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HIV Controllers Are Distinguished by Chemokine Expression Profile and HIV-Specific T-Cell Proliferative Potential

Card, Catherine M. BSc*; Keynan, Yoav MD*,†,‡; Lajoie, Julie PhD*; Bell, Courtney P. BSc*; Dawood, Magdy PhD*,§; Becker, Marissa MD*,†; Kasper, Ken MD*; Fowke, Keith R. PhD*,†,‡

JAIDS Journal of Acquired Immune Deficiency Syndromes: April 15th, 2012 - Volume 59 - Issue 5 - p 427–437
doi: 10.1097/QAI.0b013e3182454fcd
Basic and Translational Science

Background: HIV controllers demonstrate a natural ability to control HIV replication in the absence of antiretroviral therapy. We performed a comprehensive evaluation of inflammation and T-cell activation in a demographically unique cohort of HIV controllers and noncontrollers.

Methods: Plasma concentrations of 22 cytokines and chemokines were evaluated using a multiplex bead array approach. Multicolor flow cytometry was used to measure baseline levels of T-cell activation and regulatory T cells (Tregs) and HIV-specific T-cell cytokine (interferon γ, interleukin 2) and proliferation responses.

Results: HIV controllers were characterized by elevated macrophage inflammatory protein 1α and low levels of interferon γ–induced protein 10, monocyte chemotactic protein 1, and Transforming growth factor beta. Activated (CD38+ HLA DR+) CD4+ and CD8+ T cells were reduced in HIV controllers relative to noncontrollers. HIV controllers and noncontrollers had comparable proportions of Tregs within the CD4+ T-cell compartment, but absolute Treg counts were depleted in noncontrollers. Absolute Treg counts correlated inversely with T-cell activation. Proliferative CD4+ and CD8+ T-cell responses directed against HIV gag epitopes were found most frequently among HIV controllers with the lowest viral loads (elite controllers) and were rarely detected among noncontrollers, supporting a relationship between HIV-specific T-cell proliferation and viral control.

Conclusions: Collectively, these data suggest a model in which HIV controllers maintain low levels of viral replication through robust HIV-specific T-cell responses in an environment of low inflammation and reduced availability of activated target cells.

Supplemental Digital Content is Available in the Text.

Departments of *Medical Microbiology

Community Health Sciences, University of Manitoba, Winnipeg, Canada

Department of Medical Microbiology, University of Nairobi, Nairobi, Kenya

§Department of Serology, Cadham Provincial Laboratory, Winnipeg, Canada.

Correspondence to: Keith R. Fowke, PhD, Department of Medical Microbiology, University of Manitoba, 539-745 Bannatyne Avenue, Winnipeg, MB R3E 0J9, Canada (e-mail:

Supported by Canadian Institutes of Health Research (grant MOP86721). Additional support was provided by Manitoba Health Research Council Graduate Studentships (C.M.C.), Canadian Institutes of Health Research postdoctoral fellowships (J.L.), Network Environment for Aboriginal Health Research scholarships (C.P.B.), and the Canadian Institutes of Health Research's International Infectious Diseases and Global Health Training Program (C.M.C., Y.K., and J.L.). K. R. Fowke holds a Manitoba Research Chair from the Manitoba Health Research Council. For the remaining authors, none were declared.

Presented at the Keystone HIV Evolution, Genomics, and Pathogenesis; March 20–25, 2011; Whistler, Canada; and Canadian Association for HIV Research; May 13–16, 2010; Saskatoon, Canada.

The authors have no conflicts of interest to disclose.

