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Originally published online as doi:10.1189/jlb.0404234 on June 24, 2004

Published online before print June 24, 2004
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(Journal of Leukocyte Biology. 2004;76:562-570.)
© 2004 by Society for Leukocyte Biology

HIV-1 compartmentalization in diverse leukocyte populations during antiretroviral therapy

Simon J. Potter*, Philippe Lemey{dagger}, Guillaume Achaz{ddagger}, Choo Beng Chew§, Anne-Mieke Vandamme{dagger}, Dominic E. Dwyer§ and Nitin K. Saksena*,1

* Retroviral Genetics Laboratory, Center for Virus Research, Westmead Millennium Institute and The University of Sydney, Australia;
§ Department of Virology, Center for Infectious Diseases and Microbiology Laboratory Services, ICPMR, Westmead Hospital, Sydney, Australia;
{dagger} Clinical and Epidemiological Virology, Rega Institute for Medical Research, Leuven, Belgium; and
{ddagger} 2102 Biological Laboratories, Harvard University, Cambridge, Massachusetts

1 Correspondence: Retroviral Genetics Laboratory, Center for Virus Research, Westmead Millennium Institute, Westmead Hospital, The University of Sydney, Darcy Road, Westmead, Sydney NSW 2145, Australia. E-mail: nitin_saksena{at}wmi.usyd.edu.au


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ABSTRACT
 
CD4+ T lymphocytes are the primary target of human immunodeficiency virus type 1 (HIV-1), but there is increasing evidence that other immune cells in the blood, including CD8+ T lymphocytes and monocytes, are also productively infected. The extent to which these additional cellular reservoirs contribute to ongoing immunodeficiency and viral persistence during therapy remains unclear. In this study, we conducted a detailed investigation of HIV-1 diversity and genetic structure in CD4+ T cells, CD8+ T cells, and monocytes of 13 patients receiving highly active antiretroviral therapy (HAART). Analysis of molecular variance and nonparametric tests performed on HIV-1 envelope sequences provided statistically significant evidence of viral compartmentalization in different leukocyte populations. Signature pattern analysis and predictions of coreceptor use provided no evidence that selection arising from viral tropism was responsible for the genetic structure observed. Analysis of viral genetic variation in different leukocyte populations demonstrated the action of founder effects as well as significant variation in the extent of genetic differentiation between subpopulations among patients. In the absence of evidence for leukocyte-specific selection, these features were supportive of a metapopulation model of HIV-1 replication as described previously among HIV-1 populations in the spleen. Compartmentalization of the virus in different leukocytes may have significant implications for current models of HIV-1 population genetics and contribute to the highly variable way in which drug resistance evolves in different individuals during HAART.

Key Words: HAART • HIV-1 population genetics • CD4+ T lymphocytes • CD8+ T lymphocytes • monocytes


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INTRODUCTION
 
The depletion of CD4+ T lymphocytes by human immunodeficiency virus type 1 (HIV-1) infection is known to be the primary cause of immune system collapse and progression to AIDS. However, there is now considerable evidence for the productive HIV-1 infection in vivo of CD8+ T cells [1 2 3 4 5 6 7 8 9 10 11 ] and monocytes [12 13 14 15 16 17 18 19 ]. It is uncertain whether the destruction of CD4+ T lymphocytes accounts wholly for the immunodeficiency observed in HIV-infected patients, and the extent to which the infection of other blood cell types contributes to the deterioration of host immune responses remains an open question. The emergence of discrete cellular HIV-1 populations within the blood compartment may contribute to viral persistence despite highly active antiretroviral therapy (HAART) and assist the spread of the virus to new cell types and tissues during the course of disease.

HIV-1 infection of CD4+ T lymphocytes is thought to occur predominantly in lymphoid tissue, where there is close contact with other infected leukocytes including antigen-presenting cells [20 21 22 23 ]. In contrast, the origins of CD8+ T lymphocyte infection by HIV-1 are less clear. Recent evidence suggests that CD8+ T cells may become infected at the double-positive stage of thymic maturation, when CD4 is coexpressed with CD8. Implants of human thymic tissue containing infected double-positive thymocytes in severe combined immunodeficiency mice have been shown to produce infected, single-positive CD8+ T lymphocytes in the peripheral circulation [5 , 24 , 25 ]. HIV-1 proviral DNA has also been found to be preferentially distributed in the naïve (CD45RA+) subset of CD8+ T cells, further supporting the thymus as a source of CD8+ T cell infection [7 ]. In contrast, a recent study has reported that HIV-1 infection is reduced in naïve CD8+ T cells [2 ], suggesting the thymus may not be a strong contributor to CD8+ T cell infection. In addition to possible intrathymic mechanisms, stimulation of highly purified CD8+ T cells with mitogens, allogeneic dendritic cells, or anti-CD3 and anti-CD28 antibodies in vitro leads to de novo synthesis of CD4 and susceptibility to HIV-1 infection [4 , 6 , 26 ]. Activated subsets of CD8+ T lymphocytes express high frequencies of CD4 in vivo, rendering these cells vulnerable to viral destruction [9 , 27 ]. Monocytes are a further target of HIV-1 infection, and recent reports have shown that they constitute a continuing source of infectious virus in HAART patients regardless of the length of treatment [19 ]. These precursor cells to tissue macrophages have the potential to migrate to new tissues [28 ] and establish additional reservoirs of virus [29 30 31 32 ]. Although the source of monocyte infection is uncertain, they are a potential reservoir of HIV-1, and their ability to resist the effects of protease inhibitors may constitute a problem for the control of HIV-1 replication in patients receiving HAART.

The objective of this study was to assess HIV-1 population structure and genetic diversity in different leukocyte reservoirs during HAART and to provide a unique look at the evolution of the virus in specific cellular compartments within the blood. We performed population genetic analyses on HIV-1 envelope sequences derived from CD4+ T cells, CD8+ T cells, monocytes, and plasma to test for viral compartmentalization, assess viral genetic diversity, and examine the role of selection, founder effects, and recombination in the evolution of leukocyte-specific HIV-1 populations. Our analyses provided strong evidence against a panmictic (randomly mixed) distribution of HIV-1 in different leukocytes. The combination of genetic structure, variation in the extent of genetic differentiation between individual populations among patients, and evidence for founder effects in leukocyte-specific HIV-1 populations we observed was consistent with the metapopulation model of HIV replication [33 34 35 ]. Under this model, the total population of infected cells is comprised of a number of cell-dependent subpopulations that turn over at a high rate, each established by a small number of founding viruses. The compartmentalization of HIV-1 in diverse leukocyte populations demonstrated in this study may have important implications for within host evolution and dynamics of HIV-1 during HAART.


