Coevolutionary Epidemiology of Hosts and Their Infectious Pathogens

点击翻译按钮获取中文摘要

My new research program addresses fundamental questions at the intersection of evolution, ecology, and epidemiology. Inspired by the Red Queen hypothesis and the putative importance of biotic interactions in shaping diversity, the long-term goal of my research program is to characterize the emergent consequences of coevolution between hosts and their infectious pathogens. Over the next 5 years, my team will develop and apply mathematical and statistical models to support the study of infectious pathogens as model systems of coevolution via two short-term objectives: 1) Develop phylogenetic methods to characterize epidemiological mechanisms of infectious diseases, and 2) Develop population genetic models and inference methods to characterize host-pathogen coevolution in spatially structured environments. To reassess the Red Queen hypothesis in the context of directly-transmitted diseases, my team will employ complementary genomic and genetic approaches to understand feedbacks between evolutionary, epidemiological, and ecological (E3) processes. In Objective 1 we will develop a new class of methods that will allow us to characterize epidemiological mechanisms from the evolutionary relationship between pathogen genome sequences. We will then optimize these methods to infer coinfection and coevolution. Finally, we will apply these methods to cytomegalovirus, to quantify coinfection in the multimammate rat, and hence its suitability as a vector for a transmissible vaccine. In Objective 2 we will use population genetic models to understand the emergent evolutionary consequences of coevolution in spatially structured environments. Local adaptation is a powerful metric connecting theoretical and empirical understandings of coevolution. By first adapting the measurement of local adaptation for coevolution between hosts and their infectious pathogens, we will use population genetic models to assess the consequences of disease on spatial diversity. We will then use these models to develop an inferential method for identifying the strength of trade-offs between epidemiological traits, knowledge which is essential for predicting disease evolution. Finally, we will adapt this inferential framework to design and test the robustness of genomic conservation strategies for rescuing species at risk of extinction from disease. We are faced with numerous existential crises at the E3 intersection: from the unprecedented loss of biodiversity from anthropogenic environmental change and the control of zoonotic pathogens. The outcomes of the proposed research will advance genomic epidemiology and provide a theoretical framework for developing evolutionarily robust conservation strategies for wildlife populations threatened by infectious disease. My research program applies mathematical and statistical approaches to both address fundamental questions in evolution and will contribute scientific knowledge and methods to address these crises for the benefit of Canadians.