Supplemental digital content is available for this article. Direct URL citations appear in the printed text and are provided in the HTML and PDF versions of this article on the journal's Web site (

Received September 17, 2011

Accepted December 6, 2011

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The HIV epidemic in Canada is marked by diversity in demographic representation. Canadian First Nations, Métis, and people from HIV-endemic countries are overrepresented among the Canadian HIV-infected population.1 In Manitoba, First Nations and Métis people account for approximately 15% of the population2 but 38% of HIV diagnoses.3 HIV cases in Manitoba are also characterized by higher rates of heterosexual transmission, late clinical presentation, and rapid disease progression.3 Although high levels of viral replication and progressive CD4+ T-cell decline mark the majority of untreated HIV cases, a small proportion of individuals demonstrate natural control of HIV. Elite controllers and viremic controllers (collectively termed HIV controllers) are HIV-infected individuals who, in the absence of antiretroviral therapy, maintain undetectable or low viral load measurements, respectively.4 A cohort of HIV controllers was established in Manitoba to evaluate mechanisms of viral control in this unique population.

Several potential mechanisms of HIV control have been proposed. The weight of the evidence implicates HIV-specific CD8+ T cells in suppression of viral replication.5–9 CD8+ T cells restricted by protective HLA class I types, such as HLA B*57,9,10 exert selective pressure on HIV, forcing the emergence of escape mutations that render the virus less fit for replication.6,11–13 CD4+ T cells are crucial for establishing effective adaptive immunity and have also been associated with protection in several studies.9,14,15 However, not all HIV controllers express protective HLA alleles, and there are several cases of control in the absence of robust HIV-specific responses,9 suggesting alternate mechanisms of protection.

Immune activation is a critical factor driving disease progression in HIV-infected individuals. A potential mechanism of HIV control involves a limitation of the pool of activated target CD4+ T cells. Indeed, HIV controllers tend to demonstrate drastically reduced T-cell activation in comparison to noncontrollers.14,16–18 Levels of immune activation may be controlled by several mechanisms including regulatory T cells (Tregs), a subset of CD4+ T cells important for controlling lymphocyte activation and responses to antigen.19 The fate and function of Tregs in HIV infection is unclear. Elevated Treg frequencies have been associated with resistance to infection in HIV-exposed seronegative individuals,20 and the literature points to a relative expansion of this subset during progressive disease, likely in response to increases in inflammatory signals.18,21,22 However, due to the gradual loss of CD4+ T cells, absolute Treg numbers steadily decline throughout infection,21–23 potentially contributing to aberrant T-cell activation and disease progression.16

Within the Manitoba HIV controller population, there is a potential for identification of unique correlates of protection and variation from known associations of HIV control due to the unique demographic nature of the cohort. Thus, we sought to comprehensively evaluate multiple potential correlates of immune control within this population and identify novel correlates of protection through a screen of inflammatory biomarkers. To this end, we measured blood plasma levels of 22 cytokines and chemokines and expression of markers of T-cell activation and Tregs in HIV controllers and noncontrollers. In a subset of patients, HIV-specific cytokine and proliferative T-cell responses were also evaluated. We demonstrate that HIV controllers are characterized by a distinct chemokine profile, reduced T-cell activation, and enhanced T-cell proliferative capacity.

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Study Participants

HIV-infected subjects were enrolled into the Manitoba Elite Controller Cohort Study through the Winnipeg Health Sciences Center and Nine Circles Community Health Clinic. All participants were antiretroviral naive, with the occasional exception of therapy during pregnancy. A minimum of 3 consecutive plasma viral load measurements were used to define elite controllers (<50 copies/mL), viremic controllers (50–2000 copies/mL), and HIV-infected noncontrollers (>5000 copies/mL). Healthy HIV-uninfected volunteers recruited from the University of Manitoba were also included as a comparison group in this study. Written informed consent was obtained from all study participants. The Research Ethics Board at the University of Manitoba approved the study protocols.

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CD4 T-Cell Count and Plasma Viral Load Determination

CD4+ and CD8+ counts were assessed from whole blood using the Tritest CD3/CD4/CD8 flow cytometry assay (BD Biosciences, Mississauga, Canada). To determine plasma HIV viral loads, HIV RNA in EDTA plasma was extracted and quantified using the automated Roche COBAS AmpliPrep/COBAS TaqMan HIV-1 Test (Roche Diagnostics, Laval, Canada).

