Speaker
Description
Recombination significantly influences the evolutionary dynamics of HIV. A high recombination rate promotes extensive diversity upon which selection can act, while also enabling the purging of deleterious mutations from the viral population. Despite acknowledging the prevalence of recombination, common phylogenetic methods often assume a single evolutionary history. The repercussions on evolutionary analyses and results, in particular the mismatch in within- and between-host evolutionary rates, are not fully understood. Here, we leveraged a large dataset obtained from a cohort of untreated individuals with dense temporal sampling and representing numerous HIV subtypes, and with populations sequenced using both whole-genome short-read (Illumina) and long-read (PacBio) technologies. We examined the impact of recombination on the measured within-host evolutionary rates, and explored how different methods for measuring evolutionary rates are impacted.
We applied existing recombination rate estimation methods designed for longitudinal within-host sequencing data but extended their application to a larger dataset with shorter sampling intervals, thereby capturing rapid evolutionary dynamics more precisely. The richness of the dataset enabled us to investigate the relationship between recombination rate and several parameters, including viral load, subtype, sex, sequencing method, read length and genome region. Additionally, we distinguish between mutations evolving neutrally from those under selection in order to mitigate the influence of positive selection on recombination inferences. Our results show that previous studies of within-host recombination have underestimated the recombination rate due to long intervals between sampling and the inclusion of positively selected sites.
In order to understand the implications of the high rates we observed on the methods applied to measure evolutionary rates, we take a simulation approach. By simulating alignments and corresponding phylogenetic trees in populations evolving with and without recombination, we quantify the impact of recombination on evolutionary rates as assessed by various methodological approaches and contextualise our findings to the rate mismatch phenomenon.
