Coevolutionary Epidemiology of Hosts and Their Infectious Pathogens

我的新研究计划解决了进化论、生态学和流行病学的交叉点上的基本问题。受到红皇后假说和生物相互作用在塑造多样性方面的假定重要性的启发，我的研究计划的长期目标是描述宿主和他们的传染性病原体之间共同进化的紧急后果。在接下来的5年里，我的团队将通过两个短期目标开发和应用数学和统计模型来支持作为协同进化模式系统的传染病病原体的研究：1)开发系统发育方法来表征传染病的流行病学机制；2)开发种群遗传模型和推断方法来表征空间结构环境中宿主-病原体的协同进化。为了在直接传播疾病的背景下重新评估红皇后假说，我的团队将使用互补的基因组和遗传方法来理解进化、流行病学和生态(E3)过程之间的反馈。在目标1中，我们将开发一类新的方法，使我们能够从病原体基因组序列之间的进化关系来表征流行病学机制。然后，我们将优化这些方法，以推断共同感染和共同进化。最后，我们将把这些方法应用于巨细胞病毒，以量化多哺乳动物大鼠的混合感染，从而使其适合作为可传播疫苗的载体。在目标2中，我们将使用种群遗传模型来理解空间结构环境中共同进化的紧急进化后果。局部适应是连接对共同进化的理论和经验理解的一个强大的度量。通过首次采用宿主和其感染病原体之间协同进化的局部适应度量，我们将使用种群遗传模型来评估疾病对空间多样性的影响。然后，我们将使用这些模型来开发一种推断方法，以确定流行病特征之间的权衡强度，这些知识对于预测疾病演变至关重要。最后，我们将采用这个推理框架来设计和测试基因组保护策略的稳健性，以拯救濒临灭绝的物种。我们在E3交界处面临着许多生存危机：人为环境变化造成的生物多样性前所未有的丧失，以及人畜共患病原体的控制。拟议的研究结果将推进基因组流行病学，并为制定受传染病威胁的野生动物种群的进化稳健保护战略提供理论框架。我的研究计划应用数学和统计学方法来解决进化论中的基本问题，并将为加拿大人的利益贡献科学知识和方法来解决这些危机。

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.