BACH2 shapes tissue resident memory T cell programs to promote HIV-1 persistence

Yulong Wei; Haocong Katherine Ma; Michelle E Wong; Hyein Back; Emmanouil Papasavvas; Karam Mounzer; Fatema Aberra; Ricardo Morgenstern; Pablo Tebas; Liza Konnikova; Luis J Montaner; Ya-Chi Ho

doi:10.1016/j.immuni.2025.07.022

. Author manuscript; available in PMC: 2026 Feb 27.

Published in final edited form as: Immunity. 2025 Aug 21;58(11):2878–2898.e11. doi: 10.1016/j.immuni.2025.07.022

BACH2 shapes tissue resident memory T cell programs to promote HIV-1 persistence

Yulong Wei ¹, Haocong Katherine Ma ¹, Michelle E Wong ¹, Hyein Back ¹, Emmanouil Papasavvas ², Karam Mounzer ³, Fatema Aberra ⁴, Ricardo Morgenstern ⁴, Pablo Tebas ⁵, Liza Konnikova ⁶, Luis J Montaner ², Ya-Chi Ho ^1,^*

PMCID: PMC12385486 NIHMSID: NIHMS2105715 PMID: 40845842

SUMMARY

Tissue resident memory T cells (Trms) are essential for mucosal immunity. We postulated that their long-lived tissue residency and restricted effector function promoted HIV-1 persistence in the gut. We coupled single-cell-DOGMA-seq and TREK-seq to capture chromatin accessibility, transcriptome, surface proteins, TCR, HIV-1 DNA, and HIV-1 RNA in gut CD4⁺ and CD8⁺ T cells from ten aviremic HIV-1⁺ individuals and five HIV⁻ donors. BACH2, a transcription repressor that establishes long-lived memory in T cells, was a key transcription factor that shaped gut Trms into long-lived memory and restrained interferon-driven effector function. BACH2-ablation shifted long-lived central memory T cells to effector memory. HIV-1-infected cells were predominantly identified among BACH2^high Trms, and HIV-1 preferentially infected and persisted in gut Trms in vitro. HIV-1-specific CD8⁺ T cells exhibited tissue residency and epigenetic scars of exhaustion, contributing to HIV-1 immune evasion in the gut. Overall, our findings indicate that HIV-1 persists in BACH2-shaped long-lived Trms.

Keywords: BACH2, tissue resident memory T cell, gut, mucosal immunity, HIV-1 reservoir, HIV-1 persistence, HIV-1-specific CD8⁺ T cells, T cell clonal expansion

Graphical Abstract

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eTOC blurb

Tissue resident memory T lymphocytes (Trms) are key to mucosal immunity. Wei et al. show that BACH2 restrains Trm effector function to reduce excessive inflammation and promote long-term tissue residence. HIV persists in gut Trm Th17 cells through a BACH2-shaped long-lived memory program, a mechanism of persistence in tissue reservoirs that is distinct from that in the blood.

INTRODUCTION

T cell fate, namely differentiation, effector function, migration, and survival, is governed by transcription factor activity and cytokine cues in the tissue microenvironment. Tissue resident memory T cells (Trms), programmed by Hobit and Blimp-1¹, protect the gut mucosal barrier against microbial invasion by exerting rapid and localized effector functions while restraining excessive inflammation^2–5. Tissue residency is maintained by integrin α1 (ITGA1, CD49a)-mediated binding to tissue collagen and laminin and integrin αE (ITGAE, CD103)-mediated binding to E-cadherin on the gut epithelium. CD69 further prevents egress by inhibiting sphingosine 1-phosphate receptor-1 (S1PR1)⁶. Upon antigen stimulation, gut CD4-Trms and CD8-Trms differentiate into effector T cells. While most effector T cells die within weeks after antigen stimulation to prevent persistent inflammation⁷, some survive activation-induced cell death and become long-lived memory. We postulate that the transition of T cells from short-lived effector to long-lived memory promotes the survival of HIV-1-infected cells in long-lived memory CD4⁺ T cells.

HIV-1 establishes a major tissue reservoir in gut CD4⁺ T cells^8–11. Current understanding of mechanisms of HIV-1 persistence heavily relies on CD4⁺ T cell profiling in the blood^12–15, where HIV-1-infected cells are enriched in cytotoxic¹², Th1¹⁶ (expressing CCR5¹⁷ coreceptor and CXCR3¹⁸), Th17¹⁹, CD161⁺²⁰, central memory (Tcm)²¹, and effector memory (Tem)²² CD4⁺ T cells. Yet, the gut accounts for up to 80–95% of HIV-1-infected cells in the body^8–11. HIV-1 replicates in gut CD4⁺ T cells^9,23–27, including intraepithelial lymphocytes (IELs), lamina propria lymphocytes (LPLs), and gut-associated lymphoid tissue (GALT) lymphocytes. How HIV-1-infected cells survive viral cytopathic effect, evade immune clearance, and persist in the gut remain unknown. On the other hand, HIV-1-specific CD8⁺ T cells are key immune effectors that control HIV-1 infection²⁸. HIV-1-specific CD8⁺ T cells recruited to the gut^29,30 may become exhausted during chronic infection^31–33 or rejuvenated after ART³⁴. Whether HIV-1-specific CD8⁺ T cells maintain effector function in the gut remains unknown. Given the rarity of HIV-1⁺ CD4⁺ T cells (<0.1%)^12,14,15 and HIV-1-specific CD8⁺ T cells, and limited accessibility to tissues, understanding mechanisms of HIV-1 persistence and immune evasion in the gut remains challenging.

BACH2 is a transcription repressor that promotes long-lived memory and stemness while repressing effector T cell functions^35–38. BACH2 competes with AP-1 binding to enhancers and attenuates AP-1-induced proinflammatory activity³⁷. While both BACH2 and AP-1 may be key transcription regulators that shape early TRM development across tissues such as the skin and lung³⁹, the role of BACH2 in the gut remains elusive. HIV-1 integration into BACH2 is highly enriched in people living with HIV (PLWH) under long-term antiretroviral therapy (ART)^40–42, leading to aberrant HIV-1-driven BACH2 expression⁴³ and clonal expansion of HIV-1-infected cells^41,42. We postulate that BACH2-mediated long-lived memory promotes HIV-1 persistence, even if HIV-1 is neither integrated into BACH2 nor driving BACH2 transcription factor activity in these cells.

We aim to identify key transcription factors that shape gut T cell immune programs and promote HIV-1 persistence. We combined single-cell DOGMA-seq⁴⁴ and TREK-seq⁴⁵ to simultaneously profile chromatin accessibility, transcriptome, surface proteins, HIV-1 DNA, HIV-1 RNA, and T cell receptor (TCR) in the same single cells from the gut of people with and without HIV-1. We found that BACH2 was the leading transcription factor that shaped long-lived memory and restrained effector function in CD4-Trms and CD8-Trms. HIV-1-specific CD8⁺ T cells were enriched in TRMs and exhibited epigenetic scars of exhaustion, while CMV-specific CD8⁺ T cells were enriched in effector memory and were not exhausted. Distinct from HIV-1-infected cells in the blood (typically effector memory Th1), HIV-1-infected cells in the gut exhibited high BACH2 transcription factor accessibility, long-lived memory, and tissue resident Th17 phenotype. Finally, we found that HIV preferentially infects and persists in gut CD4-Trm, and BACH2 ablation shifted both gut CD4⁺ and CD8⁺ T cells from central memory to effector memory. Our study uncovers a fundamental dichotomy between gut and blood in mechanisms of HIV-1 persistence.

RESULTS

Single-cell DOGMA-seq identifies integrative single-cell epigenetic, transcriptional, and protein profiles of human gut

To examine immune cell types in the gut, we obtained colon biopsy from ten PLWH under ART (plasma viral load <200 copies/ml) and five HIV-1 negative individuals (HIV⁻) (Table S1) for single-cell DOGMA-seq and TREK-seq to capture chromatin accessibility (scATAC-seq), transcriptome (scRNA-seq), 155 surface proteins (antibody-derived tags), and TCR within the same single cells. Briefly, 10–14 colon biopsies per participant were processed into single-cell suspensions and cryopreserved into aliquots on the day of biopsy⁴⁶. One aliquot of gut immune cells and another aliquot of CD3-enriched T lymphocytes were examined separately.

Collagenase treatment is required to isolate immune cells from the gut lamina propria. However, collagenase treatment may degrade surface protein epitopes and reduce surface protein staining⁴⁷. We performed DOGMA-seq on peripheral blood mononuclear cells (PBMC) treated with mock, collagenase II, or collagenase IV. We found that collagenase treatment reduced detection of certain T cell surface proteins (Figure S1A, S1B, S1C). Results from protein expression analyses should be interpreted with caution.

We captured the chromatin accessibility landscape, cellular transcriptome, and surface protein expression in 17,642 cells from PLWH and 22,422 cells from HIV⁻ individuals in the gut cell aliquot. After batch effect removal and weighted nearest neighbor (WNN) integration of ATAC and RNA profiles, gut cell phenotypes were visualized by Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP)⁴⁸ (Figure 1A). We identified CD4⁺ T cells (16.9%), CD8⁺ T cells (16.1%), B cells (15.4%), IgA-producing plasma cells (39.1%), IgG-producing plasma cells (19.1%), innate immune cells (natural killer cells, mast cells, and monocytes) (2.2%), tissue structural cells (epithelium, subepithelium, endothelium, and myofibroblast) (6.4%), and the enteric nervous system (myenteric ganglia and enteric glia)(1.6%) (Figure 1B, Table S2). Combining gut cell with CD3⁺ T enriched aliquot, single-cell DOGMA-seq captured 43,113 CD4⁺ T cells from PLWH, 6,092 CD4⁺ T cells from HIV⁻ individuals, 45,475 CD8⁺ T cells from PLWH, and 6,064 CD8⁺ T cells from HIV⁻ individuals.

