Capturing Within Host HIV-1 Evolution Dynamics Using Simulation Methods
Abstract
The persistent latent reservoir of long-lived cells carrying integrated HIV DNA is the source of reinfection upon treatment interruption, and a primary focus for cure research. The reservoir is difficult to study because these cells are relatively rare or located in tissues that are difficult to sample. Sequencing proviral DNA in the latent reservoir is an important source of information about reservoir establishment and persistence, especially from the presence of identical (clonal) sequences. I evaluated the relationship between select measures of these clonal sequences and drivers of reservoir persistence, e.g., clonal expansion, by implementing a simulation model of within-host HIV dynamics in actively and latently infected cells. I implemented a discrete event simulation in the R package treeswithintrees, with four populations of cells corresponding to active, latent, replenishment and death compartments. To simulate molecular evolution on the resulting trees, I collapsed branches representing infected cells in a latent state and ran the program INDELible with parameters calibrated to HIV-1 on a representative env sequence. I propose a new clonality statistic (pairwise clonality) that can capture the genetic diversity of a sample with less information loss. I then evaluated the response of two clonality statistics used in literature (the proportion of identical sequences, Gini coefficient) and my proposed clonality statistic (number of identical pairwise comparisons) to changes in simulation parameter values by fitting a General linear Model (GLM). I found that the former clonality statistics were not as robust as the proposed pairwise clonality score. In addition, there were significant associations between clonality statistics and simulation parameters. Finally, I implemented a particle filtering method to evaluate non-linear relationships between simulation parameters and the clonality scores.