Antiretroviral therapy (ART) has transformed HIV infection from a uniformly deadly disease into a chronic lifelong condition, saving millions of lives. However, ART interruption leads to rapid viral rebound within weeks due to the persistence of proviral latency in rare, long-lived resting CD4⁺ T cells and possibly in tissue macrophages (Honeycutt et al., 2017). HIV latency is defined as the presence of a transcriptionally silent but replication-competent proviral genome. Latency allows infected cells to evade both immune clearance mechanisms and currently available ART, which is based solely on the elimination of actively replicating virus.

An extensively investigated approach to purging latent HIV is the ‘shock and kill’ strategy, which consists of forcing the reactivation of latent proviruses (‘shock’ phase) with the use of latency reversing agents (LRAs), while maintaining ART to prevent de novo infections. Subsequently, reactivation of HIV expression would expose such cells (shocked cells) to killing by viral cytopathic effects and immune clearance (‘kill’ phase). A variety of LRAs have been explored in vitro and ex vivo, with only a few candidates being advanced to testing in pilot human clinical trials. Use of histone deacetylase inhibitors (HDACi: vorinostat, panobinostat, romidepsin, and disulfiram) in clinical studies has shown increases in cell-associated HIV RNA production and/or plasma viremia after in vivo administration (Archin et al., 2012a; Elliott et al., 2015; Elliott et al., 2014; Rasmussen et al., 2014; Søgaard et al., 2015). However, none of these interventions alone has succeeded in significantly reducing the size of the latent HIV reservoir (Rasmussen and Lewin, 2016).

Several obstacles can explain the failure of LRAs, as reviewed in (Margolis et al., 2016; Rasmussen et al., 2016). However, the biggest challenge to date is our inability to accurately quantify the size of the reservoir. The absolute quantification (number of cells) of the latent reservoir in vivo (and ex vivo) has thus far been technically impossible. The most sensitive, quickest, and easiest assays to measure the prevalence of HIV-infected cells are PCR-based, quantifying total or integrated HIV DNA or RNA transcripts. However these assays substantially overestimate the number of latently infected cells, due to the predominance of defective HIV DNA genomes in vivo (Bruner et al., 2016; Ho et al., 2013). The best currently available assay to measure the latent reservoir is the relatively cumbersome viral outgrowth assay (VOA), which is based on quantification of the number of resting CD4⁺ T cells that produce infectious virus after a single round of maximum in vitro T-cell activation. After several weeks of culture, viral outgrowth is assessed by an ELISA assay for HIV-1 p24 antigen or a PCR assay for HIV-1 RNA in the culture supernatant. Importantly, the number of latently infected cells detected in the VOA is 300-fold lower than the number of resting CD4⁺ T cells that harbor proviruses detectable by PCR.

This reliance on a single round of T-cell activation likely incorrectly estimates the viral reservoir for two reasons. First, the discovery of intact non-induced proviruses indicates that the size of the latent reservoir may be much greater than previously thought: the authors estimate that the number may be at least 60 fold higher than estimates based on VOA (Ho et al., 2013; Sanyal et al., 2017). This work and that of others (Chen et al., 2017) highlight the heterogeneous nature of HIV latency and suggest that HIV reactivation is a stochastic process that only reactivates a small fraction of latent viruses at any given time (Dar et al., 2012; Ho et al., 2013; Singh et al., 2010; Weinberger et al., 2005). Second, the ability of defective proviruses to be transcribed and translated in vivo (Pollack et al., 2017): this study shows that, although defective proviruses cannot produce infectious particles, they express viral RNA and proteins, which can be detectable by any p24 antigen or PCR assays used for the reservoir-size quantification.

Thus, current assays underestimate the actual number of latently infected cells, both in vivo and ex vivo, and the real size of HIV reservoir is still to be determined. Therefore, it has been difficult to judge the potential of LRAs in in vitro (latency primary models), ex-vivo (patients’ samples) and in vivo (clinical trial) experiments.

HIV latency is a complex, multi-factorial process (reviewed in [Dahabieh et al., 2015]). Its establishment and maintenance depend on: (a) viral factors, such as integrase that specifically interacts with cellular proteins, including LEDGF, (b) trans-acting factors (e.g., transcription factors) and their regulation by the activation state of T cells and the environmental cues that these cells receive, and (c) cis-acting mechanisms, such as the local chromatin environment at the site of integration of the virus into the genome. Recent evidence has also highlighted the association of specific HIV-1 integration sites with clonal expansion of latently infected cells (reviewed in [Maldarelli, 2016]).