Figure 1. — (A) WNN UMAP plot of distinct cell subsets in human gut (n = 17,642 cells in PLWH and 22,422 cells in HIV⁻ individuals). (B) Proportions of gut cell subsets in PLWH and HIV⁻ individuals. (C) WNN UMAP plot of distinct CD4⁺ T cell subsets in human gut (n = 43,113 cells in PLWH and 6,092 cells in HIV⁻ individuals), including CD4⁺ T cells from gut cell aliquots and CD3⁺ enriched aliquots. (D) Proportions of gut CD4⁺ T cell subsets PLWH and HIV⁻ individuals. (E) Mean regulation scores (signed, −log10 scale) across domains of regulatory chromatin (DORC) per transcription factor (TF) for all significant TF-DORC interactions determined in PLWH (top) and HIV⁻ individuals (bottom). Top TF activators (left) and top TF repressors (right) are highlighted. (F) WNN UMAP plot (top) and violin plots (bottom) of BACH2 motif accessibility in gut CD4⁺ T cells. (G) WNN UMAP plot (top) and violin plot (bottom) of per cell RNA module scores in gut CD4⁺ T cells. The module score was built on DORC genes that had upregulated gene expression in PLWH and were predicted to be activated by BACH2 (BACH2 regulation scores > 0). (H) Heatmap indicating differential transcription factors binding motifs accessibility between CD4-Trm-BACH2^high, CD4-Trm-BACH2^low, and Tem-Th1 cells. False discovery rate (FDR)-adjusted P < 0.05, average Z-score difference > 0.2. (I) Heatmap indicating differentially expressed genes between CD4-Trm-BACH2^high, CD4-Trm-BACH2^low, and Tem-Th1 cells. FDR-adjusted P < 0.05, min.pct. > 0.25, log2FC > 0.6. (J) Heatmap indicating differentially expressed surface proteins between CD4-Trm-BACH2^high, CD4-Trm-BACH2^low, and Tem-Th1 cells. FDR-adjusted P < 0.05, min.pct. > 0.15, log2FC > 0.2. Significantly enriched negative regulation of immune effector process (K) and TCR calcium pathway (L) were identified using GSEA with leading-edge genes shown. * P < 0.05, ** P < 0.01, *** P < 0.001, Wilcoxon rank-sum test. See also Figure S1 – S2.

BACH2 shapes tissue resident memory programs in gut CD4⁺ T cells

After batch effect correction on 43,113 CD4⁺ cells from ten PLWH and 6,092 CD4⁺ T cells from five HIV− individuals (Figure S2A), we identified 13 CD4⁺ T cell subsets based on chromatin accessibility of transcription factors, transcriptional profile, and surface protein expression (Figure S2B – S2D, Table S2). Overall, 55.1% of CD4⁺ T cells were tissue resident memory T cells (Trms) (Figure 1C, Table S2), evidenced by high ITGA1 (integrin α1) and CD69 gene expression and high CD49a (encoded by ITGA1) and/or CD103 (encoded by ITGAE) protein expression^49,50, including two Trm-Th1 populations (Trm-BACH2^high-Th1 and Trm-BACH2^low-Th1), Trm-BACH2^high-Th17, and Trm. Of note, gut CD4-Trms exhibited an overall effector memory (Tem) phenotype (Figure S2C, S2D), consistent with a previous study⁵¹. Among non-Trms, we identified naïve, Tcm, Tem-Th1, Th17, regulatory T cells (Treg), induced Treg (iTreg), follicular regulatory T cells (Tfr), T follicular helper cells (Tfh), and proliferating cells (Figure 1D).

We performed gene regulatory network (GRN) analysis to unbiasedly identify transcriptional regulators that govern gene expression in the same cells. We identified BACH2 as a prominent transcription factor that regulated gut CD4⁺ T cell transcriptional profiles in both PLWH and HIV− individuals (Figure 1E, S2E). We found significantly higher BACH2 motif accessibility (suggesting higher BACH2 transcription factor binding activity) in cells from both Trm-BACH2^high-Th1 and Trm-BACH2^high-Th17 (hereafter referred to as CD4-Trm-BACH2^high) when compared to cells from Trm-BACH2^low-Th1 (hereafter referred to as CD4-Trm-BACH2^low) and Tem-Th1, in both PLWH and HIV− individuals (Figure 1F). GRN-identified BACH2-target genes included tissue residency (ITGA1, ITGAE) and long-lived memory cytokine and chemokine receptors (IL2RB, IL4R, IL7R, IL21R, IL23R, CCR7) (Table S3). The expression of these BACH2-driven genes were significantly higher in CD4-Trm-BACH2^high cells when compared to CD4-Trm-BACH2^low and Tem-Th1, in both PLWH and HIV− individuals (Figure 1G). These BACH2-driven genes were involved in T cell activation, differentiation, and adhesion (Figure S2F).

BACH2-high CD4-Trms exhibit long-lived memory programs

We next dissected profile differences between CD4-Trm-BACH2^high and CD4-Trm-BACH2^low cells. At the chromatin level, CD4-Trm-BACH2^high cells had higher motif accessibility of TBX21 (T-bet) and RORC (RORγt) (Figure 1H). Indeed, CD4-Trm-BACH2^high cells were also Th1 (68.0%) and Th17 (32.0%) polarized. At the transcription level, CD4-Trm-BACH2^high cells had higher CCR6 (Th17 tissue homing receptor) and long-lived memory cytokine receptor (IL2RA, IL2RB, IL4R, IL7R, IL12RB2) gene expression, whereas CD4-Trm-BACH2^low cells had higher expression of effector molecules (IFNG and TNF) (Figure 1I). At the protein level, CD4-Trm-BACH2^high cells had higher CD49a (tissue residency), IL-7R (long-lived memory), and CD161 (Th17⁵²) protein expression, whereas CD4-Trm-BACH2^low cells had higher exhaustion marker PD-1 and TIGIT expression (Figure 1J). Overall, CD4-Trm-BACH2^high cells exhibit a long-lived memory Th1 and Th17 phenotypes, whereas CD4-Trm-BACH2^low cells exhibit effector and exhaustion phenotypes.

BACH2 is a transcription repressor that limits AP-1 activity by competing at the same transcription factor binding site³⁷. Because BACH2 and AP-1 (JUN and FOS) share highly similar binding motifs (Figure S2G, top), accessibility of the AP-1 heterodimer JUN:FOS was therefore also higher in CD4-Trm-BACH2^high than CD4-Trm-BACH2^low and Tem-Th1 (Figure S2G, bottom) in both PLWH and HIV− individuals. To tease out BACH2 versus AP-1 activity in BACH2-high versus BACH2-low cells, we examined the chromatin accessibility at their gene loci. We found that CD4-Trm-BACH2^high cells had higher chromatin accessibility at BACH2 locus compared with CD4-Trm-BACH2^low cells (Figure S2H), while CD4-Trm-BACH2^low cells had higher chromatin accessibility at JUN and FOS loci (Figure S2I, S2J) in both PLWH and HIV⁻ individuals. To tease out whether BACH2 or AP-1 shaped the immune programs in BACH2-high versus BACH2-low cells, we performed gene set enrichment analysis (GSEA) to identify enriched immune programs in CD4-Trm-BACH2^high and CD4-Trm-BACH2^low cells, against naïve cells and Tem. We found that CD4-Trm-BACH2^high cells, but not CD4-Trm-BACH2^low cells, had enrichment in gene sets involving negative regulation of immune effector function (upregulating IL7R, Figure 1K, S2K). In contrast, CD4-Trm-BACH2^low cells had enrichment in gene sets involving AP-1 and proinflammatory mediators (Figure 1L, S2K). Of note, key AP-1 pathway genes and proinflammatory mediators (TNF, IFNG, CCL4, CCL5) were upregulated in CD4-Trm-BACH2^low cells but not in CD4-Trm-BACH2^high cells (Figure S2L). Together, BACH2 shapes CD4-Trms by restraining effector function and increasing long-lived memory gene expression.

BACH2 shapes tissue resident memory programs in gut CD8⁺ T cells

After batch effect correction on 45,475 gut CD8⁺ T cells from PLWH and 6,064 gut CD8⁺ T cells from HIV− individuals (Figure S3A), we identified 13 CD8⁺ T cell clusters (Figure 2A) by transcription factor accessibility, transcriptional profile, and surface protein expression (Figure S3B – S3D, Table S2). Overall, 56.0% of CD8⁺ T cells were Trms (Figure 2B), evidenced by ITGAE and ITGA1 gene expression and CD103 protein expression (Figure S3C, S3D). Of note, gut CD8-Trms exhibited a Tem phenotype (lower CCR7 and LEF1 gene expression and lower CD45RA protein expression compared with naïve cells, Figure S3C, S3D), consistent with a previous study⁵¹. Among CD8⁺ non-Trms, we identified naïve, memory, Tem, Tem-activated, effector, mucosal-associated invariant T cell (MAIT), γδ T, and proliferating CD8⁺ T cells. In addition, we identified a subset of natural killer CD8⁺ T (NKT) cells (Figure 2B).

Figure 2. — (A) WNN UMAP plot of distinct CD8⁺ T cell subsets in human gut (n = 45,475 cells in PLWH and 6,064 cells in HIV⁻ individuals), including CD8⁺ T cells from gut cell aliquots and CD3⁺ enriched aliquots. (B) Proportions of CD8⁺ T cell subsets in PLWH and HIV⁻ individuals. (C) Mean regulation scores (signed, −log10 scale) across DORCs per TF for significant TF-DORC interactions determined in PLWH (top) and HIV⁻ individuals (bottom) (D) WNN UMAP plot (top) and violin plots (bottom) of BACH2 motif accessibility in CD8⁺ T cells. (E) WNN UMAP plot (top) and violin plot (bottom) of per cell RNA module scores in gut CD8⁺ T cells. The module score was built on DORC genes that had upregulated gene expression in PLWH and were predicted to be activated by BACH2 (BACH2 regulation scores > 0) in PLWH CD8⁺ T cells. (F) Heatmap showing differential transcription factor binding motif accessibility between CD8-Trm-BACH2^high, CD8-Trm-BACH2^low, and Tem cells. FDR-adjusted P < 0.05, average Z-score difference > 0.15. (G) Heatmap indicating differentially expressed genes between CD8-Trm-BACH2^high, CD8-Trm-BACH2^low, and CD8-Tem cells. FDR-adjusted P < 0.05, min.pct > 0.25, log2FC > 0.6. (H) Heatmap indicating differentially expressed surface proteins between CD8-rm -BACH2^high, CD8-Trm-BACH2^low, and CD8-Tem cells. FDR-adjusted P < 0.05, min.pct > 0.15, log2FC > 0.25. Significantly enriched negative regulation of immune effector process (I) and AP-1 pathway (J) were identified using GSEA with leading-edge genes shown. * P < 0.05, ** P < 0.01, *** P < 0.001, Wilcoxon rank-sum test. See also Figure S3.

By GRN analysis, we also identified BACH2 as a prominent regulator that shaped gene expression in gut CD8⁺ T cells in PLWH and HIV⁻ individuals (Figure 2C, Figure S3E). We identified higher BACH2 motif accessibility in CD8-Trm-BACH2^high cells in both PLWH and HIV⁻ individuals (Figure 2D). As in CD4-Trms, BACH2 drove gene expression involving tissue residency (ITGA1, ITGAE) and long-lived memory cytokine and chemokine receptors (including IL7R, IL21R, CCR7) (Table S3). The expression of these BACH2-driven genes were higher in CD8-Trm-BACH2^high cells in both PLWH and HIV⁻ individuals (Figure 2E). These BACH-2 driven genes were involved in T cell activation, differentiation, and adhesion (Figure S3F).

