Antiretroviral therapy (ART) has transformed human immunodeficiency virus type 1 (HIV-1) from a fatal disease to a chronic condition. However, ART must be taken life-long as interruption of therapy in people living with HIV-1 (PLWH) mostly results in viral rebound within weeks (Davey et al., 1999; Crowell et al., 2019; Bar et al., 2016; Buzon et al., 2014). This rebound results from cells harboring HIV-1 DNA that is integrated into the host genome. While >95% of proviral DNA is replication-incompetent, the remaining fraction that we define here as the ‘HIV-1 reservoir’, retains the capacity to produce infectious virus particles, either stochastically (Chun et al., 1997; Finzi, 1997; Siliciano et al., 2003; Eisele and Siliciano, 2012), or through interventions to induce coordinated reactivation (Archin et al., 2012; Søgaard et al., 2015; Elliott et al., 2013; Elliott et al., 2015). The largest and best characterized HIV-1 reservoir resides in circulating resting CD4+ T cells. The inducible form of the HIV-1 reservoir is present in blood cells at a low frequency (~1 in 10⁶ resting CD4+ T cells), but is highly stable, with an estimated half-life of 44 months (Crooks et al., 2015; Siliciano and Siliciano, 2015; Siliciano and Siliciano, 2004). Natural decay of the HIV-1 reservoir would therefore take >70 years (Crooks et al., 2015; Siliciano and Siliciano, 2015; Siliciano and Siliciano, 2004).

Strategies to allow PLWH to cease ART without viral rebound aim to either eliminate all HIV-1 infected cells harboring persistent, replication-competent virus (HIV-1 eradication), or to achieve a state of durable HIV-1 suppression without rebound (HIV-1 remission). Both approaches may harness HIV-1-specific CD8+ T cells to achieve reservoir reduction or elimination. T cells recognize infected cells through T cell receptors (TCR) that bind viral peptides presented on human leukocyte antigen (HLA) on the surface of the infected cell (Townsend and McMichael, 1985; Townsend et al., 1984; Townsend et al., 1986). On peptide recognition, CD8+ T cells can produce cytokines or release lytic molecules killing the infected cell. There is significant evidence that CD8+ T cells contribute to the control of untreated HIV-1, supported by studies of simian immunodeficiency virus (SIV) in non-human primates (Pandrea et al., 2011; Barouch et al., 2002; Schmitz et al., 2005; McBrien et al., 2020). First, certain HLA-I types, such as B*57:01/:03 and B*27:05, are overrepresented in HIV-1 elite virus controllers, defined as PLWH who exhibit sustained HIV-1 control below 50 copies/ml without ART. This suggests that specific subpopulations of CD8+ T cells are capable of controlling HIV-1 replication in the absence of therapy. Second, HIV-1-specific CD8+ T cells exert significant immune pressure on HIV-1, resulting in the accumulation of non-synonymous mutations in and around reactive T cell epitopes that can confer escape from CD8+ T cell recognition and killing (Borrow et al., 1997; Goulder et al., 1997; Phillips et al., 1991; Price et al., 1997). HIV-1 escape from CD8+ T cells, which is typically measured as a ≥ 50% decrease in the magnitude of the T cell response, can occur within days to weeks of infection (Borrow et al., 1997; Price et al., 1997; Koup et al., 1994; Goonetilleke et al., 2009). Both T cell immunodominance (relative strength of a T cell response) and viral entropy (sequence conservation of HIV-1 at a population level) are major determinants of the rate of virus escape in untreated infection (Liu et al., 2013; Ferrari et al., 2011; Barton et al., 2016). Notably, virus escape is ongoing in untreated HIV-1 infection (Allen et al., 2005), with new virus variants emerging over time in response to newly induced T cell responses, shifts in T cell clonotypes, or to compensate for fitness costs of earlier mutations. Lastly, when T cell selection pressure is removed, as in the case of virus transmission to recipients with HLA alleles different from the donor, reversion of mutations can occur (Leslie et al., 2004; Carlson et al., 2014).

