. 2025 Aug 21;6(9):102311. doi: 10.1016/j.xcrm.2025.102311

BACH2 promotes seeding and establishment of long-lived HIV-1 reservoir in memory CD4⁺ T cells

Hongbo Gao ^1,⁹, Yuhao Li ¹, Ritudwhaj Tiwari ¹, Marilia Pinzone ¹, Xiwen Qin ¹, Kolin M Clark ¹, Sara K Nicholson ¹, Tony Yao ², Kelly Rome ^3,⁴, Michael Scaglione ^3,⁴, Will Bailis ^3,⁴, Rachel M Presti ¹, Irini Sereti ⁵, Naresha Saligrama ^2,⁶, Leyao Wang ^7,⁹, Liang Shan ^1,^6,^8,^9,^∗

PMCID: PMC12490260 PMID: 40845840

Summary

Despite antiretroviral therapy, HIV-1 mainly persists in memory CD4⁺ T cells in people living with HIV-1. Most long-lived viral reservoir cells are infected by the virus near the time of therapy initiation. A better understanding of the early events in viral reservoir seeding presents opportunities for preventing latent reservoir formation. Here, we demonstrate that CD4⁺ T cells expressing CCR5, permissive to HIV-1 infection, are effector or terminally differentiated cells. BTB domain and CNC homolog 2 (BACH2) is expressed by a small subset of CCR5⁺ cells and reverses their terminal differentiation. BACH2-mediated memory differentiation is impeded due to heightened inflammation before treatment initiation. Mice with a BACH2-knockout human immune system have a reduced frequency of HIV-1 reservoir cells and do not experience virus rebound after treatment discontinuation. Our study reveals that BACH2 is essential to the seeding and establishment of long-lived HIV-1 reservoir in memory CD4⁺ T cells.

Keywords: HIV-1, viral reservoirs, CCR5, CD4⁺ T cells, BACH2, terminal differentiation, memory differentiation, humanized mice, ART

Graphical abstract

Highlights

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CCR5 expression requires the fate commitment of T cells to terminal differentiation
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BACH2 demarcates effector and memory differentiation of activated CCR5⁺CD4⁺ cells
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BACH2-mediated memory differentiation drives seeding of long-lived HIV-1 reservoirs
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Seeding of long-lived HIV-1 reservoirs is partially inhibited before antiretroviral therapy

Gao et al. dissect the cellular and molecular mechanisms governing the seeding of long-lived HIV-1 reservoirs in memory CD4⁺ T cells. They identify BACH2 as the key transcription factor that controls the fate determination of HIV-1-infected cells, highlighting potential therapeutic targets for HIV-1 eradication.

Introduction

Although antiretroviral therapy (ART) suppresses viral replication in people living with HIV-1 (PLWH), it cannot cure HIV-1 infection due to the lifelong persistence of integrated, transcriptionally inactive, and replication-competent proviruses primarily in a small pool of resting memory CD4⁺ T cells,¹^,²^,³^,⁴^,⁵^,⁶ referred to as latent viral reservoir cells. In theory, any host cells that express the CD4 receptor and the CCR5 co-receptor on their surface at sufficient levels for HIV-1 infection could contribute to the viral reservoir. It is puzzling that the size of the HIV-1 reservoir does not correlate with the duration of untreated infections, suggesting that viral reservoir cells do not accumulate before ART. Although the seeding of viral reservoirs begins within the first few days after infection⁷^,⁸^,⁹ and continues until viral replication is suppressed by ART,¹⁰^,¹¹^,¹² viral reservoirs established during untreated infections are relatively unstable with a rapid turnover rate,¹³^,¹⁴ likely due to chronic immune activation and viral superinfection. Phylogenetic analyses revealed that the majority of HIV-1-infected cells that persist for years during ART are seeded near the time of therapy initiation.¹⁰^,¹¹^,¹⁴^,¹⁵^,¹⁶ After initial seeding, viral reservoirs continue to evolve during ART due to the host immune system, which results in limited viral transcription and resistance to immune clearance.¹⁷^,¹⁸^,¹⁹^,²⁰^,²¹ The dynamics of HIV-1 reservoirs represent a major challenge to understanding the initial seeding and the force driving the rapid turnover of the pre-ART viral reservoir.

CCR5 and CXCR4 are two co-receptors for HIV-1 entry.²²^,²³ CCR5-tropic HIV-1 isolates are preferentially transmitted by all routes and almost always dominate the pool of viruses in blood and tissues.²⁴^,²⁵ CXCR4-tropic viruses usually emerge during the later stage of infection in approximately 50% of individuals without ART.²⁶^,²⁷ Latent HIV-1 in PLWH on suppressive ART is predominantly CCR5 tropic.²⁸^,²⁹^,³⁰ HIV-1 eradication had been achieved in a few people who received hematopoietic stem cell transplantation mostly from donors who were homozygous for the CCR5Δ32 deletion,³¹^,³²^,³³^,³⁴ which corroborates the report that most rebound viruses after therapy interruption use CCR5.³⁵^,³⁶ In PLWH on ART, HIV-1 DNA is detected in a variety of subsets of CD4⁺ T cells with distinct biological properties.³⁷^,³⁸^,³⁹^,⁴⁰^,⁴¹^,⁴²^,⁴³^,⁴⁴^,⁴⁵ Paradoxically, CCR5 expression is enriched within a small number of CD4⁺ T cells, including activated T helper 1 (Th1) and Th17 effector CD4⁺ T cells⁴⁶^,⁴⁷^,⁴⁸^,⁴⁹ and follicular regulatory T cells,⁵⁰ which is consistent with the observation that activated CD4⁺ T cells (human leukocyte antigen [HLA]-DR⁺CD38⁺) in untreated individuals contribute a median of 86% of the total HIV-1 RNA in their lymph nodes.⁵¹ The incongruence between the cells permissive to HIV-1 infection and the ones harboring latent HIV-1 suggests phenotypic and functional transformations of CCR5-expressing effector cells after HIV-1 entry. In this study, we dissected the immunological process that gives rise to CCR5⁺CD4⁺ T cells, which are cellular targets for HIV-1 infection and initial reservoir seeding. Furthermore, we identified BTB domain and CNC homolog 2 (BACH2) as the key transcription factor that reprograms CCR5⁺CD4⁺ T cells into long-lived memory cells. Lastly, we demonstrated that targeting BACH2 could be a useful strategy to prevent HIV-1 reservoir seeding, which might lead to viral remission after ART discontinuation.

Results

CCR5⁺CD4⁺ T cells are short-lived effector cells

We first utilized the available single-cell RNA sequencing (scRNA-seq) datasets⁵² (E-MTAB-11536) to reveal the features of CCR5-expressing CD4⁺ T cells in lymphoid tissues. CCR5 transcripts were detected in clusters of effector or terminal effector cells with transcripts for Th1, Th17, or regulatory T cell (Treg) cytokines (Figures 1A, S1A, and S1B). Next, we performed a 34-parameter single-cell analysis using cytometry by time of flight (CyTOF) to profile CCR5⁺CD4⁺ T cells. The naive and memory subsets of cells were determined based on the expression of activation and memory markers (Figure 1B). The vast majority of blood CD4⁺ T cells did not express any T cell activation markers or inhibitory receptors. By contrast, CCR5 was mostly expressed by activated CD4⁺ T cells, because the cell cluster that included the majority of CCR5⁺CD4⁺ T cells had minimal expressions of CCR7, CD27, and CD127, but they produced high levels of CD25, HLA-DR, and PD-1 (Figures 1B and S2A). We validated these results by flow cytometry analyses, and we demonstrated that CCR5-expressing CD4⁺ T cells expressed higher levels of activation and exhaustion markers, such as HLA-DR, CD25, and PD-1 (Figures S2B–S2D), and expressed higher levels of inflammatory cytokines, including interferon-gamma (IFN-γ), tumor necrosis factor alpha (TNF-α), and interleukin (IL)-2 at both mRNA and protein levels (Figures 1C, S2E, and S2F). We also showed that CCR5⁺CD4⁺ T cells in blood and secondary lymphoid tissues had similar characteristics (Figure S2G). Furthermore, CCR5-expressing CD4⁺ T cells from blood and tonsillar tissues were more prone to cell death compared with their CCR5⁻ counterparts (Figures 1D–1G). Next, we used puromycin labeling as an unbiased approach to compare protein synthesis activities in CCR5⁺ and CCR5⁻CD4⁺ T cells. CCR5⁺ cells from both HIV-1-negative donors and PLWH were more actively producing proteins, as evidenced by higher levels of puromycin incorporation (Figures 1H–1J). Taken together, these results suggest that CCR5⁺CD4⁺ T cells are short-lived effector cells.

CCR5 expression requires the fate commitment of T cells to terminal differentiation

Naive CD4⁺ T cells do not express CCR5, and T cell activation drives CCR5 expression in some but not all cells. To understand why CCR5 expression is enriched among short-lived effector cells, we recapitulated the T cell activation process by in vitro stimulation with αCD3 and αCD28 antibodies and carefully monitored CCR5 post stimulation (Figure S3A). When bulk CD4⁺ T cells were stimulated, cells that became CCR5 positive exhibited strong effector phenotypes with high expression levels of CD69, HLA-DR, and PD-1. By contrast, the cells that showed an early memory precursor (MP) phenotype (CCR7⁺CD127⁺CD27⁺) remained CCR5 negative (Figures 2A, 2B, and S3B). In addition, compared to CCR5⁻ cells, the cells turning on CCR5 had undergone more rounds of cell division (Figure 2C) as well as substantially increased rates of apoptosis (Figure 2D). Consistent results were obtained from transcriptome analyses of purified CCR5⁺ and CCR5⁻ cells, which showed significant enrichment of several functional categories in CCR5⁺ cells, mainly involving cell division and immune activation (Figures 2E, S3C, and S3D). By contrast, transcription factors associated with T cell memory and recall responses, including BACH2 and TCF7, were enriched in CCR5⁻ cells (Figure 2E) (GSE275936). We also confirmed that CCR5⁺ cells almost completely lost TCF1 expression (Figure 2F), a DNA-binding protein encoded by the TCF7 gene that is essential to preserving recall responses of memory T cells.⁵³ Meanwhile, the HMG-box transcription factor TOX, which initiates the fate commitment and programming of exhausted T cells,⁵⁴^,⁵⁵^,⁵⁶ is upregulated in CCR5⁺ cells (Figure 2G). In addition, B lymphocyte-induced maturation protein-1 (BLIMP-1), a transcriptional repressor that promotes T cell terminal differentiation,⁵⁷ is also upregulated in CCR5⁺ cells (Figure 2H). Next, we determined whether the cells turning on CCR5 became functionally impaired and terminally differentiated. Alterations in mitochondrial physiology, including mitochondrial mass and its membrane potential, are commonly linked to apoptotic cell death. MitoTracker and JC-1 dyes validated lower mitochondrial content and membrane potential in CCR5⁺ cells (Figures 2I, 2J, and S3E). As expected, purified CCR5⁺ cells died rapidly and failed to proliferate upon restimulation (Figures S3F and S3G). Bromodomain and extra-terminal motif (BET) proteins sustain effector T cell differentiation, and BET bromodomain inhibitors, such as JQ1, suppress terminal differentiation and help T cells preserve functional properties of central memory cells.⁵⁸^,⁵⁹^,⁶⁰ We observed that JQ1 treatment reduced the transcription of TOX in activated CD4⁺ T cells (Figure 2K). More importantly, the differentiation of CCR5⁺ cells was blocked by both JQ1 and AZD5153, another BET bromodomain inhibitor (Figures 2L and S3H). These results demonstrated that only cells committed to effector and terminal differentiation turned on CCR5. One caveat is that stimulation with αCD3 and αCD28 antibodies leads to global T cell activation, which does not reflect the specificity and strength of individual T cell receptors.

