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. Author manuscript; available in PMC: 2020 May 18.
Published in final edited form as: Sci Transl Med. 2019 Oct 9;11(513):eaaw5589. doi: 10.1126/scitranslmed.aaw5589

The Replication-Competent HIV-1 Latent Reservoir is Primarily Established Near the Time of Therapy Initiation

Melissa-Rose Abrahams 1,†, Sarah B Joseph 2,3,†, Nigel Garrett 4, Lynn Tyers 1, Matthew Moeser 3, Nancie Archin 5, Olivia D Council 2, David Matten 1, Shuntai Zhou 3, Deelan Doolabh 1, Colin Anthony 1, Nilu Goonetilleke 2,5, Salim Abdool Karim 4,6, David M Margolis 2,5, Sergei Kosakovsky Pond 7, Carolyn Williamson 1,8,*, Ronald Swanstrom 3,9,*
PMCID: PMC7233356  NIHMSID: NIHMS1060174  PMID: 31597754

Abstract

Although antiretroviral therapy (ART) is highly effective at suppressing HIV-1 replication, the virus persists as a latent reservoir in resting CD4+ T cells during therapy. This reservoir forms even when ART is initiated early after infection, but the dynamics of its formation are largely unknown. The viral reservoirs of individuals who initiate ART in chronic infection are generally larger and genetically more diverse than those of individuals who initiate therapy in acute infection, consistent with the hypothesis that the reservoir is formed continuously throughout untreated infection. To determine when viruses enter the latent reservoir, we compared sequences of replication-competent viruses from resting CD4+ T cells from nine women on therapy to viral sequences circulating in blood collected longitudinally prior to therapy. We found that, on average, 71% of the unique viruses from the latent reservoir were most genetically similar to viruses replicating just prior to ART initiation. This proportion is far greater than would be expected if the reservoir formed continuously and was always long-lived. We conclude that ART alters the host environment in a way that allows the formation or stabilization of a majority of the long-lived latent HIV-1 reservoir.

One Sentence Summary:

Most of the long-lived, replication-competent HIV-1 reservoir is formed near the time of therapy initiation.

Introduction

Infection with human immunodeficiency virus type 1 (HIV-1) results in active viral replication in the face of the host immune response, eventually leading to the loss of its primary target cell for replication, CD4+ T cells, and immunodeficiency. The use of multiple potent antiviral drugs stops viral replication and disease progression. However, discontinuation of antiretroviral therapy (ART) results in the rapid rebound of virus, indicating that while therapy suppresses viral replication, HIV-1 is able to persist in an infectious state for years. The best characterized reservoir in individuals on ART is integrated viral DNA in resting memory CD4+ T cells (1–3). One quantitative measure of the reservoir is the number of resting CD4+ T cells that can be induced to produce replication-competent virus after stimulation of the cells in culture, called the quantitative viral outgrowth assay (QVOA). Using this assay, it has been estimated that in people on therapy approximately one in a million resting CD4+ T cells can be induced to produce replication-competent virus, and that this latent reservoir has a half-life of 44 months (4, 5). Given the large number of resting CD4+ T cells in the body, it is impossible to cure HIV-1 by waiting for the infected cells to decay. In addition, clonal expansion of latently infected T cells adds another mechanism for persistence of virus in the body over time (6–13).

In people on ART, greater than 90% of the proviral genomes in resting CD4+ T cells are defective (14, 15). These defective genomes may contribute to continued immune activation and exhaustion (16, 17), but are not the source of the rebound virus if ART is stopped. In contrast, most intact proviruses are theoretically capable of producing replication-competent virus, but the frequency of cells harboring intact proviruses is approximately 30 times higher than the frequency of cells that can be induced to produce virus in a QVOA (15). In addition, the number of resting CD4+ T cells producing outgrowth viruses in QVOA increases with extra rounds of cell stimulation (14), indicating that the typical QVOA using a single round of stimulation underestimates the number of inducible proviruses. Taken together, these results imply that the reservoir of replication-competent proviruses is larger than that measured by standard QVOA. It is currently unknown whether the discrepancy arises because virus expression and outgrowth from resting CD4+ T cells is a noisy stochastic process, or because latency is generated by multiple mechanisms, some of which are not readily reversed in a standard QVOA, or both.

The most widely accepted model of how the reservoir forms involves the infection of a CD4+ T cell as it is transitioning to a resting state (18). However, little is known about when this happens during the course of infection. The reservoir is established even when ART is initiated early (19–21), i.e. virus rebounds with the subsequent discontinuation of therapy even when starting ART soon (e.g. within days) after infection. This suggests that there is early and continuous formation of the reservoir during the period prior to therapy initiation. However, studies of viral DNA have yielded conflicting data about the timing of reservoir formation. One report claimed evidence of continuous introduction of viral DNA into the long-lived reservoir (22), while another report found that most of the viral DNA in the reservoir comes from virus replicating around the time of therapy initiation (23). The timing of reservoir formation is an important question since an understanding of when the reservoir forms could provide new opportunities for limiting its formation. In this study we perform the first quantitative analysis aimed at understanding when replication-competent virus enters the long-lived latent reservoir.