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Measurement of Plasma Cytokines and Chemokines

Cytokine and chemokine concentrations were determined in plasma samples using a custom multiplex bead array kit (Millipore Corporation, Burlington, MA), which allowed for simultaneous detection of 21 analytes: fractalkine/CX3CL1, interferon (IFN) α2, IFNγ, interleukin (IL) 1α, IL-1β, IL-1 receptor agonist (ra), IL-2, soluble IL-2 receptor α, IL-6, IL-7, IL-8, IL-10, IL-15, IL-17, IFNγ-induced protein (IP) 10/CXCL10, monocyte chemotactic protein (MCP) 1/CCL2, MCP-3/CCL7, macrophage-derived chemokine/CCL22, macrophage inflammatory protein (MIP) 1α/CCL3, MIP-1β/CCL4, and tumor necrosis factor α. Transforming growth factor (TGF) beta was analyzed separately using a singleplex kit (Millipore Corporation), which required acidification of samples before analysis. Data were acquired on a Bioplex200 and analyzed using Bioplex Manager software, version 5.0 (BioRad Corporation, Mississauga, Canada). The sensitivity ranged between 0.2 and 10 pg/mL, depending on the analyte measured. Analyte concentrations below the lower limit of detection were assigned a value of 0 pg/mL.

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T-Cell Phenotyping

Thawed peripheral blood mononuclear cells (PBMCs) were immunophenotyped using multicolor flow cytometry to determine levels of T-cell activation and Treg frequency. Antibodies used to detect surface markers included anti-CD3 Pacific Blue, anti-CD4 AlexaFluor700, anti-CD8 V500, anti-CD25 phycoerythrin (PE), anti-CD38 PECy5, and anti-HLA DR allophycocyanin (APC)-Cy7 (BD Biosciences). After surface staining, intracellular staining for FOXP3 was done using the eBioscience FOXP3 staining buffer set and anti-FOXP3 APC (eBioscience, San Diego, CA).

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PBMC Stimulation, Intracellular Cytokine Staining, and Proliferation Assays

Thawed PBMCs were stimulated with anti-CD3/CD28 Dynabeads (bead to cell ratio 1:1; Invitrogen, Burlington, Canada), a pool of overlapping synthetic peptides (12–15 amino acids in length) covering the HIV-1 ancestral gag protein (2 μg/mL per peptide, Sigma), or left unstimulated (media containing DMSO vehicle control) in 24-well plates. Costimulatory antibodies (1 μL/mL of anti-CD28 and anti-CD49d; BD Biosciences) were added to each well. For intracellular cytokine staining assays, PBMCs were stimulated in the presence of 1 μL/mL of GolgiPlug containing brefeldin A (BD Biosciences) for 14 hours. After antigen stimulation, PBMCs were stained with anti-CD3 APC-H7, anti-CD4 AlexaFluor700, and anti-CD8 APC and then were fixed and permeabilized using the BD Cytofix/Cytoperm Fixation/Permeabilization Kit (BD Biosciences) and intracellular cytokine staining was performed using anti-IFNγ fluorescein isothiocyanate and anti–IL-2 PE. For proliferation assays, cells were loaded with carboxyfluorescein diacetate succinimidyl ester (Invitrogen) before stimulation. After 6 days, cells were stained with the dead cell discriminator Aqua amine reactive dye (Invitrogen) and anti-CD3 APC-H7, anti-CD4 AlexaFluor700, and anti-CD8 APC. Data were acquired on an LSRII flow cytometer (BD Biosciences) and were analyzed using FlowJo software, version 9.1 (Treestar, Ashland, OR).

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Statistical Analysis

Continuous variables were analyzed using the nonparametric Mann–Whitney U test when comparing 2 groups or the Kruskal–Wallis nonparametric analysis of variance when comparing 3 groups. Relationships between continuous variables were assessed using Spearman nonparametric correlation. Dichotomous variables were compared with Fisher exact test or χ2 test for trend. Differences were considered to be statistically significant if P <0.05. All statistical analyses were performed using GraphPad Prism for Mac OS X, version 5.0a (GraphPad software).

