The clarifying role of time series data in the population genetics of HIV

• Published in: PLoS genetics 2021-01, Vol.17 (1), p.e1009050
• Main Authors: Feder, Alison F, Pennings, Pleuni S, Petrov, Dmitri A
• Format: Article
• Language: English
• Subjects: Anti-HIV Agents - adverse effects; Antiretroviral Therapy, Highly Active - adverse effects; Biology and Life Sciences; Demographic aspects; Disease Resistance - genetics; Ecology and Environmental Sciences; Evolution, Molecular; Forecasts and trends; Genetic aspects; Genetic Variation; Genetics, Population; HIV - drug effects; HIV - genetics; HIV - pathogenicity; HIV infection; HIV Infections - drug therapy; HIV Infections - genetics; HIV Infections - virology; Humans; Medicine and Health Sciences; Mutation - genetics; Mutation Rate; Selection, Genetic - genetics; Viewpoints
• ISSN: 1553-7404; 1553-7390; 1553-7404
• DOI: 10.1371/journal.pgen.1009050

HIV can evolve remarkably quickly in response to antiretroviral therapies and the immune system. This evolution stymies treatment effectiveness and prevents the development of an HIV vaccine. Consequently, there has been a great interest in using population genetics to disentangle the forces that govern the HIV adaptive landscape (selection, drift, mutation, and recombination). Traditional population genetics approaches look at the current state of genetic variation and infer the processes that can generate it. However, because HIV evolves rapidly, we can also sample populations repeatedly over time and watch evolution in action. In this paper, we demonstrate how time series data can bound evolutionary parameters in a way that complements and informs traditional population genetic approaches. Specifically, we focus on our recent paper (Feder et al., 2016, eLife), in which we show that, as improved HIV drugs have led to fewer patients failing therapy due to resistance evolution, less genetic diversity has been maintained following the fixation of drug resistance mutations. Because soft sweeps of multiple drug resistance mutations spreading simultaneously have been previously documented in response to the less effective HIV therapies used early in the epidemic, we interpret the maintenance of post-sweep diversity in response to poor therapies as further evidence of soft sweeps and therefore a high population mutation rate (θ) in these intra-patient HIV populations. Because improved drugs resulted in rarer resistance evolution accompanied by lower post-sweep diversity, we suggest that both observations can be explained by decreased population mutation rates and a resultant transition to hard selective sweeps. A recent paper (Harris et al., 2018, PLOS Genetics) proposed an alternative interpretation: Diversity maintenance following drug resistance evolution in response to poor therapies may have been driven by recombination during slow, hard selective sweeps of single mutations. Then, if better drugs have led to faster hard selective sweeps of resistance, recombination will have less time to rescue diversity during the sweep, recapitulating the decrease in post-sweep diversity as drugs have improved. In this paper, we use time series data to show that drug resistance evolution during ineffective treatment is very fast, providing new evidence that soft sweeps drove early HIV treatment failure.