Genotypic prediction of HIV-1 subtype D tropism

Abstract

Background: HIV-1 subtype D infections have been associated with rapid disease progression and phenotypic assays have shown that CXCR4-using viruses are very prevalent. Recent studies indicate that the genotypic algorithms used routinely to assess HIV-1 tropism may lack accuracy for non-B subtypes. Little is known about the genotypic determinants of HIV-1 subtype D tropism.

Results: We determined the HIV-1 coreceptor usage for 32 patients infected with subtype D by both a recombinant virus phenotypic entry assay and V3-loop sequencing to determine the correlation between them. The sensitivity of the Geno2pheno10 genotypic algorithm was 75% and that of the combined 11/25 and net charge rule was 100% for predicting subtype D CXCR4 usage, but their specificities were poor (54% and 68%). We have identified subtype D determinants in the V3 region associated with CXCR4 use and built a new simple genotypic rule for optimizing the genotypic prediction of HIV-1 subtype D tropism. We validated this algorithm using an independent GenBank data set of 67 subtype D V3 sequences of viruses of known phenotype. The subtype D genotypic algorithm was 68% sensitive and 95% specific for predicting X4 viruses in this data set, approaching the performance of genotypic prediction of HIV-1 subtype B tropism.

Conclusion: The genotypic determinants of HIV-1 subtype D coreceptor usage are slightly different from those for subtype B viruses. Genotypic predictions based on a subtype D-specific algorithm appear to be preferable for characterizing coreceptor usage in epidemiological and pathophysiological studies.

Figure 1

V3 amino acid sequence alignments and matched phenotypes of the 26 subtype D viruses (1a) and of 13 reference subtype B viruses (1b). V3 amino acid sequence alignments were obtained by bulk sequencing env PCR products from the 26 subtype D-infected patients, 10 subtype B-infected patients and 3 reference subtype B viruses. These sequences are shown with the following abbreviations with reference to the consensus sequences: dot, identity with amino acid baseline sequence; dash, gap inserted to maintain alignment; slash, amino acid position related to dual virus population. Replacements are indicated by the appropriate code letters. Residues at positions 11 and 25 and mutated N-linked glycosylation sites are boxed to highlight the substitutions noted. The V3 net charge (calculated by subtracting the number of negatively charged amino acids [D and E] from the number of positively charged ones [K and R]); the number of amino acids in V3; the genotype predicted by the combined 11/25 and net charge rules built for subtype B viruses and the Geno2pheno are shown, together with the observed phenotype. Discordances between the genotypic predictions and the phenotype are boxed.

Figure 2

Clonal analysis of the virus populations of three patients whose genotypic prediction and phenotype were discordant. Clonal composition of the HIV-1 quasispecies of three patients harboring R5 phenotyped viruses mispredicted X4 by the genotypic algorithms built for subtype B viruses. V3 amino acid sequence alignments were obtained by sequencing molecular clones of env PCR products. Theses sequences are shown with the following abbreviations with reference to the direct sequence: dot, identity with amino acid baseline sequence; dash, gap inserted to maintain alignment; slash, amino acid position related to dual virus population. Replacements are indicated by the appropriate code letters. Residues at positions 11 and 25 are boxed to highlight the substitutions noted. The V3 net charge (calculated by subtracting the number of negatively charged amino acids [D and E] from the number of positively charged ones [K and R]); the number of amino acids in V3; the genotype predicted by the combined 11/25 and net charge rules built for subtype B viruses and the Geno2pheno are shown, together with the observed phenotype.

Figure 3

Neighbour-joining phylogenetic tree of HIV-1 subtype D V3 sequences from 26 patients and 72 sequences from GenBank. Patients are identified with the same number than in Figure 1 and the GenBank sequences are identified with the country (two letters code) and the accession number. The corresponding phenotype is indicated by symbols: open circles indicate sequences from R5 viruses, solid circles indicate sequences from R5X4 viruses and solid squares indicate sequences from X4 viruses. Percentage bootstrap values are indicated on branches have been calculated for 1000 replicates. The genetic relatedness of two different sequences is represented by the horizontal distance that separates them, with the length of the bar at the bottom denoting a sequence divergence of 0.10.