ISSN: 2475-7640

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Abstrakt

Differential Association Between Responders HLA-DR Phenotypes and HLD-DR Antibody Production in Patients Awaiting for Renal Transplantation; Analysis of UNOS Database

Ahmed Shoker, Qingyong Xu and Hyun June Lim

Background: Immunogenicity of the Human Leukocytes Antigens (HLA) is highly variable. It is hypothesized that responder’s HLA-DR phenotypes contribute preferentially to produce antibodies against certain HLA-DR antigens. This study aims to stratify the immunogenicity of donor/responder’s HLA-DR phenotype combinations. Methods: Subjects studied were HLA-DR-homozygous patients waiting for kidney transplant in UNOS (United Network for Organ Sharing) database (n=2294) with anti-HLA-DR antibodies. Immunogenicity of recipient/donor DR combinations was determined by presence of significant positive and negative associations between HLA-DR phenotypes and HLA-DR antibodies as determined by likelihood analysis including odds ratios. Peptide binding affinity was determined with computer algorithms to corroborate our findings. Results: Out of 146 associations analyzed between HLA-DR phenotypes and antibody specificities, 88 combinations were significantly positive, 24 combinations were significantly negative, while 34 combinations were statistically insignificant. The highest positive association was seen between HLA-DR4-homogenous responders and anti-HLA-DR17 antibody (odds ratio= 4.05, p<0.0001). Directionality was found in the majority (68%) of acceptable mismatches. Some directionality in acceptability was explained by binding affinity between HLA-DR in responders and sensitizing indirect-pathway allopeptides. Conclusions: Generation of HLA-DR antibodies is influenced by recipient’s HLA-DR phenotypes. The results identified three groups of strong, irrelevant and acceptable HLA-DR mismatches. These findings support the possibility to predict, and therefore avoid, highly immunogenic donor-recipient HLA-DR combinations before kidney transplantation.

Haftungsausschluss: Dieser Abstract wurde mit Hilfe von Künstlicher Intelligenz übersetzt und wurde noch nicht überprüft oder verifiziert.