ART is highly effective in inhibiting viral replication with multiple studies reporting that HIV-1 does not undergo directional selection during suppressive ART (Kearney et al., 2014; Kearney et al., 2016; Rosenbloom et al., 2017; Van Zyl et al., 2017). Recent studies have reported that the majority of proviruses, both total HIV-1 DNA and replication-competent viruses, that persist during ART, reflect viruses circulating during the year prior to ART initiation (Brodin et al., 2016; Abrahams et al., 2019; Pankau et al., 2020). These studies suggest that ART initiation is the major driver of latency formation, including the formation of the replication-competent stable HIV-1 reservoir. Proviruses that persist during ART would therefore include HIV-1 variants that have escaped from T cell selection over the entire course of untreated infection (Phillips et al., 1991; Deng et al., 2015). ART also impacts the HIV-1-specific T cell response, with reduced HIV-1 loads resulting in lower antigen drive and over time, yielding improved proliferation and a less exhausted T cell phenotype (Rehr et al., 2008; Rutishauser et al., 2017) (reviewed in Warren et al., 2019). We have recently shown that HIV-1-specific T cell responses are detectable ex vivo and are highly stable in PLWH on durable ART suppression (Xu et al., 2019), with others showing that ex vivo isolated CD8+ T cells can suppress HIV-1 superinfected CD4+ T cells as well as in vitro reactivated reservoir virus (Sung et al., 2015; Shan et al., 2012).

Collectively, these data suggest that HIV-1-specific T cell responses on ART should reflect those detected prior to the onset of ART. Likewise, the persistent HIV-1 reservoir should reflect the burden of virus escape mutants circulating pre-ART. Of note, a recent study reported that up to 98% of proviral HIV-1 sequences harbored mutations in immunoprevalent viral epitopes, though unmutated targets were also identified (Deng et al., 2015). A high burden of HIV-1 escape variants in the reservoir could limit approaches that aim to increase anti-HIV-1 immunity (Ondondo et al., 2016; Borducchi et al., 2016; Mothe et al., 2015b; Achenbach et al., 2015; Hanke, 2014).

Here, we present the first study reporting within-host HIV-1 escape frequencies in PLWH on ART. A non-selective approach was taken, specifically sequencing HIV-1 outgrowth viruses (OGVs) from the reservoir induced from resting CD4+ T cells, and mapping circulating T cell responses against the entire HIV-1 proteome in 25 PLWH on ART. These strategies enabled us to empirically determine the extent and patterns of pre-ART escape within the stable HIV-1 reservoir. We observed that the majority, 68%, of T cell epitopes in the HIV-1 reservoir did not harbor escape variants and were recognized by circulating T cells. Furthermore, analysis of ‘escape’ epitopes (the remaining 32%) showed that escape was significantly lower in regions of HIV-1 that are more conserved. We conclude that the majority of circulating HIV-1-specific T cells in PLWH on ART can recognize the stable HIV-1 reservoir.

In this study, we employed two unbiased approaches to comprehensively examine the landscape of escape in the HIV-1 reservoir in 25 PLWH durably suppressed on ART. First, we examined the frequency of published HLA-associated polymorphisms in HIV-1 subtype B OGV sequences. Overall, 20% of HLA-associated sites in the HIV-1 reservoir harbored the known HLA-adapted viral form, suggesting potential viral escape from T cell responses. This number increased to 48% if both known and putative HLA-adapted forms were included in the calculation. These estimates are consistent with substantial immune escape in the reservoir, but nevertheless support the notion that the majority of HLA-associated sites in the reservoir maintain susceptibility to host T cell responses. Next, in all participants, we mapped HIV-1-specific T cell epitopes across the full HIV-1 proteome. Across the cohort, the mean breadth of HIV-1 epitopes targeted per person was 7. T cell targeting of HIV-1 proteins was consistent with studies of untreated infection, with T cells most often targeting Gag, Pol, and Nef (Addo et al., 2003). No differences were observed between treatment initiated in acute (n = 3) or chronic infection (n = 22). We then systematically examined whether non-synonymous mutations in reactive epitopes impacted T cell recognition and functional responses. Consistent with the overall HLA-adaptation estimates from the bioinformatics analysis, we experimentally confirmed that 32% (49/151) of all reactive T cell epitopes in HIV-1 OGVs contained mutations that conferred T cell escape, and that conversely, 68% of circulating HIV-specific T cell responses were able to fully recognize the autologous replication-competent-reservoir virus.