CCR5 expression requires T cell terminal differentiation post stimulation

Peripheral blood CD4⁺ T cells were stimulated with αCD3 & αCD28 antibodies for 3 days in the presence of IL-2. Activated cells were then cultured in the presence of IL-2 without antibodies for 3 more days before cell sorting or phenotypic and functional analyses.

(A) Phenotypic profiling of activated CD4⁺ T cells by a panel of 16 markers using flow cytometry. The cell clusters were defined using t-distributed stochastic neighbor embedding (tSNE) and FlowSOM analysis performed with FlowJo.

(B) Expression of CD69, HLA-DR, and PD-1 by CCR5⁺ and CCR5⁻ cells. CD4⁺ T cells from six healthy blood donors were used.

(C and D) Cell proliferation and viability of CCR5⁺ and CCR5⁻ cells determined by CFSE and Annexin V, respectively. Bulk activated CD4⁺ T cells from four and seven healthy blood donors were used in (C) and (D), respectively.

(E) Transcriptome analyses of activated CCR5⁺ and CCR5⁻ blood CD4⁺ T cells. Activated CCR5⁺ and CCR5⁻ cells were purified from four healthy blood donors by sorting, for bulk RNA-seq. Transcription factors expressed in this dataset were identified from the list of non-redundant human transcription factors from the JASPAR database. Differential expression of CCR5⁺ vs. CCR5⁻ cells was analyzed using DESeq2. The false discovery rate-corrected p values and log2 fold changes were used to plot the volcano plot using the Enhanced Volcano package. Gray: not significant. Green: log2 fold change >1. Purple: p value < 0.01. Red: both.

(F) TCF1 expression in CCR5⁺ and CCR5⁻ cells by intracellular staining. CD4⁺ T cells from six healthy blood donors were used.

(G and H) *TOX* and *BLIMP-1* expression in activated CCR5⁺ and CCR5⁻ cells. Cells were purified by sorting before the total RNA extraction and RT-qPCR. Data were normalized by *POLR2A* transcripts. CD4⁺ T cells from six healthy blood donors were used.

(I and J) Mitochondria membrane potential of activated CCR5⁺ and CCR5⁻ cells measured by flow cytometry. CD4⁺ T cells from five healthy blood donors were used.

(K and L) JQ1 reduces *TOX* expression and blocks differentiation of CCR5⁺ cells. JQ1 (1 μM) was added to activated bulk CD4⁺ T cells for 1 day to measure gene transcripts or for 3 days to assess CCR5 expression. *TOX* mRNA was measured by RT-qPCR and normalized by *POLR2A* transcripts. CCR5 expression was measured by flow cytometry. CD4⁺ T cells from six and three healthy blood donors were used in (J) and (K), respectively.

In (B), p values were calculated using the two-way ANOVA and Holm-Sidak multiple comparisons test. Other p values were calculated using the paired t test. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗∗p < 0.0001.

BACH2 demarcates effector and memory differentiation of activated CCR5⁺CD4⁺ cells

Since commitment to terminal differentiation is a prerequisite for producing CCR5⁺CD4⁺ T cells, long-lived HIV-1 reservoir cells must go through the transdifferentiation into memory cells at some point post viral entry. To this end, we aimed to understand whether and how CCR5⁺ cells could be rewired to quiescent memory cells. We purified the activated CCR5⁺ cells and cultured them for four additional days to allow further differentiation (Figure S3A). We found that the vast majority of these cells were effector (EFF) cells, because they maintained an effector cell phenotype with high levels of activation markers. By contrast, a small fraction of them became MP cells, because they down-regulated activation markers, acquired an MP phenotype (CCR7⁺CD127⁺CD27⁺), and lost expression of activation markers tested, including CCR5 (Figures 3A, 3B, and S4A–S4C). Although activated CCR5⁺ cells lost TCF1 likely due to hyperproliferation, these CCR5⁺-derived MP cells had high levels of TCF1 expression compared to the EFF cells (Figure 3C). Next, we purified the MP cells for functional analysis. Since CCR7 was expressed by the vast majority of the MP cells and was exclusively expressed by the MP cluster, we used CCR7 as the marker to purify the MP (CCR7⁺CD45RA⁻) and the EFF (CCR7⁻CD45RA⁻) clusters. The MP cells also responded to restimulation more effectively and proliferated with higher viability than the EFF cells (Figures 3D–3F). We next infected activated CCR5⁺ cells and allowed them to continue to differentiate in vitro. Although most of the HIV-1-infected cells were detected within the effector clusters, a small portion of the infected cells were also found in the MP cluster (Figures 3G, 3H, and S4D), suggesting that HIV-1 did not impede re-differentiation of CCR5⁺ cells into memory cells. It appears that a relatively smaller proportion of MP cells were infected with HIV-1, likely because of the active CCR5 down-regulation during memory differentiation in this cell population.

Bifurcated differentiation of CCR5⁺ cells

(A–C) Differentiation of activated CCR5⁺ cells. Blood CD4⁺ T cells were stimulated with αCD3 & αCD28 antibodies for 3 days with the presence of IL-2. Activated CCR5⁺ cells were purified by sorting and then cultured without antibodies for 4 more days with IL-2 (4 ng/mL), IL-7 (200 pg/mL), and IL-15 (4 ng/mL). Profiling of differentiated CCR5⁺ cells by a panel of 16 markers using flow cytometry. In (C), expression of TCF1 by MP and EFF cells was determined by intracellular staining. CD4⁺ T cells from five healthy blood donors were used.

(D–F) Viability and proliferation of MP and EFF cells upon restimulation. MP and EFF cells were differentiated from activated CCR5⁺ cells and then purified by sorting. Purified cells were labeled with CFSE before restimulation with αCD3 & αCD28 antibodies for 3 days with IL-2. Cell viability and proliferation were determined by AnnexinV/7-AAD (D and E) and CFSE staining (F), respectively. CD4⁺ T cells from three healthy blood donors were used.

(G and H) Activated CCR5⁺ cells were infected with an HIV-1 reporter virus pseudotyped with the BaL envelope for 3 days. Phenotypic profiling was performed as described in (A). Distribution of GFP⁺ cells across cell clusters was shown in (H).

In (A) and (G), tSNE plots were generated as described in Figure 2A. In (B), p values were calculated using the two-way ANOVA and Holm-Sidak multiple comparisons test. Other p values were calculated using the paired t test. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗∗p < 0.0001.

According to the RNA sequencing (RNA-seq) analysis of differentially expressed transcription factors between CCR5⁺ and CCR5⁻ cells, three transcription factors, including BACH2, TCF7, and LEF1, were among the top differentially expressed genes in CCR5⁻ cells (Figure 2E). Thus, we purified MP and EFF cells by sorting and confirmed that the MP cells re-differentiated from activated CCR5⁺ cells had higher levels of BACH2 and TCF7 transcripts and reduced levels of TOX and BLIMP-1 compared to the effectors that failed to re-differentiate (Figure 4A). BACH2 represses effector differentiation in CD4⁺ and CD8⁺ T cells by controlling the access of activator protein-1 (AP-1) factors to enhancers⁶¹^,⁶² and forces memory differentiation in exhausted CD8⁺ T cells in mice infected with lymphocytic choriomeningitis virus.⁶³^,⁶⁴ We also confirmed that the BACH2 transcripts were more abundant in less differentiated CD4⁺ T cells (Figure S5A), suggesting a negative correlation between BACH2 and T cell differentiation. To test the requirement of BACH2 for re-differentiation of activated CCR5⁺ cells, we generated BACH2-knockout (KO) and Cas9 control CD4⁺ T cells (Figures S3A, S5B, and S5C) and found that the MP differentiation from CCR5⁺ cells was prevented when the BACH2 coding gene was deleted (Figures 4B, 4C, and S5D). Notably, MP cells generated from BACH2-KO CCR5⁺ cells did not fully regain memory cell functions despite phenotypically resembling MP cells, as evidenced by the fact that BACH2-KO MP cells lacked TCF1 expression (Figure 4D) and were unable to respond to restimulation (Figure 4E). By contrast, the differentiation of activated CCR5⁺ cells into the CCR7⁺ MP was not affected in TCF7-KO cells (Figure S5E), suggesting that TCF1 was not required to re-differentiate activated CCR5⁺ cells. Interestingly, TCF7-KO MP cells were also unable to respond to restimulation (Figures S5F and S5G), which is consistent with previous studies that demonstrated the critical role of TCF1 in the T cell recall response. This also explains why BACH2-KO MP cells were functionally incompetent due to their loss of TCF1 expression. These results demonstrate that BACH2 promotes the conversion of CCR5⁺ cell-derived MP cells and that MP differentiation of CCR5⁺ cells independent of BACH2 results in functionally defective memory cells. BACH2 ablation in activated CCR5⁺ cells not only reduced the number of MP cells but also caused the loss of TCF1 in CCR5⁺ cells that acquired the MP phenotype. Given the heterogeneity of CCR5⁺ effector cells, we hypothesize that some EFF populations could not differentiate into MP cells without BACH2, whereas the TCF1⁻ MP cells were likely derived from a subset of less active EFF populations. The reversibility of TCF7 epigenetic silencing enables a late CD8⁺ T cell memory decision.⁶⁵ Our results suggest that BACH2 is essential for this memory decision and may be involved in re-expression of TCF1.

BACH2 controls differentiation and functional preservation of CCR5-derived MP cells

(A) *BACH2*, *TCF7*, *TOX*, and *BLIMP-1* expression in MP and EFF cells. MP and EFF cells were differentiated from activated blood CCR5⁺ cells and then purified by sorting before total RNA extraction and RT-qPCR. Data were normalized by *POLR2A* transcripts. CD4⁺ T cells from four healthy blood donors were used.

(B and C) Phenotypic profiling of differentiated *BACH2*-KO or control CCR5⁺ cells by a panel of 16 markers using flow cytometry. *BACH2*-KO and Cas9 control CD4⁺ T cells were stimulated with αCD3 & αCD28 antibodies. Activated CCR5⁺ cells were then purified for further differentiation as described in Figure 3A. tSNE plots were generated as described in Figure 2A. The percentage of MP conversion is shown in (C). CD4⁺ T cells from five healthy blood donors were used.

