Ancient viral threats through the lens of adaptation in human genomes

Information

  • Research Project
  • 10274677
  • ApplicationId
    10274677
  • Core Project Number
    R35GM142677
  • Full Project Number
    1R35GM142677-01
  • Serial Number
    142677
  • FOA Number
    PAR-20-117
  • Sub Project Id
  • Project Start Date
    9/17/2021 - 2 years ago
  • Project End Date
    7/30/2026 - 2 years from now
  • Program Officer Name
    JANES, DANIEL E
  • Budget Start Date
    9/17/2021 - 2 years ago
  • Budget End Date
    7/31/2022 - a year ago
  • Fiscal Year
    2021
  • Support Year
    01
  • Suffix
  • Award Notice Date
    9/17/2021 - 2 years ago
Organizations

Ancient viral threats through the lens of adaptation in human genomes

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.

IC Name
NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES
  • Activity
    R35
  • Administering IC
    GM
  • Application Type
    1
  • Direct Cost Amount
    250000
  • Indirect Cost Amount
    127363
  • Total Cost
    377363
  • Sub Project Total Cost
  • ARRA Funded
    False
  • CFDA Code
    859
  • Ed Inst. Type
    SCHOOLS OF ARTS AND SCIENCES
  • Funding ICs
    NIGMS:377363\
  • Funding Mechanism
    Non-SBIR/STTR RPGs
  • Study Section
    ZRG1
  • Study Section Name
    Special Emphasis Panel
  • Organization Name
    UNIVERSITY OF ARIZONA
  • Organization Department
    BIOLOGY
  • Organization DUNS
    806345617
  • Organization City
    TUCSON
  • Organization State
    AZ
  • Organization Country
    UNITED STATES
  • Organization Zip Code
    857210158
  • Organization District
    UNITED STATES