Evolutionary dynamics of influenza virus across spatiotemporal scales

Abstract

RNA viruses like influenza mutate rapidly to form genetically diverse populations. Recent high-throughput deep sequencing techniques make it possible to track influenza’s evolutionary dynamics at high resolution, showing how viral populations diversify and adapt in just days or weeks. In my dissertation, I examine how influenza viruses evolve across different spatiotemporal scales. First, I characterize a cooperative interaction between two distinct influenza variants that differ by a single nucleotide mutation. In cell culture, a mixture of the two viral variants grows to higher titers than either variant alone, and populations maintain an equal mixture of the two variants through several passages. Next, I show that this mixture of cooperative variants arises primarily in cell culture rather than in clinical samples. This work provides one of the first examples of a specific cooperative interaction between RNA viruses. In the rest of my thesis, I focus on how influenza viruses evolve within infected hosts. First, I characterize influenza’s evolutionary dynamics within chronically infected individuals. In multi-week infections, I observe extensive parallelism in the mutations that arise within and between hosts. The same small set of antigenic variants arises recurrently within an individual, in multiple individuals in our study, and in the global influenza population. Next, I resolve a discrepancy between two recent estimates of how much genetic diversity is present within acute influenza infections and what proportion of this genetic diversity is transmitted. I identify a major technical issue in the raw sequencing data for one study that contributes to that study’s estimate of high genetic diversity and a large transmission bottleneck. Altogether, this work expands our understanding of the evolutionary forces that shape viral populations across multiple spatiotemporal scales.