1. Microbiology and Infectious Disease
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Transcriptional down-regulation of ccr5 in a subset of HIV+ controllers and their family members

  1. Elena Gonzalo-Gil
  2. Patrick B Rapuano
  3. Uchenna Ikediobi
  4. Rebecca Leibowitz
  5. Sameet Mehta
  6. Ayse K Coskun
  7. J Zachary Porterfield
  8. Teagan D Lampkin
  9. Vincent C Marconi
  10. David Rimland
  11. Bruce D Walker
  12. Steven Deeks
  13. Richard E Sutton  Is a corresponding author
  1. Yale University School of Medicine, United States
  2. Dallas VA Medical Center, United States
  3. Atlanta VA Medical Center, Emory University School of Medicine, United States
  4. MIT and Harvard University, United States
  5. University of California San Francisco, United States
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Cite this article as: eLife 2019;8:e44360 doi: 10.7554/eLife.44360

Abstract

HIV +Elite and Viremic controllers (EC/VCs) are able to control virus infection, perhaps because of host genetic determinants. We identified 16% (21 of 131) EC/VCs with CD4 +T cells with resistance specific to R5-tropic HIV, reversed after introduction of ccr5. R5 resistance was not observed in macrophages and depended upon the method of T cell activation. CD4 +T cells of these EC/VCs had lower ccr2 and ccr5 RNA levels, reduced CCR2 and CCR5 cell-surface expression, and decreased levels of secreted chemokines. T cells had no changes in chemokine receptor mRNA half-life but instead had lower levels of active transcription of ccr2 and ccr5, despite having more accessible chromatin by ATAC-seq. Other nearby genes were also down-regulated, over a region of ~500 kb on chromosome 3p21. This same R5 resistance phenotype was observed in family members of an index VC, also associated with ccr2/ccr5 down-regulation, suggesting that the phenotype is heritable.

https://doi.org/10.7554/eLife.44360.001

Introduction

Human immunodeficiency virus type 1 (HIV-1) is pandemic, with more than 36 million people infected world-wide. Anti-retroviral therapy (ART) is a mainstay of treatment, but once therapy is stopped or drug resistance develops, viral rebound occurs within weeks and CD4 +T cell counts decline (Holkmann Olsen et al., 2007). A small population of HIV-infected individuals termed elite controllers (ECs) and viremic controllers (VCs), however, are able to control viral replication (plasma viral load, VL <50 [ECs] or 50 < VL < 2000 [VCs] for at least 6–12 months) in the absence of ART by a mechanism that is not fully elucidated (Deeks and Walker, 2007; Gonzalo-Gil et al., 2017; Lambotte et al., 2005). EC/VCs are considered examples of ‘functional’ cures, in which virus is not fully eradicated and yet for the most part the patient does not develop immune dysfunction over time. The clinical status of most EC/VCs cannot be explained by defective HIV particles or genomes (Wang et al., 2002; Blankson et al., 2007). Rather, these individuals appear to have an intrinsic ability to control HIV infection, perhaps because of host genetic determinants. A genome-wide association study (GWAS) identified certain human leukocyte antigens (HLA)-B and HLA-C alleles that are associated with viral control in ECs (Pereyra et al., 2010). However, these protective alleles only accounted for ~20% of the effect, suggesting that there are other mechanisms responsible for the suppressed viral loads in EC/VCs. Identifying novel mechanisms involved in HIV control is paramount to HIV research and the cure agenda.

C-X-C chemokine receptor 4 (CXCR4) and C-C chemokine receptor 5 (CCR5) serve as co-receptor for X4-tropic and R5-tropic HIV-1 entry into CD4 +T cells, respectively, and CCR5 is essential for sexual transmission of HIV (Feng et al., 1996). The presence of the CCR5 delta 32 (Δ32CCR5) allele confers protection against seroconversion, with homozygotes being completely resistant to infection via mucosal routes (Liu et al., 1996; Samson et al., 1996). There is, however, no evidence that Δ32CCR5 ± is associated with EC/VC phenotype. Conflicting results have been obtained regarding the susceptibility of EC/VC CD4 +T cells to HIV infection in vitro. Activated CD4 +T cells from EC/VCs have been shown to be susceptible to both R5- and X4-tropic HIV (Blankson et al., 2007; Lamine et al., 2007) but opposite results have also been reported, with CD4 +T cells of EC/VCs being resistant to HIV (Chen et al., 2011; Sáez-Cirión et al., 2011; Walker et al., 2015; Julg et al., 2010).

Previously we had observed that three of roughly a dozen ECs tested had CD4 +T cells with intrinsic resistance to R5 virus, due to increased chemokine gene expression (Walker et al., 2015). To extend those findings and to determine whether R5 resistance is a consequence of a transcriptional mechanism and if there is a hereditary basis associated with the phenotype, we analyzed the in vitro susceptibility to HIV of purified CD4 +T cells from 131 EC/VCs, along with normal, healthy donors. Here we report that a subset of EC/VCs have resistance to HIV, specific to R5-tropic virus. For these subjects, however, the resistance phenotype was due to lower levels of CCR5, at both the RNA and protein levels, and was likely due to reduced active transcription of ccr5, despite highly accessible chromatin. The fact that CD4 +T cells from multiple family members of an index VC had a similar phenotype and also down-regulation of ccr5 suggests that the phenotype is hereditary in nature.

Results

Clinical characteristics of EC/VC cohort

The total number of EC/VCs studied was 131, with a majority coming from the UCSF SCOPE cohort. Forty-four percent (58/131) were ECs, with 56% (73/131) being VCs (See Supplementary file 1). The year of initial HIV diagnosis or likely exposure ranged from 1980 to 2014, and subjects were 48 ± 12 years old (mean ±SD, range of 19 to 79 years), the majority being men (78.62%). CD4 +T cell count at time of enrollment was 689 ± 358 (mean ±SD). Most had never received ART except under the circumstances of pregnancy or malignancy (Supplementary file 1). Although occasional viral blips were observed, none of the EC/VCs ever lost virologic control necessitating ART. A number of subjects (54/125) had documented protective HLA alleles, being 32.06% HLA-B*57:03, 25.95% HLA-B*57:01, 22.9% Cw*08:02, 10.69% B*14:02, 4.58% HLA-B*27:05, and 3.05% B*52:01.

In vitro CD4 +T cell intrinsic resistance specifically to R5-tropic virus in a subset of HIV +EC/VCs

To determine whether T cells of EC/VCs were resistant to X4- or R5-tropic virus in vitro, we activated CD4 +T cells from 131 EC/VC and 35 Ctrl, and then infected them overnight using single cycle HIV encoding YFP and pseudotyped with either X4, R5, or VSV G glycoprotein and analyzed cells by flow cytometry 72 hr later. We observed relative resistance to R5-tropic HIV in CD4 +T cells from EC/VCs (% cells eYFP+: EC/VC 0.99 ± 0.79) compared to Ctrl (1.22 ± 0.66; p=0.01; Figure 1—figure supplement 1A, left panel). In contrast, we saw equal susceptibility to X4-tropic HIV (Ctrl 3.08 ± 1.32; EC/VCs 3.33 ± 1.91) and VSV G pseudoviral particles among the groups (Ctrl 34.8 ± 9.36; EC/VCs 30.66 ± 11.22; Figure 1—figure supplement 1B). Post-hoc analysis identified 16% of EC/VCs (21 of 131 analyzed, termed ECr/VCr) with resistance specific to R5-tropic HIV, compared to remaining EC/VC subjects and healthy Ctrl, with no resistance observed (% cells YFP+: Ctrl 1.22 ± 0.66; EC/VC 1.2 ± 0.77; ECr/VCr 0.2 ± 0.07; p<0.0001; Figure 1—figure supplement 1A, right panel), pointing to an early block of infection in a subset of EC/VCs. These data confirmed that the phenotype was specific to EC/VC, not observed in Ctrl. To confirm the R5 resistance phenotype, we then selected ECr/VCr samples for further study, based upon % eYFP +cells being lower than any value in Ctrl group. We retested these ECr/VCr samples prospectively in at least triplicate, using two R5-tropic envelopes, in comparison to a subset of EC/VC (n = 38, selected based upon sample availability and representativeness of the population from the initial test) and Ctrl (n = 35). Our results redemonstrated R5 resistance, as manifested as a 5-fold reduction in CD4 +T cell susceptibility to YU2-pseudotyped virus, on average, in ECr/VCr compared to remaining EC/VC and Ctrl (Figure 1A, % cells eYFP+: Ctrl 1.05 ± 0.81; EC/VC 1.09 ± 0.75; ECr/VCr 0.20 ± 0.16; p<0.0001). Similar results were observed using ADA-pseudotyped virus (% cells YFP+: Ctrl 1.27 ± 0.5; EC/VC 1.13 ± 0.75; ECr/VCr 0.34 ± 0.16; p<0.0001, Figure 1A). Similar to the post-hoc analysis, in this prospective testing we observed equal susceptibility to X4-tropic and VSV-G-pseudotyped HIV particles in activated CD4 +T cells from ECr/VCr compared to EC/VC without the phenotype and Ctrl (Figure 1B). In multiple cases, based upon sample and subject availability, we retested ECr/VCr CD4 +T cells isolated from independent, separate blood draws and observed consistent results (i.e., R5 resistance was seen repeatedly, not just on a single blood draw). Taken together, these data identify a subset of EC/VCs with intrinsic, reproducible resistance specific to R5-tropic virus in T cells, a phenotype only observed in EC/VC. From the 21 EC/VCs with the resistance phenotype, 43% were ECs (9/21) and 57% VCs (12/21). Figure 1—figure supplement 2A shows virus infectivity data for all 21 ECr/VCr, with Figure 1—figure supplement 2B demonstrating absence of correlation between R5 and X4 and R5 and VSV G susceptibility.

Figure 1 with 2 supplements see all
CD4 +T cell resistance to infection in prospective single cycle assay, specific to R5-tropic viruses in a subset of EC/VCs.

(A) Five-fold resistance to R5-tropic viruses in 16% of EC/VC (ECr/VCr) infected using replication defective HIV-cycT1-IRES-eYFP (CIY) with R5-tropic envelopes YU2 and ADA. A > 95% power was determined based on comparisons of means using PASS statistical software between ECr/VCr and all other groups (Ctrl and EC/VC). (B) Equivalent susceptibility to both X4-tropic (NL4-3) and VSV G pseudoviral particles in ECr/VCr. A and B are pooled results from different experiments with samples tested at least in triplicate (Ctrl n = 35, EC/VC n = 38, representative from the initial population (Figure 1—figure supplement 1) and selected based upon specimen availability, and ECr/VCr (n = 21). (C) Comparable levels of chemokines (MIP-1α and MIP-1β) in cell culture supernatants from activated CD4 +T cells, measured by ELISA. (D) CD4 +T cells from Ctrl were exposed to cell culture supernatants from activated T cells of Ctrl and EC/VC with or without the resistance phenotype, in the presence of HIV particles pseudotyped with YU2 or VSV G. C and D are pooled results from different experiments with n = 10 (Ctrl and EC/VCs) and n = 21 (ECr/VCr). Shown are individual values with Means ± Standard Deviation (SD). Data were analyzed by using the Kruskal-Wallis test and Dunn’s multiple-comparison test. *p<0.05; ****p<0.0001.

https://doi.org/10.7554/eLife.44360.002

We next analyzed whether any clinical characteristics (VL, CD4 +T cell count, and age) were associated with the R5 resistance phenotype in the EC/VC population. Comparable VLs and CD4 +T cell counts were observed in both groups (Figure 1—figure supplement 1C). However, ECr/VCr were significantly younger than EC/VC (43 ± 14 vs 49 ± 12 years; p=0.047; Figure 1—figure supplement 1C). Analyzed by gender, most of the subjects in both groups were men (EC/VC 78% or 85/109% and 86% or 18/21 in ECr/VCr).

To investigate whether this resistance was associated with increased levels of chemokines or other soluble factors, which could block viral entry by competitively binding to the chemokine co-receptor CCR5 (Paxton et al., 1996; Saha et al., 1998), chemokine levels were quantified in cell culture supernatants from activated CD4 +T cells. We selected samples from each group (Ctrl and EC/VCs based upon specimen availability and representative from the initial testing) and compared with ECr/VCr (n = 21). oup (Ctrl and EC/VCs basing on sample availability and representativeness from the initCD4 +T cells from ECr/VCr, however, had decreased levels of secreted MIP-1α and MIP-1β, compared to the other groups (Figure 1C), which was statistically significant compared to Ctrl (MIP-1α: Ctrl 16 ± 9.42 vs ECr/VCr 7.24 ± 5.21 ng/ml; p=0.048 and MIP-1β: ECr/VCr 4.52 ± 2.61 vs Ctrl 9.12 ± 4.73 ng/ml; p=0.01). Additionally, we performed media transfer experiments to explore whether other factors elaborated by activated CD4 +T cells were responsible to the resistance phenotype in this ECr/VCr subset. Our results revealed comparable T cell susceptibility to infection in ECr/VCr and EC/VCs without the phenotype (Figure 1D), suggesting that the culture supernatants did not contain soluble factors that could confer resistance to R5-tropic virus in the ECr/VCrs.

