EAGER: New genomic resources and models for predicting evolving vector-borne disease dynamics in a changing world

全球气候变化加速了我们了解疾病动态的需求。由蚊子或蜱等小型无脊椎动物（病媒）在宿主之间传播的疾病在世界各地呈上升趋势，但与气候变化的联系尚不清楚。气候变化可通过改变有能力的宿主和病媒或寄生虫的出现，或更微妙地通过改变它们相互作用的时间或性质，直接影响病媒传播的疾病。预测病媒传播疾病对气候变化的反应，既需要了解所有相关物种可能如何受到影响，也需要找到新的方法来确定和预测它们如何相互作用，以及它们及其相互作用如何演变。这项研究将开发和测试包含共同进化变化的病媒传播疾病的功能模型。具体而言，该项目将开发模型，说明过去和预测未来演变的反应，禽疟疾，蚊子传播的疾病，使用数据库的疾病流行率在濒危夏威夷蜜。这项研究将回答流行病学中的重要问题，通过测量和整合宿主，媒介和寄生虫的进化变化到疾病动态预测模型在未来的气候scenaries.An理想的疾病系统，开发和训练这样一个模型是本地和引进夏威夷鸟类的禽疟疾。夏威夷的禽疟疾病原体是一种非本地的血孢子虫寄生虫，即残余疟原虫，由两种非本地的致倦库蚊（Culex quinquefasciatus）混合传播。夏威夷的禽疟疾是一系列重复的自然实验，其中病媒和寄生虫的流行率在几个岛屿上沿着海拔梯度变化，据报道，一些鸟类宿主的耐受性存在平行梯度。虽然P. relictum以前对所有夏威夷的采蜜鸟都有很高的毒性，但在几个物种中观察到了耐受性（或抗性）的进化，如Amakihi（Hemignatus sp）。内和外物种的耐受性程度的差异表明，基因组变异的基础上，这种差异，此外，有地理变异内和跨岛屿的宿主物种组成，宿主耐受性，气候（温度和降水），载体丰度，载体的能力，和病原体健身。对这一系统的研究已有几十年，因此，对宿主和病媒的许多人口统计学特征都有了很好的了解。具体而言，该项目将（1）使用机器学习方法来开发将相关物种的遗传数据与可能影响其进化的生态，环境和流行病学压力相关联的方法;（2）使用下一代基因组学分析过去和当前的寄生虫多样性。虽然构建参考夏威夷疟原虫系统，如果成功验证，这种模型方法将可广泛应用到任何疾病系统中，预计会发生变化的气候，环境或生态条件的演变。

Global climate change has accelerated our need to understand disease dynamics. Diseases transmitted among hosts by small invertebrates such as mosquitoes or ticks (vectors) are on the rise across the world but links to climate change are unclear. Climate change can impact vector-borne disease transmission directly by shifting the occurrence of competent hosts and vectors, or a parasite, or more subtly by changing the timing or nature of their interaction. Predicting the response of vector-borne diseases to climate change requires both an understanding of how all the species involved are likely to be affected as well as new ways to identify and predict how they interact and furthermore how they and their interactions may evolve. This research will develop and test functional models of vector-borne diseases that incorporate co-evolutionary change. Specifically, the project will develop models that account for past and predict future evolution of responses to avian malaria, a mosquito borne disease, using a database of disease prevalence in the endangered Hawaiian honeycreepers. This research will answer important questions in epidemiology by measuring and integrating evolutionary changes in hosts, vectors and parasites into predictive models of disease dynamics under future climate scenarios.An ideal disease system in which to develop and train such a model is avian malaria in native and introduced Hawaiian birds. The agent of avian malaria in Hawaii is a non-native Haemosporidian parasite, Plasmodium relictum, vectored by mixes of two non-native strains of the mosquito Culex quinquefasciatus. Avian malaria in Hawaii occurs as a series of replicated natural experiments in which vector and parasite prevalence vary along elevational gradients on several islands, and a parallel gradient in tolerance among some bird hosts has been reported. Although P. relictum was previously highly virulent to all Hawaiian honeycreepers, evolution of tolerance (or resistance) has been observed in several species such as the Amakihi (Hemignatus sp). Within- and among-species differences in the degree of tolerance suggest that genomic variation underlie such differences; moreover, there is geographic variation within and across islands in host species composition, host tolerance, climate (temperature and precipitation), vector abundance, vector competence, and pathogen fitness. This system has been studied for several decades; thus, many of the host and vector demographic characteristics are well understood. Specifically this project will (1) use a machine learning approach to develop ways to correlate the genetic data from the relevant species with the ecological, environmental, and epidemiological pressures that might shape their evolution; (2) analyze past and current parasite diversity using Next Generation genomics to be fed into the model. Though constructed with reference specifically to the Hawaiian Plasmodium system, if successfully validated, this model methodology will then be available for broad application into any disease system in which evolution is expected to occur in response to shifting climatological, environmental, or ecological conditions.