This collaborative project involves two early career female researchers, one from the U.S. and the other from Israel. Their goal is to determine the fitness cost of all possible mutations in the human immunodeficiency virus (HIV) genome using new laboratory techniques and new statistical methods. Mutations are the ultimate source of genetic variation and the fuel of evolution. But whether a particular mutation persists and contributes to variation within a population is determined by its fitness cost. Mutations with high fitness costs will likely be eliminated from the population versus those with little or no fitness costs. Knowledge of mutational fitness is central to many basic questions in evolutionary biology, but also has critical practical application, for example, for predicting evolution of resistance to antibiotics, antiviral drugs, and pesticides. Despite this importance, estimating fitness costs remains one of the key challenges in modern evolutionary genomics. The researchers will analyze clinical samples from a combination of 75 HIV subtype B and subtype C patients using innovative genomic and computational methods. Resulting data will ultimately allow them to infer the fitness cost of every single point mutation in the HIV genome. The project includes intensive mentoring and research opportunities for undergraduate students from underrepresented groups in STEM fields. These students will also gain international experiences. A new graduate level course on communicating science to the public will be developed. Public outreach will occur via videos, public lectures, and visits to local schools.
HIV is an ideal model system for studying in vivo fitness costs: Genetic diversity accumulates quickly in every host, and samples from different patients can be treated as independent replicate populations. Fitness costs can then be inferred by using the theory of mutation-selection balance and averaging mutation frequencies across patients. The researchers have three primary objectives. 1) Develop statistical methods for inferring fitness costs from mutation frequencies. 2) Develop highly accurate next generation sequencing approaches for low biomass samples, and use these to sequence HIV from patient samples and infer mutation frequencies. 3) Infer high-resolution maps of fitness costs in HIV-1 subtypes B and C, the two most prevalent subtypes across the globe, and quantify context-dependent fitness effects. Accomplishing these objectives will lead to the first complete in vivo distribution of fitness costs for a genome. Development of these innovative methods will also be generalizable to any system for which next generation sequencing (NGS) data exist for independent populations. This award is co-funded by the Office of International Science and Engineering.
