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Early immunologic and virologic predictors of clinical HIV-1 disease progression

Mahnke, Yolanda D.a; Song, Kaimeia; Sauer, Mariana M.b; Nason, Martha C.c; Giret, Maria Teresa M.b,d; Carvalho, Karina I.b; Costa, Priscilla R.b; Roederer, Marioa; Kallás, Esper G.b

doi: 10.1097/QAD.0b013e32835ce2e9
Basic Science

Objective: To identify early determinants of HIV-1 disease progression, which could potentially enable individualized patient treatment, and provide correlates of progression applicable as reference phenotypes to evaluate breakthrough infections in vaccine development.

Design: High-throughput technologies were employed to interrogate multiple parameters on cryopreserved, retrospective peripheral blood mononuclear cell (PBMC) samples from 51 individuals from São Paulo, Brazil, obtained within 1 year of diagnosing early Clade B HIV-1 infection. Fast Progressors, Slow Progressors, and Controllers were identified based on a 2-year clinical follow-up.

Methods: Phenotypic and functional T-cell parameters were tested by flow cytometry and qPCR to identify potential early determinants of subsequent HIV-1 disease progression.

Results: Major differences were observed between Controllers and Progressors, especially in cell-associated viral load (CAVL), the differentiation pattern and CD38 expression of CD8+ T cells, and the cytokine pattern and activation phenotype of HIV-1-specific CD8+ T cells. Despite remarkably few other differences between the two Progressor groups, the CAVL had predictive power independent of plasma viral load.

Conclusion: Analysis of three parameters (% CD38+ CD8+ T cells, total CAVL, % CCR5+ CD8+ T cells) was sufficient to predict subsequent disease progression (P < 0.001). Use of such prognostic correlates may be crucial when early CD4+ T-cell counts and plasma viral load levels fail to discriminate among groups with differing subsequent clinical progression.

Supplemental Digital Content is available in the text

aImmunoTechnology Section, VRC, NIAID, NIH, Bethesda, Maryland, USA

bDivision of Clinical Immunology and Allergy, School of Medicine, University of São Paulo, São Paulo, Brazil

cBiostatistics Research Branch, NIAID, NIH, Bethesda, Maryland

dTwo Story Lab, Miller School of Medicine, University of Florida, Miami, Florida, USA.

Correspondence to Yolanda D. Mahnke, PhD, 40 Convent Drive, Rm 5608, Bethesda, MD 20892, USA. Tel: +1 301 594 8655; fax: +1 301 480 2788; e-mail:

Received 31 August, 2012

Revised 1 November, 2012

Accepted 21 November, 2012

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 Website (

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The ability to predict an HIV-1 patient's disease progression rate will provide an ability to deliver a customized treatment plan, for example, by identifying individuals who may benefit from early intervention. HIV-1 Controllers and Progressors exhibit immunophenotypic differences, even early after infection [1–3]. However, even within Progressors there exists a large variation in the rate of progression. Although largely disparate CD4+ T-cell count and plasma HIV-1 viral load (PVL) values can differentiate fast and slow progression to immunodeficiency [4–6], no parameters have been identified that address the ‘gray zone’ of Progressors where CD4+ T-cell counts and PVL have limited power to predict outcome.

After decades of searching for useful predictors of HIV-1 disease progression, CD4+ T-cell count [5], PVL [4,6], and cell-associated viral load (CAVL) [5] remain the strongest correlates and predictors, though other factors, notably immune activation parameters, can serve as independent predictors. CD38 expression has long been a focus because both elevated frequencies of positive cells [7] and molecular density [4,8,9] among CD8+ T cells correlate with poor prognosis, whereas HLA-DR+ CD38 CD8+ T cells correlate with slower disease progression [3].

T-cell differentiation status might also correlate with immune protection [1]. In a simian vaccination model, increased CD4+ central memory T-cell levels were a better predictor of survival than either CD4+ T-cell count or PVL [10]. However, such an association was not found in HIV-1 patients [2]. Instead, analysis of acute/early infection samples showed strong predictive power for the frequency of proliferating CD8+ T cells, the frequency of naive and CD127+ CD8+ T cells, and CAVL [2].

CD8+ T-cell responses are crucial in containing the virus during acute and early infection, and in determining the viral set point [11,12]. A positive correlation was identified between PVL and the magnitude of total HIV-1-, Env- and Nef-specific CD8+ T-cell responses [13]. In vaccinated rhesus macaques, higher ex-vivo virus-specific T-cell responses correlated strongly with survival [10]. In a mixed cohort of acute and chronic HIV-1 patients (untreated and treated), increased response quality (i.e. more polyfunctional cells), rather than magnitude or phenotype of HIV-specific CD8+ T cells, provided a correlate for better prognosis [14]. No conclusive association with protection has been found in either the magnitude or the breadth of HIV-1-specific T-cell responses [15].

