Abstract
Our adaptive immune system has the remarkable ability to distinguish previously unseen foreign peptides from harmless self. This self-foreign discrimination was long thought to arise from the silencing of self-reactive T cells during negative selection in the thymus, but recent data show that negative selection is far from complete. Here we ask how a repertoire containing many self-reactive T cells can nevertheless discriminate self from foreign. We address this question using realistic-scale computational models of the T cell repertoire. Our models show that moderate T cell cross-reactivity automatically skews the post-selection repertoire towards peptides that differ systematically from self. But even when no systematic differences between self and foreign exist, discrimination remains possible if the peptides presented in the thymus are chosen in a way that minimizes the co-occurrence of similar, redundant self peptides. Thus, our model predicts that negative selection on a well-chosen subset of self peptides biases the resulting repertoire towards better detection of both self-similar and -dissimilar pathogens. This effect would allow the immune system to “learn self by example”, an ability shared with cognitive systems.
- Negative selection
- Central tolerance
- Self-nonself discrimination
- T cell repertoires
- Artificial immune system
- Learning by example