In-vivo mutation rates and fitness landscape of HIV-1

Abstract

Mutation rates and fitness costs of deleterious mutations are difficult to measure in vivo but essential for a quantitative understanding of evolution. Using whole genome deep sequencing data from longitudinal samples during untreated HIV-1 infection, we estimated mutation rates and fitness costs in HIV-1 from the temporal dynamics of genetic variation. At approximately neutral sites, mutations accumulate with a rate of 1.2 × 10 −5 per site per day, in agreement with the rate measured in cell cultures. The rate from G to A is largest, followed by the other transitions C to T, T to C, and A to G, while transversions are more rare. At non-neutral sites, most mutations reduce virus replication; using a model of mutation selection balance, we estimated the fitness cost of mutations at every site in the HIV-1 genome. About half of all nonsynonymous mutations have large fitness costs (greater than 10%), while most synonymous mutations have costs below 1%. The cost of synonymous mutations is especially low in most of gag and pol, while much higher costs are observed in important RNA structures and regulatory regions. The intrapatient fitness cost estimates are consistent across multiple patients, suggesting that the deleterious part of the fitness landscape is universal and explains a large fraction of global HIV-1 group M diversity. IntroductionHIV-1 evolves rapidly within individual hosts: mutations allow it to evade immune predation but can also impair viral replication. Genetic changes arise during reverse transcription, during forward transcription by the human RNA polymerase II, or are caused by the innate immune system (Abram et al., 2010;Cuevas et al., 2015;Malim, 2009;Mansky and Temin, 1995). These changes are the source of genetic diversity, from which selection amplifies beneficial variants and filters deleterious mutations. Characterization of the mutation rate matrix and the genome wide landscape of fitness effects is a prerequisite a quantitative understanding of the evolutionary dynamics of HIV and for rational design of both vaccines and resistance proof drugs.The majority of mutations are deleterious, some mutations are neutral and have little or no effect, and a minority of mutations are beneficial. While beneficial mutations rapidly spread through the virus population within a patient, deleterious mutations stay at low frequency in a balance between mutation and selection. Beneficial mutations are often patient-specific and mediate escape from cytotoxic T-lymphocytes (CTL) and neutralizing antibodies (Bar et al., 2012;Goonetilleke et al., 2009;Walker and McMichael, 2012). At the same time, substitutions in response to immune selection are expected to lower intrinsic viral fitness; host-specific adaptation is a tradeoff between immune evasion and fitness costs of escape mutations.Since HIV-1 proteins serve the same function in different hosts, the landscape of fitness costs might be expected to be similar in different hosts. However, the effect of a particular mutation can depend on other sites in the...

Experimental Estimation of the Effects of All Amino-Acid Mutations to HIV’s Envelope Protein on Viral Replication in Cell Culture

“…More generally, our experiments provide high-throughput experimental data that can augment computational efforts to infer features of HIV’s fitness landscape [18–20, 22, 121]. Such data will aid in efforts to understand viral evolutionary dynamics both within and between patients.…”

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“…; Zanini et al . ), almost all single nucleotide mutations are present as SGV at any time. With values of Θ l ∼10 to 10 4 , one would expect that adaptation occurs only via soft selective sweeps.…”

“…In the absence of treatment, within-patient HIV populations have large census sizes with around 10 8 -10 9 active virusproducing cells (Haase 1994;Coffin & Swanstrom 2013). Since HIV has a high mutation rate of 10 À7 -10 À5 , depending on mutation type (Abram et al 2010;Zanini et al 2016), almost all single nucleotide mutations are present as SGV at any time. With values of Θ l $ 10 to 10 4 , one would expect that adaptation occurs only via soft selective sweeps.…”

Measuring Selection Across HIV Gag: Combining Physico-Chemistry and Population Genetics

“…Ideally, HIV fitness landscapes can help researchers predict when and where a virus will escape immune control (Barton et al, 2016a) and develop effective vaccines (Ferguson et al, 2013; Shekhar et al, 2013). In a few cases HIV fitness landscapes have been estimated directly from experimental data (Hinkley et al, 2011; Kouyos et al, 2012; Mann et al, 2014; Rihn et al, 2013), but in most studies the landscape is inferred from the ever growing libraries of genotype frequency data (Barton et al, 2016a; Zanini et al, 2016; Mann et al, 2014; Ferguson et al, 2013; Deforche et al, 2008; Seifert et al, 2015).…”