(D) TCF1 expression in *BACH2*-KO or control MP and EFF cells. *BACH2*-KO or control cells generated in (B) and (C) were subject to intracellular TCF1 staining. CD4⁺ T cells from three healthy blood donors were used.

(E) Viability and proliferation of *BACH2*-KO or control MP cells upon restimulation. *BACH2*-KO or control MP cells generated in (B) and (C) were purified by sorting and stained with CFSE before restimulation with αCD3 & αCD28 antibodies for 3 days with IL-2. Cell viability was determined by AnnexinV/7-AAD.

(F–H) BACH2 overexpression suppresses the differentiation of CCR5⁺ CD4⁺ T cells *in vitro* and *in vivo*. Bulk blood CD4⁺ T cells were stimulated with αCD3 & αCD28 antibodies for 1 day before being transduced with lentiviral vectors overexpressing mCherry and BACH2 (BACH2-OP) or BΔZip (BΔZip-OP). Transduced cells were either cultured *in vitro* with the presence of IL-2 for 3 days (F) or immediately transferred into unengrafted MISTRG-6-15 mice (G and H). CCR5 and CCR7 expression of mCherry⁺ cells was analyzed by flow cytometry 6 days later. In (F), CD4⁺ T cells from five healthy donors were used. In (G) and (H), cells were isolated from one blood donor. n = 5.

(I) BACH2 overexpression forces differentiation of activated CCR5⁺ cells into MP cells. Activated CCR5⁺ cells were transduced with lentiviral vectors expressing mCherry and BACH2 or BΔZip and then cultured *in vitro* with the presence of IL-2 for 3 more days before analyses of CCR5 and CCR7 expression. CD4⁺ T cells from five healthy blood donors were used.

In (G) and (H), p values were calculated using the two-way ANOVA and Holm-Sidak multiple comparisons test. Other p values were calculated using the paired t test. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗∗p < 0.0001.

Since BACH2 suppressed effector T cell differentiation, it may modulate the differentiation of CCR5⁺ cells at two separate stages. First, it may impair the generation of CCR5⁺CD4⁺ T cells during T cell activation. Second, it may promote the memory re-differentiation of activated CCR5⁺ cells. BACH2 overexpression (BACH2-OP) and BΔzip overexpression (BΔzip-OP) lentiviral vectors (Figure S5H) were introduced into bulk CD4⁺ T cells. BACH2 overexpression restrained the differentiation of activated CCR5⁺ cells (Figure 4F), similar to the BET bromodomain inhibitor treatment. We also observed that two other chemokine receptors, CCR2 and CXCR6, which are located adjacent to CCR5 and expressed by effector T cells, were also suppressed by BACH2 (Figures S5I and S5J). To gain in vivo evidence, we adoptively transferred activated CD4⁺ T cells overexpressing BACH2 or BΔzip into immunodeficient mice. In mice that received BACH2-OP cells, the generation of activated CCR5⁺ cells was blocked, and most cells acquired a central memory phenotype (CCR7⁺) in all tissues (Figures 4G and 4H). Regarding the second stage, the BACH2-OP or BΔzip-OP vector was introduced into CD4⁺ T cells that had already turned on CCR5 expression. In the control group transduced with the BΔzip-OP lentiviral vector, around 10% of the activated CCR5⁺ cells differentiated into CCR7⁺ cells. By contrast, in the BACH2-OP group, 40%–60% of the activated CCR5⁺ cells differentiated into CCR7⁺ cells (Figure 4I). Therefore, the results from BACH2-KO and BACH2 overexpression consistently showed that BACH2 played a key role in the memory differentiation of CCR5⁺CD4⁺ T cells. Notably, memory differentiation of CCR5⁺ cells can begin either shortly after or long after the effector commitment, both in a BACH2-dependent manner.

Memory differentiation of CCR5⁺CD4⁺ T cells is impeded by heightened inflammation prior to ART

Although cellular reservoirs for HIV-1 are seeded throughout the course of untreated infection, a vast majority of them are short lived, whereas the ones seeded near the time of therapy initiation are more likely to persist. In PLWH before ART initiation, most productively infected cells (HIV-1 RNA⁺ or HIVp24⁺) in blood and lymph nodes exhibit features of Th1 cells and frequently express activation markers.⁶⁶ Th1 CD4⁺ T cells carrying intact HIV-1 proviruses represent a driving force for HIV-1 persistence in PLWH on suppressive ART.³⁹^,⁶⁷ Given such a necessity for progressive transformation, we hypothesized that cells infected by HIV-1 before ART initiation, the precursors of viral reservoirs, cannot undergo memory differentiation to become long-lived until the inflammation is resolved by ART. Thus, latent HIV-1 residing in these cells is unable to persist. To test this hypothesis, we obtained matched pre- and post-ART plasma samples from the study participants enrolled in the AIDS Clinical Trials Group (ACTG) A5142 study of initial ART⁶⁸ to determine the immediate impact of ART on proinflammatory cytokine production (Figure 5A). Through single and multiplex ELISA analyses, we found that many proinflammatory cytokines were drastically reduced after only a 7-day period of ART (Figures 5B and S6A). Interestingly, activated CD4⁺ cells that turned on CCR5 had higher levels of IL-18 receptor alpha (IL-18Rα) expression (Figures 5C and 5D), which was further increased in cells polarized toward the Th1 direction (Figure S6B). These results indicated a potentially prominent role of IL-18 on the fate of differentiating CCR5⁺ cells. We next confirmed that IL-18 at inflammatory plasma concentrations was sufficient to suppress BACH2 expression in CCR5⁺ cells (Figure 5E) and block the MP conversion (Figure 5F). These results demonstrate that inflammatory cytokines, such as IL-18, may impede the necessary cellular transformation process required for establishing long-lived HIV-1 reservoirs.

Memory differentiation of CCR5⁺CD4⁺ T cells is impeded by heightened inflammation

(A) Sample collection scheme.

(B) Plasma IL-18 concentration determined by ELISA. Indicated pre- and post-ART samples from 83 study participants were included.

(C and D) IL-18Rα expression in activated CD4⁺ T cells. CD4⁺ T cells from four healthy blood donors were used.

(E and F) Activated CCR5⁺ cells were purified as described in Figure 3A and were further differentiated under the Th1 polarization condition with or without the presence of IL-18 (5 ng/mL). BACH2 transcripts were measured by RT-qPCR and normalized by *POLR2A*. Conversion to MP cells was determined by flow cytometry. CD4⁺ T cells from seven healthy blood donors were used.

(G–L) The impact of ART on CD4 recovery (G), TCF1 expression (H–K), and BACH2 transcription (L). The percentage of CD4⁺ T cells and TCF1 expression in total and memory (CD45RA⁻) CD4⁺ T cells from 10 PLWH were analyzed by flow cytometry. BACH2 transcripts in CD4⁺ T cells from five PLWH were measured by qPCR. Error bars indicate mean standard error of the mean (SEM).

(M and N) The role of BACH2 in HIV-1 transcription in latent reservoir cells. CD4⁺ T cells from eight PLWH on suppressive ART were collected to generate Cas9 ctrl and *BACH2*-KO cells. After electroporation, cells were rested for 3 days before treatment with either PBS or anti-CD3 and anti-CD28 antibodies for 24 h. Cellular RNA was extracted to measure cell-associated HIV-1 RNA.

In (G), (J), (K), and (L), p values were calculated using the one-way ANOVA and Holm-Sidak multiple comparisons test. Other p values were calculated using the paired t test. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗∗p < 0.001; ∗∗∗∗p < 0.0001.

To seek evidence beyond the in vitro system, we obtained peripheral blood mononuclear cells (PBMCs) from PLWH before and during ART (Figure 5A). Since all the study participants had been on ART for many years, it was impossible to capture the moment when their cells underwent memory differentiation at the time of ART initiation. BACH2 is essential to both MP conversion and the functional preservation of converted MP cells, as evidenced by the loss of TCF1 in memory cells differentiated from BACH2-KO CCR5⁺ cells. Although we were unable to monitor the MP conversion process in PLWH, we found that total and memory CD4⁺ T cells collected pre-ART had low levels of TCF1 expression, which then gradually returned to normal levels after ART (Figures 5H–5K) and eventually became comparable to cells from HIV-1-negative participants (Figure S6C). Additionally, the abundance of the BACH2 transcripts was also gradually increased following ART initiation (Figure 5L). These results demonstrated that HIV-1 reservoir cells are unable to differentiate into long-lived memory cells due to chronic inflammation, which explains why reservoir cells seeded immediately prior to ART are more likely to undergo memory differentiation, persist, and regain proliferation capacity. Next, we determined whether BACH2 directly impacts HIV-1 transcription or latency reversal using both in vitro and ex vivo approaches. No difference in viral gene expression was observed in HIV-1-infected BACH2-KO and Cas9 ctrl CD4⁺ T cells (Figure S6D). HIV-1-infected BACH2-KO and Cas9 ctrl CD4⁺ T cells were equally susceptible to viral cytopathic effects (Figure S6E). We then investigated whether HIV-1 transcription is affected in cells overexpressing BACH2. Overexpression of BACH2 impacts T cell activation and differentiation, which may indirectly influence HIV-1 transcription. Therefore, we utilized J-Lat cell lines that are latently infected with a reporter HIV-1. We generated cell clones that were stably transduced with either BACH2-OP or BΔzip-OP lentiviral vectors and then reactivated the virus with TNF-α. The frequency of latency reversal (%GFP) and the levels of HIV-1 expression (GFP intensity) were not affected by BACH2 overexpression (Figure S6F). This suggests that, despite BACH2 overexpression, HIV-1 gene expression is not altered in these cells. Lastly, we generated BACH2-KO and Cas9 ctrl CD4⁺ T cells from PLWH on suppressive ART and measured cell-associated HIV-1 RNA including unspliced (gag) and total (polyA) HIV-1 RNA as previously described.⁶⁹^,⁷⁰^,⁷¹ The loss of BACH2 did not alter baseline cell-associated HIV-1 RNA levels and did not enhance or diminish virus reactivation by anti-CD3 and anti-CD28 antibodies (Figures 5M and 5N). Taken together, our results demonstrate that BACH2 does not directly control HIV-1 gene expression in CD4⁺ T cells. Our study suggests that the role BACH2 plays in HIV-1 reservoir seeding and expansion is more related to how HIV-1 target cells differentiate into long-lived memory cells. It is unclear whether HIV-1 actively alters BACH2 transcription in favor of reservoir seeding.