We compared sequences of replication-competent viruses induced from on-ART resting CD4+ T cells (outgrowth viruses, OGVs) to viral sequences in blood collected longitudinally pre-ART. We found that, on average, 71% of OGVs were most genetically similar to viruses replicating just prior to therapy initiation. This proportion is far greater than would be expected if the reservoir were formed continuously. Our results indicate that the initiation of ART alters the host environment to promote latency and/or dramatically extends the half-life of latently infected cells, suggesting new approaches that could be employed at the time of therapy initiation to block formation of a majority of the long-lived CD4+ T cell reservoir.

Results

Cohort and source of viral sequences for the analysis of the latent reservoir

We investigated when replication-competent viruses enter the reservoir by examining viruses induced to replicate from resting CD4+ T cells in the context of QVOA. OGVs were isolated from blood-derived cells of nine women on ART. The women were participants in the CAPRISA 002 cohort, based in KwaZulu-Natal, South Africa, who were originally enrolled into the cohort during acute/primary HIV-1 infection (24). QVOA measures the replication-competent viral reservoir and estimates the number of resting (CD25low, CD69-, HLA-DR-) CD4+ T cells that can produce replication-competent virus following stimulation. This provides a representation of viruses capable of rebound upon ART interruption as opposed to assays that evaluate total viral DNA genomes, the majority of which cannot give rise to replicating virus. The women were ART-naïve for an average of 4.5 years. ART was initiated in accordance with national guidelines in place at the time. The participants had been on suppressive ART for an average of 5.0 years when blood samples were collected for QVOA (Table 1, Fig. 1). Plasma-derived viral RNA genomes were sequenced at multiple time points pre-ART (on average every six months from acute/early infection (Supplementary Data Fig. 1), and these evolving sequences were compared to the sequences of the replication-competent OGVs subsequently grown out of the latent reservoir.

Table 1.

Clinical information for the nine women from the CAPRISA 002 cohort

Participant ID Drug regimen* Time ART naïve (weeks) Time on ART (weeks) CD4 count at ART initiation (cells/μl) CD4 count at QVOA blood draw (cells/μl) Number of outgrowth viruses Percent clonal outgrowth viruses Percent outgrowth viruses from 1 year pre-ART Percent Outgrowth viruses from within one year of transmission**
CAP188 EFV/3TC/TDF
EFV/FTC/TDF		 247 246 364 430 12 0 50 0
CAP206 EFV/3TC/TDF
EFV/FTC/TDF		 274 286 289 978 21 19 78 5
CAP217 EFV/3TC/TDF 360 239 287 657 17 12 44 0
CAP257 3TC/NVP/TDF
EFV/3TC/TDF
EFV/FTC/TDF		 249 318 170 596 48 17 89 0
CAP287 EFV/FTC/TDF 260 223 411 559 6 0 67 17
CAP288 EFV/FTC/TDF 210 275 318 860 7 0 100 0
CAP302 EFV/3TC/AZT
EFV/3TC/AZT
EFV/FTC/TDF		 164 270 307 541 8 38 17 17
CAP316 3TC/AZT/LPV/r
EFV/FTC/TDF		 215 215 436 888 11 0 91 0
CAP336 EFV/3TC/TDF 142 258 164 558 17 47 100 0
Average = 236 259 305 674 16 15 71 4
Median = 247 258 307 596 12 12 78 0
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EFV = Efavirenz, 3TC = Lamivudine, TDF = Tenofovir, NVP = Nevirapine, AZT = Zidovudine, LPV/r = Lopinavir/Ritonavir,

FTC = Emtricitabine

ART = antiretroviral therapy; QVOA = quantitative virus outgrowth assay

*

Regimen changes indicated below original/prior regimen

**

Acute/early infection here refers to sampling time points in the first year of study enrolment

Fig 1. Viral Load and Suppression History of Nine Participants from the CAPRISA 002 Acute Infection Cohort.

Fig 1.

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The graph shows the viral loads of HIV-1 RNA in the blood pre- and post-therapy, with the time of therapy initiation designated as T=0. Each participant is designated by a different color and the time of blood collection for assessing the reservoir is designated by verticle arrows.

Sequencing plasma virus from throughout untreated infection and OGVs

Phylogenetic trees of sequences from longitudinally sampled viruses generally possess a ladder-like structure as the viral population diverges from its homogeneous founder (25–27) thereby providing a strong phylogenetic and temporal signal for distinguishing populations over time. This allowed us to estimate when each OGV entered the long-lived reservoir by identifying the pre-ART viral sequences that it was most closely related to, and therefore the pre-ART time point when it most likely entered the long-lived reservoir. We sequenced five regions (one in gag, one in nef and three in env), in three viral genes known to have rapid rates of evolution driven by immune responses in the host (28, 29).

