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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
AIDS. Author manuscript; available in PMC 2010 May 3.
Published in final edited form as:
PMCID: PMC2862484

HIV integration site distributions in resting and activated CD4+ T cells infected in culture



The goal of this study was to investigate whether the location of HIV integration differs in resting versus activated T cells, a feature that could contribute to the formation of latent viral reservoirs via effects on integration targeting.


Primary resting or activated CD4+ T cells were infected with purified X4-tropic HIV in the presence and absence of nucleoside triphosphates and genomic locations of integrated provirus determined.


We sequenced and analyzed a total of 2661 HIV integration sites using linker-mediated PCR and 454 sequencing. Integration site data sets were then compared to each other and to computationally generated random distributions.


HIV integration was favored in active transcription units in both cell types, but integration sites from activated cells were found more often in genomic regions that were dense in genes, dense in CpG islands, and enriched in G/C bases. Integration sites from activated cells were also more strongly correlated with histone methylation patterns associated with active genes.


These data indicate that integration site distributions show modest but significant differences between resting and activated CD4+ T cells, and that integration in resting cells occurs more often in regions that may be suboptimal for proviral gene expression.

Keywords: CD4+ T cells, gene expression, HIV integration, latency


HIV infection is able to persist in the presence of antiretroviral therapy (ART) due in part to persistence of latentviral reservoirs found in resting CD4+ T cells [14]. It is unclear how proviruses establish latent infection, but invitro models suggest two possibilities. One possibility is that HIV infects activated cells, which then revert to a resting state [2,5] and another possibility is that HIV directly infects and integrates into resting CD4+ T cells [68].

Previous studies have suggested that the location of proviral integration in the host cell genome may influence viral gene activity, possibly contributing to latency [9]. In the extensively studied Jurkat cell model, inducible (i.e. latent) proviruses were found to be enriched in alphoid repeats, which are characteristic of centromeric heterochromatin; in gene deserts, which correlate with low host gene expression; and in very highly expressed host cell genes, consistent with gene repression by transcriptional interference (see also [10,11]). These studies were limited by the use of transformed cell models. However, studies of latently infected cells in individuals on long-term successful ART are severely limited by the background of replication-incompetent proviruses that accumulate in circulating cells; thus, so far, it has not been possible to study integration site distributions directly in latently infected cells due to masking by much larger numbers of integrated sequences from genetically inactive proviruses [12].

Here we investigate possible mechanisms of latency in resting and activated CD4+ T cells by asking whether the distributions of de-novo integration sites might differ between the two cell types, a feature that could bias proviruses in resting cells toward entering the latent state. We used DNA bar coding and pyrosequencing [13] to recover 1474 sites from resting CD4+ T cells and 1187 sites from activated cells infected in cell culture. We found that integration sites were enriched within active transcription units in both cell types. However, quantitatively modest but significant and reproducible differences could be detected between the resting and activated data sets, in which the bias toward integration in actively transcribed regions and associated features was reduced in resting cells. Although the magnitudes of these changes were small, the differences in integration site distributions in the resting cell pool were in the direction that would be predicted to result in less efficient viral gene expression following integration.


Purification, activation, and maintenance of CD4+ T cells

CD4+ T cells were purified by the University of Pennsylvania Immunology Core from a mononuclear leukapheresis product using the RosetteSep Human CD4+ T Cell Enrichment kit (Stem Cell Technologies, Bethesda, Maryland, USA) following the recommendations of the manufacturer. To obtain pure resting CD4+ T cells, rosette-purified CD4+ T cells were then stained with saturating concentrations of phycoerythrin-labeled antibodies that recognize the T cell activation markers CD25, CD69, and human leukocyte antigen (HLA-DR; BD Biosciences, San Jose, California, USA). Following staining against activation markers, the cells were labeled with anti-phycoerythrin magnetic beads and applied to a magnetic column (Miltenyi Biotec, Auburn, California, USA) to separate activated from resting CD4+ T cells. Cell purity and level of activation was monitored by flow cytometry using a FACSCalibur instrument (BD Biosciences) and the data were analyzed using FlowJo software (Treestar, Ashland, Oregon, USA). To obtain activated CD4+ T cells, rosette-purified cells were cultured in the presence of CD3/CD28 beads (Invitrogen, Madison, Wisconsin, USA) at approximately 1 bead/cell for 72 h at 378C. CD4+ T cells were cultured at 378C in RPMI 1640 media supplemented with 10% fetal bovine serum (FBS) and 1% penicillin–streptomycin (Gibco/Invitrogen, Carlsbad, California, USA).