Previous reports have suggested that expression of HLA-B*27/HLA-B*57 and other specific HLA alleles can account for some of the controller phenotype. We examined whether the presence of protective HLA alleles was associated with viral control in ECr/VCr subset. Of the 16% of ECr/VCr with the R5 resistance phenotype, only five individuals (5/19 or 26.3%) had documented protective alleles, with four of them being HLA-B*57 positive and only one HLA-B*27. Analyzing the remaining EC/VC, the percentage was higher, with 46% (49/106) of them having protective HLA alleles. Although this difference in frequency of protective alleles was not significant (p=0.086) due to the low number of ECr/VCr, these data confirm that protective alleles were not more frequent in ECr/VCr.

We next investigated whether the ECr/VCr CD4 +T cells were also relatively resistant to replication-competent virus. Activated CD4 +T cells from EC/VC, ECr/VCr (based upon prior experiments) and Ctrl (n = 2 per group, tested in triplicate, selected based upon cell availability) were infected with X4- and R5-tropic viruses, at low MOI. Viruses were prepared in 293 T cells by co-transfection with VSV G expression plasmid to facilitate the first round of replication. Replication of NL4-3 (X4) and BaL (R5) was quantified using TZMbl cells as a reporter, measuring firefly luciferase activity over a period of 3 weeks. We observed significantly reduced replication of BaL in ECr/VCr, compared to EC/VC and Ctrl over the 21 days analyzed (Mean ±SD Area Under Curve [AUC] R5: Ctrl 177828 ± 53736; EC/VC 125548 ± 31577; ECr/VCr 62006 ± 4179; Figure 1—figure supplement 2C, right panel). The absence of differences in viral replication at day three post-infection may be explained by the addition of VSV G as described above. Infection using NL4-3 also showed significant resistance in all EC/VC (AUCs: Ctrl 19679 ± 12897; EC/VC 5880 ± 1319; ECr/VCr 2125 ± 60.1, Figure 1—figure supplement 2C, left panel). The fact that EC/VC (with or without the R5-resistance phenotype) had reduced infectivity, with virtual absence of X4 replication in ECr/VCr, suggests a more complex mechanism of virologic resistance that should be further explored.

RNA-Seq identifies several genes down-regulated in EC/VC with R5-tropic resistance

To further investigate the mechanism of R5-tropic resistance in early infection, we next performed RNA-Seq to identify genes that were significantly up- or down-regulated in activated CD4 +T cells from ECr/VCr compared to Ctrl. We examined RNA levels in activated T cells because those are the cells in which we observed the R5 resistance phenotype (unactivated T cells are extremely difficult to infect). Several of the differentially expressed genes were located on chromosome 3 (chr 3), including ccr1, ccr2, and ccr5, which were significantly down-regulated in ECr/VCr (corrected p values=0.005). To quantify mRNA levels of these genes in ECr/VCr, we performed RT-qPCR in ECr/VCr, and compared results to remaining EC/VCs and Ctrl. These data confirmed a 7-fold decreased expression in ccr2 mRNA levels, on average, in T cells of ECr/VCr (0.13 ± 0.09) compared to those of EC/VC without the resistance phenotype (0.89 ± 0.41; p<0.0001) and Ctrl (0.91 ± 0.72; p<0.0001; Figure 2A). Similarly, we observed down-regulation of ccr5 RNA in T cells of ECr/VCr (0.076 ± 0.047; 9-fold decrease on average) compared to those of the other groups (EC/VC 0.79 ± 0.63 and Ctrl 0.68 ± 0.63; p<0.0001, Figure 2A).

Figure 2 with 1 supplement see all
Decreased mRNA levels of several chromosomal three genes in ECr/VCrs.

(A) Decreased ccr2/ccr5 RNA levels in activated CD4 +T cells from EC/VCs with the resistance phenotype, with comparable cxcr4 and cd4 RNA levels in all groups. Shown are individual values with Means ± SD. Pooled results from different experiments are shown with representative samples per group, n = 19 (Ctrl), n = 8 (EC/VC) and n = 21 (ECr/VCr) per group. (B) Positive correlation between ccr2 and ccr5 RNA levels in activated CD4 +T cells. ccr5 RNA levels positively correlated with % of YFP +infected cells by single cycle assay using R5-tropic viruses but not with cd4 or cxcr4 (Figure 2—figure supplement 1A). (C) Decreased RNA levels in multiple chromosomal 3p21 genes in T cells of HIV +infected individuals (Figure 2—figure supplement 1C). Statistical analysis performed using Kruskal-Wallis test and Dunn’s multiple-comparison test. r value calculated using the non-parametric Spearman correlation test. Graphs show individual values with Means ± SD. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

https://doi.org/10.7554/eLife.44360.005

Conversely, we did not observe significant differences between groups in cxcr4 and cd4 RNA levels (Figure 2A). Interestingly, ccr5 RNA highly correlated with ccr2 RNA levels (r = 0.88; p<0.0001, Figure 2B), suggesting a common regulatory mechanism for both genes in all subjects. Moreover, ccr5 mRNA levels were positively correlated with transduction by R5-tropic virus (r = 0.71; p<0.0001, Figure 2B), indicating that subjects whose CD4 +T cells were more resistant to R5-tropic virus had lower ccr5 mRNA expression. However, there was no correlation between ccr2 and cd4 or cxcr4 RNA levels, nor between ccr5 and cd4 or cxcr4 RNA levels (Figure 2—figure supplement 1A).

Because several genes near ccr2/ccr5 appeared to be down-regulated, we analyzed levels of gene expression both centromeric and telomeric of that region. We observed down-regulation in several genes in that locus of 3p21, including fyco1, cxcr6, ccr1, and ccr3 in all HIV +infected groups (EC/VC, and ECr/VCr) compared to Ctrl, although only EC/VC and ECr/VCr groups reached statistical significance compared to Ctrl (Figure 2C). Taken together, these data point towards RNA down-regulation involving a region of approximately 500 Kb, surrounding ccr2/ccr5 in EC/VC (Figure 2—figure supplement 1C), with a more profound decrease of ccr2/ccr5 specifically in ECr/VCr, not observed in the remaining EC/VCs.

LOC102724291 is a poorly characterized long non-coding RNA (lncRNA) of unknown function, present on chr3, antisense to ccr5 and ccr2. To ascertain if loc102724291 was involved in ccr2/ccr5 RNA down-regulation, we quantified its expression in CD4 +T cells by RT-qPCR. Comparable levels were observed between ECr/VCr and EC/VCs without the phenotype using a primer pair within exons 1 and 2. We did observe, however, lower lncRNA levels using a primer pair within exon 3, within intron 2 of ccr5 (Figure 2—figure supplement 1D), in CD4 +T cells of ECr/VCr compared to other groups. The absence of a negative correlation between ccr2 or ccr5 and loc102724291 makes it unlikely that an antisense effect from this lncRNA is responsible for the down-regulation of ccr2/ccr5 in ECr/VCr and is more consistent with loc102724291 also being down-regulated by a more global mechanism, similar to other genes in the region.

Lower CCR2 and CCR5 surface expression in EC/VC with the resistance phenotype

We confirmed the activation status of CD4 +T cells by analyzing CD69 and CD25 up-regulation by flow cytometry, cell-surface markers of early and late cell activation, respectively, after CD4 +T cell activation with aCD3/CD28 for three days. Results showed a strong late activation of CD4 +T cells in all groups, with comparable CD25 levels in ECr/VCr and remaining EC/VC and Ctrl groups (Figure 3A). However, we observed lower levels of CD69 (%+) in activated CD4 +T cells from ECr/VCr (22.26 ± 6.54) compared to EC/VC without the resistance phenotype (30.59 ± 5.15; p=0.011) and Ctrl (32.12 ± 4.15; p=0.0003; Figure 3A).

Figure 3 with 2 supplements see all
Lower proliferative responses and CCR2 and CCR5 cell surface levels in activated CD4 +T cells from ECr/VCrs.

(A) Reduced CD69, but not CD25 levels in activated CD4 +T cells from ECr/VCrs. Graph shows representative data N = 13 (Ctrl), n = 9 (EC/VC) and n = 21 (ECr/VCr). (B) Comparable frequencies of naïve CD45RA + and memory CD45RO + T cells after anti-CD3/CD28 activation between groups (n = 2 per group). (C) CCR5 and CCR2 cell surface levels measured by flow cytometry are reduced in freshly thawed (NS, non-stimulated) and activated CD4 +T cells (anti-CD3/28) from ECr/VCr. (D) Percentages of CCR5 +in effector memory (EM) and central memory (CM) compartments of activated CD4 +T cells (n = 2 per group). (E) Reduced CCR2 and CCR5 cell surface levels, expressed as MFI, in activated (anti-CD3/28) CD4 +T cells from ECr/VCr. Data in D-E shown pooled results from different experiments with n = 10 (Ctrl and EC/VC) and n = 19 (ECr/VCr). (F) Positive correlation between CCR2 and CCR5 cell surface levels. (G) Positive correlation observed between ccr2/ccr5 RNA levels and cell surface expression. Values obtained using the non-parametric Spearman correlation test. *p<0.05.

https://doi.org/10.7554/eLife.44360.007

It is important to note that we infected the T cells 72 hr after activation, not at 24 hr, and that the resistance phenotype was specific to R5 virus, with equal susceptibility to X4- and VSV G-pseudotyped particles. In fact, positive correlations were observed between ccr5 mRNA or cell surface expression levels and % CD69 +cells, confirming that lower ccr5 expression, but not cxcr4, was observed in CD4 +T cells with lower levels of the early activation marker (Figure 2—figure supplement 1B). In select samples we also analyzed the percentage of memory T cells after anti-CD3/CD28 co-stimulation. Results showed a high percentage of CD45RO + memory T cells of between 60–80%, and low percentage of naïve CD45RA + T cells (~10%), with no differences between groups (Figure 3B). These data, taken together, confirm efficient activation of CD4 +T cells in all subject groups studied, with a high percentage of memory T cells after activation.

To determine whether the resistance phenotype was associated with an alteration in the expression of CCR2 and CCR5, cell surface levels were quantified by flow cytometry in activated CD4 +T cells. Our results revealed lower surface expression of CCR2 in ECr/VCr (15.5 ± 10.17 %+) compared to other EC/VCs (22.99 ± 11.04; p=0.021) and Ctrl (23.64 ± 9.13; p=0.023; Figure 3C, CD3/28 panel. Figure 3—figure supplement 1 shows individual flow cytometric histograms, comparing cell surface expression of Ctrl and all 21 ECr/VCr). Similarly, differences in CCR5 expression also reached significance, being lower in ECr/VCr (21.39 ± 13.65) than in other EC/VC (37.99 ± 12.3; p=0.005) or Ctrl (36.08 ± 10.29; p=0.003; Figure 3C, CD3/28 panel. Figure 3—figure supplement 2 shows individual flow cytometric histograms comparing cell surface expression between Ctrl and all 21 ECr/VCr). Similar results were observed analyzing the data as MFI, with lower CCR2 and CCR5 in ECr/VCr compared to the other groups (Figure 3E). Interestingly, we observed a positive correlation between CCR2 and CCR5 surface expression (r = 0.35; p=0.029; Figure 3F). We next investigated whether there were differences in surface expression levels in non-stimulated (NS) CD4 +T cells. Similarly, CCR2 and CCR5 expression levels in NS CD4 +T cells were significantly lower in ECr/VCr, compared to EC/VC without the resistance phenotype (Figure 3C). Since most CCR5 +CD4+T cells show an effector memory phenotype (EM, defined as CD45RO+/CD27-), and these T cells may be more easily infected by R5-tropic virus, we investigated whether CCR5 levels were lower in Effector Memory (EM) from ECr/VCr. Our results, however, demonstrated lower CCR5 expression in both EM and central memory T cells (CM, defined as CD45RO+/CD27+; Figure 3D) in ECr/VCr, compared to Ctrl and EC/VC. In addition, percentages of EM trended higher in ECr/VCr T cells compared to other groups, suggesting that the R5-resistance phenotype is not due to a lower percentage of EM T cells.

We then investigated whether activated CD4 +T cells from individuals with lower ccr2/ccr5 RNA levels also had lower surface expression of both CCR2 and CCR5. We saw a positive correlation between CCR2 protein expression and ccr2 RNA levels (r = 0.45; p=0.02, Figure 3G). Similar results were observed with CCR5 (r = 0.36; p=0.01), suggesting that down-regulation of ccr5 RNA was responsible for lower cell surface expression and consequent resistance to R5 virus in ECr/VCr CD4 +T cells.

