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

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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.