Abstract
The dynamic nature of the SIV population during disease progression in the SIV/macaque model of AIDS and the factors responsible for its behavior have not been documented, largely owing to the lack of sufficient spatial and temporal sampling of both viral and host data from SIV-infected animals. In this study, we detail Bayesian coalescent inference of the changing collective intra-host viral effective population size (Ne ) from various tissues over the course of infection and its relationship with what we demonstrate is a continuously changing immune cell repertoire within the blood. Although the relative contribution of these factors varied among hosts and time points, the adaptive immune response best explained the overall periodic dynamic behavior of the effective virus population. Data exposing the nature of the relationship between the virus and immune cell populations revealed the plausibility of an eco-evolutionary mathematical model, which was able to mimic the large-scale oscillations in Ne through virus escape from relatively few, early immunodominant responses, followed by slower escape from several subdominant and weakened immune populations. The results of this study suggest that SIV diversity within the untreated host is governed by a predator-prey relationship, wherein differing phases of infection are the result of adaptation in response to varying immune responses. Previous investigations into viral population dynamics using sequence data have focused on single estimates of the effective viral population size (Ne ) or point estimates over sparse sampling data to provide insight into the precise impact of immune selection on virus adaptive behavior. Herein, we describe the use of the coalescent phylogenetic frame- work to estimate the relative changes in Ne over time in order to quantify the relationship with empirical data on the dynamic immune composition of the host. This relationship has allowed us to expand on earlier simulations to build a predator-prey model that explains the deterministic behavior of the virus over the course of disease progression. We show that sequential viral adaptation can occur in response to phases of varying immune pressure, providing a broader picture of the viral response throughout the entire course of progression to AIDS.
Highlights
For RNA viruses such as human immunodeficiency virus (HIV) and its pathogenic simian relative (SIV), high mutation rates and short generation time are the constant fuel for rapid evolutionary change [1]
Infected patients the gp40 region does contain sites where mutations can lead to viral escape, we decided to focus on gp120 because, besides including known immunodominant epitopes as well as the CD4 binding domain, it displays the highest phylogenetic signal, which is optimal for intra-host evolutionary studies in the SIV macaque model [45]
Often 2-3 distinct lineages were observed for each macaque phylogeny, dating back as early as the pretransmission interval [46] (Figures 1 and S1), each of these lineages appeared to be temporally structured, giving rise to multiple population turnover events in the estimated within-host viral effective population size (Ne) (Figures 2A and S2) and a periodicity demonstrated by auto-correlation (Figure S3)
Summary
For RNA viruses such as human immunodeficiency virus (HIV) and its pathogenic simian relative (SIV), high mutation rates and short generation time are the constant fuel for rapid evolutionary change [1]. The long-term fate of these changes, both among and within infected hosts [2], depends on the interplay of several population-level processes, such as genetic drift, selective forces from the environment, migration, and recombination [3]. Determining which population genetic process (selection or drift) is the major driving force of viral population dynamics is critical to understanding the likelihood of immune escape and drug resistance in response to natural and synthetic antiviral defenses. For viruses like HIV and SIV that persist for long periods of time, knowledge of this interplay is just as important during later stages of infection as it is at the time of transmission
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