Infection of CD4+ T cells

Resting or activated CD4+ T cells were inoculated by spinoculation with the X4-tropic HIV molecular clone pNL4-3 [14] (obtained from the University of Pennsylvania Center for AIDS Research Virology Core) as previously described [15]. When testing the effect of deoxynucleosides on integration, the cells were treated with a deoxynucleoside mixture at 50 mmol/l (Sigma, StLouis, Missouri, USA; with equal content of each deoxynucleoside) during spinoculation. Following spinoculation, the cells were washed twice to remove unbound virus. The cells were then resuspended in medium (RPMI 1640 supplemented with 10% FBS and 1% penicillin-streptomycin) containing 1.25 mmol/l of saquinavir (Roche Pharmaceuticals, Nutley, New Jersey, USA) to prevent viral spread and 50 mmol/l of deoxynucleosides (wherever indicated). The inoculated cells were then incubated at 378C for 36 h (activated cells) or 60 h (resting cells).

Measuring HIV integration

HIV integration was measured at multiple time points postinoculation by quantitative Alu-PCR as previously described [8,16,17]. To determine the number of integration events per cell, cell numbers were estimated by quantitative PCR using primers that detect β-globin [15].

Integration site analysis

Recovery of integration sites was performed as described [18]. DNA was analyzed from the sample collected 36 h after inoculation from the activated T cells and from the sample collected 60 h after inoculation from the resting T cells. Two micrograms of genomic DNA was digested overnight with MseI, ligated to linkers overnight at 16°C, and digested a second time with SacI. Nested PCR was then performed using primers and conditions described in [18,19]. DNA barcodes were included in the second round PCR primers in order to track sample origin [20]. Amplification products were gel purified and sequenced by massively parallel pyrophosphate sequencing. Only sequences that showed unique best alignments to the human genome by BLAT (BLAST-like alignment tool, hg18, version 36.1, >98% match score) and began within three base pairs of the long terminal repeat (LTR) end were used in downstream analyses. All sequences will be deposited in publicly accessible databases (NCBI) upon acceptance of this article for publication.

Comparisons to genomic features and histone modifications were carried out as described [21,22]. Details of statistical analyses can be found in study by Berry et al. [22]. Analyses of gene expression utilized data from SupT1 cells, with expression measured using the Affymetrix HU133 plus 2.0 gene chip array. Transcriptional profiles from resting and activated T cells showed relatively modest differences, and analysis of integration site placement against either type of data showed no major differences. Spinoculation did not have a measurable effect on chromatin structure, as revealed by a comparison of the activated cell data sets with other data sets for HIV vectors in activated T cells, in which the frequency of integration near DNAseI-hypersensitive sites showed no significant differences after correction for multiple comparisons. Consensus sequence analysis at the point of integration was performed using WebLogo ( LEDGF expression levels of resting and activated T cells [23] were compared using an unpaired t-test.

Interactive supplementary data

An interactive heat map, summarizing statistical tests of integration frequency relative to genomic features, can be found at The interactive supplementary data can be viewed using a standard web browser. Please download and unzip the supplementary data file and follow the instructions in the ReadMe file to load into a web browser.

To view statistical comparisons of experimental data to matched random controls, click on the text to the right of the screen ‘Compare to Area = 0.05’. To view comparisons between data sets (columns), click on the column headings (e.g. ‘Act + NTP’). To view comparisons of genomic features to each other (rows), click on the labels to the left of the heat map (e.g. ‘gc10000’). The P value, determined by a logistic regression method that respects the pairing in the data (clogit), is overlaid on each heatmap tile (*P < 0.05; **P < 0.01; ***P < 0.001).


