Is T Cell Negative Selection a Learning Algorithm?

Is T Cell Negative Selection a Learning Algorithm?

Blog Article

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 cocktail tree for sale 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 read more 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.