BACH2-high CD8-Trms exhibit long-lived memory programs

We compared the cellular profiles between CD8-Trm-BACH2^high cells to CD8-Trm-BACH2^low cells. At the chromatin level, CD8-Trm-BACH2^high had higher BACH2, AP-1, and RORγt motif accessibility (Figure 2D, 2F, S3G). However, comparing chromatin accessibility at BACH2, JUN, and FOS gene loci between CD8-Trm-BACH2^high and CD8-Trm-BACH2^low cells, we found higher accessibility at BACH2 in CD8-Trm-BACH2^high (Figure S3H) and no significant difference at JUN and FOS (Figure S3I, S3J), in both PLWH and HIV⁻ individuals. At the transcription level, CD8-Trm-BACH2^high cells had higher BACH2 and long-lived memory cytokine receptors IL4R and IL7R gene expression, whereas CD8-Trm-BACH2^low cells had higher AP-1 (FOS and JUN) and exhaustion (ENTPD1) expression (Figure 2G). At the protein level, CD8-Trm-BACH2^high cells had higher long-lived memory cytokine receptor CD127 (IL7R) protein expression (Figure 2H). Notably, differences between Trm-BACH2^high and Trm-BACH2^low cells are similar for gut CD8⁺ and CD4⁺ T cells (Figure 1F, 1G, 1H), highlighting a consistent BACH2-shaped, long-lived memory phenotype in both gut CD4⁺ and CD8-Trms.

We next performed GSEA to examine enriched immune programs in CD8-Trm-BACH2^high and CD8-Trm-BACH2^low cells, against naïve cells and Tem. We found that CD8-Trm-BACH2^high cells, but not CD8-Trm-BACH2^low cells and Tem, had enrichment in gene sets involving negative regulation of immune effector function (upregulating IL-7R, Figure 2I, S3K). In contrast, CD8-Tem cells (low BACH2 accessibility) had enrichment in gene sets involving AP-1 and proinflammatory mediators (Figure 2J, S3K). Of note, key AP-1 and proinflammatory mediators (TNF, IFNG, CCL3, CCL4) were upregulated in CD8-Tem cells but not in CD8-Trm-BACH2^high cells (Figure S3L). Together, BACH2, not AP-1, shapes long-lived CD8-Trms.

BACH2-shaped tissue resident memory program restrains terminal effector function and promotes long-lived memory

We postulate that higher BACH2 motif accessibility correlates with lower immune effector and higher long-lived memory transcription factor motif accessibility and gene expression. To test this, we ranked CD4-Trm-Th1 and CD8-Trms from low to high BACH2 accessibility (Figure 3A, 3B). We then binned cells into 10 groups by BACH2 accessibility and examined differential transcription factor accessibility and gene expression per group (Figure 3C–F). In CD4-Trm-Th1 and CD8-Trm, decrease in BACH2 accessibility was associated with increased interferon regulatory factor (IRF) accessibility (Figure 3C, 3E, 3G, 3I) and effector gene expression (TNF, IFNG, and GZMA) (Figure 3D, 3F, 3H, 3J). In contrast, increase in BACH2 accessibility was associated with increased IKZF1 (which silences effector genes by inhibiting AP-1 binding⁵³) and IKZF3 (which restrains cytotoxic CD4⁺ T cell programming⁵⁴ and promotes HIV-1 persistence¹⁴) accessibility in CD4-Trm-Th1, increased stemness (LEF1 and TCF7) in CD8-Trm (Figure 3C, 3E, 3K, 3M), and increased expression of long-lived memory cytokines in both (such as IL7R, IL2RB, and CCR7) (Figure 3D, 3F, 3L, 3M). Overall, interferon responses drive effector function in BACH2-low cells, while BACH2 restrains effector function and drives long-lived memory.

Figure 3. — (A) WNN UMAP plot highlighting CD4-Trm-BACH2^high-Th1 and CD4-Trm-BACH2^low-Th1 cells (left) and BACH2 motif accessibility in CD4⁺ T cells. (B) WNN UMAP plot highlighting CD8-Trm-BACH2^high and CD8-Trm-BACH2^low cells (left) and BACH2 motif accessibility in CD8⁺ T cells. The black arrows indicate increased in BACH2 motif accessibility in highlighted cells. (**C – F**) Diffusion heatmaps and feature expression plots indicating dynamic changes in CD4-Trm-Th1 and CD8-Trms ordered from low to high BACH2 accessibility. Cells were binned into 10 groups by BACH2 accessibility and enriched features were determined in each bin. (C) Changes in global chromatin accessibility of transcription factors from CD4-Trm-BACH2^low-Th1 to CD4-Trm-CD4⁺ BACH2^high-Th1 cells. FDR-adjusted P < 0.05, average Z-score difference > 0.3. (D) Changes in gene expression from CD4-Trm-BACH2^low-Th1 to CD4-Trm-BACH2^low-Th1 cells. FDR-adjusted P < 0.05, min.pct. > 0.1, log₂FC > 0.5. (E) Changes in global chromatin accessibility of transcription factors from CD8-Trm-BACH2^low to CD8-Trm-BACH2^high cells. FDR-adjusted P < 0.05, average Z-score difference > 0.3. (F) Changes in gene expression from CD8-Trm-BACH2^low to CD8-Trm-BACH2^high cells. FDR-adjusted P < 0.05, min.pct. > 0.1, log₂FC > 0.4. (**G – N**) Transcription factor accessibility and gene expression of cells highlighted in (**A – B**), as ranked by their BACH2 motif accessibility. (**G, H**) IRF-driven effector function decreases when BACH2 motif accessibility increases in CD4: accessibility of IRF1 and IRF4 binding motifs decreased (G) while *IFNG* and *TNF* expression decreased (H) with increase in BACH2 motif accessibility in CD4-Trm-Th1 cells. (**I, J**) IRF-driven effector function decreases when BACH2 motif accessibility increases in CD8: Accessibility of IRF1 and IRF4 binding motifs decreased (I) while *IFNG* and *GZMA* expression decreased with increase in BACH2 motif accessibility in CD8-Trms (J). (K, L) Long-lived memory program increases when BACH2 motif accessibility increases in CD4: IKZF1 and IKZF3 binding motifs increased (K) while *IL7R* and *IL2RB* expression increased (L) with increase in BACH2 motif accessibility in CD4-Trm-Th1 cells. (M, N) Long-lived memory program increases when BACH2 motif accessibility increases in CD8: LEF1 and TCF7 binding motifs increased (M) while *IL7R* and *CCR7* expression increased (N) with increase in BACH2 motif accessibility in CD8-Trms.

TNF and IFN-γ ligand–receptor interactions from BACH2-low effectors to BACH2-high Trms shape long-lived memory

Our results showed that Trm-BACH2^low cells had higher IFNG and TNF cytokine expression while Trm-BACH2^high cells expressed cytokine receptors. We speculate that IFN-γ and TNF signaling from Trm-BACH2^low cells may shape the cellular programs of Trm-BACH2^high cytokine receiving cells. We performed cell-cell communication analysis between gut CD4⁺ and CD8⁺ T cells by merging CD4⁺ T cell and CD8⁺ T cell datasets (Figure S4A). Using Scriabin⁵⁵, we identified two statistically significant ligand–receptor interaction programs, Interaction Program 2 and 12 (IP-2, IP-12). Together, IP-2 and IP-12 identified significant signaling between ligand-expressing CD8-Tem-activated and Trm-BACH2^low-Th1 cells and receptor-expressing Trm-BACH2^high-Th1 and Trm-BACH2^high-Th17 cells (Figure S4B, S4C, top). TNF and IFNG ligand–receptor interactions were enriched especially in PLWH in comparison to HIV⁻ individuals (Figure S4B, S4C, bottom), suggesting TNF and IFN-γ effector molecule expression from CD8-Tem-activated cells and Trm-BACH2^low-Th1 senders interact with TNF and IFN-γ receptors in BACH2-high CD4-Trm (Figure S4D, S4E).

To determine how TNF and IFNG ligands shaped cellular programs in receptor-expressing Trm-BACH2^high-Th1 and Trm-BACH2^high-Th17 cells, we applied NicheNet⁵⁶ and identified cellular responses to TNF and IFN-γ signaling in both receiver cell groups (Figure S4F, S4G). We found higher TNF and IFN-γ signaling in PLWH, based on TNF and IFNG mean ligand score in receiver cells. TNF and IFN-γ signaling increased long-lived memory (IL7R and CCR7) and survival (BCL2) gene expression in the receiver BACH2-high CD4-Trms (Figure S4H–S4I).

CD4⁺ T cells are less proliferative than CD8⁺ T cells in human gut

CD8⁺ T cells are known to be more proliferative than CD4⁺ T cells.⁵⁷ The clonal expansion of HIV-1⁺ CD4⁺ T cells promotes HIV-1 persistence¹², while the clonal expansion capacity of HIV-1-specific CD8⁺ T cells predicts protective immunity⁵⁸. T cell clone size, captured by TCR sequencing, reflects proliferation in vivo. We performed TREK-seq⁴⁵ to capture TCR sequences using an aliquot of DOGMA-seq cDNA library. Overall, we captured TCRβ sequences in 30.2% CD4⁺ T cells in PLWH, 18.1% CD4⁺ T cells in HIV⁻ individuals, 23.9% CD8⁺ T cells in PLWH, and 14.4% CD8⁺ T cells in HIV⁻ individuals. Among them, T cell clones were found in 11.4% CD4⁺ T cells in PLWH, 13.6% CD4⁺ T cells in HIV⁻ individuals, 49.3% CD8⁺ T cells in PLWH, and 35.2% CD8⁺ T cells HIV⁻ individuals (Table S2, Table S4). We found that CD4⁺ T cells had smaller T cell clone size than CD8⁺ T cells, indicating lower proliferative capacity in CD4⁺ T cells than CD8⁺ T cells in the human gut (Figure 4A).

Interferon responses promote clonal expansion of gut CD8⁺ T cell effectors

In CD4⁺ T cells, there was no significant difference in cell type composition of CD4⁺ T cell clones in PLWH versus HIV⁻ individuals (Figure 4B–4D). In PLWH and HIV⁻ individuals, most clones were Trm-BACH2^high-Th1 (37.6%, 26.1%), Trm-BACH2^high-Th17 (22.4%, 15.9%), and Tcm (8.9%, 17.4%) (Figure 4D, Table S2), as they were the most abundant CD4⁺ T cell subsets in the gut (Figure 4E, 4F).

In CD8⁺ T cells, there was a higher proportion of CD8⁺ T cell clones in PLWH (49.3%) than HIV⁻ individuals (35.2%) (Figure 4G, 4H). Most clones in PLWH were CD8-Trm-BACH2^low (35.1%) and CD8-Tem (23.7%), whereas most clones in HIV⁻ individuals were CD8-Trm-BACH2^low (58.1%) (Figure 4I, 4J, 4K). Of note, there was a higher proportion of proliferated CD8-Tem in PLWH (23.7%) than HIV⁻ individuals (10.1%), which drove higher proportion of CD8⁺ T cell clones in PLWH (Figure 4J, 4K).