The role of the site of HIV integration into the cellular genome in the establishment and maintenance of HIV latency has remained controversial. While early studies found that the HIV integration site does affect both the entry into latency (Chen et al., 2017; Jordan et al., 2003; Jordan et al., 2001), and the viral response to LRAs (Chen et al., 2017), other studies have failed to find a significant role of integration sites in regulating the fate of HIV infection (Dahabieh et al., 2014; Sherrill-Mix et al., 2013).

In this study, we have used a new dual color reporter virus, HIV_GKO, to investigate the reactivation potential of various LRAs in pure latent population. We find that latency is heterogeneous and that only a small fraction (<5%) of the latently infected cells is reactivated by LRAs. We also show that both genomic localization and chromatin context of the integration site affect the fate of HIV infection and the reversal of viral latency.

Dual-color HIV-1 reporters are unique and powerful tools (Calvanese et al., 2013; Dahabieh et al., 2013), that allow for the identification and the isolation of latently infected cells from productively infected cells and uninfected cells. Latency is established very early in the course of HIV-1 infection (Archin et al., 2012b; Chun et al., 1998; Whitney et al., 2014) and, until the advent of dual-reporter constructs, no primary HIV-1 latency models have allowed the study of latency heterogeneity at this very early stage. Importantly, the comparison of data obtained from distinct primary HIV-1 latency models is complicated as some models are better suited to detect latency establishment (e.g., dual-reporters), while others are biased towards latency maintenance (e.g., Bcl2-transduced CD4⁺ T cells). The use of env-defective viruses limits HIV replication to a single-round and, thereby limits the appearance of defective viruses (Bruner et al., 2016).

In this study, we describe and validate an improved version of HIV_DuoFluoI, previously developed in our laboratory (Calvanese et al., 2013), which accurately allows for: (a) the quantification of latently infected cells, (b) the purification of latently infected cells, and (c) the evaluation of the ‘shock and kill’ strategy. Our data highlight two important facts: (a) cell-associated HIV RNA quantification does not reflect the number of cells undergoing viral reactivation, and (b) a small portion of the cells carrying latent proviruses (<5%) is reactivated, although LRAs target the whole latent population. Hence, even if cells harboring reactivated virus die, this small reduction would likely remain undetectable when quantifying the latent reservoir in vivo. Our data are in agreement with previous reports, which show that levels of cellular HIV RNA and virion production are not correlated, and that the absolute number of cells being reactivated by αCD3/CD28 is indeed limited to a small fraction of latently infected cells (Cillo et al., 2014; Sanyal et al., 2017; Yucha et al., 2017). Using our dual-fluorescence reporter, we confirm these findings, and extend these observations to LRAs combinations. However, although LRAs combinations show synergy when measuring cell-associated HIV RNA, we do not find such synergy at the level of individual cells, but rather only partial additive effect. Our work, as well as that of others (Cillo et al., 2014; Sanyal et al., 2017; Yucha et al., 2017), demonstrate the importance of single cell analysis when it comes to the evaluation of potential LRAs. Indeed, it is necessary to determine wheter potential increases in HIV RNA after stimulation in a bulk population result from a small number of highly productive cells, or from a larger but less productive population, as these two mechanisms likely have very different impacts on the latent reservoir.

Our data further highlight the heterogeneous nature of the latent reservoir (Chen et al., 2017; Ho et al., 2013). We currently have a limited understanding of why some latently infected cells are capable of being induced while others are not. It is possible that different chromatin environments impose different degrees of transcriptional repression on the integrated HIV genome, with the non reactivatable latent HIV corresponding to the most repressive environment. (Chen et al., 2017). Since HIV_GKO allows for the isolation of productively infected cells and reactivated latent cells from those that do not reactivate, it provides a unique opportunity to explore the impact of HIV integration on the fate of the infection.

Different integration site-specific features contribute to latency, such as the chromatin structure, including adjacent loci but also the provirus location in the nucleus (Lusic and Giacca, 2015; Lusic et al., 2013). Viral integration is a semi-random process (Bushman et al., 2005) in which HIV-1 preferentially integrates into active genes (Barr et al., 2006; Bushman et al., 2005; Demeulemeester et al., 2015; Ferris et al., 2010; Han et al., 2004; Lewinski et al., 2006; Mitchell et al., 2004; Schröder et al., 2002; Sowd et al., 2016; Wang et al., 2007). LEDGF, one of the main chromatin-tethering factors of HIV-1, binds to the viral integrase and to H3K36me3, and to a lesser extent to H3K4me1, thus directing the integration of HIV-1 into transcriptional units (Daugaard et al., 2012; Eidahl et al., 2013; Pradeepa et al., 2012). Also CPSF6, which binds to the viral capsid, markedly influences integration into transcriptionally active genes and regions of euchromatin (Sowd et al., 2016), explaining how HIV-1 maintains its integration in the euchromatin regions of the genome independently of LEDGF (Quercioli et al., 2016). Several studies have characterized the integration sites, however, these analyses have been restricted to productive infections.

