Understanding the Effects of Complex Phage-Bacteria Infection Networks on Marine Ecosystems

细菌和它们的病毒(噬菌体)构成了海洋中最丰富、基因最多样化的两类生物。随着环境测序的出现，这种多样性的程度变得越来越明显。然而，到目前为止，正在进行的新分类多样性的发现在量化噬菌体和细菌之间的功能和相互作用方面取得了更快的进展。提高对不同噬菌体群体如何利用细菌宿主的定量理解将改善对微生物种群动态、生态系统功能和全球生物地球化学循环的大规模动态的预测。该项目将开发一个理论框架来描述复杂的噬菌体-细菌相互作用对海洋生态系统结构和功能的影响。该理论框架基于噬菌体与其细菌宿主的交叉感染分析，称为噬菌体-细菌感染网络(PBIN)。关于PBIN结构的最新发现将与一个新的生态进化动力学模型框架相结合，以服务于以下目标：目的1.发展理论方法来分析PBIN，包括定量感染数据，以表征海洋生态系统中发现的复杂的交叉感染模式。目的2.建立包含复杂PBIN数据的生态进化多菌株模型，以评估关于PBIN内交叉感染如何影响群落稳定性的假说。目的3.利用多菌株模型预测PBIN如何影响：(I)病毒与细菌种群丰度的比率；以及(Ii)生态系统水平上的碳和营养物质通量。该项目发展的理论将改进对海洋生态系统中噬菌体-细菌相互作用的描述，并建立一个将噬菌体-细菌相互作用与生态系统功能联系起来的框架。首先，该项目将通过开发可应用于定量感染数据的新网络理论，推广经验性PBIN内多尺度结构的初步发现。将分析海洋PBIN的属性，以评估它们是否按层次组织、组织成模块和/或具有多尺度结构。多国信息网的统计结构将与多尺度共同进化模型相结合。这些共同进化模型将被用来评估关于交叉感染结构如何影响社区稳定性的假设。最后，这些共同进化模型将被用来考虑通过细菌宿主的病毒裂解来实现碳和营养再生。PBIN的结构将有所不同，以建立交叉感染与生态系统结构和功能的关键指标之间的联系，并具体应用于玫瑰杆菌和聚球藻宿主。将利用分析方法和大规模模拟来实现这些目标，与经验数据密切相关。广泛影响：教育目标将围绕培养对微生物系统感兴趣的下一代量化生物学家的主题(目标4)。为此，国际生物研究所将：(I)为定量生物学家提供培训计划，使他们能够与不同背景的学生进行直接互动；(Ii)引入一门侧重于定量病毒生态学的新课程；(Iii)开发和传播软件工具，使生物学家能够将严格的定量方法应用于病毒-宿主相互作用数据和病毒-宿主群落的研究。该项目将直接资助两名研究生和八名本科生。这些学生将拥有从物理到生物的学术背景，并在协作团队中工作。这些研究生将前往亚利桑那大学和田纳西-诺克斯维尔大学的病毒生态学实验室进行长期访问。佐治亚理工学院将开发一门关于定量病毒生态学的新研究生课程，为获得这笔补助金的学员和越来越多的对环境微生物学感兴趣的学生提供服务。本项目中提出的理论将作为开放源码软件工具加以实施和传播。

Bacteria and their viruses (phages) make up two of the most abundant and genetically diverse groups of organisms in the oceans. The extent of this diversity has become increasingly apparent with the advent of environmental sequencing. However, the ongoing discovery of new taxonomic diversity has, thus far, out-paced gains in quantifying the function of and interactions among phages and bacteria. Improved quantitative understanding of how diverse groups of phages exploit bacterial hosts will improve predictions of microbial population dynamics, ecosystem functioning, and the large-scale dynamics of global biogeochemical cycles. This project will develop a theoretical framework for characterizing the effect of complex phage-bacteria interactions on marine ecosystem structure and function. The theoretical framework is grounded in the analysis of cross-infection assays of bacteriophages with their bacterial hosts, termed phage-bacteria infection networks (PBINs). Recent discoveries concerning the structure of PBINs will be combined with a novel eco-evolutionary dynamics modeling framework in the service of the following aims: Aim 1. Develop theoretical methods to analyze PBINs that include quantitative infection data to characterize complex patterns of cross-infection found in marine ecosystems.Aim 2. Establish eco-evolutionary multi-strain models that incorporate complex PBIN data to evaluate hypotheses regarding how cross-infection within PBINs affects community stability.Aim 3. Utilize the multi-strain model to predict how PBINs influence: (i) the ratio of viral to bacterial population abundances; and (ii) the flux of carbon and nutrients at the ecosystem level.The theory developed in this project will improve characterizations of phage- bacteria interactions in marine ecosystems and establish a framework to link phage-bacteria in- teractions with ecosystem function. First, the project will generalize preliminary findings of multi-scale structure within empirical PBINs by developing novel network theories that can be applied to quantitative infection data. Properties of marine PBINs will be analyzed to assess whether they are hierarchically organized, organized into modules, and/or possess multi-scale structure. The statistical structure of PBINs will be integrated with multi-scale coevolutionary models. These co- evolutionary models will be utilized to evaluate hypotheses regarding how cross-infection structure affects community stability. Finally, these coevolutionary models will be used to consider carbon and nutrient regeneration via viral lysis of bacterial hosts. PBIN structure will be varied to establish a link between cross-infection and key indices of ecosystem structure and function, with specific applications to Roseobacter and Synechococcus hosts. Analytical methods and large-scale simulations will be utilized to achieve these goals, closely linked to empirical datasets.Broader impacts: Educational objectives will be centered around the theme of fostering the next generation of quantitative biologists interested in microbial systems (Aim 4). In doing so, the PI will: (i) provide a training program for quantitative biologists that enables them to have direct interactions with students of different backgrounds; (ii) introduce a new course focusing on quantitative viral ecology; (iii) develop and disseminate software tools that enable biologists to apply rigorous quantitative methods to viral-host interaction data and to the study of viral-host communities. Two graduate students and eight undergraduates will be directly supported on this project. These students will have academic backgrounds spanning physics to biology and work in collaborative teams. The graduate students will travel for extended visits to the viral ecology laboratories at the U of Arizona and U of Tennessee-Knoxville. A new graduate course will be developed on quantitative viral ecology to serve trainees on this grant and the growing number of students interested in environmental microbiology at Georgia Tech. The theories developed in this project will be implemented and disseminated as open-source software tools.