Infections and integration site sequencing

CD4+ T cells were isolated from healthy volunteers. Cell subsets were prepared as described in Fig. 1a. Briefly, peripheral blood mononuclear cells (PBMCs) were depleted with antibodies against T cell receptor (TCR)-γ/δ, CD8, CD16, CD19, CD36, CD56, and CD66b to yield CD4+ T cells. Cells expressing the activation markers for HLA-DR, CD69, and CD25 were depleted using phycoerythrin-labeled antibodies against these markers and antiphycoerythrin magnetic beads. In what follows, cells purified by this method are referred to as ‘resting cells’. This method for purifying resting CD4+ T cells yields more than 97% activation marker negative CD4+ T cells. Furthermore, a few (<3%) contaminating cells express very low levels of the activation markers CD25, CD69, and HLA-DR (Fig. 1b). To prepare the ‘activated cell’ subset, CD3/CD28 beads were added to the culture for 3 days. Resting cells purified as described above do not proliferate detectably over the time period of the study as assessed by DNA/RNA analysis and BrdU incorporation [24], whereas the activated cells enter the cell cycle and divide as shown by carboxyfluorescein succinimidyl ester (CFSE) staining [2527]. Previous studies have also shown that activation induces the resting CD4+ T cells to express high levels of activation markers [7].

Fig. 1
Diagram of the experiment and purification of CD4+ T cells

Cells were infected by spinoculation as described [15] using pNL4-3 derived virus particles packaged with the native HIVenvelope for cell entry. As reported previously, resting cells do not divide after spinoculation as measured by BrdU incorporation, nor does the treatment induce expression of activation markers, and viability of resting cells after spinoculation is typically 100% and the yield of cells 3 days after infection is typically 50% [24]. Because low pools of deoxynucleoside triphosphate substrates in resting T cells are associated with inefficient reverse transcription [7], we also infected some aliquots of cells in the presence of added deoxynucleosides to boost efficiency (though this did not turn out to be necessary to recover sufficient integration sites). Quantitative PCR [16,17] showed approximately one to three proviruses per cell for all DNA pools (Fig. 1c). Consistent with earlier studies [6,7,24], the kinetics of HIV integration in activated cells was faster than in resting cells.

Integration sites were isolated as described previously [18,19,21]. Briefly, genomic DNA was purified, digested with the restriction enzyme MseI, and linkers ligated to the digested ends. Proviral-host DNA junctions were amplified by PCR in a first round using primers annealing to the linker and to the U5 region of the LTR. A second round of nested PCR introduced DNA barcodes and binding sites for the 454/Roche sequencing primers. Samples were pooled and sequenced using 454/Roche pyrosequencing [28].

A total of 2661 unique sites were recovered and mapped to the human genome (Table 1). For comparison, matched random control sets were generated computationally by randomly choosing three genomic sites lying the same distance from an MseI cut site as each of the integration sites. This method for generating matched random controls accounts for biases in the recovery of integration sites based on their proximity to MseI sites and allows for more accurate statistical analysis [18,21,22,2931].

Table 1
Integration site data sets used in the study.

Integration site sequences were judged to be authentic if they showed a more than 98% match to a cellular sequence; showed a single best match to the human genome; and if the 5′-CA-3′ sequence of the terminal viral DNA was within three bases of the start of the high quality match to the human genome sequence. Representative junctions are shown in Fig. 2a. Note that 1474 sites from the resting cell sequences met these criteria, indicating that integrase-mediated integration takes place in the resting cell pools studied here. This is consistent with data showing that HIV DNA can be detected within the chromosomal DNA of resting CD4+ T cells inoculated with HIV by Alu-PCR [68,24].

Fig. 2
Primary human DNA sequences at sites of integration

Comparison of primary sequences at integration sites

HIV is known to favor integration at the weakly conserved palindromic sequence 5′-GT(A/T)AC-3′ at the point of integration [22,3235]. Although this preference is weak, it can be a strong predictor of integration targeting in comparisons to randomly chosen sequences [22]. To investigate sequence preferences at the point of integration in the resting and activated data sets, we examined 20 bp of genomic sequence surrounding the point of integration for each. Consensus sequences at the point of integration did not differ whether or not infections were supplemented with deoxynucleosides or in resting versus dividing cells (Fig. 2b). These data support the idea that correct integrase-mediated integration took place in the cell populations studied here.

