The IMGT/HLA database

The IPD-IMGT/HLA Database

It is 24 years since the IPD-IMGT/HLA Database, http://www.ebi.ac.uk/ipd/imgt/hla/, was first released, providing the HLA community with a searchable repository of highly curated HLA sequences. The database now contains over 35 000 alleles of the human Major Histocompatibility Complex (MHC) named by the WHO Nomenclature Committee for Factors of the HLA System. This complex contains the most polymorphic genes in the human genome and is now considered hyperpolymorphic. The IPD-IMGT/HLA Database provides a stable and user-friendly repository for this information. Uptake of Next Generation Sequencing technology in recent years has driven an increase in the number of alleles and the length of sequences submitted. As the size of the database has grown the traditional methods of accessing and presenting this data have been challenged, in response, we have developed a suite of tools providing an enhanced user experience to our traditional web-based users while creating new programmatic access for our bioinformatics user base. This suite of tools is powered by the IPD-API, an Application Programming Interface (API), providing scalable and flexible access to the database. The IPD-API provides a stable platform for our future development allowing us to meet the future challenges of the HLA field and needs of the community.

IPD-IMGT/HLA Database

The IPD-IMGT/HLA Database, http://www.ebi.ac.uk/ipd/imgt/hla/, currently contains over 25 000 allele sequence for 45 genes, which are located within the Major Histocompatibility Complex (MHC) of the human genome. This region is the most polymorphic region of the human genome, and the levels of polymorphism seen exceed most other genes. Some of the genes have several thousand variants and are now termed hyperpolymorphic, rather than just simply polymorphic. The IPD-IMGT/HLA Database has provided a stable, highly accessible, user-friendly repository for this information, providing the scientific and medical community access to the many variant sequences of this gene system, that are critical for the successful outcome of transplantation. The number of currently known variants, and dramatic increase in the number of new variants being identified has necessitated a dedicated resource with custom tools for curation and publication. The challenge for the database is to continue to provide a highly curated database of sequence variants, while supporting the increased number of submissions and complexity of sequences. In order to do this, traditional methods of accessing and presenting data will be challenged, and new methods will need to be utilized to keep pace with new discoveries.

Identification of a novel HLA allele, HLA-A* 24:02:90, in a Han Chinese individual

HLA-A*24:02:09 shows one nucleotide difference from HLA-A*24:02:01:01 at position 408 in exon 3 (codon 112 GGG>GGC).

Identification of a novel DRB1 allele through intergenic recombination between HLA-DRB1 and HLA-DRB3∗02 in a Chinese family

In this study, a novel DRB1 allele was revealed by routine HLA-SBT typing noted for its extensive mismatches to any known DRB1 alleles within the exon 2. Sequences containing the exons 2, 3 of HLA-DRB1, their surrounding introns, and the full-length cDNA of DRB1 were analyzed to determine a possible recombination event. Interestingly, the sequences of entire exon 2 were characterized as DRB3(∗)02:02:01:01/02; while exon 3 were characterized as DRB1(∗)14 like alleles. Further analysis of the sequences using Simplot software suggested that an intergenic recombinant event (i.e. exchange of sequence between non-allelic genes) may have occurred between DRB3(∗)02 allele and DRB1(∗)14 like allele, and the recombination sites are located at intron 1 and the boundary of exon 2 and intron 2 of DRB1. There are 5 CGGGG sequences flanking each side of exon 2 could serve as potential recombination site. Moreover, the full-length cDNA of the novel allele has been identified. The exon 1 and exon 3 to exon 6 share the same sequence as DRB1(∗)14 like alleles. At the mRNA level, the new allele has no significant difference when compared with the other DRB1 allele. This novel recombinant allele is also found to be paternally inherited. In conclusion, this is the first report of a DRB1 and DRB3 intergenic recombination event involving whole exon 2, which generate a new DRB1(∗)14:141.

MHCcluster, a method for functional clustering of MHC molecules

The identification of peptides binding to major histocompatibility complexes (MHC) is a critical step in the understanding of T cell immune responses. The human MHC genomic region (HLA) is extremely polymorphic comprising several thousand alleles, many encoding a distinct molecule. The potentially unique specificities remain experimentally uncharacterized for the vast majority of HLA molecules. Likewise, for nonhuman species, only a minor fraction of the known MHC molecules have been characterized. Here, we describe a tool, MHCcluster, to functionally cluster MHC molecules based on their predicted binding specificity. The method has a flexible web interface that allows the user to include any MHC of interest in the analysis. The output consists of a static heat map and graphical tree-based visualizations of the functional relationship between MHC variants and a dynamic TreeViewer interface where both the functional relationship and the individual binding specificities of MHC molecules are visualized. We demonstrate that conventional sequence-based clustering will fail to identify the functional relationship between molecules, when applied to MHC system, and only through the use of the predicted binding specificity can a correct clustering be found. Clustering of prevalent HLA-A and HLA-B alleles using MHCcluster confirms the presence of 12 major specificity groups (supertypes) some however with highly divergent specificities. Importantly, some HLA molecules are shown not to fit any supertype classification. Also, we use MHCcluster to show that chimpanzee MHC class I molecules have a reduced functional diversity compared to that of HLA class I molecules. MHCcluster is available at www.cbs.dtu.dk/services/MHCcluster-2.0.

