Ancient viral threats through the lens of adaptation in human genomes

从人类基因组适应的角度看古代病毒的威胁

• 批准号: 10665076
• 负责人: David Enard
• 金额: $ 37.74万
• 依托单位: UNIVERSITY OF ARIZONA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-17 至 2026-07-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10665076
• 关键词: Automobile Driving; Bayesian Analysis; COVID-19 pandemic; Complex; Development; Epidemic; Event; Evolution; Future; Genes; Genetic Predisposition to Disease; Genetic Recombination; Genomics; Graph; Human; Human Genome; Immune; Immune system; Knowledge; Left; Link; Machine Learning; Mutation; Population; Shapes; Signal Transduction; Testing; Time; Viral; Virus; Work; arms race; deep learning; future epidemic; future pandemic; lens; novel strategies; pandemic potential; pathogen; reconstruction; tool; viral epidemic

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.

项目概要 当前的 SARS-COV2 大流行表明需要付出更多努力来评估大流行 病毒可能在人群中传播。评估特定地区大流行的可能性 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时期引起了流行病 记录了过去，但在人类进化的更长的时间范围内。引起流行病的病毒 过去确实最有可能在未来再次引发流行病，数百种病毒流行 很可能在人类进化过程中困扰着人类。这项工作将填补人们对流行病知识的空白 祖先人群，通过这样做，将能够更好地评估代表人类祖先的病毒 未来的大流行威胁。 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。 病毒的军备竞赛通过驱动大量的适应来塑造宿主的免疫系统。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，而且在整个系统中都留下了丰富的适应信号。 整个人类基因组。该实验室将检查人类基因组中特定病毒留下的适应信号，以 检测、测定古代流行病的年代并对其进行功能表征。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 （ARG）。这些新方法具有更强的检测和测定基因组适应性的能力，这将使我们能够询问 以下问题： 1）哪些病毒推动了人类进化中的古代流行病？ 我的实验室将创建具有高能力的深度学习测试，以检测过去复杂的基因组适应 人类进化约 20 万年。 2）特定病毒何时引发古代流行病？ 我们将使用 ARG 和近似贝叶斯计算，通过与宿主约会来确定古代流行病的年代 由特定病毒驱动的适应性事件。 3）古代流行病期间选择了哪些功能宿主基因变化，其中 基因，以及它们如何影响对现有病毒的遗传易感性？ 我们将研究对病毒的监管适应以及病毒驱动的宿主适应对病毒的总体影响 不同人群对特定病毒的遗传易感性，从而提供 病毒学家具有强大的候选宿主基因以供进一步探究。 我的实验室特别适合通过将宿主与病原体的相互作用联系在一起来破译古代流行病 适应群体基因组学的最新进展。

项目成果

An efficient and robust ABC approach to infer the rate and strength of adaptation.

一种高效且稳健的 ABC 方法，用于推断适应率和强度。

• DOI: 10.1101/2023.08.29.555322
• 发表时间: 2023
• 期刊: bioRxiv : the preprint server for biology
• 作者: Murga-Moreno,Jesús;Casillas,Sònia;Barbadilla,Antonio;Uricchio,Lawrence;Enard,David
• 通讯作者: Enard,David

Decreased recent adaptation at human mendelian disease genes as a possible consequence of interference between advantageous and deleterious variants.

• DOI: 10.7554/elife.69026
• 发表时间: 2021-10-12
• 期刊: eLife
• 影响因子: 7.7
• 作者: Di C;Murga Moreno J;Salazar-Tortosa DF;Lauterbur ME;Enard D
• 通讯作者: Enard D

Versatile Detection of Diverse Selective Sweeps with Flex-Sweep.

• DOI: 10.1093/molbev/msad139
• 发表时间: 2023-06-01
• 期刊: MOLECULAR BIOLOGY AND EVOLUTION
• 影响因子: 10.7
• 作者: Lauterbur, M. Elise;Munch, Kasper;Enard, David
• 通讯作者: Enard, David

Assessing the Presence of Recent Adaptation in the Human Genome With Mixture Density Regression.

• DOI: 10.1093/gbe/evad170
• 发表时间: 2023-10-06
• 期刊: GENOME BIOLOGY AND EVOLUTION
• 影响因子: 3.3
• 作者: Salazar-Tortosa, Diego F.;Huang, Yi-Fei;Enard, David
• 通讯作者: Enard, David

Adaptive duplication and genetic diversification of protein kinase R contribute to the specificity of bat-virus interactions.

• DOI: 10.1126/sciadv.add7540
• 发表时间: 2022-11-25
• 期刊: Science advances
• 影响因子: 13.6