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Fundacio irsiCaixa, Hospital Universitari Germans Trias i Pujol, Universitat Autònoma de Barcelona (UAB), 08916 Badalona, Spain
Correspondence
Miguel Angel Martínez
mmartinez{at}irsicaixa.es
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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The GenBank/EMBL/DDBJ accession numbers for the sequences reported in this paper are AY601922 [GenBank] –AY602164 [GenBank] .
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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). The emergence of SI viruses is associated with an accelerated decrease in CD4+ T cell count, rapid disease progression and the establishment of AIDS (Fauci, 1996; Glushakova et al., 1998). Although both variants temporally co-exist and X4 variant abundance increases with respect to R5 variants during disease progression, few studies have detected interaction between both virus populations (Schuitemaker et al., 1992; Xiao et al., 2000). Consequently, X4 variants may infect the same cell type as R5 variants and the interaction between both variants may be more common than previously thought. The high mutation rate associated with HIV-1 replication (10–4–10–5 mutations per nucleotide and per replication cycle) (Mansky & Temin, 1995) and the host's selective forces determine a continuous process of intrahost virus evolution and diversification that gives rise to variants with different biological properties (Shankarappa et al., 1999). The increase in HIV-1 diversity is related to increased HIV-1 replication capacity (fitness) and pathogenesis (Troyer et al., 2005). X4 variants tend to have a higher ex vivo virus fitness than R5 variants (Asjo et al., 1986; Cheng-Mayer et al., 1988; Tersmette et al., 1989; van't Wout et al., 1998). This could explain why the detection of X4 variants in infected subjects is associated with fast evolution to AIDS (Campbell et al., 2003; Kimata et al., 1999; Kwa et al., 2003). It is expected that with a low immunological response at late stages of the disease, virus evolution and selective pressure will be reduced, decreasing the virus population diversity and stabilizing its divergence with respect to the founder virus or viruses. Several studies have described a decrease in diversity at late stages of disease (McDonald et al., 1997; Wolinsky et al., 1996), whereas others have not found a reduction in virus diversity (Markham et al., 1998; Shankarappa et al., 1999). Recently, it has been found that ex vivo HIV-1 fitness correlated strongly with HIV-1 env C2V3 genetic diversity, suggesting that these parameters may be linked (Troyer et al., 2005). Here, virus evolution at late stages of the disease and the effects of selective pressure on the diversity of R5 and X4 variants were studied.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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). Eight samples showed an NSI phenotype (Group 1, g1), whereas the other four samples presented an SI phenotype in MT-2 cells (Group 2, g2) (Llano et al., 2001). CD4+ T cell counts, CD8+ T cell counts, provirus load and HIV-1 RNA levels are shown in Table 1. Plasma HIV-1 RNA levels were measured using the Amplicor monitor assay (Roche) and HIV-1 proviral DNA quantification was performed by end-point limiting dilution, as previously described (Ibanez et al., 1999, 2001; Parera et al., 2004).
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, 2001; Parera et al., 2004). End-point dilution of PBMC DNA was performed before nested PCR to ensure products were derived from a single provirus (Ibanez et al., 1999, 2001; Parera et al., 2004). For each sample, at least 20 amplicons were directly sequenced in an ABI 310 automated sequencer using dRhodamine chain terminator chemistry (Applied Biosystems). The env V3–V5 coding region was amplified using the published oligonucleotides Env 1 (5'-CCAATTCCCATACATTATTGT-3'; position 6885–6904 on the HIV-1 HXB2) and Env 5 (5'-CTTCCTGCTGCTCCCAAGAACC-3'; HXB2 position 7786–7807) for the first PCR and primers Env 2 (5'-CAGTCTAGCAGAAGAAGA-3'; HXB2 position 7013–7030) and Env 4 (5'-TTCTCCAATTGTCCCTCAT-3'; HXB2 position 7647–7665) for the nested PCR (Ibanez et al., 1999). Fifty nanomoles of each oligonucleotide was added to a standard PCR mixture (final volume of 50 µl) containing 1x PCR buffer, 5 mM MgCl2, 200 µM dNTPs and 2 U Taq polymerase (Promega). Cycling parameters were one cycle of denaturation at 94 °C for 2 min followed by 35 cycles of 94 °C for 30 s, 55 °C for 30 s and 72 °C for 1 min. A final extension step at 72 °C for 7 min was carried out. Sequencing oligonucleotides were Env 2, Env 4 and Env 3 (5'-TCCTCAGGAGGGGACCCAG-3'; HXB2 position 7314–7332). Sequences were aligned using CLUSTAL_W (Thompson et al., 1994).
