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, 81 (14), 7620-8

Contribution of Recombination to the Evolution of Human Immunodeficiency Viruses Expressing Resistance to Antiretroviral Treatment

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Contribution of Recombination to the Evolution of Human Immunodeficiency Viruses Expressing Resistance to Antiretroviral Treatment

Tamara Nora et al. J Virol.

Abstract

Viral recombination has been postulated to play two roles in the development of human immunodeficiency virus (HIV) resistance to antiretroviral drugs. First, recombination has the capacity to associate resistance mutations expressed by distinct viruses, thereby contributing to the development of viruses with improved drug resistance. In addition, recombination could preserve diversity in regions outside those subject to strong selective pressure. In this study, we sought direct evidence for the occurrence of these processes in vivo by evaluating clonal virus populations obtained from the same patient before and after a treatment change that, while unsuccessful in controlling viral replication, led to the emergence of viruses expressing a different profile of resistance mutations. Phylogenetic studies supported the conclusion that the genotype arising after the treatment change resulted from the emergence of recombinant viruses carrying previously existing resistance mutations in novel combinations, whereas alternative explanations, including convergent evolution, were not consistent with observed genotypic changes. Despite evidence for a strong loss of genetic diversity in genomic regions coding for the protease and reverse transcriptase, diversity in regions coding for Gag and envelope was considerably higher, and recombination between the emerging viruses expressing the new pattern of resistance mutations and viral quasispecies in the previously dominant population contributed to this preservation of diversity in the envelope gene. These findings emphasize that recombination can participate in the adaptation of HIV to changing selective pressure, both by generating novel combinations of resistance mutations and by maintaining diversity in genomic regions outside those implicated in a selective sweep.

Figures

FIG. 1.
FIG. 1.
Treatment history and evolution of viral load and CD4+ T-cell counts in the study subject. The log10 viral load (solid symbols) and CD4+ T-cell counts (open symbols) are shown as a function of time after initiating treatment with antiretroviral agents. The agents received by the patient are indicated below the graph. Abbreviations: ABC, abacavir; APV, amprenavir; ddI, didanosine; EFV, efavirenz; 3TC, lamivudine; LPV, lopinavir; NFV, nelfinavir; NVP, nevirapine; RTV, ritonavir; SQV, saquinavir; d4T, stavudine; TDF, tenofovir; ddC, zalcitabine; AZT, zidovudine. The open triangles indicate the times at which clonal viral populations were obtained (left and right, M77 and M90, respectively).
FIG. 2.
FIG. 2.
Graphical representation of the protease (top) and RT (bottom) genotypes obtained for the study subject. Each bar represents a codon that was polymorphic in at least one of the sequences obtained from the patient. The bars are color coded as follows: red, resistance mutation as defined in Materials and Methods; green, alternative resistance mutation found at the same position; dark blue, other nonsynonymous polymorphism; light blue, alternative nonsynonymous polymorphism found at the same position; violet, synonymous polymorphism. For the genotypes obtained by sequencing bulk cDNA obtained from plasma by RT-PCR (M40, M45, M64, M66, M69, and M71), the consensus sequence is shown. For sequences representing a single clonal virus (M77 clone D11D and M90 clone C8G), all polymorphisms are shown. For sequences representing groups of clones (the group of 17 M90 clones [M90 clones ×17], M90 clones ×16, and M77 clones ×28, etc.), only polymorphisms expressed by the majority of the sequences in the given group are shown, although most polymorphisms were present in all clones in a given group. Resistance mutations were expressed by all the clones in a given group with the following exceptions: for protease, M77 clones ×28, one clone expressed the V82I resistance mutation, not the V82A mutation, and for RT, M77 clones ×24, one clone did not express the K70R mutation. Boxes surround genotypes obtained at earlier times that are most similar to the sequences of the M90 clones in the regions coding for amino acids 15 to 99 of protease (region 1), 41 to 100 of RT (region 2), and 101 to 236 of RT (region 3). The positions of all resistance mutations encountered in these sequences are also identified.
FIG. 3.
FIG. 3.
Neighbor-joining phylogenetic trees for the three genomic regions identified in Fig. 2 and covering the sequences coding for amino acids 15 to 99 of protease (A), 41 to 100 of RT (B), and 101 to 236 of RT (C). The genotypes obtained by sequencing bulk cDNA obtained from plasma by RT-PCR (Geno) and sequences from individual clones obtained at M77 and M90 are shown. The names of the bulk genotypes that grouped most closely in each region with the clones obtained at M90 have been boxed. Selected bootstrap scores are also shown. The scale line at the bottom of each tree represents 0.5% divergence.
FIG. 4.
FIG. 4.
Comparison of nucleotide diversities and Tajima's D statistic values for different genomic regions of clones obtained at months 77 (open bars) and 90 (solid bars). (A) Nucleotide distance was calculated pairwise for all clones using the method of Tajima and Nei (52), and results are expressed as the mean ± standard error of the mean. (B) Tajima's D statistic was determined as described previously (51). Significant departure from neutrality is indicated by asterisks (*, P < 0.05; **, P < 0.01).
FIG. 5.
FIG. 5.
Fay and Wu's H statistic values for different genomic regions of clonal viruses obtained at month 90. The H statistic was determined using the consensus sequence of M77 clones as an outgroup. In the upper panel, a sliding window representation of the H statistic is shown (window length, 100; step size, 25); the corresponding regions of the genome are shown in boxes below. The H statistic was also determined for each of the genomic regions, and the significance of the value compared to neutral expectations was determined by coalescent simulation, as implemented in DNA-SP.

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