Using ENCODE reference datasets, our data are consistent with previous results, showing that HIV-1 preferentially targets actively transcribed regions (Marini et al., 2015; Wang et al., 2007; Chen et al., 2017). However, non-inducible latent proviruses are observed to be integrated to a higher extent into silenced chromatin. In addition, even though HIV integration is normally strongly disfavored in the heterochromatic condensed regions in LADs due to low chromatin accessibility, we show that some HIV integration does occur in LADs when using a previously published dataset of LADs (Guelen et al., 2008; Marini et al., 2015), and that latent proviruses that are not readily reactivatable are integrated at higher extent in LADs.

Importantly, we identify a unique rare population among the latent cells that can be reactivated. In contrast to the non-inducible latent infections, the latency reversal of inducible latent proviruses might be explained by integration in an open chromatin context, similar to integration sites for productive proviruses, followed by subsequent heterochromatin formation and proviral silencing. As a consequence, the distinct integration sites between induced and non-induced latent proviruses highlight new possibilities for cure strategies. Indeed, the ‘shock and kill’ strategy aims to reactivate and eliminate every single replication-competent latent provirus, since a single remaining cell carrying a latent inducible provirus could, in theory, reseed the infection. However, our study and others point out several significant barriers to successful implementation of the ‘shock and kill’ strategy. First, LRAs only reactivate a limited fraction of latent proviruses. It is likely that some of the non-induced proviruses, such as those integrated into enhancers and transcriptionnal active regions of the genome, will reactivate after several rounds of activation, due to the stochastic nature of HIV activation (Dar et al., 2012; Ho et al., 2013; Singh et al., 2010; Weinberger et al., 2005). It is also likely that better suited LRAs combinations (two or more LRAs) will reactivate some of the non-induced proviruses integrated into silenced chromatin marked by H3K27me3 and H3K9me3. Indeed, several studies have shown that the pharmaceutical inhibition of H3K27me3 and H3K9me2/3 could sensitize latent proviruses to LRAs (Friedman et al., 2011; Nguyen et al., 2017; Tripathy et al., 2015). Second, Shan et al. have shown that latently reactivated cells are not cleared due to cytopathic effects or CTL response implying that immunomodulatory approaches, in addition of more potent LRAs, are likely required to achieve a cure for HIV infection (Shan et al., 2012).

In conclusion, the heterogeneity of the latent reservoir calls for therapies addressing the different pools of latently infected cells. While ‘shock and kill’ might be helpful in reactivating and possibly eliminating a small subset of highly reactivatable latent HIV genomes, other approaches will be necessary to control or eliminate the less readily reactivatable population identified here and in patients. Perhaps, this latter population should rather be ‘blocked and locked’ using latency-promoting agents (LPAs), as described by several groups (Besnard et al., 2016; Kessing et al., 2017; Kim et al., 2016; Vranckx et al., 2016). For a functional cure, a stably silenced, non-reactivatable provirus is preferable to a lifetime of chronic active infection.

All sequencing data generated during this study are included in the Integration sites Source data file 1

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Thank you for submitting your article "Chromatin Functional States Correlate with HIV Latency Reversal in Infected Primary CD4⁺ T Cells" for consideration by eLife. Your article has been reviewed by three peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Prabhat Jha as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Marina Lusic (Reviewer #1); Vicente Planelles (Reviewer #2).

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

In this elegant study, the authors report on the establishment of latency and reactivation of latent proviruses using a dual reporter HIV-1 virus (HIV_GKO), which allows accurate separation and quantification of cells actively producing HIV from those harboring LTR silent, latent viruses.

By using several latency-reversing agents (LRAs) the authors compare the efficacy of HIV-1 reactivation in cells from HIV-1 infected individuals and in cells infected in vitro with the HIV_GKO. HDAC inhibitor panobinostat, BRD4 inhibitor JQ1, and the PKC inhibitor bryostatin-1 showed limited reactivation unless used in combination. The similar trends in reactivation potential with single or combination LRAs recapitulates well the existing literature. The observation that the synergistic effect of these drugs in reactivating HIV in patient samples was stronger than that observed in the cells infected in vitro is very interesting.