The minimal empirically defined T cell escape frequency of 32% observed in PLWH on ART in this study is consistent with studies of T cell escape in untreated HIV-1 infection. A 30% escape frequency was observed in primary untreated HIV-1 infection, in which HIV-1-specific T cell responses were comprehensively mapped against the autologous transmitted/founder virus in participants, and T cell reactivity was assessed against mutant viruses that emerged over the first 6 months of infection (Liu et al., 2013). In that study, similar to ours, CD8+ T cell escape occurred more frequently in epitopes of higher Shannon entropy (Liu et al., 2013; Ganusov et al., 2011; Bansal et al., 2005). A similar escape frequency of 33% was also observed in a large cross-sectional study of chronically infected HIV-1 Clade C untreated individuals (n >2,000), in which the T cell escape frequency was determined by examining participant HLA-associated polymorphisms in immunoprevalent HIV-1 CD8+ T cell epitopes (Carlson et al., 2012). In PLWH durably suppressed on ART, HIV-1-specific T cell responses are stable and maintain immunodominance hierarchies over years (Xu et al., 2019). Both the stability of the HIV-1-specific T cell response and the comparable level of population-level virus escape in treated and untreated individuals is consistent with studies of HIV-1 reservoir formation and kinetics. These studies showed that the HIV-1 reservoir is mostly formed at the time of ART initiation (Brodin et al., 2016; Abrahams et al., 2019; Pankau et al., 2020) and its subsequent rate of decay is very slow (Crooks et al., 2015; Siliciano and Siliciano, 2015; Siliciano and Siliciano, 2004), with only limited variation in the number of proviruses, or the types of proviral defects observed in PLWH on ART over time (Lu et al., 2018). More recently, longitudinal analyses of near full-length proviral sequences in PLWH on ART showed no enrichment in escaped epitopes over time suggesting that HIV-1-specific T cells do not significantly alter the provirus landscape in people durably suppressed (Antar et al., 2020). Furthermore, a recent study reported that brief analytical treatment interruption (ATI) (median of 5 weeks) does not significantly impact the composition of virus populations (phylogenetic analyses sequencing of HIV-1 env) or size (total DNA, cell-associated RNA, IUPM) of the latent HIV-1 reservoir (Salantes et al., 2018). Collectively, these studies suggest a model in which circulating T cells, as well as virus escape variants in infected cells at the time of ART initiation, are maintained throughout durable viral suppression, and during brief periods of ATI.

Our observations contrast somewhat with those of Deng and colleagues who reported a predominance of CD8+ T cell-resistant viruses in proviral HIV-1 DNA (Deng et al., 2015). Deng et al did not empirically investigate proteome-wide patterns of escape (i.e. escape in 32% of reactive T cell epitopes) in the replication-competent HIV-1 reservoir, as reported here. Rather, their study focused on predicting the ‘proportion’ of escape within epitopes in Gag. They mostly used sequencing and bioinformatics approaches to conclude that, in epitopes in which virus variation was observed, in individuals treated in chronic infection, most (>98%) proviruses contained variants previously reported to confer T cell escape. Our empirical testing, which focused on T cell escape in the persistent HIV-1 reservoir, did not yield the same high proportion of escape variants reported in that study. Indeed, the average within-epitope escape frequency in 49 empirically tested epitopes was much lower at 27% (Supplementary file 5). This dataset included escape variants that can emerge early after infection such as the Gag T242N escape in Gag 240–249, restricted by B*58:01 and B57:01/3 (Leslie et al., 2004; Brumme et al., 2009). Deng et al also noted a higher within-epitope escape in the DNA reservoir in individuals treated in chronic versus acute infection. While only a few participants in this study were treated in acute infection, we observed no difference in within-epitope escape frequencies between participants treated in chronic or acute HIV-1 infection. Together this suggests that the differences in the studies did not arise from the regions of HIV-1 studied, or the differences in stage of HIV-1 infection at time of ART initiation. Rather, we propose that differences in within-epitope escape frequencies between studies derived from two sources. First, our empirical approach showed clearly that HIV-1 variants often do not escape T cell recognition and therefore prediction methods may overestimate the level of escape. Indeed, Deng et al did include examples where virus variants predicted to confer escape did not decrease T cell responses following experimental testing. Second, the HIV template, HIV DNA versus OGV RNA, used for virus sequencing largely differed between studies.

Our observations that epitopes mostly harbor a mix of escape and non-escape variants are consistent with previous studies in untreated infection reporting that T cell escape is rarely all-or-nothing, but rather occurs at different rates (days to years) with multiple variants emerging over time (Goonetilleke et al., 2009; Liu et al., 2013; Asquith et al., 2006). These changes reflect both ongoing compensatory changes to maintain viral fitness and selection pressure from different T cell clonotypes (Brockman et al., 2010; Ladell et al., 2013; Ritchie et al., 2014), and progressive dysregulation of the immune response. While the HIV-1 reservoir is mostly formed at ART initiation, almost 30% of sequences enter the HIV-1 reservoir much earlier. The mixture of escape and non-escape OGVs observed in this study likely represent OGVs at different stages of T cell escape entering the reservoir at different stages of infection.

