Home Current Issue Previous Issues Published Ahead-of-Print Collections For Authors Journal Info
Skip Navigation LinksHome > January 1, 2009 - Volume 50 - Issue 1 > Viral Load and CD4+ T-Cell Dynamics in Primary HIV-1 Subtype...
JAIDS Journal of Acquired Immune Deficiency Syndromes:
doi: 10.1097/QAI.0b013e3181900141
Epidemiology and Social Science

Viral Load and CD4+ T-Cell Dynamics in Primary HIV-1 Subtype C Infection

Novitsky, Vladimir MD, PhD*†; Woldegabriel, Elias MD†; Kebaabetswe, Lemme MSc†; Rossenkhan, Raabya MSc†; Mlotshwa, Busisiwe BA†; Bonney, Caitlin BA*; Finucane, Mariel BA‡; Musonda, Rosemary PhD*†; Moyo, Sikhulile MSc, MPH†; Wester, Carolyn MD*†; Widenfelt, Erik van BSc†; Makhema, Joseph MBChB*†; Lagakos, Stephen PhD‡; Essex, M DVM, PhD*†

Free Access
Article Outline
Collapse Box

Author Information

From the *Department of Immunology and Infectious Diseases, Harvard School of Public Health, Boston, MA; †Botswana-Harvard School of Public Health AIDS Initiative Partnership for HIV Research and Education, Gaborone, Botswana; and the ‡Department of Biostatistics, Harvard School of Public Health, Boston, MA.

Received for publication March 28, 2008; accepted September 23, 2008.

The primary HIV-1 subtype C infection study in Botswana, the Tshedimoso study, was supported and funded by National Institutes of Health grant R01 AI057027 to. This work was supported in part by the National Institutes of Health grant D43 TW000004 (B.M.) and also through the Association of American Medical Colleges, Fogarty International Center, and Ellison Overseas Fellowships in Global Health and Clinical Research (L.K. and R.R.).

Correspondence to: M. Essex, DVM, PhD, Chair, Harvard School of Public Health AIDS Initiative, FXB 402, 651 Huntington Avenue, Boston, MA 02115 (e-mail: messex@hsph.harvard.edu;).

Collapse Box

Abstract

Background: Most knowledge of primary HIV-1 infection is based on subtype B studies, whereas the evolution of viral parameters in the early phase of HIV-1 subtype C infection is not well characterized.

Methods: The kinetics of viral RNA, proviral DNA, CD4+ T-cell count, and subsets of CD4+ T cells expressing CCR5 or CXCR4 were characterized in 8 acute and 62 recent subtype C infections over the first year postseroconversion.

Results: The viral RNA peak was 6.25 ± 0.92 log10 copies per milliliter. After seroconversion, heterogeneity among acute cases was evident by patterns of change in viral load and CD4+ T-cell count over time. The patterns were supported by the rate of viral RNA decline from peak (P = 0.022), viral RNA means (P = 0.005), CD4 levels (P < 0.001), and CD4 decline to 350 (P = 0.011) or 200 (P = 0.046). Proviral DNA had no apparent peak and its mean was 2.59 ± 0.69 log10 per 106 peripheral blood mononuclear cell. In recent infections, viral RNA set point was 4.00 ± 0.97 log10 and viral RNA correlated inversely with CD4+ T cells (P < 0.001) and directly with proviral DNA (P < 0.001).

Conclusions: Distinct patterns of viral RNA evolution may exist shortly after seroconversion in HIV-1 subtype C infection. The study provides better understanding of the early phase of subtype C infection.

Back to Top | Article Outline

INTRODUCTION

The nonuniform distribution of HIV-1 subtypes in the worldwide AIDS epidemic has resulted in a gradual increase of HIV-1 subtype C,1,2 which became the predominant viral subtype causing the HIV/AIDS epidemic in countries with the highest HIV-1 prevalence.3,4 Southern Africa is experiencing the most severe HIV/AIDS epidemic in the world. In 2007, this region accounted for 35% of all people living with HIV and 32% of all new HIV infections and AIDS deaths globally.5

Most studies on viral dynamics in primary HIV-1 infection have been performed in subtype B settings in cohorts of men having sex with men, injection drug users, or blood donors.6-14 It is known that the initial peak of plasma viremia reaches 105-107 copies per milliliter,15-17 lasts approximately 2-3 weeks,9,10,18 and then drops to a steady state level of viral replication, “viral set point” within 4-6 months after infection.7,13,14,19-21 The rate of viral RNA decline10 and the viral RNA in plasma13 vary among infected individuals in a wide range. The peak of viral RNA coincides with the time of seroconversion12 and is accompanied by a profound depletion of CD4+ T cells expressing CCR5 at the mucosal effector site.22-25 In the peripheral blood, a fast initial decline of CD4+ T cells is followed by a slower rate after 5-6 months.13 Less is known about proviral DNA in primary HIV-1 infection, although it might be critical to HIV-1 pathogenesis because of its predictive value for disease progression.26-29

The courses of natural HIV-1 subtype B infection and disease outcome are greatly affected by early events in primary HIV infection.8,11,20,21,30-35 The peak, nadir, and median of viral RNA load in early HIV-1 infection are associated with RNA load at 6-12 months, and nadir RNA load can predict the level of CD4+ T cells during the first year of infection.8,33,36

In non-subtype B epidemics, both similarities and differences in relation to primary HIV-1 subtype B infection have been reported.37-42 In Kenya, where subtype A is a widespread HIV-1 subtype, viral load at the peak of acute infection was 4.7 log1043 to 5.1 log10,39 whereas viral set point at 4 months was 4.6 log10, and was associated with disease progression.39 In Senegal, viral RNA in the early stage of CRF02_AG HIV-1 infection was found to be 4.5 log1038 but was about 0.7 log10 lower in HIV-1 subtype A and subtype G infections.38 In subtype C epidemic in Malawi, an RNA peak of 6.10 log10 was reported.44,45

Relatively little is known about the evolution and variance of virologic and immunologic parameters in primary HIV-1 subtype C infection. The rationale for the current study is the need to better understand the pathogenesis of the early phase of HIV-1 subtype C infection and to identify subtype-specific characteristics of viral dynamics, if any. We analyzed key viral parameters in a cohort with primary HIV-1 subtype C infection including 8 acute and 62 recent subjects and assessed potential associations between analyzed parameters.

