1. Microbiology and Infectious Disease
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Infection with a newly designed dual fluorescent reporter HIV-1 effectively identifies latently infected CD4+ T cells

  1. Jinfeng Cai
  2. Hongbo Gao
  3. Jiacong Zhao
  4. Shujing Hu
  5. Xinyu Liang
  6. Yanyan Yang
  7. Zhuanglin Dai
  8. Zhongsi Hong
  9. Kai Deng  Is a corresponding author
  1. Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, China
  2. Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, China
  3. Department of Infectious Diseases, Fifth Affiliated Hospital, Sun Yat-sen University, China
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Cite this article as: eLife 2021;10:e63810 doi: 10.7554/eLife.63810

Abstract

The major barrier to curing HIV-1 infection is a small pool of latently infected cells that harbor replication-competent viruses, which are widely considered the origin of viral rebound when antiretroviral therapy (ART) is interrupted. The difficulty in distinguishing latently infected cells from the vast majority of uninfected cells has represented a significant bottleneck precluding comprehensive understandings of HIV-1 latency. Here we reported and validated a newly designed dual fluorescent reporter virus, DFV-B, infection with which primary CD4+ T cells can directly label latently infected cells and generate a latency model that was highly physiological relevant. Applying DFV-B infection in Jurkat T cells, we generated a stable cell line model of HIV-1 latency with diverse viral integration sites. High-throughput compound screening with this model identified ACY-1215 as a potent latency reversing agent, which could be verified in other cell models and in primary CD4+ T cells from ART-suppressed individuals ex vivo. In summary, we have generated a meaningful and feasible model to directly study latently infected cells, which could open up new avenues to explore the critical events of HIV-1 latency and become a valuable tool for the research of AIDS functional cure.

Introduction

Despite antiretroviral therapy (ART) efficiently blocks productive HIV-1 replication, viruses persist in a small pool of latently infected CD4+ T cells, which are widely considered the major barrier to curing HIV-1 infection. These latently infected cells harbor transcriptional silent but replication-competent proviral genome, which was demonstrated to be the origin of rapid viral rebound after ART is interrupted (Finzi et al., 1997; Chun et al., 2008). Latently infected CD4+ T cells are essentially indistinguishable from uninfected cells and are therefore not recognized by immune responses, including broadly neutralizing antibodies or cytotoxic T lymphocytes (CTLs). Current technologies also do not allow direct identification or purification of live latently infected cells from the infected individuals on ART. It is therefore vitally important to create novel and physiological relevant cell models of HIV-1 latency, which can be further exploited to study the mechanisms underlying HIV-1 latency and to develop novel strategies for AIDS functional cure.

The difficulty in distinguishing latently infected cells from the vast majority of uninfected cells has represented a major bottleneck precluding comprehensive understandings of HIV-1 latency. To tackle this question, researchers have tried to establish reporter virus systems to differentiate productively and latently infected cells. The reporter viruses usually incorporate two fluorescent proteins, with one dependent on LTR activity while the other under the control of a constitutive promoter (Calvanese et al., 2013; Dahabieh et al., 2013; Battivelli et al., 2018; Kim et al., 2019). However, some apparent defects existed with the previously reported systems, especially when infecting primary CD4+ T cells: (a) unclear cell subpopulations, for example, the boundaries of productively infected cells were obscured (Calvanese et al., 2013; Battivelli et al., 2018; Kim et al., 2019); (b) potential interference of cytokines on HIV-1 latency, as all infected cells (Dahabieh et al., 2013; Battivelli et al., 2018) were maintained in IL-2 containing medium for latency establishment; and (c) low frequency of reactivation of the identified latently infected cells, for instance, only 5% of HIVGKO cells were reactivated by bryostatin-1 and panobinostat (Battivelli et al., 2018), which bona fide limit its application to explore the mechanism of HIV latency maintenance and reactivation in vitro.

To improve on the cell model of HIV-1 latency, here we set up a newly designed dual fluorescent reporter virus, DFV-B, infection of which can distinguish latently infected from productively infected and uninfected cells. With DFV-B infection, we can sort out latently infected primary CD4+ T cells, in which more than 50% can be reactivated by subsequent PMA and ionomycin stimulation. We also showed that DFV-B infection in Jurkat cells can generate a bunch of stable latently infected cells (named J-mC cells), which were obtained by natural infection, therefore circumventing the clonal-derived biases of some previous cell lines like J-Lat. We went on to show that the J-mC system is a robust model of HIV-1 latency, as a high-throughput compound screening with this model successfully identifies a novel latency reversal agent (LRA) ACY-1215, whose activity could be verified in other cell models and in primary CD4+ T cells from ART-suppressed individuals. As an oral HDAC6-selective inhibitor, ACY-1215 has a higher safety and tolerability profile than the currently available pan-HDAC inhibitors such as SAHA or panobinastat, making it a promising LRA in future applications for HIV-1 functional cure.

Results

DFV-B infection directly identifies latently infected primary CD4+ T cells

To specifically identify latently infected cells, we constructed a new reporter virus by engineering GFP and mCherry into the HIV-1 genome, in which GFP expression was controlled by LTR, while mCherry expression was independently controlled by the constitutive promoter EF-1α. This viral construct was named DFV-B (dual fluorescent virus-B) (Figure 1A). To test the infectivity of DFV-B, activated primary CD4+ T cells from HIV-1 naïve individuals were infected with DFV-B incorporating a CXCR4-tropic env (Figure 1B). Productively infected (double-positive, DP) and latently infected (single mCherry-positive, mC) CD4+ T cells were clearly distinguishable (Figure 1B), and the percentage of the DP cells was roughly 16 times more than the single mC-positive cells on day 3 post-infection (p.i.) in STCM (Super T cell medium) (Figure 1B). To mimic the process of infected activated CD4+ T cells transitioning back to a more resting state, and monitor how the dynamics of productive and latent infection change during this process, DFV-B-infected cells were transferred to cytokine-free medium (CFM) on day 3 p.i. As culture time increased, the proportion of DP cells decreased significantly, while the proportion of mC cells gradually increased (Figure 1C). mC cells accounted for up to 30% of the total infected cells on day 10 p.i. (Figure 1C), suggesting some of the productively infected cells survived, reverted to a more resting state, and turned off viral gene (GFP) expression, which may add to the increased proportion of latently infected cells.

Infection with a newly designed dual fluorescent reporter HIV-1 identifies latently infected primary CD4T cells.

(A) Diagram of the derivation of DFV-B from the original NL4-3 strain of HIV-1. Top panel depicts the genomic structure of HIV-1 NL4-3; bottom panel shows the genomic structure of DFV-B and the major modifications comparing to NL4-3. (B) Representative experiment of DFV-B infection in activated primary CD4+ T cells. Top panel depicts the scheme of experimental procedure. Isolated primary CD4+ T cells from HIV naïve individuals were stimulated with αCD3/αCD28 antibodies in the presence of IL-2 and TCGF for 3 days, and the activated CD4+ T cells were then infected with DFV-B incorporating a CXCR4-tropic Env at the MOI of 0.1 to 1 for 3 days in STCM and were subsequently transferred to CFM. Bottom panel shows the infection profiles of stimulated primary CD4+ T cells by DFV-B at indicated time points. (C) Data from panel B are displayed graphically. Line charts show the percentages of latent infection (mC) and productive infection (DP). Histogram represents the cell viability. Data indicate mean ± SD from three different donors. TCGF: T cell growth factor; CFM: cytokine-free medium; STCM: super T cell medium, CFM with IL-2 and TCGF.

To validate the single mC-positive cells that were indeed latently infected, a series of experiments were performed as illustrated in Figure 2A. We sorted the DP, the single mC-positive, and the double-negative (DN) cells by flow cytometry at 7 days post DFV-B infection and validated the purity of each population (Figure 2—figure supplement 1A). The sorted mC cells were then cultured in CFM and activated by PMA and ionomycin for 3 days. During this process, the viability of mC cell population was more than 40% (Figure 2—figure supplement 1B). Compared to the DMSO control, over 50% of total cells were converted from mC to DP cells (Figure 2B), suggesting that more than half of the latently infected cells were reactivated. At the same time, DP and DN cells were not affected (Figure 2B). In addition, these sorted populations were analyzed for HIV-1 RNA and protein expression. As expected, the DP cells transcribed significantly higher amounts of HIV-1 mRNA, nearly 10 times as the mC cells (Figure 2C). HIV-1 Gag and Vif protein production were detected by western blot in DP cells, but not in mC cells (Figure 2D). To further verify that the absence of GFP production was due to viral latency but not possible genomic deletion, we quantified the cell-associated DNA and RNA of fluorescent protein from different populations respectively. Comparing to the uninfected DN cells, both GFP-DNA and mCherry-DNA were comparable between mC and DP cells, indicating no difference in viral integration (Figure 2E). Meanwhile, levels of GFP mRNA in DP cells were much higher than that in mC cells, but mCherry mRNA levels remained the same (Figure 2E), consistent with the phenotypes observed by FACS. A small fraction of single GFP+ cells existed after DFV-B infection (Figure 1B), and we traced and found that the proportion of single GFP+ cells decreased from 0.7% to 0.2% (similar to uninfected cells) as the culture time increased (Figure 2—figure supplement 1C), which is significantly lower than the previous reported dual reporter viral systems (Calvanese et al., 2013; Kim et al., 2019). The integrated DNA levels of GFP and mCherry in this single GFP+ cells were not different (Figure 2—figure supplement 1D), suggesting both reporters were still stably integrated. We speculated that this unlikely phenotype of single GFP+ cells could be due to the differential dynamics of fluorescent protein degradation. Taken together, these results demonstrated that infection of DFV-B reporter virus in primary CD4+ T cells could directly identify live latently infected cells.

