The Journal of general virology vol:91 issue:Pt 8 pages:1898-1908
A better understanding of Human Immunodeficiency Virus Type 1 drug resistance evolution under the selective pressure of combination treatment is important for the design of long-term effective treatment strategies. We applied Bayesian Network Learning on sequences from patients treated with the reverse transcriptase inhibitor combination of zidovudine and lamivudine to identify the role of many treatment-selected mutations in the development of resistance. Based on the Bayesian Network structure, an in vivo fitness landscape was build, reflecting the necessary selective pressure under treatment, to evolve naive sequences to sequences obtained from patients treated with the combination. This landscape, combined with an evolutionary model, was used to predict resistance evolution in longitudinal sequence pairs. In our analysis, mutations 41L, 70R, 184V and 215F/Y were identified as major resistance mutations to the combination of zidovudine and lamivudine, since they were directly associated with treatment experience. The network also suggested a possible role in resistance development for a number of novel mutations. Fitness, estimated using the landscape, correlated significantly with in vitro resistance phenotype in genotype-phenotype pairs (R2=0.70). Variation in predicted evolution under selective pressure correlated significantly with observed in vivo evolution during zidovudine plus lamivudine treatment. In conclusion, we confirmed current knowledge on resistance development to the combination of zidovudine and lamivudine, but additional, novel mutations were identified. Moreover, a model to predict resistance evolution during zidovudine and lamivudine treatment has been built and validated.