BACH2 is required for HIV-1 persistence in vivo

To this end, we used the MISTRG-6-15 humanized mouse model⁷² to study how BACH2 impacts HIV-1 persistence. To generate humanized mice with a BACH2-deficient immune system, BACH2-KO or Cas9 ctrl CD34⁺ cells were generated by electroporation of Cas9 protein and two BACH2-specific single-guide RNAs (sgRNAs) or non-targeting sgRNA as described previously.⁷³ Modified or control cells were then used to engraft the newborn MISTRG-6-15 mice (Figure 6A). We confirmed the reconstitution of the BACH2-KO human immune system in the mice by deep sequencing analysis, which confirmed that 80%–100% of the human immune cells in mice were BACH2 edited (Figures S7A and S7B). To test how BACH2 impacts human hematopoiesis in vivo, we first characterized the human immune cell reconstitution in mice and found that the BACH2-KO group had a modest reduction in total human CD45⁺ cells in blood, which was primarily due to reduced blood B cell counts (Figure 6B). By contrast, T cell development and CD4-to-CD8 ratio were unaffected. We further analyzed macrophages and different subsets of CD4⁺ T cells in the spleen and found that the only difference between the BACH2-KO and control groups was in the counts of naive CD4⁺ T cells, while their memory T cell populations and macrophages were unaffected (Figures 6C and 6D). This is consistent with other mouse studies showing that BACH2 is essential to the maintenance of T cells at a naive state but has no impact on circulating and tissue memory T cells.⁷⁴ In addition, TCF1 expression levels in memory CD4⁺ T cells were comparable between the BACH2-KO and control groups (Figure S7C). This indicates that BACH2 ablation preferentially affects TCF1 expression in effector cells transitioning into memory stage such as the CCR5-derived MP cells. When infected with HIV_BaL, plasma viral loads (Figure 6E), loss of blood CD4⁺ T cells (Figure 6F), frequency of p24⁺ cells (Figure 6G), and frequency of HIV-1 DNA (Figures 6H and 6I) were comparable in both groups of the mice, suggesting that the loss of BACH2 does not alter the course of untreated HIV-1 infection in the MISTRG-6-15 mice.

Generation of *BACH2*-KO humanized mice for HIV-1 infection

(A) Strategy for generating *BACH2*-edited humanized mice.

(B) Human immune cell reconstitution and T cell development in mice engrafted with *BACH2*-KO or *Cas9* ctrl CD34⁺ cells. Blood samples were collected 10–11 weeks after transplantation.

(C and D) Human T cell and macrophage development in mice engrafted with *BACH2*-KO or *Cas9* ctrl CD34⁺ cells. Spleen samples were collected 12 weeks after transplantation. The total naive (CD45RA⁺CCR7⁺), central memory (CD45RA⁻CCR7⁺), effector memory (CD45RA⁻CCR7⁻), and regulatory (CD25⁺Foxp3⁺) CD4⁺ T cells as well as macrophages were determined by flow cytometry.

(E and F) HIV-1 infection in mice engrafted with *BACH2*-KO or *Cas9* ctrl CD34⁺ cells. Plasma viral loads (E) and loss of CD4⁺ T cells (F) were determined at indicated time points.

(G–I) Quantification of HIV-1 infection. The frequency of p24⁺ of the CD3⁺CD8⁻ population was measured by flow cytometry. The frequency of HIV-1 DNA⁺ cells was determined by qPCR.

In (F), p values were calculated using the two-way ANOVA and Holm-Sidak multiple comparisons test. Other p values were calculated using the unpaired t test. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

Next, we generated animal cohorts to interrogate the role of BACH2 in the early HIV-1 reservoir seeding. Mice reconstituted with a control or BACH2-KO human immune system were infected with HIV_BaL for 2–3 weeks prior to ART as described previously.⁷² ART led to a bi-phasic decay of plasma HIV-1 RNA, which dropped below the detection limit between 3 and 4 weeks post treatment initiation (Figure 7A). Aviremic animals stayed on ART for another 3–4 weeks before HIV-1 rebound or reservoir analyses. To investigate virus rebound, we collected CD8-depleted total splenocytes from the ART-treated mice and adoptively transferred these into the HIV-1-negative recipient mice. Each recipient mouse received the whole splenocyte population from one ART mice. This adoptive transfer strategy not only removed viral-specific CD8⁺ T cells, a potential confounding variable in virus rebound, but also allows for prolonged monitoring of HIV-1 rebound in humanized mouse systems. Plasma HIV-1 RNA levels in the recipient mice were monitored once per week (Figure 7B). Among the mice that received cells from the BACH2-KO group, 10 out of 14 did not experience virus rebound (Figure 7C). Furthermore, the median time-to-virus rebound was 3 weeks and was not reached within 12 weeks in the Cas9 ctrl and BACH2-KO groups, respectively (Figure 7D). In animals that experienced virus rebound, there was no difference in the levels of rebound viremia, suggesting that the difference in virus rebound between the two groups was unlikely driven by viral fitness or immune control. Next, we generated additional mouse cohorts for HIV-1 infection and measured residual HIV-1 DNA and RNA in the spleens, lungs, and livers. The frequency of HIV-1 DNA was lower in the BACH2-KO group in all the tissues tested (Figures 7E and S7D). Since BACH2 is essential to T cell memory differentiation, we hypothesized that the difference in HIV-1 DNA should be more profound in long-lived memory T cells. As expected, the loss of BACH2 only impacted the size of HIV-1 reservoirs in central memory T (T_CM) CD4⁺ T cells, whereas the frequency of HIV-1 DNA was comparable in non-T_CM CD4⁺ T cells or macrophages (Figure 7F), suggesting a prominent role of BACH2 in long-lived HIV-1 reservoirs. More importantly, the remaining HIV-1 reservoir in T_CM CD4⁺ T cells from the BACH2-KO group animals was unable to cause virus rebound, which is probably because BACH2-independent MP differentiation of CCR5⁺ cells only gives rise to functionally defective cells exhibiting a memory phenotype (Figures 4D and 4E). These results demonstrate that BACH2 is essential to the seeding and establishment of long-lived HIV-1 reservoir in both lymphoid and non-lymphoid tissues.

BACH2 is required for the seeding and establishment of long-lived HIV-1 reservoir in memory CD4⁺ T cells

(A) HIV-1 infection and ART in the control and *BACH2*-KO groups. ART was provided 2–3 weeks post HIV_BaL infection. Plasma HIV-1 RNA levels determined by RT-qPCR. *Cas9* ctrl, n = 10; *BACH2*-KO, n = 9.

(B–D) Viral rebound. CD8-depleted total splenocytes from ART-treated mice are adoptively transferred into the HIV-1-negative unengrafted MISTRG-6-15 mice. Each recipient mouse receives the whole splenocyte population from one ART mice. Blood samples were collected once per week to monitor virus rebound. Virus rebound is confirmed when plasma viral load is >1,000 copies per mL. *Cas9* ctrl, n = 10; *BACH2*-KO, n = 14. In (D), time to rebound is set at 12 weeks (open circles) for mice without viral rebound.

(E and F) Frequency of HIV-1 DNA⁺ cells in tissues from aviremic mice on suppressive ART. In (F), T_CM cells (CD3⁺CD4⁺CD45RA⁻CCR7⁺), non-T_CM cells (CD3⁺CD4⁺CD45RA⁻CCR7⁻), and macrophages (CD14⁺) were purified by sorting before HIV-1 DNA measurement. Open circle: undetected. In (E), *Cas9* ctrl, n = 10; *BACH2*-KO, n = 14. In (F), *Cas9* ctrl, n = 10; *BACH2*-KO, n = 9.

In (C), the p value was calculated by the log rank (Mantel-Cox) test. Other p values were calculated using the unpaired t test. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

Discussions

The enormous diversity and heterogeneity of residual viral reservoirs in PLWH are among the central challenges to finding a cure for HIV-1. They are established prior to ART initiation⁷⁵ and continue to evolve under ART to overcome cell death signaling, immune surveillance, and viral cytopathic effects.⁷⁶ As a result, viral reservoir cells in PLWH on ART differ in terms of viral sequences, integration sites, and transcriptional activities, as well as host cell transcriptional profiles and anatomical locations,⁷⁷^,⁷⁸ making viral eradication nearly impossible. While HIV cure research has mainly focused on characterizing and targeting reservoirs that have evolved for many years or decades, the molecular and cellular pathways involved in the initial reservoir seeding before its diversification remain unclear. Recently, phylogenetic analyses of residual HIV-1 proviral sequences helped reveal the age of HIV-1 reservoir cells. From these studies, we understand that the pool of latent HIV-1 is dominated by genetically young and clonally enriched proviruses, which suggests that most virus-infected cells throughout the course of untreated infections are not long lived and that preventing HIV-1 reservoir cells from transforming into long-lived cells at the time of ART initiation could have a major impact on the total reservoir and viral persistence.

To this end, we aimed to identify the precursors to reservoir cells, which are cells that have been infected by HIV-1 immediately before ART initiation and have the potential to differentiate into long-lived memory cells. These precursors must share common features, such as expressing the viral co-receptor CCR5 and permissiveness to viral reverse transcription and integration. CCR5 expression is enriched within a small fraction of CD4⁺ T cells with effector functions,⁷⁹ which, according to our study, is because turning on CCR5 requires T cell commitment to the terminal differentiation program. Therefore, viral reservoir cells are much less diverse and heterogenic at the time of or shortly after viral infection before they transform into various types of memory cells. Here, we demonstrated that BACH2 is the key transcription factor that reprograms short-lived HIV-1 reservoir precursors into long-lived memory cells. We further demonstrated that the BACH2-mediated memory differentiation is impeded by inflammation during the untreated stage and that most memory CD4⁺ T cells developed before ART are functionally defective. Thus, our findings explain why memory cells carrying latent HIV-1 before ART are unable to persist and why ART helps produce stable viral reservoirs that persist for years. CCR5 expression is a result of T cell activation and requires prolonged activation process, which usually leads to terminal differentiation. By contrast, CD4⁺ T cells undergoing early commitment to MPs rarely express CCR5 (Figure 2A). Thus, we focused on CD4⁺ T cells that initially committed to an effector fate, some of which retained the plasticity to regain memory potential and long-term survival. Without a tracking system, we cannot rule out the possibility of transient expression of CCR5 by CD4⁺ T cells with an early commitment to MPs.