Increased susceptibility to R5-tropic virus infection in activated CD4 +T cells after overexpression of CCR5

To confirm that the R5-tropic resistance in ECr/VCr CD4 +T cells was due to down-regulation of CCR5, activated CD4 +T cells from ECr/VCr, EC/VC, and Ctrl were infected with R5-tropic pseudotyped HIV particles after cell transduction using pan-tropic pseudotyped viral particles encoding both CCR5 and eYFP. First, we observed an increase in the percentage of CCR5 +cells in ECr/VCr transduced with pHIV-CCR5-IRES-YFP (VSV G) (8.09 ± 3.86%), compared to vector encoding YFP alone (3.16 ± 1.14%; p=0.032, Figure 4A). These CD4 +T cells were more susceptible than those of Ctrl to subsequent infection using two different R5-tropic viruses (YU2: Ctrl 1.15 ± 0.05% vs ECr/VCr 2.74 ± 1.11%; p=0.009; ADA: Ctrl 0.70 ± 0.04% vs ECr/VCr 1.79 ± 1.3%; p=0.008).

Resistance to R5-tropic viruses is due to down-regulation of CCR5 in ECr/VCr.

(A) Overexpression of CCR5 in CD4 +T cells using a lentiviral vector (YFP-CCR5). Increased susceptibility to R5-tropic virus after overexpression of CCR5 in EC/VCs with R5 resistance, as measured by YFP+/mRFP +double positive cells (n = 5 per group). (B) Comparable susceptibility to infection specific to R5-tropic virus in MDMs from ECr/VCr, EC/VCs and Ctrl (n = 6 per group; samples tested in duplicate for YU2). (C–D) Similar ccr2/ccr5 mRNA (C) and cell surface protein levels (D) in MDMs from EC/VCs (n = 3 per group). Shown in all cases are individual values with Means ± SD, analyzed using U-Mann Whitney test. *p<0.05; **p<0.01.

https://doi.org/10.7554/eLife.44360.010

Also, and more interestingly, higher susceptibility was observed in ECr/VCr than EC/VC (for YU2: EC/VC 1.56 ± 0.39% vs ECr/VCr 2.47 ± 0.51%; p=0.03, Figure 4A). We did not, however, observe any differences when we used VSV G-pseudotyped viral particles, confirming that the observed R5-resistance phenotype in ECr/VCr was in fact due to decreased cell surface expression of CCR5.

To determine whether this R5-resistance phenotype was observed in other circulating mononuclear cells, macrophages derived from monocytes (MDMs) were infected using pseudotyped lentiviral particles and analyzed by flow cytometry (Figure 4B). We observed comparable R5 susceptibility in MDMs from ECr/VCr and remaining EC/VCs. We next analyzed ccr5 and ccr2 RNA expression levels in MDMs from EC/VCs, and equivalent levels were present in all groups (Figure 4C). Similarly, the percentages of CCR5 +and CCR2 +in CD14+cells were comparable between groups (Figure 4D), suggesting that the R5-tropic resistance phenotype and ccr2/ccr5 down-regulation observed in a subset of EC/VCs were specific to activated CD4 +T cells.

Other investigators have attempted to determine with limited success whether EC/VC CD4 +T cells are resistant to infection in vitro. To ascertain whether the conflicting results are a consequence of varying experimental conditions or clinical characteristics of the EC/VCs, we activated CD4 +T cells using PHA or PMA/ionomycin from a representative number of samples from different groups, and T cells then infected with pseudotyped viral particles. We observed comparable CD4 +T cell susceptibility to X4- and VSV G-pseudotyped particles in EC/VCs (Figure 5A).

Resistance specific to R5-tropic virus is dependent upon T cell activation method.

(A) Comparable CD4 +T cell susceptibility to X4- and VSV G or (B) R5- pseudotyped particles in all groups after PMA plus ionomycin or PHA stimulation. Decreased susceptibility to R5-tropic infection in ECr/VCr compared to Ctrl and remaining EC/VCs after PHA stimulation was not significant. Shown are Means ± SD. (C) Comparable ccr2 and ccr5 mRNA expression levels between experimental groups after PMA plus ionomycin or PHA treatment. (D) Comparable frequency of CCR5 +cells between samples after PMA plus ionomycin or PHA stimulation in activated cells, analyzed as the MFI (n = 8 per experimental group).

https://doi.org/10.7554/eLife.44360.011

Additionally, we did not observe significant differences in R5-tropic virus susceptibility of EC/VC CD4 +T cells after PMA/ionomycin stimulation (Figure 5B), although T cell susceptibility trended lower in ECr/VCr compared to Ctrl and EC/VCs after PHA stimulation. Next, we analyzed ccr2 and ccr5 transcript levels by qPCR in the same samples after both experimental conditions and results were comparable between groups (Figure 5C). Similarly, no differences were found in CCR5 cell surface expression between groups after both non-specific stimulations (Figure 5D). Our data thus suggest that the R5-tropic resistance phenotype in ECr/VCr is limited to CD4 +T cells activated by anti-CD3/CD28 co-stimulation, which in vitro is the most physiological method of stimulation, short of using cognate antigen and antigen presenting cells.

Frequencies of Δ32CCR5 and promoter polymorphism in EC/VC with resistant phenotype

In order to exclude the possibility that the observed R5-tropic resistance in ECr/VCr was due to the ccr5 promoter polymorphism −2459 A/G (Hladik et al., 2005; Joshi et al., 2017), we analyzed the frequency of those genotypes in our populations. 76.5% of the Ctrls were A/G heterozygotes, with absence of the polymorphism in 23.5% of the Ctrl population. Interestingly, we only found A/G homozygotes in EC/VC population (8.51%). When analyzed as presence vs. absence of the polymorphism, we identified a lower frequency of homo +heterozygotes in EC/VCs (60.64%) compared to Ctrl (p=0.03). Although a significantly lower frequency was also observed in ECr/VCr (52.38%; p=0.04) compared to Ctrl, we did not observe a significant difference between ECr/VCr and remaining EC/VC (p=0.41). Thus, the presence of this known promoter polymorphism does not contribute to the R5 resistance phenotype in the ECr/VCr population.

By PCR and agarose gel electrophoresis we also analyzed the frequencies of Δ32CCR5 in our cohort (Samson et al., 1996; Rappaport et al., 1997), with 14.8% of the Ctrl (4 of 27) being ∆32CCR5 heterozygotes. We did observe a higher frequency of Δ32CCR5 heterozygotes in ECr/VCr (33.33% or 7/21) compared to remaining EC/VCs (18.42%, 14/76; p=0.027), suggesting that the presence of this variant contributes in part to the R5 resistance phenotype observed in ECr/VCr subset.

ATAC-Seq identifies open chromatin regions in ECr/VCr

Given the reduced ccr2/ccr5 RNA levels observed in ECr/VCr, we decided to examine whether there were differences in chromatin accessibility in this region of chromosome 3, inclusive of ccr2 and ccr5 (chr3:45,920,704–46,497,303). DNA libraries were prepared in activated CD4 +T cells from ECr/VCr (n = 4 replicates) and compared to Ctrl samples (n = 4 replicates) and Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-Seq) was performed to quantify differences in open chromatin. Our results identified 64 peaks enriched in ECr/VCr compared to Ctrl (Figure 6A), consistent with ~500 kb of highly accessible chromatin in this region of 3p21 in ECr/VCr patients. We explored a small region including ccr2 and ccr5 (chr3:46,392,331–46,418,348), and we identified more open chromatin in the ccr2- and ccr5-promoter regions in ECr/VCr compared to Ctrl (Figure 6A).

Figure 6 with 1 supplement see all
Increased chromatin accessibility and lower active transcription in activated CD4 +T cells from ECr/VCr.

(A) Left panel: ATAC-Seq coverage profiles of region of chr 3p21 (45,920,704-46,497,303) of ECr/VCr CD4 +T cells, compared to those of Ctrl (n = 4 replicates per group). Heat map showing gene TSS aligned, with a window of −250 bp to +250 bp, calculated as a normalized coverage around each TSS. Matrix was divided it into two clusters, based upon Ctrl data. At top is average coverage profile for each of the clusters (cluster one in red and cluster two in green). Right panel: ATAC-Seq peaks of chr 3p21 (46,392,331-46,418,348) of ECr/VCr vs Ctrl visualized using Integrated Genome Browser (IGB), see also Figure 6—figure supplement 1. Green arrows highlight increased peaks near the TSS of both genes, ccr2/ccr5, in ECr/VCr relative to Ctrl. (B–C) ChIP-qPCR, using either Tri-Methyl-Histone H3 (Lys4) (B) or Rpb1 (C) antibodies, with ccr2 and ccr5 DNA quantified by qPCR. Data normalized by the % total input DNA. Shown are Means ± SD (n = 4 and n = 5 per group in B and C, respectively), with statistical analysis performed using Kruskal-Wallis with Dunn’s multiple-comparison test. *p<0.05. (D) Quantitation of mRNA half-lives of indicated genes in activated CD4 +T cells, using Act D as a transcription inhibitor. T cells were incubated with Act D and harvested (from time 0 to 8 hr). RNA was extracted, and RNA levels quantified by RT-qPCR and half-life calculated using GraphPad PRISM software.

https://doi.org/10.7554/eLife.44360.012

We also examined chromatin accessibility both upstream and downstream of this ~500 kb region. The coverage matrices of clusters 1 and 2 (upstream) showed a slight increase in ECr/VCr compared to Ctrl whereas there were no observable differences in the downstream ATAC-Seq peaks (Figure 6—figure supplement 1). These results suggest that the increase in chromatin accessibility is relatively specific to the ~500 kb region encompassing ccr2 and ccr5 in ECr/VCr.

In order to confirm the increased chromatin accessibility in ECr/VCr, we analyzed by ChIP ccr2 and ccr5 DNA levels using Tri-Methyl Histone H3 (Lys4) antibody (H3K4Me3) and qPCR (Figure 6B). We saw a trend towards greater H3K4Me3 levels in ECr/VCr compared to EC/VC and Ctrl, although differences were not significant (ccr5: p=0.42 and p=0.12, respectively). These data, taken together, demonstrate that the down-regulation of ccr2/ccr5 mRNA levels is accompanied by an increase in open chromatin in ECr/VCr in a specific region of 3p21.

CCR5 transcriptional down-regulation in ECr/VCr

To determine whether the down-regulation of ccr2/ccr5 RNA in ECr/VCr was attributable to a decrease in active transcription, we performed ChIP in activated CD4 +T cells using antibodies against Rpb1 CTD, the carboxy terminal domain of the large subunit of RNA polymerase II, followed by qPCR. We observed lower ccr5 DNA levels in chromatin samples from ECr/VCr (0.069 ± 0.02) compared to those of EC/VCs without the resistance phenotype (0.36 ± 0.21; p=0.02) and Ctrl (0.27 ± 0.13; p=0.03; Figure 6C). We also observed comparable results with ccr2, with decreased DNA levels in chromatin samples from ECr/VCr (0.09 ± 0.01) compared to remaining EC/VCs (0.36 ± 0.21; p=0.04) and Ctrl (0.22 ± 0.07; p=0.02). These data are consistent with reduced transcriptional initiation or activity of ccr2/ccr5 in ECr/VCrs compared to remaining EC/VCs and Ctrl.

We next determined whether the differences in ccr2/ccr5 RNA levels were a result of changes in RNA stability. Activated CD4 +T cells were incubated in presence of Actinomycin D for varying lengths of time, RNA isolated, RT-qPCR performed, and RNA half-life calculated from the decay curves for ECr/VCr, Ctrl, and remaining EC/VC populations. We observed comparable half-lives of ccr2, ccr5, and gapdh RNAs in CD4 +T cells from ECr/VCr, Ctrl, and remaining EC/VC groups (Figure 6D), indicating that the down-regulation of ccr2/ccr5 RNA in ECr/VCr was likely a result of differences in transcriptional initiation, rather than due to changes in RNA stability, consistent with the Rpb1 ChIP results above.

Down-regulation of ccr2/ccr5 RNA levels in family members of an index VC with R5-tropic resistance

To determine whether there is a hereditary basis associated with R5 resistance, we recruited family members of an index VCr and investigated whether the associated CD4 +T cells had the same in vitro phenotype. Activated CD4 +T cells from several ATL2 family members were infected with pseudotyped viral particles of varying tropisms, and viral susceptibility analyzed by flow cytometry. We observed resistance specific to R5-tropic virus in the T cells of two of three ATL2 family members analyzed, with full susceptibility to X4- and VSV G-pseudotyped HIV (Figure 7A and B).

Pedigree analysis of an Index VC with R5 resistance phenotype.

