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Immunopeptidome Analysis of HLA-DPB1 Allelic Variants Reveals New Functional Hierarchies.

Peter van BalenMichel G D KesterWendy de KlerkPietro CrivelloEsteban Arrieta-BolañosArnoud H de RuInge JedemaYassene MohammedMirjam H M HeemskerkKatharina FleischhauerPeter A van VeelenJ H Frederik Falkenburg
Published in: Journal of immunology (Baltimore, Md. : 1950) (2020)
HLA-DP alleles can be classified into functional T cell epitope (TCE) groups. TCE-1 and TCE-2 are clearly defined, but TCE-3 still represents an heterogeneous group. Because polymorphisms in HLA-DP influence the presented peptidome, we investigated whether the composition of peptides binding in HLA-DP may be used to refine the HLA-DP group classification. Peptidomes of human HLA-DP-typed B cell lines were analyzed with mass spectrometry after immunoaffinity chromatography and peptide elution. Gibbs clustering was performed to identify motifs of binding peptides. HLA-DP peptide-binding motifs showed a clear association with the HLA-DP allele-specific sequences of the binding groove. Hierarchical clustering of HLA-DP immunopeptidomes was performed to investigate the similarities and differences in peptidomes of different HLA-DP molecules, and this clustering resulted in the categorization of HLA-DP alleles into 3-DP peptidome clusters (DPC). The peptidomes of HLA-DPB1*09:01, -10:01, and -17:01 (TCE-1 alleles) and HLA-DPB1*04:01, -04:02, and -02:01 (TCE-3 alleles) were separated in two maximal distinct clusters, DPC-1 and DPC-3, respectively, reflecting their previous TCE classification. HLA-DP alleles categorized in DPC-2 shared certain similar peptide-binding motifs with DPC-1 or DPC-3 alleles, but significant differences were observed for other positions. Within DPC-2, divergence between the alleles was observed based on the preference for different peptide residues at position 9. In summary, immunopeptidome analysis was used to unravel functional hierarchies among HLA-DP alleles, providing new molecular insights into HLA-DP classification.
Keyphrases
  • mass spectrometry
  • machine learning
  • deep learning
  • endothelial cells
  • blood pressure
  • single cell
  • transcription factor
  • rna seq
  • liquid chromatography
  • body composition
  • data analysis