Figure 2 with 1 supplement see all
Validation of the DFV-B infected mC population as latently infected primary CD4T cells.

(A) Schematic of experimental design. (B) Reactivation of the sorted different cell populations. Cells were cultured in CFM with 50 ng/ml PMA and 1 μM ionomycin and measured by flow cytometry 3 days later. Data shown from three different donors (mean ± SD). p-values calculated using unpaired t-test, ***p<0.001; ****p<0.0001; ns, not significant. (C) Quantification of cell-associated HIV-1 mRNA of different sorted populations. HIV-1 mRNAs were measured by real-time qPCR. RNA copies were normalized to the uninfected group. Each symbol represents the mean of three replicates (n = 8). (D) Western blot analysis of HIV-1 protein production in different sorted cell populations. (E) Quantification of the cell-associated DNA and RNA of GFP and mCherry in the sorted populations. The DNA and mRNA of GFP and mCherry were quantified relative to cellular GAPDH, respectively. Data shown from three biological replicates (mean ± SD). p-values calculated using unpaired t-test, ns, not significant; ****p<0.0001.

DFV-B infection confirms effector-to-memory transitioning (EMT) CD4+ T cells are the primary targets for latency establishment

The latent HIV-1 reservoir mainly resides in resting memory CD4+ T cells (Chun et al., 1997; Finzi et al., 1997; Chomont et al., 2009), but they are normally not the direct targets for infection, due to the blocks in reverse transcription (Korin and Zack, 1998), integration (Bukrinsky et al., 1992) and host restriction by SAMHD1 (Baldauf et al., 2012). It has been recently demonstrated that HIV-1 latency was preferentially established in EMT CD4+ T cells (Shan et al., 2017). Therefore, we investigated whether DFV-B infection in EMT CD4+ T cells recapitulated the tendency of latency establishment. We first confirmed the primary CD4+ T cells were in the process of EMT after 5–7 days in CFM culture, as the expression of CD25/CD69/HLA-DR and T cell exhaustion markers (PD-1 and TIM-3) had been greatly reduced (Figure 3A and B). mC cells expressed significantly lower levels of activation markers and exhaustion markers than DP cells (Figure 3C and D), again supporting our results that mC cells were latently infected. The infection profiles were then analyzed when infection was administered independently at different time points indicated (Figure 3E). In general, EMT CD4+ T cells were less permissive to DFV-B infection; however, the ratio of mC cells to total infected cells (mC+DP) steadily increased from 15.7% (day 0 infection) to 64.8% (day 10 infection) (Figure 3F). This data strongly supports that infection during EMT process of CD4+ T cells is most likely to generate latent infection, which also highlights the significance and applicability of our DFV-B system as a viable cell model for HIV-1 latency.

Effector-to-memory transitioning CD4T cells are the primary targets for HIV-1 latency establishment.

(A and B) Expression of activation markers (CD25, CD69, HLA-DR; panel A) and exhaustion markers (LAG-3, TIM-3, PD-1; panel B) of the DFV-B infected primary CD4+ T cells. Primary CD4+ T cells were infected as described in Figure 1B and the surface markers were measured by FACS at indicated time points. Data indicate mean ± SD for three different donors. p-values calculated using unpaired t-test, ns, not significant; **p<0.01; ***p<0.001. (C and D) Expression of activation markers and exhaustion markers in the DN, DP, and mC population from A and B of this figure. Panel C only shows the results of day 8 post-infection. Red dots represented marker-positive cells; blue dots represented negative cells. Panel D shows quantified values of markers expression. Data indicate mean ± SD for three different donors. (E) Infection profile of EMT CD4+ T cells when infected by DFV-B at indicated time points. Top panel depicts scheme of experimental procedure. Bottom panel shows the flow cytometric chart of infected EMT CD4+ T cells on day 5 post-infection. (F) Infected EMT CD4+ T cells are more likely to become latently infected cells. The ratios of DP cells or mC cells to total infected cells were calculated using data from panel E. Data represents the average of three donors.

Generation of a stable cell line model of HIV-1 latency

Although some cell line models of HIV-1 latency (such as the widely used J-Lat model) have been reported, all of which are derived from single-cell clones, and therefore with identical integration sites, which significantly underrepresented the heterogenous nature of latently infected cells in vivo. Here, by taking the advantage of direct labeling of latently infected cells with DFV-B infection, we generated a Jurkat-based cell line model of HIV-1 latency (J-mC), which were derived from random infection events, and therefore should harbor diverse integration sites. We found that DFV-B infection in Jurkat cells closely resembled primary CD4+ T cells. Latently infected cells were readily identifiable on day 7 p.i. (Figure 4—figure supplement 1A). Then the DN, DP, and mC Jurkat T cells were sorted by flow cytometry and stimulated by 10 ng/ml TNF-α for 3 days in CFM. Over 80% of mC cells were converted to DP cells following the treatment, but DP and DN cells were not affected (Figure 4—figure supplement 1B), confirming DFV-B infection could directly identify latent infection in Jurkat cells.

The process of model generation is illustrated in Figure 4A. Thirty days after the first sorting, a small dose of TNF-α (5 ng/ml) was added to the culture to activate those unstable cells that were probably in the borderline state of latency (Figure 4A and B). To assess the activatability of J-mC cells, we stimulated them with different stimuli. As expected, J-mC cells were activated efficiently by all stimuli to express both fluorescent proteins (Figure 4C and D). The potent induction of cell-associated HIV-1 mRNA after PMA treatment further confirmed the integrated proviruses were reactivated in this model (Figure 4E). Besides, mRNA expression levels and DNA levels of GFP and mCherry remained relatively stable over the course of a year (Figure 4F and G). Together, these results demonstrated that J-mC cells were indeed a stable group of latently infected cells and could be reactivated efficiently upon stimulation, suggesting that this would become a useful cell line model to study HIV-1 latency.

Figure 4 with 1 supplement see all
Generation of a novel cell line model of HIV-1 latency.

(A) The experimental procedure of generating a Jurkat-based cell line model of HIV-1 latency (J-mC). Jurkat cells were infected by DFV-B at day 0 in CFM. (B) Flow cytometric charts during the process of J-mC model generation. Detailed steps can be found in ‘Generation of J-mC cells’ in the Materials and methods section. (C) Reactivation of J-mC cells by different stimuli. J-mC cells were stimulated, respectively, with 1 μM JQ1, 1 μM SAHA, 10 ng/ml TNF-α, 50 ng/ml PMA, and 50 ng/ml PMA and 1 μM ionomycin for 48 hr prior to analysis by flow cytometry. (D) Data from panel C are displayed graphically. Data indicate mean ± SD for three different treatments. (E) Cell-associated HIV-1 mRNA in J-mC cells after reactivation. J-mC cells were treated with DMSO or 50 ng/ml PMA for 48 hr. HIV-1 gag, tat, vif, and LTR mRNAs were quantified relative to cellular GAPDH, and values within each group were normalized to the DMSO treatment. Error bars represent SDs of three technical replicates. p-values calculated using unpaired t-test, ***p<0.001; ****p<0.0001. (F) Long-term tracking of the phenotype of J-mC cells. J-mC cells were cultured in CFM and the expression of GFP and mCherry were monitored over the course of a year. (G) Long-term tracking of the integrated DNA level of GFP and mCherry in J-mC cells. J-mC cells were collected on the date indicated, and then total DNA were isolated. The DNA of GFP and mCherry were quantified relative to cellular GAPDH, respectively. Data shown from three biological replicates (mean ± SD).

The integration landscape of HIV-1 in J-mC cells

To verify whether J-mC cells harbored diverse HIV-1 integration sites, we adopted a method called linear amplification–mediated high-throughput genome-wide sequencing (LAM-HTGTS) (Hu et al., 2016) to analyze HIV-1 integration sites. We first tested the applicability of the method on J-Lat 8.4 and J-Lat 15.4, which had been reported to have only one HIV-1 integration site respectively (Symons et al., 2017). J-Lat 8.4 and 15.4 were mixed in equal amounts and then subjected to LAM-HTGTS. As shown in Figure 5A and B, 49.09% of the integration sites were located in the same position 77946384 of chromosome 1% and 46.92% counts were located in the same position 34441293 of chromosome 19 (Supplementary file 1), which were exactly the integration sites of J-Lat 8.4 and J-Lat 15.4, suggesting that LAM-HTGTS was a robust method for analyzing HIV-1 integration sites. For J-mC cells, all integration sites recovered were diffusely distributed on 24 chromosomes (Figure 5C). Chromosome 17 harbored the most abundant integration sites, which at the same time were extremely diverse (Figure 5D and Supplementary file 2). We also noticed that more than 70% of the integration sites were distributed on intronic regions (Figure 5E), which is consistent with previous reports on integration sites recovered from ART-treated HIV-1 infected individuals (Han et al., 2004; Cohn et al., 2015). We performed gene ontology (GO) and GO pathway analysis on the genes at all integration sites, and found that the proviruses were enriched in genes related to ‘Regulation of transcription’, ‘Membrane’, and ‘Binding’ (Figure 5F). In addition, HIV-1 integrated more frequently in cancer-related pathways (Figure 5G), which is also consistent with previously reported ex vivo data (Wagner et al., 2014; Cohn et al., 2015). Together, these data clearly demonstrated that J-mC cells harbored tremendously diverse HIV-1 integration sites, which were highly physiologically relevant, suggesting this cell line model as a valuable tool to study HIV-1 latency.