Quantifying genome-wide distributions of integration sites relative to mapped genomic features

We examined the distribution of integration site patterns in resting and activated T cells relative to annotated features on the human genome sequence. For this, a heatmap format was developed to summarize many relationships using the receiver operating characteristic (ROC) area method introduced in [22]. Figure 3 summarizes the construction of an ROC curve. Figure 3a shows a conventional histogram illustrating the highly significant (P < 2.22e–16) correlation of integration frequency in activated T cells with relatively higher gene density compared with matched random controls. The experimental integration sites are more frequently found in bins of high gene density (right side of histogram) compared with the matched random control.

Fig. 3
Construction of heat maps using the receiver operating characteristic area method

Figure 3(b–d) provides an example of how the comparison shown in Fig. 3a can be converted into a single colored tile of a heat map. The genomic interval surrounding each experimental or control site was extracted and the number of genes found within it quantified. In the example, 1 Mb windows were studied (Fig. 3b). Each sequence was then ranked by relative gene density in the flanking 1 Mb (Fig. 3b, numbers beside the sequences). ROC areas were then calculated by determining the number of matched random control sites with ranks lower than the integration sites, and this number was divided by the total number of matched random controls (Fig. 3c). All values for all sets of integration sites and matched random controls were then averaged, yielding the final ROC (0.667 in Fig. 3c). An ROC area greater than 0.5, as in the example, indicates positive correlation between the experimental integration site data set and the genomic feature studied. An ROC area less than 0.5 indicates negative correlation.

A single colored tile can be used to represent the resulting ROC area (Fig. 3d). Enriched associations are shown as increasing shades of red, negative associations as increasing shades of blue, and no difference from random as white (Fig. 3d). The statistical significance can be determined by regression and is represented by asterisks overlaid on the tile (*P < 0.05; **P < 0.01; ***P < 0.001) [22].

Many forms of statistical comparisons are possible among the four data sets and the dozens of genomic features queried (Fig. 4) [22]. For example, one might wish to know whether ROC areas for the experimental data sets differ from the matched random controls, or alternatively whether data sets differ from each other (e.g. resting versus activated cells). The heat maps described above have been further developed to allow users to carry out interactive statistical comparisons to investigate these questions. In interactive supplementary data 1 (found at; see the Methods section for further instructions), we introduce user-configurable statistical tests, where clicking on any row of the heat map allows statistical comparison to all other rows, clicking on any column allows statistical comparison to all columns, and clicking on the indicated button to the right of the heat map allows comparison of all experimental data to matched random controls. Thus, readers can use this automated tool to visualize the statistical significance for any comparison of interest.

Fig. 4
Genomic heat map of integration frequency relative to genomic features

Comparison of HIV integration site distributions in activated and resting CD4+ T cells

Heat maps of the four HIV integration site data sets are shown in Fig. 4a and b. For both the activated and resting cell infections, deoxynucleosides were added or not to replicate cultures and integration sites analyzed separately. Addition of deoxynucleosides had only very slight effects on integration site selection. Figure 4b shows that for the two activated sets with or without deoxynucleoside addition, only one of 47 comparisons achieved significance. All comparisons between a given data set and all other sets can be carried out using the interactive tool in supplementary data 1. In what follows, the closely similar sets with or without deoxynucleosides serve as replicates documenting the reproducibility of differences between the activated and resting cell data sets.