The authors mapped a total of 1,803 integration sites mostly located in introns of actively transcribing genes. The transcription profiles of these genes as well as several histone marks using ENCODE data were also analyzed. Proviruses appear to be integrated in intragenic regions, with proviruses integrated more frequently in enhancer and actively transcribed regions of the genome compared to silenced regions. Non-inducible latently infected cells harbored virus integrated in enhancer and activated regions to a higher extent than in silenced regions, although reduced compared to productively infecting and reactivated proviruses. Non-inducible latently infected cells showed an increased level of integration into heterochromatin compared to productively and reactivated proviruses. The authors conclude that HIV-1 reversal from latency highly depends on the host transcriptional activity and chromatin context. Integration sites from PICs are outside of lamin-associated domains (LADs) whereas those from RLIC and NRLIC are found more often within LADs compared to latent cells.

In conclusion, this well-written manuscript highlights the heterogeneous nature of the HIV-1 latent reservoir and the challenges it poses to strategies seeking to induce virus from latency as a method to eradicate HIV infection. Moreover, it underlines several important aspects regarding latent HIV, such as that viral RNA does not truly reflect the number of cells that are reactivated and that LRAs and T cell stimulation with CD3/CD28 results in reactivation of only a small portion of latent viruses. This powerful new dual color HIV reagent will be exciting to those in the field.

A revised manuscript will be considered if the authors can address the major points outlined below within a reasonable amount of time.

Essential revisions:

1) Throughout the paper the definition of productively infected cells appears to change. For example, in Figure 1D the ratio of latent to productively infected cells, appears to be calculated by combining csGFP+mKO2- and csGFP+mKO2+ populations whereas in the text corresponding to Figure 1E and 1F the productively infected population is defined as csGFP+mKO2+ cells only. While the csGFP+mKO2- population is reduced in the HIV_GKO model, this population is not negligible and should be consistently included when examining the productively infected population.

2) As the authors aim to establish a superior HIV latency model they should directly quantify the percent increase in latently infected cells compared to their previous DuoFluo reporter instead of only stating that they can identify a 'much larger' number of latent cells.

3) Throughout the paper the authors express surprise at the low level of reactivation of the latent reservoir observed while also stating that this observation is in agreement with the current literature giving a confusing impression of the novelty of their results. Please amend.

4) The fact that JQ1 and bryostatin induce reactivation in patient samples but only little effect on in vitro infected cells is a very interesting finding, pointing to differences in the sites of viral integration between patient cells and in vitro infected cells. It would be interesting if the authors could comment on these findings in the Discussion section.

5) The authors convincingly show that only a minor fraction of latently infected cells becomes GFP+ upon treatment with LRAs. Are there GFP- KO2-cells in the culture before and after treatment with LRAs (and if yes, which percentage?).

It would be important to analyze the effect of sequential (e.g., 2) rounds of treatment with LRAs. Another important experiment, which the authors may consider to strengthen the conclusions, is to inhibit H3K27me3 and H3K9me3 followed by treatment with LRAs. Lastly, please clarify why the time of LRAs application is different for in vitro infected versus patient samples (such as 6 hours vs 24 hours).

6) The CD3/CD28 beads (positive control) only reactivated HIV in 3.1% of the latently infected cells. Can the authors comment on why they used 0.5 CD3/CD28 beads/cell for their infection as this is less than the 1 bead per cell recommended by the manufacturer.

Did the authors try other positive controls such as PMA and Ionomycin as reported elsewhere (Laird et al., 2015)? Additionally, can the authors comment on why the latently infected cells were not incubated with the stimuli for longer than 24 hours? This might have resulted in a different outcome.

7) The authors compare the activation status of non-infected, reactivated and non-reactivated cells and conclude that "Overall, the comparison of the latent populations showed very little difference". Judging from the data, the conclusion appears too strong to me. There is a significantly lower percentage of CD25+CD69+ in non-reactivated cells which leaves open the possibility that, at least in part, this population has an incomplete/lower activation (which might partially account for lack of reactivation). The addition of a late marker of activation such as HLA-DR would also greatly strengthen the conclusions.

An important control would be to repeat this experiment with another reactivating stimulation (e.g. PHA) and see if these differences persist. If so, the possibility that lack of full activation accounts for lower viral reactivation must be acknowledged and discussed.