The integration landscape of HIV-1 in J-mC cells.

(A and B) Validation of integration sites of J-Lat cell lines by LAM-HTGTS. J-Lat 8.4 and J-Lat 15.4 were mixed in equal amounts and subjected to LAM-HTGTS. A: Chromosome distribution of all integration sites. B: Integration sites analysis of J-Lat 8.4 and J-Lat 15.4. (C and D) Diversity of HIV-1 integration sites in J-mC cells. C: Chromosome distribution of all integration sites. A total of 5154 identified integration sites were obtained from J-mC cells. D: Distribution of integration sites on chromosome 17. The percentage of top 10 integration sites was shown. A total of 499 identified integration sites were obtained from chromosome 17. (E) Proportion of integration sites of J-mC cells in the indicated genomic regions. (F) The gene ontology analysis of HIV-1 integration sites detected in J-mC cells. (G) The GO pathway enrichment analysis of HIV-1 integration sites in J-mC cells.

High-throughput screening of small compound library in J-mC cells discovers ACY-1215 as a potent latency reversing agent

To test the applicability of our J-mC model, we performed a high-throughput screening of a small-molecule library containing 1700 FDA-approved compounds to discover novel LRAs. The screening was performed from high concentrations to low concentrations (Figure 6—figure supplement 1A), and with this system we successfully identified several previously reported LRAs, including SAHA, panobinostat, and romidepsin (Figure 6A). A new compound, ricolinistat (ACY-1215), was discovered in this screen (Figure 6A and B), displaying excellent potential to reactivate latent HIV-1 in a dose-dependent manner (Figure 6C, Figure 6—figure supplement 1B). Viral transcription was induced eight fold in average after 24 hr of ACY-1215 treatment (Figure 6D). In addition, we found that ACY-1215 showed better latency reversing activities in J-mC cells than in different J-Lat cell lines (Figure 6—figure supplement 1C–E), which probably explained why it was not discovered in previous drug screenings. Besides, we observed that ACY-1215 displayed a synergistic effect with several other LRAs, but not disulfiram (Figure 6—figure supplement 1F).

Figure 6 with 3 supplements see all
High-throughput screening with the J-mC model identifies ACY-1215 as a potent latency reversing agent.

(A) Summary of screening results in J-mC cells. The results were shown as the percentage of the double positive cells and were normalized to the reactivation level of 50 ng/ml PMA. Each bar represents the average value of triplicates. (B) The chemical structure of ricolinostat (ACY-1215). (C) Reactivation effects of ACY-1215 on J-mC cells at the indicated concentrations. 0.5 million J-mC cells were stimulated with ACY-1215 for 3 days in each condition. Error bars represent SDs of three technical replicates. (D) Cell-associated HIV-1 mRNA expression of J-mC cells after ACY-1215 treatment. Data are representative of at least three independent experiments (mean ± SD). (E) ACY-1215 induced HIV-1 transcription in a primary CD4+ T cells model of HIV-1 latency. (F) Data from panel E are displayed graphically. Histograms show quantification of the percent population in the active gate. Values represent the mean ± SD, N = 3. **p<0.01. Results were analyzed by unpaired t-tests. (G) Quantification of ACY-1215 induced transcription of cell-associated HIV-1 mRNA in CD4+ T cells from ART-suppressed infected individuals. HIV-1 transcription was measured by HIV-1 viral quality assurance assay. 1 million CD4+ T cells were analyzed per condition. Data indicate mean ± SD for three technical replicates. p-values calculated using unpaired t-test, **p<0.01. (H) Fold induction of cell-associated HIV-1 mRNA was shown relative to the negative control (DMSO). Data indicate mean ± SD for three technical replicates. p-values calculated using unpaired t-test, **p<0.01.

Figure 6—source data 1

Characteristics of ART-suppressed, aviremic people living with HIV-1 (PLWH) in this study.

https://cdn.elifesciences.org/articles/63810/elife-63810-fig6-data1-v2.docx

ACY-1215 is an oral HDAC6-selective inhibitor, and our data suggested that it was less toxic than SAHA as measured by annexin-V/propidium iodide (PI) staining (Figure 6—figure supplement 2A and B) and CCK-8 assay (Figure 6—figure supplement 2C). ACY-1215 does not induce upregulation of classic T cell activation markers on freshly isolated primary CD4+ T cells (Figure 6—figure supplement 3A). In addition, ACY-1215 had no significant effect on CCR5 and CXCR4 expression of CD4+ T cells (Figure 6—figure supplement 3B and C). No significant induction of IFN-γ and TNF-α was detected in CD8+ T cells and CD4+ T cells from the same donors following ACY-1215 treatment (Figure 6—figure supplement 3D–G). Taken together, these data indicated that ACY-1215 does not cause global T cell activation, T cell dysfunction, or cytotoxicity to CD4+ and CD8+ T cells.

To further verify the physiological relevance of the J-mC model, we tested the latency reversal activities of ACY-1215 in a primary cell model of HIV-1 latency and in primary cells from ART-treated infected individuals. Average 20 million of GFP- Bcl-2-transduced CD4+ T cells were obtained from one healthy donor according to the protocol (Kim et al., 2014). At 48 hr post-treatment, we found that ACY-1215 worked as well as SAHA, reactivating over 80% of latent HIV-1 when comparing to PMA and ionomycin (Figure 6E and F). We collected five PBMC samples from ART-suppressed, HIV-1 infected individuals (Figure 6—source data 1) and isolated CD4+ T cells. As shown in the Figure 6G and H, ACY-1215 robustly induced the level of cell-associated HIV-1 mRNA by an average of 50-fold comparing to the negative controls. Taken together, our data strongly supports that ACY-1215 is a promising LRA candidate and J-mC is a highly physiological relevant cell model for HIV-1 latency.

Discussion

In this study, we presented a newly designed dual fluorescent virus, DFV-B. With DFV-B infection, latently infected cells can be easily distinguished from productively infected or uninfected cells. Compared with the previously reported infection models, DFV-B displayed a better infection profile with a clearer cell population and a lower percentage of single GFP positive cells (<1%) (Figure 1B) than RGH (Dahabieh et al., 2013), Duo-Fluo I (Calvanese et al., 2013), mdHIV (Hashemi et al., 2016), and HIVGKO (Battivelli et al., 2018).

More than 50% of the latently infected primary cells by DFV-B were reactivated by PMA and ionomycin in CFM (Figure 2B), while only 5% were reactivatable in the HIVGKO model (Battivelli et al., 2018), indicating the robustness of the DFV-B system to research the mechanism of HIV-1 latency maintenance and reactivation in vitro. A small fraction of single GFP positive cells existed after DFV-B infection (Figure 1B), but compared with Duo-Fluo I or HIVGKO infected cells, the proportion of the cells was extremely small (<1%). The eGFP and mCherry have 20–30 bp regions of homology at the C- and N-termini (Salamango et al., 2013), which may cause genomic deletion due to homologous recombination. However, our data showed that the absence of GFP production was due to viral latency but not genomic deletion (Figure 2E). Nevertheless, the risk of recombination still exists between GFP and mCherry, which may need further optimizations. Based on sequence similarity of fluorescent proteins that have been reported, E2 crimson/mKO2 may be a good choice to replace mCherry, which have been used previously to construct a dual-fluorescence reporter system (Battivelli et al., 2018; Kim et al., 2019). Besides, the insertion location of the fluorescent protein in the construct needs to be considered, because there was a big difference in protein expression depending on whether 31 bp nef was retained or not, or two fluorescent proteins were connected or not in DFV-B.

How HIV-1 latency is established is still not fully understood. A widely accepted view is that after infection occurs in activated CD4+ T cells, a tiny fraction of the infected cells survive viral cytopathic effect and host immune elimination and revert to a more resting and memory phenotype, becoming latently infected cells (Sengupta and Siliciano, 2018; Margolis et al., 2020). Our data here was in line with the mainstream view (Figure 1B and C). It is also suggested that latent infection is preferentially established in CD4+ T cells undergoing EMT (Chavez et al., 2015; Shan et al., 2017). Our results from DFV-B infection indicated that EMT CD4+ T cells were less permissive to infection, but once infected were much more likely to establish latency (Figure 3E and F), which supports that EMT CD4+ T cells are the primary targets for latent infection, and highlights the feasibility of our DFV-B system.

