Augmented virtual crossmatch for donor-induced antibody prediction by using high resolution human leukocyte antigen typing and human leukocyte antigen epitope mapping for better donor match
dc.contributor.advisor | Yelekçi, Kemal | en_US |
dc.contributor.author | Karadeniz, Sedat Tanju | |
dc.contributor.author | Yelekçi, Kemal | |
dc.date | 2023-01 | |
dc.date.accessioned | 2023-07-25T12:44:44Z | |
dc.date.available | 2023-07-25T12:44:44Z | |
dc.date.issued | 2023 | |
dc.department | Enstitüler, Lisansüstü Eğitim Enstitüsü, Biyoinformatik ve Genetik Ana Bilim Dalı | en_US |
dc.description.abstract | The Human Leukocyte Antigen (HLA) disparity between donors and recipients is the primary driver of Donor Specific Antibodies (DSA) formation and graft rejection after transplantation. We aimed to predict the DSA by finding the HLA antigen mismatches, searching the eplets of antigens that bind to the recipient's anti-HLA antibodies, calculating the number of shared eplets between the mismatched donor HLA antigens and the recipient's pre-transplantation anti-HLA antibody-bound antigens. We have used recipient-donor HLA Typing results and the recipient's pre-transplantation and post-transplantation anti-HLA antibody detection results by Luminex single antigen bead (Luminex-SAB) assay as retrospective data for calculation in five steps. We have compared the HLA Typing results to find the mismatched antigens in the first step and searched the relevant eplets for the recipient's pre-transplantation anti-HLA antibodies in the second step. Then we calculated the shared eplets between the donor's mismatched HLA antigens and the recipient's pre-transplantation anti-HLA antibodies to find the highest number of shares, then listed the most shared anti-HLA antibodies as the most probable DSA in the fourth step. Then, we confirmed the possible epitope's peptide AA (amino acid) sequences with the IEDB Bepipred-1.0 Antibody Epitope Prediction method using the donor's HLA antigen AA sequence. | en_US |
dc.identifier.uri | https://hdl.handle.net/20.500.12469/4386 | |
dc.identifier.yoktezid | 793509 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Kadir Has Üniversitesi | en_US |
dc.relation.publicationcategory | Tez | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | Virtual Crossmatch | en_US |
dc.subject | Antibody Prediction | en_US |
dc.subject | Epitope Mapping | en_US |
dc.subject | Donor Specific Antibody | en_US |
dc.subject | Kidney Transplantation | en_US |
dc.title | Augmented virtual crossmatch for donor-induced antibody prediction by using high resolution human leukocyte antigen typing and human leukocyte antigen epitope mapping for better donor match | en_US |
dc.type | Doctoral Thesis | en_US |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 9407938e-3d31-453b-9199-aaa8280a66c5 | |
relation.isAuthorOfPublication.latestForDiscovery | 9407938e-3d31-453b-9199-aaa8280a66c5 |
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