Seasonal influenza viruses: Limited predictability of evolution & Inference of reassortment networks

Seasonal influenza viruses repeatedly infect humans in part because they rapidly change their antigenic properties and evade host immune responses, necessitating frequent updates of the vaccine composition. A better understanding of this evolution could allow for better predictions and would be important for vaccine design.
We investigated the predictability of frequency dynamics and fixation of amino acid substitutions and found that the current frequency was the strongest predictor of eventual fixation, as expected in neutral evolution. Other properties, such as occurrence in previously characterized epitopes or high Local Branching Index (LBI) had little predictive power. Simulations of models of adapting populations, in contrast, show clear signals of predictability. This indicates that the evolution of influenza HA and NA, while driven by strong selection pressure to change, is poorly described by common models of directional selection such as traveling fitness waves.
Influenza diversity is not only generated by mutation but also by reassortment: the exchange of genome segments between virus co-infecting a cell. While reassortment is known to be at the root of most influenza pandemics, little is known about its role in the evolution of the virus. This is largely due to the difficulty in identifying reassortments events from viral sequences. Here, we discuss the problem of inferring Ancestral Reassortment Graphs (ARG), objects that describe ancestry relations in the presence of reassortment. We introduce a simple heuristic, TreeKnit, that is able to accurately solve this problem while being orders of magnitude faster than other existing methods.