Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.
Evolutionary Studies Initiative, Vanderbilt University, Nashville, Tennessee, United States of America.
PLoS Comput Biol. 2023 Apr 6;19(4):e1010445. doi: 10.1371/journal.pcbi.1010445. eCollection 2023 Apr.
Components of immune systems face significant selective pressure to efficiently use organismal resources, mitigate infection, and resist parasitic manipulation. A theoretically optimal immune defense balances investment in constitutive and inducible immune components depending on the kinds of parasites encountered, but genetic and dynamic constraints can force deviation away from theoretical optima. One such potential constraint is pleiotropy, the phenomenon where a single gene affects multiple phenotypes. Although pleiotropy can prevent or dramatically slow adaptive evolution, it is prevalent in the signaling networks that compose metazoan immune systems. We hypothesized that pleiotropy is maintained in immune signaling networks despite slowed adaptive evolution because it provides some other advantage, such as forcing network evolution to compensate in ways that increase host fitness during infection. To study the effects of pleiotropy on the evolution of immune signaling networks, we used an agent-based modeling approach to evolve a population of host immune systems infected by simultaneously co-evolving parasites. Four kinds of pleiotropic restrictions on evolvability were incorporated into the networks, and their evolutionary outcomes were compared to, and competed against, non-pleiotropic networks. As the networks evolved, we tracked several metrics of immune network complexity, relative investment in inducible and constitutive defenses, and features associated with the winners and losers of competitive simulations. Our results suggest non-pleiotropic networks evolve to deploy highly constitutive immune responses regardless of parasite prevalence, but some implementations of pleiotropy favor the evolution of highly inducible immunity. These inducible pleiotropic networks are no less fit than non-pleiotropic networks and can out-compete non-pleiotropic networks in competitive simulations. These provide a theoretical explanation for the prevalence of pleiotropic genes in immune systems and highlight a mechanism that could facilitate the evolution of inducible immune responses.
免疫系统的组成部分面临着巨大的选择压力，需要有效地利用机体资源、减轻感染并抵抗寄生虫的操纵。一种理论上最佳的免疫防御取决于所遇到的寄生虫种类，在构成性和诱导性免疫成分之间进行投资平衡，但遗传和动态限制可能迫使偏离理论最优状态。一个潜在的限制因素是多效性，即单个基因影响多种表型的现象。尽管多效性可以阻止或显著减缓适应性进化，但它在构成后生动物免疫系统的信号网络中普遍存在。我们假设，尽管适应性进化缓慢，但多效性仍然存在于免疫信号网络中，因为它提供了其他一些优势，例如迫使网络进化以在感染期间以增加宿主适应性的方式进行补偿。为了研究多效性对免疫信号网络进化的影响，我们使用基于代理的建模方法来进化一组同时受到共同进化寄生虫感染的宿主免疫系统。将四种对可进化性的多效性限制纳入网络中，并将其进化结果与非多效性网络进行比较和竞争。随着网络的进化，我们跟踪了几种免疫网络复杂性的指标、诱导性和构成性防御的相对投资，以及与竞争模拟的赢家和输家相关的特征。我们的结果表明，非多效性网络进化为部署高度构成性免疫反应，而与寄生虫流行率无关，但多效性的某些实现有利于高度诱导性免疫的进化。这些诱导性多效性网络与非多效性网络一样具有适应性，并且在竞争模拟中可以胜过非多效性网络。这为免疫系统中多效性基因的普遍性提供了理论解释，并强调了一种可以促进诱导性免疫反应进化的机制。