Here we report on the identification of correlates of progression measured early after HIV infection. We specifically evaluated previously identified correlates on cryopreserved blood samples from a well characterized, early HIV-1 infection cohort from São Paulo, Brazil. We also explored the predictive potential of additional T-cell differentiation and activation markers, as well as of T-cell responses directed to different HIV-1-derived peptide pools. Overall, we evaluated a wide breadth of bulk and antigen-specific markers with the goal of identifying potentially clinically useful measures for staging early HIV-1-infected adults.

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Patient cohort

Gender distribution and age at diagnosis of HIV-1 infection was comparable between patients maintaining low levels of viremia (Controllers), with progressive disease requiring antiretroviral therapy within 2 years of diagnosis (Fast Progressors), and with progressive disease not requiring antiretroviral therapy within 2 years (Slow Progressors) (Table 1; a full description of the patient cohort can be found in Supplemental Methods, Healthy Donors were on average slightly younger (P = 0.035, Wilcoxon signed-rank test). Patient samples were obtained within 1 year of HIV-1 diagnosis by Standard Algorithm for Recent HIV Seroconversion (STAHRS). The CD4+ T-cell counts were lower in all patients compared with Healthy Donors (P < 0.0001, Wilcoxon signed-rank test).

Table 1

Table 1

HLA-B*27, B*57, and B*58 are associated with delayed HIV-1 disease progression [16–18]. Nevertheless, recent studies reported that the virus-specific CD8+ T-cell responses are better predictors of disease progression than the HLA genotype [19,20]. In our patients, beneficial HLA types were enriched in Controllers (57%), but also expressed by 23% of Slow Progressors (Table 1). Coinfection with GB virus type C (GBV-C) has also been proposed to correlate with a slower HIV-1 disease progression and to modulate T-cell activation [21]. Interestingly, Progressors had a higher GBV-C prevalence than Controllers, ruling out GBV-C coinfection as a factor for delayed HIV-1 disease progression of Controllers. This is reminiscent of findings showing similar GBV-C prevalence between elite controllers and chronically infected HIV-1 patients [22]. The CCR5Δ32 mutation, known to confer resistance to HIV-1 [23,24], was found in about 10% of study participants (all heterozygous), and distributed equally in the groups (Table 1).

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Cell-associated viral load

To evaluate the potential role of CAVL among CD4+ T-cell subsets, we used flow cytometric cell sorting to isolate naive (Nv) cells, all CCR5-expressing CD45RO+ memory cells (MR5), as well as CCR7+ (MR5−R7+) and CCR7 (MR5−R7−) cells within CCR5 memory cells (Fig. 1a). The total CD4-associated CAVL was computed by summing the frequency-weighted subset CAVL measurements.

Fig. 1

Fig. 1

Although PVL were not statistically different between Fast and Slow Progressors (Table 1), CAVL was significantly elevated in Fast Progressors (P = 0.0014, Mann–Whitney; Fig. 1b). CAVL was lowest in Controllers, and altogether undetectable, by our method, in 42% of this group. This is not surprising as tissues such as lymph nodes and gut are the major viral reservoir [25]; ultrasensitive CAVL measurements are required to identify peripheral blood mononuclear cell (PBMC)-associated virus in patients with very low viral burden.

Although the total CAVL was significantly different between study groups, the viral burden in Nv CD4+ T cells was comparable in all study groups (Fig. 1c), supporting the hypothesis that infection of Nv occurs during homeostatic proliferation of these cells and is not a pathogenic mechanism of HIV-1 infection [26]. Notably, a large fraction of Slow Progressors (39%) and Controllers (42%) had undetectable CAVL in Nv cells, consistent with decreased homeostatic proliferation in the face of decreased CD4+ T-cell loss [27]. In all other subpopulations examined, Fast Progressors had higher CAVL than Slow Progressors, with the lowest CAVL found in Controllers (Fig. 1d, Suppl. Fig. 1a and b, Overall, as expected, MR5 carried the highest and Nv the lowest viral burden among CD4+ T-cell populations (Fig. 1e). The distribution was comparable in individual study groups (Suppl. Fig. 1c and d,, except in Controllers where a high frequency of samples had undetectable CAVL (Suppl. Fig. 1e, Within Progressors, the viral burden in MR5 was over 1 log higher than in Nv cells from the same patients, whereas within Controllers no such difference was found (Suppl. Fig. 1f, In contrast, the relative CAVL between CCR5+ and CCR5 memory subsets was similar within each patient group (Fig. 1f), in agreement with data from nonhuman primates [28].