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MATERIALS AND METHODS
 
Cell-type purification and sequence data
Thirteen HIV-1 seropositive individuals were enrolled in this study after informed consent (Supplementary Table 1 ). The patient group included subjects with a range of plasma viremia and CD4+ T cell counts. At the time of blood sampling, each was receiving HAART, which for the purposes of this study, was defined as simultaneously receiving three or more antiretroviral drugs. A single blood sample (10–20 ml) was obtained from each patient. After separation of plasma, peripheral blood mononuclear cells (PBMC) were purified into constituent blood cell types (CD4+ T cells, CD8+ T cells, and CD14+ monocytes) by positive isolation with antibody-conjugated magnetic beads (Dynal Biotech, Oslo, Norway). Fluorescein-activated cell sorter analysis performed on separated CD4+ T cell, CD8+ T cell, and CD14+ monocyte populations demonstrated high levels of purity in all cases (99.3–99.9%; data not shown). CD8+ T lymphocyte fractions had very low numbers of contaminating CD4+ T lymphocytes, in no case exceeding 0.5% of the total population (Supplementary Fig. 1 ). HIV-1 sequences from separated leukocyte populations differed genetically in almost all patients, providing evidence that they were indeed derived from independent cellular sources, thus further confirming the purity of separations.


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  		Table 1. Nucleotide Diversity among HIV-1 Subpopulations in Different Patients



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  		Figure 1. Unrooted, ML trees generated from compartmental HIV-1 envelope sequences in patients where multiple cell types were successfully sampled. A scale accompanies each tree for estimation of genetic distances. Bootstrap values are displayed when equal to or greater than 75.

A nested polymerase chain reaction (PCR) was used to amplify a 600-bp fragment (C2-V5) of the envelope gene (primers/PCR conditions available on request). Independent PCR experiments were performed in triplicate on each separated fraction, and pooled PCR products were used to generate compartment-specific clones. Cloning experiments were carried out using methods described previously [36 ]. PCR amplification from the total PBMC and CD4+ T cell fractions was successful in all patients. Reverse transcriptase-PCR from plasma was successful in all but three cases, where plasma viremia was at below-detectable levels (Patients 6, 10, and 13). Amplification from CD8+ T cells was unsuccessful in two of 13 subjects (Patients 6 and 9), and monocyte amplification failed in four subjects (Patients 6, 7, 9, and 10). This was despite multiple PCR attempts with several different primer pairs. Amplification of human genomic DNA confirmed the integrity of extracted DNA in these cases. Five clones were sequenced from each separated fraction (plasma, total PBMC, CD4+ T cells, CD8+ T cells, monocytes). PCR experiments to determine the taq-polymerase error rate were also conducted in parallel.

Population genetic studies
Phylogenetic analyses
All sequences were aligned using clustalw [37 ] with default parameters and were visually inspected. Phylogenetic trees were reconstructed using a maximum likelihood (ML) heuristic search algorithm under the general time-reversible (GTR) model with {gamma}-distributed rate heterogeneity among sites in PAUP*v4.0b2 [38 ]. Bootstrap values were calculated using 1000 replicates.

Analysis of genetic diversity
The genetic diversity was calculated in ARLEQUIN [39 ] using pairwise distances as estimated under the Kimura two-parameter model [40 ] with {gamma}-distributed rate variability among sites. The nucleotide diversity is given by = {sum}i=1k{sum}j<i pipjij, where ij is the estimate of the number of substitutions having occurred, as with the divergence of haplotypes i and j, k is the number of haplotypes, and pi is the frequency of haplotype i.

Tests for genetic structure
Nonparametric testing
To account for the sequence length variation evident in many patients, alignments were first recoded using barcod (available at <http://wwwabi.snv.jussieu.fr/~public/barcod>). This is a direct software implementation of a method described previously, which determines the minimum number of insertions/deletions (indels) in all sequences using information derived from multiple alignments [41 ]. Additional characters denoting indels are incorporated into alignments, allowing them to be counted as a difference. Recoded alignments were subsequently used in nonparametric tests for population subdivision. The nonparametric test used was essentially identical to the method of Hudson and co-workers [42 ] (we adapted the test for use on "n" samples). Briefly, K*s is defined as the mean of the log of the pairwise difference within each subpopulation: K*S = {sum}i=1Sni/N K*i, where S is the number of subpopulations (here up to five), ni is the number of sequences in the subpopulation i, N is the total number of sequences, and K*i is further defined as

, where Dj,k is the number of differences between sequence j and sequence k (in the subpopulation i). The test measures how many times randomly labeled samples of the same sequences (here, we used 100,000 replicates) exhibited a smaller K*s than the real samples. Bonferroni corrections were applied to all calculations.

Analysis of molecular variance (AMOVA)
The genetic structure between HIV-1 populations from different blood compartments was quantified using estimates of FST, the fraction of total genetic variation between subpopulations [43 ]. FST estimates were calculated using AMOVA [44 , 45 ] in ARLEQUIN ver.2000. The significance for rejecting the null hypothesis of a random distribution of genetic variation was determined using 10,000 randomizations of sequences between populations. Pairwise genetic differences used in AMOVA analysis were obtained using ML assuming a GTR model with {gamma}-distributed rate variation among sites. AMOVA analyses were conducted in two stages. First, a comparison was made between sequences from the following two populations: plasma and total PBMC (Group 1) and the three cell-type populations (Group 2). The two-group analysis had three variance components: Va = variance "among groups", Vb = variance "among populations within groups", Vc = variance "within populations" (VT=total variance). Hence, the fraction of variation between the two groups was defined as FCT = (Va/VT) and the fraction of variation within groups between the subpopulations, by FSC = Vb/(Vb+Vc). To test the genetic structure of the cell-type HIV populations more specifically, calculations were performed separately on the cell-type group (Group 2 above) treated as a single-group analysis. Under this single-group analysis, there were two variance components: Va = variance "among populations", and Vb = variance "within populations". Here, the fraction of variation between the cell populations was defined by FST = (Va/Va+Vb).

