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.

项目概要 从考古样本中回收古代 DNA (aDNA) 分子的可能性已经产生 研究人类进化和历史的绝佳机会。与传统数据集相比 aDNA 由当前收集的单个时间点组成，可以检测丢失的遗传基因 谱系并能够直接评估不同时间横断面的等位基因频率。 从考古材料中获取基因组数据的技术推动了技术的发展 古基因组学领域，但利用时间序列遗传信息的统计方法 数据集落后于这些技术进步。未来五年，阿莫林实验室将 开发并应用使用 aDNA 研究人类宿主-病原体协同进化和适应的方法。 我们寻求通过生成包含大量数据的 aDNA 数据集来推进古基因组学领域的发展 每个考古遗址的样本量并开发新的方法和途径来利用 来自时间序列遗传数据的信息。我们将利用这些新资源来研究宿主病原体 欧亚大陆瘟疫（例如黑死病）爆发期间和 南美洲原住民与欧洲殖民者之间的接触。对比型号 人口进化的分析和计算，以及观察到的等位基因频率和其他 汇总不同时间横断面的统计数据，我们将识别宿主病原体的遗传特征 相互作用，描述人类对病原体反应所涉及的进化过程，以及 推断选择性扫描的强度和时间。此外，我们将描述分布 不同时间横断面的人类基因组新突变的适应度效应（DFE） 根据疾病爆发和其他事件引起的环境变化来分析其演变。 这项研究代表了古基因组学最先进知识的进步，因为它的重点是 尚未用aDNA表征的进化过程（特别是宿主-病原体共同进化）， 使用时间序列数据表征 DFE，以及新资源（数据集）的开发 和方法）。阿莫林实验室在实现这些目标方面具有独特的优势，因为我们 将基于模型的方法与古代和现代基因组数据分析相结合的经验 DNA，以及我们之前研究中世纪欧洲人和美洲原住民的经验 人口。