Learning predictive signatures of HLA type from T-cell repertoires.
T cells recognize a wide range of pathogens using surface receptors that interact directly with peptides presented on major histocompatibility complexes (MHC) encoded by the HLA loci in humans. Understanding the association between T cell receptors (TCR) and HLA alleles is an important step towards...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012724 |
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| _version_ | 1832540362649894912 |
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| author | María Ruiz Ortega Mikhail V Pogorelyy Anastasia A Minervina Paul G Thomas Thierry Mora Aleksandra M Walczak |
| author_facet | María Ruiz Ortega Mikhail V Pogorelyy Anastasia A Minervina Paul G Thomas Thierry Mora Aleksandra M Walczak |
| author_sort | María Ruiz Ortega |
| collection | DOAJ |
| description | T cells recognize a wide range of pathogens using surface receptors that interact directly with peptides presented on major histocompatibility complexes (MHC) encoded by the HLA loci in humans. Understanding the association between T cell receptors (TCR) and HLA alleles is an important step towards predicting TCR-antigen specificity from sequences. Here we analyze the TCR alpha and beta repertoires of large cohorts of HLA-typed donors to systematically infer such associations, by looking for overrepresentation of TCRs in individuals with a common allele.TCRs, associated with a specific HLA allele, exhibit sequence similarities that suggest prior antigen exposure. Immune repertoire sequencing has produced large numbers of datasets, however the HLA type of the corresponding donors is rarely available. Using our TCR-HLA associations, we trained a computational model to predict the HLA type of individuals from their TCR repertoire alone. We propose an iterative procedure to refine this model by using data from large cohorts of untyped individuals, by recursively typing them using the model itself. The resulting model shows good predictive performance, even for relatively rare HLA alleles. |
| format | Article |
| id | doaj-art-f6eecde544854f1f83b7518ac88087b7 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-f6eecde544854f1f83b7518ac88087b72025-02-05T05:30:39ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-01-01211e101272410.1371/journal.pcbi.1012724Learning predictive signatures of HLA type from T-cell repertoires.María Ruiz OrtegaMikhail V PogorelyyAnastasia A MinervinaPaul G ThomasThierry MoraAleksandra M WalczakT cells recognize a wide range of pathogens using surface receptors that interact directly with peptides presented on major histocompatibility complexes (MHC) encoded by the HLA loci in humans. Understanding the association between T cell receptors (TCR) and HLA alleles is an important step towards predicting TCR-antigen specificity from sequences. Here we analyze the TCR alpha and beta repertoires of large cohorts of HLA-typed donors to systematically infer such associations, by looking for overrepresentation of TCRs in individuals with a common allele.TCRs, associated with a specific HLA allele, exhibit sequence similarities that suggest prior antigen exposure. Immune repertoire sequencing has produced large numbers of datasets, however the HLA type of the corresponding donors is rarely available. Using our TCR-HLA associations, we trained a computational model to predict the HLA type of individuals from their TCR repertoire alone. We propose an iterative procedure to refine this model by using data from large cohorts of untyped individuals, by recursively typing them using the model itself. The resulting model shows good predictive performance, even for relatively rare HLA alleles.https://doi.org/10.1371/journal.pcbi.1012724 |
| spellingShingle | María Ruiz Ortega Mikhail V Pogorelyy Anastasia A Minervina Paul G Thomas Thierry Mora Aleksandra M Walczak Learning predictive signatures of HLA type from T-cell repertoires. PLoS Computational Biology |
| title | Learning predictive signatures of HLA type from T-cell repertoires. |
| title_full | Learning predictive signatures of HLA type from T-cell repertoires. |
| title_fullStr | Learning predictive signatures of HLA type from T-cell repertoires. |
| title_full_unstemmed | Learning predictive signatures of HLA type from T-cell repertoires. |
| title_short | Learning predictive signatures of HLA type from T-cell repertoires. |
| title_sort | learning predictive signatures of hla type from t cell repertoires |
| url | https://doi.org/10.1371/journal.pcbi.1012724 |
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