Additional sequence analyses
Searches for cell type-specific amino acid changes were performed using viral epidemiology signature pattern analysis (VESPA) [46 ]. Predictions of coreceptor use were made using software available at <http://ubik.microbiol.washington.edu/computing/pssm/> [47 ]. Tajima’s D statistics were calculated in ARLEQUIN ver.2000. Nonsynonymous/synonymous substitution rate ratios (dN/dS) were estimated using a codon model allowing for variation among sites with three discrete categories (M3). All calculations were performed using the codeml program from the PAML package [48 ]. Recombination was investigated with the Informative Sites Test (IST) applied to the third codon position in the program PIST [49 ]. As a result of the sampling procedure, recombination between viral genomes from different cell populations could only have happened in vivo, as the cell populations were separated before PCR cloning.


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RESULTS
 
Phylogenetic reconstructions
Reconstruction of a tree using all 278 viral sequences showed that all clones clustered in a patient-specific manner, confirming the absence of PCR contamination (Supplementary Fig. 2 ). Unrooted trees reconstructed from compartmental HIV-1 envelope sequences of each patient suggested genetic differentiation and structure amongst cell type-specific HIV-1 populations unlikely to be observed under a hypothesis of panmixis (Fig. 1 ). Although there was a tendency to cluster according to population, CD4+ T cell viral clones were generally the most diverse population. CD8+ T cells appeared to be the most confined population and for the most part, clustered separately with the support of high bootstrap values from other cell-type sequences. Despite this, CD8+ T cell sequences were often still associated with viral clone(s) from the total PBMC and/or plasma compartments. Monocyte-derived HIV-1 sequences also demonstrated an appreciable degree of segregation from other cell-type populations, particularly in Patients 1, 2, 4, 5, and 8.



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  		Figure 2. Probability distribution graph showing the degree of separation between individual cell-type HIV-1 populations in each patient. A nonparametric test was used to compare sequences from each individual compartment against those of the other compartments pooled together in a patient-specific manner. Counts represent the number of individual patients for which the probability of the specified cell type being identical to other HIV-1 populations occurred within the specified range. For example, 0.0 < P < 0.005 for CD8+ T cells in seven patients, indicating this compartment has a very low degree of similarity to other cell-type HIV-1 populations.

Analysis of genetic diversity
Differences in the degree of genetic diversity within subpopulations provided further evidence for genetic structure (Table 1 ). The total PBMC and CD4+ T cell populations exhibited the highest diversity, and a modest correlation was evident between these compartments among patients (Pearson correlation coefficient, r=0.57). The plasma pool diversity was somewhat lower, most probably because it only reflects the diversity of recently produced virions. There was a modest correlation between monocyte and plasma diversity (r=0.54), suggesting a possible linkage between these compartments, although phylogenetic analyses showed a relationship only in some cases. The CD8+ T cell population exhibited lower diversity, indicating fewer founder viruses in the tissues where these cells are infected. Despite the close relatedness of CD8+ T cell clones in some patients, each clone had a distinct sequence, and multiple substitutions between individual clones were evident in sequence alignments. Statistical analyses demonstrated that the diversity observed amongst CD8+ T cell clones was much higher than that which could be attributed to possible taq-polymerase error, supported by highly significant values in all cases (Supplementary Table 2 ). This confirmed that although closely related, HIV-1 clones from the CD8+ compartment did not arise from "resampling" of the same starting material. The extremely high purity of CD8+ fractions, coupled with the clustering of individual CD8+ T cell clones away from CD4-derived sequences strongly attested to the independence of CD8-derived sequences.


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  		Table 2. Nonparametric Tests for Population Subdivision

Tests for genetic structure
Two complementary analyses were performed to assess the genetic differentiation between blood cell-type HIV-1 populations. First, we used nonparametric tests based on pair-wise differences within and between samples. Table 2 shows the degree of likelihood that HIV-1 sequences from separated fractions were derived from a single population. This test was performed for patients where multiple blood cell types were successfully sampled. Homogeneity within samples was strongly rejected in nine of 11 patients (P~0.024–P<10–5). Patient 10 was close to statistical significance (i.e., 0.0045 after Bonferroni correction) at 0.0078. Only Patient 3 showed a P value >0.01, suggesting an absence of subpopulation structure in this individual, a finding later confirmed by AMOVA analysis (Table 3 ). Additional tests were performed by comparing sequences from each individual compartment against those of the other compartments pooled together. Figure 2 summarizes the probability distribution of individual cell-type populations being identical in each patient. Counts on the vertical axis represent the number of patients for which the specified compartment occurred between the given P values (x-axis). CD8+ T cells showed the strongest statistical support for population subdivision with the majority of patients (7/11 counts) occurring at highly significant P values (i.e., P<0.005). Monocytes and plasma also showed evidence of segregation at this level in several patients, whereas CD4+ T cells were the least separated population, suggesting a substantial degree of migration to and from this compartment.


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  		Table 3. Statistics from AMOVA Analysis

Second, the degree of genetic differentiation between blood compartments was assessed using AMOVA. We tested a structure defined by two groups, one including the plasma and total PBMC populations (Group 1) and the other including the three different cell populations (Group 2). This was done for all patients from whom all populations were successfully sampled. The fraction of the total genetic variation found between these two groups (FCT) was never higher than expected from a random distribution of variation (Table 3) . The predominantly negative values of FCT reflect the homogeneity of viral genetic variation over the two predefined groups. This was expected, as the three populations in Group 2 (the three different cell types isolated from PBMC) are subpopulations of the PBMC population from Group 1 (plasma and PBMC populations). However, within groups, there was a significant contribution of the variation found between the populations (FSC) for all patients except Patient 12 (Table 3) .