Back to Top | Article Outline

METHODS

Definition of Terms

Acute HIV infection was defined as a preseroconversion phase that starts at the time when infection occurs and ends at detectable seroconversion. Recent HIV infection was defined as a phase that starts at seroconversion and lasts for approximately 1 year. A similar designation was described previously.46 Primary HIV-1 infection was defined as the initial period of infection that includes the acute and recent phases. The viral RNA load peak was the observed highest level and may underestimate the true peak.

Back to Top | Article Outline
Study Subjects

Acute and recent HIV-1 subtype C infections were identified in Botswana using a combination of a prospective cohort of postnatal HIV-negative women and a voluntary counseling and testing-based referral sample (Fig. 1). Participants in the prospective cohort were screened for HIV infection bimonthly. Referred subjects were screened for HIV infection cross sectionally.47 Acute HIV-1 infections were defined by a negative HIV-1 serology using double enzyme-linked immunosorbent assay (ELISA) tests combined with a positive HIV-1 reverse transcriptase-polymerase chain reaction test. The clinical status at first presentation for acutely infected subjects is shown in Table 1. Recent HIV-1 infections were identified by detuned ELISA testing according to the standardized algorithm for recent HIV-1 seroconversion algorithm, which was developed for HIV-1B infection.48 Fiebig staging12 was determined for each case of recent infection. Subjects identified with acute or recent HIV infection were enrolled and followed for 12 months. Acutely infected subjects had weekly visits for the first 2 months, biweekly visits for the second 2 months, and monthly visits for the following 8 months. Subjects with recent HIV infection had monthly visits. Individuals whose CD4+ T-cell count dropped below 200 cells per cubic millimeter or developed opportunistic infection had access to antiretroviral therapy (ART) [Combivir (azidothymidine/lamivudine) 300/150 mg twice a day plus nevirapine 200 mg twice a day if female, or efavirenz 600 mg every day if male] free of charge, in accordance with Botswana National Program guidelines.

Table 1
Table 1
Image Tools
Figure 1
Figure 1
Image Tools

The study was approved by Institutional Review Boards in Botswana and the United States. Written informed consent was obtained from each participant.

A total of 80 subjects including 9 acute and 71 recent cases were enrolled from April 2004 to December 2007 (Fig. 1). The 70 subjects who completed at least 4 study visits were included in the current analysis. The analysis cohort consisted of 8 acute and 62 recent cases. The distribution of 62 recent cases by Fiebig staging12 was as follows: 10 (16.1%) cases were in stage IV; 18 (29.04%) cases were in stage V; 4 (6.5%) cases were on the edge of stages V and VI (extremely faint p31 band evident for a transition from stage V to stage VI); and 30 (48.4%) cases were in stage VI. There were 18 males (2 acute) and 52 females (6 acute). Analysis of virologic and immunologic parameters in recent infection was performed with adjustment for Feibig stage.12 The time since seroconversion was adjusted by 6 days for subjects in stage IV (3 days of phase 3 and 3 days to the midpoint of phase 4), by 44 days for subjects in stage V (9 days of phases 3 and 4 and 35 days to the midpoint of phase 5), by 79 days for stage V-VI (9 days of phases 3 and 4 and 70 days of phase 5), and by 117 days for subjects in stage VI (79 days for phases 3-5 and 38 days to the midpoint between the beginning of phase 6 and the mean of the estimated “recency window” of detuned ELISA). Participants' ages ranged from 19 to 53 years. There were no significant age differences between the acute and recent infection groups or between male and female participants. The demographics of the study population have been described elsewhere.47 All subjects were Botswana nationals.

Back to Top | Article Outline
Laboratory Methods

Antibodies to HIV-1/2 were tested by rapid ELISA Determine HIV-1/2 (Abbott Diagnostic Division, Belgium/Luxemburg) and Uni-Gold HIV Kit (Trinity Biotech, Wicklow Bay, Ireland); and/or regular ELISA Murex HIV 1.2.0 (Murex Biotech Ltd, Dartford, Kent, England) and Ortho HIV-1/2 Ab Capture (Ortho Diagnostics Systems Inc., Raritan, NJ). The ELISA kits used represented third-generation immunoassays that have an estimated “window period” of approximately 2 weeks.49 The detuned ELISA was performed by Vironostika HIV-1 Plus O Microelisa System (BioMerieux Inc, Durham, NC) according to the protocol published elsewhere.50,51 Plasma RNA load was quantified by the COBAS AmpliPrep/COBAS AMPLICOR HIV-1 MONITOR Test, version 1.5, according to the manufacturer's instructions. The method of viral load quantification used in the study has been certified by the Virology Quality Assurance at Rush University, Chicago, IL, as a part of the laboratory proficiency testing. The level of detection was from 50 copies per milliliter for the ultrasensitive method and 400 copies per milliliter for the standard method to 750,000 copies per milliliter. Specimens exceeding the upper level of detection were retested by 10-fold dilutions. Quantification of total proviral DNA was performed by a previously published method52 that is used routinely in our lab and targets HIV-1 gag p24 using the TaqMan Universal PCR master mix on the Applied Biosystems 7500 Real-Time PCR system. Quantification of CD4+ and CD8+ T cells was combined with CCR5 and CXCR4 enumeration and was performed by flow cytometry using the 4-color FACSCalibur. The TriTest kit (BD Biosciences, San Diego, CA) that includes a mixture of CD4-FITC/CD3-PE/CD8-PerCP monoclonal antibodies (mAb) was complemented by the CCR5-APC (clone 2D7, Pharmingen) or CXCR4-APC (clone 12G5, Pharmingen) mAb. To determine the HIV-1 subtype of the acute and recent infections studied, HIV-1 subtyping was performed. The V1C5 region of gp120 was amplified and sequenced in 8 of 8 acute cases and in a subset of 41 of 62 recent infections (the subset of recent infections includes those enrolled by December 2006; 21 cases enrolled in 2007 have not been sequenced). Three sequences per patient from the earliest available time points were aligned with 37 reference sequences from the Los Alamos HIV Sequence Database (http://www.hiv.lanl.gov/) and analyzed by the maximum likelihood (ML) and neighbor-joining (NJ) methods. The ML phylogeny was inferred by PhyML53 using the HKY model of nucleotide substitution and the tree was visualized by FigTree v.1.1.2.54 Analysis by the NJ method was performed with the MEGA4 program using the Kimura 2-parameter model of nucleotide substitution.55

Back to Top | Article Outline
Statistical Analysis

Data were summarized with means ± SDs. Comparisons between groups were based on t tests and χ2 tests for continuous and binary outcomes, respectively. Linear regression was used to assess time trends and correlations among CD4+ T-cell count, viral RNA, and proviral DNA. All reported P values are 2 sided.