The heterogeneous HLA genetic makeup of the Swiss population

This study aims at investigating the HLA molecular variation across Switzerland in order to determine possible regional differences, which would be highly relevant to several purposes: optimizing donor recruitment strategies in hematopoietic stem cell transplantation (HSCT), providing reliable reference data in HLA and disease association studies, and understanding the population genetic background(s) of this culturally heterogeneous country. HLA molecular data of more than 20,000 HSCT donors from 9–13 recruitment centers of the whole country were analyzed. Allele and haplotype frequencies were estimated by using new computer tools adapted to the heterogeneity and ambiguity of the data. Non-parametric and resampling statistical tests were performed to assess Hardy-Weinberg equilibrium, selective neutrality and linkage disequilibrium among different loci, both in each recruitment center and in the whole national registry. Genetic variation was explored through genetic distance and hierarchical analysis of variance taking into account both geographic and linguistic subdivisions in Switzerland. The results indicate a heterogeneous genetic makeup of the Swiss population: first, allele frequencies estimated on the whole national registry strongly deviate from Hardy-Weinberg equilibrium, by contrast with the results obtained for individual centers; second, a pronounced differentiation is observed for Ticino, Graubünden, and, to a lesser extent, Wallis, suggesting that the Alps represent(ed) a barrier to gene flow; finally, although cultural (linguistic) boundaries do not represent a main genetic differentiation factor in Switzerland, the genetic relatedness between population from south-eastern Switzerland and Italy agrees with historical and linguistic data. Overall, this study justifies the maintenance of a decentralized donor recruitment structure in Switzerland allowing increasing the genetic diversity of the national—and hence global—donor registry. It also indicates that HLA data of local donor recruitment centers can be used as reference data in both epidemiological and population genetic studies focusing on the genetic history of present European populations.

HLA DNA sequence variation among human populations: molecular signatures of demographic and selective events

Molecular differences between HLA alleles vary up to 57 nucleotides within the peptide binding coding region of human Major Histocompatibility Complex (MHC) genes, but it is still unclear whether this variation results from a stochastic process or from selective constraints related to functional differences among HLA molecules. Although HLA alleles are generally treated as equidistant molecular units in population genetic studies, DNA sequence diversity among populations is also crucial to interpret the observed HLA polymorphism. In this study, we used a large dataset of 2,062 DNA sequences defined for the different HLA alleles to analyze nucleotide diversity of seven HLA genes in 23,500 individuals of about 200 populations spread worldwide. We first analyzed the HLA molecular structure and diversity of these populations in relation to geographic variation and we further investigated possible departures from selective neutrality through Tajima's tests and mismatch distributions. All results were compared to those obtained by classical approaches applied to HLA allele frequencies.Our study shows that the global patterns of HLA nucleotide diversity among populations are significantly correlated to geography, although in some specific cases the molecular information reveals unexpected genetic relationships. At all loci except HLA-DPB1, populations have accumulated a high proportion of very divergent alleles, suggesting an advantage of heterozygotes expressing molecularly distant HLA molecules (asymmetric overdominant selection model). However, both different intensities of selection and unequal levels of gene conversion may explain the heterogeneous mismatch distributions observed among the loci. Also, distinctive patterns of sequence divergence observed at the HLA-DPB1 locus suggest current neutrality but old selective pressures on this gene. We conclude that HLA DNA sequences advantageously complement HLA allele frequencies as a source of data used to explore the genetic history of human populations, and that their analysis allows a more thorough investigation of human MHC molecular evolution.