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CD8+ T-cell differentiation reveals correlates of HIV-1 infection, as well as predictors of rapid and slow disease progression

Naive and memory T-cell subsets were defined by differential expression of CD45RO, CCR7, and CD27. This results in eight subpopulations, including naive (TNV: CD45RO CCR7+ CD27+; a more stringent definition of TNV than used for the CAVL analyses), central memory (TCM: CD45RO+ CCR7+ CD27+), transitional memory (TTM: CD45RO+ CCR7+ CD27), effector memory (TEM: CD45RO+ CCR7 CD27), and terminal effector cells (TTE: CD45RO CCR7 CD27), as well as three subsets that are not named in the literature. As they most closely resemble TCM, TTM, and TTE (unpublished observation), we refer to these as TCM* (CD45RO CCR7+ CD27), TTM* (CD45RO+ CCR7+ CD27), and TTE* (CD45RO CCR7 CD27+) (Suppl. Fig. 2a,

The overall signature of CD8+ T-cell differentiation states differed significantly between HIV-1+ and HIV-1 individuals, as well as between Progressors and Controllers (Fig. 2a). Remarkably, distinct signatures correlated with a more rapid disease progression. All HIV-1+ groups had significantly lower frequencies of TNV cells, possibly reflecting ongoing inflammation and resulting cellular differentiation (Fig. 2b). Controllers had comparable levels of TTM, TCM*, and TTE* subsets to Healthy Donors, whereas both groups of Progressors revealed significantly elevated frequencies of TTM and TTE*, as well as reduced levels of TCM* cells. Interestingly, Controllers had significantly higher frequencies of CD8+ TTE cells (Fig. 2b) than Progressors or Healthy Donors, potentially the result of an effective early anti-HIV-1 immune response and concomitant inflammation driving a large number of cells into the TTE stage.

Fig. 2

Fig. 2

No major changes were observed in the memory signatures of CD4+ T cells from HIV-1-infected individuals compared with Healthy Donors (Suppl. Fig. 2b and c, This is somewhat surprising, as a reduction in the CD4+ T-cell count had already taken place (Table 1).

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The frequency of CD38+ CD8+ T cells is a strong predictor of disease progression within the first year after HIV-1 infection

The activation status of both CD4+ and CD8+ T cells was investigated by analyzing the expression of several cell surface markers associated with activation and differentiation, as well as the intracellular lytic molecule Granzyme B and the nuclear proliferation marker Ki67 (Fig. 2c). Some antigens (CD25 and CD57 on CD4+ T cells; CD38 and CD57 on CD8+ T cells) were elevated in all HIV-1-infected groups compared with healthy controls, possibly resulting from the systemic inflammation accompanying HIV-1-infection. Other markers (CD38 and Ki67 on both CD4+ and CD8+ T cells) were elevated in Progressors as compared with Controllers (Fig. 2d and e), reflecting even higher activation levels in these patients. No differences were found between Fast and Slow Progressors.

Elevations in both the frequency [7] and median fluorescence intensity (MFI) [4,8,9], a measure of the average molecular density of a given Ag at the cell surface, of the ectoenzyme CD38 on CD8+ T cells have long been correlated with a poor prognosis in HIV infection. Indeed, although all patient groups had significantly elevated frequencies of CD38+ CD8+ T cells compared with Healthy Donors, the proportion of CD38+ cells among CD8+ T cells was 10-fold higher in Progressors than Controllers, with no overlap between these groups. Although significantly different between Progressors, Controllers, and Healthy Donors, the MFI of CD38 on CD8+ T cells did not provide such a clear-cut distinction (Fig. 2f). Furthermore, although all CD4+ and CD8+ T-cell subsets demonstrated elevated MFI of CD38 in HIV-1-infected individuals, the differences were much less pronounced for very early and very late differentiation stages (Suppl. Fig. 3a and b,

As the Controllers from our cohort were defined as maintaining PVL less than 2000 copies/ml, with no overlap in PVL between Controllers and Progressors, and the frequency of CD38+ CD8+ T cells had previously been shown to correlate with plasma viremia [29], this parameter alone was sufficient to segregate Progressors and Controllers in our study of early infection samples.