Some primary cell models of HIV-1 latency have been developed in CD4+ T cells or thymocytes (Sahu et al., 2006; Burke et al., 2007; Marini et al., 2008; Bosque and Planelles, 2009). However, the limited source and poor long-term viability of primary CD4+ T cells preclude their extensive use. Yang et al reported a Bcl-2-transduced primary CD4+ T cell model of HIV-1 latency, which could obtain a large amount of viable resting CD4+ T cells long term and mimic the process of latency establishment in vitro. However, the over-expression of Bcl-2 may alter the global transcriptional profile and affect the physiological characteristics of the latently infected cells (Yang et al., 2009). In addition, the in vitro cell line model of latency, such as the J-Lat cell lines (Jordan et al., 2003), was derived from single-cell clone with identical HIV-1 integration sites, which significantly underrepresented the heterogeneous nature of latently infected cells (Golumbeanu et al., 2018; Ait-Ammar et al., 2019). Our novel model of J-mC cells harbored extremely diverse HIV-1 integration sites, with a majority of them located in intronic regions, which was in line with data recovered from infected individuals (Han et al., 2004, Craigie and Bushman, 2012, Cohn et al., 2015). In general, our DFV-B model system in primary CD4+ T cells or Jurkat cells circumvented the problems mentioned above, and therefore could be utilized as a robust model to study the characteristics and mechanisms of HIV-1 latent infection.

By applying the J-mC model, we discovered a novel compound ricolinistat (ACY-1215) that can reactivate latent HIV-1 efficiently in different cell models and primary CD4+ T cells from ART-treated infected individuals. ACY-1215, an orally bioavailable HDAC6-selective inhibitor, had previously completed Phase 1 and 2 clinical trials for treatment of multiple myeloma (Santo et al., 2012). In vitro, ACY-1215 selectively increased acetylated a-tubulin (a marker of HDAC6 inhibition) without altering acetylated histone H4 (a marker of global HDAC inhibition) at biologically relevant concentrations (<10 μM) (Carew et al., 2019). In osteoarthritis chondrocytes, the function of ACY-1215 was investigated at concentrations of 1–10 μM, and decreased cell viability was observed at a concentration of 50 μM (Cheng et al., 2019; Li et al., 2019). In the present study, we used 3 μM ACY-1215 in all additional assays to achieve selective inhibition of HDAC6 activity, which was in line with the studies mentioned above and was in the achievable range for clinical trials.

HDAC6 belongs to class II b HDACs and is a cytosolic microtubule-associated deacetylase. Selective HDAC6 inhibition stops aggresome formation, thence inhibiting the degradation leading to accumulation of misfolded proteins within cells, and may reduce the toxicity related to the off-target effects of pan-HDAC inhibitors (Amengual et al., 2021). Besides, HDAC6-KO mice were normal, as opposed to the lethal effect of genetic ablation of class I HDACs (Haberland et al., 2009). ACY-1215 and ACY-241 (another HDAC6-selective inhibitor) can downregulate Th2 cytokine production, decrease T-cell exhaustion, increase T-cell killing ability, and augment the proportion of central memory cells and expression of cytolytic markers in vitro (Laino et al., 2019; Bae et al., 2018). ACY-1215 can also change the chromatin accessibility of T cells (Laino et al., 2019). Besides, ACY-1215 has anti-inflammatory effect and is considered a potential anti-inflammatory drug (Cheng et al., 2019; Zhang et al., 2018; Zhang et al., 2019). A recent study reported that ACY-1215 could prevent the neurotoxicity of HIV-1 envelope protein gp120 (Wenzel et al., 2019). Our data suggested that ACY-1215 was less toxic than SAHA as described clinically (Santo et al., 2012), did not elevate the expression of classic T cell activation markers, and had no significant effect on HIV-1 co-receptors. Taken together, ACY-1215 could become a promising LRA for future AIDS functional cure studies.

In summary, our study designed and put forward a new dual fluorescent reporter virus, DFV-B, infection of which can directly mark latently infected primary CD4+ T cells. We have also developed a stable latently infected cell line model based on DFV-B infection, which can be utilized as a valuable tool to study HIV-1 latency. We further identified a novel small compound ACY-1215 that can effectively reactivate latent HIV-1 and will be of great importance for the future applications of HIV-1 functional cure strategy.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional
information
AntibodyAnti-human CD4-FITC (mouse monoclonal)BiolegendCat# 317408; RRID:AB_571951Dilution 1:1000
AntibodyAnti-human CD8-APC (mouse monoclonal)BiolegendCat# 301049; RRID:AB_2562054Dilution 1:1000
AntibodyAnti-human Bcl2-AF647 (mouse monoclonal)BiolegendCat# 658706; RRID:AB_2563280Dilution 1:1000
AntibodyAnti-human CD69-APC (mouse monoclonal)BiolegendCat# 310910; RRID:AB_314845Dilution 1:1000
AntibodyAnti-human CD25-APC (mouse monoclonal)BiolegendCat# 302610; RRID:AB_314280Dilution 1:1000
AntibodyAnti-human HLA-DR-APC (mouse monoclonal)BiolegendCat# 307610; RRID:AB_314688Dilution 1:1000
AntibodyAnti-human PD-1-APC (mouse monoclonal)BiolegendCat# 329908; RRID:AB_940475Dilution 1:1000
AntibodyAnti-human LAG-3-APC (mouse monoclonal)BiolegendCat# 369212; RRID:AB_2728373Dilution 1:1000
AntibodyAnti-human CCR5-APC (mouse monoclonal)BiolegendCat# 359122; RRID:AB_2564073Dilution 1:1000
AntibodyAnti-human CXCR4-APC (mouse monoclonal)BiolegendCat# 306510; RRID:AB_314616Dilution 1:1000
AntibodyAnti-human IFN-γ-APC (mouse monoclonal)BiolegendCat# 502512; RRID:AB_315237Dilution 1:1000
AntibodyAnti-human TNF-α-APC (mouse monoclonal)BiolegendCat# 502912; RRID:AB_315264Dilution 1:1000
AntibodyAnti-HIV-1 p24 antibody (mouse monoclonal)ABcamCat# ab9071; RRID:AB_306981Dilution 1:1000
AntibodyAnti-HIV-1 Vif antibody (mouse monoclonal)ABcamCat# ab66643; RRID:AB_1139534Dilution 1:1000
AntibodyAnti-β-actin antibody (mouse monoclonal)ABcamCat# ab8226; RRID:AB_306371Dilution 1:1000
AntibodyGoat Anti-Mouse IgG H and L (Alexa Fluor 680) preadsorbedABcamCat# ab186694Dilution 1:20000
AntibodyPurified anti-human CD3 Antibody (mouse monoclonal)BiolegendCat# 300302; RRID:AB_314038Dilution 1:1000
AntibodyPurified anti-human CD28 Antibody (mouse monoclonal)BiolegendCat# 302902; RRID:AB_314304Dilution 1:1000
Strain, strain background (Escherichia coli)Stbl3 E. coliThermoFisherCat#C7381201
Biological sample (Homo sapiens)Blood samplesGuangzhou Blood Center, Guangzhouhttp://www.gzbc.org/Blood samples from healthy individuals
Biological sample (Homo sapiens)Blood samplesThe Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Chinahttp://www.zsufivehos.com/Blood samples from HIV-1- infected individuals
Tissue culture mediaRPMI 1640GIBCOCat# 11875093
Tissue culture mediaDMEMGIBCOCat# 11995065
Tissue culture mediaPenicillin-Streptomycin SolutionBBI Life SciencesCat# E607011
Tissue culture media1 M Hepes SolutionBBI Life SciencesCat# E607018
Chemical compound, drugDMSOSigma-AldrichCat# D2650-100ML
Chemical compound, drugDisulfiramSelleckchemCat# S1680
Chemical compound, drugBryostatin-1Sigma-AldrichCat# B7431
Chemical compound, drugPanobinostatSelleckchemCat# S1030
Chemical compound, drugACY-1215SelleckchemCat# S8001
Chemical compound, drug(+)-JQ-1SelleckchemCat#S7110
Chemical compound, drugSAHASelleckchemCat#S1047
Chemical compound, drugApproved Drug LibraryTargetMOICat# L1000
Chemical compound, drugPhorbol 12-myristate 13-acetate (PMA)SelleckchemCat#S7791
Chemical compound, drugIonomycinMERCKat#407952–5 MG
Chemical compound, drugPhytohemagglutinin-M(PHA-M)Sigma-AldrichCat#11082132001
Chemical compound, drugTRIzol ReagentThermoFisherCat#15596018
Chemical compound, drugPropidium Iodide (PI)BiolegendCat# 421301
Chemical compound, drugAnnexin-V-APCBiolegendCat# 640941; RRID:AB_2616657
Recombinant proteinsRecombinant Human TNF-aPeproTechCat#300-01A
Recombinant proteinsRecombinant Human IL-2R and D SystemsCat#202-IL-500
Critical commercial assaysHuman CD4+T Lymphocyte Enrichment Set-DMBD BiosciencesCat#557939
Critical commercial assaysHIV-1 p24 ELISA KitAbcamCat#ab218268
Critical commercial assaysCell Counting Kit-8MedChemExpressCat# HY-K0301
Critical commercial assaysPlasmid Mini KitOMEGACat# D6943-02
Critical commercial assaysEndo-free Plasmid Mini KitOMEGACat# D6950-02
Critical commercial assaysGel ExtractionOMEGACat# D2500-02
Critical commercial assaysCycle-Pure KitOMEGACat# D6492-02
Critical commercial assaysTissue DNA kitOMEGACat# D3396-02
Cell line (Homo sapiens)HEK293TATCCCRL-3216; RRID:CVCL_0063
Cell line (Homo sapiens)J-Lat 6.3NIH AIDS Reagents Program (Jordan et al., 2003)Cat# 9846; RRID:CVCL_8280
Cell line (Homo sapiens)J-Lat 8.4NIH AIDS Reagents Program (Jordan et al., 2003)Cat#9847; RRID:CVCL_8284
Cell line (Homo sapiens)J-Lat 9.2NIH AIDS Reagents Program (Jordan et al., 2003)Cat# 9848; RRID:CVCL_8285
Cell line (Homo sapiens)J-Lat 10.6NIH AIDS Reagents Program (Jordan et al., 2003)Cat#9849; RRID:CVCL_8281
Cell line (Homo sapiens)J-Lat 15.4NIH AIDS Reagents Program (Jordan et al., 2003)Cat# 9850; RRID:CVCL_8282
Cell line (Homo sapiens)J-Lat A2NIH AIDS Reagents Program (Jordan et al., 2003)Cat#9867; RRID:CVCL_1G43
SoftwarePrism 6GraphPadhttps://www.graphpad.com/scientific-software/prism/; RRID:SCR_002798
SoftwareBD LSRFortessa cell analyzerBD Bioscienceshttp://www.bdbiosciences.com/in/instruments/lsr/index.jsp; RRID:SCR_018655
SoftwareFlowJo V10Tree Starhttps://www.flowjo.com/; RRID:SCR_008520
SoftwareGloMax 96 Microplate Luminometer SoftwarePromegahttps://www.promega.com/resources/softwarefirmware/; RRID:SCR_018614
Sequence-based reagentHIV-1-VQA-FThis paperPCR primersCAGATGCTGCATATAAGCAGCTG
Sequence-based reagentHIV-1-VQA-RThis paperPCR primersTTTTTTTTTTTTTTTTTTTTTTTTGAAGCAC
Sequence-based reagentHIV-1-VQA-ProbeThis paperPCR primersFAM-CCTGTACTGGGTCTCTCTGG-MGB
Sequence-based reagentqPCR-GFP-FThis paperPCR primersGTGCAGTGCTTCAGCCGCTACC
Sequence-based reagentqPCR-GFP-RThis paperPCR primersACCTCGGCGCGGGTCTTGTA
Sequence-based reagentqPCR-mCherry-FThis paperPCR primersGTGGTGACCGTGACCCAGGACT
Sequence-based reagentqPCR-mCherry-RThis paperPCR primersTGGTCTTGACCTCAGCGTCGTAGTG
Sequence-based reagentqPCR-GAPDH-FThis paperPCR primersCTCTGCTCCTCCTGTTCGAC
Sequence-based reagentqPCR-GAPDH-RThis paperPCR primersAGTTAAAAGCAGCCCTGGTGA
Sequence-based reagentHIV-1-gag-FThis paperPCR primersACATCAAGCAGCCATGCAAAT
Sequence-based reagentHIV-1-gag-RThis paperPCR primersTCTGGCCTGGTGCAATAGG
Sequence-based reagentHIV-1-tat-FThis paperPCR primersATGGAGCCAGTAGATCCTAGAC
Sequence-based reagentHIV-1-tat-RThis paperPCR primersCGCTTCTTCCTGCCATAGG
Sequence-based reagentHIV-1-vif-FThis paperPCR primersCACACAAGTAGACCCTGACCT
Sequence-based reagentHIV-1-vif-RThis paperPCR primersCCCTACCTTGTTATGTCCTGCT
Sequence-based reagentHIV-1-LTR-FThis paperPCR primersCCACAAAGGGAGCCATACAATG
Sequence-based reagentHIV-1-LTR-RThis paperPCR primersTTATGGCTTCCACTCCTGCC