Figure 4a shows that most comparisons to random for all four data sets were significant (asterisks on each tile). Starting at the top two rows of Fig. 4a, integration was favored within transcription units called by the uniGene and RefSeq databases, as seen in all previous studies of lentiviral integration site distributions [18,21,22,29,31,3642]. The next two rows (intergenic width and gene width) indicate that integration was less frequent in intervals of relatively long distances between transcription units and less frequent in transcription units of relatively long lengths. Both of these features are characteristic of gene-rich regions. Tests of correlations with distances to gene 5′ and 3′ ends (distance to start, distance to gene boundary) did not achieve significance, reflecting the previous finding that HIV integration is evenly distributed over the length of transcription units [29]. Integration within 50 kb of proto-oncogene 5′ ends was increased compared with random, consistent with favored integration in gene-rich regions. Integration was enriched near sites of DNAseI cleavage and CpG islands, with the effects stronger over longer interval sizes. This length dependence reflects the fact that over long intervals DNAseI sites and CpG islands are characteristic of gene-rich regions, which is favored for HIV integration, whereas over shorter intervals these sites are enriched near gene promoters, which are disfavored for HIV integration.

Most measures of gene density were positively correlated with HIV integration frequency, and statistical tests achieved high significance. The correlation between gene density and integration frequency was tested over multiple interval sizes (10 kb to 1 Mb) and found to be significant for each. Transcriptional intensity was also compared. In this measure, gene activity in T cells was quantified using Affymetrix microarrays, and then genes ranked for relative expression levels. The expression intensity measure was quantified as for gene density; except only genes in the upper half of the expression ranks (‘top ½ expression’) or upper 16th (‘top 1/16 expression’) were scored. Most expression intensity measures were positively correlated with integration frequency.

In the human genome, G/C-rich regions are correlated with regions of high gene density, high expression intensity, high densities of CpG islands, and high densities of DNAseI sites. Correlations with integration site densities and G/C richness were analyzed over interval sizes ranging from 20 bp to 10 Mb. Over longer chromosomal intervals, HIV integration was positively correlated with G/C richness, paralleling the preference for integration in gene-rich regions. Over short intervals, the strength of the trend is reduced near to or equal to random. Previous work has shown that HIV integration is favored in DNAwrapped on nucleosomes [18,4345], and nucleosome wrapping is favored by periodic A/T-rich motifs in DNA [46], probably explaining the diminished G/C sequence preference over short intervals (<2 kb).

The resting and activated cell sets differed over many of the forms of annotation analyzed, but in most cases the differences were modest in magnitude. Table 1 shows the quantitative differences for a few values (%GC content, number of transcription units, and number of CpG islands, all measured over 1 Mb intervals). Compared with resting cells, in the 1 Mb intervals surrounding integration sites in activated cells, G/C content was 2–3% higher, about two more genes were found, and about 25 more CpG islands were found. The asterisks on Fig. 4b summarize the statistical significance of pair-wise comparisons of the activated cell data set to the other three data sets. Statistical comparisons of any data set (columns in Fig. 4) to all others are available in the interactive supplementary data.

In all cases in which significant differences were seen, the integration site distributions in the resting cell data sets more resembled the matched random controls than did the activated cell data sets. There were some slight differences in the frequencies of integration within transcription units between the resting and activated cell data sets (Fig. 4b), but onlyone of fourcomparisons between resting and activated achieved statistical significance, so we conclude that integration in transcription units was about equally favored in the resting and activated cells. However, for resting cells, integration frequency within gene-rich regions was reduced compared with activated, and integration near associated features such as DNAseI sites, CpG islands, and regions of high G/C-rich content was diminished as well. Effects were most pronounced over longer intervals. The strongest effects involved the G/C-rich regions and CpG islands.

Thus, differences between the activated and resting cell data sets could be discerned, though they were all quantitative in nature, involving less extreme departures from random for the resting cell data sets.

HIV integration frequency in activated versus resting T cells compared with histone modifications and chromatin-bound proteins

We also investigated the integration site distribution for the resting and activated cells relative to 20 types of histone modifications and selected chromatin-bound proteins (Fig. 4c and d). For this we used data from a study of resting T cells in which chromatin immunoprecipitation and Solexa sequencing were used to map between 1 and 16 million sequence tags for each of these histone modifications and proteins [47]. As with the genomic heat maps, comparisons were done over multiple window sizes to maximize detection of differences between sets. Detailed information on these epigenetic marks can be found in [47,48].