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Study Participants

A total of 31 HIV-infected individuals enrolled in our study. This population consisted of 5 elite controllers, 14 viremic controllers, and 12 noncontrollers. The majority of analyses in our study combined elite controllers and viremic controllers into a single group (HIV controllers). HIV-uninfected healthy subjects (n = 20) were also included in our study as controls.

Demographic and clinical characteristics of HIV+ study participants are shown in Supplemental Digital Content 1 (see Table, No differences were observed between groups in terms of age, sex, or years since HIV diagnosis. HIV+ elite and viremic controllers had significantly higher CD4 counts than noncontrollers (P = 0.0061 and P < 0.0004, respectively). As expected, plasma viral load was significantly higher in noncontrollers than elite and viremic controllers (both P < 0.0001). The ethnic representation of the cohort was comparable to the greater HIV-infected population in Manitoba. Specifically, the cohort included individuals of Canadian Aboriginal, European, and African descent (48.4%, 35.5%, and 16.1%, respectively). This is in contrast to previously described HIV controller cohorts, which primarily enrolled white and African American individuals, but included minimal or no Aboriginal representation.10,24,25

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HIV Controllers Are Characterized by a Unique Cytokine and Chemokine Expression Pattern

Plasma concentrations of 22 cytokines and chemokines were compared between HIV-uninfected (n = 13), HIV controllers (n = 18) and noncontrollers (n = 11). Nine of 22 analytes were found to vary between groups. These included various chemokines (IP-10, IL-8/CXCL8, MCP-1, and MIP-1α), proinflammatory cytokines (IFNγ and IL-17), anti-inflammatory cytokines (TGF-β), and homeostatic cytokines (IL-2 and IL-7) (see Table, Supplemental Digital Content 2, Pairwise comparisons revealed that the observed differences in concentrations of IL-8, IFNγ, IL-17, IL-2, and IL-7 were attributable to differences between the HIV-uninfected and HIV-infected groups. However, concentrations of IP-10, MCP-1, and TGF-β were lower in HIV controllers (P = 0.0003, P = 0.017, and P = 0.085, respectively), whereas MIP-1α was found to be significantly higher in HIV controllers (P = 0.013) relative to noncontrollers (Fig. 1A). We therefore focused our subsequent analyses on these 4 analytes.



We next investigated the relationship between concentrations of these 4 analytes with CD4 count and detectable plasma viral load (Fig. 1B). CD4 count inversely correlated with IP-10 (r = −0.36, P = 0.017) but positively with MIP-1α (r = 0.36, P = 0.001). Although CD4 count is important for determining disease stage and rates of disease progression, we were more interested in factors that may play a role in control of viral replication, as indicated by plasma viral load. Plasma viral load was correlated positively with levels of IP-10 and TGF-β (r = 0.59, P = 0.003 and r = 0.46, P = 0.005, respectively) and inversely with MIP-1α (r = −0.50, P = 0.003). Together, these results suggest that elevated levels of MIP-1α and lower levels of IP-10 and TGF-β associate with control of viral replication.

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HIV Controllers Are Characterized by Reduced Frequencies of Activated T Cells and Maintenance of Absolute Treg Counts

To assess levels of T-cell activation, we measured coexpression of CD38 and HLA-DR on CD4+ and CD8+ T cells from HIV-uninfected (n = 12), HIV controllers (n = 15), and noncontrollers (n = 11). Representative gating is shown in Supplemental Digital Content 3 (see Figure, Frequencies of activated CD4+ and CD8+ T cells differed significantly between groups (both P < 0.0001, Fig. 2A). Pairwise comparisons confirmed that noncontrollers had more activated CD4+ and CD8+ T cells than both uninfected (CD4+ P = 0.0001, CD8+ P < 0.0001) and HIV controllers (CD4+ P = 0.007, CD8+ P = 0.020). Additionally, HIV controllers had more activated CD4+ and CD8+ T cells than uninfected controls (P = 0.002 and P = 0.0002, respectively). As shown in Figure 2B, CD4+ and CD8+ T-cell activation correlated positively with CD4 count (r = −0.51, P = 0.0008 and r = −0.47, P = 0.003, respectively) and inversely with plasma viral load (r = 0.76, P < 0.0001 and r = 0.67, P = 0.0002, respectively). These results lend support to previous observations that viral control is associated with low levels of baseline T-cell activation.14,16–18,21



