Epistasis and pleiotropy in viral protein evolution

Abstract

Viruses evolve under Darwinian selection, and forecasting their evolution requires understanding which mutations confer fitness advantages. Over the past decade, advances in high-throughput experiments have made it possible to measure the phenotypic effects of all mutations to key viral proteins. Yet we continue to fall short when predicting which viral mutations will rise in frequency. The main problem is that the effect of a mutation is not fixed—it depends on the genetic background in which it occurs and on an immune context that is often unknown and dynamic. A mutation that is deleterious in one background may become tolerated in another. A mutation that benefits one phenotype but harms another can create conflicts that constrain selection. These phenomena, known as epistasis and pleiotropy, complicate efforts to make accurate viral forecasts. In chapter 2, we examine how viral mutations combine to escape antibodies in human sera. We introduce a simple biophysical model that explains how the effect of a mutation on antibody escape depends on epistatic interactions with other mutations. We then show how these mutation effects can be inferred directly from deep mutational scanning datasets. In chapter 3, we investigate how pleiotropy constrains viral protein evolution. We use deep mutational scanning to measure the effects of mutations to human influenza virus hemagglutinin on three phenotypes: cell entry, acid stability, and serum antibody neutralization. By quantifying mutation effects across multiple phenotypes, we identify viral mutations that are beneficial in one context but deleterious in another, revealing evolutionary trade-offs. Finally, in chapter 4, we compare the effects of viral mutations to H3, H5, and H7 influenza virus hemagglutinin to explore how mutation effects differ across three sequentially divergent but structurally conserved proteins.