Back to Top | Article Outline

RESULTS

Subjects are infected with HIV-1 subtype C. The branching topology of the ML phylogenetic tree demonstrated that env sequences spanning the V1C5 region from all genotyped subjects clustered with subtype C references. The ML results were comparable to the NJ analysis by MEGA4. Clustering of analyzed env sequences with subtype C references in the NJ tree was supported by a high bootstrap value of 99 of 100 replicates (data not shown). Therefore, the phylogenetic analysis indicated that subjects in this study were infected with HIV-1 subtype C (Fig. 2).

Figure 2
Figure 2
Image Tools
Back to Top | Article Outline
Viral RNA in Acute HIV-1 Subtype C Infection

The preseroconversion RNA+Ab status of 8 acutely infected subjects was confirmed by laboratory results of positive reverse transcriptase-polymerase chain reaction and negative double ELISA tests. Individual trajectories of viral RNA, proviral DNA, and CD4+ T cells are shown in Figure 3. A summary of viral RNA kinetics is presented in Table 2. The first viral RNA load corresponds to preseroconversion time point for all acutely infected subjects and is presented in Table 2. For our analyses, we assigned the peak of viral load to the higher value of viral RNA either before or at seroconversion.12 As evident from Figure 3 and Table 2, a high peak of viral RNA was evident in all acute cases, with a mean value of 6.25 ± 0.92 log10 copies per milliliter.

Table 2
Table 2
Image Tools
Figure 3
Figure 3
Image Tools

After seroconversion, heterogeneity was evident among acute cases with respect to patterns of change over time in viral load and CD4+ T-cell count. To assess this heterogeneity, we categorized acute cases into 1 of 2 categories. The patterns were apparent from differences in decline of viral RNA from peak, differences in levels of viral RNA within the first 2 months and from 50 to 200 days postseroconversion, and differences in levels and decline of CD4+ T cells to the clinically important thresholds of 350 and 200 (Fig. 4). We tested whether the parameters analyzed differ between groups with apparently different patterns, assuming that biological significance of the patterns can be confirmed or rejected based on statistical analyses between groups. Four of 8 subjects-1811, 2865, 3312, and 5018-showed slow decline of viral RNA load from peak and had high levels of viral RNA accompanied by low levels and faster decline of CD4+ T cells. In contrast, subjects 3430, 3505, 3603, and 5582 experienced a relatively fast decline of viral RNA from peak, lower levels of viral RNA, and higher levels of CD4+ T-cell count. Therefore we refer to the observed patterns as groups with slow and fast decline of viral RNA as the first indicator of potential differences between groups.

Figure 4
Figure 4
Image Tools

The peak values of viral RNA did not significantly differ between the defined groups [6.79 ± 0.97 log10 vs. 5.72 ± 0.53 log10 in groups with slow and fast decline of viral RNA, respectively; 95% confidence interval (CI) for difference of means: −0.28 to 2.41; P = 0.10]. No difference was found between groups in the rate of viral RNA decline within the first month after seroconversion, (mean slopes −0.046 ± 0.028 vs. −0.043 ± 0.028, P = 0.87). However, by 2 months after seroconversion, the rate of viral RNA decline from peak differed between groups, which was evident from a difference in RNA slopes, −0.014 ± 0.005 log10 copies per milliliter per day in subjects with slow decline of viral RNA vs. −0.028 ± 0.008 log10 copies per milliliter per day in subjects with fast decline (P = 0.022; 95% CI for difference of means 0.003 to 0.026). The rate of viral RNA decline from peak also differed between groups at later time points (mean slopes −0.008 ± 0.006 vs. −0.016 ± 0.002 at 4 months after seroconversion, P = 0.029; 95% CI for difference of means 0.001 to 0.015). The levels of viral RNA differed between groups at 2 months (Fig. 4B; 5.47 ± 0.46 vs. 3.72 ± 0.65 log10 copies/mL; P = 0.005) and at 6 months (Fig. 4C; 5.21 ± 0.21 vs. 3.71 ± 0.32 log10 copies/mL; P < 0.001). Differences in CD4+ T-cell count between groups were significant and are shown in the results section “Trajectories of CD4+ T-Cell Count in Acute Cases” below. In 3 of 4 subjects with slow decline of viral RNA-1811, 2865, and 3312-initiation of ART within the first year of infection was triggered by a drop of their CD4+ T-cell counts below 200 cells per cubic millimeter and occurred at 6, 7, and 12 months, respectively. As expected, ART resulted in reduction of viral RNA to below the detectable level within 1 month.

Back to Top | Article Outline
Proviral DNA Load in Acute HIV-1 Subtype C Infection

No peak or obvious time trends in the kinetics of cell-associated proviral DNA load over the 12 months after seroconversion were observed (Fig. 3). The mean of proviral DNA was 2.59 ± 0.69 log10. The range of proviral DNA varied from 0.58 log10 to 1.73 log10, suggesting a substantial intersubject variation. In contrast to viral RNA in plasma, no immediate effect of ART on the level of proviral DNA load was observed, although the follow-up time was only up to 12 months after seroconversion. No difference in proviral DNA means or range was evident between groups of subjects with fast and slow decline of viral RNA.