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Increased differentiation of HIV-1-reactive cells indicates poor prognosis

As CD8+ T-cell responses are key in containing the virus during acute and early infection [11,30], we hypothesized that some characteristic of the HIV-1-specific T-cell responses might have prognostic value. However, the total magnitude [interferon-γ (IFN-γ), tumor necrosis factor (TNF), and/or interleukin-2 (IL-2)] of the HIV-1-specific CD4+ and CD8+ T-cell responses, measured after stimulating with HIV-1-derived peptide pools for Env, Gag, Nef, Pol, or Tat+Rev+Vpr+Vpu (TRVV), was not statistically different between patient groups (Fig. 3a). Similarly, there was no statistically significant difference between groups in the absolute frequency of CD4+ T cells reactive to any of the HIV-1 peptide pools tested (Suppl. Fig. 4a, Within CD8+ T cells, there was a trend for more elevated Env- and lower Nef-specific response magnitudes in Fast Progressors, but these did not prove statistically significant (Suppl. Fig. 4b,

Fig. 3

Fig. 3

However, significant differences in the scope of the response (i.e., which HIV-1 proteins were targeted) occurred between patient groups (Suppl. Fig. 4c, On average, close to half of the HIV-1-specific CD8+ T cells of Controllers were Gag-specific, significantly more than either in Fast (P = 0.017, Wilcoxon signed-rank test) or Slow Progressors (P = 0.004, Wilcoxon signed-rank test). In contrast, Progressors demonstrated more diversity in the targeted HIV-1 proteins (Suppl. Fig. 4c, Fast Progressors exhibited a higher proportion of Env-specific cells compared with Slow Progressors, in agreement with a report that early Env-specific responses might be more prevalent in patients with higher PVL [20]. Finally, we found Slow Progressors to have a larger proportion of Nef-specific cells than Controllers.

The cytokine pattern of total HIV-reactive cells differed significantly between groups (Fig. 3b), with Slow Progressors exhibiting a cytokine pattern more similar to Controllers among CD4+ T cells, but more like Fast Progressors among CD8+ T cells. We investigated the frequency of subsets producing either of the possible cytokine combinations in response to individual HIV-1 peptide pools. Few statistically significant differences were observed, although a pattern emerged wherein increases in the frequency of CD8+ T cells producing only TNF correlated with poor prognosis, whereas increases in Gag-specific CD8+ T cells producing IFN-γ and IL-2 in the presence or absence of TNF correlated with a better prognosis (Fig. 3c).

CD57+ cells were less frequent within total HIV-1-reactive CD4+ T cells from Controllers, whereas CD127+ and Programmed Death-1+ (PD-1+) cells were more prevalent (Fig. 3d). Their CD8+ counterparts were more likely to express CD28 and PD-1 than within Progressors (Fig. 3d). Most of these patterns were also found in cytokine+ cells after stimulation with individual HIV-1-derived peptide pools (Fig. 3e), indicating that the activation and differentiation phenotypes of HIV-1-reactive T cells are independent of the targeted Ag. Although fewer statistically significant differences were observed in the proportion of T-cell differentiation subsets within cytokine+ CD4+ or CD8+ T cells, cells from Controllers had higher proportions of less differentiated cells, whereas more differentiated cells were more prevalent in Progressors (Fig. 3f).

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Early prediction of disease progression

Even within the first year of HIV-1 infection, immunophenotypic analysis of peripheral blood T cells can provide an indication of subsequent disease progression within individual patients. Although the frequency of CD38+ CD8+ T cells alone is sufficient to segregate Progressors from Controllers (Figs 2e and 4), no such major differences were observed between Fast and Slow Progressors in any one parameter. We tested the power of previously identified correlates of HIV-1 disease progression to distinguish either between Controllers and Progressors (Fast and Slow Progressors combined) or between Fast and Slow Progressors from our patient cohort (Suppl. Table 1, A number of those correlates, namely PVL and CAVL (the former being part of the selection criteria in the present cohort), as well as T-cell activation (frequency of CD38+ and Ki67+ cells, molecular density of CD38 per cell), proved useful markers to predict outcome between Controllers and Progressors. However, neither CD4+ T-cell counts nor the proportions of CD4+ TCM, CD8+ TNV, or CD127+ CD8+ T cells were significantly different between patient groups at this early time point. None of the previously identified correlates were significantly different between Fast and Slow Progressors, with the exception of CAVL.

