Development of Elvitegravir Resistance and Linkage of Integrase Inhibitor Mutations with Protease and Reverse Transcriptase Resistance Mutations

Abstract

Failure of antiretroviral regimens containing elvitegravir (EVG) and raltegravir (RAL) can result in the appearance of integrase inhibitor (INI) drug-resistance mutations (DRMs). While several INI DRMs have been identified, the evolution of EVG DRMs and the linkage of these DRMs with protease inhibitor (PI) and reverse transcriptase inhibitor (RTI) DRMs have not been studied at the clonal level. We examined the development of INI DRMs in 10 patients failing EVG-containing regimens over time, and the linkage of INI DRMs with PI and RTI DRMs in these patients plus 6 RAL-treated patients. A one-step RT-nested PCR protocol was used to generate a 2.7 kB amplicon that included the PR, RT, and IN coding region, and standard cloning and sequencing techniques were used to determine DRMs in 1,277 clones (mean 21 clones per time point). Results showed all patients had multiple PI, NRTI, and/or NNRTI DRMs at baseline, but no primary INI DRM. EVG-treated patients developed from 2 to 6 strains with different primary INI DRMs as early as 2 weeks after initiation of treatment, predominantly as single mutations. The prevalence of these strains fluctuated and new strains, and/or strains with new combinations of INI DRMs, developed over time. Final failure samples (weeks 14 to 48) typically showed a dominant strain with multiple mutations or N155H alone. Single N155H or multiple mutations were also observed in RAL-treated patients at virologic failure. All patient strains showed evidence of INI DRM co-located with single or multiple PI and/or RTI DRMs on the same viral strand. Our study shows that EVG treatment can select for a number of distinct INI-resistant strains whose prevalence fluctuates over time. Continued appearance of new INI DRMs after initial INI failure suggests a potent, highly dynamic selection of INI resistant strains that is unaffected by co-location with PI and RTI DRMs.

Identifying Signatures of Selection in Genetic Time Series

“…Thus, our tests are perhaps most readily applicable to selection scans in full-genome time-series data like those now actively generated in evolution experiments in Drosophila (Burke et al 2010;Orozco-terWengel et al 2012). They will also be applicable to asexual organisms when clonal interference is absent or weak, for example in competitive fitness assays (Lenski et al 1991;Gallet et al 2012;Kryazhimskiy et al 2012) or in tracking known polymorphisms in natural populations for a relatively short time (Barrett et al 2008;Winters et al 2012;Pennings et al 2013). By contrast, inferring selection coefficients when allele dynamics are influenced by multiple linked sites is a substantially more difficult problem, which has begun to be addressed elsewhere (Illingworth and Mustonen 2011;Illingworth et al 2012), although not within the same rigorous population-genetic framework that treats all genotypic dynamics stochastically.…”

“…P OPULATION geneticists typically seek to understand the forces responsible for patterns observed in contemporaneous samples of genetic data, such as the nucleotide differences fixed between species, polymorphisms within populations, and the structure of linkage disequilibrium. Recently, however, there has been a rapid increase in the availability of dynamic data, where the frequencies of segregating alleles in an evolving population are monitored through time, both in laboratory experiments (Hegreness et al 2006;Bollback and Huelsenbeck 2007;Barrick et al 2009;Lang et al 2011;Orozco-terWengel et al 2012;Lang et al 2013) and in natural populations (Barrett et al 2008;Reid et al 2011;Denef and Banfield 2012;Winters et al 2012;Daniels et al 2013;Maldarelli et al 2013; Pennings et al 2013). One important question is whether the changes in allele frequencies observed in such data are the result of natural selection or are simply consequences of genetic drift or sampling noise.…”

“…The underlying reason for this problem is that the likelihood-ratio statistic is x 2 -distributed only asymptotically, and convergence to this distribution is slow (Wilks 1938). In most practical applications, such as when sampling from natural populations (Reid et al 2011;Denef and Banfield 2012;Winters et al 2012;Daniels et al 2013;Maldarelli et al 2013; Pennings et al 2013) or competing two microbial strains (Lenski et al 1991;Bollback and Huelsenbeck 2007;Lang et al 2013), the number of sampled time points is typically small (,10) and the distribution of the likelihood-ratio statistic is far from x 2 under neutrality, leading to more false positives then expected. We propose two solutions to fix this problem, providing unbiased tests for natural selection in time-series data sampled at a single genetic locus.…”

Multiple Genetic Pathways Involving Amino Acid Position 143 of HIV-1 Integrase Are Preferentially Associated with Specific Secondary Amino Acid Substitutions and Confer Resistance to Raltegravir and Cross-Resistance to Elvitegravir

“…In previous in vitro and in vivo studies, Y143C and Y143R virus populations emerged in response to RAL exposure but not EVG exposure (20,33,34). We therefore investigated the susceptibility of patient viruses and a panel of Y143X SDMs with and without various RAL resistance-associated secondary substitutions ( …”

Characterization of the Drug Resistance Profiles of Integrase Strand Transfer Inhibitors in Simian Immunodeficiency Virus SIVmac239

“…E92Q is a nonpolymorphic mutation that has been selected in patients receiving either EVG (40)(41)(42)(43)(44) or RAL (39,40,45) and is associated with virological failure on EVG-based regimens (45). The A54A/V, A265A/V, and V277V/M partial substitutions were selected under INSTI pressure, but only the A265 position is conserved among HIV integrases of different subtypes.…”