Our goal is to examine the rules of virus adaptation and evolution and to determine how the genetic composition of a RNA virus population modulates viral fitness, adaptation and pathogenesis. We will integrate experimental innovations and concepts into a platform that measures diversity and adaptation and use these measurements to deepen and extend our understanding of how viral genetic diversity and population structure contribute to adaptation and disease. Our approach is to generate virus populations with finely characterized genetic diversity profiles and mutation compositions and link these measurements to fitness and evolution rates. To this end, we have developed novel and a highly accurate sequencing strategy to describe the genetic structure of a viral population. This new approach allows us to detect rare mutational events with unprecedented accuracy. To determine fitness and evolution rates of the viral population we will employ standard tissue culture assays to characterize the phenotypes of the individual populations under well-defined evolutionary paradigms. We will examine virus adaptation and fitness both in tissue culture and animal models of infection. We then will link the mutation distribution of the initial viral quasispecies to the experimental measures of its fitness using established computer algorithms and mathematical models. The integration of rich deep-sequencing information corresponding to the composition of virus populations as they evolve through various selective pressures, including in infected animals, together with our capacity to manipulate the quasispecies structure of viruses, will open the door to a better understanding of virus evolutionary capacity;will better define the functional relationship between population structure and adaptation dynamics;and will identify specific mutations and interactions with the host that are adaptive and lead to disease.

Public Health Relevance

We have developed an experimental and computational platform to examine the evolutionary behavior of an RNA virus. The integration of rich deep-sequencing information on a virus population as it evolves through various selective pressures, including in infected animals, together with our capacity to manipulate quasispecies structure of virus population, may allow for a better understanding and to predict viral evolution and identify which types of mutations will be adaptive and lead to disease.

Agency
National Institute of Health (NIH)
Type
High Priority, Short Term Project Award (R56)
Project #
2R56AI036178-17A1
Application #
8880656
Study Section
Virology - B Study Section (VIRB)
Program Officer
Park, Eun-Chung
Project Start
Project End
Budget Start
Budget End
Support Year
17
Fiscal Year
2014
Total Cost
Indirect Cost
Name
University of California San Francisco
Department
Microbiology/Immun/Virology
Type
Schools of Medicine
DUNS #
City
San Francisco
State
CA
Country
United States
Zip Code
94143