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Immunogenetics

, Volume 63, Issue 6, pp 325–335 | Cite as

Functional classification of class II human leukocyte antigen (HLA) molecules reveals seven different supertypes and a surprising degree of repertoire sharing across supertypes

  • Jason Greenbaum
  • John Sidney
  • Jolan Chung
  • Christian Brander
  • Bjoern Peters
  • Alessandro SetteEmail author
Original Paper

Abstract

Previous studies have attempted to define human leukocyte antigen (HLA) class II supertypes, analogous to the case for class I, on the basis of shared peptide-binding motifs or structure. In the present study, we determined the binding capacity of a large panel of non-redundant peptides for a set of 27 common HLA DR, DQ, and DP molecules. The measured binding data were then used to define class II supertypes on the basis of shared binding repertoires. Seven different supertypes (main DR, DR4, DRB3, main DQ, DQ7, main DP, and DP2) were defined. The molecules associated with the respective supertypes fell largely along lines defined by MHC locus and reflect, in broad terms, commonalities in reported peptide-binding motifs. Repertoire overlaps between molecules within the same class II supertype were found to be similar in magnitude to what has been observed for HLA class I supertypes. Surprisingly, however, the degree to which repertoires between molecules in the different class II supertypes also overlapped was found to be five to tenfold higher than repertoire overlaps noted between molecules in different class I supertypes. These results highlight a high degree of repertoire overlap amongst all HLA class II molecules, perhaps reflecting binding in multiple registers, and more pronounced dependence on backbone interactions rather than peptide anchor residues. This fundamental difference between HLA class I and class II would not have been predicted on the basis of analysis of either binding motifs or the sequence/predicted structures of the HLA molecules.

Keywords

MHC HLA class I HLA class II Peptide binding T cell epitopes 

Notes

Acknowledgments

This work was supported with funds from the National Institutes of Health, National Institute of Allergy and Infectious Diseases (NIH-NIAID) contracts HHSN266200400006C, HHSN272200900044C, HHSN272200900042C, and HHSN272200700048C (all to AS). We thank Carla Oseroff for MHC purification and tissue culture; Amiyah Steen, Carrie Moore, and Sandy Ngo for help with the binding assays; and Howard Grey for his comments and helpful discussions.

Supplementary material

251_2011_513_MOESM1_ESM.pdf (185 kb)
Supplemental Figure 1 Bootstrapping analysis of MHC allele clusters. Bootstrapping was performed using the BootstrapClusterTest function of the ClassDiscovery R package. The number of times each allele fell into the same cluster with every other allele was measured. Blue indicates a low frequency of co-clustering and yellow indicates a high frequency. The large yellow blocks that emanate away from the diagonal and generally coincide with cluster borders indicate that the dataset is intrinsically structured according to the defined clusters (PDF 185 kb)
251_2011_513_MOESM2_ESM.pdf (178 kb)
Supplemental Figure 2 Bootstrapping analysis of shuffled dataset. Bootstrapping was performed on a dataset with shuffled binding events. The presence of yellow only along the diagonal indicates that there is no structure to this dataset, in contrast to the original dataset (PDF 178 kb)
251_2011_513_MOESM3_ESM.doc (33 kb)
Supplemental Table 1 Average co-clustering frequency (DOC 33 kb)
251_2011_513_MOESM4_ESM.xls (383 kb)
Supplemental Table 2 (XLS 383 kb)

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Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Jason Greenbaum
    • 1
  • John Sidney
    • 1
  • Jolan Chung
    • 1
  • Christian Brander
    • 2
  • Bjoern Peters
    • 1
  • Alessandro Sette
    • 1
    Email author
  1. 1.La Jolla Institute for Allergy and ImmunologyLa JollaUSA
  2. 2.AIDS Research Institute, Fundacio irsiCaixa-HIVACATHospital Universitari Germans Trias i PujolBadalona, BarcelonaSpain

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