Cell lines

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HEK293T and Jurkat cells were obtained from ATCC. HEK293T cells were cultured in DMEM supplemented with 1% penicillin–streptomycin (ThermoFisher), 1% L-glutamine (ThermoFisher), and 10% FBS (ThermoFisher). Jurkat T cells were cultured in CFM (RPMI 1640 supplemented with 1% penicillin–streptomycin, 1% L-glutamine, and 10% FBS). J-Lat 6.3, J-Lat 8.4, J-Lat 9.3, J-Lat 10.6, J-Lat 15.4, and J-Lat A2 cell lines were derived from the human Jurkat T cell line and were gifts from Dr. Robert F. Siliciano (Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD) Laboratory, which were originally generated from Dr. Eric Verdin (The Buck Institute for Research on Aging, Novato, CA). All the J-Lat cell lines were cultured in CFM. All cells have been tested for mycoplasma using a PCR assay and confirmed to be mycoplasma-free. All cells were cultured in a sterile incubator at 37°C and 5% CO2.

Plasmids and vector construction

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DFV-B was derived from the original NL4-3 strain of HIV-1, which contains an HIV-1 genome. To construct DFV-B, GFP (Fragment 2) and EF-1α-mCherry (Fragment 3) were obtained by cloning in the respective plasmids. A 347 bp sequence (Fragment 1) was amplified from pNL4-3 plasmid using primers F1: 5’-ATTAGTGAACGGATCCTTAGCACTTATCTGGGACG-3’ and R1: 5’-GCCCTTGCTCACCATCTTATAGCAAAATCCTTTCCA-3’. A 3’LTR sequence (Fragment 4) was amplified from pNL4-3 plasmid using primers F2: 5’-CTTAGCCACTTTTTAAAAGAAA-3’ and R2: 5’- ACCCTGCACTCCATGGATCAGCTGGCACCCCCGGA-3’. Next, a skeleton sequence was obtained from pNL4-3 using the BamH I and Nco I restriction sites. Finally, Fragment 1, GFP (Fragment 2), EF-1α-mCherry (Fragment 3), and Fragment 4 were cloned into the skeleton sequence by a modified SLIC technique (Li and Elledge, 2012). Of note, a portion of env was deleted for a single round of infection of the virus, and the vpr gene had a frameshift mutation to reduce the toxicity of the pseudovirus. Most of the nef sequence was replaced by GFP- EF-1α-kozak-mCherry, and the remaining 31 bp of nef sequence was reserved in order not to disturb the function of LTR.

Virus stock production

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Pseudotyped viral stocks were produced in HEK293 T cells by co-transfecting 2.2 μg of VSV-G glycoprotein-expression or pCXCR4 HIV-1 envelope-expression vector, 4.4 μg of packaging vector pC-Help, and 4.4 μg pEB-FLV or pDFV-B construct using a polyethylenimine (Invitrogen) transfection system according to the manufacturer’s instructions. Supernatants were harvested after 48 hr, centrifuged (10 min, 500 × g, room temperature), and filtered through a 0.45 μm pore size membrane to remove the cell debris. Viruses were concentrated by centrifuging with a 25% vol of 50% polyethylene glycol 6000 and a 10% vol of 4 M NaCl. Concentrated virions were resuspended in complete medium and stored at −80°C.

Generation of J-mC cells

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The cell line model of HIV-1 latency was generated as described (Figure 4A and B). Briefly, Jurkat cells were infected by DFV-B incorporating a VSVG envelope at 250 ng virus per million cells on day 0 in CFM. At day 7, the single mCherry positive cells were sorted according to the red gate in Figure 4B. The purity of single mCherry positive cells was greater than 99%. Next, the cells were cultured for another 30 days in CFM. At day 37, a small dose of TNF-α (5 ng/ml) was added to the culture and maintained for 7 days to further activate those unstable cells that were probably in the borderline state of latency. At day 44 the second sorting of single mCherry positive cells was performed by the red gate. Similarly, the third and fourth sorting of single mCherry positive cells were performed at days 58 and 72. J-mC cells were finalized at day 86. Each sorting was performed by the red gate in Figure 4B.

Generation of latently HIV-1 infected Bcl-2–transduced cells

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This primary cell of HIV-1 latency was generated as described previously (Kim et al., 2014). In brief, primary CD4+ T cells were isolated from PBMCs with Human CD4+ T lymphocyte Enrichment Set-DM (BD, USA) according to manufacturer’s instructions. CD4+ T cells were co-stimulated with anti-CD3 and anti-CD28 antibodies and were transduced with EB-FLV at the MOI of 5–10 by spinoculation of cells at 1200 g at room temperature for 2 hr. Following 3–4 weeks of culture, viable cells were isolated using Ficoll density gradient centrifugation. Activated Bcl-2-transduced cells were then infected with reporter virus NL4-3-Δ6-drEGFP at a MOI of less than 0.1. The infected cells were maintained in IL-2 and T cell growth factor–enriched medium for 3 days after infection, and then the medium was changed to RPMI 1640 with 10% FBS and 1% penicillin/streptomycin without exogenous cytokines and cultured for more than 1 month. Finally, the GFP-negative cells were sorted to more than 99.9% purity using fluorescence-activated cell sorting (FACS Aria II, BD).