Back to Top | Article Outline
Trajectories of CD4+ T-Cell Count in Acute Infection

Subjects in the groups with slow and fast decline of viral RNA seemed to experience different CD4+ T-cell count trajectories during the first 6 months (Table 3). Subjects with slow decline of viral RNA exhibited lower means of CD4+ T cells, whereas subjects with fast decline of viral load had more stable CD4 trajectories (Fig. 3). For the periods 0-2 and 2-6 months, the CD4+ T-cell counts of the 2 groups were significantly different (Table 3; P < 0.001 and P = 0.002, respectively). The Kaplan-Meier plots showed significant differences between groups with slow and fast decline of viral RNA over time in reaching the clinically important thresholds of 350 (Fig. 4D; P = 0.011) and 200 (Fig. 4E; P = 0.046) CD4+ T-cell count per cubic millimeter. The CD4+ T-cell counts varied more in subjects with fast decline of viral RNA but remained relatively stable in subjects with slow decline (P = 0.014 and P = 0.037 for comparison of CD4 variance between groups during the periods 0-2 and 2-6 months, respectively).

Table 3
Table 3
Image Tools

The percentage of CCR5+CD4+ and CXCR4+CD4+ T cells fluctuated in a wide range between patients over the first year of HIV-1 subtype C infection (Fig. 3; pink and yellow trajectories in the CD4+ T cells graphs). The average of CCR5+CD4+ T cells over the first year of infection was 14.3% ± 5.5%. The overall mean value of CXCR4+CD4+ T cells was 57.6% ± 11.7%. The average level of CCR5+CD4+ T cells remained relatively stable during the first year after seroconversion. By contrast, relatively high fluctuations in the percentage of CXCR4+CD4+ T cells between patients were accompanied by a gradual decrease over the first year of infection.

Back to Top | Article Outline
Evolution of Viral Load and CD4+ T-Cell Count in Recent HIV-1 Subtype C Infection

The linear regression curve of viral RNA quantified in 62 patients with recent HIV-1 subtype C infection over 1 year summarizes and averages the viral set point at the level of about 4.0 log10 copies per milliliter (Fig. 5A). Most subjects had established viral RNA set points by the end of the first year of infection, which is evident from the shape of individual regression curves (Fig. 5B). Thirty-three subjects had shown an increase in viral RNA (mean rate of 0.0020 ± 0.0021 log10 copies/mL/day) and 29 had shown a decrease of viral RNA (mean rate of -0.0028 ± 0.0024 log10 copies/mL/day) over 1 year. The mean value of viral RNA in recent HIV-1 subtype C infection was 4.00 ± 0.97 log10 copies per milliliter and the median was 4.10 log10 copies per milliliter (Fig. 5C). The distribution of viral RNA means (Fig. 5D) was close to normal.

Figure 5
Figure 5
Image Tools

The average proviral DNA set point was at the level of 2.3 log10 copies per 106 peripheral blood mononuclear cell (PBMC) over 1 year of HIV-1 subtype C infection (Fig. 5E). Individual fluctuations of proviral DNA resulted in heterogeneous proviral set points (Fig. 5F), suggesting that more than 1 year may be required to establish a stable proviral DNA set point.

Approximately half of the patients (29/62 patients; 46.8%) showed an increase in proviral load at the rate of 0.0029 ± 0.0032 log10 copies per 106 PBMC per day with a relatively high interpatient variability evident from the SD value. Patients with negative slopes of proviral DNA (33/62 measured; 53.2%) demonstrated decline of DNA load at the rate of −0.0030 ± 0.0057 log10 copies per 106 PBMC per day with high interpatient variation. The average of proviral DNA load was 2.30 ± 0.64 log10 copies per 106 PBMC, whereas the median was 2.21 log10 copies per 106 PBMC (Fig. 5G), suggesting a relatively normal distribution of means (Fig. 5H).

The kinetics of CD4+ T cells in recent HIV-1 subtype C infection was relatively stable over 1 year (Fig. 5I). The CD4 curves of most subjects demonstrated little change over time (Fig. 5J), although a majority of patients (44/62; 71.0%) had negative slopes with declines of CD4+ T cells at −0.48 ± 0.47 cells per day. The average (median) of CD4+ T-cell counts in recent infections was 472 ± 225 (449) (Fig. 5K). The means of CD4+ T cells varied from 145 to 1437 between patients. The distribution of CD4+ T-cell means was unimodal and skewed to the right (Fig. 5L). The kinetics of CCR5+CD4+ and CCR5+CD4+ T cells was similar to acutely infected cases. The CCR5+CD4+ T-cell mean was 16.3% ± 6.5% ranging from 5.0% to 31.8%, whereas the CXCR4+CD4+ T-cell mean was 59.6% ± 13.3% fluctuating widely from 25.3% to 85.4%.

Back to Top | Article Outline
Associations in Recent HIV-1 Subtype C Infection

As expected, a positive correlation was evident between mean viral RNA and mean proviral DNA (Fig. 6A, R = 0.68, P < 0.001). Both mean plasma RNA and mean proviral DNA inversely correlated with mean CD4+ T-cell count (Fig. 6B, R = −0.57, P < 0.001; and Fig. 6C, R = −0.51, P < 0.001; respectively). There was a direct association between mean and range of CD4+ T cells (Fig. 6D, R = 0.78, P < 0.001), suggesting higher fluctuations of CD4+ T cells in patients with higher CD4 counts and a weak inverse association between mean and range of viral RNA (R = −0.253, P = 0.048). In contrast, no correlation between mean and range was found for viral RNA or proviral DNA. Mean viral RNA load seemed to be higher in younger participants. Regression analysis revealed a weak but statistically significant inverse association between mean plasma RNA and age (Fig. 6E, R = −0.36, P = 0.004).

Figure 6
Figure 6
Image Tools

Analysis of proviral DNA and CD4+ T-cell counts in viral RNA quartiles provide an alternative look at potential associations between virologic and immunologic markers in primary HIV-1 subtype C infection (Fig. 7). Lower CD4+ T-cell counts with median below 400 cells per cubic millimeter were associated with higher viral RNA quartiles, whereas subjects with median CD4+ T-cell counts above 400 cells per cubic millimeter had lower levels of viral RNA (Fig. 7A). Similarly, high proviral DNA was associated with high viral RNA (Fig. 7B).