To investigate the degree of subpopulation structure between individual blood cell types, we further analyzed in detail the genetic structure of the three cell populations (called Group 2 above). Except for Patients 3 and 12, the fraction of variation between the cell populations (FST) was significantly higher than expected from a random distribution (Table 3) . Therefore, for most patients, the HIV-1-infected cell populations within the blood compartment were inconsistent with an assumption of panmixis. There was a considerable variation of FST between individuals, ranging from very low (<0.03) to very high (>0.8) levels of divergence between cell populations (Table 3) .

Further sequence analyses
Founder effects
As previously shown [33 ], founder effects can be detected using the Tajima’s D statistic [50 ]. A small number of founders lead to a large founder effect (bottleneck) and a negative Tajima’s D, and a larger number of founders lead to a small founder effect and higher Tajima’s D values. There was a strong association between genetic diversity and the Tajima’s D statistic in different leukocyte populations (Pearson correlation coefficient, r=0.85, P<0.01; Fig. 3 ), a finding consistent with the action of founder effects. The incidence of selection can also result in deviations from a 0 (neutral) Tajima’s D value. Despite Tajima’s D values being negative for most patients, there was no correlation between the dN/dS rate ratio and Tajima’s D (data not shown). Although Tajima’s D is often used as a test for neutrality, the absence of a correlation with dN/dS values argued that founder effects mainly influenced the Tajima’s D statistic rather than selection in these cases.



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  		Figure 3. Estimates of Tajima’s D against nucleotide diversity in leukocyte HIV-1 populations. The correlation between Tajima’s D and nucleotide diversity is indicated by the line (Pearson correlation coefficient, r=0.85, P<0.01). {blacksquare}, CD4+ T cells; {blacktriangleup}, CD8+ T cells; •, monocytes.

Recombination analysis
Recombination is difficult to observe in highly related populations arising from small numbers of founders but is easier to detect in panmictic (well-mixed) populations. In agreement with the genetic subdivision observed in most patients, the hypothesis of pure clonal evolution could only be rejected at the 99% confidence level in three cases (Table 4 ). It is not surprising that these three individuals (Patients 3, 5, 12) had the lowest value of FST, and thus, it was only in patients with the least evidence of population structure that recombination could be detected. In two of these patients (Patients 3 and 12), a random distribution of genetic variation could not be rejected (Table 3) . For Patient 9, the hypothesis of pure clonal evolution was rejected at the 95% confidence limit (Table 4) , and it is important to consider that only the most well-mixed populations were sampled in this patient (plasma, total PBMC, and CD4+ T cells). Therefore, our data suggest that subdivision had a strong effect in the history of the sample and determined our ability to detect recombination.


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  		Table 4. P Values from the IST

VESPA analysis
Signature pattern analysis of the 600-bp envelope region (C2-V5) revealed no leukocyte-specific amino acid changes across patients (data not shown). Analysis of the third variable (V3) region predicted that coreceptor use among clones from individual leukocyte populations was often mixed and did not follow a cell type-specific pattern. Thus, we could find no evidence of selection associated with cell type-specific determinants of viral tropism in the envelope region, and differential coreceptor use did not suffice as an explanation for the genetic structure observed.


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DISCUSSION
 
The compartmentalization of genetically diverse HIV-1 populations within infected individuals represents an impediment to effective antiretroviral treatment and ultimately a barrier to viral eradication. The existence of genetically discrete HIV-1 populations has been demonstrated in tissue compartments such as the central nervous system [51 52 53 54 ], the reproductive tract [55 , 56 ], and gastrointestinal mucosa [57 ], and cellular reservoirs including latent memory CD4+ T cells [58 59 60 ] and tissue macrophages [61 , 62 ]. The emergence of temporal phylogenetic and phenetic structure in different compartments over time in HIV-infected individuals is likely to arise from a continual process of migration of one or a few infected cells into each compartment, followed by localized expansion and evolution of that population [63 ]. In this study, we provide a unique look at the evolution of HIV with respect to specific cellular compartments that exist within the blood of patients receiving HAART. Our data show significant evidence of genetic differentiation between HIV-1 populations in different leukocytes, a finding that may have important implications for within host evolution and dynamics of HIV-1 during antiretroviral therapy.

Many population models assume that HIV populations are well mixed [64 , 65 ], however, the data presented here demonstrate that HIV subpopulations in the blood compartment are unlikely to be under panmixis. The genetic analyses conducted in this study showed a significant degree of population structure between individual HIV-1-infected leukocytes. This compartmentalization appeared to be unrelated to the level of plasma viremia or the disease status of the patient. We first investigated whether the genetic structure could be attributed to cell type-specific determinants of viral tropism in the envelope gene. Signature pattern analysis across the 600-bp region (C2-V5) revealed no specific amino acid changes for individual cell-type populations across patients. To investigate whether coreceptor use influenced the genetic structure, we conducted further analyses of the V3 region. Although monocytes contained variants predicted to use CC chemokine receptor 5 (CCR5), strains predicted to use CXC chemokine receptor 4 (CXCR4) were also present in monocytes of most patients. Moreover, analysis of the CD4 and CD8 compartments also revealed CXCR4 and CCR5 strains. These findings may reflect the limitations of current algorithms used for the prediction of coreceptor use or suggest that the propensity of certain HIV variants to use one coreceptor does not completely exclude the use of another. Despite the absence of any apparent signature patterns amongst individual cell types, envelope changes responsible for viral tropism may be strain-specific and thus, vary among individuals [66 ]. Therefore, a role for selection arising from viral tropism or other cell type-specific traits cannot entirely be ruled out.

Recently, Frost et al. [33 ] showed that infected spleen pulps follow a metapopulation model of HIV-1 replication: The total population of infected cells consists of a large number of smaller subpopulations that turn over at a high rate (in this case, white pulps), each of which is established by a small number of founding cells [34 , 35 ]. The genetic structure observed in this study was consistent with the metapopulation model. Nonparametric tests provided statistically significant evidence for population subdivision, and the fraction of variation between cell-type populations (FST) was significantly higher than expected from a random distribution in almost all patients. Considerable differences in the level of viral genetic structuring between subpopulations were also observed between individuals. These differences may arise because of relatively small differences in the number of founders, with high levels of population structure, low levels of diversity within subpopulations, and a lesser role of recombination arising when the number of founders is low. Viral genetic diversity differed between leukocyte subsets, and there was evidence for founder effects in cell type-specific HIV populations. A strong correlation was observed between genetic diversity and the Tajima’s D statistic, a feature consistent with the metapopulation model [33 ]. Recombination analyses indicated only a small contribution to genetic diversity. In all cases where there was evidence for subpopulation structure, recombination did not appear to contribute significantly to viral genetic variation, findings also supportive of metapopulation dynamics. Therefore, in the absence of evidence implicating selection as a possible mechanism for HIV-1 compartmentalization in different leukocytes, the metapopulation model appeared to fit the data set reasonably well.

