Genetic Consequences of Antiviral Therapy on HIV-1

Abstract

A variety of enzyme inhibitors have been developed in combating HIV-1, however the fast evolutionary rate of this virus commonly leads to the emergence of resistance mutations that finally allows the mutant virus to survive. This review explores the main genetic consequences of HIV-1 molecular evolution during antiviral therapies, including the viral genetic diversity and molecular adaptation. The role of recombination in the generation of drug resistance is also analyzed. Besides the investigation and discussion of published works, an evolutionary analysis of protease-coding genes collected from patients before and after treatment with different protease inhibitors was included to validate previous studies. Finally, the review discusses the importance of considering genetic consequences of antiviral therapies in models of HIV-1 evolution that could improve current genotypic resistance testing and treatments design.

Figure 1

Overall sequence diversity variation and Kullback-Leibler divergence. (a) Variation of overall sequence difference between the two datasets of each evolutionary scenario (d _{after  treatment} − d _{before  treatment}). Indels are considered as missing data. Error bars indicate standard error. Reference values are shown in Table S1 (Supplementary Material). (b): Kullback-Leibler distance, nucleotide diversity distribution, between the two datasets of each evolutionary scenario. Dark grey bars consider indels as a new state whereas clear grey bars consider indels as missing data. Error bars indicate standard error across sites. “−” indicates naïve-treatment patients.

Figure 2

Sequence difference variation as a function of time interval between samples. (a) Variation of the overall sequence difference between the two datasets of each evolutionary scenario (d _{after  treatment} − d _{before  treatment}) is represented in the “y-axis” (mean and standard error). The time period between both samples (t _{after  treatment} − t _{before  treatment}) is represented in the “x-axis” (mean and standard error from all patients of the scenario). The correlation coefficient between both parameters among all the scenarios is r = 0.056, suggesting absence of correlation. Correlation coefficients within each evolutionary scenario (among patients) between these parameters are also shown in the plot and ranges from 0.0003 to 0.507, although most of them are under 0.1. (b) Genetic diversity gradient divided by the corresponding time period. Error bars indicate standard error. “−” indicates naïve-treatment patients. Reference values are shown in Table S1 (Supplementary Material).

Figure 3

Global nonsynonymous to synonymous substitution rates ratio (dN/dS) variation. Variation of global dN/dS between the two datasets of each evolutionary scenario (dN/dS _{after  treatment} − dN/dS _{before  treatment}). Error bars indicate 95% CI. “−” indicates naïve-treatment patients. Reference values are shown in Table S3 (Supplementary Material).