Infection of primary CD4+ T cells

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Primary CD4+ T cells were isolated from HIV naïve individuals. CD4+ T cells were co-stimulated with anti-CD3 and anti-CD28 antibodies for 3 days. Activated CD4+ T cells were then infected with DFV-B incorporating a CXCR4-tropic Env at a MOI of 0.05–0.1. The infected cells were maintained in STCM for 3 days after infection, then the medium was changed to CFM, and cultured for 1 week.

Flow cytometry and cell sorting

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GFP and mCherry fluorescence were measured in a BD LSR Fortessa cell analyzer and sorted in a FACS Aria II (BD). Data were analyzed using Flowjo (version 10.4.0).

DNA, RNA extraction, and qPCR

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Total DNA was extracted with tissue DNA kit (OMEGA). Cells were incubated with different drugs for different time intervals, total RNA was extracted using TRIzol (Invitrogen) and chloroform, and then precipitated with isopropyl alcohol. RNA reverse transcription to cDNA was done according to the procedure of the SuperScript IV Control Reactions (ThermoFisher). Relatively quantitative PCR was performed using SYBR qPCR Master Mix (Vazyme) on the QuantStudio 3 (Thermo Fisher Scientific) using a standard two-step procedure (denatured: 95°C/10 s, annealed/extended: 60°C/30 s, 40 cycles). The RT-PCR primer sequences used were shown in Key resources table. The 2−ΔΔCT method was adopted to analyze the relative expression of mRNAs, using GAPDH as the reference gene.

Viral quality assurance

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Total cellular RNA was extracted from 1 million CD4+ T cells after 24 hr of stimulation followed by reverse transcription. Real-time PCR was performed using the TaqMan (Life Technologies) gene expression assay. HIV-1 mRNAs were detected using the following primers and probe, modified from Shan et al., 2013. Forward (5′→3′) CAGATGCTGCATATAAGCAGCTG (9501–9523), Reverse (5′→3′) TTTTTTTTTTTTTTTTTTTTTTTTGAAGCAC (9629-poly A), and Probe (5′→3′) FAM-CCTGTACTGGGTCTCTCTGG-MGB (9531–9550). The cycling parameters were as follows: 2 min at 50°C, 10 min at 95°C, and 45–50 cycles at 95°C for 15 s and then 60°C for 60 s. Molecular standard curves were generated using serial dilutions of a TOPO plasmid containing the last 352 nucleotides of viral genomic RNA plus 30 deoxyadenosines (pVQA). Results for each drug treatment were presented as fold change relative to DMSO control (mean ± SD).

Protein extraction and western blot

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After treatment, cells were lysed in RIPA lysis buffer (10 mM Tris-HCl buffered at pH 7.5, 150 mM NaCl, 0.5 % NP-40, 1% Triton X-100, 10% glycerol, 2 mM EDTA, 1 mM NaF, 1 Mm Na3VO4, 1× protease and phosphatase inhibitor cocktail) and incubated in ice for 30 min, followed by centrifugation at 12,000 g for 10 min at 4°C. The supernatants were collected as a whole protein extract. Protein samples were stored at −80°C or directly used for western blotting analysis. The protein extract was denatured by addition of NuPAGE running buffer (ThermoFisher), followed by denaturation at 100°C for 10 min, 50 μg of total protein were electrophoresis for 1.5 hr on a 10% polyacrylamide gel to separate the proteins, transferred onto PVDF membranes (Biorad), and co-incubation with primary and secondary antibodies. The antibodies were diluted by 5% BSA. Subsequently, Blots were imaged on an infrared (IR) laser-based fluorescence imaging system (LI-COR Odyssey).

Linear amplification–mediated high-throughput genome-wide sequencing (LAM-HTGTS)

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Genomic DNA is sheared by sonication, and the HIV-1-human genome junctions are then amplified by LAM-PCR (Schmidt et al., 2007), using directional primers lying on HIV-1 LTR. LAM-PCR with a single 5′ biotinylated primer amplifies across the HIV-1 LTR into the unknown human genome. Junction-containing single-stranded DNAs (ssDNAs) are enriched via binding to streptavidin-coated magnetic beads. After washing, bead-bound ssDNAs are unidirectionally ligated to a bridge adapter. Adapter-ligated, bead-bound ssDNA fragments are then subjected to nested PCR to incorporate a barcode sequence that is necessary for demultiplexing. A final PCR step fully reconstructs Illumina Miseq adapter sequences at the ends of the amplified HIV-1-human junction sequence. Samples are then separated on an agarose gel, and the resulting population of 0.5–1 kb fragments is collected and quantified before Miseq paired-end sequencing, with a typical 2 × 250 bp HTGTS library. The sequence of the matched read pairs after the LTR or adapter were trimmed to 50 base pairs and subsequently the trimmed read pairs were grouped according to 100% sequence match. Well-represented sequences were used for chromosomal alignment which was determined using the Blat-UCSC Genome Browser (GRCH38/hg38). An HIV integration site was identified if the position had ≥10 counts.

Drug screening

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We screened a library of 1700 drugs from the FDA-approved drug library. 1 × 105 J-mC cells were resuspended in 200 μl of medium in round-bottom 96-well plates. For the first round of screening, J-mC cells were treated with compounds (10 μM) for 48 hr and were analyzed by FACS. Then drugs with less than 30% of live cells are selected from the result of the first round of screening for a second round of screening with 5 μM drug concentration. Each plate contained cells treated with 50 ng/ml PMA, PBS and DMSO as controls. After 48 hr at 37°C, reactivation of latent HIV-1 was detected as above.

Cytotoxicity assays

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Cell death radio was evaluated by Annexin V/propidium iodide (PI) double staining assay. The cells incubate Annexin V-FITC at room temperature for 15 min. Then add PI before analyzing the stained cells by flow cytometry. Cell viability was measured by a Cell Count Kit-8 (MCE). PBMCs plated as 1 million cells per well in a 96-well plate were incubated with drugs for 48 hr. Then 10 μl of CCK-8 reagent was added to 100 μl of cell culture mixture and incubated for an additional 4 hr. Optical density (OD) was recorded at a wavelength of 450 nm on a microplate reader (Mοlecular Devices). The cell viability was calculated as follows. (OD treatment – OD blank control) / (OD untreatment – OD blank control) × 100%.

Statistical analysis

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All experiments were repeated at least three times. Statistics were performed with GraphPad Prism 8 (GraphPad), and all data are presented as mean values ± SD. p-values were calculated based on the Mann–Whitney U-test or Student’s t-test (large sample size) using SPSS software. * denotes p<0.05, ** denotes p<0.01, *** denotes p<0.001, **** denotes p<0.0001, and NS denotes non-significant.

Data availability

All data generated or analysed during this study are included in the manuscript.

References

    1. Craigie R
    2. Bushman FD
    (2012) HIV DNA integration
    Cold Spring Harbor Perspectives in Medicine 2:a006890.
    https://doi.org/10.1101/cshperspect.a006890

Decision letter

  1. Miles P Davenport
    Senior Editor; University of New South Wales, Australia
  2. Jori Symons
    Reviewing Editor; University Medical Center, Netherlands
  3. Thomas Rasmussen
    Reviewer; Peter Doherty Institute of Infection and Immunity, Australia

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

The authors demonstrate that their dual reporter virus is an efficient tool to determine latency and to test latency reversal agents. These findings will contribute to the armoury to study and combat HIV latency.

Decision letter after peer review:

Thank you for submitting your article "Infection with a newly-designed dual fluorescent reporter HIV-1 effectively identifies latently infected CD4+ T cells" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Miles Davenport as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Thomas Rasmussen (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.

As the editors have judged that your manuscript is of interest, but as described below that additional experiments are required before it is published, we would like to draw your attention to changes in our revision policy that we have made in response to COVID-19 (https://elifesciences.org/articles/57162). First, because many researchers have temporarily lost access to the labs, we will give authors as much time as they need to submit revised manuscripts. We are also offering, if you choose, to post the manuscript to bioRxiv (if it is not already there) along with this decision letter and a formal designation that the manuscript is "in revision at eLife". Please let us know if you would like to pursue this option. (If your work is more suitable for medRxiv, you will need to post the preprint yourself, as the mechanisms for us to do so are still in development.)

Summary:

In this paper, Cai and colleagues describe a dual fluorescent reporter virus, DFV-B, that was used to infect primary CD4+ T cells and Jurkat cells. In DFV-B infected primary CD4+ T cells, latently infected cells could be sorted out and in those more than 50% could be activated to produce GFP (under control of LTR) using PMA/ionomycin stimulation. In Jurkat cells the authors demonstrated the establishment of a latently infected cell line model of HIV latency that was used to identify ACY-1215, a selective inhibitor of HDAC6, as a potential latency-reversing agent.

The manuscript is well written and in general, data are clearly and soundly presented. As such, this work should be of interest for the field of HIV latency and cure, but there are a few outstanding issues that may require further clarification or modification:

Essential revisions:

1. The results show that the ratio of mC cells tot total infected cells increased over prolonged culturing in CD4 T cells. However, as the mC signal also dropped 10 fold. Additionally, there seems to be cells that solely transcribe the productive reporter.