An average of nine pre-ART plasma samples were sequenced per participant, using the Illumina MiSeq platform with a Primer ID protocol that allowed us to deeply sample the replicating and evolving viral populations in plasma pre-ART while correcting for sequencing errors and template resampling (30, 31). Phylogenetic trees were constructed for each region that yielded sequences at most pre-ART time points. Two regions were analyzed for one partipicant, and at least three regions were analyzed for the remaining eight particpants. In addition, viral RNA was isolated from the culture supernatant from QVOA wells for analysis of OGV sequences from the replication-competent latent reservoir. Near full-length OGV sequences were generated using the Pacific Biosciences (PacBio) SMRT platform with barcoded primers that likewise allowed for virtually complete error correction; we estimate a residual sequencing error rate of 1 per 50,000 bases based on repeated sequencing of a defective virus produced from a cell clone (8E5 cells; (32)). The sequence of each OGV was determined from a bulk PCR product of the supernatant virus given that the resting CD4+ T cells had been diluted to end point so that a single virus was present in the culture well in most cases. Within the different OGVs from a subject, an average of 15% of the sequences were clonal using a definition in which sequences are clonal if they are identical or differ by 1 base (i.e. 1 difference out of approximately 8850 bases sequenced, likely due to rare sequencing errors and errors within the first round of viral replication in the well; Table 1). Since these OGVs were likely derived from cells that had undergone clonal proliferation in the host (8, 12), we assumed that clonal OGVs represent a single entry into the reservoir followed by subsequent cellular expansion and therefore included only one representative sequence from each putative clonal population in summarizing our findings.

Analysis of the timing of reservoir formation

Phylogenetic analysis revealed that reservoir OGV sequences predominantly clustered in the tree with plasma viruses from the year preceding ART initiation (representative trees for two participants are shown in Fig. 2A–B), although for some participants the OGVs were distributed throughout the tree (two examples are shown in Fig. 3A–B; trees for all participants are shown in Supplementary Data Figs. 2–10). Three methods: patristic distance, clade support and phylogenetic placement (33, 34), were used to estimate when each individual OGV entered the reservoir (pipeline and additional description of the methods are available at https://github.com/veg/ogv-dating). The three methods were applied to each of the multiple regions of the genome sequenced, and the weighted median of these estimates was used to summarize when each OGV entered the reservoir (Fig. 4 for the data shown in Figs. 2 and 3, and Supplementary Data Fig. 11).

Fig. 2. The Majority of Outgrowth Viruses (OGVs) Are Most Closely Related to Viruses Replicating Near the Time of ART Initiation.

Fig. 2.

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Phylogenetic analyses of one genomic region for each of two participants are shown. Pre-ART sequences are colored red (year after transmission) to blue (year before ART) and outgrowth viruses are shown in pink. The clustering of outgrowth sequences (pink) with sequences from the year before ART (blue) indicates that these participants each had a majority of OGVs with timing assignments for entry into the reservoir in the year preceding ART. (a) CAP257 had 44 unique OGVs of which 89% were most closely related to viruses replicating within one year pre-ART, and 7% to viruses replicating 142 weeks pre-ART. (b) CAP288 had 7 unique OGVs, all (100%) of which were most closely related to viruses replicating within one year pre-ART.

Fig. 3. A Subset of Participants Had Outgrowth Viruses (OGVs) Most Closely Related to Viruses Replicating Throughout Untreated Infection.

Fig. 3.

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Phylogenetic analyses of one genomic region for each of two participants are shown. Selected participants each had OGVs with timing assignments from a wide range of times pre-ART, as illustrated by the clustering of outgrowth sequences (pink) with sequences from different pre-ART time points (red to blue). (a) CAP217 had 17 OGVs, two of which were clones. Of the genetically unique OGVs, 44% were most closely related to viruses replicating within one year pre-ART, and 31% to viruses replicating more than 2 years before ART. (b) CAP302 had 6 unique OGVs of which one was from the year before ART and one was from the year after transmission.

Fig. 4. Timing of Reservoir Entry for OGVs as Estimated by Combining Three Methods.

Fig. 4.

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Three methods (patristic distance, clade support and phylogenetic placement) were used to generate estimates of OGV entry into the reservoir by comparing multiple regions of the OGV genome to that of viral RNA isolated from the plasma pre-ART. (a, b) Estimates are for two participants with OGVs that typically entered the reservoir near the time of ART initiation (CAP257 and CAP288) and (c, d) two with OGVs from throughout untreated infection (CAP217 and CAP302). Each OGV is represented as a vertical line with up to 15 estimates along that line. For each OGV, estimates of reservoir entry were summarized as a weighted median, shown as a horizonal line and represented in a colored bar along the x axis. Boxes indicate OGVs with highly variable estimates (SD > 1 yr). Additional phylogenetic analyses of OGVs with variable estimates found that one (designated with an ‘x’) was in a time-specific lineage that did not correspond to the weighted median. All other OGVs were found in time-specific lineages that corresponded to the weighted median.

The percentage of the OGVs representing viruses replicating in the last year before the initiation of therapy (i.e. “late” virus) was calculated for each of the nine participants (Table 1, Fig. 5A). The median of these values was 78%, and the average percent was 71%. In four of nine women, over 90% of OGVs, and in six of nine women at least 67% of OGVs, entered the reservoir in the year proceeding ART. In contrast, on average only 4% of OGVs entered the reservoir within the first year of infection (Table 1, Fig. 5A).

Fig. 5. Skewing of Reservoir Virus to Virus Replicating Near the Time of ART Initiation May be Explained by a Change in the Half-Life of Latently Infected Cells in the Presence of ART.

Fig. 5.