Fig. 4

Fig. 4

As no single parameter was able to differentiate between Fast and Slow Progressors in the absence of differences in CD4+ T-cell count or PVL, we explored multivariate prediction using a classification tree. With the goal of developing a prediction rule based on parameters that would be feasible to assess in a clinical setting, we excluded the cytokine data from this analysis (indeed, including the cytokine data did not increase the power of this analysis). A multistep prognostic tool was established that predicts 2-year outcome based on clinically measured parameters early after infection (Fig. 4). As in previous studies [2,5], among the progressors CAVL proved to be the strongest predictor, with a total CAVL of 9.0 × 10−4 gag copies/cell chosen as the threshold. Ninety-five percent (19/20) of patients who had a CAVL below this threshold were categorized as Slow Progressors. For patients with a CAVL of at least 9.0 × 10−4 gag copies/cell, our model uses a subsequent threshold of 43% CCR5+ CD8+ T cells, with a high value on both of these variables predicting slow progression. Patients with a low value on % CCR5+ CD8+ T cells among those who had elevated CAVL were predicted as Fast Progressors.

The present decision tree analysis shows that even early after HIV-1 infection when CD4+ T-cell counts are not predictive for disease progression and PVL also fails to differentiate between Fast and Slow Progressors, relatively few additional parameters can be employed to determine individualized patient follow-up schedules and treatment regimens. As both the frequency of CD38 expressing CD8+ T cells and CAVL had already previously been identified as important measurements that can be used to predict outcome, only the relevance of CCR5+ CD8+ T cells within Progressors with elevated CAVL would need to be confirmed in an independent study.

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Predicting the rate of progression to immunodeficiency after HIV-1 infection is an important challenge in clinical research. Understanding the mechanisms involved in spontaneous viral control provides an informed selection of milestones in patient follow-up, including the important decision of when to commence antiretrovirals. In this retrospective study, we assessed several immune and viral markers in a well characterized cohort of HIV-1-infected individuals from whom early samples had been collected prospectively, to identify surrogate markers for protection or development of immunodeficiency. Our analysis yielded a rich dataset for both CD4+ and CD8+ T cells, including three functional and 12 phenotypic parameters, in addition to the CAVL measurements (CD4+ T cells only).

In concert with previous findings [7], the frequency of CD8+ T cells expressing CD38 proved to be a very strong predictor of disease progression, and in itself was sufficient to distinguish between Progressors and Controllers. This is likely linked [29] to the differences in PVL between Progressors and Controllers in our patient cohort. In addition to being a useful phenotypic marker, such CD38+ CD8+ T cells from HIV-1-infected individuals with high levels of CD38+ CD8+ T cells have been shown to express higher levels of CD95 and to be more susceptible to both spontaneous and FasL-mediated apoptosis [29], which could in part explain the reduced CD8+ T-cell-mediated viral control in such patients.

As in previous studies [26], we detected HIV gag DNA in all CD4+ T-cell subsets investigated, including TNV cells. Interestingly, there was no difference between patient groups in the CAVL in TNV cells, though the fraction of patients with HIV gag DNA+ TNV cells increased with more rapid disease progression. Although CCR5 is a coreceptor for HIV-1 and its expression is obligate for in-vitro infections with transmitted viruses, there is still substantial viral burden among nominally CCR5 cells. TNV, which express no CCR5 [31], have substantially lower but easily detectable CAVL. Among memory cells, CCR5 cells also carry substantial viral genomes. Although this might be a consequence of CCR5 downregulation following infection, we have shown that memory cells that have too little CCR5 expression to be detected by immunophenotyping still express CCR5 mRNA [28].

Previously, TCM were reported to carry the highest viral burden [26,32]; however, we detected little difference in the CAVL between CCR5 CCR7+ CD45RO+ and the more differentiated CCR5 CCR7 CD45RO+ memory cells. Both these subsets had a lower viral content than CCR5+ CD45RO+ memory cells. We found no difference in predictive power of CAVL within any subset (vs. total), suggesting that the relative seeding of the different subsets measured here, though biologically important, is not clinically relevant. Notably, out of all the parameters investigated in the present study, the total CAVL proved to be the strongest correlate of progression in Progressors, for whom CD4+ T-cell counts and PVL fail to predict outcome.

No differences were observed in the total HIV-1-specific response magnitude between Progressors and Controllers, similar to a previous report [33]. However, cytokine responses of Controllers were more polyfunctional, comprising fewer cells producing one cytokine only. Additionally, the phenotype of CD4+ and CD8+ HIV-1-specific T cells differed significantly between Controllers and Progressors, cytokine+ cells from Progressors being more likely to express CD57. Among cytomegalovirus (CMV)-specific CD8+ T cells, those lacking CD57 expression appear to exert a more protective effect against CMV in stem cell transplant recipients [34]. A larger proportion of CD4+ T cells from Controllers expressed CD127 or PD-1, indicating a potential difference in longevity of HIV-1-reactive cells between patient groups.

