Affinity maturation for an optimal balance between long-term immune coverage and short-term resource constraints

TL;DR

This paper models the immune system's affinity maturation as an optimal decision process balancing long-term pathogen coverage with short-term resource costs, revealing a trade-off that explains observed immune repertoire patterns.

Contribution

It introduces a novel mathematical framework for understanding affinity maturation as an optimal control problem, predicting immune repertoire diversity and size regimes.

Findings

Predicts power-law distributions of clonotype sizes.

Identifies a trade-off between immune protection and metabolic costs.

Explains antigenic imprinting as an optimal strategy.

Abstract

In order to target threatening pathogens, the adaptive immune system performs a continuous reorganization of its lymphocyte repertoire. Following an immune challenge, the B cell repertoire can evolve cells of increased specificity for the encountered strain. This process of affinity maturation generates a memory pool whose diversity and size remain difficult to predict. We assume that the immune system follows a strategy that maximizes the long-term immune coverage and minimizes the short-term metabolic costs associated with affinity maturation. This strategy is defined as an optimal decision process on a finite dimensional phenotypic space, where a pre-existing population of naive cells is sequentially challenged with a neutrally evolving strain. We unveil a trade-off between immune protection against future strains and the necessary reorganization of the repertoire. This plasticity of the repertoire drives the emergence of distinct regimes for the size and diversity of the memory pool, depending on the density of naive cells and on the mutation rate of the strain. The model predicts power-law distributions of clonotype sizes observed in data, and rationalizes antigenic imprinting as a strategy to minimize metabolic costs while keeping good immune protection against future strains.