There is considerable evidence that the primary anatomical sites at which individual blood cell types become infected may differ, and local turnover in distantly related tissues may therefore explain the genetic differences observed. There are many tissues in which the infection of CD4+ T cells may occur, but it is generally acknowledged that lymph nodes represent the primary site of CD4+ T cell infection [20 21 22 23 ]. Although the infection of circulating, activated CD8+ T cells through transient expression of CD4 is a likely contributor to the total CD8+ viral pool [9 , 27 ], other evidence suggests the infection of CD8+ T lymphocytes may occur during the double-positive (CD4+/CD8+) stage of thymic maturation [5 , 7 , 24 , 25 ]. In this study, CD8+ T cells showed the lowest degree of genetic diversity, suggesting fewer founder viruses and reduced viral turnover. Supporting this, we have recently shown that HIV-1 variants from CD8+ T cells often contain markedly fewer antiretroviral drug resistance mutations than those from CD4+ T cells and monocytes, giving evidence of reduced viral evolution [36 ]. McBreen and co-workers [7 ] showed that the frequency of infected, naïve CD8+ T cell populations during suppressive antiretroviral therapy remained largely static despite a corresponding decline in the frequency of infected naïve and memory CD4+ T lymphocytes. Genetic diversity is a reflection of the effective population size, and thus, the lower CD8 population diversity we observed may also indicate a smaller effective population size. Previous reports have suggested that HIV infection in CD8+ T cells is reduced compared with CD4+ T lymphocytes, although quantitative studies performed on CD8 cells have produced conflicting results [2 , 7 ].

The source of HIV-1 in circulating blood monocytes is still uncertain, however, a report by Spear et al. [17 ] suggested that monocyte HIV-1 DNA does not originate in myeloid tissue precursors. Sonza and colleagues [19 ] demonstrated the presence of labile forms of HIV-1 DNA in circulating monocytes and were able to recover virus successfully, suggesting recent rather than latent infection. Supporting this, Zhu et al. [14 ] showed that monocyte HIV-1 populations were related or identical to those in plasma, and the moderate linkage between plasma-monocyte diversity we observed is in agreement with these results. However, phylogenetic analyses did not reveal a relationship in all cases. Despite uncertainty regarding the source of monocyte infection, the genetic structure observed between leukocyte-specific HIV-1 populations may suggest differential sources of infecting virus. Selective pressures imposed by antiretroviral drug regimens and cell-type differences in the rate of viral turnover may also contribute.

This study represents a "snapshot" of HIV-1 population structure in the blood compartment, and further investigation into the dynamics of individual leukocyte subpopulations over multiple time-points will be of immense interest. As specific subpopulations turn over, it is likely they will break down over time and be reconstituted by new founders. However, this process may differ in specific leukocyte compartments, and the extent to which distinct lineages that arise from new founder viruses persist or go on to evolve over time remains unclear. The genetic structure demonstrated in this study suggests that rather than constituting a random mix of HIV-1 variants, the blood HIV-1 pool could better be described as a "population of populations", arising from compartmentalization of the virus in distinct leukocyte subsets. Local turnover of cell type-specific HIV-1 populations in distantly related tissues may contribute to the highly variable way in which drug resistance evolves in different individuals, and functional assessment of leukocyte-specific variants should be a priority to assess their impact on treatment and disease progression.


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ACKNOWLEDGEMENTS
 
This work was supported in part by the Westmead Hospital Charitable Trust Fund and the Flemish Fonds voor Wetenschappelijk Onderzoek (FWO G.0288.01). S. J. P. is a recipient of the Australian Postgraduate Award (APA) scholarship from The University of Sydney. P. L. was supported by the Flemish Institute for Scientific-Technological Research in Industry (IWT). G. A. was supported by La Fondation pour la Recherche Medicale. The authors thank Gavin Morrow and Emma Keating for their assistance during this project. Thanks also go to Filip Volckaert for helpful discussions on the AMOVA analysis and to Allen Rodrigo for constructive suggestions.

Received April 12, 2004; accepted May 14, 2004.


