Evidence of differential selection of HIV-1 variants carrying drug-resistant mutations in seroconverters

Objectives: Among the factors that drive the transmission of HIV-1-resistant variants, we evaluated i) the proportion of drug resistance mutations in seroconverters (SCs) and patients who showed a virological failure to antiretrovirals and ii) the transmissibility of viruses carrying resistant mutations over the last 7 years, by using an ecological case control study. Methods: We studied 139 RT and 140 protease (PR) sequences from 147 SCs and 4736 RT and 4430 PR sequences from 2156 and 2065 patients who failed treatment, respectively. The transmission efficiency of variants with drug mutations was assessed by evaluating the odds ratio (OR) between SC and individuals failing therapy at each RT and major PR mutations listed by the IAS-USA Panel, 2003. In this analysis, OR values were inversely related to the efficiency of transmission. The absence of mutations in SC was tested as a random event by a binomial probability model. Results: Overall prevalence of any major drug mutations was 16% in SCs. Class resistance in SCs and failing patients for NRTIs, NNRTs and PIs was as follows: 14%, 3% and 3%, and 70%, 25% and 34%, respectively. A number of RT and PR mutations were absent in SCs. Among these, the proportion of RT mutations 44D and 69D, and PR mutations 48V, 82A and 84V in failing patients allowed to establish that their absence was due to a negative selection (P<0.005). The transmission of RT mutations present in SCs and the degree of its efficiency was as follows: 118I (OR: 3.9, CI: 1.7–8.9), 67N (OR: 6.6, CI: 3.1–14.1), 210W (OR: 8.6, CI: 3.2–23.3), 103N (OR: 9.3, CI: 2.3–37.5), 70R (OR: 12.8, CI: 4.7–34.8), 41L (OR: 13.8, CI: 5.7–33.9), 215Y/F (OR: 22.7, CI: 8.4–61.5), 184V (OR: 23.9, CI: 7.6–75). For PR mutations 46I and 90M, the odds ratios were 7.2 (CI: 1.8–29.1) and 19.7 (CI: 4.9–79.6),respectively. Conclusions: HIV-1 variants carrying specific drug-resistant mutations seem to have a selective advantage in establishing new infections, as evaluated by odds ratio analysis and a binomial probability model. The analysis of larger databases may further investigate the transmission efficiency of low prevalent mutations.