Single-cell multiome analysis provides evidence of epigenetic regulation of HIV-1 reservoirs by BACH2 in blood and tissue CD4⁺ T cells from PLWH.³⁹^,⁸⁰ Future studies evaluating BACH2 transcription in HIV-1 RNA⁺ or DNA⁺ cells following ART will provide more insight into when and how BACH2 influences the memory differentiation of the viral reservoir precursor cells. Interestingly, although HIV-1 integration sites are widely distributed in transcriptionally active genes, latent HIV-1 proviruses are frequently detected within a few genes, indicating survival advantages associated with viral integration into these genes. BACH2 is one of the top genes recurrently detected in almost all HIV-1 integration studies, and nearly all integration sites mapped within BACH2 are located upstream of the first coding exon in the same transcriptional orientation as the host gene.⁸¹^,⁸²^,⁸³^,⁸⁴^,⁸⁵ In fact, insertion of HIV-1 genome or the viral promoter immediately preceding the BACH2 open reading frame triggers the production of the chimeric mRNA that contains fused HIV-1 and BACH2 sequences, which leads to increased abundance of the BACH2 protein and enhanced T cell outgrowth in favor of a T regulatory cell phenotype.⁸⁶^,⁸⁷ According to our study, it is not a coincidence that HIV-1 integration-driven BACH2 overexpression leads to selective survival of the infected cells. Notably, most reservoir cells carry proviruses in genes other than BACH2⁸⁸ and thus rely on the host BACH2 transcription to drive memory differentiation. Integration site enrichment in BACH2 is the extreme example supporting the role of BACH2 in the long-term survival of HIV-1 reservoir cells. Notably, latent HIV-1 can also be detected in naive CD4⁺ T cells, albeit less frequently.³⁸^,⁴⁵ Due to the lack of CCR5 expression, the naive reservoir cells most likely harbor CXCR4-tropic viruses. Thus, the memory differentiation of CCR5⁺ effector cells described in this study is likely not critical to the establishment of latent infection by CXCR4-tropic HIV-1 in naive CD4⁺ T cells. It may be interesting to study the naive reservoirs in BACH2-KO animals infected with CXCR4-tropic viruses, given that naive CD4⁺ T cells require BACH2 to maintain their quiescent naive status.

In summary, our study explains why most cells infected by HIV-1 are unable to contribute to viral reservoirs and preventing HIV-1 reservoir cells from transforming into long-lived cells at the time of ART initiation by targeting the BACH2 pathway could have a major impact on the total viral reservoir and viral persistence. Our study elucidates the essential role of BACH2 in memory differentiation of CCR5⁺ effector cells and their subsequent proliferation capacity upon restimulation. Although BACH2 acts at the memory transition stage, its impact is essential not only to viral reservoir seeding but also to persistence during long-term ART, when host cell proliferation is the driving force behind reservoir cell maintenance. Our findings help lay a foundation for developing future therapeutic strategies to broadly target HIV-1 reservoirs at their infancy stage by interfering with a unique cellular re-differentiation process, and thus, such strategies will not be hindered by the diversity of the viruses and the host cells. It remains unclear how BACH2 regulates the differentiation of CCR5⁺CD4⁺ T cells. It either directly binds to the CCR5 promoter or local regulatory elements to inhibit CCR5 transcription or modulates distal promoters or enhancers that influence CCR5 transcriptional activity. Interestingly, we found that in activated CD4⁺ T cells, BACH2 suppressed not only CCR5 expression but also the two adjacent chemokine receptors, CCR2 and CXCR6. These results suggest a global role of BACH2 in reprogramming CD4⁺ T cell differentiation. BLIMP-1 is one of the highly expressed genes in BACH2^−/− murine CD4⁺ T cells⁷⁴ and is also highly expressed in human CCR5⁺CD4⁺ T cells but not in CCR5-derived MP cells as found in this study. One possible explanation is that BACH2 aborts the terminal differentiation of human CCR5⁺CD4⁺ T cells through suppressing BLIMP-1. Besides, it is also unclear how IL-18 reduces BACH2 levels and whether IL-18 is the major inflammatory cytokine that blocks BACH2-driven memory differentiation. In the present study, we focused on IL-18 because IL-18Rα was highly expressed by CCR5⁺ cells. This does not preclude the possibility that other inflammatory cytokines can also reduce BACH2 levels. It is possible that cytokines promoting effector functions are able to directly or indirectly reduce BACH2 levels, while IL-18 is likely more effective than others due to the high expression of its receptor by CCR5⁺ cells. Future studies are needed to elucidate the pathway through which BACH2 re-differentiates CCR5⁺CD4⁺ T cells. This could pave the way for developing more targeted approaches to prevent the seeding of long-lived HIV-1 reservoirs.

Limitations of the study

We have demonstrated both in vitro and in humanized mice how BACH2 reprograms CCR5⁺CD4⁺ T cells to acquire an MP phenotype, which is essential for the seeding and establishment of HIV-1 reservoirs. In mice with a BACH2-KO human immune system, HIV-1 DNA was still detectable, albeit at a lower frequency. Due to the limited lifespan of mouse models, the duration of ART was not sufficient to allow for a complete decay of short-lived viral reservoir cells, although these cells were unable to rekindle HIV-1 replication. Additionally, due to the short-term treatment and suboptimal immune response, humanized mice cannot be used to accurately model the continuous evolution of HIV-1 reservoir cells under ART. Therefore, our study primarily focuses on the early events in viral reservoir seeding, rather than its heterogeneity.

Resource availability

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Liang Shan (shanliang@smart.org.cn).

Materials availability

Requests of plasmids and cell lines generated in this study will be available upon request from the lead contact with a completed materials transfer agreement.

Data and code availability

All data reported in this paper will be shared by the lead contact upon request. RNA-seq data have been deposited at the NCBI Gene Expression Omnibus repository, and the accession numbers are listed in the key resources table. This paper does not report original code. For data related to the ACTG A5142 study, the authors confirm that all data underlying the findings are fully available without restriction. Due to ethical restrictions, study data are available upon request from sdac.data@sdac.harvard.edu with the written agreement from Advancing Clinical Therapeutics Globally for HIV/AIDS and Other Infections (ACTG). Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

Acknowledgments

We gratefully acknowledge all the members of the ACTG A5142 team and commend all the staff at the ACTG sites for their effort. We particularly thank Ronald J. Bosch and Kevin Wongsodirdjo for their support in this study. We thank the Regeneron Pharmaceuticals and the Richard Flavell Laboratory at Yale University for generating the MISTRG-6-15 human cytokine knockin mice, the Immunomonitoring Laboratory at the Bursky Center for Human Immunology and Immunotherapy at Washington University for the immune profiling experiments, and Dr. Francesco Simonetti (Johns Hopkins University) for critical insight and manuscript review. The following reagents were obtained through the AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH: pNL4-3-ΔEnv-EGFP (ARP-11100), HIV-1 BaL.01 Env Expression Vector (ARP-11445), HIV_BaL (ARP-510), Molt4-CCR5 (ARP-4984), human T lymphocyte J-Lat full-length cells (6.3) (ARP-9846), human T lymphocyte J-Lat full-length cells (9.2) (ARP-9848), and human T lymphocyte J-Lat full-length cells (10.6) (ARP-9849). This work was supported by NIH grants R01AI162203, R01AI155162, R56AI186666, and R01AI176594 to L.S.; R21AI181675 to L.W.; F31AI165251 to K.M.C.; and in part by the CRISPR for Cure Martin Delaney Collaboratory for HIV cure UM1AI164568, co-funded by NIAID, NIMH, NIDA, NINDS, NIDDK, and NHLB; and in part by the NIH/National Center for Advancing Translational Sciences (NCATS), CTSA grant UL1TR002345. NIAID/NIH UM1AI068634, UM1AI068636, and UM1AI106701 were used to support ACTG&SDAC studies. The work of I.S. was supported by the intramural research program of NIAID/NIH.

Author contributions

L.S. and H.G. designed the study and analyzed the data. H.G., Y.L., and X.Q. performed the in vitro experiments. H.G. and R.T. performed the humanized mice experiments. K.M.C., S.K.N., and L.W. analyzed RNA-seq and scRNA-seq data. N.S., T.Y., and H.G. designed the multiplex flow cytometry experiments. K.R., M.S., and W.B. developed and refined the puromycin staining protocol for primary CD4⁺ T cells. H.G. and M.P. designed and performed plasma cytokine experiments. I.S., R.M.P., and L.S. designed the studies using clinical samples. L.S. and H.G. wrote the manuscript. I.S. and L.W. revised the manuscript.

Declaration of interests

The authors declare no competing interests.