8) The number of integration sites appears to be too low for RLIC (only 162). Please disclose how many genes are hit by these 162 IS. There is no tabular representation of the IS sequencing information. A file with all sorted IS is missing.

Is the value for RLIC really 0% in Figure 5? A significantly higher expression (upon CD3-CD28 activation) of genes containing integration sites of non-reactivated HIV versus reactivated virus seems counterintuitive. As mentioned before the possibility that the lack of complete activation upon CD3-CD28 treatment characterizes the non-reactivated population must be further analyzed and discussed.

9) Clarification on which method was used for the quantification of gene expression is needed. The method presented is not as cited as it requires at least two housekeeping genes. (ddCT method is referenced in the Materials and methods section).

10) In the Discussion section the authors state that "productive proviruses predominantly target actively transcribed regions….On the other hand, non-inducible latent proviruses are observed to integrate into silenced chromatin…". The data in Figure 7A show that the majority of NRLIC viruses still integrate into enhancer and transcriptionally active regions of the genome although integrations in the NRLIC population occur to a higher extent in silenced regions of the genome compared to PIC and RLIC populations. This observation is not addressed in the discussion and the statement is, therefore, a bit misleading.

Essential revisions:

1) Throughout the paper the definition of productively infected cells appears to change. For example, in Figure 1D the ratio of latent to productively infected cells, appears to be calculated by combining csGFP+mKO2- and csGFP+mKO2+ populations whereas in the text corresponding to Figure 1E and 1F the productively infected population is defined as csGFP+mKO2+ cells only. While the csGFP+mKO2- population is reduced in the HIV_GKO model, this population is not negligible and should be consistently included when examining the productively infected population.

We agree with the reviewer and have edited the text corresponding to Figure 1E and 1F according to the reviewers’ suggestion. The productively infected cells indeed comprise both csGFP+mKO2- and csGFP+mKO2+ populations. GFP+ mKO2- cells are now included in productively infected cells in all experiments.

2) As the authors aim to establish a superior HIV latency model they should directly quantify the percent increase in latently infected cells compared to their previous DuoFluo reporter instead of only stating that they can identify a 'much larger' number of latent cells.

We have addressed this concern in the new version of the manuscript and added a supplemental figure (Figure 1—figure supplement 1A–C). The data shows that increasing the input of HIV_GKO proportionately increased latently and productively infected cells (Figure 1—figure supplement 1A and 1B) with no change in the ratio of latent/productive infection (Figure 1—figure supplement 1C). This was not observed with our previous DuoFluo reporter (Figure 1—figure supplement 1A–C).

3) Throughout the paper the authors express surprise at the low level of reactivation of the latent reservoir observed while also stating that this observation is in agreement with the current literature giving a confusing impression of the novelty of their results. Please amend.

The novelty of the results is our ability to quantify the fraction of latently infected cells that reactivate using our novel dual fluorescent virus. These results are in agreement with what has been observed in latently affected cells isolated from patients and reactivated in vitro. We have edited our manuscript to clarify this point.

4) The fact that JQ1 and bryostatin induce reactivation in patient samples but only little effect on in vitro infected cells is a very interesting finding, pointing to differences in the sites of viral integration between patient cells and in vitro infected cells. It would be interesting if the authors could comment on these findings in the Discussion section.

We are somewhat confused by this question since JQ1 and bryostatin respectively induce 1.7- and 6.8-fold increase of HIV mRNA in patients’ samples, and 1.1- and 6.2-fold increase in HIV_GKO latently infected cells. These reactivation levels appear very similar. We are not sure what the reviewers want us to comment on.

5) The authors convincingly show that only a minor fraction of latently infected cells becomes GFP+ upon treatment with LRAs. Are there GFP- KO2-cells in the culture before and after treatment with LRAs (and if yes, which percentage?).

There were indeed csGFP-, mKO2- cells before and after LRA treatment in the case of Figure 2B (RT-qPCR quantification of HIV latency reactivation) (about 97% of csGFP-, mKO2-, and 3% of GFP-, mKO2+ after sort). In the experiment shown in Figure 3 (Flow cytometry of HIV latency reactivation), there were no csGFP-, mKO2- cells after the sort. We isolated pure latent cells (GFP-, mKO2+), and proceeded to LRA reactivation on this pure population.

It would be important to analyze the effect of sequential (e.g., 2) rounds of treatment with LRAs.

We agree with the reviewers since HIV latency reversal has been shown to be stochastic (discussed in the Discussion section). We have tried this experiment several times. Briefly, primary CD4 T cells were infected with HIV_GKO, and latent cells were isolated by sorting at day 5 post-infection. We, then, isolated again the non-induced HIV_GKO latently infected cells. However, these cells showed very low viability and we were unable to restimulate them and answer this question.

