Tregs self-organize into a "computing ecosystem" and implement a sophisticated optimization algorithm for mediating immune response

TL;DR

This paper models Tregs as a self-organizing system that optimizes immune response, revealing how diversity in Tregs influences self-tolerance and autoimmunity, with implications for understanding immune regulation and disease.

Contribution

It introduces a biophysical model linking Treg behavior to constrained optimization, providing a new algorithmic perspective on immune self-tolerance.

Findings

High Treg diversity ensures robust self-tolerance.

Lower Treg diversity leads to a sharp transition to autoimmunity.

Proposes an experimental test for the Treg diversity transition.

Abstract

Regulatory T cells (Tregs) play a crucial role in mediating immune response. Yet an algorithmic understanding of the role of Tregs in adaptive immunity remains lacking. Here, we present a biophysically realistic model of Treg mediated self-tolerance in which Tregs bind to self-antigens and locally inhibit the proliferation of nearby activated T cells. By exploiting a duality between ecological dynamics and constrained optimization, we show that Tregs tile the potential antigen space while simultaneously minimizing the overlap between Treg activation profiles. We find that for sufficiently high Treg diversity, Treg mediated self-tolerance is robust to fluctuations in self-antigen concentrations but lowering the Treg diversity results in a sharp transition -- related to the Gardner transition in perceptrons -- to a regime where changes in self-antigen concentrations can result in an auto-immune response. We propose a novel experimental test of this transition in immune-deficient mice and discuss potential implications for autoimmune diseases.