STAR★Methods

Key resources table

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies

anti-Puromycin-AF 647	Sigma	Cat# MABE343-AF647; RRID:AB_2736876
anti-human CD197 (CCR7)-BV421	BD Biosciences	Cat# 562555; RRID: AB_2728119
anti-human CD57-Pacific Blue	BioLegend	Cat# 359608; RRID: AB_2562459
anti-human CD69-BV605	BioLegend	Cat# 310938; RRID: AB_2562307
anti-human CD69-FITC	BioLegend	Cat# 310904; RRID: AB_314839
anti-human CD45RA-BV650	BioLegend	Cat# 304136; RRID: AB_2563653
anti-human/mouse/rat CD278 (ICOS)-BV711	BioLegend	Cat# 313548; RRID: AB_2734289
anti-human CD27-BV750	BioLegend	Cat# 302849; RRID: AB_2750389
anti-human HLA-DR-BV785	BioLegend	Cat# 307642; RRID: AB_2563461
anti-human TIGIT-Vio® Bright B515	Miltenyi Biotec	Cat# 130-116-938; RRID: AB_2751351
anti-human CD195 (CCR5)-PE	BioLegend	Cat# 359106; RRID: AB_2562335
anti-human CD127 PE/Dazzle594	BD Biosciences	Cat# 562397; RRID: AB_11154212
anti-human CD223 (LAG3)-PE/Fire640	BioLegend	Cat# 369221; RRID: AB_2922583
anti-human CD95 (Fas)-PE-Cy7	BioLegend	Cat# 305622; RRID: AB_2100369
anti-human CD45RO-APC	BioLegend	Cat# 304210; RRID: AB_314426
anti-human CD62L-Spark NIR 685	BioLegend	Cat# 304861; RRID: AB_2860808
anti-human CD279 (PD-1)-AF700	BioLegend	Cat# 329951; RRID: AB_2566363
anti-human CD279 (PD-1)-PE-Cy7	BioLegend	Cat# 329918; RRID: AB_2159324
anti-human CD38-APC/Fire810	BioLegend	Cat# 303549; RRID: AB_2860783
anti-human TCF1	BioLegend	Cat# 655204; RRID: AB_2566620
anti-human Ki-67	BioLegend	Cat# 350514; RRID: AB_10959327
Human TruStain FcX	BioLegend	Cat# 422302; RRID: AB_2818986
TruStain FcX anti-mouse CD16/32	BioLegend	Cat# 101320; RRID: AB_1574975
anti-mouse CD45-FITC	BioLegend	Cat# 103108; RRID: AB_312973
anti-human CD45-Pacific Blue	BioLegend	Cat# 304029; RRID: AB_2174123
anti-human CD3-BV785	BioLegend	Cat# 317330; RRID: AB_2563507
anti-human CD19-PE-Cy7	BioLegend	Cat# 302216; RRID: AB_314246
anti-human CD56-PE	BioLegend	Cat# 362508; RRID: AB_2563925
anti-human CD14-APC	BioLegend	Cat# 301808; RRID: AB_314190
anti-human CD4-BUV395	BD Biosciences	Cat# 563550; RRID: AB_2738273
anti-human CD8-BUV737	BD Biosciences	Cat# 612754; RRID: AB_2870085
anti-human CD195 (CCR5)-BV421	BioLegend	Cat# 359118; RRID: AB_2563577
anti-human CD197 (CCR7)-AF647	BioLegend	Cat# 353218; RRID: AB_10917385
anti-human CCR7-PE	BD Biosciences	Cat# 566742; RRID: AB_2864275
anti-human CD27-PE-Cy7	BioLegend	Cat# 302838; RRID: AB_2561919
anti-human CD3-BUV737	BioLegend	Cat# 612752; RRID: AB_2870083
anti-human CD45-BV785	BioLegend	Cat# 304048; RRID: AB_2563129
anti-human Foxp3-BV421	BioLegend	Cat# 320124; RRID: AB_2565972
anti-human CD25-APC	BioLegend	Cat# 302610; RRID: AB_314280
anti-p24-PE	Beckman Coulter	Cat# KC57-RD1; RRID: AB_1575989
anti-human IFN-γ-FITC	BD Biosciences	Cat# 554551; RRID: AB_395473
anti-human TNF-α-BV421	BioLegend	Cat# 502931; RRID: AB_10898321
anti-human IL-2-AF 647	BioLegend	Cat# 500315; RRID: AB_493370
anti-human CD192 (CCR2)-BV785	BioLegend	Cat# 357233; RRID: AB_2800971
anti-human CD186 (CXCR6)-BV421	BioLegend	Cat# 356013; RRID: AB_2562514
anti-Human CD218a (IL-18Rα)	BioLegend	Cat# 564675; RRID: AB_2738885
Anti-human CD45-089Y	Fluidigm	Cat# 3089003B; RRID: AB_2938863
Anti-human CD57-142ND	Fluidigm	Cat# 3142007B; RRID: AB_2868399
Anti-human CD45RA −143ND	Fluidigm	Cat# 3143006B; RRID: AB_2651156
Anti-human CD270 (HVEM, TR2)-144ND	Fluidigm	Cat# 3144022B; RRID:
Anti-human CD4-145ND	Fluidigm	Cat# 3145001B; RRID: AB_2661789
Anti-human CD8a-146ND	Fluidigm	Cat# 3146001B; RRID: AB_2687641
Anti-human CD278/ICOS-148ND	Fluidigm	Cat# 3148019B; RRID: AB_2756435
Anti-human CD45RO-149Sm	Fluidigm	Cat# 3149001B; RRID: AB_2687851
Anti-human CD134 (OX40)-150ND	Fluidigm	Cat# 3150023B; RRID: AB_2938869
Anti-human CD95/Fas-152Sm	Fluidigm	Cat# 3152017B; RRID: AB_2920762
Anti-human TIM-3-153Eu	Fluidigm	Cat# 3153008B; RRID: AB_2938868
Anti-human TIGIT-154Sm	Fluidigm	Cat# 3154016B; RRID: AB_2888926
Anti-human CD27-155Gd	Fluidigm	Cat# 3155001B; RRID: AB_2687645
Anti-human CD184 (CXCR4)-156Gd	Fluidigm	Cat# 3156029B; RRID:
Anti-human CD137/4-1BB-158Gd	Fluidigm	Cat# 3158013B; RRID: AB_2888927
Anti-human CD357 (GITR)-159Tb	Fluidigm	Cat# 3159020B; RRID: AB_2858232
Anti-human CD28-160Gd	Fluidigm	Cat# 3160003B; RRID: AB_2868400
Anti-human CD152 (CTLA-4)-161Dy	Fluidigm	Cat# 3161004B; RRID: AB_2687649
Anti-human CD69-162Dy	Fluidigm	Cat# 3162001B; RRID: AB_3096016
Anti-human CD272 (BTLA)-163Dy	Fluidigm	Cat# 3163009B; RRID: AB_2910546
Anti-human CD161-164Dy	Fluidigm	Cat# 3164009B; RRID: AB_2687651
Anti-human CD40-165Ho	Fluidigm	Cat# 3165005B; RRID:
Anti-human CD197 (CCR7)-167Er	Fluidigm	Cat# 3167009A; RRID: AB_2858236
Anti-human CD127 (IL-7Ra)-168Er	Fluidigm	Cat# 3168017B; RRID: AB_2756425
Anti-human CD25 (IL-2R)-169Tm	Fluidigm	Cat# 3169003B; RRID: AB_2938861
Anti-human CD122-170Er	Fluidigm	Cat# 3170004B; RRID:
Anti-human CD38-172Yb	Fluidigm	Cat# 3172007B; RRID: AB_2756288
Anti-human HLA-DR-173Yb	Fluidigm	Cat# 3173005B; RRID: AB_2810248
Anti-human CD279 (PD-1)-174Yb	Fluidigm	Cat# 3174020B; RRID: AB_2868402
Anti-human CD223/LAG-3-175Lu	Fluidigm	Cat# 3175033B; RRID: AB_2888932
Mouse monoclonal anti-BACH2	BioLegend	Cat# 695602; RRID: AB_2650879
Mouse monoclonal anti-Beta-actin	Invitrogen	Cat# MA1-140; RRID: AB_2536844
HRP-conjugated goat anti-mouse IgG	Cell Signaling Technology	Cat# 7076; RRID: AB_330924

Virus strains

pNL4-3-ΔEnv-EGFP	NIH HIV Reagent Program	ARP-11100
HIV-1 BaL.01 Env Expression Vector	NIH HIV Reagent Program	ARP-11445
HIV_BaL	NIH HIV Reagent Program	ARP-510
psPAX2	Addgene	12260
pLenti-mC-BACH2	this paper	N/A
pLenti-mC-BΔzip	this paper	N/A
pVSVG	Addgene	8454

Chemicals, peptides, and recombinant proteins

APC Annexin V	BioLegend	640941
7-AAD Viability Staining Solution	BioLegend	420404
Zombie NIR™ Fixable Viability Kit	BioLegend	423106
Zombie Aqua™ Fixable Viability Kit	BioLegend	423102
(+)-JQ1	Selleckchem	S7110
AZD5153 6-hydroxy-2-naphthoic acid	Selleckchem	S8344
Ultra-LEAF™ Purified anti-human CD3	Biolegend	300333
Ultra-LEAF™ Purified anti-human CD28	Biolegend	302943
Recombinant Human IL-2	BioLegend	589106
Recombinant Human IL-7	BioLegend	581904
Recombinant Human IL-12(p70)	BioLegend	573004
Recombinant Human IL-15	BioLegend	570306
Recombinant Human IL-18	BioLegend	592106
Recombinant Human TNF-α	BioLegend	570104
Recombinant Human SCF	STEMCELL Technologies	78062
Recombinant Human FLT3	STEMCELL Technologies	78009
Recombinant Human TPO	STEMCELL Technologies	78210
Phytohemagglutinin-L (PHA-L)	Sigma	11249738001
Enfuvirtide (T-20)	MedChemExpress	HY-P0052
Raltegravir (RAL)	MedChemExpress	HY-10353
Recombinant Cas9 protein	IDT	1081059
Random Primers	Invitrogen	48190–011
ProtoScript II Reverse Transcriptase	NEB	M0368
RNase Inhibitor, Murine	NEB	M0314L
Oligo(dT)12–18 Primer	Invitrogen	18418012
TaqMan™ Fast Advanced Master Mix for qPCR	Applied Biosystems	4444556
Lenti-X Concentrator	TaKaRa	631232
Ficoll-Paque PLUS	GE Healthcare	17144003
Percoll®	Sigma	GE17-0891-01
HEPARIN Sodium	Sagent Pharmaceuticals	25021
Citrate-dextrose solution (ACD)	Sigma-Aldrich	C3821
RBC Lysis Buffer (10×)	Biolegend	420302
Paraformaldehyde Solution, 4% in PBS	Thermo Fisher Scientific	J19943K2
DNAse I	Sigma	11284932001
Collagenase D	Sigma	11088858001
EDTA (0.5 M), pH 8.0, RNase-free	Invitrogen	AM9260G
Puromycin (hydrochloride)	Cayman Chemical	13884

Critical commercial assays

MojoSort™ Human CD4 T cell Isolation Kit	BioLegend	480010
Human cord blood CD34 isolation kit	STEMCELL Technologies	17896
MojoSort™ Human CD8 Nanobeads	BioLegend	480108
Human IFN-alpha 2 Quantikine HS ELISA Kit	R&D Systems	HSIFNA2
Human Total IL-18 DuoSet ELISA	R&D Systems	DY318-05
P3 Primary Cell 4D-Nucleofector X Kit	Lonza	V4XP-3032
Quick-RNA Viral Kit	Zymo Research	R1035
Quick-RNA Microprep Kit	Zymo Research	R1051
Direct-zol RNA Miniprep Kit	Zymo Research	R2051
CFSE Cell Division Tracker Kit	BioLegend	423801
MitoProbe™ JC-1 Assay Kit	Invitrogen	M34152
MitoTracker™ Dyes for Mitochondria Labeling	Invitrogen	M22426
QIAamp DNA Mini Kit	Qiagen	51304
ZymoPURE II Plasmid Maxiprep Kit	Zymo Research	D4202
BD Cytofix/Cytoperm™ Fixation/Permeabilization Kit	BD Biosciences	554714
eBioscience™ Foxp3/Transcription Factor Staining Buffer Set	Invitrogen	00-5523-00
Human ProcartaPlex Mix&Match 15-plex	Invitrogen	PPX-15-MX9HK3C
TaqMan® Gene Expression Assay-human TOX	Thermo Fisher Scientific	Hs01049519_m1
TaqMan® Gene Expression Assay-human BACH2	Thermo Fisher Scientific	Hs00935338_m1
TaqMan® Gene Expression Assay-human POLR2A	Thermo Fisher Scientific	Hs00172187_m1
TaqMan® Gene Expression Assay-human PRDM-1	Thermo Fisher Scientific	Hs00153357_m1

Deposited data

RNA-seq data	This paper	GSE275936

Experimental models: Cell lines

HEK 293T	ATCC	CRL-3216
Molt4-CCR5	NIH HIV Reagent Program	ARP-4984
Human T Lymphocyte J-Lat Full Length Cells (6.3)	NIH HIV Reagent Program	ARP-9846
Human T Lymphocyte J-Lat Full-Length Cells (Clone 9.2)	NIH HIV Reagent Program	ARP-9848
Human T Lymphocyte J-Lat Full Length Cells (10.6)	NIH HIV Reagent Program	ARP-9849