Another important experiment, which the authors may consider to strengthen the conclusions, is to inhibit H3K27me3 and H3K9me3 followed by treatment with LRAs.

We thank the reviewers for their suggestion. Several teams have already performed this experiment using different models, and they all reached the same conclusion: the inhibition of H3K27me3 sensitizes latent provirus to LRAs (Friedman et al., 2011; Nguyen et al., 2017; Tripathy et al., 2015).

EZH2 is a key component of the Polycomb Repressive Complex 2 (PRC2) silencing machinery, and the enzyme is required for H3K27me3 deposition. In their study, Friedman et al. found that knocking down of EZH2 in latent Jurkat T-cells induced up to 40% of the latent HIV proviruses (Friedman et al., 2011). Knockdown of EZH2 also sensitized latent proviruses to external stimuli, such as T-cell receptor stimulation, and slowed the reversion of reactivated proviruses to latency. Similarly, cell populations that responded poorly to external stimuli carried HIV proviruses that were enriched in H3K27me3. Similarly, but with a different model, Tripathy et al. show that GSK343, a potent and selective EZH2/EZH1 inhibitor, reduced H3K27me3 of the HIV provirus in primary resting cells (Tripathy et al., 2015). This epigenetic change was not associated with increased proviral expression in latently infected resting cells. However, following the reduction in H3K27me3 at the HIV LTR, subsequent exposure to the HDACi vorinostat resulted in increases in HIV gag RNA and HIV p24 antigen production that were up to 2.5-fold greater than those induced by vorinostat alone. Finally, a recent study from Nguyen et al. found that the pharmaceutical inhibition of EZH2 in the Th17 primary cell model of HIV latency or resting memory T cells isolated from HIV-1-infected patients receiving highly active antiretroviral therapy, was sufficient to induce the reactivation of latent proviruses (Nguyen et al., 2017). The methyltransferase inhibitors showed synergy with interleukin-15 and VOR.

EHMT2 and SUV39H1 are respectively components of the H3K9me2 and H3K9me3 silencing machinery. In their study, Friedman et al. show that SUV39H1 can contribute to HIV-1 latency in Jurkat T cells, but it appears to be less effective than EZH2 (Friedman et al., 2011). Nguyen et al. found that the inhibition of H3K9me2 in the Th17 primary cell model of HIV latency or resting memory T cells isolated from HIV-1-infected patients receiving highly active antiretroviral therapy, was sufficient to induce the reactivation of latent proviruses (Nguyen et al., 2017).

In conclusion, data showing that inhibiting H3K27me3 and H3K27me2/3 sensitizes latent provirus to downstream LRAs treatment is available and supports our model. We have added text in our Discussion section describing these studies.

Lastly, please clarify why the time of LRAs application is different for in vitro infected versus patient samples (such as 6 hours vs 24 hours).

1) Experiments with patients’ samples and in vitro latently infected cells are different. In the case of in vitro experiment, we are sorting cells after infection, while we are not sorting cells in the case of patients’ samples.

2) In preliminary experiments, we noticed that sorted cells showed a spontaneous reactivation of latent HIV which became more prominent after 24 hours and was minimum at 6 hours (Author response image 1). We therefore focused on analyzing the cells 6 hours after sorting and addition of LRA to eliminate this confounding variable.

Author response image 1

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Briefly, CD4⁺ T cells were purified from healthy donors’ blood and activated for 72 hours with aCD3/CD28 beads + 100 U/ml IL-2 before infection with HIV_GKO. Five days post-infection, latent cells (csGFP- mKO2+) cells were sorted, put in culture, and every 6 hours post-sort, aliquots of cells were stained with live/dead marker and fixed before FACS analysis.</Author response image 1 title/legend>

6) The CD3/CD28 beads (positive control) only reactivated HIV in 3.1% of the latently infected cells. Can the authors comment on why they used 0.5 CD3/CD28 beads/cell for their infection as this is less than the 1 bead per cell recommended by the manufacturer.

In our preliminary experiments, we did not observe any difference between 0.5 bead/cell and 1 bead/cell when infecting cells (data not shown). Given the high price of these beads, we decided to use 0.5 bead/cell to activate cells before infection. However, when we reactivated latently infected cells, we used 1 bead/cell (specified in the Materials and methods section).

Did the authors try other positive controls such as PMA and Ionomycin as reported elsewhere (Laird et al., 2015)?

