Communications Biology (Apr 2023)

Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome

  • Jonas Birkelund Nilsson,
  • Saghar Kaabinejadian,
  • Hooman Yari,
  • Bjoern Peters,
  • Carolina Barra,
  • Loren Gragert,
  • William Hildebrand,
  • Morten Nielsen

DOI
https://doi.org/10.1038/s42003-023-04749-7
Journal volume & issue
Vol. 6, no. 1
pp. 1 – 13

Abstract

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Abstract Human leukocyte antigen (HLA) class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the α- and β-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation. In addition, the contribution of trans-only variants (i.e. variants not observed in the population as cis) in shaping the HLA-DQ immunopeptidome remains largely unresolved. Here, we seek to address these issues by integrating state-of-the-art immunoinformatics data mining models with large volumes of high-quality HLA-DQ specific mass spectrometry immunopeptidomics data. The analysis demonstrates highly improved predictive power and molecular coverage for models trained including these novel HLA-DQ data. More importantly, investigating the role of trans-only HLA-DQ variants reveals a limited to no contribution to the overall HLA-DQ immunopeptidome. In conclusion, this study furthers our understanding of HLA-DQ specificities and casts light on the relative role of cis versus trans-only HLA-DQ variants in the HLA class II antigen presentation space. The developed method, NetMHCIIpan-4.2, is available at https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.2 .