Experimental models: Organisms/strains

MISTRG-6-15	This paper	N/A

Software and algorithms

FlowJo v10	BD Biosciences	https://www.flowjo.com/
GraphPad Prism 10	GraphPad Software	https://www.graphpad.com/
Image Lab Software	Bio-Rad	https://www.bio-rad.com/en-us/product/image-lab-software?ID=KRE6P5E8Z
Adobe Illustrator	Adobe	https://www.adobe.com/products/illustrator.html
Biorender	Biorender	https://www.biorender.com/
Mega11	Tamura, Stecher, and Kumar⁸⁹	https://www.megasoftware.net/
Python v3.8.5	Python Software Foundation	https://www.python.org
ETE3 v3.1.3	Huertas-Cepas, Serra, and Bork⁹⁰	http://etetoolkit.org/
Phylo Pic	PhyloPic	https://www.phylopic.org/
PyMSAviz v.0.4.0	Yuki Shimoyama⁹¹	https://pypi.org/project/pyMSAviz/
Expasy Translator Tool	Gasteiger et al.⁹²	https://web.expasy.org/translate/
STAR v2.7.9a	Dobin et al.⁹³	https://github.com/alexdobin/STAR
Integrative Genomics Viewer v2.16.2	Robinson et al.⁹⁴	https://igv.org/
Cytobank	Beckman Coulter	https://premium.cytobank.org/

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Experimental model and subject details

Human subjects

Anonymous peripheral blood samples were collected from the Mississippi Valley Regional Blood Center as waste cellular products. Anonymous human cord blood samples were acquired from the Cleveland Cord Blood Center. Human tonsils were collected from elective tonsillectomies from the Saint Louis Children’s Hospital, which were provided as surgical waste without identifying information. For inflammatory cytokine analyses, pre- and post-ART plasma samples were obtained from the ACTG trial A5142.⁶⁸ To measure TCF-1 and BACH2 expression in CD4⁺ T cells from PLWH, PBMCs were collected from study participants before ART initiation and at various time points post-ART initiation under clinical protocols NCT00286767 and NCT02147405. In addition, PLWH on ART for at least two years with undetectable plasma HIV-1 RNA levels (<20 copies per mL) for at least six months were recruited by the Infectious Diseases Clinic at Barnes-Jewish Hospital for blood collection, approved by the Washington University School of Medicine Internal Review Board. All study participants signed a written informed consent.

Mice strains

The MISTRG-6-15 human cytokine knock-in mice was described previously.⁷² The mouse colony was maintained at Washington University School of Medicine. Both male and female mice were included in the experiments. All animal experiments were approved by the Institutional Animal Care and Use Committee of Washington University School of Medicine (approval #23–0383).

Cell lines & primary cells

HEK 293T cells were cultured in DMEM containing 10% heat-inactivated fetal bovine serum (FBS), 1 U/mL penicillin, and 100 mg/mL streptomycin. Molt4-CCR5 cells were cultured in RPMI 1640 containing 10% heat-inactivated FBS, 1 U/mL penicillin, 100 mg/mL streptomycin, and 1 mg/mL G418. Human T Lymphocyte J-Lat Full Length Cells (6.3), Human T Lymphocyte J-Lat Full Length Cells (9.2) and Human T Lymphocyte J-Lat Full Length Cells (10.6) were cultured in RPMI 1640 containing 10% heat-inactivated FBS. PBMCs were isolated by ficoll from human peripheral blood. CD4⁺ T cells were isolated from blood or tonsil samples using the MojoSort Human CD4 T cell Isolation Kit. CD4⁺ T cells were cultured in RPMI 1640 containing 10% heat-inactivated FBS, 1 U/mL penicillin, and 100 mg/mL streptomycin in the presence of 200pg/mL IL7, and 4ng/mL IL-15 until the time of stimulation. To mimic TCR stimulation, CD4⁺ T cells were co-stimulated with plate-bound αCD3 and soluble αCD28 antibodies in the presence of 20 ng/mL IL-2 for three days. Human CD34⁺ cells were isolated from cord blood using the EasySep human cord blood CD34 positive selection kit. The CD34⁺ cells with a purity greater than 90% were utilized to engraft mice.

Method details

Plasmids and viruses

To prepare replication-defective HIV-1 reporter viruses, HEK293T cells were transfected with pNL4-3-ΔEnv-EGFP and CCR5 tropic envelope expressing plasmid. The replication-competent HIV_BaL was propagated using CD8-depleted PHA-stimulated PBMCs. To collect viruses from infection and transfection, culture supernatant was collected and concentrated via the Lenti-X concentrator. Lenti-BACH2 or Lenti-BΔzip were produced by co-transfecting HEK293T cells with the packaging plasmid psPAX2, envelope plasmid pVSVG, and either the BACH2-expressing plasmid pLenti-mCherry-BACH2 or plasmid pLenti-mCherry-BΔzip, which expresses a BACH2 mutant with an L-zip deletion.

In vitro culture assay

To investigate the effect of BET bromodomain inhibitors on the commitment to effector differentiation, activated CD4⁺ T cells were treated with 1μM JQ1 or 333nM AZD5153 after stimulation. Cells were collected after 24 h post-treatment to measure TOX transcripts and collected at three days post-treatment to assess the surface CCR5 expression. To evaluate the role of BACH2 in HIV-1 latency reversal, J-Lat clone 6.3, 9.2, and 10.6 were stably transduced with BACH2-OP or BΔzip-OP lentiviral vectors. Transduced cells were stimulated with TNFα (10ng/mL) for 24h before FACS analysis.

To assess the differentiation of CCR5⁺ cells after in vitro stimulation, CD4⁺ T cells were co-stimulated with plate-bound αCD3 and soluble αCD28 antibodies in the presence of 20 ng/mL IL-2 for three days. Cells were then cultured in the presence of 20 ng/mL IL-2 for three days. CCR5⁺ cells were sorted and cultured in the presence of 4ng/mL IL-2, 4 ng/mL IL-15, and 200pg/mL IL-7 for an additional four days. In the IL-18 culture assay, CD4⁺ T cells were co-stimulated with plate-bound αCD3 and soluble αCD28 antibodies in the presence of 20 ng/mL IL-2 and 1ng/mL IL-12 for three days to differentiate Th1 cells, then cultured with 20 ng/mL IL-2 and 1ng/mL IL-12 for three days. CCR5⁺ cells were sorted and cultured in the presence of 1ng/mL IL-12 with or without IL-18 (5ng/mL) for three days.

Cell viability and mitochondria assays

Cell viability was determined by using Annexin V and 7-AAD according to the manufacturer’s instructions. The mitochondrial transmembrane potential of cells was measured with the JC-1 dye according to the manufacturer’s instructions. The MitoTracker dye was used to access the mitochondrial mass according to the manufacturer’s instructions. To track cell proliferation in vitro, activated CD4⁺ T cells were labeled with the CellTrace CFSE according to the manufacturer’s instructions.

For cellular biosynthesis measurement, unstimulated CD4⁺ T cells were cultured in 20ng/mL IL-2 with puromycin hydrochloride (10μg/mL) for two hours. Cells were washed with PBS and stained with surface antibodies for 30 min on ice. Subsequently, the cells were fixed with Foxp3/Transcription Factor Staining Buffer Set according to the manufacturer’s instructions. Fixed cells were stained with puromycin antibody (1:300 dilution) for 30 min at room temperature.

Mouse tissue cell suspensions were incubated with Zombie Aqua or NIR for 15 min at 4°C, followed by washing and incubation with Human and mouse TruStain FcX blocking antibodies for 15 min at room temperature. After another wash, the cells were incubated with antibodies for 30 min on ice. For tissue samples from HIV-1-infected mice, cells were fixed with 4% paraformaldehyde for 10 min after antibody staining.

Immunoblotting, ELISA, and multiplex bead array

Lysates from 1×10⁶ BACH2-KO or Cas9 ctrl Molt4-CCR5 cells were collected for protein expression analysis by immunoblotting. Total cellular proteins (30 μg per sample) were separated by SDS–PAGE immunoblotted and visualized using ChemiDoc Imaging System (Bio-Rad).

To quantify the level of inflammatory cytokines in PLWH before and after ART, plasma samples were analyzed using ProcartaPlex multiplex immunoassays according to the manufacturer’s instructions. The 15-plex kit included the following analytes: IFN-γ, IL-1β, IL-10, IL-12p70, IL-15, IL-17A, IL-18, IL-2, IL-22, IL-6, IL-8, IP-10, MCP-1, RANKL and TNFα. In addition, IL-18 concentration was determined using the Human Total IL-18 DuoSet ELISA (R&D Systems).

Gene expression analysis

To determine host gene transcription levels, total RNA was using the Quick-RNA Microprep Kit before reverse transcription using the ProtoScript II Reverse Transcriptase and random primers. Expression of target genes was measured using pre-designed gene-specific TaqMan probes on a QuantStudio 5 Real-Time PCR System. RNA polymerase II subunit A (POLR2A) was used as the reference gene.

BACH2 overexpression assay

Bulk CD4⁺ T cells or sorted CD4⁺CCR5⁺ T cells were co-stimulated with plate-bound αCD3 and soluble αCD28 antibodies in the presence of 20 ng/mL IL-2. On day one, cells were spin-transfected with Lenti-BACH2 or Lenti-BΔzip at 1200×g for two hours at 30°C. On day four, cells were cultured in the presence of 20 ng/mL IL-2 for three days. The expression of CCR5 and CCR7 was then measured by flow cytometry. For mouse transfusion assay, each 10-week-old MISTRG-6-15 mouse received 4×10⁶ cells transfected with either Lenti-BACH2 or Lenti-BΔzip via retro-orbital injection. The blood, lung, liver, and spleen of each mouse were collected 6 days post-transfusion for analysis.

CRISPR-Cas9 editing in CD4⁺ T cells and CD34⁺ cells

CRISPR editing of CD4⁺ T cells and CD34⁺ cells were described previously.⁷³ Briefly, Cas9-sgRNA ribonucleoprotein (RNP) complexes were electroporated into Molt4-CCR5 cells, CD4⁺ T cells, and cord blood CD34⁺ cells using the Lonza 4D Nucleofector system. The sgRNAs targeting BACH2 or TCF1 were purchased from Synthego (Table S1). RNP complexes were prepared by mixing recombinant Cas9 protein (40 pmol) with BACH2 or TCF7 sgRNA (100 pmol) and incubating them for 10 min at room temperature. 4 × 10⁶ unstimulated CD4⁺ T cells or 0.4 × 10⁶ CD34⁺ cells were resuspended in 20 μL buffer P3. Each RNP complex was then mixed with the cell suspension. CD4⁺ T cells and MOLT4-CCR5 cells were electroplated using the programs EH-100, and CD34⁺ cells were electroplated using the programs DZ-100. Electroporated CD4⁺ T cells were cultured for three days under 4 ng/mL IL-15, and 200pg/mL IL-7 before stimulation. Electroporated CD34⁺ cells were transplanted into the mice. The knockout efficacy of BACH2 in Molt4-CCR5 cells or CD4⁺ T cells was assessed by the Western blot, while the knockout efficacy in CD34⁺ cells was evaluated by amplicon deep sequencing (see below). The knockout efficacy of TCF7 of CD4⁺ T cells was assessed by flow cytometry.

