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: 10490279
• 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/10490279
• 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; Signal Transduction; Testing; Time; Viral; Virus; Work; arms race; base; deep learning; lens; novel strategies; pandemic disease; pathogen; reconstruction; tool

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.

项目总结