Elucidating host–microbe interactions in vivo by studying population dynamics using neutral genetic tags. Issue 4 (19th October 2020)

• Record Type: Journal Article
• Authors: Hausmann, Annika; Hardt, Wolf‐Dietrich
• Is Part Of: Immunology. Volume 162:Issue 4(2021)
• Page Start: 341
• Page End: 356
• Publication Date: 2020-10-19
• Subjects: host–microbe interaction -- in vivo models -- population dynamics; Immunology -- Periodicals
• DOI: 10.1111/imm.13266
• Languages: English
• ISSNs: 0019-2805

Abstract

Summary: 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. Abstract : Host‐microbe interactions are highly dynamic in space and time. Recent approaches – combining experiments using neutral genetic tags with stochastic population dynamic models – allow precise quantification of biologically relevant parameters that govern the interaction between microbe and host cell populations. In this review, we present tools to study population dynamics in vivo using genetic tags, explain examples for their implementation, and discuss future applications.