Mass cytometry

For mass cytometry analysis, metal-tagged antibodies were either purchased from Fluidigm or custom-conjugated using the Maxpar X8 Antibody Labeling Kit according to the manufacturer’s instructions. The following metal-conjugated antibodies were used in this study: anti-hCD45, anti-hCD57, anti-hCD45RA, anti-hCD270, anti-hCD4, anti-hCD8a, anti-hICOS, anti-hCD45RO, anti-hOX40, anti-hTIM-3, anti-hCD95, anti-hTIGIT, anti-hCD27, anti-h4-1BB, anti-hCXCR4, anti-hCD357, anti-hCD28, anti-hCTLA-4, anti-hCD272, anti-hCD69, anti-hCD161, anti-hCD40, anti-anti-hCD127, hCCR7, anti-hCD25, anti-hCD38, anti-hCD122, anti-hHLA-DR, anti-hPD-1, and anti-hLAG-3. 5∗10⁶ freshly isolated blood CD4⁺ T cells were resuspended in CyFACS buffer (0.1% BSA, 0.02% NaN2, 2 mM EDTA in CyPBS) and then incubated with human Fc receptor blocking solution for 10 min at room temperature. Subsequently, these cells were stained with surface antibodies for 60 min on ice. Excess antibodies were removed by washing with CyPBS. Cells were then incubated with 2.5 μM cisplatin as a viability indicator and fixed in 4% paraformaldehyde. Fixed cells were barcoded using the Fluidigm Cell-ID 20-Plex Pd Barcoding Kit. Mass cytometry data were acquired on a CyTOF2 mass cytometer (Fluidigm) and analyzed with Cytobank. Cell gating was performed according to the following criteria: EQ1-, cisplatin-, DNA+, singlet^lo, CD45⁺, CD3⁺, and CD4⁺. viSNE analysis was conducted using the Barnes-Hut implementation of the t-SNE algorithm performed with Cytobank.⁹⁵^,⁹⁶

RNA-seq and data analysis

CCR5⁺ and CCR5^- CD4⁺ T cells were sorted by flow cytometry for total RNA extraction using Direct-zol RNA kit (Zymo Research). The cDNA library was prepared according to the library kit manufacturer’s protocol, indexed, pooled, and sequenced on an Illumina HiSeq. Basecalls and demultiplexing were performed using the Illumina’s bcl2fastq software and a custom python demultiplexing program with a maximum of one mismatch in the indexing read. RNA-seq reads were then aligned to the Ensembl release 76 primary assembly with STAR version 2.5.1a. Gene counts were derived from the number of uniquely aligned unambiguous reads by Subread:featureCount version 1.4.6-p5. Sequencing performance was assessed for the total number of aligned reads, total number of uniquely aligned reads, and features detected. The ribosomal fraction, known junction saturation, and read distribution over known gene models were quantified with RSeQC version 2.6.2. Counts were first filtered by removing genes where greater than half of the samples had counts per million (cpm) values less than one using python version 3.8.5. Differential expression analysis was assessed using the R/Bioconductor package DESeq2 version 1.44.0.⁹⁷ For Gene Set Enrichment Analysis, genes were pre-ranked using the Log2 fold change, and analysis was done via the MSigDB’s Gene Set Enrichment Analysis desktop software version 4.3.3 using the Hallmark gene sets. For transcription factor expression analysis, the 2024 version of non-redundant human transcription factors was downloaded from the JASPAR database.⁹⁸ Duplicates were removed, and the factors expressed in the current dataset were identified using Microsoft Excel. Volcano plots of differentially expressed genes were created using the R/Bioconductor package EnhancedVolcano version 1.22.0.⁹⁸

Single cell RNA-seq data analysis

Processed single-Cell sequencing data from various tissues were downloaded from https://www.tissueimmunecellatlas.org.⁵² The dataset was filtered to include only CD4⁺ T cells from the mesenteric lymph node (MLN), lung-draining lymph node (LLN), and spleen (SPL). Cells with less than 1000 unique molecular identifier counts, or less than 600 genes present, or identified as a doublet were filtered out. Additionally, donor samples containing fewer than 300 cells were omitted from the analysis. SCTransform from the Seurat R-Package (v4.0.3) was used for normalization and variance stabilization on the raw data of each sample individually before merging. Next, variable features were detected and PCA dimensional reduction was performed. Samples across the different organs and donors were integrated using the R Package harmony (v0.1.0). The harmony-transformed PCA embeddings were then used for clustering and visualization.⁹⁹ UMAP was used to visualize the clusters, which were annotated to the desired resolution using FeaturePlot() in the Seurat Package to identify the distribution of marker genes (genes on Dot Plot).¹⁰⁰ Seurat’s DimPlot(), DotPlot() & FeaturePlot() functions in combination with ggplot2 (v3.3.5) were used for all visualization (https://ggplot2.tidyverse.org.). Signature genes used to define clusters of CD4⁺ T cells were selected based on previous studies¹⁰¹^,¹⁰²^,¹⁰³ and were listed in Figure S1C.

Flow cytometry analysis

For surface staining, cells were washed and then stained with antibodies for 30 min on ice. For surface staining of chemokine receptors such as CCR5 and CCR7, cells were stained at room temperature using pre-warmed buffer. Dead cells were labeled with either 7-AAD or the Zombie Fixable Viability Kit. Intracellular IFNγ, TNFα, IL-2 and HIV-1 p24 staining was performed using the BD Cytofix/Cytoperm Fixation/Permeabilization kit. eBioscience Foxp3/Transcription Factor Staining Buffer Set was used for Foxp3, TCF1, or Ki67 staining according to the manufacturer’s instructions. For intracellular cytokine staining, cells were stimulated with 50ng/mL PMA+1 μg/mL ionomycin with the presence of monensin for 5h according to the manufacturer’s instructions. To phenotypically profile total CD4⁺ T cells as well as infected or uninfected CD4⁺CCR5⁺ T cells, cells were stained with Zombie NIR, followed by the incubation with a panel of 16 surface antibodies for 30 min at room temperature (anti-hCCR7, anti-hCD57, anti-hCD69, anti-h45RA, anti-h/mICOS, anti-hCD27, anti-hHLA-DR, anti-hTIGIT, anti-hCCR5, anti-hCD127, anti-hLAG3, anti-hCD95, anti-h45RO, anti-hCD62L, anti-hPD-1, and anti-hCD38). After staining, cells were washed and fixed using 4% paraformaldehyde for 10 min. Cell clusters were defined using tSNE and FlowSOM analysis¹⁰⁴ performed with FlowJo. Flow cytometry was performed on a BD LSRFortessa X-20 or Cytek Aurora, and the data were analyzed with FlowJo. Cell sorting experiments were used with a BD FACSAria II.

Virus reactivation in CD4⁺ T cells from PLWH

CD4⁺ T cells were isolated from PLWH to generate Cas9 control and BACH2-KO cells as described above. To prevent viral spread, 10 μM T-20 and 5 μM raltegravir were added to the culture medium throughout the entire assay. Electroporated CD4⁺ T cells were cultured for three days in the presence of 4 ng/mL IL-15 and 200 pg/mL IL-7, followed by treatment with either PBS or anti-CD3 and anti-CD28 antibodies along with 20 ng/mL IL-2 for 24 h. A minimum of 2.5 million cells were used per group, and cells from each group were harvested and lysed with 0.6 mL TRIzol reagent (Invitrogen). Total RNA extraction was performed using the Zymo Direct-zol RNA kit, followed by reverse transcription with the ProtoScript II Reverse Transcriptase, Oligo(dT)12–18 primer and random primers. Virus reactivation in Cas9 control and BACH2-KO CD4⁺ T cells were measured using HIV-1 gag qPCR or HIV-1 total RNA (PolyA) qPCR (Table S2).

Generation of humanized mice for HIV-1 infection and ART

One-to three-day-old newborn MISTRG-6-15 mice received an intrahepatic injection of 1.5–2×10⁴ unmodified or Cas9-edited CD34⁺ cells. Nine weeks after engraftment, 100 μL whole blood of each mouse was collected by facial vein bleed, and reconstitution of human immune cells was assessed by flow cytometry. Experimental groups were assigned randomly. Engrafted mice were infected with HIV_BaL (15 ng p24/mouse) through retro-orbital injection. In some studies, ART was administered to humanized mice via irradiated Teklad chow pellets containing emtricitabine (1000 mg/kg), tenofovir disoproxil fumarate (1000 mg/kg), and raltegravir (320 mg/kg). The special rodent chow was manufactured by the Research Diets. ART was initiated two to three weeks after infection.

Quantification of HIV-1 infection in humanized mice

Blood samples were collected weekly in ACD-A tubes via facial vein bleeding to quantify plasma HIV-1 RNA. The viral RNA was extracted using the Quick-RNA Viral Kit. For tissue analyses, spleens and liver of each mouse were processed through cell strainer (100μm) into single-cell suspensions. Lung samples were collected, chopped, and incubated in Collagenase D (2mg/mL) with DNase I (100μg/mL) for 30 min at 37°C. Leukocytes from liver and lung were isolated using 27% Percoll. Subsequently, cells from each tissue were subject to red blood cell lysis to obtain single cell suspensions for genomic DNA extraction using the QIAamp DNA Mini Kit (Qiagen). Plasma viral RNA was reverse transcribed with ProtoScript II Reverse Transcriptase. Genomic DNA samples and cDNA products from plasma viral RNA were used for qPCR analyses using an HIV-1 gag-based qPCR assay.⁷¹ All samples were run and analyzed on a QuantStudio 5 Real-Time PCR System.

To study viral rebound, splenic cell suspensions from each mouse in both the BACH2-KO and Cas9 ctrl groups were transfused into unengrafted MISTRG-6-15 mice via intraperitoneal injection. Blood samples were collected once per week to determine plasma viral loads. Viral rebound was confirmed when plasma viral load exceeded 1000 copies/mL.

Amplicon deep sequencing

In the first step, specific primers were used for the BACH2 loci (BACH2-F and BACH2-R). In a second round of PCR, primers containing sample-specific barcodes and adapters were employed (Table S3). The amplicons were sequenced using MiSeq Sequencing (Illumina) for 2 × 250 paired end reads. The CRISPResso software was used to analyze the deep sequencing data.¹⁰⁵

Quantification and statistical analysis

The statistical analysis was conducted using Prism 10. Phylogenetic statistics and analysis were performed as above. The methods for statistical analysis are described in the figure legends. The error bars indicate the standard error of the mean.

Published: August 21, 2025

Footnotes

Supplemental information can be found online at https://doi.org/10.1016/j.xcrm.2025.102311.

Supplemental information

Document S1. Figures S1–S7 and Tables S1–S3

mmc1.pdf^{(3.1MB, pdf)}

Document S2. Article plus supplemental information

mmc2.pdf^{(11.7MB, pdf)}

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Supplementary Materials

Document S1. Figures S1–S7 and Tables S1–S3

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Document S2. Article plus supplemental information

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PERMALINK

BACH2 promotes seeding and establishment of long-lived HIV-1 reservoir in memory CD4+ T cells

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