| First Author | Textor J | Year | 2023 |
| Journal | Cell Syst | Volume | 14 |
| Issue | 12 | Pages | 1059-1073.e5 |
| PubMed ID | 38061355 | Mgi Jnum | J:353512 |
| Mgi Id | MGI:7642287 | Doi | 10.1016/j.cels.2023.11.004 |
| Citation | Textor J, et al. (2023) Machine learning analysis of the T cell receptor repertoire identifies sequence features of self-reactivity. Cell Syst 14(12):1059-1073.e5 |
| abstractText | The T cell receptor (TCR) determines specificity and affinity for both foreign and self-peptides presented by the major histocompatibility complex (MHC). Although the strength of TCR interactions with self-pMHC impacts T cell function, it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naive CD4(+) T cells with low versus high self-reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that identifies population-level differences between TCRbeta sequence sets. This approach revealed that weakly self-reactive T cell populations were enriched for longer CDR3beta regions and acidic amino acids. We tested our ML predictions of self-reactivity using retrogenic mice with fixed TCRbeta sequences. Extrapolating our analyses to independent datasets, we predicted high self-reactivity for regulatory T cells and slightly reduced self-reactivity for T cells responding to chronic infections. Our analyses suggest a potential trade-off between TCR repertoire diversity and self-reactivity. A record of this paper's transparent peer review process is included in the supplemental information. |
