Impact of CCR5delta32 Host Genetic Background and Disease Progression on HIV-1 Intrahost Evolutionary Processes: Efficient Hypothesis Testing through Hierarchical Phylogenetic Models

Abstract

The interplay between C-C chemokine receptor type 5 (CCR5) host genetic background, disease progression, and intrahost HIV-1 evolutionary dynamics remains unclear because differences in viral evolution between hosts limit the ability to draw conclusions across hosts stratified into clinically relevant populations. Similar inference problems are proliferating across many measurably evolving pathogens for which intrahost sequence samples are readily available. To this end, we propose novel hierarchical phylogenetic models (HPMs) that incorporate fixed effects to test for differences in dynamics across host populations in a formal statistical framework employing stochastic search variable selection and model averaging. To clarify the role of CCR5 host genetic background and disease progression on viral evolutionary patterns, we obtain gp120 envelope sequences from clonal HIV-1 variants isolated at multiple time points in the course of infection from populations of HIV-1-infected individuals who only harbored CCR5-using HIV-1 variants at all time points. Presence or absence of a CCR5 wt/Δ32 genotype and progressive or long-term nonprogressive course of infection stratify the clinical populations in a two-way design. As compared with the standard approach of analyzing sequences from each patient independently, the HPM provides more efficient estimation of evolutionary parameters such as nucleotide substitution rates and d(N)/d(S) rate ratios, as shown by significant shrinkage of the estimator variance. The fixed effects also correct for nonindependence of data between populations and results in even further shrinkage of individual patient estimates. Model selection suggests an association between nucleotide substitution rate and disease progression, but a role for CCR5 genotype remains elusive. Given the absence of clear d(N)/d(S) differences between patient groups, delayed onset of AIDS symptoms appears to be solely associated with lower viral replication rates rather than with differences in selection on amino acid fixation.

Disentangling the impact of within-host evolution and transmission dynamics on the tempo of HIV-1 evolution

“…To this end, we compiled a large collection of datasets for previously described HIV-1 transmission chains. We describe the viral evolutionary histories with a recently introduced transmission model in BEAST [17] and test for transmission bottleneck size differences with a Bayesian hierarchical phylogenetic model (HPM) approach [27] that incorporates fixed effects [28]. We find no support for a difference in the loss of genetic diversity between the HET and MSM groups.…”

Graph hierarchies for phylogeography

“…HPMs have been used in phylodynamic studies of the intrahost behaviour of HIV-1 [15,26]. The long timespan of HIV-1 infections, which sometimes last more than 10 years, combined with high mutation rates, makes phylodynamics a useful tool for assessing viral intrahost biology.…”

“…These hierarchical phylogenetic models (HPMs) generally have the property of reducing variability in estimates for phylogenetic parameters of individual partitions, while providing a framework for assessing overall tendencies. Examples of HPMs involving the sequence substitution processes in infectious diseases include examining selective pressures in HIV intrahost data, where information pools across multiple patients [15], and estimating the time to most recent common ancestor across the multiple gene segments of influenza [16].…”

HIV-1 evolution in patients undergoing immunotherapy with Tat, Rev, and Nef expressing dendritic cells followed by treatment interruption

“…The potential effect of vaccination on viral evolution was explored using a fixed-effects extension of Bayesian hierarchical phylogenetic models (HPMs) [17,18] implemented in BEAST (Bayesian Evolutionary Analysis Sampling Trees) [19]. By specifying hierarchical prior distributions, HPMs allow pooling information across different patients to improve estimate precision of withinhost viral evolutionary parameters, in this case the HIV evolutionary rate, in individual patients.…”