The drop in the latent reporter needs to be explained. My understanding is that this reporter should be continuously transcribed. A loss of signal of the latent reporter is a known problem with dual reporter viral systems. Why does this system show less efficiency in primary cells compared to cell lines.

2. Figure 2: I suggest including the PMA/ionomycin stimulation of DN and DP cells in the actual Figure 2 (currently in the supplement) as these are good controls and add further credibility to the conclusion.

3. Figure 4: The process of model generation is not quite clear from text and Figure 4. Please further clarify identification of latently infected (mCherry) and DP cells. In that process I recommend incorporating the gating plots from the supplementary figure to further clarify gating strategy and how this is different to the sorting plots shown in Figure 4B. It is also not clear at which time points in the model (shown in Figure 4B) cells were sorted and then subjected to the various stimuli.

4. The authors mention that previous J-Lat models are disadvantaged due to their identical integration sites, but that their J-mC model of DFB-V infection should harbour diverse integration sites. Yet, no data are presented to confirm this assumption.

5. The use of the developed model systems in the identification of ACY-1215 is

interesting and relevant, but several important pieces of information are missing, which will further strengthen the presentation of these data:

a. The dose-dependent activation of GFP expression with increasing concentration of ricolinistat looks consistent and credible, but information on how long cells were stimulated for should be included in the figure or figure text, as should the utilised concentration of SAHA. It would also strengthen the interpretation if the authors could relate the used concentrations of ricolinistat to therapeutic levels during clinical dosing, ie are these levels physiologically relevant?

b. Figure 5G: Given the significant variation among individuals in cell-associated HIV RNA in the absence of stimulation, it would useful to also display the actual measured levels across DMSO, PMA/ionomycin and ACY conditions in Figure 5G. Also, which concentration of ACY was used here and is this therapeutically relevant? Finally, how many isolated CD4+ T cells were analysed per condition?

6. Depending on the above clarifications with regard to the most relevant concentration of ricolinistat for ex vivo and in vitro use, the compound looks like it could have similar potency to vorinostat, which is not overly impressive and may not offer any advantage compared to previously tested LRAs. One difference could be its HDAC6 selectivity, which may offer a better effect/toxicity index as hinted by the authors, but this is not really elaborated on any further. I recommend adding this aspect to the discussion of this compound by comparing clinically available data with this compound and selected pan-HDAC inhibitors.

7. Figure 1c demonstrates that the viability of the total cell population after day 6 of viral infection is rapidly decreasing. Was the viability of the mC cell population after 7 days (moment of reactivation) also reduced and if so, how does this impact latency and the subsequent reactivation of HIV.

https://doi.org/10.7554/eLife.63810.sa1

Author response

Essential revisions:

1. The results show that the ratio of mC cells tot total infected cells increased over prolonged culturing in CD4 T cells. However, as the mC signal also dropped 10 fold. Additionally, there seems to be cells that solely transcribe the productive reporter.

The drop in the latent reporter needs to be explained. My understanding is that this reporter should be continuously transcribed. A loss of signal of the latent reporter is a known problem with dual reporter viral systems. Why does this system show less efficiency in primary cells compared to cell lines.

We thank the reviewers for the helpful comment. In Figure 1B, as described by the reviewers that the ratio of mC cells to total infected cells increased over prolonged culturing in CD4+ T cells, but DP cells to total infected cells decreased. We think this phenotype can be attributed to two factors: 1) the productive infected DP cells are more prone to apoptosis caused by the cytotoxicity of HIV-1 proteins; 2) a portion of survived DP cells gradually transform to mC cells because HIV-1 transcription is turned off during the culturing process, this is also evidenced by previous works on primary cell models of HIV-1 latency, as some infected cells gradually lost the fluorescent signal of the reporter viruses (Yang et al., JCI 2009; 119: 3473–3485, He et al., eLife 2019; 8: e46181 and Scandella et al., Scientific Reports 2020; 10: 14642). To further verify these DP cells are more prone to apoptosis, we monitored the percentage of apoptotic cells after DFV-B infection by Annexin V and Zombie (Cat#423105, Biolegend) staining. We found that DP cells had significantly higher proportion of Annexin V-positive cells than mC cells on 3-7 days post-infection (Author response image 1), which clearly demonstrated that DP cells contained more early apoptotic cells, that contributed to the decreased ratio of DP cells in the total infected cell population.

Author response image 1
The expression of apoptotic markers on DP and mC cells during post-infection culturing of primary CD4+ T cells.

(A) The expression of apoptotic markers on DP and mC cells at 3 days post-infection. Flow cytometry chart shows the representative measurement of annexin V and zombie in DFV-B infected primary CD4+ T cells. (B) Data from Day 3, 5, 7 post-infection are displayed graphically on each indicated time point. Histograms show the percentage of positive cells. Error bars represent SDs of three technical replicates. p-values calculated using unpaired t test. ns, not significant; *p < 0.05, **p < 0.01.

In Figure 3E, we showed that the level of mC cells dropped 10-fold from 3.09% to 0.33% as concerned by the reviewers. We apologize for not stating it clear enough in the original manuscript. In fact, this data here was not generated by one-time infection and follow-up (such as Figure 1B), but was generated by multiple independent infections administered at different indicated time points, which was not the case of loss of signal of the latent reporter. The purpose here was to further verify whether HIV-1 latency was preferentially established in EMT CD4+ T cells, which our previous work had reported (Shan et al., Immunity 2017; 47: 766-775). Our data here showed that when the infection was administered at EMT stage (Day 6, 8, 10 after αCD3/28 stimulation), although the overall infectivity decreased, the percentages of directly generated latent infection (mC cells) significantly increased. This was demonstrated by the ratio of mC cells to total infected cells in Figure 3F, which was increased from 15.7% (Day 0 infection) to 64.8% (Day 10 infection). Our experiment confirmed that HIV-1 latency was preferentially established in EMT CD4+ T cells.

Additionally, regarding the potential problem of loss of signal of the latent reporter, we indeed observed that a tiny portion of single GFP+ cells existed after DFV-B infection (Figure 1B and Figure 3E). We traced and found that the proportion of single GFP+ cells decreased from 0.7% to 0.2% (similar to uninfected cells) as the culture time increased (updated Figure 2—figure supplement 1C), which is significantly lower than the previous reported dual reporter viral systems (Calvanese et al., Virology 2013; 446: 283–292 and Kim Y et al., Journal of the International AIDS Society 2019; 22: e25425). Meanwhile, the integrated DNA levels of GFP and mCherry in this single GFP+ cells were not different (updated Figure 2—figure supplement 1D), suggesting both reporters were still stably integrated. We speculated that this unlikely phenotype of single GFP+ cells could be due to the differential dynamics of fluorescent protein degradation. We agree with the reviewer’s comment about the problem of potential loss of signal of the dual reporter viral systems, we have since added additional words (lines 287-294 in revised manuscript) in the Discussion section to address.

2. Figure 2: I suggest including the PMA/ionomycin stimulation of DN and DP cells in the actual Figure 2 (currently in the supplement) as these are good controls and add further credibility to the conclusion.

We thank the reviewers for the insightful suggestion. We have modified Figure 2 by adding the PMA/ionomycin stimulation of DN and DP cells (Figure 2B). This data showed DP and DN cells were not affected by PMA/ionomycin stimulation, but more than half of the mC cells were reactivated, again suggesting that infection of DFV-B in primary CD4+ T cells could directly identify live latently infected cells.

3. Figure 4: The process of model generation is not quite clear from text and Figure 4. Please further clarify identification of latently infected (mCherry) and DP cells. In that process I recommend incorporating the gating plots from the supplementary figure to further clarify gating strategy and how this is different to the sorting plots shown in Figure 4B. It is also not clear at which time points in the model (shown in Figure 4B) cells were sorted and then subjected to the various stimuli.

We apologize for not stating it clear enough in the original manuscript about the process of J-mC cells generation. We have adopted the reviewer`s suggestion to show gating strategy in the modified Figure 4B. To clarify the process of J-mC generation, firstly, Jurkat cells were infected by DFV-B incorporating a VSVG envelope at 250 ng virus per million cells on Day 0 in CFM. At Day 7, the single mCherry positive cells were sorted according to the red-gated plots in Figure 4B, in which the purity of the single mCherry positive cells was greater than 99%. Next, the cells were cultured for another 30 days in CFM. At Day 37, 23.9% of the mC cells were converted to productive cells, probably due to spontaneous activation. At the same time, a small dose of TNF-α (5 ng/ml) was added to the culture and maintained for 7 days to further activate those unstable cells that were probably in the borderline state of latency. More than 70% of mC cells were reactivated at day 44. Meanwhile, the second sorting of single mCherry positive cells were performed, and subsequently the third and fourth sorting of single mCherry positive cells were performed at Day 58 and 72. J-mC cells were finalized at Day 86. Each sorting strategy was performed by the red-gated plots in Figure 4B. Besides, we have updated the experimental procedure in “Generation of J-mC cells” in the Materials and methods (lines 432-443 in revised manuscript).

4. The authors mention that previous J-Lat models are disadvantaged due to their identical integration sites, but that their J-mC model of DFB-V infection should harbour diverse integration sites. Yet, no data are presented to confirm this assumption.