Due to their role in controlling T-cell activation, we were interested in investigating Treg levels in HIV controllers. In a subset of subjects (HIV-N n = 5, HIV+ C n = 14, and HIV+ NC n = 11), we assessed levels of Tregs by measuring coexpression of the Treg transcription factor FOXP3 and CD25 on CD4+ T cells. Representative gating is shown in Supplemental Digital Content 3 (see Figure, No significant differences in frequency of Tregs within the CD4+ T-cell compartment were observed between groups (Fig. 2C), and Treg frequency did not correlate with CD4 count or detectable plasma viral load (Fig. 2D). However, absolute Treg counts were elevated in HIV controllers when compared with noncontrollers (P = 0.044) (Fig. 2C), presumably due to the loss of CD4+ T cells with disease progression as has been shown in previous studies.21–23 Absolute Treg counts correlated positively with CD4 count (r = 0.44, P = 0.014) and inversely with detectable plasma viral load (r = −0.42, P = 0.047) (Fig. 2D). Our data suggest that peripheral Tregs decline at a rate comparable to other CD4+ T cells during HIV disease progression. This gradual decline in absolute numbers of Tregs could be expected to affect regulation of T-cell activation. We did not have sufficient sample material to analyze Treg function in vitro, so we addressed this possibility by investigating the relationship between absolute Treg counts and levels of T-cell activation (Fig. 2E). Absolute Treg counts were inversely correlated with frequencies of activated CD8+ T cells (r = −0.62, P = 0.0003). A similar trend was observed with CD4+ T cells (r = −0.31, P = 0.097). These observations do not imply causation between Treg function and immune activation. Indeed, loss of Tregs and increased immune activation may both be independently driven by other factors related to HIV replication and CD4+ T-cell loss. However, these data are supportive of the hypothesis that progressive Treg loss may be one factor contributing to aberrant immune activation, a well-established driving factor in HIV disease progression.

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HIV Controllers Readily Respond to HIV Gag by T-Cell Proliferation

Robust CD8+ T-cell responses are thought to contribute to the ability of HIV controllers to maintain low levels of viral replication. Full characterization of HLA-restricted epitope-specific responses was beyond the scope of our study, but we sought to characterize bulk HIV gag–specific CD4+ and CD8+ T-cell responses in a subset of patients. We focused on HIV gag, as responses directed against this protein have been shown to be important for viral control.26,27 PBMCs were stimulated with a pool of HIV gag peptides, followed by measurement of cytokine (IFNγ and IL-2) and proliferation responses.

IFNγ and IL-2 production by CD4+ and CD8+ T cells in response to stimulation with HIV gag peptides was measured in HIV controllers (n = 12) and noncontrollers (n = 6). Representative gating of cytokine responses is shown in Supplemental Digital Content 4 (see Figure, A trend was observed in which the magnitude of HIV-specific CD4+ IFNγ+ IL-2− and CD8+ IFNγ− IL-2+ responses (ie, percentage of cytokine-producing cells) was higher in HIV controllers than noncontrollers (P = 0.09 and P = 0.016, respectively) (Figs. 3A, 4A). The frequency of positive responses (ie, proportion of individuals responding by a particular cytokine combination) was also compared between groups. Although no statistically significant differences were observed between groups, CD4+ and CD8+ IFNγ− IL-2+ responses were only observed in the HIV controller group (Figs. 3B, 4B). Furthermore, when the HIV controller group was broken down into elite controllers (n = 5) and viremic controllers (n = 7), the presence of CD4+ IFNγ− IL-2+ responses was found to decrease with progressive loss of viral control (P = 0.10; Fig. 3C). This trend was absent in the CD8+ T-cell compartment (Fig. 4C).