(A) Resistance specific to R5-tropic virus, with equivalent susceptibility to X4- and VSV G, in activated CD4 +T cells from 2 of 3 analyzed ATL2 VC family members. Shown are pooled results from different experiments, with samples tested at least in triplicate. Statistical differences between ECr/VCr and other groups (Ctrl, EC/VC, and ATL2 FMnr) are also shown (**). (B) Pedigree analysis of ATL2 EC. Red are individuals with the R5 resistance phenotype (ATL2 FMr); grey represents full susceptibility to infection (ATL2 FMnr); black not available for testing. (C) Decreased ccr2/ccr5 RNA levels in activated CD4 +T cells from family members with R5 resistance. Samples were tested in duplicate. (D) Decreased CCR2 and CCR5 surface expression in resting (NS) and activated CD4 +T cells in family members with the resistance phenotype. Samples were tested at least in duplicate; shown are individual values with Mean ±SD. Statistical analysis was performed by using the U-Mann Whitney test or Kruskal-Wallis with Dunn’s multiple-comparison test. *p<0.05; **p<0.01. FMr: family member with R5 resistance. FMnr: family member without R5 resistance.

https://doi.org/10.7554/eLife.44360.014

This included the mother and daughter, but not the son. Other family members were not available for testing. Of note, by self-report all family members were HIV seronegative and we were not allowed to do further testing. The percentage of infected cells using R5-tropic virus was significantly lower in activated CD4 +T cells of ATL2 and some family members (ATL2 +FMr 0.51 ± 0.24) compared to those of Ctrl (1.58 ± 0.66; p=0.0015), EC/VC (1.42 ± 0.72; p=0.007), and the family member without the phenotype, FMnr (1.05 ± 0.1; p=0.04).

We next asked whether the observed phenotype seen in family members was associated with down-regulation of ccr2 and ccr5 RNA and other genes. RNA-Seq data identified 315 genes significantly differentially expressed between ATL-2 and ATL-2 FMr, compared to Ctrl and ATL-2 FMnr. A complete list of the genes is included in Supplementary file 2. More than half (51%, 160/315) were significantly down-regulated in activated CD4 +T cells from ATL2 and FMr compared to Ctrl and FMnr, including ccr2 and ccr5 and several genes in 3p21. RT-qPCR confirmed down-regulation in ccr2/ccr5 RNA levels in activated CD4 +T cells from ATL2 and FM with R5-resistance phenotype (ccr2: FMnr 0.24 ± 0.01 vs ATL2 +FMr 0.05 ± 0.02; p=0.03 and ccr5: FMnr 0.17 ± 0.005 vs ATL2 +FMr 0.03 ± 0.01; p=0.05, Figure 7C). By flow cytometry, we also measured CCR2 and CCR5 cell surface expression in non-stimulated and stimulated CD4 +T cells in EC/VC family members with and without the R5 resistance phenotype (Figure 7D). The expression of CCR5 in activated CD4 +T cells from those family members with the resistant phenotype was significantly reduced (% CCR5 +FMnr 12.33 ± 1.55 vs FMr 7.48 ± 2.57; p=0.02). These data point towards a hereditary basis of R5-tropic resistance, at least for the ATL2 pedigree, and that the observed CCR2/CCR5 down-regulation is genetic in nature.

Discussion

Here we studied CD4 +T cells purified from PBMCs of 131 EC/VCs and identified a subset of HIV EC/VCs whose T cells were relatively resistant to infection by R5-tropic pseudotyped viral particles, in single cycle, cell-based in vitro assays. This R5-resistance phenotype was associated with transcriptional down-regulation of both ccr2 and ccr5. This same phenotype was observed in family members of an index VC with R5 resistance, and it was also associated with ccr5 RNA and protein down-regulation, providing strong evidence for a hereditary basis of the phenotype.

The in vitro R5 resistance phenotype was most strongly observed after CD4 +T cell co-stimulation. In agreement with our results, prior studies have demonstrated that PHA-activated CD4 +T cells from ECs were susceptible to both R5- and X4-tropic HIV infection (Blankson et al., 2007; Bailey et al., 2006; Sáez-Cirión et al., 2010). Other groups have demonstrated that anti-CD3-activated CD4 +T cells from ECs were resistant to HIV infection, independent of co-receptor usage (Chen et al., 2011; Sáez-Cirión et al., 2011; Paxton et al., 1996; Saha et al., 1998; Yu and Lichterfeld, 2011). Only one prior report, from our group, observed T cell resistance specific to R5-tropic virus (Walker et al., 2015), and the current results are consistent with those data. Our prior study, however, suggested the mechanism was mediated by increased chemokine produced and secreted by activated CD4 +T cells, which would then confer resistance by sterically interfering with Env binding to co-receptor (Saha et al., 1998).

In the experiments here, performed on a much larger scale compared to our initial report, chemokine RNA and protein levels were actually decreased in CD4 +T cells of EC/VCs with the R5 resistance phenotype, suggesting that another mechanism was operational. It should be pointed out that there was some overlap in subjects between the two studies. Despite repeated testing, we did not confirm increased chemokine expression in EC11, but instead down-regulation of both ccr2 and ccr5 RNA. Of note, this sample was obtained at a later time point, perhaps explaining the observed differences. We also included VCs in the current report, and they were excluded from the previous study. Prior investigations have suggested that CD4 +T cells from ECs retain the ability to proliferate and produce IL-2 in response to HIV (Emu et al., 2005) and are highly activated (Bello et al., 2009). EC/VCs with the R5-tropic resistant phenotype expressed significantly lower levels of the early activation marker CD69. There were no differences, however, in levels of the late activation marker CD25, which is when the T cells were infected. In addition, those T cells remained fully susceptible to VSV G- and X4 Env-pseudotyped HIV, thus the significance of the subtly lower CD69 levels in the T cells of the ECr/VCr subset is not known.

Of interest was the fact that we observed the R5 resistance phenotype only in activated CD4 +T cells and not MDMs. The observed phenotype correlates with ccr2/ccr5 RNA down-regulation in CD4 +T cells, whereas in MDMs there was no down-regulation of those two co-receptor genes, demonstrating a strong correlation between resistance to R5 tropic viruses and down-regulation of ccr2/ccr5. It is known that there are large differences in the transcriptional profiles between T cells and MDMs (Woelk et al., 2004; Xue et al., 2014), and even in T cells different activation protocols result in altered gene expression patterns (Marrack et al., 2000; Xu et al., 2013). Thus, it is quite conceivable that non-specific T cell stimulation leads to production of transcription factors not present after co-stimulation, resulting in altered RNA and cell-surface levels of CCR5.

The presence of the homozygous CCR5Δ32 mutation confers protection against mucosal HIV infection (Liu et al., 1996; Samson et al., 1996), and heterozygotes have slower disease progression (Rappaport et al., 1997; Rodés et al., 2004). That the frequency of CCR5Δ32 ± was significantly higher in EC/VCs with the R5-resistance phenotype compared to other ECs suggests heterozygosity could contribute in part to the R5 resistance phenotype, likely by inactivating one ccr5 allele and decreasing cell surface expression. Here, we also observed that both ccr2 and CCR5 mRNA and cell surface protein levels were down-regulated in ECr/VCrs, supporting the idea that the R5 resistance phenotype is mediated by a transcriptional mechanism. It is unlikely that CCR5Δ32 affects mRNA levels since nonsense-mediated decay of RNA is not operational if the stop codon is present in the last exon, as it is here. In addition, several lines of evidence presented here favor a transcriptional mechanism for the RNA down-regulation of ccr2/ccr5. There was no difference in the half-lives of these RNAs in activated T cells, and ChIP-qPCR data using anti-Rpb1 demonstrated decreased levels of active transcription on ccr2/ccr5 in ECr/VCrs. Rpb1 is the largest subunit of RNA polymerase II and its presence on DNA correlates strongly with active transcription (Shin et al., 2016; Brookes and Pombo, 2009; Phatnani and Greenleaf, 2006).

Two decades ago, the cis- and trans-acting sequences and factors influencing ccr5 transcription were studied, and a promoter upstream of ccr5 was localized and dissected by functional assays (Liu et al., 1998; Mummidi et al., 1997). Given the lack of upstream sequence conservation and distance of >10 kb, it is highly unlikely that those DNA sequences and transcription factors would also modulate ccr2 expression. In addition, we observed decreased RNA levels of multiple genes spanning ~500 kb of 3p21, both centromeric and telomeric to ccr2/ccr5, consistent with a more global and coordinate down-regulation of multiple chemokines and their receptors in the activated CD4 +T cells from ECr/VCr.

ATAC-Seq is an established method for quantifying chromatin accessibility in different cell populations (Corces et al., 2016). Previous reports have suggested that histone modifications upstream of coding regions play a role in transcriptional regulation (Bernstein et al., 2002). In general, H3K4Me3 is associated with open chromatin, specifically marking the promoters of active genes, and correlates with higher levels of transcripts (Heintzman et al., 2007; Bernstein et al., 2005). In our study, however, we observe CD4 +T cells from ECr/VCr have more open chromatin over ~500 Kb region in chr3, including ccr2 and ccr5, which surprisingly was associated with lower transcription of both genes. It had also been shown that levels of DNA methylation in the ccr5 locus correlated inversely with CCR5 levels on T cells (Gornalusse et al., 2015), which is also a typical transcriptional control mechanism. The fact that CD4 +T cells of ECr/VCr have decreased transcriptional initiation/transcript levels of ccr2/ccr5 and yet more open chromatin suggests that there is a dissociation between chromatin access and transcription of these genes, for inapparent reasons.

Interestingly, two of the three family members of an Index VCr had CD4 +T cells with a similar R5 resistance phenotype, with associated down regulation of CCR5 RNA and protein levels. The fact that it was multi-generational and in both sexes is highly suggestive but is not definitive evidence that the phenotype is autosomal dominant. Additional family studies will be necessary to determine whether the R5 tropic resistance phenotype has hereditary dominance. Autosomal dominant inheritance would be consistent with altered cell signaling or DNA binding factor, acting in a trans-dominant fashion and negatively influencing transcription of both ccr2/ccr5 alleles (Liu et al., 1998). Precedents include naturally-occurring dominantly suppressive variants of human stat5 (Crotti et al., 2007; Yamashita et al., 2003), those of human stat6 that are amino terminus truncated for the SH2 domain (Mikita et al., 1996; Patel et al., 1998), or an alternatively spliced form of human stat3 that functioned as a dominant negative regulator of transcription (Zammarchi et al., 2011). The JAK/STAT signaling pathway is important for expression of multiple chemokines and their receptors, including ccr5, and becomes activated after T cell co-stimulation (Shuai and Liu, 2003; Wong and Fish, 1998; Zi et al., 2017). It is an open question whether T cell co-stimulation leads to the production of a dominant-negative transcription factor in the ECr/VCr subset, resulting in reduced ccr2/ccr5 or more global transcriptional down-regulation.

LOC102724291 is transcribed antisense to ccr5 and it has been suggested that loc102724291 may contribute to virus set-point (McLaren et al., 2015). Our results revealed a down-regulation in loc102724291 RNA levels in activated T cells in ECr/VCr, with no correlation between ccr2 and ccr5 gene expression, making it unlikely that it is modulating the expression of those genes. Without invoking a more global mechanism of transcriptional control, it is difficult to understand how loc102724291 would be capable of inhibiting transcription of other genes in that chromosome region. Instead, it appears that lncRNA may be similarly down-regulated to other genes in the region.