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(a) All outgrowth virus (OGV) sequences are shown in an approximate maximum-likelihood tree. Branch tips are colored according to the estimated time when each OGV entered the reservoir. With OGVs from the year after transmission in red and OGVs from the year before ART in blue. For each participant, the OGV timing distribution is illustrated in a pie chart with the percentage of OGVs from the year pre-ART listed. Overall, 71% of OGVs were produced by cells infected near the time of ART initiation. (b) Two models were considered to explain this pattern. One model assumes that latently infected cells have a long half-life of 176 weeks (44 months) both prior to and during therapy, and predicts that the reservoir will contain variants from throughout untreated infection. The other model assumes that latently infected cells have a short half-life in untreated infection (here chosen as 2 weeks) that then stabilizes to a long half-life during therapy. For the majority of participants (four shown here), the observed data most closely resembles the predicted pattern if latently infected cells have a short half-life prior to ART initiation and then decay slowly after ART initiation.

We estimate that 15% of OGVs were induced from clonally expanded cells (i.e. differed from another OGV by no more than 1 mutation). However, because we sampled a small number of OGVs from some participants, we were only able to identify an OGV as being from an expanded cell clone if that clone was present at a high frequency in the population. In this analysis of 25 clonal OGVs (25 total clones in 10 clone groups), 60% of the clone groups formed with a virus that was replicating in the year prior to ART, with the remaining 40% forming from viruses that had been replicating earlier. There was no statistical difference between the frequency of “late” viruses observed as clones in the OGVs versus their frequency in unique OGVs (Fisher’s exact test P=0.33). Thus there is no evidence (from this limited sampling) that clonal expansion differentially impacted OGVs that entered the reservoir earlier in untreated infection vs. those that entered near the time of ART initiation.

Modeling formation of the long-lived HIV-1 reservoir

Our analyses suggest that cells infected with HIV-1 near the time of ART initiation (or clones of those cells) are more likely to contribute to the long-lived, replication-competent reservoir than cells infected at earlier time points. We considered two simple models to explain our findings. The first model assumes that infected resting CD4+ T cells, which have been observed in untreated infection (35), have a half-life of 44 months (176 weeks), comparable to their half-life on therapy (Fig. 5B, left panel) (4, 5). Under this model the reservoir is expected to contain variants sampled throughout untreated infection with those from later in infection being only slightly more common. The second model assumes that infected resting CD4+ T cells have a short half-life in untreated HIV-1 infection (here set at 2 weeks) that then stabilizes to a longer half-life with the onset of effective ART (Fig. 5B, middle panel). Similar to our observations for two participants with predominantly late reservoir virus (CAP257 and CAP288, Fig. 5B, right panel), this second model predicts that most of the reservoir is seeded by viruses replicating around the time of ART initiation. Thus, the observed high similarity of viruses replicating proximal to ART initiation to those in the latent reservoir can be explained by infected resting CD4+ T cells from early in infection rapidly decaying before they contribute to the long-lived reservoir. However, the second model does not fit the data for the subset of participants (e.g., CAP217 and CAP302) who had virus enter the reservoir over the entire period prior to the initiation of therapy (Fig. 5B, right panel).

Discussion

In this study we have taken advantage of the availability of archived pre-ART viremic samples in the CAPRISA 002 cohort to establish a detailed temporal record of viral evolution in each of nine women enrolled from the time of primary HIV-1 infection. Using this temporal record of viral evolution, we estimated when viruses that had persisted after more than four years on therapy entered the long-lived latent reservoir. In most women the majority of the viruses entered the reservoir around the time of therapy initiation, suggesting that the initiation of ART indirectly changes the host environment by suppressing viral replication to favor the establishment of long-lived cells, some of which are latently infected with replication-competent HIV-1.

Brodin et al. (23) and Jones et al. (22) recently characterized the formation of the long-lived proviral DNA reservoir, which is predominantly defective (14, 15). Our study, which focused on the replication-competent component of the reservoir, is consistent with the observations of Brodin et al. (23) who found that the DNA reservoir in ten individuals was skewed toward variants present in the year preceding ART, although to a lesser extent than identified here. The results of Brodin et al. and our study differ from those of Jones et al. who inferred diversity in the viral DNA reservoir that was reflective of viral evolution over the entire course of infection in two individuals. However, their analysis was based on sequences from a limited number of pre-ART time points (all during chronic infection) and thus required extrapolation based on the model of a molecular clock. In contrast, we were able to sequence virus from acute/early infection onward to the start of therapy, and directly estimate that only a small fraction of the sampled OGV populations were seeded prior to the initiation of therapy. In eight of nine participants there was no evidence of continuous reservoir formation. Only one participant (CAP302) had the replication-competent reservoir seeded primarily with virus from early to mid-stages of the untreated infection (Fig. 3B, Supplementary Data Fig. 8). Overall our data and modeling analysis suggest that much of the long-lived HIV-1 reservoir forms following the initiation of ART which also coincides with rapid declines in HIV-1 viral load, increases in CD4+ T cell counts, and reductions in markers of immune activation. One limitation is that in those subjects where the number of OGVs is small (Table 1) we are able to glean only a coarse picture of reservoir timing. However, even such a broad-strokes picture makes it clear that on average there is preferential entry into the reservoir by viruses replicating around the time of therapy initiation.

