Department of Integrative Biology, University of California, Berkeley, California, USA; email:
Wissenschaftskolleg zu Berlin-Institute for Advanced Study, Berlin, Germany.
Annu Rev Virol. 2022 Sep 29;9(1):57-78. doi: 10.1146/annurev-virology-091919-075914. Epub 2022 May 18.
Viruses of bacteria (bacteriophages or phage) have broad effects on bacterial ecology and evolution in nature that mediate microbial interactions, shape bacterial diversity, and influence nutrient cycling and ecosystem function. The unrelenting impact of phages within the microbial realm is the result, in large part, of their ability to rapidly evolve in response to bacterial host dynamics. The knowledge gained from laboratory systems, typically using pairwise interactions between single-host and single-phage systems, has made clear that phages coevolve with their bacterial hosts rapidly, somewhat predictably, and primarily by counteradapting to host resistance. Recent advancement in metagenomics approaches, as well as a shifting focus toward natural microbial communities and host-associated microbiomes, is beginning to uncover the full picture of phage evolution and ecology within more complex settings. As these data reach their full potential, it will be critical to ask when and how insights gained from studies of phage evolution in vitro can be meaningfully applied to understanding bacteria-phage interactions in nature. In this review, we explore the myriad ways that phagesshape and are themselves shaped by bacterial host populations and communities, with a particular focus on observed and predicted differences between the laboratory and complex microbial communities.
细菌病毒（噬菌体或 phage）对自然界中细菌的生态和进化具有广泛的影响，它们介导微生物之间的相互作用，塑造细菌的多样性，并影响营养循环和生态系统功能。噬菌体在微生物领域的持续影响在很大程度上是由于它们能够快速进化以响应细菌宿主的动态变化。从实验室系统中获得的知识，通常使用单一宿主和单一噬菌体系统之间的两两相互作用，已经清楚地表明噬菌体与它们的细菌宿主迅速共同进化，有些可以预测，主要是通过对抗宿主的抗性来适应。最近在宏基因组学方法上的进展，以及对自然微生物群落和宿主相关微生物组的关注焦点的转变，开始揭示噬菌体进化和生态学在更复杂环境中的全貌。随着这些数据充分发挥潜力，至关重要的是要问从噬菌体体外进化研究中获得的见解在何时以及如何能够有意义地应用于理解自然界中细菌-噬菌体的相互作用。在这篇综述中，我们探讨了噬菌体塑造和被细菌宿主种群和群落塑造的多种方式，特别关注实验室和复杂微生物群落之间观察到的和预测的差异。