In conclusion, our data suggest that the R5-tropic resistance phenotype seen in a subset of EC/VCs is associated with transcriptional down-regulation of ccr5, which appears to be heritable, across multiple generations. That the chromatin of this region of 3p21 appears to be more accessible yet multiple genes are down-regulated implies a complex but coordinate mode of transcriptional regulation. Because these ECs are able to persistently suppress viral replication, further investigation into the mechanisms underlying these findings should inform the HIV cure effort.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or referenceIdentifiersAdditional
information
Antibodyanti-CD3 mouse Monoclonal Antibody (OKT3), PerCP-Cyanine5.5eBioscienceCat # 45-0037-42; RRID: AB_10548513Dilution (1:100)
Antibodyanti-CD4 mouse Monoclonal Antibody (RPA-T4), APCeBioscienceCat # 17-0049-42; RRID: AB_1272048Dilution (1:100)
Antibodyanti-CD14 mouse Monoclonal Antibody (61D3), FITCeBioscienceCat # 11-0149-42; RRID: AB_10597597Dilution (1:100)
Antibodyanti-CD8a mouse Monoclonal Antibody (HIT8a), PEeBioscienceCat # 12-0089-42; RRID: AB_10804039Dilution (1:100)
AntibodyCD3 mouse Monoclonal Antibody (OKT3), Functional GradeeBioscienceCat # 16-0037-81; RRID: AB_46885410 µg/ml
AntibodyCD28 mouse Monoclonal
Antibody (CD28.2), Functional Grade
eBioscienceCat # 16-0289-81; RRID: AB_4689264 µg/ml
AntibodyCD25 mouse Monoclonal Antibody (BC96), PEeBioscienceCat # 12-0259-42; RRID: AB_1659682Dilution (1:200)
AntibodyCD69 mouse Monoclonal Antibody (FN50), FITCeBioscienceCat # 11-0699-42; RRID: AB_10853975Dilution (1:200)
AntibodyCD45RA mouse Monoclonal Antibody (HI100), FITCeBioscienceCat # 11-0458-42; RRID: AB_11219672Dilution (1:100)
AntibodyCD45RO, mouse Monoclonal PE-Cyanine5, clone: UCHL1eBioscienceCat # 15597726; Gene ID: 5788Dilution (1:100)
AntibodyPE anti-human CD195 (CCR5) rat Monoclonal AntibodyBiolegendCat # 313707; RRID: AB_345307Dilution (1:100)
AntibodyAPC anti-human CD192 (CCR2) mouse Monoclonal AntibodyBiolegendCat # 357207; AB_2562238Dilution (1:100)
Antibodyanti-Rpb1 CTD mouse MonoclonalCell SignalingCat # 2629; 4H8ChIP (1:50)
AntibodyTri-Methyl-Histone H3-Lysine 4 (H3Lys4) rabbit MonoclonalCell SignalingCat # 9727ChIP (1:50)
Peptide, recombinant
protein
Recombinant Human IL-2E. coli-derived human IL-2 proteinR and D: P60568
Recombinant DNA reagentHIV-cycT1-IRES-YFP (HIV-CIY)this paperSutton labplasmid
Recombinant DNA reagentpSM-ADA Envthis paperSutton labplasmid
Recombinant DNA reagentpSRα-YU2 Envthis paperHeinrich Gottlinger, UMass Medical Cenerplasmid
Recombinant DNA reagentpSRα-NL4-3 Envthis paperHeinrich Gottlinger, UMass Medical Cenerplasmid
Recombinant DNA reagentpME-VSV Gthis paperSutton labplasmid
Recombinant DNA reagentpCCL3L1OrigeneNM_021006.4, NP_066286plasmid
Recombinant DNA reagentpCCL4this papergenerated by PCR using pcDNA3/1 + CAT plasmid; Sutton labplasmid
Recombinant DNA reagentVpx-myc-hisNed Landau laboratory, NYU Medical Centerplasmid
Recombinant DNA reagentpMDL-Chp6Ned Landau laboratory, NYU Medical Centerplasmid
Cell line (H. Sapiens)HEK 293TATCCCat# CRL-3216, RRID:CVCL_0063
Cell line (H. Sapiens)GHOST.Hi5NIH AIDS Reagent ProgramNIH-ARP Cat# 3944–343, RRID:CVCL_1E17
Cell line (H. Sapiens)GHOST.CXCR4NIH AIDS Reagent ProgramNIH-ARP Cat# 3685–448, RRID:CVCL_S492
Cell line (H. Sapiens)TZM-bl cellsNIH AIDS Reagent ProgramNIH-ARP Cat# 8129–442, RRID:CVCL_B478
Commercial assay or kitRNeasy Mini KitQiagenID: 74104
Commercial assay or kitMouse MIP-1 alpha (CCL3) ELISAInvitrogenLS885601322
Commercial assay or kitHuman CCL4 (MIP-1 beta) ELISAInvitrogenInvitrogen 88703476
Commercial assay or kitHigh-Capacity cDNA Reverse Transcription KiThermoFisherID: 4368814
Commercial
assay or kit
DNeasy blood and tissue kitQiagenCat No./ID: 69504
Commercial assay or kitSimpleChIP enzymatic ChIP kit agarose beadsCell SignalingCat #9002
Commercial assay or kitMinElute Reaction Cleanup kitQiagenCat No./ID: 28204
Commercial assay or kitTransposase mixtureIlluminaNextera DNA library prep kit; FC-131–1024
Chemical
compound, drug
Phorbol 12-myristate 13-acetateSigmaPubChem CID: 27924
Chemical compound, drugIonomycin calcium saltSigmaI3909
Chemical
compound, drug
Actinomycin DSigma. From Streptomyces spCat # A1410
Chemical
compound, drug
DigitoninPromegaG944A
OtherPower SYBR Green PCR Master MixThermoFisherCat # 4367659Commercial reagent
OtherNEBnext PCR master mixNew England BioLabsCat # M0541SCommercial reagent
Software, algorithmCummeRbundR package version 2.24.0DOI: 10.18129/B9.bioc.cummeRbund
Software, algorithmIllumina's CASAVA 1.8.2IlluminaRef. 15011197
Software, algorithmGraphPad PrismGraphPad Prism (https://graphpad.com)RRID:SCR_015807
Software, algorithmFlowJohttps://www.flowjo.com/solutions/flowjoRRID:SCR_008520

Study subjects

131 HIV +EC/VC subjects were recruited from Yale New Haven Hospital and other HIV clinics in USA. Inclusion criteria for EC/VCs were HIV seropositivity and plasma VL < 50 (ECs) or 50 < VL < 2000 (VCs) for at least 6–12 months in the absence of ART, except in some special circumstances, as specified (Supplementary file 1). Occasional viral blips were allowed but not virologic escape or clear trends in viremia. Exclusion criteria included contraindication to peripheral phlebotomy and inability to provide informed consent. Clinical characteristics recorded included gender, age, CD4 +T cell count, VL, and year of HIV diagnosis. Also, HIV acquisition risk factor, major comorbidities, and protective HLA alleles data were collected, if known. The study was approved by both the Yale IRB (Yale New Haven Hospital and other Yale-affiliated HIV clinics in Connecticut), and the local IRBs (the SCOPE cohort from UCSF, the Ragon Institute of MGH, MIT and Harvard, and from Veterans Medical Center HIV clinics from Atlanta and Dallas) and informed, written consent was obtained from all subjects.

Anonymized, leukocyte-enriched fractions of peripheral blood from 35 normal, healthy donors were obtained and used as controls. Three family members (FM) of an Index VC (Atl2) were enrolled and whole blood obtained by peripheral phlebotomy. Based upon self-report, all FM included in the study were HIV seronegative. CFAR relies on self-reporting with respect to HIV-uninfected cases. Our IRB protocol did not allow us to perform HIV testing on FM because of privacy concerns.

Peripheral blood mononuclear cell collection and CD4 +T cell purification

Mononuclear cells were obtained after Ficoll-Paque PLUS (GE Healthcare Life Sciences, Piscataway, NJ) centrifugation of leukocyte-enriched fractions of whole blood. CD4 +T cells were purified by positive selection, using anti-CD4 magnetic microbeads (Miltenyi Biotech, San Diego, CA) following the manufacturers’ recommendations. The purity of the CD4 +T cells was confirmed by flow cytometric analysis using anti-human CD3-PerCP-Cyanine5.5 (clone OKT3; eBioscience, San Diego, CA) and CD4-APC (clone RPA-T4; eBioscience) antibodies. Antibodies against human CD14 and CD8 were included to confirm absence of contaminating monocytes and CD8 +T cells (anti human CD14-FITC, clone 61D3; anti-human CD8a-PE, clone HIT8a; eBioscience). Purity of CD4 +T cells was >95%. The remaining cells were predominantly CD4-low monocytes with <1% contaminating CD8 +T cells. T cells were resuspended in staining buffer (2% FBS in PBS) on ice for 30 min, washed, and then placed in IC fixation buffer (eBioscience) on ice for 10 min. Cells were washed, resuspended in staining buffer, and analyzed by flow cytometry (LSRII, BD; Franklin Lakes, NJ). Data were analyzed using FlowJo software (version 10.1 Ashland, OR).

CD4 +T cell activation and staining

CD4 +T cells were activated for 72 hr, using tissue culture plates pre-coated with 1 µg/mL anti-CD3 (clone OKT3; eBioscience) in the presence of 2 µg/mL soluble anti-CD28 (clone 28.2; eBioscience) and 100IU/mL IL-2 (recombinant, R and D Systems, Minneapolis, MN). To check activation status, activated CD4 +T cells were analyzed by light microscopy to confirm refractility and aggregation. The percentage of activated cells was calculated by flow cytometry as above, using anti-human CD25-PE (clone BC96) and CD69-FITC (clone FN50; eBioscience) antibodies. Percentage of naïve and memory CD4 +T cells was analyzed using anti-human CD45RA-FITC (clone HI100) and CD45RO-PeCy5 (clone UCHL1; eBioscience), respectively. To differentiate CM from EM T cells, activated CD4 +T cells were stained with CD45RO-PeCy5 and CD27-FITC (clone M-T271; BD) and analyzed by flow cytometry. To assess CCR2 and CCR5 cell surface levels, non-activated and activated CD4 +T cells were stained for 30 min with fluorescently labeled antibodies against human CD195-PE (CCR5; clone HEK/1/85a; Biolegend, San Diego, CA) or CD195-APC (clone 3A9; BD), and CD192-APC (CCR2; clone K036C2; Biolegend). PE-rat IgG2a, k (clone RTK2758) and APC-mouse IgG2a, k (clone MOPC-173) antibodies were used as isotype controls (Biolegend). Cells were fixed, resuspended in 2% FBS in PBS, and analyzed by flow cytometry as percentage of positive cells and as MFI.

Alternatively, T cells were activated using 1 mg/ml phytohaemagglutinin (PHA; Sigma-Aldrich, St. Louis, MO), or 10 ng/ml PMA (Sigma) plus 500 ng/ml ionomycin (Sigma) for 72 or 48 hr, respectively, in the presence of 100IU/ml IL-2.

Cell transfection, virus production and single cycle HIV infection

Pseudotyped lentiviral particles were produced by transient transfection of 293 T cells using the calcium phosphate method and the following plasmids: HIV-cycT1-IRES-YFP (HIV-CIY) as packaging/transfer vector, pSM-ADA Env and pSRα-YU2 Env (both R5-tropic), and pSRα-NL4-3 Env (X4-tropic), with pME-VSV G (pan-tropic control). Viral particles were harvested 72 hr after transfection and frozen after confirming the efficiency of the transfection by flow cytometry and fluorescence microscope observation. Vector supernatants were tested on GHOST HI5 (R5-tropic) or GHOST CXCR4 (X4-tropic) cells by end-point dilution and also by flow cytometry, with a range of infectivity between 2.5 × 105 U/ml to 3.0 × 106 U/ml. VSV G pseudotyped particles were used as positive control, with an infectivity of ~2.5×107 U/ml. For normalization purposes, for each pseudotyped virus the same amount of IU was used to infect activated CD4 +T cells in the same total volume and plate format by spinoculation at 1800 rpm for 30 min, and at 72 hr percentage of YFP+T cells was quantified by flow cytometry.

HIV replication-competent assay

1 × 105 activated CD4 +T cells (anti-CD3/CD28) were infected in triplicate with 0.001 ml of pNL-BaL or 0.01 ml of HIV-NL4-3ΔR1, in the presence of IL-2ample l of PBS and lysis in 201 CD28)U using Luciferase assay.se to infect TzmBL cells . Both of these viruses were prepared by plasmid co-transfection of 293 T cells with pME VSV G, to facilitate initial rounds of viral replication. On alternate days post-infection (from day 1 to 21), supernatant was removed, centrifuged, and used to infect 10,000 TZM-bl cells (obtained from the NIH AIDS Reagent Program). Reporter cells were harvested 72 hr post-infection, washed with 0.5 ml of ost-infection, akes, NJ). PBS and lysed in 0.2 ml of lysis buffer (25 mM Tris-phosphate (pH 7.8), 2 mM DTT, 2 mM 1,2-diaminocyclohexane-N,N,N´,N´-tetraacetic acid, 10% glycerol, and 1% Triton X-100). FFLUC assay was performed by incubating 0.1 ml of lysate with 0.1 ml of assay buffer (25 mM Gly-Gly, 15 mM potasium phosphate pH 7.8, 15 mM magnesium sulfate, 4 mM EGTA, 2 mM ATP and 1 mM DTT) and 0.015 ml Luciferin solution (0.2 mM, Sigma). Bioluminescence was immediately measured in a Gen5 (BioTek) Instrument (Winooski, VT).

Enzyme-linked immunosorbent assays and conditioned media transfer

CD4 +T cells were activated for 3 days with anti-CD3/CD28 in presence of IL-2 and culture supernatants were harvested and frozen at −80 degrees. Human MIP-1α (CCL3) and MIP-1β (CCL4) instant ELISA kits (eBioscience) were used to measure chemokine levels in culture supernatants, according to the manufacturer’s instructions. Media transfer experiments were performed to investigate whether soluble factors were responsible for the inhibition of HIV replication. Activated CD4 +T cells from healthy controls were incubated in presence of supernatant from activated CD4 +T cells from EC/VCs and Ctrl and T cells were then infected with different pseudotyped HIV particles. As control, we included supernatants from 293 T cells transfected with the following plasmids: (i) pCCL3L1 encoding MIP1α (Origene, Rockville, MD); (ii) pCCL4 encoding MIP1β (generated by PCR-amplifying the ccl4 coding sequence from human cDNA and ligating the product into pcDNA3/1 + CAT plasmid). After 30 min of incubation with culture supernatant, cells were infected with pseudotyped HIV particles. T cells were harvested after three days and infectivity was analyzed by flow cytometry for YFP conferred by virus infection.