In preliminary experiment, we indeed used PMA+Ionomycin, but saw similar reactivation as aCD3/CD28 in patients’ samples and HIV_GKO latently infected cells after 24 hours of treatment (Author response image 2).

Author response image 2

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(2) Intracellular HIV-1 mRNA levels in HIV_GKO latently infected CD4⁺ T-cells, and treated with a single LRA or a combination of two LRAs for 24 hours in presence of raltegravir, presented as fold induction relative to DMSO control.

(n = 3 (different donors), mean + SEM, paired t-test).

Experiments were performed as for Figure 2 in the main manuscript. </Author response image 2 title/legend>

Additionally, can the authors comment on why the latently infected cells were not incubated with the stimuli for longer than 24 hours? This might have resulted in a different outcome.

We did not treat latent cells with stimuli for longer than 24 hours since most of the literature is using 24 hours. However, we looked at latency reversal at 24, 36 and 48 hours post-treatment, and did not see much differences. We actually did observe some decrease of latency reversal after 48 hours of treatment with panobinostat, likely due to drug toxicity (Author response image 3).

Author response image 3

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Briefly, CD4⁺ T cells were purified from blood of healthy donors and activated for 72 hours with aCD3/CD28 beads and 100 U/ml IL-2 before infection with HIV_GKO. Five days post-infection, latently infected cells (csGFP- mKO2+) cells were sorted, put back in culture, and stimulated with different LRAs in presence of raltegravir for 24-, 36- or 48 hours before performing FACS analysis.</Author response image 3 title/legend>

7) The authors compare the activation status of non-infected, reactivated and non-reactivated cells and conclude that "Overall, the comparison of the latent populations showed very little difference". Judging from the data, the conclusion appears too strong to me. There is a significantly lower percentage of CD25+CD69+ in non-reactivated cells which leaves open the possibility that, at least in part, this population has an incomplete/lower activation (which might partially account for lack of reactivation). The addition of a late marker of activation such as HLA DR would also greatly strengthen the conclusions.

We agree with the reviewers’ comment and repeated the experiment with HLA-DR as a marker along with CD69 and CD25 (Figure 4 of the revised manuscript).

However, the data with these 4 donors do not show significant lower percentage of CD25+/CD69+. Furthermore, the data still show that the majority of the phenotype for both RLIC and NRLIC are highly activated cells (around 70% of cells expressing both early and intermediate activation markers CD69 and CD25 respectively). No difference is observed when looking at HLA-DR.

We conclude that there is no reproducible difference in activation markers between reactivated vs. unreactivated cells. These new data are shown in the revised manuscript (Figure 4).

An important control would be to repeat this experiment with another reactivating stimulation (e.g. PHA) and see if these differences persist. If so, the possibility that lack of full activation accounts for lower viral reactivation must be acknowledged and discussed.

In addition of looking at the RLIC and NLRIC populations activation status after aCD3/CD28, we also looked at these populations’ phenotype after PHA treatment (Author response image 4(2)) as suggested by the reviewers, and at populations with no stimuli (DMSO – Author response image 4(1)).

Interestingly, PHA treated cells have a very similar profile to untreated (DMSO) cells at 24 hours post treatment, and it seems that PHA is not as effective as aCD3/CD28 in activating cells, at least 24 hours post-treatment.

Author response image 4

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8) The number of integration sites appears to be too low for RLIC (only 162). Please disclose how many genes are hit by these 162 IS.

There is indeed a small number of RLIC, since we do have a very small population to work with. As shown in Figure 6A of the manuscript, 80.9% of RLIC IS are within genes (= 131 IS are within genes).

There is no tabular representation of the IS sequencing information. A file with all sorted IS is missing.

We provided an excel file Source data 1 with all IS for each donor and each population. This file comprises 4 tabs. The first tab is the complete list of integration sites for all populations (PIC, RLIC, NRLIC), and the 3 donors. The second tab is the complete list of integration sites for PIC population from the 3 donors, the third tab is the complete list of integration sites for RLIC population from the 3 donors, and the last tab is the complete list of integration sites for NRLIC population from the 3 donors. Each excel tab has 20 columns (Donor, Status, Chromosome, Start, End, Size, Strand, Expression over integration site (same strand), expression over integration site (opposite strand), gene symbol, Ensemble accession number, Description gene, Start gene, End gene, Distance to closest gene, Gene strand, Expression gene, Integration UTR/intron, Direction integration and gene).

Is the value for RLIC really 0% in Figure 5?

Yes, the value is truly 0% in Figure 5.