It is not clear why only a small fraction of the full-length proviruses from resting CD4+ T cells produce detectable virus in a QVOA (14). It is possible that most proviruses are induced in QVOA, but that the assay is very inefficient at amplifying the small amounts of virus produced by a single resting cell. Alternatively, proviruses may exist in different states of latency with only a small fraction of them inducible in cell culture. Consistent with these possibilities, Ho et al. (14) has shown that within a QVOA repeated rounds of stimulation can give rise to an additional 24% of induced viruses, either pointing to a stochastic nature of the assay or a distinct set of proviruses/cells that are inducible only with multiple rounds of stimulation.

If proviruses exist in different states of latency then it raises the possibility that OGVs recovered by QVOA represent a skewed sample of the viral reservoir, some of which may have been seeded earlier in untreated infection. While it is likely that proviruses experience different degrees of latency, perhaps representing different host mechanisms of gene silencing, the work of Brodin et al. (23) finding that the viral DNA reservoir is also skewed toward variants circulating late in untreated infection indicates that the majority of both the induced and uninduced reservoir is seeded near the time of ART initiation. While we cannot rule out the possibility that a small fraction of the reservoir is seeded early in untreated infection and persists in a state of “deep latency”, current evidence indicates that the majority of the reservoir (both inducible and uninducible) is seeded near the time of ART initiation.

In this study we focused on virus induced to grow from resting CD4+ T cells isolated from the blood of virologically suppressed women. We have not examined the reservior in activated cells (CD25High, CD69+, HLA-DR+), cells in tissues, or cells in HIV-positive men. Also, several studies have found that viral DNA, including intact viral genomes and replication-competent genomes, are found in a wide variety of different CD4+ T cell subsets (13, 36–38). While there is a need to confirm our observations more broadly, they are likely generalizable. For example, Boritz et al. (39) observed that in elite controllers, HIV-infected cells migrate between lymphoid tissue and the blood, suggesting that viral populations at these sites are genetically linked. In addition, analysis of gut tissue biopsies suggests that viral populations in latently infected cells are largely equilibrated between blood and tissue (40), making it less likely that there is a distinct population of latent virus residing within tissue. As mentioned above, the results of Brodin et al. (23), indicate that genetically distinct viruses contained in other cell types such as activated CD4+ T cells would have to represent a small fraction of the long-lived HIV-1 reservoir since they obtained similar results to those reported here analyzing viral DNA present in total peripheral blood cells which would include all subtypes of CD4+ T cells. In addition, while our study of biologically active, inducible virus in the reservoir was done using women, the analogous results of Brodin et al. (23) were obtained in a cohort largely composed of men, suggesting that sex is not a major determinant of when viral sequences enter the long-lived reservoir.

There is ongoing interest as to whether HIV-1 replicates during suppressive antiviral therapy. We were able to account for all OGVs as being phylogenetically close to, and at times identical to, viruses that were replicating before the initiation of therapy, i.e. the OGVs did not represent an outlier group of sequences that had undergone further sequence evolution due to ongoing replication during the average of 5 years on therapy. These results are consistent with those of Brodin et al. (23) and Van Zyl et al. (41) who have failed to find any evidence for viral sequence evolution during suppressive antiretroviral therapy, an observation most compatible with the latent reservoir not being sustained by ongoing viral replication.

Our results point to the long-lived reservoir forming under two very distinct conditions. The majority of the reservoir (on average) forms around the time of therapy initiation and is likely driven by the rapidly changing immune environment within the host that occurs in response to lowering viral loads and decreased antigen drive. These immunologic changes include absolute increases in peripheral CD4+ T cells, reductions in CD4+ T cell turnover (42, 43), and increased frequencies of long-lived memory CD4+ T cells (44). Initiation of ART may therefore promote the increased transition of both infected and uninfected CD4+ T cells from being short-lived effectors to being long-lived memory T cells (45). Similarly, ART-mediated reduction of HIV-1 antigen restores the proliferative ability of HIV-specific CD4+ T cells (46). These changes may directly impact HIV-specific CD4+ T cells by allowing them to become long-lived memory cells capable of proliferation. As this model would predict, HIV-infected people on ART have been shown to have HIV-specific CD4+ T cells that are infected at rates greater than CMV-specific T cells or total memory CD4+ T cells, although the HIV-specific CD4+ T cells represent only a small fraction of the total memory CD4+ T cells and contain only a small fraction of the total viral DNA (47). Together, these findings suggest that profound immunologic changes at the time of ART may allow HIV-infected cells to become long-lived memory cells and form the majority of the stable reservoir.

An additional mechanism is needed to explain how a variable fraction of the reservoir forms in the presence of active viral replication (see examples in Fig. 3). The fraction of OGVs that entered the reservoir at earlier time points prior to therapy initiation was highly variable, with the majority of OGVs from one particpant (CAP 302) seeded earlier in untreated infection. It is not clear what factors contribute to early viral entry into the reservoir. For this particular woman, the infection course was only moderately different from the cohort overall: three years in study prior to therapy, setpoint viral load about two times higher than the mean, nadir CD4+ T cell count only slightly lower than the mean. An important question about the phenomenon of early entry into the reservoir is whether it is continuous or episodic where it might be linked to some other biological events that occur in the person, such as transient exposure to other infectious agents.