There are limitations of this study. First, detection of escape variants may have been limited by the sequencing depth obtained from OGVs from QVOAs. In addition, different stimuli can activate different viruses, and even the negative wells from the QVOA can generate new viruses after new stimulation, contributing to an underestimation of, or a bias in the replication-competent viruses present in the culture (Ho et al., 2013; Hosmane et al., 2017). Applying a rule of three calculation, our average depth of sequencing (25 sequences/participant) provided 95% confidence that any undetected variant comprised <12% of the population. While sampling biases were minimized by our choice of sequencing methods (nearly full-length and Primer ID), which have a low probability of introducing PCR mutations (Zhou et al., 2015; Salazar-Gonzalez et al., 2008), we cannot exclude that a low-frequency variants not detected by sequencing may have conferred escape (Abrahams et al., 2019). It is notable that that participant OGVs sequenced over time did not exhibit directional selection suggesting that the QVOA method recovers outgrowth virus that is representative of all variants (escaped and non-escaped) in the HIV-1 reservoir. Second, our bioinformatics analysis of HLA-associated polymorphisms in HIV-1 reservoir sequences simply identifies viral variants that are enriched among individuals expressing a particular HLA allele, as defined using population-level analyses (Carlson et al., 2012), and thus provides only a crude estimate of adaptation burden. This is because these lists of HLA-associated polymorphisms do not capture every escape variant that occurs in vivo; moreover, not every HLA-associated viral site defined at the population level is necessarily targeted by every individual expressing the relevant HLA allele. It is notable, however, that the results of the bioinformatics approach were consistent with our empirical studies, suggesting that the estimates made correctly captured the range of escape frequencies within this cohort. Third, in our empirical studies, we cannot exclude that the use of clade B consensus peptides underestimated the total breadth of the T cell response (Goonetilleke et al., 2009; Altfeld et al., 2003). However, our approach of synthesizing autologous peptides at reactive epitopes, and experimentally testing all ‘stripes’ (non-synonymous changes in >40% of sequences, see Materials and Methods) in our OGVs to identify additional epitopes to study, minimizes this limitation. Fourth, our empirical approach did not detect all mechanisms of escape such as possible immune dysfunction and processing mutations. To fully assess loss of CD8+ T cell responses due to immune dysfunction, longitudinal mapping and sequencing studies pre -and post-ART are needed. While no previously documented processing mutations were detected in our dataset, we anticipate the bioinformatics analysis which enumerated all HLA-associated polymorphisms regardless of their location with respect to known (or novel) T cell epitopes, will have captured some processing mutations. Lastly, we did not confirm the CD4 or CD8 restriction of all reactive and escaped T cell epitopes in this study. However, both consistent with previous studies (McNeil et al., 2001; Appay et al., 2002; Betts et al., 2001; Riou et al., 2012) and the optimal epitope mapping performed here, it is likely that the bulk of T cell responses identified in this study were CD8-restricted.

In summary, empirical testing supported by bioinformatics analysis, showed that 68% of circulating HIV-1 specific T cells across our cohort were able to recognize the replication-competent virus from the latent reservoir. Moreover, T cells in most individuals targeted one or more epitopes with no detectable escape and within epitopes, on average, only a minority of OGVs conferred escape. Treatment interruption studies have shown that circulating HIV-1 specific T cell responses are insufficient to fully suppress virus rebound in most individuals. Recent work has shown recombinant viruses rapidly emerge following HIV rebound (Vibholm et al., 2019; Lu et al., 2018; Cohen et al., 2018). Our study, supported by animal models (Pandrea et al., 2011) and case reports investigating viral rebound (Smith et al., 2015), suggest that circulating HIV-1-specific T cells very likely afford some contribution in control of virus rebound. However, T cell immunity is rapidly overwhelmed, in part due to recombination-mediated escape (Ritchie et al., 2014; Streeck et al., 2008). Altogether, this suggests that HIV-1 cure strategies that include efforts to augment T cell immunity in PLWH on ART should include immunogen designs that target existing T cell epitopes, particularly those with low entropy which, as we show here, are less likely to harbor escape in the replication-competent reservoir. Ultimately, success of these therapies will likely be dependent on the ability to exert very rapid virus control and to limit the emergence of viral recombinants.

