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.
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.
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.
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).
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).
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).
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 log10 38 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.
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
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.
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