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Is T Cell Negative Selection a Learning Algorithm?

Department of Tumor Immunology, Radboud Institute for Molecular Life Sciences, Geert Grooteplein 26-28, 6525 GA Nijmegen, The Netherlands
Theoretical Biology, Department of Biology, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands
Department of Physiology, McGill University, 3649 Promenade Sir William Osler, Montreal, QC H3G 0B1, Canada
Authors to whom correspondence should be addressed.
Cells 2020, 9(3), 690;
Received: 21 January 2020 / Revised: 6 March 2020 / Accepted: 7 March 2020 / Published: 11 March 2020
(This article belongs to the Special Issue Quantitative Models of Autoimmunity)
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. View Full-Text
Keywords: negative selection; central tolerance; self-nonself discrimination; T cell repertoires; artificial immune system; learning by example negative selection; central tolerance; self-nonself discrimination; T cell repertoires; artificial immune system; learning by example
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MDPI and ACS Style

Wortel, I.M.N.; Keşmir, C.; de Boer, R.J.; Mandl, J.N.; Textor, J. Is T Cell Negative Selection a Learning Algorithm? Cells 2020, 9, 690.

AMA Style

Wortel IMN, Keşmir C, de Boer RJ, Mandl JN, Textor J. Is T Cell Negative Selection a Learning Algorithm? Cells. 2020; 9(3):690.

Chicago/Turabian Style

Wortel, Inge M.N., Can Keşmir, Rob J. de Boer, Judith N. Mandl, and Johannes Textor. 2020. "Is T Cell Negative Selection a Learning Algorithm?" Cells 9, no. 3: 690.

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