PT  - JOURNAL ARTICLE
AU  - Johannes Textor
AU  - Franka Buytenhuijs
AU  - Dakota Rogers
AU  - Ève Mallet Gauthier
AU  - Shabaz Sultan
AU  - Inge M. N. Wortel
AU  - Kathrin Kalies
AU  - Anke Fähnrich
AU  - René Pagel
AU  - Heather J. Melichar
AU  - Jürgen Westermann
AU  - Judith N. Mandl
TI  - Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity
AID  - 10.1101/2022.11.23.517563
DP  - 2023 Jan 01
TA  - bioRxiv
PG  - 2022.11.23.517563
4099  - http://biorxiv.org/content/early/2023/03/22/2022.11.23.517563.short
4100  - http://biorxiv.org/content/early/2023/03/22/2022.11.23.517563.full
AB  - The T cell receptor (TCR) determines the specificity and affinity for both foreign and self-peptides presented by MHC. It is established that self-pMHC reactivity impacts T cell function, but it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naïve CD4+ T cells with low versus high self-pMHC reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that predicts self-reactivity directly from TCRβ sequences. This approach revealed that n-nucleotide additions and acidic amino acids weaken self-reactivity. We tested our ML predictions of TCRβ sequence self-reactivity using retrogenic mice. Extrapolating our analyses to independent datasets, we found high predicted self-reactivity for regulatory CD4+ T cells and low predicted self-reactivity for T cells responding to chronic infection. Our analyses suggest a potential trade-off between repertoire diversity and self-reactivity intrinsic to the architecture of a TCR repertoire.Competing Interest StatementThe authors have declared no competing interest.