Shannon entropy has been defined in terms of the probabilities of the different sequences or clusters of sequences that can be present at a given time point. The normalized entropy, Sn, was calculated as Sn=–
i(pilnpi)/lnN, where N is the total number of sequences analysed and pi is the frequency of each sequence in the virus quasispecies. Sn varies from 0 (no complexity) to 1 (maximum complexity) (Wolinsky et al., 1996). Pairwise nucleotide distances were calculated with the Tamura–Nei model of evolution and the phylogenetic reconstruction was generated using the neighbour-joining method implemented in the PAUP* 4.0 beta 8 software package (Sinauer Associates). Bootstrap resampling (Felsenstein, 1988) (1000 replicates) was applied to the neighbour-joining trees to assign approximate confidence limits to individual branches. The final graphical output was created with the program TREEVIEW (Page, 1996). The amino acid distances with the Poisson correction were obtained from the program MEGA2 (Kumar et al., 2001). The proportion of synonymous substitutions (ds) per potential synonymous site and the proportion of non-synonymous substitutions (dn) per potential non-synonymous site were calculated with the program SNAP (http://www.hiv.lanl.gov/content/hiv-db/SNAP) using the Nei–Gojobori model of evolution (Nei & Gojobori, 1986) incorporating a statistic developed by Ota & Nei (1994).
A maximum-likelihood method was used to estimate codon-specific selection pressures implemented in the program CODEML from the package PAML version 3.14 (Yang, 1997). To assess evidence for positive selection, different models of codon evolution were compared using a likelihood ratio test: M0 vs M3, M1 vs M2 and M7 vs M8. Single codons subjected to positive selection can be determined by a Bayesian method implemented in the same software package.
Co-receptor usage and SI phenotype determination.
The X4 (SI) phenotype was predicted from virus sequence data using position-specific scoring matrices (PSSM) (Jensen et al., 2003). This analysis is a simple bioinformatic method of scoring V3 amino acid sequences that reliably predicts CXCR4 usage. This determination allowed us, within the positive SI samples previously analysed in MT-2 cells (Llano et al., 2001), to distinguish between sequences predicted to use CXCR4 or CCR5 as the main co-receptor.
Statistical analysis.
To test significant differences between g1 and g2 CD4+ T cell count, CD8+ T cell count, virus load, provirus load, distribution of nucleotide (dg) and amino acid (da) distances, ds, dn and the ratio of ds per synonymous site to dn per non-synonymous site (ds/dn), groups were subjected to non-parametric statistical treatment using the Mann–Whitney test included in the GraphPad Prism version 4.00 for Windows (http://www.graphpad.com). Correlation between genetic distance and CD4+ T cell count was analysed with Pearson's correlation test.
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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. No significant differences were found between g1 (NSI) and g2 (SI) when mean blood CD4+ T cell counts (P=0·5697), mean blood CD8+ T cell counts (P=0·1714), virus load (P=0·4606) or provirus load (P=0·6828) were compared, suggesting a similar immunological status for both groups of subjects included in this study.
Association of intrahost sequence diversity of the HIV-1 env V3–V5 coding region and NSI/SI MT-2 phenotype
Neighbour-joining phylogenetic reconstruction of all env V3–V5 nucleotide sequences was performed to determine the evolutionary relationships of the virus variants. Fig. 1 shows that sequences from each subject produced a monophyletic group, which was supported by bootstrap analysis. Of note, the two samples from subject F formed distinct temporal clustering, that is, intermingling of sequences from the two time points was not observed. This subject F sequence temporal clustering was also supported by high bootstrap values (Fig. 1). All env (V3) amino acid sequences were used to predict main co-receptor usage with the PSSM matrix. Predictive X4 virus phenotypes were only detected in those samples that tested as SI in MT-2 cells (Table 2), highlighting the correlation between both methods. Thus, g2 sequences were subdivided into subgroups g2r5 for the variants predicted to use CCR5 and g2x4 for the variants predicted to use CXCR4 for each sample. To determine whether SI or NSI virus samples from subjects with CD4+ T cell counts below 200 cells µl–1 were under different selective pressures, the HIV-1 env V3–V5 proviral sequence heterogeneity, Shannon entropy (complexity), genetic distance, amino acid distance, and ds and dn of the 11 study subjects were analysed (Table 2).