Figure 7
Figure 7
Image Tools
Back to Top | Article Outline

DISCUSSION

Despite the predominance of HIV-1 subtype C worldwide, the evolution of virologic and immunologic parameters in primary subtype C infection have not been well characterized. Better understanding of early events in HIV-1 subtype C infection could guide prevention strategies, facilitate proper initiation of antiretroviral treatment, and possibly help in the design of a more efficient vaccine. The current prospective follow-up study of 8 (62) subjects identified with acute (recent) HIV-1 subtype C infection addresses the magnitude and kinetics of viral RNA, proviral DNA, CD4+ T cells, and their potential associations.

Our data might suggest differential although transient evolution of viral RNA after seroconversion. We did not mean to suggest that all acute HIV infections are of 2 distinct types, but rather to provide evidence of heterogeneity among acute cases with respect to patterns of change over time in viral load and CD4+ T-cell count. Based on substantial heterogeneity in viral RNA among subjects, we categorized acutely infected subjects into 1 of 2 categories. The patterns were apparent from differences in decline of viral RNA from peak (P = 0.022), in levels of viral RNA within the first 2 months (P = 0.005) and from 50 to 200 days (P < 0.001) postseroconversion and in levels (P < 0.001 and P = 0.002 for 0-2 and 2-6 months, respectively), and decline of CD4+ T cells to the clinically important thresholds of 350 (P = 0.011) and 200 (P = 0.046). Taken together, the differences between subjects described here might represent a composite pattern of viral dynamics in the early phase of HIV-1 subtype C infection that requires further analysis using a larger sample.

It is possible that reduction of viral RNA load in subjects with fast decline of viral RNA may be a result of successful innate and efficient adaptive immune responses at the very early stage of infection. If this assumption can be confirmed, a detailed analysis of immune responses in patients with fast decline of viral RNA may provide important insights for HIV vaccine design. A fraction of HIV-1 subtype C-infected subjects with slow decline of viral RNA may contribute disproportionally to the transmission of virus during the early stage of infection, and therefore, their identification might be of high public health priority.

Similarly to our findings, patterns of differential viral evolution have been described at the early phase of HIV-1 subtype B infection.7,11 Viral kinetics differed between patients with rapid and slow viral load drop during the first 3 months of infection,7 but not after 12 months. In contrast to our findings, no association with CD4+ T-cell dynamics, even transient, was found.7 Blattner et al11 reported that patients with rapid clearance of viral RNA had lower set points, whereas patients with less-rapid clearance had high set points.

In this study, the peak observed viral RNA before seroconversion, which might underestimate the true peak value, was high in all subjects with a mean value of 6.25 ± 0.92 log10 copies per milliliter, which was close to the RNA peak of 6.35 ± 0.71 log10 reported in subtype B in the Syndey cohort.7 In southern Africa, viral set point in subtype C infection has been reported to be similar to that of HIV-1 subtype B,37 which is consistent with our data. In Kenya, a viral RNA set point of 4.60 log10 was found in the adult population.39 In Senegal, a plasma RNA set point of 3.76 log1038 was reported in HIV-1 CRF02_AG infection, whereas lower levels of set point were observed in other non-CRF02_AG HIV-1 subtypes.38

The viral RNA set point in our study was at the level of 4.0 log10, although the mean in the higher RNA quartile was 5.21 ± 0.23 log10, suggesting that a fraction of HIV-1 subtype C-infected participants maintain high viral set points. In fact, 3 of 8 acutely infected subjects in this study dropped their CD4+ T cells below 200 and initiated ART within the first year postseroconversion. Potential reasons for such a rapid progression could include contributions from both host and virus. The host could be responsible for an altered frequency, copy number, or expression of AIDS-related genes, which could account for impaired immune response (eg, homozygosity of major histocompatibility complex class I HLA alleles). Viral infection could be caused by multiple viral variants (eg, dual or superinfection), and the transmitted population of viral quasispecies might have increased evolutionary rates or higher levels of transcription and/or replication. Interestingly, the initial fast decline of viral RNA in patients 3430 and 3505 was followed by fluctuations and increase of viral RNA at about 6 months, which might suggest a potential viral escape from immune recognition by 6 months after seroconversion. The kinetics of viral load and CD4+ T-cell count in acutely infected subjects in this study suggest that monitoring of viral RNA during the first few months postseroconversion may guide treatment strategies including selective early initiation of ART.

The importance of cell-associated proviral DNA load in the course of HIV-1 infection has been well documented.26-28,56-61 Although the evolution of proviral DNA in primary HIV-1 subtype C infection has not been described previously, levels of subtype C proviral DNA were associated with mother-to-child transmission62 and HIV transmission by breast feeding has been linked to proviral DNA in breast milk.63 We found no peak of cell-associated proviral DNA in acute HIV-1 subtype C infection. The levels of proviral DNA varied within and between patients, suggesting that no stable set point of proviral DNA has been reached by the end of the first year of infection. Subjects in the higher RNA quartile had high levels of proviral DNA. Variations in the provirus DNA testing cannot be ruled out as a potential cause of proviral DNA fluctuations. In contrast to the RNA trajectories, no immediate reduction of proviral DNA was observed in patients who started ART.

A gradual decrease of the CD4+ T-cell count was observed on a background of seemingly random fluctuations in both acute and recent infection. The level of CCR5+CD4+ T cells was relatively stable over the first year of HIV-1 subtype C infection with no evidence for depletion of CCR5+CD4+ T cells after seroconversion. In contrast, CXCR4+CD4+ T cells varied in a wide range both within and between subjects and demonstrated a trend toward reduction by the end of the first year. The mechanistic reason for the relative stability of CCR5+CD4+ T cells and declining CXCR4+CD4+ T cells is not clear at the moment and requires further studies.

Markers of viral replication, viral RNA, and proviral DNA correlated with one another, and were inversely related to CD4+ T cells, resembling a pattern described in subtype B studies.64 No significant gender differences in the relationships among viral RNA, proviral DNA, and CD4+ T cells were found during primary HIV-1 subtype C infection, which is consistent with previous reports on other non-B subtypes from Kenya39 and Uganda,65 but differs from subtype B studies that reported higher viral RNA load in men.14,66,67 The association between mean viral RNA and age is of interest and highlights the importance of HIV-1 prevention efforts which target a predominantly younger population. This finding is in agreement with the study from Kenya reporting a higher plasma RNA load in infants as compared with RNA load in adults39 and the age-dependent trend for RNA load in primary HIV-1 infection in the cohort of commercial sex worker from Senegal.38

In summary, we characterized viral dynamics in primary HIV-1 subtype C infection. The patterns of viral load and CD4+ T-cell evolution in acute and recent HIV-1 subtype C infection share many common characteristics with HIV-1 subtype B infection. It is likely that the peak of viral RNA in subtype C infection is higher than in other non-B subtype settings. Our results suggest that distinct patterns of viral RNA evolution may exist shortly after seroconversion in HIV-1 subtype C infection. Monitoring of key viral parameters including viral RNA in plasma and cell-associated proviral DNA load in the early phase of infection might be useful for timely initiation of ART.

