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

了解复杂噬菌体细菌感染网络对海洋生态系统的影响

• 批准号: 1233760
• 负责人: Joshua Weitz
• 金额: $ 47.11万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2017-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1233760&HistoricalAwards=false
• 关键词: Understanding; Effects; Complex; Phage; Bacteria

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.

细菌和它们的病毒（噬菌体）构成了海洋中数量最多、基因最多样化的两类生物。随着环境排序的出现，这种多样性的程度变得越来越明显。然而，到目前为止，新的分类多样性的发现已经超过了噬菌体和细菌之间的功能和相互作用的量化进展。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大尺度动态的预测。本项目将建立一个理论框架，以表征复杂的噬菌体-细菌相互作用对海洋生态系统结构和功能的影响。理论框架的基础是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（pins）。关于ppin结构的最新发现将与一个新的生态进化动力学建模框架相结合，以服务于以下目标：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交叉感染的复杂模式。目标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内部交叉感染如何影响社区稳定性的假设。目标3。利用多菌株模型预测ppin如何影响：(i)病毒与细菌种群丰度的比例；（ii）生态系统层面的碳和养分通量。本项目建立的理论将改善海洋生态系统中噬菌体-细菌相互作用的特征，并建立噬菌体-细菌相互作用与生态系统功能联系的框架。首先，该项目将通过开发可应用于定量感染数据的新型网络理论，概括经验ppin中多尺度结构的初步发现。将分析海洋pins的性质，以评估它们是否具有分层组织，组织成模块和/或具有多尺度结构。ppin的统计结构将与多尺度协同进化模型相结合。这些共同进化模型将用于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用于考虑通过病毒裂解细菌宿主的碳和营养再生。PBIN结构将发生变化，以建立交叉感染与生态系统结构和功能关键指标之间的联系，具体应用于玫瑰杆菌和聚珠球菌宿主。将利用与经验数据集密切相关的分析方法和大规模模拟来实现这些目标。更广泛的影响：教育目标将围绕培养对微生物系统感兴趣的下一代定量生物学家的主题（目标4）。为此，PI将：(i)为数量生物学家提供培训计划，使他们能够与不同背景的学生直接互动；（ii）开设以定量病毒生态学为重点的新课程；（三）开发和传播软件工具，使生物学家能够对病毒-宿主相互作用数据和对病毒-宿主群落的研究应用严格的定量方法。本项目将直接资助2名研究生和8名本科生。这些学生将具有从物理学到生物学的学术背景，并在协作团队中工作。研究生们将前往亚利桑那大学和田纳西大学诺克斯维尔分校的病毒生态学实验室进行长期访问。将开设一门新的关于定量病毒生态学的研究生课程，以服务于这项资助的受训者和越来越多对佐治亚理工学院环境微生物学感兴趣的学生。在这个项目中开发的理论将作为开源软件工具实施和传播。