Ancient viral threats through the lens of adaptation in human genomes

项目摘要 目前的SARS-COV2大流行表明，需要做出更多努力来评估这一大流行 病毒可能会在人类群体中蔓延。评估特定疾病大流行的可能性 病毒，在接下来的五年里，我的实验室将询问类似的病毒是否不仅在最近的几年中引起了流行病 有记录的过去，但在人类进化的更长时间范围内。在中国引发疫情的病毒 过去确实是最有可能在未来再次引发疫情的，而数百种病毒疫情 很可能在人类进化过程中困扰着人类。这项工作将填补在流行病知识方面的空白 通过这样做，将能够更好地评估代表 未来的流行病威胁。 为了研究古代流行病，我的实验室将利用古代病毒驱动的宿主基因组适应。 与病毒的军备竞赛通过驱动大量适应来塑造宿主免疫系统。我 最近表明，病毒不仅在免疫基因中留下了丰富的适应信号，而且在整个 整个人类基因组。该实验室将研究人类基因组中特定病毒留下的适应信号，以 对古代流行病进行检测、确定日期并确定其功能。为此，我们将开发新的统计工具 基于机器学习和祖先重组图重建的最新进展 (参数)。这些新的方法具有更强的检测和测定基因组适应的能力，将使我们能够问 以下问题： 1)在人类进化过程中，哪些病毒推动了古老的流行病？ 我的实验室将创建高功率的深度学习测试，以检测过去复杂的基因组适应 大约20万年的人类进化。 2)特定的病毒是在什么时候驱动古代流行病的？ 我们将使用ARGS和近似贝叶斯计算来确定古代流行病的年代，方法是确定宿主的年龄 由特定病毒驱动的适应性事件。 3)在古代流行期间，选择了哪些功能宿主的遗传变化，其中 基因，以及它们如何影响目前病毒的遗传易感性？ 我们将研究对病毒的调节适应以及病毒驱动的宿主适应对 不同人群对目前特定病毒的遗传易感性，从而提供 病毒学家有很强的候选宿主基因以供进一步研究。 我的实验室特别适合通过将宿主和病原体的相互作用联系在一起来破译古老的流行病 随着适应的种群基因组学的最新发展。

Project Summary The current SARS-COV2 pandemic has brought to light that more efforts are needed to evaluate the pandemic potential of viruses that can spill over in human populations. To assess the pandemic potential of specific viruses, over the next five years my lab will ask if similar viruses caused epidemics not only during the recent documented past, but during the much longer time scale of human evolution. Viruses that caused epidemics in the past are indeed the most likely to cause epidemics again in the future, and hundreds of viral epidemics likely plagued human populations during their evolution. This work will fill gaps in knowledge on epidemics in ancestral human populations, and by doing so, will enable a better assessment of the viruses that represent a future pandemic threat. To study ancient epidemics, my lab will exploit host genomic adaptation driven by ancient viruses. Arms races with viruses have shaped the host immune system by driving a large number of adaptations. I recently showed that viruses left abundant signals of adaptation not only in immune genes, but across the entire human genome. The lab will examine signals of adaptation left by specific viruses in human genomes, to detect, date, and functionally characterize ancient epidemics. To this aim, we will develop new statistical tools based on recent advances in machine learning and in the reconstruction of Ancestral Recombination Graphs (ARGs). These new approaches with increased power to detect and date genomic adaptation will allow us to ask the following questions: 1) Which viruses drove ancient epidemics in human evolution? My lab will create deep learning tests with high power to detect complex genomic adaptation within the past ~200,000 years of human evolution. 2) When did specific viruses drive ancient epidemics? We will use ARGs and Approximate Bayesian Computation to date ancient epidemics, by dating the host adaptive events driven by specific viruses. 3) Which functional host genetic changes were selected during ancient epidemics, in which genes, and how do they influence genetic susceptibility to present viruses? We will investigate regulatory adaptation to viruses and the overall impact of virus-driven host adaptation on the genetic susceptibility of different human populations to specific present viruses, thereby providing virologists with strong candidate host genes for further inquiry. My lab is uniquely suited to decipher ancient epidemics by linking host-pathogen interactions together with the latest developments in the population genomics of adaptation.