RNA-Seq

High quality RNA was isolated from 1 × 106 activated CD4 +T cells (aCD3/CD28) using the RNeasy Mini kit (Qiagen, Germantown, MD). RNA integrity was verified by running an Agilent Bioanalyzer gel. For the RNAseq library preparation, mRNA was purified from total RNA with oligo-dT beads and sheared by incubation at 94 degrees. Following first-strand synthesis with random primers, second strand synthesis was performed with dUTP for generating strand-specific sequencing libraries. The cDNA library was then end-repaired, and A-tailed, adapters ligated, and second-strand digestion was performed by U-DNA-Glycosylase. Indexed libraries that meet appropriate cut-offs were quantified by qRT-PCR and insert size distribution determined with the LabChip GX or Agilent Bioanalyzer. Samples were sequenced using 75 bp single or paired-end sequencing on an Illumina HiSeq 2500 according to Illumina protocols. Signal intensities were converted to individual base calls during a run using the system's Real Time Analysis software. Multiplexing and alignment to the human genome was performed using Illumina's CASAVA 1.8.2 software. DNA sequence data generated were stored in FASTQ format and quality control was performed using FastQC version 0.10.1. Quality-filtered reads (low quality reads <20 were removed) were aligned to sequences of the human genome (hg19) downloaded from Illumina's iGenome resource (Illumina, San Diego, CA), as previously described (Garber et al., 2011). Reads were analyzed using Cuffdiff (Trapnell et al., 2012) in order to allow estimation of differential gene expression using functions of the R package ‘cummeRbund’.

Reverse transcription and real time quantitative PCR

RNA levels of ccr2, ccr5, cxcr4, cd4, ccr1, ccr3, fyco1, cxcr6 and loc102724297 were measured by real time quantitative PCR (RT-qPCR). Total RNA was extracted from activated CD4 +T cells using the RNeasy mini kit (Qiagen). A260/280 was determined to confirm the RNA was of high quality, and 1 μg was used for first-strand complementary DNA synthesis using High Capacity cDNA Transcription Kit (Life Technologies; Warrington, UK). Quantitative RT-PCR was performed on an Applied Biosystems 7500 Fast Real-Time PCR System using Power SYBR Green PCR Master Mix (Life Technologies) and the following primers:

  • ccr5-F:5’-AAAAAGAAGGTCTTCATTACACC-3’ and ccr5-R:5’-CTGTGCCTCTTCTTCTCATTTCG-3’;

  • ccr2-F:5'-CACATCTCGTTCTCGGTTTATC-3' and ccr2-R:5'-AGGGAGCACCGTAATCATAATC-3';

  • cd4-F:5’-TGCCTCAGTATGCTGGCTCT-3’ and cd4-R:5’-GAGACCTTTGCCTCCTTGTTC-3’;

  • cxcr4-F:5'-CTACACCGAGGAAATGGGCT-3' and cxcr4-R:5'-CCACAATGCCAGTTAAGAAGA-3';

  • fyco1-F:5’-CGCCTCACTTGCTTGGTAG 3' and fyco1-R:5’-CTGTGTGGTAGTCCTCCTCC-3';

  • cxcr6-F:5’-GACTATGGGTTCAGCAGTTTCA-3' and cxcr6-R:5’-GGCTCTGCAACTTATGGTAGAAG-3';

  • ccr1-F:5’-ACTATGACACGACCACAGAGT-3' and ccr1-R:5’-CAACCAGGCCAATGACAAATA-3';

  • ccr3-F:5’-GTCATCATGGCGGTGTTTTTC-3' and ccr3-R:5’-CAGTGGGAGTAGGCGATCAC-3';

  • loce1-2 F:5'-CTCACCAGTGTTCGCAGAAA-3' and loce1-2 R:5'-TCATGTAGGTGCAGGCAGAC-3’;

  • loce3-F:5’-GCATCTCACTGGAGAGGGTTT-3’ and loce3-R:5’-TTTGCAGAGAGATGAGTCTTAGC-3’;

  • gapdh-F:5'-TTGCCATCAATGACCCCTT-3' and gapdh-R:5'-CTCCACGACGTACTCAGCG-3'.

For relative quantification, we compared the amount of target to the values obtained for gapdh as a normalization control. Data obtained were compared to a standard curve generated by serial dilution of a template complementary DNA and expressed as target gene:gapdh ratios.

Overexpression of CCR5 in activated CD4+ T cells and single-cycle assay

To confirm that the R5-resistance to infection in EC/VC was due to down-regulation of CCR5, we overexpressed CCR5 in EC/VC T cells with R5-tropic resistance in comparison to those of EC/VC without the phenotype and Ctrl, and those T cells were then infected with HIV pseudotyped particles to determine whether they now had increased susceptibility to R5 virus. CD4+ T cells activated by anti-CD3/CD28 co-stimulation were first transduced with VSV G-pseudotyped HIV vector encoding both CCR5 and YFP (pHIV-CCR5-IRES-YFP) or YFP alone (HIV-IRES-YFP). T cells were then infected with an HIV vector encoding mRFP and pseudotyped with either R5 Envelopes or VSV G. After 72 hr, cells were analyzed by flow cytometry to quantify the percentage of double positive cells (YFP+/mRFP+), normalized to HIV-IRES-YFP transduction results.

CCR5∆32 and promoter polymorphism detection by PCR

Genomic DNA extracted from mononuclear cells was purified using DNeasy blood and tissue kit (Qiagen). CCR5 genotype (∆32 vs. WT) was determined by agarose gel electrophoresis following PCR using the following primers: CCR5 ∆32 F:5’-ATAGGTACCTGGCTGTCGTCCAT-3′; CCR5 ∆32 R:5′-GATAGTCATCTTGGGGCTGGT-3′ (de Roda Husman et al., 1997). Promoter polymorphism A/G −2459CCR5 was performed by restriction fragment length polymorphism analysis as previously described (McDermott et al., 1998), using the following primers CCR5 2459 F:5'-CCGTGAGCCCATAGTTAAAACTC-3'; CCR5 2459 R:5'-CACAGGGCTTTTCAACAGTAAGG-3'. PCR products were electrophoresed on a 2% agarose gel and genotypes were determined by visual inspection of ethidium bromide stained banding pattern.

Measurement of mRNA stability

CD4 +T cells activated by anti-CD3/CD28 co-stimulation were treated with 5 µg/ml Actinomycin D (Sigma) for varying lengths of time. ccr2, ccr5, and gapdh mRNA levels were quantified at each time point by RT-qPCR using SYBR Green. mRNA decay and half-lives were calculated using a time-point standard curve.

ATAC-Seq

ATAC-Seq was performed as previously described (Buenrostro et al., 2015), with some modifications. CD4 +T cells were activated with anti-CD3/CD28 in presence of IL-2 for 3 days. 50,000 cells were lysed and transpositions were performed using transposase mixture (Nextera DNA library prep kit, Illumina), supplemented with 0.01% digitonin (Promega; Madison, WI). Transposition reactions were incubated for 30 min at 37°C in a ThermoMixer (Eppendorf) with agitation at 300 rpm. DNA was purified using the MinElute Reaction Cleanup kit (Qiagen), and libraries amplified using NEBnext PCR master mix with the following primers:

  • Ad1_noMX:AATGATACGGCGACCACCGAGATCTACACTCGTCGGCAGCGTCAGATGTG;

  • Ad2.1_TAAGGCGA:CAAGCAGAAGACGGCATACGAGATTCGCCTTAGTCTCGTGGGCTCGGAGATGT;

  • Ad2.2_CGTACTAG:CAAGCAGAAGACGGCATACGAGATCTAGTACGGTCTCGTGGGCTCGGAGATGT;

  • Ad2.3_AGGCAGAA:CAAGCAGAAGACGGCATACGAGATTTCTGCCTGTCTCGTGGGCTCGGAGATGT;

  • Ad2.4_TCCTGAGC:CAAGCAGAAGACGGCATACGAGATGCTCAGGAGTCTCGTGGGCTCGGAGATGT;

  • Ad2.5_GGACTCCT:CAAGCAGAAGACGGCATACGAGATAGGAGTCCGTCTCGTGGGCTCGGAGATGT;

  • Ad2.6_TAGGCATG:CAAGCAGAAGACGGCATACGAGATCATGCCTAGTCTCGTGGGCTCGGAGATGT.

Libraries were quantified using RT-qPCR prior to sequencing. All Fast-ATAC libraries were paired-end sequenced, 75 bp using a HiSeq2500 instrument. Quality of FASTQ files was performed using FASTX trimmer. More than 50 million reads were mapped, with <10% mapped, on average, to the mitochondrial genome. The reads were aligned to the hg19 (UCSC) version using Burrows-Wheeler Aligner (BWA-MEM). Peaks were called using MACS2 (Zhang et al., 2008) peak-caller, and the reads from input DNA sample were used as control. Visualization of the peaks was done using R Software.

Chromatin immunoprecipitation-qPCR

Chromatin immunoprecipitation (ChIP) was performed using SimpleChIP enzymatic ChIP kit agarose beads (Cell Signaling) according to the manufacturer’s protocol. Three million CD4 +T cells were activated for 3 days with anti-CD3/CD28. Cells were fixed, and chromatin was sonicated after digestion with micrococcal nuclease. IP was performed with anti-Rpb1 CTD (4H8; Cell Signaling, #2629) or anti-Tri-Methyl-Histone H3-Lysine 4 (H3Lys4) mouse monoclonal antibody (Cell Signaling, #9727), with Histone H3 XP and rabbit IgG serving as positive and negative controls, respectively. DNA was purified by spin column, measured, and amplified by RT-qPCR to quantify ccr2 and ccr5 DNA. Primers for gapdh were used as a control.

Generation of human monocyte-derived macrophages and infectivity assays

Mononuclear cells were obtained via peripheral phlebotomy and Ficoll-Paque density gradient centrifugation. Monocytes were purified using anti-human CD14 +microbeads (Miltenyi). Cell purity was confirmed by flow cytometry using anti-CD14-FITC antibody (eBoscience). To differentiate monocytes to macrophages, monocytes were cultured for 7 days in RPMI 1640 supplemented with 10% FBS and 10 ng/ml M-CSF (eBioscience), adding fresh growth factor every 2 days. CCR2 and CCR5 cell surface expression was assessed by FACS analysis. Macrophages were then infected using HIV-CIY prepared with Vpx-myc-his and pMDL-Chp6 (kind gifts of Ned Landau, NYU Medical Center), pseudotyped with either R5 Envelope or VSV G. Macrophages were analyzed by flow cytometry after 72 hr to determine infection efficiency.

Cell lines

HEK 293 T cells were originally obtained from ATCC and authenticated by transfection testing in vitro, their gross morphology, resistance to 1 mg/ml G418, susceptibility to first generation adenoviral vectors, and growth characteristics. GHOST.Hi5 and GHOST.CXCR4 cells were obtained from the NIH AIDS Reagent Program. Their identity was authenticated by gross morphology, growth characteristics, expression of eGFP after infection with HIV of the appropriate tropism, confirmation of CCR5 (GHOST.Hi5) and CXCR4 (GHOST.CXCR4) cell surface expression by flow cytometry, and also testing for CD4 expression (both lines). TZM-bl cells were also obtained from the NIH AIDS Reagent Program and authenticated by gross morphology and growth characteristics, cell surface expression of both co-receptors and CD4 by flow cytometry, and susceptibility in vitro to HIV, with readout being both FFLUC activity in infected cell lysates and lacZ expression in fixed cells, the latter using X-Gal. All cell lines were tested to confirm absence of mycoplasma contamination.

Statistics

Correlations between mRNA and cell surface expression levels, and percentage of infected CD4 +T cells were assessed by Spearman´s test. Statistical differences between groups were determined using Mann-Whitney U test for two independent samples or one-way ANOVA using Kruskal-Wallis non-parametric test, as required. Frequencies of HLA alleles and presence of polymorphisms were compared between groups using Chi-Square analysis. Power calculations for sample comparisons were determined based on the comparisons of means/proportions using PASS statistical software. Analysis was performed using GraphPad PRISM (version 7.01; CA, USA), Minitab Statistical (version 17) and/or R Softwares. P values for pairwise tests, or multiplicity-adjusted post-tests of selected pairs, are reported in the Figure Legends. p<0.05 was considered significant.