To investigate the immune programs driving gut CD8⁺ T cell proliferation, we compared CD8⁺ T cell clones with non-clonal cells in PLWH. At the chromatin level, CD8⁺ T cell clones had higher interferon responses (IRF, STAT1, STAT2) and lower BACH2 motif accessibility (Figure 4L). At the transcription level, CD8⁺ T cell clones had higher effector molecule (GZMA, CCL5, ZEB2, NKG7), cytokine receptor (IL2RB, and IL12RB2), and tissue residency (ITGAE and ITGA1) gene expression but lower BACH2 gene expression (Figure 4M). At the protein level, CD8⁺ T cell clones had higher activation (CD38, HLA-DR, DP, DQ) and tissue residency (CD103, CD49a, and CD69) protein expression (Figure 4N). We next compared CD8⁺ T cell clones between PLWH and HIV⁻ individuals. CD8⁺ T cell clones in PLWH had higher IRFs and lower BACH2 motif accessibility (Figure S5A), higher effector (CCL5, GZMA, and NKG7) gene expression (Figure S5B), and higher activation (HLA-DR, DP, DQ) and tissue residency (CD69, CD103) protein expression (Figure S5C). Overall, IRF dominates over BACH2 and promoted the proliferation of effector memory CD8⁺ T cells in PLWH.

Clonally expanded gut CD4⁺ T cells exhibit Trm-Th1 and Trm-Th17 phenotypes

To determine the immune programs in clonally expanded gut CD4⁺ T cells, we compared CD4⁺ T cell clones to non-clonal cells in PLWH. At the chromatin level, CD4⁺ T cell clones had higher BACH2, AP-1, TBX21, EOMES (Th1), RORC (Th17) motif accessibility (Figure S5D). At the transcription level, CD4⁺ T cell clones had higher CCL5, IL12RB2 (Th1), RORA, KLRB1 (Th17), and SERPINB9 (granzyme B inhibitor) gene expression (Figure S5E). At the protein level, CD4⁺ T cell clones had higher tissue residency (CD103 and CD49a), Th1 (CCR5), and Th17 (CD161) protein expression (Figure S5F). Overall, the cellular profiles of CD4⁺ T cell clones resemble those of Trm-BACH2^high-Th1 and Trm-BACH2^high-Th17. This finding is not surprising since 59.9% of all CD4⁺ T cell clones in PLWH were identified as Trm-BACH2^high cells (Table S2).

Comparing CD4⁺ T cell clones between PLWH and HIV⁻ individuals, CD4⁺ T cell clones in PLWH had higher AP-1 motif accessibility (Figure S5G), higher CCL5 gene expression (Figure S5H), and higher Th1 (CXCR3, CCR5), Th17 (CD161), and activation (HLA-DR-DP-DQ) protein expression (Figure S5I), suggesting that CD4⁺ T cell clones in PLWH were more activated than that in HIV⁻ individuals. Overall, IRF transcription factors promote effector function and proliferation, which is restrained by BACH2 activity, in gut CD8⁺ T cells. In contrast, IRF transcription factors do not drive the clonal expansion of CD4⁺ T cells, and gut CD4⁺ T cell clones exhibit BACH2-shaped tissue resident Th1 and Th17 programs.

HIV-1-specific CD8⁺ T cells exhibit tissue residency, lower proliferation capacity, and epigenetic scar of exhaustion

HIV-1-specific CD8⁺ T cells were thought to be exhausted during chronic infection, while CMV-specific CD8⁺ T cells were known to retain effector function in HIV-1 infection⁵⁹. By mapping TCRβ CDR3 amino acid sequences against the McPAS-TCR database⁶⁰ of TCRs of known HIV-1 and CMV antigen specificity (see Methods), we identified 56 HIV-1-specific CD8⁺ T cells and 429 CMV-specific CD8⁺ T cells (Figure 5A–5D, Table S2, Table S4). Trms accounted for 67.9% of HIV-1-specific CD8⁺ T cells, 47.8% of CMV-specific CD8⁺ T cells, and 57.2% of other T cells having mapped TCR. Compared to CMV-specific CD8⁺ T cells, HIV-1-specific CD8⁺ T cells were more enriched in Trm-BACH2^low (44.6% vs 38.9%) but less enriched in Tem (16.1% vs 28.0%) (Figure 5B, Table S2). Furthermore, 44.6% HIV-1-specific CD8⁺ T cells were clonal while 60.4% of CMV-specific CD8⁺ T cells were clonal (Figure 5C, 5D, Table S2).

Figure 5. — (A) WNN UMAP plot of 10,868 CD8⁺ T cells having TCRβ CDR3 junction sequences (CDR3β) captured, including 56 predicted HIV-1-specific (red) and 429 CMV-specific (blue) CD8⁺ T cells. (B) Cell subset proportions of HIV-1-specific, CMV-specific, and other CD8⁺ T cells with mapped TCRβ. (C) Cell subset proportions of HIV-1-specific (n = 25), CMV-specific (n = 262), and other (n = 5,379) CD8⁺ T cell clones. (D) Alluvial plot indicating the proportions of CD8⁺ T cell clones (n = 5,666) by shared CDR3β junction sequences in each participant (indicated by bar size), the size of each clone per participant (indicated by bar size), and antigen-specificity for each clone (HIV-1 antigen-specific in red, CMV antigen-specific in blue). (E) Differentially expressed genes between HIV-1-specifc CD8⁺ T cells and CMV-specific CD8⁺ T cells. FDR-adjusted bootstrap P < 0.05, min.pct > 0.25, log2FC > 0.5. (F) Differences in averaged and scaled RNA between HIV-1-specifc, CMV-specific, other (effector and memory), and naïve CD8⁺ T cells. FDR-adjusted P < 0.05, min.pct > 0.25, log2FC > 0.5. (**G, H**) Genome tracks showing differences in chromatin accessibility at *LEF1* gene locus (G) and *ENTPD1* gene locus (H) between HIV-1-specifc, CMV-specific, naïve CD8⁺ T cells, and other cells (effector and memory).

HIV-1-specific CD8⁺ T cells had higher tissue residency (ITGAE and ITGA1) gene expression than CMV-specific CD8⁺ T cells, likely reflecting enrichment of HIV-1-specific cells in Trms and enrichment of CMV-specific cells in Tem. Expression of effector genes was also different between HIV-1-specific CD8⁺ T cells (high in GZMB, PRF1, and GNLY) and CMV-specific CD8⁺ T cells (high in GZMK, IFNG) (Figure 5F), although these trends should be interpreted with caution due to the low number of HIV-1-specific cells precluding robust statistical testing.

Persistent antigen stimulation in chronic viral infections, such as HIV-1, hepatitis C virus, and lymphocytic choriomeningitis virus (LCMV), imposes epigenetic scars of exhaustion in viral antigen-specific CD8⁺ T cells^61–63. Indeed, comparing HIV-1-specific to CMV-specific CD8⁺ T cells, HIV-1-specific CD8⁺ T cells had higher exhaustion marker (ENTPD1 and LAG3) expression (Figure 5E, 5F). In line with evidence of epigenetic scarring of viral antigen-specific CD8⁺ T cell exhaustion in chronic infections^61–63, while LEF1 (a marker of naïve and resting T cells⁶⁴) had higher accessibility in naïve CD8⁺ T cell (Figure 5G), ENTPD1 had higher accessibility in HIV-1-specific CD8⁺ T cells (Figure 5H). Overall, HIV-1-specific CD8⁺ T cells exhibit tissue residency, lower proliferation capacity, and epigenetic scars of exhaustion, while CMV-specific CD8⁺ T cells exhibit effector memory and higher proliferation capacity.

The majority of HIV-1-infected cells in the gut are Trms

We aimed to interrogate how HIV-1-infected CD4⁺ T cells survive and persist in the gut. To identify HIV-1-infected cells, we mapped ATAC-seq reads and RNA-seq reads to a compendium of clade B HIV-1 sequences and identified HIV-1 DNA and HIV-1 RNA, respectively. We have previously determined the sensitivity of HIV-1 DNA detection (28.4%) and HIV-1 RNA detection (81.3%) by mapping HIV-1 DNA and RNA from HIV-1⁺, stably integrated Jurkat cell lines, at 2 HIV-1 read per cell threshold^14,65. Because of the sparsity of HIV-1 DNA captured by single-cell ATAC-seq, this method cannot determine the intactness of HIV-1 DNA. HIV-1 DNA⁺ cells determined herein likely harbored defective proviruses⁶⁶.

We identified 99 HIV-1-infected cells, including 54 HIV-1 DNA⁺ RNA⁻ cells and 45 HIV-1 RNA⁺ cells, from ten PLWH participants (Figure 6A, Figure S6B–S6D, Table S1, Table S2, Table S5). We pooled HIV-1 DNA⁺ RNA⁻ cells and HIV-1 RNA⁺ cells as HIV-1-infected cells to ensure statistical rigor for analysis. We found that 0.02%–0.74% (201–7,362 cells/million) gut CD4⁺ T cells were HIV-1⁺ (Figure S6A, Table S2), reflecting the size of the reservoir measured in blood CD4⁺ T cells [1.2–304.3 cells/million, as measured by intact provirus DNA assay (IPDA)] (Spearman correlation coefficient 0.51, P = 0.14) (Table S1).

We found that the majority (80.8%) of HIV-1-infected cells were Trms, given that 54.1% of uninfected CD4⁺ T cells were Trms in PLWH (Figure 6A, 6B). While many HIV-1-infected cells (64.6%) were detected from two participants (017 and 023, Figure S6A), enrichment of HIV-1-infected cells relative to uninfected cells in Trms was observed across PLWHs (Figure 6C).

HIV-1 persists in BACH2-shaped, long-lived, tissue resident memory cells

We compared HIV-1-infected CD4⁺ T cells versus uninfected cells in PLWH under ART. At the chromatin level, HIV-1-infected cells had higher BACH2, AP-1, and RORγt motif accessibility (Figure 6D). At the transcription level, HIV-1-infected cells had higher AP-1, long-lived memory cytokine receptor IL7R, and Th17 gut-homing receptor CCR6 gene expression (Figure 6E). At the protein level, HIV-1-infected cells had higher tissue residency (CD103, CD69, and CD49a), Th17 KLRB1, chemokine receptor CCR5, and long-lived memory IL-7R protein expression (Figure 6F). Indeed, tissue residency molecule ITGAE^49,50 (Figure 6G), Th17 gut-homing receptor CCR6⁶⁷ (Figure 6H), and long-lived memory IL7R⁶⁸ (Figure S6E) had higher chromatin accessibility at gene locus, gene expression, and protein expression in HIV-1-infected cells. Furthermore, HIV-1-infected cells were significantly enriched in TNF signaling response [GSEA leading edge genes (TNFAIP3, FOS, JUN)] (Figure S6F), IL-17 signaling pathway, and Th1 and Th17 differentiation (Figure S6G). Overall, HIV-1-infected CD4⁺ cells in the gut exhibit BACH2-shaped Trm programs, AP-1-shaped TNF responses, Th1 and Th17 polarization, and IL-7R expression.

