Optimal evolutionary decision-making to store immune memory

Abstract

The adaptive immune system provides a diverse set of molecules that can mount specific responses against a multitude of pathogens. Memory is a key feature of adaptive immunity, which allows organisms to respond more readily upon re-infections. However, differentiation of memory cells is still one of the least understood cell fate decisions. Here, we introduce a mathematical framework to characterize optimal strategies to store memory to maximize the utility of immune response over an organism's lifetime. We show that memory production should be actively regulated to balance between affinity and cross-reactivity of immune receptors for an effective protection against evolving pathogens. Moreover, we predict that specificity of memory should depend on the organism's lifespan, and shorter-lived organisms with fewer pathogenic encounters should store more cross-reactive memory. Our framework provides a baseline to gauge the efficacy of immune memory in light of an organism's coevolutionary history with pathogens.

Unconstrained generation of synthetic antibody-antigen structures to guide machine learning methodology for real-world antibody specificity prediction

“…For the purpose of developing computational and ML methods for the study of antibody-antigen binding, even coarse-grained models, such as the one presented here, are valuable as long as they (as this model does) provide features that overlap with experimental data as well as operate at least to a certain degree, within a physiological parameter space (Figure 2, Figure S6, Figure S7). In comparison to previous widely used more or less abstract coarse-grained representations of antibody-antigen binding (reviewed in ( 73)), ranging from probabilistic models (74,75), the shape-space model ( 76), to cubic ligand-receptors (76,77) or structural coefficients for antibody clones ( 78), Absolut! allows a substantially higher level of intrinsic structural complexity of antibody-antigen binding.…”

Optimal immune specificity at the intersection of host life history and parasite epidemiology

“…Different parasites may impose different selective pressures that wash out, making our approach a reasonable approximation of empirical selective pressures. However, it is also possible that co-evolution may be more influential for the specificity of immune memory–e.g., Schnaack and Nourmohammad [ 58 ] show that the extent of parasite evolution during an organism’s lifespan shapes optimal specificity of memory against that parasite, which will trade off with the cross-reactivity of that memory against evolved variants of that parasite. Long-lived organisms with many encounters of a particular evolving pathogen should thus have more specific immune memory of that pathogen, because across long durations cross-reactivity becomes less valuable [ 58 ].…”

“…However, it is also possible that co-evolution may be more influential for the specificity of immune memory–e.g., Schnaack and Nourmohammad [ 58 ] show that the extent of parasite evolution during an organism’s lifespan shapes optimal specificity of memory against that parasite, which will trade off with the cross-reactivity of that memory against evolved variants of that parasite. Long-lived organisms with many encounters of a particular evolving pathogen should thus have more specific immune memory of that pathogen, because across long durations cross-reactivity becomes less valuable [ 58 ]. Indeed, repeated exposure may be an additional selective pressure that modulates evolved specificity in adaptive immune systems, in addition to the pressures that we describe here, although the precise concepts labeled “specificity” here and in [ 58 ] are not exactly the same.…”

“…Long-lived organisms with many encounters of a particular evolving pathogen should thus have more specific immune memory of that pathogen, because across long durations cross-reactivity becomes less valuable [ 58 ]. Indeed, repeated exposure may be an additional selective pressure that modulates evolved specificity in adaptive immune systems, in addition to the pressures that we describe here, although the precise concepts labeled “specificity” here and in [ 58 ] are not exactly the same. A further selection pressure might be imposed by pathogens that occur irregularly with respect to age (including epidemic pathogens).…”

Risk-utility tradeoff shapes memory strategies for evolving patterns

“…Memory recognition, however, is not limited to static patterns and can be desirable when classifying evolving stimuli that drive the system out of equilibrium. One such example is the adaptive immune system in which memory can effectively recognize evolved variants of previously encountered pathogens [4][5][6][7][8]. In a recent work, we have demonstrated that distributed learning strategies, which are desirable for pattern recognition in the stationary setup, can fail to reliably learn and classify dynamically evolving patterns [9].…”