Is T Cell Negative Selection a Learning Algorithm?

• Published in: Cells (Basel, Switzerland) Vol. 9; no. 3; p. 690
• Main Authors: Wortel, Inge M. N., Keşmir, Can, de Boer, Rob J., Mandl, Judith N., Textor, Johannes
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI 11.03.2020; MDPI AG
• Subjects: Algorithms; artificial immune system; central tolerance; Central Tolerance - immunology; Humans; learning by example; negative selection; self-nonself discrimination; t cell repertoires; T-Lymphocytes - immunology; self-nonself discrimination; central tolerance; T cell repertoires; negative selection; learning by example; artificial immune system
• ISSN: 2073-4409; 2073-4409
• DOI: 10.3390/cells9030690

Our immune system can destroy most cells in our body, an ability that needs to be tightly controlled. To prevent autoimmunity, the thymic medulla exposes developing T cells to normal “self” peptides and prevents any responders from entering the bloodstream. However, a substantial number of self-reactive T cells nevertheless reaches the periphery, implying that T cells do not encounter all self peptides during this negative selection process. It is unclear if T cells can still discriminate foreign peptides from self peptides they haven’t encountered during negative selection. We use an “artificial immune system”—a machine learning model of the T cell repertoire—to investigate how negative selection could alter the recognition of self peptides that are absent from the thymus. Our model reveals a surprising new role for T cell cross-reactivity in this context: moderate T cell cross-reactivity should skew the post-selection repertoire towards peptides that differ systematically from self. Moreover, even some self-like foreign peptides can be distinguished provided that the peptides presented in the thymus are not too similar to each other. Thus, our model predicts that negative selection on a well-chosen subset of self peptides would generate a repertoire that tolerates even “unseen” self peptides better than foreign peptides. This effect would resemble a “generalization” process as it is found in learning systems. We discuss potential experimental approaches to test our theory.