A larger fraction of the total HIV-1 cytokine response was directed against Gag in Controllers, while Slow Progressors had a larger proportion of Nef-specific cells than Controllers, and Fast Progressors had a larger proportion of Env-specific cells than Slow Progressors. As Nef-specific responses are developed earlier than Gag-specific responses during the course of HIV-1-infection [20,35], early Gag-specific T-cell responses reflect a faster maturation of the anti-HIV-1 response and may thereby indicate a favorable prognosis.

Burgers et al. [1] found that elevated proportions of TCM and TINT (corresponding to TTM and TTE* in our study), and TEM within total and HIV-specific CD8+ T cells, correlated with a lower and higher viral set point, respectively. Our data do not confirm these findings, although discrepancies could be due to differences in sampling time points, subset definitions, origin and demographics of study populations, and viral subtypes (Clade B vs. Clade C). In our cohort, the differentiation pattern of CD8+ T cells largely mimicked that of Healthy Donors, apart from a much more elevated proportion of TTE, balanced by reduced fractions of TNV and TCM. In comparison to HIV-1 Controllers, Progressors demonstrated higher proportions of TCM (comparable to Healthy Donors), TTM and TTE*, together with lower frequencies of TCM* and TTE (comparable to Healthy Donors). Although no differences were observed in the differentiation phenotypes of Fast and Slow Progressors, the above observations provide valuable reference phenotypes for vaccine trials.

We are currently analyzing the longitudinal development of T-cell phenotypes and functionality in these patients (Mahnke et al., in preparation). This will allow us to correlate measurements made at an early time point with immunopathogenesis (rather than just clinical progression as reported here). In addition, the longitudinal analyses may reveal whether rates of change in biomarkers are clinically predictive. Although less practical (in that multiple measurements over months or years are needed), such assessments may provide more powerful correlates.

Our data demonstrate that even early after HIV-1 infection, where CD4+ T-cell counts and PVL do not serve as predictive parameters, the CAVL and immunophenotype of CD8+ T cells can be used as predictive correlates. We propose that a few additional parameters be measured in the clinic during early HIV-1 infection, namely CAVL, the frequency of CD38+ and CCR5+ CD8+ T cells, to attempt an early prediction of disease progression. These measurements enable a preemptive, individualized follow-up and ARV treatment program.

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We are grateful for Richard Nguyen's help with the sorting of CD4+ T-cell subpopulations and thank Joanne Yu and Tess Brodie for technical assistance, and Joe Casazza for providing control PBMC samples. We thank Leandro Tarosso who helped in compiling critical patient information, Nancy Gouvea and Celso Gouvea for HLA typing, as well as Helena TI Tomiyama and Claudia Tomiyama for their support in the sample repository.

Authorship: Y.D.M., M.R., and E.G.K. developed the study concept and experimental design; M.M.S. was responsible for clinical supervision of patients and PBMC sampling; PBMC were processed and stored by P.R.C.; clinical information was assembled by Y.D.M., M.M.S., and K.I.C.; M.T.M.G. produced CCR5Δ32, GBV-C, and HLA data; K.S. measured CAVL; Y.D.M. performed flow cytometry experiments, analyzed all data and wrote the manuscript; statistical analyses were done by Y.D.M. and M.C.N.; and all authors were involved in manuscript review and editing.

Funding: This work was supported by the Intramural Research Program of the National Institute of Allergy and Infectious Diseases, National Institutes of Health; the Brazilian Program for STD and AIDS, Ministry of Health (grant number 914/BRA/3014-UNESCO/Kallas); the São Paulo City Health Department (grant number 2004-0.168.922–7/Kallas); and the Fundação de Amparo a Pesquisa do Estado de São Paulo (FAPESP) (grant numbers 2004/15856–9 and 2006/50096–0). The funding bodies had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Conflicts of interest

The authors declare no competing financial interests.