To determine whether mechanisms of persistence of HIV-1-infected cells are different between blood and gut, we compared HIV-1-infected cells in the gut (identified in this study) with HIV-1-infected cells from the peripheral blood of six PLWH under ART identified in our previous study¹⁴. At the chromatin level, gut HIV-1-infected CD4⁺ T cells had higher BACH2, AP-1, and TCF7 motif accessibility, while blood HIV-1-infected CD4⁺ T cells had higher IRF5 and IRF9 motif accessibility (Figure 6I). At the transcription level, gut HIV-1-infected CD4⁺ T cells had higher BACH2 and Th17 gut-homing receptor CCR6 gene expression, while blood HIV-1-infected cells had higher AP-1 and IRF1 expression (Figure 6J). At the protein level, gut HIV-1-infected CD4⁺ T cells had higher tissue residency (CD49a, CD69, and CD103) and CCR6 protein expression, while blood HIV-1-infected CD4⁺ T cells had higher activation (HLA-DR-DP-DQ) gene expression (Figure 6K). Overall, HIV-1-infected cells in the gut exhibit BACH2-shaped tissue residency and Th17 programs, distinct from HIV-1-infected cells in the blood which exhibit IRF-shaped activation and not BACH2-shaped programs.

BACH2 ablation reduces of long-lived memory CD4⁺ and CD8⁺ T cells

Central memory T cells are known for their superior capacity for self-renewal and long-lived survival compared to effector memory T cells^69,70. We reasoned that if BACH2 restrains effector function and promotes long-lived memory, when gut CD4⁺ and CD8⁺ T cells undergo T cell activation, BACH2 ablation will shift gut T cells from long-lived central memory to short-lived effector memory. We performed CRISPR-Cas9-mediated BACH2 ablation in human primary cells isolated from the lamina propria of colon excisions from 9 HIV⁻ donors (Table S1). Nontargeting gRNA served as a negative control. BACH2 protein expression was abrogated 3 days after ablation (Figure S7A). We examined the proportions of IL-7R expression and Tcm (CD45RO⁺ CCR7⁺) versus Tem (CD45RO⁺ CCR7–) 4 days after BACH2 ablation (Figure 7A, Figure S7B). We found that BACH2 ablation decreased long-lived memory IL-7R expression, decreased Tcm, and increased Tem in gut primary CD4⁺ and CD8⁺ T cells, when compared with nontargeting controls (Figure 7B, 7C). Consistent with previous studies^37,38, in vitro BACH2 ablation reduces the maintenance of long-lived memory T cells.

Figure 7. — **(A)** Experimental scheme of BACH2 ablation of primary human gut cells. Cells from the lamina propria layer of human colon were isolated (n = 9 donors), activated with CD3/CD28, and subjected to CRISPR-Cas9-mediated BACH2 ablation. Nontargeting (NT) gRNA served as a control. BACH2 ablation efficiency was measured by western blot 3 days after CRISPR-Cas9-gRNA nucleofection. (**B, C**) Flow cytometry measurement of the proportion of IL-7R (CD127) expression, central memory (CD45RO⁺ CCR7⁺), and effector memory (CD45RO⁺ CCR7⁻) CD4⁺ (B) and CD8⁺ (C) gut T cells 4 days after CRISPR-Cas9-mediated BACH2 ablation. Each line represents a biological replicate from uninfected donors. (D) Experimental scheme of HIV-1 infection of primary human gut CD4⁺ T cells (n = 4 donors). Cells from the lamina propria layer of human colon was isolated, activated with CD3/CD28, and infected with HIV-1-dEnv-dNef-GKO pseudotyped with an R5 envelope (JR-FL). (E, F) Flow cytometry measurement HIV-1 infectivity 2 days (E) and 7 days (F) post infection. Each line represents a biological replicate from uninfected donors. CD4-Trm was defined as CD49a⁺CD69⁺ double positive CD4⁺ T cells. CD8-Trm was defined as CD103⁺CD8⁺ T cells. * P < 0.05, ** P < 0.01, *** P < 0.001, Wilcoxon signed-rank test. See also Figure S7.

HIV-1 preferentially infects and persists in Trms in the gut, particularly CCR6⁺ CD161⁺ Th17 Trm

To validate whether HIV-1 preferentially infects and persists in CD4-Trm T cells, we infected primary gut cells isolated from the lamina propria of colon excisions from four HIV⁻ donors (Table S1) with a single-round HIV-1-dEnv-dNef-GKO reporter⁷¹ pseudotyped with a R5 envelope (JR-FL) after CD3/CD28 activation. This HIV-1 reporter contains intact Vpr, Vpu, and Vif and can cause viral cytopathic effects in infected cells⁷², but does not have functional Nef and thus does not effectively downregulate CD4 expression. We examined whether HIV-1 preferentially infects CD4-Trm by measuring the proportion of HIV-1-infected cells 2 days after infection, and whether HIV-1 preferentially persists in CD4-Trm by measuring the proportion of HIV-1-infected cells 7 days after infection, when viral cytopathic effect should have killed most infected cells (Figure 7D, Figure S7C). CD4⁺ T cells expressing both CD49a and CD69 were defined as Trms. We found that the proportion of HIV-1⁺ Trms was significantly higher than those in non-Trms at both 2 and 7 days after infection, indicating preferential HIV-1 infection and persistence in Trms (Figure 7E, 7F).

Presumably, HIV-1 preferentially infects Th1 because of higher protein expression of CCR5 (HIV-1 entry co-receptor)^73–75. We tested whether HIV-1 preferentially infects or persists in Th17 (CCR6 and CD161 expression) versus Th1 (CCR5 and CXCR3 expression). We found that the proportion of HIV-1-infected cells was higher in CCR6⁺ Trms and CD161⁺ Trms both 2 days and 7 days after infection (Figure 7E, 7F), indicating preferential infection and persistence of HIV-1 infection in Th17. Although HIV-1 infection is thought to be enriched in CCR5⁺CD4⁺ T cells^73–75, we found that the proportion of HIV-1-infected cells in CCR5⁺ Trms and CXCR3⁺ Trms was not higher than CCR5⁻ Trms and CXCR3⁻ Trms (Figure 7E, 7F). Of note, the proportion of HIV-1-infected cells in CCR6⁺ CD4⁺ T cells and CD161⁺ CD4⁺ T cells (including both Trms and non-Trms) was still higher than those in CCR6⁻ CD4⁺ T cells and CD161⁻ CD4⁺ T cells (Figure S7D, S7E), indicating that CCR6 and CD161 were markers for HIV-1⁺ CD4⁺ T cells in the gut. Proportions of HIV-1-infected cells were neither higher in CCR5⁺ CD4⁺ T cells (Figure S7F) nor in CXCR3⁺ CD4⁺ T cells (Figure S7G). Consistent with previous studies^19,76, in vitro validation of HIV-1 infection of primary gut CD4⁺ T cells shows that HIV-1⁺ preferentially infects and persists gut CCR6⁺ T cells (Figure S7D), particularly gut CCR6⁺ Trms (Figure 7E, 7F).

DISCUSSION

Our single-cell multiomic profiling, supported by orthogonal in vitro validations, revealed that HIV-1 takes advantage of physiological gut mucosal T cell homeostasis programs to establish long-term persistence. We identified BACH2 as a key transcription regulator of gut mucosal T cell homeostasis that may provide survival benefit for HIV-1-infected cells to persist long-term. The effector function and clonal expansion of effector memory T cells is driven by interferon responses (IRF). However, these rapidly expanding effector memory T cells are short-lived by activation-induced cell death to restrain excessive inflammation when the antigen is removed. BACH2 restrains effector function and promotes long-lived memory.

Our study identifies how HIV takes advantage of the human immune system to persist in tissues through BACH2-shaped tissue residency programs in the gut, revealing a sharp blood versus gut dichotomy. In peripheral blood, HIV-1-infected cells are predominantly short-lived effector memory²² Th1¹⁶ (expressing CCR5^73–75, cytotoxic¹², or activation marker HLA-DR⁷⁷). In the gut, Th17 cells that express gut-homing CCR6 (the receptor of microbial-induced CCL20 (CCR6 ligand) expression) have been previously reported as a key HIV-1 reservoir^19,76,78,79. We found that HIV-1-infected cells in the gut are BACH2-shaped, long-lived tissue resident memory, Th17 having restrained effector function. Indeed, BACH2 maintains HIV-1-infected cells in gut Trms but not in the blood. Furthermore, HIV-1-specific CD8⁺ T cells, which are mainly Trms in the gut, have epigenetic scars of exhaustion. In comparison, CMV-specific CD8⁺ T cells in the gut are mainly Tems with no evidence of exhaustion. Our study identifies the importance of tissue immune homeostasis programs to guide HIV cure strategies and nominates BACH2 as a potential therapeutic target.

Limitations of the Study

A major limitation of the study was the low sensitivity of HIV-1 DNA capture by ATAC-seq and the inability to infer proviral genome intactness of HIV-1-infected cells. Additionally, TREK-seq TCR capturing efficiency is less efficient than 5’-based method in ECCITE-Seq that we previously reported¹², and this method did not yield a sufficient number of HIV-1⁺ T cell clones for analysis. Moreover, inferring HIV-1-specificity CD8⁺ T cells was limited by the availability of known antigen-specific TCRβ CDR3 sequences in public available databases, and the low number of HIV-1-specific CD8⁺ T cells identified from PLWH under suppressive ART limited the conclusions that could be drawn about these cells. Furthermore, the role of BACH2 in maintaining clones of infected cells, an essential component of HIV-1 reservoir, remains to be evaluated. In addition, although BACH2 transcription factor accessibility is increased in HIV-1⁺ cells compared to HIV⁻ cells in the gut, BACH2 was not transcriptionally upregulated in HIV-1⁺ cells. Further investigation is required to emphasize the distinct role of BACH2, relative to other factors, in supporting HIV-1 persistence in the gut. Lastly, nine out of ten PLWH studied were male, so the generalizability of our conclusions to people of different sex remains to be determined, although part of our results have been confirmed by in vitro validation in female participants.

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Ya-Chi Ho (ya-chi.ho@yale.edu).

Materials availability

This study did not generate new unique reagents.

