Rh Antigens Immunogenicity Prediction Using in silico analysis of HLA Binding

Human red blood cells (RBCs) contain more than 362 erythrocyte antigens, classified into various blood group systems. As of November 2023, the International Society of Blood Transfusion has classified blood types according to 45 blood group systems. The Rh system comprises numerous antigens located on variant forms of RhD and RhCE proteins. Alloimmunization refers to the production of alloantibodies against alloantigens expressed on RBCs following exposure to RBC antigens through transfusion, pregnancy, or transplantation. In RBC alloimmunization, there are two main types of epitopes involved: B cell epitopes, which are native three-dimensional structures on the antigen to which antibodies directly bind, and T cell epitopes, which are linear peptides presented by MHC molecules on antigen-presenting cells. This dual recognition mechanism is critical in the immune response to transfused RBCs and highlights the complexity of alloimmunization. The risk of RBC alloimmunization increases based on disease type, ethnicity, human leukocyte antigen (HLA) alleles, sex, and transfusion volume.

We performed in silico binding analysis of Rh antigens and HLA loci using the machine learning with artificial neural networks. We used the NetMHCpan-4.1 and NetMHCIIpan-4.1 algorithms to predict HLA-peptide binding. In NetMHCpan-4.1 analysis, strong binding was scattered and did not show significant positions (hot spots). However, in NetMHCIIpan-4.1 analysis, the HLA class II pilot study showed a few clustered hotspots. Rh antigens (RHD*01, RHD*01.01, RHD*01.W.1, RHD*01.W.2, RHD*01.W.3, and RHCE*01) on HLA class II (HighQ-DRB, HighQ-DP, HighQ-DQ, DRB1, DRB3, DRB4, DRB5, DP, and DQ) because HLA class II molecules present RBC antigens via antigen-presenting cells (APCs) to interact them to T-cell receptors. We investigated the frequency and hotspots of HLA-DRB1 in four ethnic groups (Caucasian, African American, Hispanic, and Asian) and used the population frequencies of HLA alleles from the Allele Frequency Net Database.

The RhD and RhCE antigens showed several distinct hotspots for each of the HLA-DRB, -DQA-DQB, and DPA-DPB peptides of HLA class II. In in silico analysis, hot spots were in the intracellular, transmembrane, and exofacial region, depending on HLA-DRB, -DQA-DQB, and DPA-DPB. For RhD and RhCE antigens in HLA-DRB, the amino acid start position of the hot spot (strong binding site) had 15 amino acid positions. They were identified: RhD (38^th, 54^th, 98^th, 125^th, 153^rd, 161^st, 165^th, 239^th, 250^th, 269^th, 303^rd, 347^th, 358^th, 390^th, and 407^th) and RhCE (38^th, 54^th, 62^nd, 124^th, 130^th, 161^st, 165^th, 239^th, 250^th, 267^th, 269^th, 303^rd, 358^th, 390^th, and 407^th). The different positions unique to RhCE were 62^nd, 124^th, 130^th, and 267^th, which were not present in RhD antigens. The unique positions in RhCE antigens indicate variations that may impact the alloimmunization of RhCE antigens, compared to the RhD antigens. The amino acids in the hot spot of RhD antigens in the HLA-DRB were as follows: 38^th (amino acid start position of hot spot) LEDQKGLVA (core amino acid), 54^th LTVMAAIGL, 98^th LSQFPSGKV, 125^th ISVDAVLGK, 153^rd LRMVISNIF, 161^st FNTDYHMNM, 165^th YHMNMMHIY, 239^th FNTYYAVAV, 250^th VTAISGSSL, 269^th YVHSAVLAG, 303^rd LISVGGAKY, 347^th LVLDTVGAG, 358^th MIGFQVLLS, 390^th LKIWKAPHE, and 407^th FWKFPHLAV. In HLA-DRB, each of the RhD antigens had the same core amino acids (epitope peptide), except for RHD*01W.1. A hot spot of RHD*01W.1 in HLA-DRB changed to p.Val270Gly (269^th YGHSAVLAG), unlike a hot spot of RHD*01W.2, RHD*01W.3, and RHD*01.01. RhD and RhCE antigens showed significant differences in hot spots across the four ethnic groups. In Caucasians, HLA-DRB1*15:01 had the highest frequency, and showed a hot spot of LSQFPSGKV at the 98^th amino acid start position in the RhD antigen, and a hot spot of LKIWKAPHV at the 390^th amino acid start position in the RhCE antigen.

Our findings highlight the significance of immunogenicity between Rh antigens and HLA-DR, suggesting the potential clinical utility of predicting antibody development in blood transfusions. This in silico approach offers novel insights into understanding and managing alloimmunization events, particularly in patients with multiple alloantibodies when RBC transfusion is required. This study is the first attempt to use these bioinformatics methods to predict the immunogenicity of Rh antigens and HLA class II.