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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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要当前的SARS-COV 2大流行揭示了需要更多的努力来评估大流行病毒在人群中传播的可能性。评估特定疾病的大流行潜力，在接下来的五年里，我的实验室将研究类似的病毒是否不仅在最近的几年里引起了流行病，记录过去，但在人类进化的更长的时间尺度。导致流行病的病毒过去确实是最有可能在未来再次引起流行病，数百种病毒流行病在人类进化过程中可能一直困扰着人类。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人类祖先的病毒，未来的流行病威胁。为了研究古代流行病，我的实验室将利用由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的适应性改变了宿主的免疫系统。我最近的研究表明，病毒不仅在免疫基因中，而且在整个细胞中留下了丰富的适应信号。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，检测，日期，并在功能上表征古代流行病。为此，我们将开发新的统计工具，基于机器学习和祖先进化图重建的最新进展，（ARGs）。这些新方法具有更强的检测和确定基因组适应日期的能力，将使我们能够问以下问题： 1)哪些病毒在人类进化中驱动了古代流行病？我的实验室将创建具有高功率的深度学习测试，以检测过去复杂的基因组适应 20万年的人类进化。 2)什么时候特定的病毒驱动了古代的流行病？我们将使用ARG和近似贝叶斯计算来确定古代流行病的年代，通过确定宿主的年代，由特定病毒驱动的适应性事件。 3)在古代流行病期间，选择了哪些功能性宿主遗传变化，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调节适应以及病毒驱动的宿主适应的总体影响，不同人群对特定病毒的遗传易感性，从而提供病毒学家有很强的候选宿主基因进行进一步调查。我的实验室非常适合通过将宿主和病原体之间的相互作用联系起来来破译古代流行病适应性种群基因组学的最新进展。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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
影响因子：
0
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.