Data and code availability

Single-cell DOGMA-seq and TREK-seq data have been deposited in the Gene Expression Omnibus (GEO) and are publicly available from the date of publication. The accession number is listed in the Key Resource Table.
Original western blot images have been deposited at Mendeley Data and are publicly available from the date of publication. The DOI is listed in the Key Resources Table.
All codes used to generate results have been deposited at Zenodo and are publicly available from the date of publication. The DOI is listed in the Key Resources Table.
Any additional information required to reanalyze the data reported in this paper is available from the lead contacts upon request.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
TotalSeq-A Human Universal Cocktail V1.0	BioLegend	CAT # 399907; RRID: AB_2888692
TotalSeq-A CD197, clone G043H7	BioLegend	CAT # 353247; RRID: AB_2750357
TotalSeq-A anti-human Hashtag 1	BioLegend	CAT # 394601; RRID: AB_2750015
TotalSeq-A anti-human Hashtag 2	BioLegend	CAT # 394603; RRID: AB_2750016
TotalSeq-A anti-human Hashtag 3	BioLegend	CAT # 394605; RRID: AB_2750017
TotalSeq-A anti-human Hashtag 4	BioLegend	CAT # 394607; RRID: AB_2750018
TotalSeq-A anti-human Hashtag 5	BioLegend	CAT # 394609; RRID: AB_2750019
TotalSeq-A anti-human Hashtag 6	BioLegend	CAT # 394611; RRID: AB_2750020
Human TruStain FcX	BioLegend	CAT # 422302; RRID: AB_2818986
Mouse Anti-Human CD4, clone SK3	BD Biosciences	CAT # 612748
Anti-human CD69, clone FN50	BioLegend	CAT # 310938; RRID: AB_2562306
Anti-human CD49a, clone TS2/7	BioLegend	CAT # 328312; RRID: AB_2566271
Anti-human CD161, clone HP-3G10	BioLegend	CAT # 339958; RRID: AB_2941516
Anti-human CD194, clone L291H4	BioLegend	CAT # 359408; RRID: AB_2562428
Anti-human CD195, clone J418F1	BioLegend	CAT # 359110; RRID: AB_2562653
Anti-human CD196, clone G034E3	BioLegend	CAT # 353424; RRID: AB_2563868
Mouse Anti-Human CD183, clone 1C6	BD Biosciences	CAT # 565223
Anti-human CD3, clone SK7	BioLegend	CAT # 344818 RRID: AB_10644011
Mouse Anti-Human CD4, clone SK3	BD Biosciences	CAT # 612748
Anti-human CD8, clone SK1	BioLegend	CAT # 344748 RRID: AB_2629583
Anti-human CD103, clone Ber-ACT8	BioLegend	CAT # 350222 RRID: AB_2629650
Mouse Anti-Human CD45RO, clone UCHL1	BD Biosciences	CAT # 564291
Anti-human CD127, clone A019D5	BioLegend	CAT # 351336 RRID: AB_2563636
Anti-human CD197, clone G043H7	BioLegend	CAT # 353214 RRID: AB_10915474
BACH2 (D3T3G) Rabbit mAb	Cell Signaling	CAT# 80775
β-Actin (D6A8) Rabbit mAb	Cell Signaling	CAT# 8457
Anti-rabbit IgG, HRP-linked Antibody	Cell Signaling	CAT# 7074
Bacterial and virus strains
HIV-GKO	Addgene	CAT # 112234
R5-tropic JR-FL envelope	NIH HIV Reagents Program	CAT # ARP-4598
Biological samples
Colon biopsy samples from HIV-BEAT Cohort (Table S1)	This study and Papasavvas et al.	N/A
PBMC from New York Blood Center	This study	N/A
Colon biopsy samples from Yale Pathology (Table S1)	This study	N/A
Chemicals, peptides, and recombinant proteins
Digitonin 5%	ThermoFisher	CAT # BN2006
xGen Lockdown Reagents	IDT	CAT # 1072281
Protector RNase Inhibitor	Sigma-Aldrich	PN-3335399001
Human Cot-1 DNA	Invitrogen	CAT # 15279011
Dynabeads M-270 Streptavidin	Invitrogen	CAT # 65306
Collagenase II	Sigma-Aldrich	CAT # C6885-5G
Collagenase IV	Sigma-Aldrich	CAT # C5138-1G
DNase I	Sigma-Aldrich	CAT # DN25-1G
Ca2⁺/Mg2⁺ free PBS	Corning	CAT # 21-040-CV
HiFBS	Gibco	CAT # A5670801
DDT	Sigma-Aldrich	CAT # D9779-1G
EDTA	Invitrogen	CAT # 15575-038
Penicillin-Streptomycin	Gibco	CAT # 15140122
Zosyn	Novaplus	CAT # 44567-801-10
Fungizone	Corning	CAT # 30-003-CF
HEPES	Gibco	CAT # 15630080
CLSPA Collagenase	Worthington	CAT # LK003240
Human AB serum	Gemini Bioproducts	CAT # 100-512
Recombinant human interleukin-2	Conn Stem	CAT # C1002
Dynabeads Human T-activator CD3/CD28	Gibco	CAT # 11131D
UltraComp eBeads Plus Compensation Beads	Invitrogen	CAT # 01-3333-42
Critical commercial assays
Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle	10x Genomics	PN-1000283
Chromium Nuclei Isolation Kit with RNase Inhibitor	10x Genomics	PN-1000494
Chromium Next GEM Chip J Single Cell	10x Genomics	PN-1000230
Dual Index Kit TT Set A	10x Genomics	PN-1000215
Single Index Kit N Set A	10x Genomics	PN-1000212
3ʹ Feature Barcode Kit	10x Genomics	PN-1000262
EasySeq Dead Cell Removal Annexin V kit	STEMCELL	CAT # 17899
EasySep Release Human CD3 Positive Seleciton Kit	STEMCELL	CAT # 17751
Live/Dead Fixable Blue Dead Cell Stain Kit	Invitrogen	CAT # L23105
Kapa Hifi Hotstart Readymix	Kapa Biosystems	CAT # KK2602
SPRIselect 5 mL reagent kit	Beckman Coulter	CAT # B23317
Alt-R S.p. Cas9 Nuclease V3	IDT	CAT # 1081059
P3 Primary Cell 4D-Nucleofector X Kit S	Lonza	CAT # V4XP-3032
4D-Nucleofector X Unit	Lonza	CAT # AAF-1003X
Deposited data
DOGMA-seq and TREK-seq sequencing data	This study	GEO: GSE299348
Original western blot images	This study	doi: 10.17632/4g722x4jfg.1
Oligonucleotides
ADT: * indicates phosphorothioate: CCTTGGCACCCGAGAATTCC	Mimitou et al.⁴⁴	N/A
HTO: * indicates phosphorothioate: GTGACTGGAGTTCAGACGTGTGCTC	Mimitou et al.⁴⁴	N/A
SIPCR: Dual index common primer for ADT/HTO	This study, see Data S1	N/A
RPX: Dual index common primer for ADT	This study, see Data S1	N/A
D7X: Dual index common primer for HTO	This study, see Data S1	N/A
TREK-seq sample indexing primers	This study, see Data S1	N/A
TREK-seq primers	This study and Miller et al.⁴⁵, see Data S1	N/A
BACH2 Alt-R CRISPR-Cas9 sgRNA: 5’-ACGTGACTTTGATCGTGGAG-3’	IDT	N/A
Non-targeting Alt-R CRISPR-Cas9 sgRNA: 5’-ACGGAGGCTAAGCGTCGCAA-3’	Prelli Bozzo et al.¹²² IDT	N/A
Software and algorithms
CellRanger-Arc v2.0	10x Genomics	https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/pipelines/latest/installation#download
CellRanger v5.0.1	10x Genomics	https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest
STAR v2.7	Dobin et al.⁹⁸	https://github.com/alexdobin/STAR
Bowtie 2 v2.4.2	Langmead and Salzberg⁹⁹	https://github.com/BenLangmead/bowtie2
Cutadapt v4.2	Martin¹¹⁴	https://cutadapt.readthedocs.io/en/stable/installation.html
Seqtk v1.3	https://github.com/lh3/seqtk	https://anaconda.org/bioconda/seqtk
Iso-Seq v3.8.2	PacBio	https://github.com/PacificBiosciences/pbbioconda
SAMtools v1.16.1	Li et al.¹²³	https://anaconda.org/bioconda/samtools
IGV v2.16	Robinson et al.¹²⁴	https://software.broadinstitute.org/software/igv/download
Seurat v5.0.0	Hao et al.⁸⁸	https://cran.r-project.org/web/packages/Seurat/index.html
Signac v1.9.0	Stuart et al.⁸⁹	https://cran.r-project.org/web/packages/Signac/index.html
Harmony v1.1.0	Korsunsky et al.⁹⁶	https://portals.broadinstitute.org/harmony/articles/quickstart.html
Clustree v0.5.0	Zappia and Oshlack⁹⁷	https://cran.r-project.org/web/packages/clustree/index.html
MACS2 v2.2.7.1	Zhang et al.⁹³	https://github.com/macs3-project/MACS
scDblFinder v1.12.0	Germain et al.⁹¹	https://bioconductor.org/packages/release/bioc/html/scDblFinder.html
Demuxafy v3.0.0	Neavin et al.⁹²	https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/Installation.html
EnsDb.Hsapiens.v86 v2.99.0	Rainer¹²⁵	https://bioconductor.org/packages/release/data/annotation/html/EnsDb.Hsapiens.v86.html
BSgenome.Hsapiens.UCSC.hg38 v1.4.5	Team TBD¹²⁶	https://bioconductor.org/packages/release/data/annotation/html/BSgenome.Hsapiens.UCSC.hg38.html
org.Hs.eg.db v3.16.0	Carlson et al.¹⁰⁸	https://bioconductor.org/packages/release/data/annotation/html/org.Hs.eg.db.html
TFBSTools v1.36.0	Tan and Lenhard¹²⁷	https://bioconductor.org/packages/release/bioc/html/TFBSTools.html
chromVAR v1.20.2	Schep et al.⁹⁴	http://bioconductor.org/packages/release/bioc/html/chromVAR.html
JASPAR2022 V0.99.7	Castro-Mondragon et al.⁹⁵	https://bioconductor.org/packages/release/data/annotation/html/JASPAR2022.html
Scriabin v0.0.0.9	Wilk et al.⁵⁵	https://github.com/BlishLab/scriabin
NicheNetr v1.1.1	Browaeys et al.⁵⁶	https://github.com/saeyslab/nichenetr
ComplexHeatmap v2.14.0	Gu et al.¹²⁸	https://bioconductor.org/packages/release/bioc/html/ComplexHeatmap.html
ACAT v0.91	Liu et al.¹⁰¹	https://github.com/yaowuliu/ACAT
FigR v0.1.0	Kartha et al.¹⁰³	https://buenrostrolab.github.io/FigR/
cisTopic v0.3.0	Gonzales-Blas et al.¹⁰⁴	https://github.com/aertslab/cisTopic
clusterProfiler v4.6.2	Xu et al.¹⁰⁶	https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html
fGSEA v1.22	Korotkevich et al.¹⁰⁹	https://bioconductor.org/packages/release/bioc/html/fgsea.html
msigdbr v7.5.1	Dolgalev¹¹²	https://cran.r-project.org/web/packages/msigdbr/vignettes/msigdbr-intro.html
pRESTO v0.7.1	Heiden et al.¹¹⁵	https://presto.readthedocs.io/en/stable/install.html
Change-O v1.2	Gupta et al.¹¹⁷	https://changeo.readthedocs.io/en/stable/install.html#
R version 4.2.0	R Core Team	https://www.r-project.org/
FlowJo V10.9.0	FlowJo	https://www.flowjo.com/solutions/flowjo/downloads
Analysis scripts	This study	https://doi.org/10.5281/zenodo.15793852

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STAR METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Human subjects

The demographics (including age, sex, race, ethnicity) of 10 de-identified people living with HIV-1 and five HIV⁻ study participants are detailed in Table S1. The study was reviewed and approved by the Institutional Review Board (IRB). All participants provided informed consent. Although this study involved Peg-IFN-α2b treatment in some participants, the samples used in this current study did not include Peg-IFN-α2b-treated time points.