Analysis of HLA-ABC locus-specific transcription in normal tissues

We developed a novel human leukocyte antigen HLA-ABC locus-specific quantitative real-time polymerase chain reaction (PCR) to determine the locus-specific gene expression of HLA-ABC in peripheral blood leukocytes (PBLs, n = 53), colon mucosa (n = 15), and larynx mucosa (n = 15). Laser-assisted tissue microdissection allowed us to study the selected cells without interference from surrounding stroma. We report evidence on the specificity of the technique, describing the HLA-ABC locus-specific gene expression patterns found in the PBLs and two solid tissues studied. PBLs showed a higher gene expression of HLA-B than of HLA-A or HLA-C (p = 4.7 × 10(-10) and p = 1.6 × 10(-6), respectively). In solid tissue, HLA-A and HLA-B gene expressions were similar and HLA-C expression lower. In particular, in larynx mucosa, significant differences were found between HLA-A and HLA-C expressions and between HLA-B and HLA-C expressions (p = 6.5 × 10(-4) and p = 8.1 × 10(-4), respectively). The same differences were observed in colon mucosa, but significance was not reached (p = 0.08 and p = 0.06, respectively). Differences in locus-specific regulation may be related to the control of cytotoxic responses of NK and CD8 positive T cells. Gene expression of HLA-ABC specific locus showed no intra-individual variability, but there was a high inter-individual variability. This may result from differences in the expression of common regulatory factors that control HLA-ABC constitutive expression.

MHC class II epitope predictive algorithms

SUMMARY

Major histocompatibility complex class II (MHC-II) molecules sample peptides from the extracellular space, allowing the immune system to detect the presence of foreign microbes from this compartment. To be able to predict the immune response to given pathogens, a number of methods have been developed to predict peptide-MHC binding. However, few methods other than the pioneering TEPITOPE/ProPred method have been developed for MHC-II. Despite recent progress in method development, the predictive performance for MHC-II remains significantly lower than what can be obtained for MHC-I. One reason for this is that the MHC-II molecule is open at both ends allowing binding of peptides extending out of the groove. The binding core of MHC-II-bound peptides is therefore not known a priori and the binding motif is hence not readily discernible. Recent progress has been obtained by including the flanking residues in the predictions. All attempts to make ab initio predictions based on protein structure have failed to reach predictive performances similar to those that can be obtained by data-driven methods. Thousands of different MHC-II alleles exist in humans. Recently developed pan-specific methods have been able to make reasonably accurate predictions for alleles that were not included in the training data. These methods can be used to define supertypes (clusters) of MHC-II alleles where alleles within each supertype have similar binding specificities. Furthermore, the pan-specific methods have been used to make a graphical atlas such as the MHCMotifviewer, which allows for visual comparison of specificities of different alleles.

The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding

MOTIVATION

Receptor-ligand interactions play an important role in controlling many biological systems. One prominent example is the binding of peptides to the major histocompatibility complex (MHC) molecules controlling the onset of cellular immune responses. Thousands of MHC allelic versions exist, making determination of the binding specificity for each variant experimentally infeasible. Here, we present a method that can extrapolate from variants with known binding specificity to those where no experimental data are available.

RESULTS

For each position in the peptide ligand, we extracted the polymorphic pocket residues in MHC molecules that are in close proximity to the peptide residue. For MHC molecules with known specificities, we established a library of pocket-residues and corresponding binding specificities. The binding specificity for a novel MHC molecule is calculated as the average of the specificities of MHC molecules in this library weighted by the similarity of their pocket-residues to the query. This PickPocket method is demonstrated to accurately predict MHC-peptide binding for a broad range of MHC alleles, including human and non-human species. In contrast to neural network-based pan-specific methods, PickPocket was shown to be robust both when data is scarce and when the similarity to MHC molecules with characterized binding specificity is low. A consensus method combining the PickPocket and NetMHCpan methods was shown to achieve superior predictive performance. This study demonstrates how integration of diverse algorithmic approaches can lead to improved prediction. The method may also be used for making ligand-binding predictions for other types of receptors where many variants exist.

Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research

BACKGROUND

Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW.

RESULTS

Because not all servers have predictors for all tested HLA-II molecules, we assessed a total of 113 predictors. The length of test peptides ranged from 15 to 19 amino acids. We tried three prediction strategies - the best 9-mer within the longer peptide, the average of best three 9-mer predictions, and the average of all 9-mer predictions within the longer peptide. The best strategy was the identification of a single best 9-mer within the longer peptide. Overall, measured by the receiver operating characteristic method (AROC), 17 predictors showed good (AROC > 0.8), 41 showed marginal (AROC > 0.7), and 55 showed poor performance (AROC < 0.7). Good performance predictors included HLA-DRB1*0101 (seven), 1101 (six), 0401 (three), and 0701 (one). The best individual predictor was NETMHCIIPAN, closely followed by PROPRED, IEDB (Consensus), and MULTIPRED (SVM). None of the individual predictors was shown to be suitable for prediction of promiscuous peptides. Current predictive capabilities allow prediction of only 50% of actual T-cell epitopes using practical thresholds.

CONCLUSION

The available HLA-II servers do not match prediction capabilities of HLA-I predictors. Currently available HLA-II prediction servers offer only a limited prediction accuracy and the development of improved predictors is needed for large-scale studies, such as proteome-wide epitope mapping. The requirements for accuracy of HLA-II binding predictions are stringent because of the substantial effect of false positives.