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1. Burgers WA, Riou C, Mlotshwa M, Maenetje P, de Assis Rosa D, Brenchley J, et al. Association of HIV-specific and total CD8+ T memory phenotypes in subtype C HIV-1 infection with viral set point. J Immunol 2009; 182:4751–4761.
2. Ganesan A, Chattopadhyay PK, Brodie TM, Qin J, Gu W, Mascola JR, et al. Immunologic and virologic events in early HIV infection predict subsequent rate of progression. J Infect Dis 2010; 201:272–284.
3. Giorgi JV, Ho HN, Hirji K, Chou CC, Hultin LE, O’Rourke S, et al. CD8+ lymphocyte activation at human immunodeficiency virus type 1 seroconversion: development of HLA-DR+ CD38- CD8+ cells is associated with subsequent stable CD4+ cell levels. The Multicenter AIDS Cohort Study Group. J Infect Dis 1994; 170:775–781.
4. Giorgi JV, Lyles RH, Matud JL, Yamashita TE, Mellors JW, Hultin LE, et al. Predictive value of immunologic and virologic markers after long or short duration of HIV-1 infection. J Acquir Immune Defic Syndr 2002; 29:346–355.
5. Goujard C, Bonarek M, Meyer L, Bonnet F, Chaix ML, Deveau C, et al. CD4 cell count and HIV DNA level are independent predictors of disease progression after primary HIV type 1 infection in untreated patients. Clin Infect Dis 2006; 42:709–715.
6. Mellors JW, Munoz A, Giorgi JV, Margolick JB, Tassoni CJ, Gupta P, et al. Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection. Ann Intern Med 1997; 126:946–954.
7. Giorgi JV, Liu Z, Hultin LE, Cumberland WG, Hennessey K, Detels R. Elevated levels of CD38+ CD8+ T cells in HIV infection add to the prognostic value of low CD4+ T cell levels: results of 6 years of follow-up. The Los Angeles Center, Multicenter AIDS Cohort Study. J Acquir Immune Defic Syndr 1993; 6:904–912.
8. Deeks SG, Kitchen CM, Liu L, Guo H, Gascon R, Narvaez AB, et al. Immune activation set point during early HIV infection predicts subsequent CD4+ T-cell changes independent of viral load. Blood 2004; 104:942–947.
9. Liu Z, Hultin LE, Cumberland WG, Hultin P, Schmid I, Matud JL, et al. Elevated relative fluorescence intensity of CD38 antigen expression on CD8+ T cells is a marker of poor prognosis in HIV infection: results of 6 years of follow-up. Cytometry 1996; 26:1–7.
10. Letvin NL, Mascola JR, Sun Y, Gorgone DA, Buzby AP, Xu L, et al. Preserved CD4+ central memory T cells and survival in vaccinated SIV-challenged monkeys. Science 2006; 312:1530–1533.
11. Goonetilleke N, Liu MK, Salazar-Gonzalez JF, Ferrari G, Giorgi E, Ganusov VV, et al. The first T cell response to transmitted/founder virus contributes to the control of acute viremia in HIV-1 infection. J Exp Med 2009; 206:1253–1272.
12. Jin X, Bauer DE, Tuttleton SE, Lewin S, Gettie A, Blanchard J, et al. Dramatic rise in plasma viremia after CD8(+) T cell depletion in simian immunodeficiency virus-infected macaques. J Exp Med 1999; 189:991–998.
13. Betts MR, Ambrozak DR, Douek DC, Bonhoeffer S, Brenchley JM, Casazza JP, et al. Analysis of total human immunodeficiency virus (HIV)-specific CD4(+) and CD8(+) T-cell responses: relationship to viral load in untreated HIV infection. J Virol 2001; 75:11983–11991.
14. Betts MR, Nason MC, West SM, De Rosa SC, Migueles SA, Abraham J, et al. HIV nonprogressors preferentially maintain highly functional HIV-specific CD8+ T cells. Blood 2006; 107:4781–4789.
15. Addo MM, Yu XG, Rathod A, Cohen D, Eldridge RL, Strick D, et al. Comprehensive epitope analysis of human immunodeficiency virus type 1 (HIV-1)-specific T-cell responses directed against the entire expressed HIV-1 genome demonstrate broadly directed responses, but no correlation to viral load. J Virol 2003; 77:2081–2092.
16. Carrington M, O’Brien SJ. The influence of HLA genotype on AIDS. Annu Rev Med 2003; 54:535–551.
17. Klein MR, Keet IP, D’Amaro J, Bende RJ, Hekman A, Mesman B, et al. Associations between HLA frequencies and pathogenic features of human immunodeficiency virus type 1 infection in seroconverters from the Amsterdam cohort of homosexual men. J Infect Dis 1994; 169:1244–1249.