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Decision letter

  1. Wendy S Garrett
    Senior Editor; Harvard TH Chan School of Public Health, United States
  2. Frank Kirchhoff
    Reviewing Editor; Ulm University Medical Center, Germany
  3. Frank Kirchhoff
    Reviewer; Ulm University Medical Center, Germany
  4. Felipe Diaz-Griffero
    Reviewer

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Transcriptional down-regulation of ccr5in a subset of HIV+ controllers and their family members" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Frank Kirchhoff as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Michel Nussenzweig as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Felipe Diaz-Griffero (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

Some HIV-1-infected individuals are capable of efficiently controlling viral replication. The reasons for this are incompletely understood and most likely complex. In the present study, the authors analyzed a large group of 131 HIV-1 controllers. Major finds are: (i) CD4 T cells from a subset of ECs/VCs are relatively resistant to R5-tropic HIV infection in vitro, compared to those from other ECs/VCs or uninfected controls; (ii) the resistance phenotype is associated with lower levels of CCR5, likely due to reduced active transcription of CCR5; (iii) CD4+ T cells from ECr/VCr have highly accessible chromatin over ~500Kb region around CCR2 and CCR5, which does not seem to fit with decreased transcription of these genes and (iv) evidence from one VCr that the resistance phenotype is heritable.

Genetic determinants of effective control of HIV-1 are of significant interest. In the present study a large number of HIV-1 controllers was examined technologies to study an important question about heterogeneous mechanisms of natural virologic control. Altogether, the data are well presented and for most part convincing. However, as appreciated by the authors the study has some limitations; e.g. that no viremic group of HIV-1-infected individuals was analyzed for comparison. The results are of significant interest but several issues should be addressed prior to publication.

Essential revisions:

1) One concern is the initial selection approach. Substantial variation is also observed for CXCR4- and VSV-G-mediated HIV-1 infection of cells from different donors (Figure 1B). If this phenotype is stable (as confirmed for CCR5), selection of the most X4 and/or VSV-G resistant subgroup of individuals would also result in significant differences to other groups in subsequent analyses. Thus, the results clearly support that CD4+ T cells of some EC/VCs express relatively low levels of CCR5. Whether this occurs more frequently than in viremic individuals and was causative for control is difficult to assess and should be critically discussed.

2) A potential reason for the ECr/VCr phenotype not explored in the manuscript is that circulating CD4 T cells from ECr/VCr might be low in effector memory (EM) cells. Among CD3+CD4+ cells, the EM population can be defined by a CD45RO+/CD27- phenotype, with the CD45RO+/CD27+ population defined as central memory (CM). In blood, most CCR5+ cells (as well as MIP-1a- and -1b-producing cells) show an EM phenotype. EM cells may be more easily infected by R5-tropic viruses than are CM cells, and may also be more likely to express the virus genes when infected. If the ECr/VCr phenotype correlated with low frequencies of EM cells among total CD4 T cells, this could mean that the phenotype is not due to atypical gene transcription on a region of 3p21 in all CD4 T cells, but instead to an atypical pattern of memory CD4 T cell differentiation in which the most susceptible target cells arise less frequently. If samples are available, additional FACS analyses should be performed, incorporating the CD27 marker in their flow panel to distinguish effector memory and central memory T cells. This will also help to better assess whether T cell differentiation rather than transcription might be different. In either case, it seems that only CD45RO/RA was determined to define "effector memory" cells in the Results section; the term "memory" seems more appropriate here since effector memory represent the CD27- subset within the RA-RO+ population.

3) Susceptibility to HIV-1 was only tested and confirmed using single-round infection assays and Envs of two macrophage-tropic HIV-1 strains. The resistance of CD4+ T cells (and PBMCs) from some representative individuals should be confirmed using primary CCR5-tropic HIV-1 strains (and X4 controls) in a spreading infection.

4) As ATAC-seq and Rpb1-ChIP results suggest more accessible chromatin structure and more RNA polymerase occupancy around CCR2 and CCR5 in ECr/VCr donors, the down-regulation of CCR2/CCR5 mRNA is unexpected. Can the authors provide additional information or speculation to help the reader understand these seemingly conflicting findings? Would it be useful to check repressive chromatin markers like H3K27me3 or H3K9me3 to confirm that these levels decrease the ~500 Kb region around CCR2 and CCR5? Either additional experiments or simply additional explanation may be sufficient to address this issue before publication.

5) Figure 1. The authors should plot the infectivity for each of the 21 samples showing infection with VSV-G, R5 and X4 in the same plot (X4, R5, and VSV-G normalized viruses for p24). The group data looks fine, but the reader cannot appreciate the differences of infectivity when comparing VSV-G (that enters T cells) and R5 tropic viruses. This will also allow the investigator to detect the more extreme cases where the difference between VSV-G and R5 infection are the greatest and illustrate this difference in the wild type samples. The reader would like to know the magnitude of resistance for each of the 21 samples.

6) One question is whether HIV-1-YU2, HIV-ADA, and HIV-1-VSV-G were normalized for p24 before infection. This is important to understand the magnitude of the differences among samples.

7) Figure 3. This figure showed one of the most important findings of the paper and should be plotted per sample showing the variability in control cells (no EC/VC). CCR2 and CCR5 for each sample like in Figure 3C should be shown. Again, this could help to identify the strongest phenotypes.

https://doi.org/10.7554/eLife.44360.025

Author response

Essential revisions:

1) One concern is the initial selection approach. Substantial variation is also observed for CXCR4- and VSV-G-mediated HIV-1 infection of cells from different donors (Figure 1B). If this phenotype is stable (as confirmed for CCR5), selection of the most X4 and/or VSV-G resistant subgroup of individuals would also result in significant differences to other groups in subsequent analyses. Thus, the results clearly support that CD4+ T cells of some EC/VCs express relatively low levels of CCR5. Whether this occurs more frequently than in viremic individuals and was causative for control is difficult to assess and should be critically discussed.

We agree with the reviewer that there is variability in percentage of infected cells using X4-tropic virus and/or VSVG; this is unfortunately unavoidable when using primary T cells. However, this variability is observed in all groups (Figure 1B), including Controls (Ctrl), EC/VC, and ECr/VCr, those with the resistant phenotype to R5-tropic viruses. Based upon the results observed in the first experiment (Figure 1—figure supplement 1A and 1B), where we initially selected R5-tropic resistant samples, we did not observe samples with both R5-tropic and X4-tropic resistance (none of the% infected cells in EC/VC group were lower than the% infected cells in Ctrl for the X4-tropic virus used). Only UCSF-56 (see Figure 1—figure supplement 2A) showed resistance to R5-tropic with relative resistance to X4-tropic virus. To investigate whether individuals with R5-tropic resistance also had lower susceptibility to either X4-tropic or VSV G-pseudotyped virus, we correlated T cell susceptibility to R5-virus to that of X4 and also R5-virus to that of VSVG (see Figure 1—figure supplement 2B). The absence of a statistically significant correlation between R5 and X4 or R5 and VSVG infectivity suggest that activated T cells of these ECr/VCr are specifically resistant to R5-tropic virus, and susceptible to X4-tropic virus. Most importantly, these ECr/VCr T cells that are resistant to R5-tropic virus are susceptible to VSVG, used as a pan-tropic control, indicating that the R5-tropic resistance is not due to a lower ability of those T cells to be infected, rather that resistance is specific to R5-tropic virus. This information is now included in Results section and in a figure legend (Figure 1—figure supplement 2).

Additional evidence that these 21 T cell samples (ECr/VCr) are only resistant to R5-tropic, but susceptible to X4-tropic, virus is the fact that ccr2 and ccr5 mRNA levels were down-regulated in this group compared to Ctrl and remaining EC/VCs, with cxcr4 mRNA levels comparable between groups (Figure 2A). The fact that ccr5 mRNA levels positively correlated with the percentage of infected cells using R5-tropic virus (Figure 2B), coupled with the absence of correlation between ccr5 and cxcr4 mRNA levels, clearly support the idea that these ECr/VCr T cells were resistant only to R5-tropic and susceptible to X4-tropic and pan-tropic virus.

It is true that we cannot prove definitively that this R5-resistance phenotype is more prevalent in EC/VC compared to viremic individuals. There are three major reasons why we are unable to study HIV+ individuals who are viremic: (i) they are now quite rare in the U.S. and very difficult to recruit, especially prior to initiating therapy, (ii) it would be unethical for us to withhold ART in order to study them longitudinally (and longitudinal study is critical here to demonstrate stability and reproducibility of the phenotype, and (iii) it is very difficult to recover sufficient quantities of T cells from viremic individuals for these sorts of studies. We were able, however, to recruit 10 HIV+ progressors (Prog) on anti-retroviral therapy. Our results show that R5-resistance was only observed in a subset of EC/VC CD4+ T cells and not in those of Prog or Ctrl. We observed relative resistance to R5-tropic HIV in CD4+ T cells from ECr/VCr compared to remaining EC/VC, Ctrl, and Prog (Ctrl 1.05 ± 0.81%; Prog 0.87 ± 0.36%; EC/VC 1.09 ± 0.75%; ECr/VCr 0.20 ± 0.16%; P<0.0001). However, our results showed equal susceptibility to X4-tropic HIV (Ctrl 3.07 ± 1.32%; Prog 2.79 ± 1.62%; EC/VCs 3.64 ± 1.78%; ECr/VCr 2.96 ± 2.01%) and VSV G pseudoviral particles among the groups (Ctrl 34.8 ± 9.35%; Prog 37.7 ± 6.4%; EC/VCs 29.32 ± 11.71%; ECr/VCr 32.87 ± 10.08%; data not shown). We readily admit, however, that these experiments were performed in a small scale with only 10 Prog, and additional experiments should be performed to confirm these results.

2) A potential reason for the ECr/VCr phenotype not explored in the manuscript is that circulating CD4 T cells from ECr/VCr might be low in effector memory (EM) cells. Among CD3+CD4+ cells, the EM population can be defined by a CD45RO+/CD27- phenotype, with the CD45RO+/CD27+ population defined as central memory (CM). In blood, most CCR5+ cells (as well as MIP-1a- and -1b-producing cells) show an EM phenotype. EM cells may be more easily infected by R5-tropic viruses than are CM cells, and may also be more likely to express the virus genes when infected. If the ECr/VCr phenotype correlated with low frequencies of EM cells among total CD4 T cells, this could mean that the phenotype is not due to atypical gene transcription on a region of 3p21 in all CD4 T cells, but instead to an atypical pattern of memory CD4 T cell differentiation in which the most susceptible target cells arise less frequently. If samples are available, additional FACS analyses should be performed, incorporating the CD27 marker in their flow panel to distinguish effector memory and central memory T cells. This will also help to better assess whether T cell differentiation rather than transcription might be different. In either case, it seems that only CD45RO/RA was determined to define "effector memory" cells in the Results section; the term "memory" seems more appropriate here since effector memory represent the CD27- subset within the RA-RO+ population.

As suggested, we analyzed additional T cell samples from a subset of Ctrl, EC/VC, and ECr/VCr to quantify the percentage of circulating Effector Memory (EM) vs Central Memory (CM) T cells and corresponding levels of CCR5 in both populations. These results are now included in Figure 3D (see also Figure 3 legend), in the Results section, and in the Materials and methods section. Overall, the percentage of EM (CD45RO+CD27-) cells in ECr/VCr was higher than in Ctrls and EC/VCs, and CM (CD45RO+CD27+) cells being relatively lower in ECr/VCr than the other two groups (but these differences were not significant). In general, and as we expected, the percentage of EM cells and CCR5+ cells with EM phenotype were higher than that of CM cells. More interestingly, the percentages of CCR5+ cells were reduced in both EM and CM cells in ECr/VCr compared to the other two groups (Ctrl and remaining EC/VC), indicating that CCR5 levels were lower in ECr/VCr not only in EM cells, the cells that are more susceptible in general to R5-tropic virus, but also in CM cells, T cells that are typically less susceptible to R5-tropic infection. Taken together, these data suggest a transcriptional mechanism of ccr5 down-regulation associated with R5-tropic resistance in a subset of EC/VC, not a difference in EM and CM T cell populations in ECr/VCr.

In addition, the term “effector memory” has been changed to “memory” accordingly (Results section and Figure 3 legend).

3) Susceptibility to HIV-1 was only tested and confirmed using single-round infection assays and Envs of two macrophage-tropic HIV-1 strains. The resistance of CD4+ T cells (and PBMCs) from some representative individuals should be confirmed using primary CCR5-tropic HIV-1 strains (and X4 controls) in a spreading infection.