Back to Top | Article Outline

ACKNOWLEDGMENTS

We are grateful to the subjects who participated in the Tshedimoso study in Botswana. We thank Gaseboloke Mothowaeng, Florence Modise, S'khatele Molefhabangwe, and Sarah Masole for their dedication and outstanding work in the clinic and outreach. We express thanks to David Nkwe for excellent laboratory support. We greatly appreciate the enthusiasm and strong commitment of Erin McDonald, Melissa Ketunuti, Carl Davis, Kenneth Onyait, and Mary Fran McLane in achieving the overall study goals. We thank the Botswana Ministry of Health, Gaborone City Council clinics, and the Gaborone voluntary counseling and testing Tebelopele for their ongoing support and collaboration. Finally, we thank Lendsey Melton for excellent editorial assistance.

Back to Top | Article Outline

REFERENCES

1. Osmanov S, Pattou C, Walker N, et al. Estimated global distribution and regional spread of HIV-1 genetic subtypes in the year 2000. J Acquir Immune Defic Syndr Hum Retrovirol. 2002;29:184-190.

2. Hemelaar J, Gouws E, Ghys PD, et al. Global and regional distribution of HIV-1 genetic subtypes and recombinants in 2004. AIDS. 2006;20:W13-W23.

3. UNAIDS. AIDS Epidemic Update: December 2005. Geneva, Switzerland: UNAIDS; 2005. Available at: http://www.unaids.org/epi/2005/doc/report_pdf.asp. Accessed November 7, 2008.

4. UNAIDS. Report on the Global AIDS Epidemic 2006. Geneva, Switzerland: UNAIDS; 2006. Available at: http://www.unaids.org/en/HIV_data/2006GlobalReport/default.asp. Accessed November 7, 2008.

5. UNAIDS. AIDS Epidemic Update 2007. Geneva, Switzerland: UNAIDS; 2007. Available at: http://www.unaids.org/en/KnowledgeCentre/HIVData/EpiUpdate/EpiUpdArchive/2006/. Accessed November 7, 2008.

6. Busch MP, Lee LL, Satten GA, et al. Time course of detection of viral and serologic markers preceding human immunodeficiency virus type 1 seroconversion: implications for screening of blood and tissue donors. Transfusion. 1995;35:91-97.

7. Kaufmann GR, Cunningham P, Kelleher AD, et al. Patterns of viral dynamics during primary human immunodeficiency virus type 1 infection. The Sydney Primary HIV Infection Study Group. J Infect Dis. 1998;178:1812-1815.

8. Kaufmann GR, Cunningham P, Zaunders J, et al. Impact of early HIV-1 RNA and T-lymphocyte dynamics during primary HIV-1 infection on the subsequent course of HIV-1 RNA levels and CD4+ T-lymphocyte counts in the first year of HIV-1 infection. Sydney Primary HIV Infection Study Group. J Acquir Immune Defic Syndr. 1999;22:437-444.

9. Kaufmann GR, Duncombe C, Zaunders J, et al. Primary HIV-1 infection: a review of clinical manifestations, immunologic and virologic changes. AIDS Patient Care STDS. 1998;12:759-767.

10. Little SJ, McLean AR, Spina CA, et al. Viral dynamics of acute HIV-1 infection. J Exp Med. 1999;190:841-850.

11. Blattner WA, Ann Oursler K, Cleghorn F, et al. Rapid clearance of virus after acute HIV-1 infection: correlates of risk of AIDS. J Infect Dis. 2004;189:1793-1801.

12. Fiebig EW, Wright DJ, Rawal BD, et al. Dynamics of HIV viremia and antibody seroconversion in plasma donors: implications for diagnosis and staging of primary HIV infection. AIDS. 2003;17:1871-1879.

13. Schacker TW, Hughes JP, Shea T, et al. Biological and virologic characteristics of primary HIV infection. Ann Intern Med. 1998;128:613-620.

14. Sterling TR, Vlahov D, Astemborski J, et al. Initial plasma HIV-1 RNA levels and progression to AIDS in women and men. N Engl J Med. 2001;344:720-725.

15. Clark S, Saag M, Decker W, et al. High titers of cytopathic virus in plasma of patients with symptomatic primary HIV-1 infection. N Engl J Med. 1991;324:954-960.

16. Daar ES, Moudgil T, Meyer RD, et al. Transient high levels of viremia in patients with primary human immunodeficiency virus type 1 infection. N Engl J Med. 1991;324:961-964.

17. Graziosi C, Pantaleo G, Butini L, et al. Kinetics of human immunodeficiency virus type 1 (HIV-1) DNA and RNA synthesis during primary HIV-1 infection. Proc Natl Acad Sci U S A. 1993;90:6405-6409.

18. Kahn JO, Walker BD. Acute human immunodeficiency virus type 1 infection. N Engl J Med. 1998;339:33-39.

19. Mellors JW, Rinaldo CR, Gupta P, et al. Prognosis in HIV-1 infection predicted by the quantity of virus in plasma. Science. 1996;272:1167-1170.

20. Mellors JW, Kingsley LA, Rinaldo CRJ, et al. Quantitation of HIV-1 RNA in plasma predicts outcome after seroconversion. Ann Intern Med. 1995;122:573-579.

21. Henrard DR, Phillips JF, Muenz LR, et al. Natural history of HIV-1 cell-free viremia. JAMA. 1995;274:554-558.

22. Picker LJ, Maino VC. The CD 4(+) T cell response to HIV-1. Curr Opin Immunol. 2000;12:381-386.

23. Picker LJ. Immunopathogenesis of acute AIDS virus infection. Curr Opin Immunol. 2006;18:399-405.

24. Brenchley JM, Schacker TW, Ruff LE, et al. CD4+ T cell depletion during all stages of HIV disease occurs predominantly in the gastrointestinal tract. J Exp Med. 2004;200:749-759.

