Vertebrate Immune System: Evolution

AbstractImmune responses evolve to balance the benefits of microbial killing against the costs of autoimmunity and energetic resource use. Models that explore the evolution of optimal immune responses generally include a term for constitutive immunity, or the level of immunological investment prior to microbial exposure, and for inducible immunity, or investment in immune function after microbial challenge. However, studies rarely consider the functional form of inducible immune responses with respect to microbial density, despite the theoretical dependence of immune system evolution on microbe-versus immune-mediated damage to the host. In this study, we analyze antimicrobial peptide (AMP) gene expression from seven wild-caught flour beetle populations (Tribolium spp.) during acute infection with the virulent bacteria Bacillus thuringiensis (Bt) and Photorhabdus luminescens (P.lum) to demonstrate that inducible immune responses mediated by the humoral IMD pathway exhibit natural variation in both microbe density-dependent and independent temporal dynamics. Beetle populations that exhibited greater AMP expression sensitivity to Bt density were also more likely to die from infection, while populations that exhibited higher microbe density-independent AMP expression were more likely to survive P. luminescens infection. Reduction in pathway signaling efficiency through RNAi-mediated knockdown of the imd gene reduced the magnitude of both microbe-independent and dependent responses and reduced host resistance to Bt growth, but had no net effect on host survival. This study provides a framework for understanding natural variation in the flexibility of investment in inducible immune responses and should inform theory on the contribution of non-equilibrium host-microbe dynamics to immune system evolution.

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Evolution of animal immunity in the light of beneficial symbioses

Philosophical Transactions of the Royal Society B Biological Sciences ◽

10.1098/rstb.2019.0601 ◽

2020 ◽

Vol 375 (1808) ◽

pp. 20190601 ◽

Cited By ~ 2

Author(s):

Nicole M. Gerardo ◽

Kim L. Hoang ◽

Kayla S. Stoy

Keyword(s):

Immune System ◽

Immune Responses ◽

Host Immune System ◽

System Evolution ◽

Symbiotic Associations ◽

Host Immune Responses ◽

Immune System Evolution ◽

System Maintenance ◽

Key Aspects ◽

Host Evolution

Immune system processes serve as the backbone of animal defences against pathogens and thus have evolved under strong selection and coevolutionary dynamics. Most microorganisms that animals encounter, however, are not harmful, and many are actually beneficial. Selection should act on hosts to maintain these associations while preventing exploitation of within-host resources. Here, we consider how several key aspects of beneficial symbiotic associations may shape host immune system evolution. When host immunity is used to regulate symbiont populations, there should be selection to evolve and maintain targeted immune responses that recognize symbionts and suppress but not eliminate symbiont populations. Associating with protective symbionts could relax selection on the maintenance of redundant host-derived immune responses. Alternatively, symbionts could facilitate the evolution of host immune responses if symbiont-conferred protection allows for persistence of host populations that can then adapt. The trajectory of immune system evolution will likely differ based on the type of immunity involved, the symbiont transmission mode and the costs and benefits of immune system function. Overall, the expected influence of beneficial symbiosis on immunity evolution depends on how the host immune system interacts with symbionts, with some interactions leading to constraints while others possibly relax selection on immune system maintenance. This article is part of the theme issue ‘The role of the microbiome in host evolution’.

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Grammatical Immune System Evolution for Reverse Engineering Nonlinear Dynamic Bayesian Models

Cancer Informatics ◽

10.4137/cin.s694 ◽

2008 ◽

Vol 6 ◽

pp. CIN.S694 ◽

Cited By ~ 3

Author(s):

B.A. McKinney ◽

D. Tian

Keyword(s):

Time Series ◽

Immune System ◽

Nonlinear Dynamic ◽

Dynamic Models ◽

Time Series Data ◽

Nonlinear System Identification ◽

Series Data ◽

Estrogen Metabolism ◽

System Evolution ◽

Immune System Evolution

An artificial immune system algorithm is introduced in which nonlinear dynamic models are evolved to fit time series of interacting biomolecules. This grammar-based machine learning method learns the structure and parameters of the underlying dynamic model. In silico immunogenetic mechanisms for the generation of model-structure diversity are implemented with the aid of a grammar, which also enforces semantic constraints of the evolved models. The grammar acts as a DNA repair polymerase that can identify recombination and hypermutation signals in the antibody (model) genome. These signals contain information interpretable by the grammar to maintain model context. Grammatical Immune System Evolution (GISE) is applied to a nonlinear system identification problem in which a generalized (nonlinear) dynamic Bayesian model is evolved to fit biologically motivated artificial time-series data. From experimental data, we use GISE to infer an improved kinetic model for the oxidative metabolism of 17β-estradiol (E2), the parent hormone of the estrogen metabolism pathway.

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