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). The division of g2 into X4 and R5 variants, g2x4 and g2r5, respectively, produced two samples (Fbr5 and Lx4) with only three sequences. Therefore, data obtained from Fbr5 and Lx4 are biased because of sample size and must be treated with caution.
Intrasample genetic distances were calculated to assess whether the different groups had their intrahost HIV-1 diversification affected by the presence of X4 variants. To estimate virus diversity, the mean and standard deviation values were determined for pairwise DNA distances from the 20 sequences obtained for each sample (Table 2). Analysis of env V3–V5 HIV-1 showed a lower genetic diversity in the g1 samples (mean±SD, 3·99±2·56 %) than in the g2 samples (4·67±3·3 %, P<0·0005). Only g2 vs g2x4 gave no significant differences (P=0·4209), highlighting the low genetic diversity of g2r5 (1·49±1·28 %) when compared with g1 sequences (R5) (3·99±2·56 %, P<0·0001) or with the same SI samples with X4 variants (4·52±2·16 %, P<0·0001) (Table 2, Fig. 2a). Correlation determined by Pearson's test between genetic diversity and CD4+ T cell count was observed (r2=0·4064; P=0·0257) (Fig. 3) without clustering of NSI or SI subjects. As with the genetic distance, the amino acid distances determined for the g1 samples (7·20±0·1 %) were statistically lower than those of the g2 samples (8·50±0·2 %, P<0·0001). As for the genetic distance, the g2 samples did not differ statistically from g2x4 samples (8·02±0·2 %, P=0·5329). Finally, g2r5 had an extremely low amino acid distance (2·36±0·14 %) compared with g1 (P<0·0001) and g2x4 (P<0·0001). Overall, subjects who developed X4 variants had highly diverse env V3–V5 quasispecies, mainly due to the X4 variants. R5 variants from SI subjects were more homogeneous when compared with the X4 variants from the same subjects or to g1 samples. Interestingly, these results showed that genetic diversity was affected by CD4+ T cell count at late stages of the disease (Fig. 3). This indicates that heterogeneity at late stages of the disease may also depend on target cell availability.
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). In g2 sequences, the ds and dn were significantly higher when compared with those of g1 [5·5±3·2 % vs 4·5±2·5 % (P<0·0001) and 4·3±3·4 % vs 3·7±2·8 % (P<0·005), respectively] (Table 2, Fig. 2b, c). Values for dng2x4 and dsg2x4 were significantly higher than that for g1 (P<0·0001), whereas dng2r5 and dsg2r5 were significantly lower (P<0·0001) when compared with g1. No significant differences were observed between dsg2 and dsg2x4 (P=0·1541) or dng2 and dng2x4 (P=0·3467). The ratio of ds per synonymous site to dn per non-synonymous site (ds/dn) was significantly higher in the g1 subject sequences than in the g2 subject sequences (2·35 vs 1·96, P<0·0001). The g2x4 ratio (1·60) was significantly lower than that of g1 (P<0·0001), whereas the g2r5 sequences were significantly higher (3·73, P<0·0001) (Table 2). As for the genetic distance data, g2r5 again greatly differed from the other groups (Fig. 2d). In this case, a low dn gave an extremely high ds/dn ratio. Since the ds/dn ratio is indicative of positive selection, these data suggested a higher selection pressure on the g2x4 env V3–V5 coding region. To explore these results in more detail, a maximum-likelihood method was used to estimate codon-specific selection pressures (Table 3). Confirming the former data, g2, divided in R5 and X4 variants, gave a more accurate picture of SI samples with CD4+ T cell counts below 200 cells µl–1. Fig. 4 depicts the specific codons in which selective pressure was being exerted for both groups (g1 and g2). No hotspots were significantly biased towards g1 or g2 samples nor R5 or X4 variants, indicating that susceptible regions for selective pressure were the same (Fig. 4). Positive selected amino acid positions were detected within all groups, 50 for g1 sequences (6 in V3, 15 in C3, 12 in V4, 6 in C4 and 11 in V5), only 3 for g2r5 sequences (1 in V3, 1 in V4 and 1 in V5) and 42 for g2x4 sequences (7 in V3, 6 in C3, 15 in V4, 2 in C4 and 12 in V5). The selective pressure was mainly circumscribed to the three variable regions V3, V4 and V5, as well as the beginning of the C3 region. Interestingly, position G165 in C4 (amino acid 441 in the HXB2 strain) was surrounded by amino acids in which positive selection was detected in both R5 and X4 sequences. In short, R5 variants from SI samples were highly homogeneous and few positive selective codons were detected, whereas X4 sequence heterogeneity and the number and distribution of positive selected codons were more similar to R5 variants from NSI samples.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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; Lamers et al., 1993; Wolfs et al., 1990). Of note, a direct correlation was found between the CD4+ T cell count and the genetic heterogeneity of the different virus samples, independent of the detection of X4 variants (Fig. 3). However, it was found that the genetic heterogeneity of g2 (SI samples) was significantly higher than that detected in g1 (NSI samples) (Table 2). This indicates the presence of a very dynamic virus population in the late stages of disease and suggests an ongoing evolution of X4 variants probably for a more efficient usage of the cellular entry complex of CD4 and CXCR4 (Stalmeijer et al., 2004).
