Speaker
Description
Understanding the forces driving intra-host HIV to evolve resistance to interventions is critical for designing effective countermeasures. While genetic linkage patterns are potentially a powerful tool to quantify the relative contributions of multiple evolutionary forces (mutation, recombination, selection), the severe population bottlenecks accompanying therapy confound these signatures. To interpret genetic linkage in the context of such major demographic changes (which are themselves driven by specific mutations), we develop a viral evolutionary forward simulation framework in which genetics and demography influence each other. This framework overcomes limitations from both dynamical modeling, in which patterns of linked variation are ignored, and from population genetic modeling, in which demography is predetermined. Using few parameters (HIV population mutation rate and viral fitness in the presence and absence of a drug), we are able to reproduce linkage patterns and population bottlenecks that broadly conform to those observed in vivo. As a case study to demonstrate this model’s utility, we consider a recent hypothesis that viral recombination is suppressed during population bottlenecks due to diminished opportunities for coinfection. In simulating populations with and without recombination suppression during population contraction, we show that this effect measurably changes genetic diversity signatures in rebounding populations, but is less visible when examining simulated viral loads or resistance mutations alone. Then, using this model as a null expectation for linkage patterns, we assess if the linkage structure in HIV populations treated with bNAbs is consistent with density-dependent recombination in vivo. Collectively, this work demonstrates that, by generating realistic null expectations of linkage under complex population demography, we can employ linkage patterns as a powerful source of information for evaluating viral evolutionary hypotheses.