Primary cell cultures

For in vitro validation, we obtained non-disease colon tissues from 13 de-identified individuals (including 5 males and 8 females) from Yale Tissue Services (Department of Pathology, Yale IRB approved) (Table S1). These participants were scheduled for elective colectomy. Participants having ongoing inflammation (such inflammatory bowel diseases) were excluded. In participants having cancer or diverticulitis, only non-cancer and non-disease parts of the colon were obtained. Tissues were preserved at 4°C in IEL buffer (1X Ca²⁺/Mg²⁺ free PBS (Corning, cat no. 21–040-CV), 5% HiFBS (Gibco, cat.no. A5670801), 10mM DTT (Sigma-Aldrich, cat no. D9779–1G), 5mM EDTA (Invitrogen, cat no. 15575–038), 1% Pen Strep (from 10,000 U/mL) (Gibco, cat no. 15140122), 500 μg/mL Zosyn (Novaplus, cat no. 44567-801-10), 1.25 μg/mL Fungizone (Corning, cat no. 30–003-CF) and processed on the same day of collection.

Following isolation of lamina propria lymphocytes (LPL) from these primary colon tissues, cells were cultured at 37°C in gut culture medium (RPMI + 10% human AB serum [Gemini Bioproducts, cat no.100–512] + 1% Pen Strep, Zosyn (500 μg/mL), Fungizone (1.25 μg/mL), and HEPES (10 mM)), in the presence of recombinant human interleukin-2 (rIL-2) (10 U/mL; Conn Stem, cat no. C1002). Dead cells were removed by immunomagnetic negative selection using EasySep Dead Cell Removal (Annexin V) Kit (STEMCELL Technologies, catalog no. 17899) following vendor-recommended manuals. Then, cells were stimulated for 1 day by Dynabeads Human T-activator CD3/CD28 (Gibco, catalog no. 11131D) at a bead-to-cell ratio of 1:2. Cells were cultured at 1 million cells/mL. Culture medium was changed every 2 days with fresh gut culture medium supplemented with 10 U/mL rIL-2.

METHOD DETAILS

Sample preparation for DOGMA-seq

A total of 12–16 rectal tissue biopsies were collected by Fiber optically guided flexible sigmoidoscopy. Rectal biopsy samples from the same individual were processed on the same day into single-cell suspensions and pooled, based on a previously established protocol⁴⁶. Briefly, colon biopsy samples were treated with collagenase II (Sigma-Aldrich, catalog no. C6885–5G) or collagenase IV (Sigma-Aldrich, catalog no. C5138–1G) and suspended by sequentially passing through 16-gauge, 18-gauge, and 20-gauge needles and 40 μm filters, and viably frozen into aliquots. On the day of single-cell DOGMA-seq, aliquots of >2.5 million viably frozen cells isolated from gut biopsy were thawed. Dead cells were removed by immunomagnetic depletion using EasySep Dead Cell Removal Annexin V kit (STEMCELL Technologies, catalog no. 17899) following vendor-recommended protocols. To pool samples, gut biopsy cells from each participant were each stained with uniquely barcoded TotalSeq-A hashing antibodies (BioLegend) and incubated at 4°C for 30 minutes. Cells were washed with 1 mL wash media and pelleted (500 g for 5 minutes at 4°C), for a total of 3 washes. Cells were then resuspended in 1 mL wash media. Each sample was then split into 2 aliquots. For the first aliquot, 10% cells were used to determine whole gut cell subsets and were pooled and demultiplexed by independent hashing antibodies (pool 1: participants 035, 037, 040; pool 2: participants 008, 012, 015; pool 3: participants 017, 023, 027, 029). For the second aliquot, 90% cells from each sample were used for CD3 positive selection following vendor-recommended protocols (STEMCELL EasySep Release Human CD3 Positive Selection Kit, catalog no. 17751) to isolate T cells. These samples were not pooled. Each biopsy sample from uninfected participants (351, 357, 360, 361, 363) was prepared independently and was neither stained with hashing antibodies to pool nor treated with CD3 Positive Selection Kit (whole gut cells were prepared).

Each sample was stained with a panel of 155 cell surface protein and 9 isotype control barcoded antibodies included in TotalSeq-A Human Universal Cocktail (V1.0, BioLegend, catalog no. 399907). We additionally stained for CD197 (CCR7, clone G043H7, BioLegend, catalog no. 353247) that was not included in the Universal Cocktail (for a total of 156 barcoded antibodies for surface proteins). All barcoded antibodies were prepared following recommended protocols. Each sample (~5 × 10⁵ cells) was resuspended in 21.5 μL staining buffer and incubated with 2.5 μL Human TruStain FcX (BioLegend, catalog no. 422302) at 4°C for 10 minutes. Next, 1 μL of CD197 (CCR7) was added, followed by incubation at 37°C for 10 minutes then chilled on ice for 5 minutes. Next, 25 μL of TotalSeq-A Human Universal Cocktail (1 test) was added to each sample at 4°C for 30 minutes. Cells were washed with 1 mL wash media and pelleted (500 g for 5 minutes at 4°C) for a total of 3 washes.

After antibody staining, cells were permeabilized without fixation with 0.01% digitonin solution according to the DOGMA-seq⁸⁰ protocol. Digitonin lysis buffer (0.01% DIG, 20 mM Tris-HCl pH 7.4, 150 mM NaCl, 3 mM MgCl₂ and 2 U/μL RNase inhibitor) and digitonin wash buffer (20 mM Tris-HCl pH 7.4, 150 mM NaCl, 3 mM MgCl₂ and 1 U/μL RNAse inhibitor) were prepared and chilled on ice. Antibody-stained cells were resuspended in 100 μL digitonin lysis buffer on ice for 5 minutes then washed with 1 mL digitonin wash buffer and pelleted (500 g for 5 minutes at 4°C). Cells were then resuspended in 1 mL digitonin wash buffer and counted using Trypan blue staining to verify permeabilization. A final concentration of ~8,000 cells per μL (in 5 μL) was prepared to load onto Chromium Next GEM chip.

DOGMA-seq library preparation and sequencing

Cells were processed according to the Chromium Next GEM Single Cell Multiome ATAC + Gene Expression protocol (10x documentation GC000338 Rev A) with modifications described in the original DOGMA-seq protocol⁴⁴. We chose digitonin (DIG) instead of the low-loss lysis (LLL) for cell permeabilization for its lower mitochondrial DNA capture (0.04% versus 25.3%) and to increase ATAC-seq-based HIV-1 DNA capture. The digitonin protocol showed superior performance in a separate benchmarking study⁸¹. See Data S1 for 10x protocol modifications and all primers used (ADT and HTO additive primers, SI-PCR, RPI-x, and D7x primers).

Each sample was split for multiple GEM generations and each was sequenced on NovaSeq 6000 S4 flow cell (n = 2 for CD3 positive selected cells from each HIV-1⁺ participants 008, 012, 015, 017, 023, 027, 029, 035, 037, 040, n = 3 for total gut cells from each uninfected participants 351, 357, 360, 361, 363, and n = 3 for hashing antibody stained and pooled total gut cells from HIV-1⁺ participants, for a total of 38 10x runs). Per run, approximately 10,000 to 40,000 cells were loaded into the 10x Genomics Chromium Controller with a final barcoded cell recovery of approximately 3,600 to 20,000. Libraries were sequenced on NovaSeq 6000 with a target of > 50,000 ATAC read pairs (500 million total per run), > 40,000 RNA read pairs (400 million total per run), and > 5,000 ADT/HTO barcode reads (100 million total per run) for a total of > 100,000 reads per cell. The final libraries were quantified using a Qubit dsDNA HS Assay Kit (Invitrogen) and a High Sensitivity D1000 DNA kit on Agilent 2200 TapeStation system.

Because our goal was to capture rare HIV-1 reads, our sequencing specifications were at-least twice the recommended sequencing requirements for Single Cell Multiome (10x Genomics, 25,000 ATAC read pairs/cell and 20,000 RNA read pairs/cell) and for CITE-seq (10x Genomics, 20,000 RNA read pairs/cell and 5,000 ADT reads/cell) to ensure good sequencing saturation and to increase sensitivity of HIV-1 read detection.

Comparing collagenase treatment in PBMC

Peripheral blood mononuclear cells (PBMC) from de-identified uninfected individuals (New York Blood Center) were treated with collagenase II (Sigma-Aldrich, catalog no. C6885–5G) and collagenase IV (Sigma-Aldrich, catalog no. C5138–1G) separately and prepared for DOGMA-seq following the same protocol as described above with the following modifications:

For each collagenase II and IV treatments, one vial of 10 million PBMCs was thawed and evenly split into two aliquots (treated and mock) then pelleted (500 g for 5 minutes at 4°C). For mock, cells were resuspended in 10mL media (RPMI + 10% HiFBS) at 37°C for 30 minutes. For treatment, cells were resuspended in collagenase digestion media (RPMI + 10% HiFBS, 500μg/mL collagenase (II or IV), 100 μL DNase I (10,000U/mL)) and incubated at 37°C for 30 minutes. Following digestion, dead cells were removed by immunomagnetic depletion (STEMCELL Technologies, catalog no. 17899) following vendor-recommended protocols. Cells from both mock and treatment groups were each evenly split into 3 aliquots, and each aliquot was stained with uniquely barcoded TotalSeq-A hashing antibodies (BioLegend) and incubated at 4°C for 30 minutes. Hashing antibody-stained cells from all 6 aliquots (3 treatment, 3 mock) were then washed with 1 mL wash media and pelleted (500 g for 5 minutes at 4°C) for a total of 3 washes before pooling with equal cell numbers per aliquot. The pooled samples were then stained with TotalSeq-A Human Universal Cocktail (V1.0, BioLegend, catalog no. 399907) and CD197 (CCR7, clone G043H7, BioLegend, catalog no. 353247), followed by cell permeabilization with 0.01% digitonin solution, using the same procedures as described above. One pooled sample contained hashing antibody-stained cells from 3 mock and 3 collagenase II treated aliquots, and the other pooled sample contained hashing antibody-stained cells from 3 mock and 3 collagenase IV treated aliquots. Each pooled sample was loaded onto Chromium Next GEM chip for independent GEM generation followed by sequencing on NovaSeq 6000 S4 flow cell with the same sequencing depth specifications as detailed above for gut-isolated cells.