HIV-specific T-cell proliferation was measured 6 days post stimulation using CFSE dye dilution in HIV controllers (n = 10) and noncontrollers (n = 4). Representative gating of proliferation responses is shown in Supplemental Digital Content 5 (see Figure, CD4+ and CD8+ proliferation responses were of significantly higher magnitude (P = 0.01 and P = 0.04, respectively), and CD4+ proliferation was observed more frequently (P = 0.07) in HIV controllers (Figs. 5A, B). When the HIV controller group was broken down into elite controllers (n = 5) and viremic controllers (n = 5), the presence of positive CD4+ and CD8+ proliferation responses decreased with progressive loss of viral control (P = 0.035 and P = 0.09, respectively; Fig. 5C).



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HIV controllers represent a heterogeneous population unified by the common ability to suppress HIV replication in the absence of antiretroviral therapy. Although multiple mechanisms for containment of viral replication have been described, no single mechanism accounts for all cases of control. Recognizing the diversity of this group and investigating alternate mechanisms of control is essential for the development of new HIV prevention and treatment strategies. In the present study, we found that differential expression of the chemokines MIP-1α, IP-10, and MCP-1 and the anti-inflammatory cytokine TGF-β distinguished HIV controllers from noncontrollers. HIV controllers also had low levels of T-cell activation, which corresponded to maintenance of absolute counts of Tregs. Finally, we found that HIV-specific T-cell proliferative capacity was greatest in individuals with superior control of viral replication.

MIP-1α is a natural ligand for CCR5, a major HIV coreceptor, which directly blocks HIV infection through competitive binding of CCR5.28 In line with this, elevated levels of MIP-1α have been associated with resistance to HIV infection.29 In addition to directly blocking cellular infection, MIP-1α may suppress HIV replication later in the life cycle.30,31 MIP-1α secretion is reduced HIV infection, but elevated levels of this chemokine has been linked to delayed disease progression.8,32 Based on these observations, we propose that MIP-1α facilitates control of HIV replication in HIV controllers by directly interfering with infection and viral replication.

We found reduced levels of the proinflammatory chemokines IP-10 and MCP-1 in HIV controllers. These chemokines attract activated T cells and macrophages to sites of inflammation,33,34 thereby recruiting target cells for HIV replication. Both chemokines have previously been observed to increase after HIV infection,35–38 IP-10 has been correlated to highly active antiretroviral therapy treatment failure,38 and IP-10 has also been observed to stimulate HIV replication in monocyte-derived macrophages and PBMC,39 demonstrating an additional mechanism by which this chemokine may influence disease progression. MCP-1 acts on CD4+ T cells, causing them to upregulate expression of CXCR4, thus rendering them more susceptible to HIV infection with X4-tropic viral variants.40 As such, maintenance of reduced levels of proinflammatory chemokines in HIV controllers may limit viral propagation by preventing infiltration of activated target cells available in sites of viral replication.

We additionally found reduced concentrations of the anti-inflammatory cytokine TGF-β in HIV controllers. This was an unexpected observation, as our findings of reduced proinflammatory chemokines suggest that low inflammation is favorable in HIV controllers. However, the observation of reduced TGF-β is consistent with studies linking this cytokine with disease progression.41,42 Although elevated levels of TGF-β could arise from multiple cell types, Tregs may express high levels of this cytokine during HIV infection.43 Thus, although the absolute numbers of Tregs decline throughout the infection, enhanced production of TGF-β may have consequences for effector T-cell responses. Indeed, the anti-inflammatory effects of TGF-β negatively impact antigen-specific T-cell responses. Thus, maintenance of low levels of TGF-β may be one mechanism by which HIV controllers are able to mount robust HIV-specific T-cell responses, resulting in killing of infected cells and viral control.

