Institute of Microbiology, Department of Biology, ETH Zurich, Zurich, Switzerland.
Immunology. 2021 Apr;162(4):341-356. doi: 10.1111/imm.13266. Epub 2020 Oct 19.
Host-microbe interactions are highly dynamic in space and time, in particular in the case of infections. Pathogen population sizes, microbial phenotypes and the nature of the host responses often change dramatically over time. These features pose particular challenges when deciphering the underlying mechanisms of these interactions experimentally, as traditional microbiological and immunological methods mostly provide snapshots of population sizes or sparse time series. Recent approaches - combining experiments using neutral genetic tags with stochastic population dynamic models - allow more precise quantification of biologically relevant parameters that govern the interaction between microbe and host cell populations. This is accomplished by exploiting the patterns of change of tag composition in the microbe or host cell population under study. These models can be used to predict the effects of immunodeficiencies or therapies (e.g. antibiotic treatment) on populations and thereby generate hypotheses and refine experimental designs. In this review, we present tools to study population dynamics in vivo using genetic tags, explain examples for their implementation and briefly discuss future applications.
宿主-微生物相互作用在空间和时间上具有高度动态性，特别是在感染的情况下。病原体种群大小、微生物表型以及宿主反应的性质往往随时间发生剧烈变化。这些特征在实验解析这些相互作用的潜在机制时带来了特殊挑战，因为传统的微生物学和免疫学方法大多只能提供种群大小的快照或稀疏的时间序列。最近的方法——结合使用中性遗传标记的实验与随机种群动态模型——允许更精确地量化控制微生物和宿主细胞种群相互作用的生物学相关参数。这是通过利用所研究的微生物或宿主细胞群体中标记组成的变化模式来实现的。这些模型可用于预测免疫缺陷或治疗（例如抗生素治疗）对种群的影响，从而生成假设并改进实验设计。在这篇综述中，我们介绍了使用遗传标记在体内研究种群动态的工具，解释了它们的实施示例，并简要讨论了未来的应用。