25. Mehandru S, Poles MA, Tenner-Racz K, et al. Primary HIV-1 infection is associated with preferential depletion of CD4+ T lymphocytes from effector sites in the gastrointestinal tract. J Exp Med. 2004;200:761-770.

26. Rouzioux C, Hubert JB, Burgard M, et al. Early levels of HIV-1 DNA in peripheral blood mononuclear cells are predictive of disease progression independently of HIV-1 RNA levels and CD4+ T cell counts. J Infect Dis. 2005;192:46-55.

27. Verhofstede C, Reniers S, Van Wanzeele F, et al. Evaluation of proviral copy number and plasma RNA level as early indicators of progression in HIV-1 infection: correlation with virological and immunological markers of disease. AIDS. 1994;8:1421-1427.

28. Strain MC, Little SJ, Daar ES, et al. Effect of treatment, during primary infection, on establishment and clearance of cellular reservoirs of HIV-1. J Infect Dis. 2005;191:1410-1418.

29. Goujard C, Bonarek M, Meyer L, 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.

30. Craib KJ, Strathdee SA, Hogg RS, et al. Serum levels of human immunodeficiency virus type 1 (HIV-1) RNA after seroconversion: a predictor of long-term mortality in HIV infection. J Infect Dis. 1997;176:798-800.

31. Lefrere JJ, Roudot-Thoraval F, Mariotti M, et al. The risk of disease progression is determined during the first year of human immunodeficiency virus type 1 infection. J Infect Dis. 1998;177:1541-1548.

32. Vanhems P, Hirschel B, Phillips AN, et al. Incubation time of acute human immunodeficiency virus (HIV) infection and duration of acute HIV infection are independent prognostic factors of progression to AIDS. J Infect Dis. 2000;182:334-337.

33. Hubert JB, Burgard M, Dussaix E, et al. Natural history of serum HIV-1 RNA levels in 330 patients with a known date of infection. The SEROCO Study Group. AIDS. 2000;14:123-131.

34. O'Brien TR, Rosenberg PS, Yellin F, et al. Longitudinal HIV-1 RNA levels in a cohort of homosexual men. J Acquir Immune Defic Syndr Hum Retrovirol. 1998;18:155-161.

35. O'Brien TR, Blattner WA, Waters D, et al. Serum HIV-1 RNA levels and time to development of AIDS in the Multicenter Hemophilia Cohort Study. JAMA. 1996;276:105-110.

36. Deeks SG, Kitchen CM, Liu L, 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.

37. Gray CM, Williamson C, Bredell H, et al. Viral dynamics and CD4+ T cell counts in subtype C human immunodeficiency virus type 1-infected individuals from southern Africa. AIDS Res Hum Retroviruses. 2005;21:285-291.

38. Sarr AD, Eisen G, Gueye-Ndiaye A, et al. Viral dynamics of primary HIV-1 infection in Senegal, West Africa. J Infect Dis. 2005;191:1460-1467.

39. Richardson BA, Mbori-Ngacha D, Lavreys L, et al. Comparison of human immunodeficiency virus type 1 viral loads in Kenyan women, men, and infants during primary and early infection. J Virol. 2003;77:7120-7123.

40. Long EM, Martin HL Jr, Kreiss JK, et al. Gender differences in HIV-1 diversity at time of infection. Nat Med. 2000;6:71-75.

41. Li B, Decker JM, Johnson RW, et al. Evidence for potent autologous neutralizing antibody titers and compact envelopes in early infection with subtype C human immunodeficiency virus type 1. J Virol. 2006;80:5211-5218.

42. Li M, Salazar-Gonzalez JF, Derdeyn CA, et al. Genetic and neutralization properties of subtype C human immunodeficiency virus type 1 molecular env clones from acute and early heterosexually acquired infections in southern Africa. J Virol. 2006;80:11776-11790.

43. Lavreys L, Baeten JM, Overbaugh J, et al. Virus load during primary human immunodeficiency virus (HIV) type 1 infection is related to the severity of acute HIV illness in Kenyan women. Clin Infect Dis. 2002;35:77-81.

44. Pilcher CD, Price MA, Hoffman IF, et al. Frequent detection of acute primary HIV infection in men in Malawi. AIDS. 2004;18:517-524.

45. Dyer JR, Kazembe P, Vernazza PL, et al. High levels of human immunodeficiency virus type 1 in blood and semen of seropositive men in sub-Saharan Africa. J Infect Dis. 1998;177:1742-1746.

46. Lacabaratz-Porret C, Urrutia A, Doisne JM, et al. Impact of antiretroviral therapy and changes in virus load on human immunodeficiency virus (HIV)-specific T cell responses in primary HIV infection. J Infect Dis. 2003;187:748-757.

47. Novitsky V, Woldegabriel E, Wester C, et al. Identification of primary HIV-1C infection in Botswana. AIDS Care. 2008;20:806-811.

48. Janssen RS, Satten GA, Stramer SL, et al. New testing strategy to detect early HIV-1 infection for use in incidence estimates and for clinical and prevention purposes. JAMA. 1998;280:42-48.

49. Speers D, Phillips P, Dyer J. Combination assay detecting both human immunodeficiency virus (HIV) p24 antigen and anti-HIV antibodies opens a second diagnostic window. J Clin Microbiol. 2005;43:5397-5399.

50. Rawal BD, Degula A, Lebedeva L, et al. Development of a new less-sensitive enzyme immunoassay for detection of early HIV-1 infection. J Acquir Immune Defic Syndr. 2003;33:349-355.

51. Kothe D, Byers RH, Caudill SP, et al. Performance characteristics of a new less sensitive HIV-1 enzyme immunoassay for use in estimating HIV seroincidence. J Acquir Immune Defic Syndr. 2003;33:625-634.

52. Novitsky VA, Gilbert PB, Shea K, et al. Interactive association of proviral load and IFN-gamma-secreting T cell responses in HIV-1C infection. Virology. 2006;349:142-155.

