the lowest receptor recognition. Consequently, HLA-B-21M SP competes with other SPs for providing epitope to HLA-E and reduces overall recognition of target cells by CD94/NKG2A, calling for reassessment of previous disease models involving HLA-B/-21M. The combined frequencies of functional SPs at each individual HLA locus are remarkably similar across populations despite well-described differences in HLA allele frequencies across these same populations, suggesting that selection pressure may maintain prescribed frequencies of functional SPs at each locus. The frequency patterns indicate a dominant role for HLA-A and HLA-C loci in HLA-E-mediated regulation of effector cells at the population level, particularly given that the single 'functional' HLA-B SP (SP-6B) confers poor CD94/NKG2 recognition. The major contribution of HLA-A and HLA-C is also supported by the frequencies of HCMV VL9 variants, which mimic HLA-A and HLA-C (but not HLA-B) functional SPs with frequencies that correlate between the virus and the host. This correlation suggests viral adaptation to mimic host. Interestingly, VMAPRTLIL, the most frequent HCMV VL9 variant, is a derivative of an HLA-C SP, which induced relatively high levels of HLA-E expression and receptor recognition in our study, suggesting that HCMV has adapted not only to the most common host classical HLA VL9 variant, but also to the most optimal in terms of binding to HLA-E and engaging CD94/NKG2A. Differential CD94/NKG2 recognition of HLA-E-VL9 is likely to impact immune responses in disease/clinical settings. Our comprehensive characterization of HLA class I SP polymorphism and its influence on HLA-E-mediated regulation of effector cells expressing CD94/NKG2 provide the foundation for building computational models to predict strength of immune responses based on HLA class I genotypes, affording more accurate interpretation of disease association data and understanding of disease pathogenesis. HLA class I and II loci are essential elements of innate and acquired immunity. Their exceptional influence on disease outcome has now been made clear by genome-wide association studies. HLA polymorphism has been the main focus for determining HLA effects on disease. However, HLA expression levels have also been implicated in disease outcome, adding another dimension to the extreme diversity of HLA that impacts variability in immune responses across individuals. To estimate HLA expression, immunogenetic studies traditionally rely on qPCR. Recently however, multiple bioinformatic methods have been developed to accurately estimate HLA expression from RNA-seq data. We analyzed three types of expression data for HLA class I genes for a matched set of individuals: RNA-seq, qPCR, and cell surface HLA-C expression. We observed a moderate correlation between expression estimates from qPCR and RNA-seq for HLA-A, -B, and -C. However, our study suggests areas that require improvement in the determination of HLA transcript expression. Comparisons between RNA-seq and qPCR, for example, should employ uniform processing of samples across methods (e.g., same RNA isolation protocol, storage/thawing time, RNA integrity) to limit artefactual differences associated with these methods. Mapping short reads to single reference genomes or transcriptomes clearly generates biases, and strategies that map reads accounting for HLA polymorphism are necessary. There is also a need to develop methods that adequately account for isoform variation. In this context, long-read data, which directly generates full transcript information, can be a powerful tool. Finally, copy number variation should also be considered when quantifying expression levels. HLA class I is loaded with peptide by the multiprotein peptide loading complex (PLC) within the ER. Tapasin, a key protein of the PLC, bridges HLA class I molecules and the transporter associated with antigen processing TAP. It also stabilizes HLA class I in a peptide-receptive conformation, promoting peptide exchange to ensure binding of sufficiently high affinity peptides and stability on the cell surface. HLA class I allotypes vary in their dependence on tapasin for peptide loading and expression on the cell surface, which may confer selective immune advantages for CTL recognition depending on disease type, ultimately affecting the quality of immune responses. We have quantified tapasin dependence (TD) values for 250 HLA allotypes commonly present across worldwide populations. The data show a continuum of TD values for these allotypes. Among these 250 allotypes, 106 pairs differed by only a single amino acid, showing a range of differences in TD level between allotypes of each pair. Single amino acid changes within some regions, such as positions 60-80 of the HLA-A molecule, consistently showed no effect on TD level. Other positions, such as 97-116 of HLA-B (within/near the F pocket), either strongly affected TD level (resulting in high log delta TD values) for certain pairs of allotypes, or had very little impact on TD level (low log delta TD). Overall, the data suggest that variation in certain regions of the HLA peptide binding groove (hotspots) are more important than others in determining TD level, but complexities of the peptide binding region (PBR) structure in general can impact whether variation within the hotspot will or will not impact TD level. Given the global contribution of the PBR in determining TD level, we have considered structural and conformational changes that may potentially impact the interactions between tapasin and HLA class I allotypes, and influence TD. Molecular dynamic (MD) simulations, which provide an estimate of the motion (and thus, stability) of a molecule in 3D, can reveal differences in stability between proteins as measured by the root mean square deviation (RMSD). We have begun to explore RMSD for pairs of alleles that differ by only a single amino acid. The structure of the more tapasin-dependent allele of a given pair generally appeared less conformationally stable (higher RMSD) compared to that of the tapasin-independent allele (lower RMSD). Overall, these data suggest that tapasin-dependent allotypes are generally less capable of adopting a peptide receptive conformation on their own, perhaps explaining their greater dependence on tapasin for optimal peptide loading.