Untreated HIV-1 infection is characterized by increased immune activation and dysfunction, which are largely reversed after ART initiation. Most relevant to this study is the loss of CD127+(IL-7 receptor) memory CD4+ T cells during untreated infection (48–50). IL-7 signaling through the CD127 receptor is essential for the transition of effector to memory CD4+ T cells as well as the subsequent maintenance of memory cells (51). With ART initiation, the number of CD127+ memory CD4+ T cells increases (42, 48, 49), most likely due to reductions in inflammation (52). These results suggest a model in which the increase in CD127 expression during ART allows latently infected cells to become long-lived as they now become responsive to IL-7 (Supplementary Data Fig. 12). Consistent with this model in which much of the reservoir should be in cells sensitive to IL-7, administering IL-7 to HIV-infected individuals on ART increases the size of the HIV-1 reservoir (53), and the rate of reservoir decay is slower when IL-7 concentrations are higher (13).

Therapies that limit CD4+ T cell memory generation, perhaps through dampening IL-7/IL-7R signaling in the window from the time of ART initiation to when all viral replication is fully suppressed, could limit reservoir formation. Such an approach would not eliminate all of the reservoir, as it is clear that latency can be seeded at different points during untreated infection. However, blocking the formation of a majority of the reservoir at the time of therapy initiation would greatly reduce the size of the reservoir, lowering the barrier to eradicating HIV-1 from an infected host.

Materials and Methods

Study design

Viral RNA from pre-ART time points ranging from acute/early HIV-1 infection to the time of ART initiation were sequenced across several regions of the viral genome using a deep sequencing protocol. After the women had been on ART for several years they provided a large blood sample that was used to grow out latent HIV-1 from the long-lived viral reservoir. The outgrowth virus sequences were compared to the sequences of the pre-ART longitudinal sampling of replicating virus to determine when the virus in the reservoir had entered the long-lived reservoir prior to the initiation of ART.

Study participants

The CAPRISA 002 cohort is comprised of women from rural and urban KwaZulu-Natal, South Africa, who were identified during the period of acute/primary infection with subtype C HIV-1 and followed longitudinally (24). Participants in the cohort gave blood samples every 3–6 months and were provided antiretroviral therapy (ART) based on the prevailing in-country guidelines. The women were retained in the cohort for up to five years after therapy initiation. We identified nine women in the cohort who had been ART naïve for at least three years prior to initiating therapy, and who had been on ART for a minimum of 4 years. The women provided a 200 ml blood draw while on therapy from which peripheral blood mononuclear cells (PBMCs) were isolated and cryopreserved in liquid nitrogen. Women were excluded based on pregnancy or if they had hemoglobin concentrations lower than 10g/dL. The use of stored samples and the collection and analysis of new samples were done under a protocol approved by the Biomedical Research Ethics Committee of the University of KwaZulu Natal (BE178/150) and the Human Research Ethics Committee of the University of Cape Town (588/2015) in South Africa, and at the University of North Carolina in the United States.

Viral outgrowth assay

Resting CD4+ T cells were isolated from PBMCs by negative selection with CD25 depletion to isolate cell that were CD69-, HLA-DR-, CD25low (Custom kit, Stem Cell). Purified cells were then examined by flow cytometry (LSRII Fortessa) for expression of CD69 (PE, BD), CD25 (APC, BD), CD8 (FITC, BD), CD4 (PerCP-Cy5.5, BD) and viability (aqua live/dead, ThermoFisher). Data were analysed using FlowJo (version 10.4.2).

Resting cells were cultured at 100,000 cells/well with 1.5 μg/ml of highly purified phytohemagglutinin (PHA, Remel-PHA, Thermo Scientific), 60 U/ml of interleukin 2, and irradiated PBMCs from a seronegative donor, and virus outgrowth was facilitated as previously described (4). On days 15 and 19, cultures were tested for HIV-1 p24 capsid protein production using an enzyme-linked immunosorbent assay. Culture supernatants positive for p24 were stored at −80°C.

Sequencing of viral populations in blood plasma pre-ART

Viral RNA copies were extracted from blood plasma using the QIAamp viral RNA Mini kit (Qiagen). The purified RNA was reverse transcribed to cDNA using Superscript III/IV Reverse Transcriptase (Invitrogen). We used the Primer ID method (30) which tags each RNA template through its cDNA primer with a unique 12 nucleotide-long identifier. This allows amplified sequences to be grouped according to their Primer ID tags, from which a consensus sequence can then be derived for each individual template. (template consensus sequence). Multiple cDNA primers were used during the cDNA synthesis step for each sample to generate cDNAs corresponding to multiple regions of the genome and allow multiplexing of the PCR amplicons followed by sequencing. The gene-specific sequence of the cDNA primer was used as an index for the specific region of the genome. Two separate cDNA reactions were performed for each sample to allow efficient use of the available RNA to sequence the different regions of the viral genome. The cDNA products were purified twice using Agencourt RNA Clean XP magnetic beads (Beckman Coulter). Multiplexing during PCR was accomplished by including a common PCR primer binding sequence at the 5’ end of each cDNA primer and a gene-specific forward primer for each amplicon/region. PCR amplification was performed using the KAPA2G Fast Multiplex Mix (Kapa Biosystems) and an equal molar amount of each forward primer and an excess of the universal reverse primer. A second PCR step using the Expand High Fidelity PCR System (Roche) allowed the incorporation of the Illumina MiSeq version 2 indexes and adapters. PCR products were purified using SPRIselect beads (Beckman Coulter), with a 0.6:1 volume ratio of beads to PCR product (to allow for size exclusion of residual primers) following both PCR steps. The final purified amplicon libraries were quantified using the Qubit double-stranded DNA HS assay (Invitrogen, CA), pooled at equimolar ratios and purified again using SPRIselect beads. Illumina MiSeq 2×300 base paired-end sequencing was performed on the multiplexed amplicons.