Sequencing data have been deposited in Gen Bank under PRJNA666896, MT307344-MT308415 and MW054719-MW054856 All data generated (raw) or analyzed during this study are included in the manuscript and supporting files (supplementary files).

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Author details

1. Joanna A Warren

   Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, United States

   Contribution

   Formal analysis, Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0595-0390

2. Shuntai Zhou

   1. Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, United States
   2. UNC Center For AIDS Research, University of North Carolina, Chapel Hill, United States

   Contribution

   Formal analysis, Investigation, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

3. Yinyan Xu

   Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

4. Matthew J Moeser

   1. Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, United States
   2. UNC Center For AIDS Research, University of North Carolina, Chapel Hill, United States

   Contribution

   Investigation

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1368-5171

5. Daniel R MacMillan

   British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada

   Contribution

   Formal analysis

   Competing interests

   No competing interests declared

6. Olivia Council

   1. Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, United States
   2. Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, United States

   Contribution

   Data curation

   Competing interests

   No competing interests declared

7. Jennifer Kirchherr

   Department of Medicine, University of North Carolina, Chapel Hill, United States

   Contribution

   Data curation, Investigation

   Competing interests

   No competing interests declared

8. Julia M Sung

   1. Department of Medicine, University of North Carolina, Chapel Hill, United States
   2. UNC HIV Cure Center, University of North Carolina, Chapel Hill, United States

   Contribution

   Resources, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

9. Nadia R Roan

   1. Department of Urology, University of California San Francisco, San Francisco, United States
   2. Gladstone Institute of Virology and Immunology, San Francisco, United States

   Contribution

   Resources, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5464-1976

10. Adaora A Adimora

    Department of Medicine, University of North Carolina, Chapel Hill, United States

    Contribution

    Resources, Data curation

    Competing interests

    No competing interests declared

11. Sarah Joseph

    1. Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, United States
    2. UNC HIV Cure Center, University of North Carolina, Chapel Hill, United States

    Contribution

    Data curation

    Competing interests

    No competing interests declared

12. JoAnn D Kuruc

    1. Department of Medicine, University of North Carolina, Chapel Hill, United States
    2. UNC HIV Cure Center, University of North Carolina, Chapel Hill, United States

    Contribution

    Resources, Data curation, Funding acquisition

    Competing interests

    No competing interests declared

13. Cynthia L Gay

    1. Department of Medicine, University of North Carolina, Chapel Hill, United States
    2. UNC HIV Cure Center, University of North Carolina, Chapel Hill, United States

    Contribution

    Resources, Data curation

    Competing interests

    No competing interests declared

14. David M Margolis

    1. Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, United States
    2. UNC Center For AIDS Research, University of North Carolina, Chapel Hill, United States
    3. Department of Medicine, University of North Carolina, Chapel Hill, United States
    4. UNC HIV Cure Center, University of North Carolina, Chapel Hill, United States

    Contribution

    Resources, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - review and editing

    Competing interests

    No competing interests declared

15. Nancie Archin

    1. Department of Medicine, University of North Carolina, Chapel Hill, United States
    2. UNC HIV Cure Center, University of North Carolina, Chapel Hill, United States

    Contribution

    Resources, Data curation, Supervision, Writing - review and editing

    Competing interests

    No competing interests declared

16. Zabrina L Brumme

    1. British Columbia Centre for Excellence in HIV/AIDS, Vancouver, Canada
    2. Faculty of Health Sciences, Simon Fraser University, Burnaby, Canada

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Methodology, Writing - original draft, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8157-1037

17. Ronald Swanstrom

    1. Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, United States
    2. Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, United States
    3. UNC Center For AIDS Research, University of North Carolina, Chapel Hill, United States
    4. UNC HIV Cure Center, University of North Carolina, Chapel Hill, United States

    Contribution

    Data curation, Supervision, Writing - review and editing

    Competing interests

    UNC is pursuing IP protection for Primer ID, and Ronald Swanstrom is listed as a coinventor and has received nominal royalties.

18. Nilu Goonetilleke

    1. Department of Microbiology and Immunology, University of North Carolina, Chapel Hill, United States
    2. Department of Medicine, University of North Carolina, Chapel Hill, United States
    3. UNC HIV Cure Center, University of North Carolina, Chapel Hill, United States

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing

    For correspondence

    nilu_goonetilleke@med.unc.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-2278-1656

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