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REFERENCES
 
    1
  1. Zloza, A., Sullivan, Y. B., Connick, E., Landay, A. L., Al-Harthi, L. (2003) CD8+ T cells that express CD4 on their surface (CD4dimCD8bright T cells) recognize an antigen-specific target, are detected in vivo, and can be productively infected by T-tropic HIV Blood 102,2156-2164[Abstract/Free Full Text]
    2. 2
  2. Brenchley, J. M., Hill, B. J., Ambrozak, D. R., Price, D. A., Guenaga, F. J., Casazza, J. P., Kuruppu, J., Yazdani, J., Migueles, S. A., Connors, M., Roederer, M., Douek, D. C., Koup, R. A. (2004) T-cell subsets that harbor human immunodeficiency virus (HIV) in vivo: implications for HIV pathogenesis J. Virol. 78,1160-1168[Abstract/Free Full Text]
    3. 3
  3. Semenzato, G., Agostini, C., Chieco-Bianchi, L., De Rossi, A. (1998) HIV load in highly purified CD8+ T cells retrieved from pulmonary and blood compartments J. Leukoc. Biol. 64,298-301[Abstract]
    4. 4
  4. Kitchen, S. G., Korin, Y. D., Roth, M. D., Landay, A., Zack, J. A. (1998) Costimulation of naive CD8(+) lymphocytes induces CD4 expression and allows human immunodeficiency virus type 1 infection J. Virol. 72,9054-9060[Abstract/Free Full Text]
    5. 5
  5. Kitchen, S. G., Uittenbogaart, C. H., Zack, J. A. (1997) Mechanism of human immunodeficiency virus type 1 localization in CD4-negative thymocytes: differentiation from a CD4-positive precursor allows productive infection J. Virol. 71,5713-5722[Abstract]
    6. 6
  6. Yang, L. P., Riley, J. L., Carroll, R. G., June, C. H., Hoxie, J., Patterson, B. K., Ohshima, Y., Hodes, R. J., Delespesse, G. (1998) Productive infection of neonatal CD8+ T lymphocytes by HIV-1 J. Exp. Med. 187,1139-1144[Abstract/Free Full Text]
    7. 7
  7. McBreen, S., Imlach, S., Shirafuji, T., Scott, G. R., Leen, C., Bell, J. E., Simmonds, P. (2001) Infection of the CD45RA+ (naive) subset of peripheral CD8+ lymphocytes by human immunodeficiency virus type 1 in vivo J. Virol. 71,4091-4102
    8. 8
  8. Livingstone, W. J., Moore, M., Innes, D., Bell, J. E., Simmonds, P. (1996) Frequent infection of peripheral blood CD8-positive T-lymphocytes with HIV-1 Lancet 348,649-654[CrossRef][Medline]
    9. 9
  9. Imlach, S., McBreen, S., Shirafuji, T., Leen, C., Bell, J. E., Simmonds, P. (2001) Activated peripheral CD8 lymphocytes express CD4 in vivo and are targets for infection by human immunodeficiency virus type 1 J. Virol. 75,11555-11564[Abstract/Free Full Text]
    10. 10
  10. Saha, K., Zhang, J., Gupta, A., Dave, R., Yimen, M., Zerhouni, B. (2001) Isolation of primary HIV-1 that target CD8+ T lymphocytes using CD8 as a receptor Nat. Med. 7,65-72[CrossRef][Medline]
    11. 11
  11. Saha, K., Zhang, J., Zerhouni, B. (2001) Evidence of productively infected CD8+ T cells in patients with AIDS: implications for HIV-1 pathogenesis J. Acquir. Immune Defic. Syndr. 26,199-207
    12. 12
  12. McElrath, M. J., Pruett, J. E., Cohn, Z. A. (1989) Mononuclear phagocytes of blood and bone marrow: comparative roles as viral reservoirs in human immunodeficiency virus type 1 infections Proc. Natl. Acad. Sci. USA 86,675-679[Abstract/Free Full Text]
    13. 13
  13. Innocenti, P., Ottmann, M., Morand, P., Leclercq, P., Seigneurin, J. M. (1992) HIV-1 in blood monocytes: frequency of detection of proviral DNA using PCR and comparison with the total CD4 count AIDS Res. Hum. Retroviruses 8,261-268[Medline]
    14. 14
  14. Zhu, T., Muthui, D., Holte, S., Nickle, D., Feng, F., Brodie, S., Hwangbo, Y., Mullins, J. I., Corey, L. (2002) Evidence for human immunodeficiency virus type 1 replication in vivo in CD14(+) monocytes and its potential role as a source of virus in patients on highly active antiretroviral therapy J. Virol. 76,707-716[Abstract/Free Full Text]
    15. 15
  15. Patterson, B. K., Carlo, D. J., Kaplan, M. H., Marecki, M., Pawha, S., Moss, R. B. (1999) Cell-associated HIV-1 messenger RNA and DNA in T-helper cell and monocytes in asymptomatic HIV-1-infected subjects on HAART plus an inactivated HIV-1 immunogen AIDS 13,1607-1611[CrossRef][Medline]
    16. 16
  16. Patterson, B. K., Mosiman, V. L., Cantarero, L., Furtado, M., Bhattacharya, M., Goolsby, C. (1998) Detection of HIV-RNA-positive monocytes in peripheral blood of HIV-positive patients by simultaneous flow cytometric analysis of intracellular HIV RNA and cellular immunophenotype Cytometry 31,265-274[CrossRef][Medline]
    17. 17
  17. Spear, G. T., Ou, C. Y., Kessler, H. A., Moore, J. L., Schochetman, G., Landay, A. L. (1990) Analysis of lymphocytes, monocytes, and neutrophils from human immunodeficiency virus (HIV)-infected persons for HIV DNA J. Infect. Dis. 162,1239-1244[Medline]
    18. 18
  18. Lambotte, O., Taoufik, Y., de Goer, M. G., Wallon, C., Goujard, C., Delfraissy, J. F. (2000) Detection of infectious HIV in circulating monocytes from patients on prolonged highly active antiretroviral therapy J. Acquir. Immune Defic. Syndr. 23,114-119
    19. 19
  19. Sonza, S., Mutimer, H. P., Oelrichs, R., Jardine, D., Harvey, K., Dunne, A., Purcell, D. F., Birch, C., Crowe, S. M. (2001) Monocytes harbour replication-competent, non-latent HIV-1 in patients on highly active antiretroviral therapy AIDS 15,17-22[CrossRef][Medline]
    20. 20
  20. Pantaleo, G., Graziosi, C., Butini, L., Pizzo, P. A., Schnittman, S. M., Kotler, D. P., Fauci, A. S. (1991) Lymphoid organs function as major reservoirs for human immunodeficiency virus Proc. Natl. Acad. Sci. USA 88,9838-9842[Abstract/Free Full Text]
    21. 21