Sequencing of outgrowth virus populations

Viral RNA was isolated from the QVOA culture supernatant of p24-positive wells and converted to cDNA using Superscript III Reverse Transcriptase and an oligo(dT) primer. The 3’ and 5’ half genomes were amplified in separate PCR reactions using barcoded primers, and the PCR products were gel purified. For CAP257, approximately 35% of the OGVs had only a 3’ half genome amplicon available for analysis. The SMRTbell Template Prep Kit (PacBio) was used to add adaptors to pooled, barcoded amplicons, and the pools of amplicons were then submitted for PacBio sequencing (movie time of 10 hours). Sequences were grouped by barcode, and high quality sequences were analyzed using the PacBio Long Amplicon Analysis (LAA) package. The 3’ and 5’ amplicons for the same virus were joined and visually screened to confirm that open reading frames were intact. Genebank Accession numbers are MN097551–MN097697.

Illumina MiSeq data processing

Raw reads were processed using a custom pipeline written in Python and R programming languages. The MotifBinner2.R program (https://github.com/HIVDiversity/MotifBinner2) carries out quality filtering, merging of overlapping paired-end reads, and implements the Primer ID processing method described by Zhou et al (31). The resulting Primer ID template consensus sequences were processed with an in-house pipeline (https://github.com/ColinAnthony/NGS_processing_pipeline) which removes sequences with degenerate bases or deletions >50 nucleotides long and filters out any contaminant non-target gene sequences.

Phylogenetic and statistical analyses

Hypervariable loop regions of env were manually removed where accurate alignment was not possible. In-frame codon alignments were generated for each genomic region using a custom pipeline (https://github.com/veg/ogv-dating). Briefly, translated protein sequences were used to identify RNA sequences that were in-frame. Sequences that contained frameshifts were aligned to each in-frame sequence using the Smith-Waterman algorithm implemented in the BioExt package (https://github.com/veg/BioExt) and the pairs with the highest score were used to correct frameshifts. MUSCLE (54) was then used to align in-frame amino acid sequences and then generate in-frame, codon-aligned RNA sequences lacking frameshifts. For each pre-ART time point analyzed, identical RNA sequences were replaced with a single representative, and approximately-Maximum-Likelihood trees were constructed using FastTree2 with more rigorous search options (55).

Three methods were used to analyze sequence data from each genomic region and estimate when the OGV entered the reservoir (pipeline and additional description of the methods are available at https://github.com/veg/ogv-dating). First, the tree was rooted to maximize root-to-tip distance versus its time linear regression coefficient. The ‘patristic distance’ method computes the path lengths between an OGV sequence and every other RNA sequence in the tree, finds the minimum distance (d) and assigns the OGV sequence to the time point from which the simple majority of RNA sequences within distance 2d come from. The ‘clade support’ method starts at the OGV sequence, and traverses the tree up to the root until it encounters a well-supported (bootstrap ≥90%) internal node that has at least one RNA sequence descendant. Each OGV sequence was assigned the time point from which the majority of RNA sequences in the clade came from. Third, phylogenetic placement (33, 34) was used to analyze phylogentic trees containing only pre-ART sequences and identify the position in these reference trees where each OGV was most likely to be found and use that new position to identify when the OGV entered the reservoir. Patristic distance and clade support estimates were excluded from subsequent analyses if their tree support value was less than 0.4. Tree support value was estimated by applying the same dating procedure to each RNA sequence, and seeing how often the correct sampling time point was recovered. A support of 0.4 means that the correct time point was recovered for 40% of RNA sequences. Because generating tree support estimates for phylogenetic placement is computationally slow, we did not exclude any estimates generated by phylogenetic placement. Fig. 4 and Supplementary Data Fig. 11 illustrate all of the estimates analyzed for each OGV.