In addition to measuring inflammation in plasma, we evaluated immune activation at the cellular level. We found HIV control to associate with reduced levels of T-cell activation, confirming the results of previous studies.14,16,17,21 Due to their role in controlling T-cell activation, we also measured levels of Tregs. The relationship between disease progression in the Treg population is a contentious issue in the literature. Although some studies describe a relative expansion of Tregs within the CD4+ T-cell compartment,18,21,22 we found that Tregs were not selectively depleted during HIV disease progression. However, consistent with other studies,21–23 our data showed a gradual loss of absolute counts of Tregs during progressive disease, with relative maintenance of these cells in HIV controllers. Absolute counts of Tregs inversely correlated with frequencies of activated T cells. Although this correlation does not imply causation, the loss of this suppressive cell type may contribute to the excessive immune activation that characterizes progressive disease. Future studies will be needed to elucidate the relative contribution of the Treg pool for control of T-cell activation during HIV disease progression.

Last, we evaluated HIV-specific T-cell responses in a subset of patients. Although we were restricted to low sample numbers, we were able to identify some interesting trends. HIV controllers were not identified by a particular cytokine expression pattern, although they tended to demonstrate IL-2 responses more often (CD4+ responses) or of higher magnitude (CD8+ responses) than noncontrollers. However, the presence of HIV-specific proliferation responses distinguished HIV controllers from noncontrollers and was directly related to the level of viral containment. Our findings are consistent with studies identifying proliferative T-cell responses as a correlate of protection in nonprogressive HIV disease.7,44–46 These data suggest that the population of HIV-specific T cells can readily expand such that they are present in sufficient quantities to effectively target infected cells and subvert viral propagation.

Collectively, these data support a model whereby HIV controllers contain HIV replication through robust, HIV-specific, proliferative, T-cell responses and direct MIP-1α activity in an environment of low inflammation and T-cell activation. Reduced levels of IP-10 and MCP-1 may prevent recruitment of activated CD4+ target cells to sites of viral replication, whereas elevated levels of MIP-1α serve to directly inhibit infection of target cells by HIV. Maintenance of absolute Treg counts may also contribute to controlling aberrant T-cell activation, further reducing the pool of activated target cells available for viral propagation.

Although we were able to identify some protective aspects of cytokine and chemokine expression, T-cell phenotypes, and T-cell response quality, we recognize that we were limited by small sample sizes for some assays. As such, some differences between groups may have been overlooked. This is particularly relevant for our evaluation of HIV-specific T-cell cytokine (HIV+ EC n = 5, HIV+ VC n = 7, HIV+ NC n = 6) and proliferation (HIV+ EC n = 5, HIV+ VC n = 5, HIV+ NC n = 4) responses. Specifically, dual cytokine-producing cells (IFNγ+/IL2+) were rare events in our analyses of HIV-specific responses. As such, we were not sufficiently powered to detect differences in polyfunctional T cells between groups, as has been previously described.7

Future studies should evaluate tissue levels of MIP-1α, IP-10, and MCP-1 in HIV controllers and noncontrollers to confirm their effects on recruitment of target cells to sites of viral replication. To our knowledge, this is the first description of a distinct chemokine expression pattern that distinguishes HIV controllers from noncontrollers. We have additionally confirmed other correlates of control in this demographically unique cohort. Understanding these mechanisms could conceivably lead to prevention and treatment strategies aimed at controlling immune activation and reducing the pool of target cells susceptible to HIV infection.

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The authors would like to thank the staff of the HIV Clinics at the Winnipeg Health Sciences Centre and Nine Circles Community Health Centre, particularly Heather Duckworth, Kim Bresler, Anne Russell, Carla Pindera, Bernie Lopko, and Jocelyn Preston for their support. The authors also thank Stephen Wayne and Jennifer Juno (University of Manitoba) for technical assistance. The authors would also like to thank the Assembly of Manitoba Chiefs for their ongoing support of this project.

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HIV/AIDS; HIV control; immune activation; Treg; proliferation; inflammation

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