The phylogenetic reconstruction performed in this study showed that R5 variants clustered apart from the X4 variants in those samples in which both virus types could be found (Fig. 1). This implies an independent evolution for both virus variant groups. The genetic diversity of g2 was mainly due to X4 variants (Table 2, Fig. 2) and was independent of the presence of R5 variants. X4 variant heterogeneity was statistically higher than that observed in g1 sequences. Moreover, R5 variants within SI samples were extremely homogeneous when compared with g1 viruses. These results show the occurrence of a different R5 variant population depending on the presence or the absence of X4 variants. The emergence of X4 strains induced a reduction in the R5 proportion in the SI subject virus population, but also resulted in the predominance of highly related strains in the R5 subpopulation, suggesting a purifying selection on R5 viruses in the presence of X4 variants. It is also important to consider that X4 variants may infect dual positive (CCR5+, CXCR4+) target cells (Collman & Yi, 1999; Naif et al., 2002; Pierson et al., 2000) and that bystander cell death increases with acquisition of the X4 variants (Jekle et al., 2003). Moreover, although it has been observed that virus fitness increases over time in subjects infected with R5 viruses, co-receptor switch significantly increases ex vivo fitness of the virus (Troyer et al., 2005). In this scenario, the better fit X4 variants could out-compete the R5 variants. Only a small proportion of R5 viruses will produce new virus particles, whereas X4 strains could expand and generate a complex mixture of variants.
It may be relevant to resolve whether the replicative capacity of R5 variants is affected by the presence of X4 variants in the same quasispecies. If a higher replication capacity is found for R5 variants from g2 (SI) samples, it would suggest that the purifying selection on R5 variants in the presence of X4 variants is due to out-competition by dual tropic viruses or by X4-only-using variants that infect dual positive target cells. Further research should resolve the basis of the evolution patterns observed for R5 variants within the SI samples.
Positive selective pressure was detected all through the env V3–V5 region within g1 and g2 samples. Similarly, no differences were found when R5 and X4 variants were compared, with the only exception of the R5 variants found within the SI samples in which few residues were found to be under positive selection pressure. Other studies performed within the V3 loop have suggested that the NSI form is more hidden from neutralizing antibodies than the SI V3 loop, that is, more amino acid residues are under selective forces in the V3 loop of the X4 variants (Callaway et al., 1999; Shiino et al., 2000; Templeton et al., 2004). In our data, this difference was not observed in the V3 loop when the NSI and SI samples were compared (Fig. 4). Moreover, V4 and V5 regions of X4 strains were also comparable to R5 strains. Interestingly, several positively selected amino acid positions were detected surrounding the G165 residue in the C4 region, indicating an apparent sensitive region to positive selective pressure. The G165 position, conserved in all primate immunodeficiency viruses, together with P162, connects the 
21 and 22 strands of gp120 to the CCR5 co-receptor (Rizzuto & Sodroski, 2000). This selective force was poorly represented in R5 strains of SI subjects, probably as a consequence of the low variability of these variants.
The issue of HIV co-receptor switching has become relevant because it may be a route to virus drug resistance to CCR5-targeting compounds that are being introduced as therapeutic options (Dorr et al., 2005; Fatkenheuer et al., 2005). The conformation of virus populations at late stages of the disease may be of crucial importance in characterizing the efficacy of these new drugs, especially if used as salvage therapy in advanced disease subjects that may carry X4 HIV-1 variants. Our results indicate that R5 strains in SI subjects are not the principal cause of HIV-1 heterogeneity and therefore different strategies for suitable treatment may be used.
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ACKNOWLEDGEMENTS |
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Received 30 November 2005;
accepted 14 January 2006.
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