18. Lazaryan A, Song W, Lobashevsky E, Tang J, Shrestha S, Zhang K, et al. Human leukocyte antigen class I supertypes and HIV-1 control in African Americans. J Virol 2010; 84:2610–2617.
19. Dinges WL, Richardt J, Friedrich D, Jalbert E, Liu Y, Stevens CE, et al. Virus-specific CD8+ T-cell responses better define HIV disease progression than HLA genotype. J Virol 2010; 84:4461–4468.
20. Kiepiela P, Ngumbela K, Thobakgale C, Ramduth D, Honeyborne I, Moodley E, et al. CD8+ T-cell responses to different HIV proteins have discordant associations with viral load. Nat Med 2007; 13:46–53.
21. Maidana-Giret MT, Silva TM, Sauer MM, Tomiyama H, Levi JE, Bassichetto KC, et al. GB virus type C infection modulates T-cell activation independently of HIV-1 viral load. AIDS 2009; 23:2277–2287.
22. Blankson JN, Klinzman D, Astemborski J, Thomas DL, Kirk GD, Stapleton JT. Low frequency of GB virus C viremia in a cohort of HIV-1-infected elite suppressors. AIDS 2008; 22:2398–2400.
23. de Roda Husman AM, Koot M, Cornelissen M, Keet IP, Brouwer M, Broersen SM, et al. Association between CCR5 genotype and the clinical course of HIV-1 infection. Ann Intern Med 1997; 127:882–890.
24. Huang Y, Paxton WA, Wolinsky SM, Neumann AU, Zhang L, He T, et al. The role of a mutant CCR5 allele in HIV-1 transmission and disease progression. Nat Med 1996; 2:1240–1243.
25. Pantaleo G, Cohen OJ, Schacker T, Vaccarezza M, Graziosi C, Rizzardi GP, et al. Evolutionary pattern of human immunodeficiency virus (HIV) replication and distribution in lymph nodes following primary infection: implications for antiviral therapy. Nat Med 1998; 4:341–345.
26. Brenchley JM, Hill BJ, Ambrozak DR, Price DA, Guenaga FJ, Casazza JP, et al. T-cell subsets that harbor human immunodeficiency virus (HIV) in vivo: implications for HIV pathogenesis. J Virol 2004; 78:1160–1168.
27. Catalfamo M, Wilhelm C, Tcheung L, Proschan M, Friesen T, Park JH, et al. CD4 and CD8 T cell immune activation during chronic HIV infection: roles of homeostasis, HIV, type I IFN, and IL-7. J Immunol 2011; 186:2106–2116.
28. Mattapallil JJ, Douek DC, Hill B, Nishimura Y, Martin M, Roederer M. Massive infection and loss of memory CD4+ T cells in multiple tissues during acute SIV infection. Nature 2005; 434:1093–1097.
29. Chun TW, Justement JS, Sanford C, Hallahan CW, Planta MA, Loutfy M, et al. Relationship between the frequency of HIV-specific CD8+ T cells and the level of CD38+CD8+ T cells in untreated HIV-infected individuals. Proc Natl Acad Sci U S A 2004; 101:2464–2469.
30. McMichael AJ, Borrow P, Tomaras GD, Goonetilleke N, Haynes BF. The immune response during acute HIV-1 infection: clues for vaccine development. Nat Rev Immunol 2010; 10:11–23.
31. Bleul CC, Wu L, Hoxie JA, Springer TA, Mackay CR. The HIV coreceptors CXCR4 and CCR5 are differentially expressed and regulated on human T lymphocytes. Proc Natl Acad Sci U S A 1997; 94:1925–1930.
32. Chomont N, El-Far M, Ancuta P, Trautmann L, Procopio FA, Yassine-Diab B, et al. HIV reservoir size and persistence are driven by T cell survival and homeostatic proliferation. Nat Med 2009; 15:893–900.
33. Schellens IM, Borghans JA, Jansen CA, De Cuyper IM, Geskus RB, van Baarle D, et al. Abundance of early functional HIV-specific CD8+ T cells does not predict AIDS-free survival time. PLoS One 2008; 3:e2745.
34. Scheinberg P, Melenhorst JJ, Brenchley JM, Hill BJ, Hensel NF, Chattopadhyay PK, et al. The transfer of adaptive immunity to CMV during hematopoietic stem cell transplantation is dependent on the specificity and phenotype of CMV-specific T cells in the donor. Blood 2009; 114:5071–5080.
35. Riviere Y, McChesney MB, Porrot F, Tanneau-Salvadori F, Sansonetti P, Lopez O, et al. Gag-specific cytotoxic responses to HIV type 1 are associated with a decreased risk of progression to AIDS-related complex or AIDS. AIDS Res Hum Retroviruses 1995; 11:903–907.

CD38; cell-associated viral load; immunophenotyping; prognosis; progression; T-cell activation

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