Metcalf C Jessica E, Tepekule Burcu, Bruijning Marjolein, Koskella Britt
Department of Ecology and Evolutionary Princeton University Princeton New Jersey 08544.
Wissenschaftskolleg zu Berlin DE-14193 Berlin Germany.
Evol Lett. 2022 Oct 27;6(6):412-425. doi: 10.1002/evl3.298. eCollection 2022 Dec.
The absence of microbial exposure early in life leaves individuals vulnerable to immune overreaction later in life, manifesting as immunopathology, autoimmunity, or allergies. A key factor is thought to be a "critical window" during which the host's immune system can "learn" tolerance, and beyond which learning is no longer possible. Animal models indicate that many mechanisms have evolved to enable critical windows, and that their time limits are distinct and consistent. Such a variety of mechanisms, and precision in their manifestation suggest the outcome of strong evolutionary selection. To strengthen our understanding of critical windows, we explore their underlying evolutionary ecology using models encompassing demographic and epidemiological transitions, identifying the length of the critical window that would maximize fitness in different environments. We characterize how direct effects of microbes on host mortality, but also indirect effects via microbial ecology, will drive the optimal length of the critical window. We find that indirect effects such as magnitude of transmission, duration of infection, rates of reinfection, vertical transmission, host demography, and seasonality in transmission all have the effect of redistributing the timing and/or likelihood of encounters with microbial taxa across age, and thus increasing or decreasing the optimal length of the critical window. Declining microbial population abundance and diversity are predicted to result in increases in immune dysfunction later in life. We also make predictions for the length of the critical window across different taxa and environments. Overall, our modeling efforts demonstrate how critical windows will be impacted over evolution as a function of both host-microbiome/pathogen interactions and dispersal, raising central questions about potential mismatches between these evolved systems and the current loss of microbial diversity and/or increases in infectious disease.
生命早期缺乏微生物接触会使个体在生命后期易发生免疫过度反应，表现为免疫病理学、自身免疫或过敏。一个关键因素被认为是一个“关键窗口期”，在此期间宿主的免疫系统能够“学习”耐受性，而超过这个时期就不再可能学习。动物模型表明，许多机制已经进化以促成关键窗口期，并且它们的时间限制是明确且一致的。如此多样的机制及其表现的精确性表明了强大的进化选择的结果。为了加强我们对关键窗口期的理解，我们使用包含人口统计学和流行病学转变的模型来探索其潜在的进化生态学，确定在不同环境中能使适应性最大化的关键窗口期的长度。我们描述了微生物对宿主死亡率的直接影响，以及通过微生物生态学产生的间接影响将如何驱动关键窗口期的最佳长度。我们发现，诸如传播规模、感染持续时间、再感染率、垂直传播、宿主人口统计学以及传播的季节性等间接影响，都会重新分配不同年龄段接触微生物类群的时间和/或可能性，从而增加或减少关键窗口期的最佳长度。预计微生物种群丰度和多样性的下降会导致生命后期免疫功能障碍增加。我们还对不同分类群和环境下关键窗口期的长度进行了预测。总体而言，我们的建模工作展示了关键窗口期将如何在进化过程中作为宿主 - 微生物组/病原体相互作用和扩散的函数而受到影响，这引发了关于这些进化系统与当前微生物多样性丧失和/或传染病增加之间潜在不匹配的核心问题。