  21. Pantaleo, G., Graziosi, C., Demarest, J. F., Butini, L., Montroni, M., Fox, C. H., Orenstein, J. M., Kotler, D. P., Fauci, A. S. (1993) HIV infection is active and progressive in lymphoid tissue during the clinically latent stage of disease Nature 362,355-358[CrossRef][Medline]
    22. 22
  22. Fox, C., Tenner-Racz, K., Racz, P., Firpo, A., Pizzo, P., Fauci, A. S. (1991) Lymphoid germinal centers are reservoirs of human immunodeficiency virus type 1 RNA J. Infect. Dis. 164,1051-1057[Medline]
    23. 23
  23. Harper, M. E., Marselle, L. M., Gallo, R. C., Wong-Staal, F. (1986) Detection of lymphocytes expressing human T-lymphotropic virus type III in lymph nodes and peripheral blood from infected individuals by in situ hybridization Proc. Natl. Acad. Sci. USA 83,772-776[Abstract/Free Full Text]
    24. 24
  24. Lee, S., Goldstein, H., Baseler, M., Adelsberger, J., Golding, H. (1997) Human immunodeficiency virus type 1 infection of mature CD3hiCD8+ thymocytes J. Virol. 71,6671-6676[Abstract]
    25. 25
  25. Brooks, D. G., Kitchen, S. G., Kitchen, C. M., Scripture-Adams, D. D., Zack, J. A. (2001) Generation of HIV latency during thymopoiesis Nat. Med. 7,459-464[CrossRef][Medline]
    26. 26
  26. Flamand, L., Crowley, R. W., Lusso, P., Colombini-Hatch, S., Margolis, D. M., Gallo, R. C. (1998) Activation of CD8+ T lymphocytes through the T cell receptor turns on CD4 gene expression: implications for HIV pathogenesis Proc. Natl. Acad. Sci. USA 95,3111-3116[Abstract/Free Full Text]
    27. 27
  27. Kitchen, S. G., LaForge, S., Patel, V. P., Kitchen, C. M., Miceli, M. C., Zack, J. A. (2002) Activation of CD8 T cells induces expression of CD4, which functions as a chemotactic receptor Blood 99,207-212[Abstract/Free Full Text]
    28. 28
  28. Meuret, G., Bammert, J., Hoffmann, G. (1974) Kinetics of human monocytopoiesis Blood 44,801-816[Abstract/Free Full Text]
    29. 29
  29. Persidsky, Y., Ghorpade, A., Rasmussen, J., Limoges, J., Liu, X. J., Stins, M., Fiala, M., Way, D., Kim, K. S., Witte, M. H., Weinand, M., Carhart, L., Gendelman, H. E. (1999) Microglial and astrocyte chemokines regulate monocyte migration through the blood-brain barrier in human immunodeficiency virus-1 encephalitis Am. J. Pathol. 155,1599-1611[Abstract/Free Full Text]
    30. 30
  30. Persidsky, Y., Stins, M., Way, D., Witte, M. H., Weinand, M., Kim, K. S., Bock, P., Gendelman, H. E., Fiala, M. (1997) A model for monocyte migration through the blood-brain barrier during HIV-1 encephalitis J. Immunol. 158,3499-3510[Abstract]
    31. 31
  31. Nottet, H. S., Persidsky, Y., Sasseville, V. G., Nukuna, A. N., Bock, P., Zhai, Q. H., Sharer, L. R., McComb, R. D., Swindells, S., Soderland, C., Gendelman, H. E. (1996) Mechanisms for the transendothelial migration of HIV-1-infected monocytes into brain J. Immunol. 156,1284-1295[Abstract]
    32. 32
  32. Lafrenie, R. M., Wahl, L. M., Epstein, J. S., Hewlett, I. K., Yamada, K. M., Dhawan, S. (1996) HIV-1-Tat protein promotes chemotaxis and invasive behavior by monocytes J. Immunol. 157,974-977[Abstract]
    33. 33
  33. Frost, S. D., Dumaurier, M. J., Wain-Hobson, S., Brown, A. J. (2001) Genetic drift and within-host metapopulation dynamics of HIV-1 infection Proc. Natl. Acad. Sci. USA 98,6975-6980[Abstract/Free Full Text]
    34. 34
  34. Pannell, J. R., Charlesworth, B. (1999) Neutral genetic diversity in a metapopulation with recurrent local extinction and recolonization Evolution 53,664-676[CrossRef]
    35. 35
  35. Levins, R. (1969) The effect of random variations of different types on population growth Proc. Natl. Acad. Sci. USA 62,1061-1065[Abstract/Free Full Text]
    36. 36
  36. Potter, S. J., Dwyer, D. E., Saksena, N. K. (2003) Differential cellular distribution of HIV-1 drug resistance in vivo: evidence for infection of CD8+ T cells during HAART Virology 305,339-352[CrossRef][Medline]
    37. 37
  37. Thompson, J. D., Higgins, D. G., Gibson, T. J. (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice Nucleic Acids Res 22,4673-4680[Abstract/Free Full Text]
    38. 38
  38. Swofford, D. L. (1999) PAUP: Phylogenetic Analysis Using Parsimony, Version 4.0b2 Illinois Natural History Survey Champaign, IL.
    39. 39
  39. Schneider, S., Roessli, D., Excoffier, L. (2000) Arlequin: A Software for Population Genetics Data Analysis, Version 2.000 Genetics and Biometry Laboratory, Department of Anthropology, University of Geneva Geneva, Switzerland.
    40. 40
  40. Kimura, M. (1980) A simple method for estimating evolutionary rate of base substitutions through comparative studies of nucleotide sequences J. Mol. Evol. 16,111-120[CrossRef][Medline]
    41. 41
  41. Barriel, V. (1994) Molecular phylogenies and nucleotide insertion-deletion C. R. Acad. Sci. III 317,693-701[Medline]
    42. 42
  42. Hudson, R. R., Boos, D. D., Kaplan, N. L. (1992) A statistical test for detecting geographic subdivision Mol. Biol. Evol. 9,138-151[Abstract]
    43. 43
  43. Wright, S. (1951) The genetical structure of populations Ann. Eugenic. 15,323-354
    44. 44
  44. Excoffier, L., Smouse, P. E., Quattro, J. M. (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data Genetics 131,479-491[Abstract]
    45. 45
  45. Weir, B. S. (1996) Genetic Data Analysis II: Methods for Discrete Population Genetic Data Sinauer Associates Sunderland, MA.
    46. 46
  46. Korber, B., Myers, G. (1992) Signature pattern analysis: a method for assessing viral sequence relatedness AIDS Res. Hum. Retroviruses 8,1549-1560[Medline]
    47. 47