The activated and resting cell data sets showed similar patterns, positively correlating with histone modifications and bound proteins found in actively transcribed regions (e.g. H2BK5me1; H3K4me1,2,3; H3K9me1; Pol II, etc.) and negatively associating with marks common to heterochromatin and gene repression (e.g. H3K9me2 and me3; H3K27me3, etc.).

Differences between resting and activated sets were modest though statistically significant, particularly when larger genomic window sizes (100 kb) were used in the comparisons. Comparisons over larger genomic lengths may achieve significance more easily because each bin contains more total tags from the ChIP-seq experiment. Comparing the resting and activated cell data sets, less negative correlation was seen in the resting cell data sets with H3K9me2 and 3 (which localize to silent chromatin) and H3K79me2 (which has no obvious localization preference) and more negative association of H3K79me3 (found at promoters) and H4R3me2 (which has no obvious localization preference). Thus, the differences in associations with histone modifications seen for resting and activated cells are generally consistent with the differences in integration targeting observed for other types of genomic features.


Recent studies [24,49,50] suggest that HIV is able to infect resting CD4+ T cells, raising the question of whether integration in resting cells may contribute to formation of the latent reservoir. Previous studies have supported the idea that integration in specific chromosomal regions can result in suboptimal HIV gene expression, and this in turn correlated with formation of inducible (i.e. latent) proviruses, at least in cell culture models of latency [9]. Thus, we sought to investigate whether HIV proviruses formed by integration in resting cells were found more commonly in genomic regions suboptimal for gene expression.

Overall the distributions of integration sites in resting and dividing CD+ T cells were similar, with integration favored in active transcription units in all data sets, but quantitative differences could be detected. The replicate experiments (i.e. with or without added deoxynucleo-sides) were closely similar, supporting the idea that the differences observed between resting and activated cells were not due to experimental error. We found that activated cells sustained more integration in regions that were gene-dense, CpG island-dense, and GC-dense than did resting cells. Significant differences in integration frequency near some types of histone modifications were also detected.

The HIV integrase-binding protein PSIP1/LEDGF/p75 is known to help direct HIV integration into active transcription units [21,41,5153], raising the question of possible involvement of LEDGF protein here. A previous report showed that the level of LEDGF expression in different cell types correlated with the proportion of HIV integration occurring in transcription units [21], so we compared LEDGF expression in published transcriptional profiling data for activated and resting T cells [23]. We found that LEDGF expression was actually slightly higher in resting T cells compared with activated T cells, inconsistent with a role for LEDGF here. In addition, when LEDGF activity is reduced, the fraction of integration sites near CpG islands actually increases [21,41], whereas we found that resting cells showed less integration near CpG islands than activated cells. Thus, variations in the level of LEDGF expression probably do not explain the differences between resting and activated cells seen here.

In summary, differences between integration targeting in resting and activated T cells are detectable and statistically significant, though quantitatively modest. In previous studies [7,24], infection of resting cells prepared as described here resulted in substantial levels of integration of viral DNA but relatively low levels of Gag protein production, providing a model for latency. Here we report that integrated proviruses are found more often in relatively less gene-dense regions in resting cells than in activated cells. Proviruses in such gene deserts may be more prone to forming latent proviruses, as in functional studies integration in gene deserts correlated with an inducible or latent phenotype [30]. Weinberger et al. [54] have shown that when levels of HIV Tat protein are low and fluctuating, drops in Tat expression switch HIV gene expression into a stable off state because Tat protein positively activates its own synthesis. The differences reported here are consistent with a model in which switching off of HIV transcription might occur more frequently in resting cells than in activated cells because of increased integration in gene deserts in resting cells. Thus, according to this idea, infection of resting cells would more often lead to latent infection due to distinctive features of integration target site selection.


We are grateful to members of the O'Doherty and Bushman laboratories for help and suggestions. This work was supported by NIH grant AI52845, the University of Pennsylvania Center for AIDS Research, and the Penn Genome Frontiers Institute with a grant with the Pennsylvania Department of Health to F.D.B. and by NIH grants AI058862-06 and AI058862-04S1 to U.O. The Department of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions.


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