In the absence of effective antiviral therapy, HIV-1 evolves in response to the within-host environment, of which the immune system is an important aspect. During the earliest stages of infection, this process of evolution is very rapid, driven by a small number of CTL escape mechanisms. As the infection progresses, immune escape variants evolve under reduced magnitudes of selection, while competition between an increasing number of polymorphic alleles (i.e., clonal interference) makes it difficult to quantify the magnitude of selection acting upon specific variant alleles. To tackle this complex problem, we developed a novel multi-locus inference method to evaluate the role of selection during the chronic stage of within-host infection. We applied this method to targeted sequence data from the p24 and gp41 regions of HIV-1 collected from 34 patients with long-term untreated HIV-1 infection. We identify a broad distribution of beneficial fitness effects during infection, with a small number of variants evolving under strong selection and very many variants evolving under weaker selection. The uniquely large number of infections analysed granted a previously unparalleled statistical power to identify loci at which selection could be inferred to act with statistical confidence. Our model makes no prior assumptions about the nature of alleles under selection, such that any synonymous or non-synonymous variant may be inferred to evolve under selection. However, the majority of variants inferred with confidence to be under selection were non-synonymous in nature, and in nearly all cases were associated with either CTL escape in p24 or neutralising antibody escape in gp41. Sites inferred to be under selection in multiple hosts have high within-host and between-host diversity albeit not all sites with high between-host diversity were inferred to be under selection at the within-host level. Our identification of selection at sites associated with resistance to broadly neutralising antibodies (bNAbs) highlights the need to fully understand the role of selection in untreated individuals when designing bNAb based therapies.Author Summary During the within-host evolution of HIV-1, the diversity of the viral population increases, with many beneficial variants competing against each other. This competition, known as clonal interference, makes the identification of variants under positive selection a challenging task. We here apply a novel method for the inference of selection to targeted within-host sequence data describing changes in the p24 and gp41 genes during HIV-1 infection in 34 patients. Our method adopts a parsimonious approach, assigning selection to the smallest number of variants necessary to explain the evolution of the system. The large size of our dataset allows for the confident identification of variants under selection, alleles at certain loci being repeatedly inferred as under selection within multiple individuals. While early CTL escape mutations have been identified to evolve under strong positive selection, we identify a distribution of beneficial fitness effects in which a large number of mutations are under weak selection. Variants that were confidently identified under selection were primarily found to be associated with either CTL escape in p24 or neutralising antibody escape in gp41, including sites associated with escape from broadly neutralising antibodies. We find that the most frequently selected loci have high diversity both within-host and at the between-host level.

A de novo approach to inferring within-host fitness effects during untreated HIV-1 infection