For each OGV, timing of reservoir entry was summarized as the weighed median of estimates generated by the three different methods and the support value for each estimate. As a way of validating this approach, we identified OGVs whose individual estimates had a standard deviation of greater than 1 year. We then visually inspected their phylogentic trees to identify whether the weighted median accurately characterized the position of each OGV in the phylogenetic trees. A total of 25 OGVs (25 of 132 unique OGVs) were identified as having a standard deviation greater than 1 year. We then inspected the phylogenetic trees to identify whether each OGV was found in a time-specific lineage and whether that lineage dated to the same time as the weighted median. This approach revealed that the weighted median accurately characterized 18 of 25 OGVs examined. However, 6 OGVs could be found in time-specific lineages that did not correspond to the weighted median. We therefore revised the estimate of timing of reservoir entry of these 6 OGVs to reflect the age of the time-specific lineage in which they were found. One additional OGV was not located in a time-specific lineage so we revised its entry into the reservoir to be ‘ambiguous’. The majority of the 7 OGVs that were mischaracterized by our automated methods had a nearby group of clonal sequences observed at multiple pre-ART time points which may have been problematic for the methods. Furthermore, 6 of these 7 OGVs were from a single participant (CAP206). Further examination of sequence data for this participant found no evidence of contamination or poor sequence alignment that could explain why our methods failed for this participant.

Supplementary Material

SupplimentalaryFigures

Fig. S1. Longitudinal MiSeq Sampling Depth for Nine Women from the CAPRISA 002 Cohort.

Fig. S2. Timing of Reservoir Outgrowth Viruses for Participant CAP188.

Fig. S3. Timing of Reservoir Outgrowth Viruses for Participant CAP206.

Fig. S4. Timing of Reservoir Outgrowth Viruses for Participant CAP217.

Fig. S5. Timing of Reservoir Outgrowth Viruses for Participant CAP257.

Fig. S6. Timing of Reservoir Outgrowth Viruses for Participant CAP287.

Fig. S7. Timing of Reservoir Outgrowth Viruses for Participant CAP288.

Fig. S8. Timing of Reservoir Outgrowth Viruses for Participant CAP302.

Fig. S9. Timing of Reservoir Outgrowth Viruses for Participant CAP316.

Fig. S10. Timing of Reservoir Outgrowth Viruses for Participant CAP336.

Fig. S11. Estimates of OGV Entry into the Long-lived Reservoir.

Fig. S12. Model of the Relationship Between Viral Suppression and Formation of the Long-lived HIV-1 Reservoir.

Table_S1

Table S1. Data of participant viral loads.

Table_S2

Table S2. Data for timing of outgrowth viruses.

Acknowledgments:

We would like to acknowledge all participants of the CAPRISA 002 acute infection cohort as well as the staff at the Vulindlela and eThekwini Clinical Research Sites, KwaZulu-Natal, South Africa.

Funding: This work was supported by the National Institute of Health(NIH) - South African Medical Research Council (MRC) US-South Africa Program for Collaborative Biomedical Research grants R01 AI115981 to CW and RS, and the Collaboratory of AIDS Researchers for Eradication (UM1 AI126619). The CAPRISA 002 acute infection cohort study has been funded by the South African Department of Science and Technology and the National Research Foundation’s Centre of Excellence in HIV Prevention (Grant # UID: 96354), the South African Department of Health and the South African Medical Research Council Special Initiative on HIV Prevention (Grant #: 96151), the National Institute of Allergy and infectious Disease of the NIH (Grant # AI51794), USAID and CONRAD (USAID co-operative Grant # GP00-08-00005-00, subproject agreement # PPA-09-046), the South African National Research Foundation (Grants # 67385, # 96354), the South African Technology Innovation Agency, and the Fogarty International Center, NIH (Grant # D43TW00231). The work was also supported by the UNC Center For AIDS Research (NIH award P30 AI50410) and the UNC Lineberger Comprehensive Cancer Center (NIH award P30 CA16068).

Footnotes

Competing interests: UNC is pursuing IP protection for Primer ID, and RS is listed as a co-inventor and has received nominal royalties.

Data and materials availability: All sequences will be deposited in public databases and accession numbers provided prior to publication. The near full-length genome sequences will be deposited in GenBank. The MiSeq sequences will be deposited in the Sequencing Read Archive.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

SupplimentalaryFigures

Fig. S1. Longitudinal MiSeq Sampling Depth for Nine Women from the CAPRISA 002 Cohort.

Fig. S2. Timing of Reservoir Outgrowth Viruses for Participant CAP188.

Fig. S3. Timing of Reservoir Outgrowth Viruses for Participant CAP206.

Fig. S4. Timing of Reservoir Outgrowth Viruses for Participant CAP217.

Fig. S5. Timing of Reservoir Outgrowth Viruses for Participant CAP257.

Fig. S6. Timing of Reservoir Outgrowth Viruses for Participant CAP287.

Fig. S7. Timing of Reservoir Outgrowth Viruses for Participant CAP288.

Fig. S8. Timing of Reservoir Outgrowth Viruses for Participant CAP302.

Fig. S9. Timing of Reservoir Outgrowth Viruses for Participant CAP316.

Fig. S10. Timing of Reservoir Outgrowth Viruses for Participant CAP336.

Fig. S11. Estimates of OGV Entry into the Long-lived Reservoir.

Fig. S12. Model of the Relationship Between Viral Suppression and Formation of the Long-lived HIV-1 Reservoir.

NIHMS1060174-supplement-SupplimentalaryFigures.docx (24.2MB, docx)
Table_S1

Table S1. Data of participant viral loads.

NIHMS1060174-supplement-Table_S1.xlsx (32.3KB, xlsx)
Table_S2

Table S2. Data for timing of outgrowth viruses.

NIHMS1060174-supplement-Table_S2.xlsx (48.7KB, xlsx)

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