Characterizing Human-Pathogen Interactions and Natural Selection with Ancient DNA

利用古代 DNA 表征人类与病原体的相互作用和自然选择

• 批准号: 10276190
• 负责人: Carlos Eduardo Guerra Amorim
• 金额: $ 35.25万
• 依托单位: CALIFORNIA STATE UNIVERSITY NORTHRIDGE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-09 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10276190
• 关键词: Archaeology; Bubonic Plague; Communicable Diseases; DNA; Data; Data Analyses; Data Set; Detection; Development; Disease Outbreaks; Epidemic; European; Event; Evolution; Future; Gene Frequency; Genetic; Genetic Variation; Genome; Goals; Health Policy; Human; Human Genetics; Human Genome; Indigenous; Knowledge; Light; Methods; Modeling; Modernization; Molecular; Mutation; Native Americans; Natural Selections; Plague; Population; Positioning Attribute; Process; Public Health; Recording of previous events; Research Proposals; Resources; Sample Size; Sampling; Series; Site; South America; Specimen; Statistical Methods; Technology; Time; base; combat; experience; fitness; genetic signature; genomic data; host-pathogen coevolution; human pathogen; improved; novel; pathogen; response; statistics; therapeutic target; time use

PROJECT SUMMARY The possibility of recovering ancient DNA (aDNA) molecules from archeological samples has yielded great opportunities for the study of human evolution and history. Compared to traditional datasets composed of a single time point collected in the present, aDNA allows for the detection of lost genetic lineages and enables the direct assessment of allele frequencies in different time transects. Technologies for obtaining genomic data from archeological material have catapulted the development of the field of paleogenomics, but statistical methods to leverage information from time-series genetic datasets lag behind these technological advances. Over the next five years, the Amorim Lab will develop and apply methods to study human host-pathogen coevolution and adaptation using aDNA. We seek to advance the field of paleogenomics by generating aDNA datasets that comprise large sample sizes per archeological site and developing new methods and approaches to leverage information from time-series genetic data. We will use these novel resources to study host-pathogen interactions during the outbreaks of the plague in Eurasia (e.g. the Black Death) and the period of contact between Indigenous peoples from South America and European colonizers. Contrasting models of population evolution, both analytical and computational, with observed allele frequencies and other summary statistics in different time transects, we will identify the genetic signatures of host-pathogen interactions, characterize the evolutionary processes involved in human response to pathogens, and infer the strength and timing of selective sweeps. In addition, we will characterize the Distribution of Fitness Effects (DFE) of new mutations in the human genome across different time transects and analyze its evolution in light of the environmental shifts caused by disease outbreaks and other events. This study represents an advance over the state-of-the-art knowledge in paleogenomics for its focus on evolutionary process not yet characterized with aDNA (in particular, host-pathogen coevolution), the characterization of the DFE using time-series data, and the development of new resources (datasets and methods). The Amorim Lab is uniquely positioned to accomplish these goals because of our experience in combining model-based approaches with genome data analysis from ancient and modern DNA, as well as our previous experience with the study of medieval Europeans and Native American populations.

项目摘要 从考古样本中恢复古代DNA（ADNA）分子的可能性已产生 研究人类进化和历史的巨大机会。与传统数据集相比 ADNA由当前收集的一个时间点组成，允许检测遗传丢失 谱系并实现在不同时间样本中对等位基因频率的直接评估。 从考古材料获得基因组数据的技术已经弹起了发展 古生物学领域，但统计方法来利用时间序列的信息遗传 数据集落后于这些技术进步。在接下来的五年中，Amorim Lab将 开发和应用方法来研究人类宿主 - 病原体共同进化和使用ADNA适应。 我们试图通过生成构成大型的ADNA数据集来推进古生物学领域 每个考古站点的样本量，并开发新方法和方法来利用 时间序列遗传数据的信息。我们将使用这些新颖的资源来研究宿主病原 欧亚大陆疫苗暴发期间的互动（例如，黑人死亡）和 来自南美洲的土著人民与欧洲殖民者之间的接触。对比模型 分析和计算的种群进化，观察到的等位基因频率和其他 摘要统计在不同时间样本中，我们将确定宿主 - 病原体的遗传特征 相互作用，表征人类对病原体反应的进化过程，以及 推断选择性扫描的强度和时机。此外，我们将表征 人类基因组新突变的适应性效应（DFE）在不同的时间横断面和 根据疾病暴发和其他事件引起的环境变化，分析其演变。 这项研究代表了对古生物学的最新知识的进步，因为它的重点是 尚未以ADNA（尤其是宿主 - 病原体进化）来表征的进化过程， 使用时间序列数据和新资源的开发（数据集）对DFE进行表征 和方法）。由于我们的 将基于模型的方法与古代和现代的基因组数据分析相结合的经验 DNA，以及我们以前在中世纪欧洲人和美洲原住民研究的经验 人群。