We totally agree with the reviewers that this is an important point, so we have added a series of new data (the updated Figure 5) to show J-mC cells indeed harbored diverse integration sites. Briefly, we performed linear amplification–mediated high-throughput genome-wide sequencing (LAM-HTGTS) (Hu et al., Nature Protocol 2016; 11: 853-871) for HIV-1 integration site analysis on J-mC cells. We first confirmed that LAM-HTGTS was feasible for HIV-1 integration sites analysis by applying the method on J-Lat 8.4 and J-Lat 15.4 cells (updated Figure 5A and 5B). For J-mC cells, 30 days after they were obtained via multiple rounds of sorting, total DNA of the obtained J-mC cells was extracted with tissue DNA kit (OMEGA) and the genomic DNA was sheared by sonication, and subjected to LAM-HTGTS. Our data clearly demonstrated that the integration landscape of latent HIV-1 in J-mC cells was highly diverse, which represents a major strength of this cell model of HIV-1 latency comparing to the other existing cell line models. Updated descriptions of the new data have been added in the main text (lines 199-224 in revised manuscript).

5. The use of the developed model systems in the identification of ACY-1215 is

interesting and relevant, but several important pieces of information are missing, which will further strengthen the presentation of these data:

a. The dose-dependent activation of GFP expression with increasing concentration of ricolinistat looks consistent and credible, but information on how long cells were stimulated for should be included in the figure or figure text, as should the utilised concentration of SAHA. It would also strengthen the interpretation if the authors could relate the used concentrations of ricolinistat to therapeutic levels during clinical dosing, ie are these levels physiologically relevant?

We thank the reviewers for pointing out this important issue. We have updated Figure 6 and added concentrations of SAHA and ACY-1215 used in the experiments. Briefly, 1 μM SAHA and 3 μM ACY-1215 were used in Figure 6 and the other supplementary figures. The recommended clinical dose of SAHA and ACY-1215 were 200-600mg once daily (QD) for 28 days (Richardson et al., Leuk Lymphoma 2008;49(3): 502–507 and Siegel et al., Blood Cancer J. 2014;4: e182) or 160 mg twice daily (BID) for 28 days (Vogl et al., Clin Cancer Res 2017; 23(13): 3307–3315 and Amengual et al., The Oncologist 2021; 10.1002/onco.13673), respectively. For in vitro system, ACY-1215 was administered at concentrations of 1–10 μM (Carew et al., Blood Advances 2018; 3:1318-1329 and Chneg et al., Biomedicine and Pharmacotherapy 2018; 109, 2464–2471). Our application of 3 μM ACY-1215 here was in line with the other studies and was in the achievable range for clinical trials.

b. Figure 5G: Given the significant variation among individuals in cell-associated HIV RNA in the absence of stimulation, it would useful to also display the actual measured levels across DMSO, PMA/ionomycin and ACY conditions in Figure 5G. Also, which concentration of ACY was used here and is this therapeutically relevant? Finally, how many isolated CD4+ T cells were analysed per condition?

We thank the reviewers for this helpful suggestion. We have now added the actual measured levels of cell-associated HIV-1 RNA in the updated Figure 6G, in which the absolute copies were quantified across DMSO, PMA/ionomycin and ACY-1215 treatments. The original Figure 5G has been changed to the updated Figure 6H. 3 μM of ACY-1215 was used in the experiment and 1 million of purified CD4+ T cells from each ART-treated patient were used per condition. The therapeutically relevant concentration for ACY-1215 was addressed in the response above.

6. Depending on the above clarifications with regard to the most relevant concentration of ricolinistat for ex vivo and in vitro use, the compound looks like it could have similar potency to vorinostat, which is not overly impressive and may not offer any advantage compared to previously tested LRAs. One difference could be its HDAC6 selectivity, which may offer a better effect/toxicity index as hinted by the authors, but this is not really elaborated on any further. I recommend adding this aspect to the discussion of this compound by comparing clinically available data with this compound and selected pan-HDAC inhibitors.

We thank the reviewers for the insightful suggestion. We have adopted this suggestion and added another paragraph in the Discussion section to elaborate this important aspect (lines 341-358 in revised manuscript).

7. Figure 1c demonstrates that the viability of the total cell population after day 6 of viral infection is rapidly decreasing. Was the viability of the mC cell population after 7 days (moment of reactivation) also reduced and if so, how does this impact latency and the subsequent reactivation of HIV.

We thank the reviewers for the comment. The viability shown in Figure 1C was monitored in primary CD4+ T cells, which were previously stimulated by αCD3/CD28. In the absence of IL-2 supplement, it was expected to see the viability of the infected cells decreased as the culturing time increased. As we stated in the response to Question 1 above, primary mC cells were significantly less apoptotic than DP cells, which also suggested that the cessation of HIV-1 protein production in mC cells improved survival. To further address the reviewer’s concern, we tested the viability of the mC, DP and DN cells at 12h post-sorting, 3 days post-DMSO and 3 days post-PMA treatment. As shown in the updated Figure 2—figure supplement 1B, the viabilities of DP, DN and mC cells were comparable between DMSO and PMA treatment, and the cells got activated by the stimulation of PMA, an indication for functional cellular response. The new data in the updated Figure 2B also demonstrated that the latency reversal effect was not affected by the viabilities of DP, DN and mC cells. Taken together, the viability of the mC cells should have minimal effect on latency and the subsequent reactivation of HIV-1.

https://doi.org/10.7554/eLife.63810.sa2

Article and author information

Author details

  1. Jinfeng Cai

    1. Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    2. Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Methodology, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8356-030X
  2. Hongbo Gao

    1. Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    2. Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    Contribution
    Data curation, Methodology
    Competing interests
    No competing interests declared
  3. Jiacong Zhao

    1. Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    2. Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    Contribution
    Data curation, Validation, Investigation
    Competing interests
    No competing interests declared
  4. Shujing Hu

    1. Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    2. Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    Contribution
    Data curation, Validation, Investigation
    Competing interests
    No competing interests declared
  5. Xinyu Liang

    1. Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    2. Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    Contribution
    Data curation, Validation, Investigation
    Competing interests
    No competing interests declared
  6. Yanyan Yang

    Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    Contribution
    Data curation, Validation, Investigation
    Competing interests
    No competing interests declared
  7. Zhuanglin Dai

    Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    Contribution
    Data curation, Validation, Investigation
    Competing interests
    No competing interests declared
  8. Zhongsi Hong

    Department of Infectious Diseases, Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, China
    Contribution
    Resources, Investigation
    Competing interests
    No competing interests declared
  9. Kai Deng

    1. Institute of Human Virology, Key Laboratory of Tropical Disease Control of Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    2. Department of Immunology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Methodology, Writing - original draft, Project administration, Writing - review and editing
    For correspondence
    dengkai6@mail.sysu.edu.cn
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9973-8130

Funding

National Natural Science Foundation of China (81672024)

  • Kai Deng

National Basic Research Program of China (2018ZX10302103)

  • Kai Deng

National Basic Research Program of China (2017ZX10202102)

  • Kai Deng

National Natural Science Foundation of China (32070159)

  • Kai Deng

Natural Science Foundation of Guangdong Province (2017A030306005)

  • Kai Deng

Guangdong Innovative and Entrepreneurial Research Team Program (2016ZT06S638)

  • Kai Deng

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank all the volunteers who donated blood samples for this study. This work was supported by National Natural Science Foundation of China (32070159 and 81672024), the National Special Research Program of China for Important Infectious Diseases (2018ZX10302103 and 2017ZX10202102), Natural Science Foundation of Guangdong Province of China (2017A030306005), and Guangdong Innovative and Entrepreneurial Research Team Program (2016ZT06S638) to KD. 

Ethics

Human subjects: The use of PBMCs from healthy individuals was approved by the Institutional Review Board of Guangzhou Blood Center (Guangzhou, Guangdong, China). We did not have any interaction with the healthy individuals or protected information, and therefore no informed consent was required. Chronically HIV-1-infected participants sampled by this study were recruited from The Fifth Affiliated Hospital of Sun Yat-sen University (Zhuhai, Guangdong, China). This study was approved by the Ethics Review Boards of the Fifth Affiliated Hospital of Sun Yat-sen University (2018K41-1). All the participants were given written informed consent with approval of the Ethics Committees. The enrollment of HIV-1-infected individuals was based on the criteria of prolonged suppression of plasma HIV-1 viremia on cART, which is undetectable plasma HIV-1 RNA levels (less than 50 copies/ml) for a minimum of 12 months, and having high CD4+ T cell count (at least 350 cells/mm3). All HIV-1-infected participants provided written informed consent for their participation in the study and agreed with the publication of the scientific results.

Senior Editor

  1. Miles P Davenport, University of New South Wales, Australia

Reviewing Editor

  1. Jori Symons, University Medical Center, Netherlands

Reviewer

  1. Thomas Rasmussen, Peter Doherty Institute of Infection and Immunity, Australia

Publication history

  1. Received: October 7, 2020
  2. Accepted: April 4, 2021
  3. Accepted Manuscript published: April 9, 2021 (version 1)
  4. Version of Record published: April 12, 2021 (version 2)

Copyright

© 2021, Cai et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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