A significantly higher expression (upon CD3-CD28 activation) of genes containing integration sites of non-reactivated HIV versus reactivated virus seems counterintuitive. As mentioned before the possibility that the lack of complete activation upon CD3-CD28 treatment characterizes the non-reactivated population must be further analyzed and discussed.

We agree with the reviewers and were surprised as well not to observe a correlation between αCD3/CD28-induced latent proviruses integration sites and genes responsive to T-cell activation signals. However, that is what our analysis reveals, and the good activation phenotype of αCD3/CD28 treated cells cannot explain the lack of correlation.

9) Clarification on which method was used for the quantification of gene expression is needed. The method presented is not as cited as it requires at least two housekeeping genes. (ddCT method is referenced in the Materials and methods section).

We edited the text and provide a more detail protocol in the Materials and methods section regarding the normalization of gene expression. Since we didn’t have the standard used in Bullen et al., 2014, we measured relative (instead of absolute HIV mRNA) copy number. We believe that this normalization is appropriate since we are reporting fold changes relative to DMSO. We used the same primers and probe as Bullen et al., 2014, but used a modified qPCR protocol that measures relative copy number (and used GAPDH to normalize the data). We changed the reference for the ddCT method.

10) In the Discussion section the authors state that "productive proviruses predominantly target actively transcribed regions….On the other hand, non-inducible latent proviruses are observed to integrate into silenced chromatin…". The data in Figure 7A show that the majority of NRLIC viruses still integrate into enhancer and transcriptionally active regions of the genome although integrations in the NRLIC population occur to a higher extent in silenced regions of the genome compared to PIC and RLIC populations. This observation is not addressed in the discussion and the statement is, therefore, a bit misleading.

We have edited the text to address this point (Discussion section).

Author details

1. Emilie Battivelli

   1. Gladstone Institute of Virology and Immunology, Gladstone Institutes, San Francisco, United States
   2. Department of Medicine, University of California San Francisco, San Francisco, United States
   3. Buck Institute for Research on Aging, Novato, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3297-0231

2. Matthew S Dahabieh

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

   Contribution

   Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

3. Mohamed Abdel-Mohsen

   1. University of California San Francisco, San Francisco, United States
   2. Blood Systems Research Institute, San Francisco, United States
   3. The Wistar Institute, Philadelphia, United States

   Contribution

   Resources, Investigation, Writing—review and editing

   Competing interests

   No competing interests declared

4. J Peter Svensson

   Department of Biosciences and Nutrition, Karolinska Institutet, Solna, Sweden

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Validation, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5863-6250

5. Israel Tojal Da Silva

   1. Laboratory of Molecular Immunology, The Rockefeller University, New York, United States
   2. Laboratory of Computational Biology and Bioinformatics, International Research Center, Sao Paulo, Brazil

   Contribution

   Data curation, Software, Formal analysis, Methodology

   Competing interests

   No competing interests declared

6. Lillian B Cohn

   Laboratory of Molecular Immunology, The Rockefeller University, New York, United States

   Contribution

   Resources, Methodology, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3485-8692

7. Andrea Gramatica

   1. Gladstone Institute of Virology and Immunology, Gladstone Institutes, San Francisco, United States
   2. Department of Medicine, University of California San Francisco, San Francisco, United States
   3. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, United States

   Contribution

   Resources, Writing—review and editing

   Competing interests

   No competing interests declared

8. Steven Deeks

   Department of Medicine, University of California San Francisco, San Francisco, United States

   Contribution

   Resources

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-6371-747X

9. Warner C Greene

   1. Gladstone Institute of Virology and Immunology, Gladstone Institutes, San Francisco, United States
   2. Department of Medicine, University of California San Francisco, San Francisco, United States
   3. Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, United States

   Contribution

   Resources, Writing—review and editing

   Competing interests

   No competing interests declared

10. Satish K Pillai

    1. University of California San Francisco, San Francisco, United States
    2. Blood Systems Research Institute, San Francisco, United States

    Contribution

    Resources, Writing—review and editing

    Competing interests

    No competing interests declared

11. Eric Verdin

    1. Gladstone Institute of Virology and Immunology, Gladstone Institutes, San Francisco, United States
    2. Department of Medicine, University of California San Francisco, San Francisco, United States
    3. Buck Institute for Research on Aging, Novato, United States

    Contribution

    Conceptualization, Supervision, Funding acquisition, Validation, Methodology, Writing—review and editing

    For correspondence

    EVerdin@buckinstitute.org

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-3703-3183

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