  47. Jensen, M. A., Li, F. S., van ’t Wout, A. B., Nickle, D. C., Shriner, D., He, H. X., McLaughlin, S., Shankarappa, R., Margolick, J. B., Mullins, J. I. (2003) Improved coreceptor usage prediction and genotypic monitoring of R5-to-X4 transition by motif analysis of HIV-1 env V3 loop sequences J. Virol. 77,13376-13388[Abstract/Free Full Text]
    48. 48
  48. Yang, Z. (1997) PAML: a program package for phylogenetic analysis by maximum likelihood Comput. Appl. Biosci. 13,555-556[Free Full Text]
    49. 49
  49. Worobey, M. (2001) A novel approach to detecting and measuring recombination: new insights into evolution in viruses, bacteria, and mitochondria Mol. Biol. Evol. 18,1425-1434[Abstract/Free Full Text]
    50. 50
  50. Tajima, F. (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism Genetics 123,585-595[Abstract/Free Full Text]
    51. 51
  51. Chang, J., Jozwiak, R., Wang, B., Ng, T., Ge, Y. C., Bolton, W., Dwyer, D. E., Randle, C., Osborn, R., Cunningham, A. L., Saksena, N. K. (1998) Unique HIV type 1 V3 region sequences derived from six different regions of brain: region-specific evolution within host-determined quasispecies AIDS Res. Hum. Retroviruses 14,25-30[Medline]
    52. 52
  52. Ait-Khaled, M., McLaughlin, J. E., Johnson, M. A., Emery, V. C. (1995) Distinct HIV-1 long terminal repeat quasispecies present in nervous tissues compared to that in lung, blood and lymphoid tissues of an AIDS patient AIDS 9,675-683[Medline]
    53. 53
  53. Stingele, K., Haas, J., Zimmermann, T., Stingele, R., Hubsch-Muller, C., Freitag, M., Storch-Hagenlocher, B., Hartmann, M., Wildemann, B. (2001) Independent HIV replication in paired CSF and blood viral isolates during antiretroviral therapy Neurology 56,355-361[Abstract/Free Full Text]
    54. 54
  54. Huang, K. J., Alter, G. M., Wooley, D. P. (2002) The reverse transcriptase sequence of human immunodeficiency virus type 1 is under positive evolutionary selection within the central nervous system J. Neurovirol. 8,281-294[CrossRef][Medline]
    55. 55
  55. Gupta, P., Leroux, C., Patterson, B. K., Kingsley, L., Rinaldo, C., Ding, M., Chen, Y., Kulka, K., Buchanan, W., McKeon, B., Montelaro, R. (2000) Human immunodeficiency virus type 1 shedding pattern in semen correlates with the compartmentalization of viral quasi species between blood and semen J. Infect. Dis. 182,79-87[CrossRef][Medline]
    56. 56
  56. Eyre, R. C., Zhen, G., Kiessling, A. A. (2000) Multiple drug resistance mutations in human immunodeficiency virus in semen but not blood of a man on antiretroviral therapy Urology 55,591[CrossRef][Medline]
    57. 57
  57. Poles, M. A., Elliott, J., Vingerhoet, J., Michiels, L., Scholliers, A., Bloor, S., Larder, B., Hertogs, K., Anton, P. A. (2001) Despite high concordance, distinct mutational and phenotypic drug resistance profiles in human immunodeficiency virus type 1 RNA are observed in gastrointestinal mucosal biopsy specimens and peripheral blood mononuclear cells compared with plasma J. Infect. Dis. 183,143-148[CrossRef][Medline]
    58. 58
  58. Furtado, M. R., Callaway, D. S., Phair, J. P., Kunstman, K. J., Stanton, J. L., Macken, C. A., Perelson, A. S., Wolinsky, S. M. (1999) Persistence of HIV-1 transcription in peripheral-blood mononuclear cells in patients receiving potent antiretroviral therapy N. Engl. J. Med. 340,1614-1622[Abstract/Free Full Text]
    59. 59
  59. Wong, J. K., Hezareh, M., Gunthard, H. F., Havlir, D. V., Ignacio, C. C., Spina, C. A., Richman, D. D. (1997) Recovery of replication-competent HIV despite prolonged suppression of plasma viremia Science 278,1291-1295[Abstract/Free Full Text]
    60. 60
  60. Zhang, L., Ramratnam, B., Tenner-Racz, K., He, Y., Vesanen, M., Lewin, S., Talal, A., Racz, P., Perelson, A. S., Korber, B. T., Markowitz, M., Ho, D. D. (1999) Quantifying residual HIV-1 replication in patients receiving combination antiretroviral therapy N. Engl. J. Med. 340,1605-1613[Abstract/Free Full Text]
    61. 61
  61. Embretson, J., Zupancic, M., Ribas, J. L., Burke, A., Racz, P., Tenner-Racz, K., Haase, A. T. (1993) Massive covert infection of helper T lymphocytes and macrophages by HIV during the incubation period of AIDS Nature 362,359-362[CrossRef][Medline]
    62. 62
  62. Meltzer, M. S., Nakamura, M., Hansen, B. D., Turpin, J. A., Kalter, D. C., Gendelman, H. E. (1990) Macrophages as susceptible targets for HIV infection, persistent viral reservoirs in tissue and key immunoregulatory cells that control levels of virus replication and extent of disease AIDS Res. Hum. Retroviruses 6,967-971[Medline]
    63. 63
  63. Poss, M., Rodrigo, A. G., Gosink, J. J., Learn, G. H., de Vange Panteleeff, D., Martin, H. L., Jr, Bwayo, J., Kreiss, J. K., Overbaugh, J. (1998) Evolution of envelope sequences from the genital tract and peripheral blood of women infected with clade A human immunodeficiency virus type 1 J. Virol. 72,8240-8251[Abstract/Free Full Text]
    64. 64
  64. Rouzine, I. M., Coffin, J. M. (1999) Linkage disequilibrium test implies a large effective population number for HIV in vivo Proc. Natl. Acad. Sci. USA 96,10758-10763[Abstract/Free Full Text]
    65. 65
  65. Coffin, J. M. (1995) HIV population dynamics in vivo: implications for genetic variation, pathogenesis, and therapy Science 267,483-489
    66. 66
  66. Hoffman, T. L., Doms, R. W. (1999) HIV-1 envelope determinants for cell tropism and chemokine receptor use Mol. Membr. Biol. 16,57-65[CrossRef][Medline]



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