53. Guindon S, Gascuel O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 2003;52:696-704.

54. Rambaut A. FigTree [computer program]. Version 1.1.2; 2008. Available at: http://tree.bio.ed.ac.uk/software/figtree/.

55. Tamura K, Dudley J, Nei M, et al. MEGA4: Molecular Evolutionary Genetics Analysis (MEGA) software version 4.0. Mol Biol Evol. 2007;24:1596-1599.

56. Re MC, Vitone F, Bon I, et al. Meaning of DNA detection during the follow-up of HIV-1 infected patients: a brief review. New Microbiol. 2006;29:81-88.

57. Lillo F, Grasso M, Lodini S, et al. HIV-1 DNA and RNA kinetics in primary HIV infection. J Biol Regul Homeost Agents. 2002;16:49-52.

58. Pierson T, McArthur J, Siliciano RF. Reservoirs for HIV-1: mechanisms for viral persistence in the presence of antiviral immune responses and antiretroviral therapy. Annu Rev Immunol. 2000;18:665-708.

59. Russell RR, Bowmer MI, Nguyen C, et al. HIV-1 DNA burden in peripheral blood CD4+ cells influences disease progression, antiretroviral efficacy, and CD4+ T-cell restoration. Viral Immunol. 2001;14:379-389.

60. Yerly S, Gunthard HF, Fagard C, et al. Proviral HIV-DNA predicts viral rebound and viral setpoint after structured treatment interruptions. AIDS. 2004;18:1951-1953.

61. Pellegrin I, Caumont A, Garrigue I, et al. Predictive value of provirus load and DNA human immunodeficiency virus genotype for successful abacavir-based simplified therapy. J Infect Dis. 2003;187:38-46.

62. Montano MA, Russell MS, Gilbert P, et al. Comparative prediction of perinatal human immunodeficiency virus type 1 transmission, using multiple virus load markers. J Infect Dis. 2003;188:406-413.

63. Koulinska IN, Villamor E, Chaplin B, et al. Transmission of cell-free and cell-associated HIV-1 through breast-feeding. J Acquir Immune Defic Syndr. 2006;41:93-99.

64. Lathey JL, Hughes MD, Fiscus SA, et al. Variability and prognostic values of virologic and CD4 cell measures in human immunodeficiency virus type 1-infected patients with 200-500 CD4 cells/mm(3) (ACTG 175). AIDS Clinical Trials Group Protocol 175 Team. J Infect Dis. 1998;177:617-624.

65. Gray RH, Li X, Wawer MJ, et al. Determinants of HIV-1 load in subjects with early and later HIV infections, in a general-population cohort of Rakai, Uganda. J Infect Dis. 2004;189:1209-1215.

66. Farzadegan H, Hoover DR, Astemborski J, et al. Sex differences in HIV-1 viral load and progression to AIDS. Lancet. 1998;352:1510-1514.

67. Rezza G, Lepri AC, d'Arminio Monforte A, et al. Plasma viral load concentrations in women and men from different exposure categories and with known duration of HIV infection. I.CO.N.A. Study Group. J Acquir Immune Defic Syndr. 2000;25:56-62.

Cited By:

This article has been cited 6 time(s).

Virology
Dynamics and timing of in vivo mutations at Gag residue 242 during primary HIV-1 subtype C infection
Novitsky, V; Wang, R; Margolin, L; Baca, J; Moyo, S; Musonda, R; Essex, M
Virology, 403(1): 37-46.
10.1016/j.virol.2010.04.001
CrossRef
Plos One
Timing Constraints of In Vivo Gag Mutations during Primary HIV-1 Subtype C Infection
Novitsky, V; Wang, R; Margolin, L; Baca, J; Kebaabetswe, L; Rossenkhan, R; Bonney, C; Herzig, M; Nkwe, D; Moyo, S; Musonda, R; Woldegabriel, E; van Widenfelt, E; Makhema, J; Lagakos, S; Essex, M
Plos One, 4(): -.
ARTN e7727
CrossRef
Nature Reviews Immunology
Monkeying around with HIV vaccines: using rhesus macaques to define 'gatekeepers' for clinical trials
Shedlock, DJ; Silvestri, G; Weiner, DB
Nature Reviews Immunology, 9(): 717-728.
10.1038/nri2636
CrossRef
Plos One
HIV-1 Subtype C-Infected Individuals Maintaining High Viral Load as Potential Targets for the "Test-and-Treat'' Approach to Reduce HIV Transmission
Novitsky, V; Wang, R; Bussmann, H; Lockman, S; Baum, M; Shapiro, R; Thior, I; Wester, C; Wester, CW; Ogwu, A; Asmelash, A; Musonda, R; Campa, A; Moyo, S; van Widenfelt, E; Mine, M; Moffat, C; Mmalane, M; Makhema, J; Marlink, R; Gilbert, P; Seage, GR; DeGruttola, V; Essex, M
Plos One, 5(4): -.
ARTN e10148
CrossRef
JAIDS Journal of Acquired Immune Deficiency Syndromes
Better Control of Early Viral Replication Is Associated With Slower Rate of Elicited Antiviral Antibodies in the Detuned Enzyme Immunoassay During Primary HIV-1C Infection
Novitsky, V; Wang, R; Kebaabetswe, L; Greenwald, J; Rossenkhan, R; Moyo, S; Musonda, R; Woldegabriel, E; Lagakos, S; Essex, M
JAIDS Journal of Acquired Immune Deficiency Syndromes, 52(2): 265-272.
10.1097/QAI.0b013e3181ab6ef0
PDF (542) | CrossRef
AIDS
High prevalence of symptomatic acute HIV infection in an outpatient ward in southern Mozambique: identification and follow-up
Ferreira, E; Alonso, P; Naniche, D; Serna-Bolea, C; Muñoz, J; Almeida, JM; Nhacolo, A; Letang, E; Nhampossa, T
AIDS, 24(4): 603-608.
10.1097/QAD.0b013e328335cda3
PDF (479) | CrossRef
Back to Top | Article Outline
Keywords:

acute HIV-1 infection; CD4 count; HIV-1 subtype C; primary HIV-1 infection; natural history; viral load

© 2009 Lippincott Williams & Wilkins, Inc.

Login

Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.