We agree that it is of interest to quantify viral kinetics using replication-competent virus. We have now included experiments with replication-competent virus using R5-tropic pNL-BaL and X4-tropic HIV-NL4-3ΔR1, showing replication curves in CD4+ T cells from representative Ctrl, EC/VC, and ECr/VCr (n=2 per group tested in triplicate, see Figure 1—figure supplement 2C). This figure demonstrates relative resistance to and reduced replication of X4- and R5-tropic virus in CD4+ T cells of ECr/VCr, compared to those of EC/VC and Ctrl, over the 21 days analyzed (see quantitation of AUC, or area under the curve). Most interestingly, infection using X4-tropic viruses demonstrated reduced infection in all EC/VC (with or without the R5-resistance phenotype by single-cycle assay), with virtual absence of replication in ECr/VCr. Importantly, this lack of replication was not associated with any cytotoxicity, as all CD4+ T cells at the end of the 3-week period were very healthy and viable, with no cell death evident. We wish to point out that the absence of differences in replication at day 3 post-infection can be explained by the fact that in the initial transfection of 293T cells to produce virus we included VSV G expression plasmid together with plasmids encoding full-length X4- or R5-tropic virus. Inclusion of the VSV G plasmid was necessary to initiate infection of the CD4+ T cells with multicycle virus (otherwise, the T cells were extremely poorly infected, at least in our hands). These data suggest relative resistance to X4-tropic virus in activated CD4+ T cells of EC/VC and suggest a more complex mechanism for ECr/VCr, other than simply down-regulation of CCR5. That single cycle X4 infectivity is normal in ECr/VCr T cells suggests an additional late block to viral replication, which is the subject of ongoing investigation. A paragraph has been included in the Results section and in the Materials and methods section.

Primary viral isolates (e.g., transmitted/founder strains) were not tested here. There is no evidence in the literature that these viruses use a co-receptor other than ccr5, and the more important conclusion of our paper is that the reason that ECr/VCr T cells are relatively resistant to R5 virus is due to RNA down-regulation of ccr5 (and not CD4), which appears to be genetic in nature. Testing PBMCs would only muddy the waters since it is a mixed cell population (especially since inhibitory CD8+ T cells will be present); we performed all single cycle experiments using highly purified CD4+ T cells. Furthermore, for most of these subjects CD4+ T cells and not PBMCs were available; thus, we performed the experiments with replication-competent virus using activated CD4+ T cells instead of PBMCs. Parenthetically, if one treats PBMCs with anti-CD3/CD28 in the presence of IL2 after a few days the cell population typically ends up being mostly CD4+/CD8+ T cells.

4) As ATAC-seq and Rpb1-ChIP results suggest more accessible chromatin structure and more RNA polymerase occupancy around CCR2 and CCR5 in ECr/VCr donors, the down-regulation of CCR2/CCR5 mRNA is unexpected. Can the authors provide additional information or speculation to help the reader understand these seemingly conflicting findings? Would it be useful to check repressive chromatin markers like H3K27me3 or H3K9me3 to confirm that these levels decrease the ~500 Kb region around CCR2 and CCR5? Either additional experiments or simply additional explanation may be sufficient to address this issue before publication.

As mentioned, it is very intriguing that the observed down-regulation of ccr2 and ccr5 and lower active transcription as measured by ccr5 and ccr2 levels after immunoprecipitation using anti-Rpb1 is associated with more accessible chromatin in ECr/VCr T cells compared to that of Ctrl and remaining EC/VC. Since our results performed using anti-H3K4me3 suggest more activated chromatin markers in ECr/VCr compared to other two groups, it would be interesting to analyze repressive chromatin markers such as H3K9me3 or H3K27me3. Unfortunately, the samples were very limiting, and this could not be performed. ChIP qPCR experiments require at least 2 million CD4+ T cells and it would be necessary to perform the experiment multiple times with enough numbers of subjects per group to be confident in the results. As mentioned in the Discussion section, H3K4Me3 has been previously associated with open chromatin, correlating with higher levels of transcripts (Heintzman et al., 2007, Bernstein et el., 2005). Since our results suggest that more open chromatin over ~500Kb region on chr3, including surrounding ccr2 and ccr5, is associated with lower transcription of both genes in ECr/VCr, it is expected that repressive chromatin markers would also be decreased, despite the down-regulation of multiple genes on 3p21. We can only speculate why this is so, but one possibility is that ECr/VCr CD4+ T cells express a dominant negative transcription factor (i.e., a DN STAT, as intimated in the Discussion section), such that the cell overcompensates for reduced transcription in this region by somehow attempting to keep the chromatin even more open. This would also explain the 5-10 fold RNA down-regulation (presumably affecting alleles or genes on both chromosomes) and the fact that the R5-resistance phenotype appears to be autosomal dominant in inheritance pattern. Despite some investigation, we do not have any direct evidence for a DN transcription factor—this is also the subject of future work—and thus are loathe to include this degree of speculation in the Discussion section.

5) Figure 1. The authors should plot the infectivity for each of the 21 samples showing infection with VSV-G, R5 and X4 in the same plot (X4, R5, and VSV-G normalized viruses for p24). The group data looks fine, but the reader cannot appreciate the differences of infectivity when comparing VSV-G (that enters T cells) and R5 tropic viruses. This will also allow the investigator to detect the more extreme cases where the difference between VSV-G and R5 infection are the greatest and illustrate this difference in the wild type samples. The reader would like to know the magnitude of resistance for each of the 21 samples.

As requested, we have now included plots showing single-cycle infectivity for all 21 ECr/VCr against X4, R5, and VSVG pseudotyped viruses in the same plot (Figure 1—figure supplement 2A). Also, a figure showing the correlation between R5 and X4 and R5 and VSVG for all 21 ECr/VCr is also now included (see Figure 1—figure supplement 2B). A sentence has been added in the Results section. The absence of a statistically significant correlation between R5 and X4 and R5 and VSVG susceptibility suggest that these ECr/VCr are specifically resistant to R5-tropic virus, being susceptible to X4-tropic virus (note that only UCSF56 showed relative lower percentages of infectivity against R5 and X4-tropic viruses—we would like to study that subject further but have been unable to obtain more PBMCs). And, as stated above, these ECr/VCr T cells are resistant to R5-tropic viruses independent of VSVG susceptibility, indicating that the R5-tropic resistance is not due to a lower ability to be infected, due to poor cell viability or activation status, for example.

For normalization of the amount of virus used, we used infectivity on GHOST cell derivatives by performing a titration of each virus using GHOST.HI5 (for R5-tropic virus) or GHOST.X4 (for X4-tropic and VSVG pan-tropic virus) cells. Based upon the end-point titration by flow cytometry, we determined the Infectious Units per ml (IU/ml) for each lot of virus and the same quantity of IU was used to infect CD4+ T cells of the subjects (Ctrl, EC/VC, and ECr/VCr). Critically, the same volumes and tissue culture plate format were also used for the infectivity assays. With regards to normalization of the multicycle virus, this was performed on TZMbls, and the same quantity of virus was used for each of the cell samples. A paragraph has been included in the Materials and methods section to clarify how the normalization was performed.

6) One question is whether HIV-1-YU2, HIV-ADA, and HIV-1-VSV-G were normalized for p24 before infection. This is important to understand the magnitude of the differences among samples.

Please refer to our response above regarding normalization using IU based upon titering in GHOST reporter cell lines, not by p24 CA.

7) Figure 3. This figure showed one of the most important findings of the paper and should be plotted per sample showing the variability in control cells (no EC/VC). CCR2 and CCR5 for each sample like in Figure 3C should be shown. Again, this could help to identify the strongest phenotypes.

As requested, we have included individual plots for all 21 EC/VC with the resistance phenotype for CCR2 (Figure 3—figure supplement 1) and CCR5 expression (Figure 3—figure supplement 2), as well as individual plots for Ctrls (normal healthy donors) for comparison. This information has been included in the Results section. Also, a graph showing the positive correlation between% ccr2 and% ccr5 has been also included (see Figure 3F) and the corresponding information has been included in the Results section and in Figure 3 legend.

https://doi.org/10.7554/eLife.44360.026

Article and author information

Author details

  1. Elena Gonzalo-Gil

    Section of Infectious Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0409-3094
  2. Patrick B Rapuano

    Section of Infectious Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, United States
    Contribution
    Data curation, Formal analysis, Methodology
    Competing interests
    No competing interests declared
  3. Uchenna Ikediobi

    Section of Infectious Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, United States
    Contribution
    Resources, Data curation, Methodology
    Competing interests
    No competing interests declared
  4. Rebecca Leibowitz

    Section of Infectious Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, United States
    Contribution
    Data curation, Methodology
    Competing interests
    No competing interests declared
  5. Sameet Mehta

    Yale Center for Genome Analysis Bioinformatics group, Yale University School of Medicine, New Haven, United States
    Contribution
    Software, Formal analysis
    Competing interests
    No competing interests declared
  6. Ayse K Coskun

    Section of Infectious Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, United States
    Contribution
    Data curation, Methodology
    Competing interests
    No competing interests declared
  7. J Zachary Porterfield

    Section of Infectious Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, United States
    Contribution
    Resources, Final approval of the version to be published
    Competing interests
    No competing interests declared
  8. Teagan D Lampkin

    Infectious Diseases Section, Dallas VA Medical Center, Dallas, United States
    Contribution
    Resources, Final approval of the version to be published
    Competing interests
    No competing interests declared
  9. Vincent C Marconi

    Atlanta VA Medical Center, Emory University School of Medicine, Atlanta, United States
    Contribution
    Resources, Final approval of the version to be published
    Competing interests
    No competing interests declared
  10. David Rimland

    Atlanta VA Medical Center, Emory University School of Medicine, Atlanta, United States
    Contribution
    Resources, Final approval of the version to be published
    Competing interests
    No competing interests declared
  11. Bruce D Walker

    Ragon Institute of MGH, MIT and Harvard University, Cambridge, United States
    Contribution
    Resources, Final approval of the version to be published
    Competing interests
    No competing interests declared
  12. Steven Deeks

    1. Department of Medicine, University of California San Francisco, San Francisco, United States
    2. Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, United States
    Contribution
    Resources, Final approval of the version to be published
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6371-747X
  13. Richard E Sutton

    Section of Infectious Diseases, Department of Internal Medicine, Yale University School of Medicine, New Haven, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Writing—review and editing
    For correspondence
    richard.sutton@yale.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7418-2378

Funding

National Institutes of Health (P30AI050409)

  • Vincent C Marconi

National Institutes of Health (U01 AA020790)

  • Vincent C Marconi

Bill and Melinda Gates Foundation

  • Bruce D Walker

Harvard University Center for AIDS Research (P30 AI060354)

  • Bruce D Walker

The Collaboration for AIDS Vaccine Discovery (CAVD)

  • Bruce D Walker

UCSF/Gladstone Institute of Virology and Immunology (P30 AI027763)

  • Steven Deeks

CFAR Network of Integrated Systems (R24 AI067039)

  • Steven Deeks

Delaney AIDS Research Enterprise (DARE; AI096109,A127966)

  • Steven Deeks

The amfAR Institute for HIV cure research (amfAR 109301)

  • Steven Deeks

National Institute on Drug Abuse (DP1DA036463)

  • Richard E Sutton

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We would like to thank the clinical coordinators at Emory University School of Medicine, especially Rincy Varughese, Cameron England, Rachel Safeek, Ramona Rai, and Clayton Carruth. The SCOPE cohort was supported the UCSF/Gladstone Institute of Virology and Immunology CFAR (P30 AI027763) and CFAR Network of Integrated Systems (R24 AI067039). Additional support was provided by the Delaney AIDS Research Enterprise (DARE; AI096109, A127966) and amfAR Institute for HIV Cure Research (amfAR 109301). This work was supported in part by the Bill and Melinda Gates Foundation and the Collaboration for AIDS Vaccine Discovery (BDW) and the Harvard University Center for AIDS Research grant P30 AI060354 (BDW), supported by the following NIH co-funding and participating Institutes and Centers: NIAID, NCI, NICHD, NHLBI, NIDA, NIMH, NIA, FIC, and OAR. This work was also supported by the following NIH grants: P30AI050409, U01 AA020790, U10 AA013566, and DP1DA036463. We thank Dr. Ned Landau of NYU Medical Center for kind gift plasmids. RES is a NIDA Avant Garde awardee.

Ethics

Human subjects: The study was approved by both the Yale IRB (Yale New Haven Hospital and other Yale-affiliated HIV clinics in Connecticut; IRB protocol HIC#1305012068), and the local IRBs (the SCOPE cohort from UCSF, the Ragon Institute of MGH, MIT and Harvard, and from Veterans Medical Center HIV clinics from Atlanta and Dallas) and informed, written consent was obtained from all subjects. All ethical guidelines regarding human subjects investigation were adhered to. None of the investigators had or currently have a real or perceived conflict of interest with regard to this work.

Senior Editor

  1. Wendy S Garrett, Harvard TH Chan School of Public Health, United States

Reviewing Editor

  1. Frank Kirchhoff, Ulm University Medical Center, Germany

Reviewers

  1. Frank Kirchhoff, Ulm University Medical Center, Germany
  2. Felipe Diaz-Griffero

Publication history

  1. Received: December 12, 2018
  2. Accepted: April 1, 2019
  3. Version of Record published: April 9, 2019 (version 1)

Copyright

This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.

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