Enrichment of TCR transcripts by TREK-seq

The TREK-seq protocol⁴⁵ was modified to perform TCR sequencing using an aliquot of DOGMA-seq cDNA libraries. TREK-seq is a modification of a previously described TCR sequencing protocol developed for Seq-well⁸², to be compatible with 10X Genomics 3’ single-cell RNA-seq cDNA libraries. See Data S1 for TREK-seq primers used. All primers were purchased from Integrated DNA Technologies (IDT Ultramer).

Wash buffers were prepared according to xGen Lockdown reagent recommended protocol (IDT, catalog no. 1072281). Mixture of 8.5 μL xGen 2x hybridization buffer, 2.7 μL hybridization buffer enhancer, 0.4 μL PartialRead1 (50 μM), 0.4 μL PartialTSO (50 μM), and 0.5 μL human cot-1 DNA (Invitrogen, catalog no. 15279011) were added to 3.5 μL of each DOGMA-seq cDNA libraries and incubated at room temperature for 10 minutes then at 95°C for 10 minutes. Then, 1 μL mixture of biotinylated TRAC and TRBC probes (1.5 μM each) was immediately added to the mixture, vortexed briefly, spun down, and incubated at 65°C for 1 hour. Then, the remainder of the xGen Lockdown protocol was followed. Finally, the sample was eluted into 20 μL nuclease-free water.

To amplify TCRA and TCRB transcripts, five PCR mixes were prepared for each sample and briefly vortexed: 2 μL of eluted sample, 1 μL PartialRead1 (10 μM) and 1 μL PartialTSO (10 μM), 8.5 μL nuclease-free water, and 12.5 μL 2x Kapa Hifi Hotstart Readymix (Kapa Biosystems, catalog no. KK2602). PCR was performed with the following cycling conditions: 1 cycle of 95°C for 3 minutes; 25 cycles of 98°C for 40 seconds, 67°C for 20 seconds, 72°C for 1 minute; 1 cycle at 72°C for 5 minutes. The five reactions were pooled to a final volume of 100 μL. Fragments of >1,000 base pairs were purified using homemade purification reagents (SPRIselect protocol from Fred Hutchinson) and eluted into 15 μL nuclease-free water.

Constructing and sequencing TCR libraries

Two separate mixtures of primers, TRAV (for TCRα V region) and TRBV (for TCRβ V region), were prepared to 10 μM each. Two PCR mixes were prepared for each purified sample: 4 μL purified product, 6μL water, 2.5 μL TRAV or TRBV mixture, and 12.5 μL 2x Kappa Readymix. PCR was performed with the following cycling conditions: 1 cycle of 95°C for 5 minutes; 1 cycle of 55°C for 30 seconds; 1 cycle of 72°C for 2 minutes. Fragments of >1,000 base pairs were purified as previously described and eluted into 11 μL nuclease-free water.

Complete sequencing handles were added to purified products and split into four PCR mixtures per sample: 2.5μL purified product, 1 μL SI_PCR_P5 (P5 sequence, 5 μM. Note: for pooling, SI_PCR_P5 primers must be unique for each cDNA library), 1 μL UPS2-N70x (P7 sequence, 5 μM), 9μL water, 12.5 μL 2x Kapa Readymix. PCR was performed with the following cycling conditions: 1 cycle of 95°C for 5 minutes; 1 cycle of 95°C for 2 minutes; 15 cycles of 95°C for 30 seconds, 60°C for 30 seconds, 72°C for 90 seconds; 1 cycle of 72°C for 5 minutes. All four reactions per sample were pooled and fragments of >1,000 base pairs were purified into 15 μL nuclease-free water.

All TCRα and TCRβ libraries, each with unique SI_PCR_P5 primer per cDNA library, were pooled at equimolar concentration. Libraries (20μL at concentration of > 2 nM) were sequenced using MiSeq with target fragment size between 1,000 bp to 1,500 bp and a density target of roughly 450K/mm2, to generate 10 – 12 million clusters per run, following a custom read configuration of 28bp for Read 1 (10x cell barcode and UMI), 150bp for Index 1 (TCR region), 8bp for Index 2 (SI_PCR_P5 sample identifier), and no Read 2. Sequencing primers (aTCRseq, bTCRseq) were added for Index 1 at a final concentration of 2.5 μM. Pooled samples were demultiplexed by Index 2.

Gut biopsy lamina propria cell isolation

For in vitro validation, we collected colon tissue samples from non-disease and non-cancer sites from 13 de-identified individuals (Yale Tissue Services, Department of Pathology, Yale IRB approved). For consistency, we isolated lamina propria lymphocytes (LPL) from these primary colon tissue. The protocol was modified from previous publications^47,83–85. Considering the effect of collagenase II and IV on surface protein staining, a collagenase with increased purity (CLSPA collagenase)⁴⁷ was used. To remove intestinal intraepithelial lymphocytes (IELs), the tissue was transferred into 20 mL of IEL buffer (1X Ca²⁺/Mg²⁺ free PBS, 5% HiFBS, 10mM DTT, 5mM EDTA, 1% Pen Strep (from 10,000U/mL), 500 μg/mL Zosyn, 1.25 μg/mL Fungizone) and incubated at 37°C for 30 minutes at 100 rpm, followed by vortexing for 10 seconds. IEL removal procedure was repeated for a second time. The tissue was then placed on a petri dish to separate the mucosa layer from the muscle and fat layers using forceps and surgical scalpel. The mucosa layer was minced into very small pieces with blunt end safety scissors and transferred into gentleMACS C Tube (Miltenyi Biotec, cat no. 130-093-237). Next, 6 mL of digestion media (10mM HEPES (Gibco, cat no. 15630080), 250 U/mL CLSPA collagenase (Worthington, cat no. LK003240), 10 μg/mL DNase I (Sigma-Aldrich, cat no. DN25–1G), 1% Pen Strep (from 10,000 U/mL), 500 μg/mL Zosyn, 1.25 μg/mL Fungizone, topped up with RPMI) was transferred into the C Tube. Minced mucosa was further dissociated using a GentleMACS dissociator with pre-set program “m-intestine-1” at RT. After dissociation, the tube was incubated at 37°C for 1 hour. After incubation, the sample was mechanically dissociated by aspiration using a blunt 16G needle 10X followed by aspiration using a blunt 20G needle 10X. Then, lamina propria lymphocytes were collected by passing dissociated mucosa through a 70 μm filter to remove debris and undissolved tissue.

R5-tropic HIV-1 production and infection

HIV-GKO (Addgene, catalog no. 112234, with deletions in Env and Nef) encoding the HIV-1 LTR-driven GFP was pseudotyped with an R5-tropic JR-FL envelope (NIH HIV Reagents Program, ARP-4598). Lamina propria primary gut cells were resuspended at the concentration of 0.2 million cells/100 μL in gut culture medium and infected with 10 μL concentrated HIV-GKO virus. Cells were cultured for 2 – 7 days under 1 million cells/mL in gut culture medium supplemented with 10 U/ml rIL-2.

BACH2 ablation by CRISPR-Cas9

Lamina propria primary gut cells were cultured for 2 days before CRISPR-Cas9 ablation. Dynabeads Human T-activator CD3/CD28 was removed by magnet prior to nucleofection. 80pmol of Alt-R S.p. Cas9 Nuclease V3 (IDT catalog no. 1081059) and 300 pmol of either BACH2 (IDT, 5’-ACGTGACTTTGATCGTGGAG-3’) or non-targeting (IDT, 5’-ACGGAGGCTAAGCGTCGCAA-3’) Alt-R CRISPR-Cas9 sgRNA were used per million viable gut cells. Cas9 and sgRNA were mixed and incubated at room temperature for at least 20 minutes. During incubation, P3 Primary Cell 4D-nucleofector master mix was made by mixing 16 μL P3 and 3.6 μL Supplement (Lonza catalog no. V4XP-3032) per million viable gut cells. After incubation, gut cell pellet was mixed with Cas9/sgRNA and 4D-nucleofector master mix and transferred to 16-well Nucleocuvette Vessels. 4D-Nucleofector System (4D-Nucleofector Core Unit and 4D-Nucleofector X Unit. Lonza Catalog no. AAF-1003X) was used for nucleofection with pulse code FL115. Cells were transferred to 12-well plates and cultured for 4 days before flow cytometry.

Western blot for BACH2 ablation efficiency

Protein lysates were extracted using RIPA lysis/protein extraction buffer with complete mini protease inhibitor cocktail (Roche). Lysates were qualified using Pierce BCA Assay (ThermoFisher). Western blot was performed using BACH2 (D3R3G) Rabbit mAb (1:1000 dilution), β-Actin (D6A8) Rabbit mAb (1:1000 dilution), and anti-rabbit IgG, HRP-linked antibody (1:1000 dilution), developed with Amersham ECL Select Western Blotting Detection Reagent (Cytiva). The blot was visualized by exposure to X-ray films (Fuji Film) for 30s.

Flow cytometry

Aliquots of uninfected and HIV-1-infected gut cells were collected 2- and 7-days post infection for flow cytometry. Cells were washed with PBS, stained with Live/Dead Fixable Blue Dead Cell Stain (Invitrogen, catalog no. L23105) for 10 minutes at room temperature and washed twice with 1 mL eBioscience Flow Cytometry Staining Buffer (invitrogen; catalog no. 00-4222-57). Cells were then treated with Human TruStain FcX (BioLegend; catalog no. 422302) for 10 minutes at room temperature prior to surface protein staining.

To characterize gut CD4⁺ T cells after BACH2 ablation by CRISPR Cas9, cells were stained with surface protein markers CD3 (APC/Cyanine7, clone SK7, BioLegend catalog no. 344818), CD4 (BUV737, clone SK3, BD Biosciences catalog no. 612748), CD8 (BV421, clone SK1, BioLegend catalog no. 344748), CD45RO (BUV395, clone UCHL1, BD Biosciences catalog no. 564291), CD127 (IL-7R) (PE/Dazzle, clone A019D5, BioLegend catalog no. 351336), CD197 (CCR7)(APC, clone G043H7, BioLegend catalog no. 353214)) for 30 minutes at 37 °C and 40 minutes at 4 °C. Flow cytometry data was acquired at Beckman CytoFLEX and analyzed using FlowJo v10.9.0. UltraComp eBeads Plus Compensation Beads (Invitrogen, catalog no. 01-3333-42) were used for setting up compensation control.

To measure HIV-1 infectivity and characterize HIV-1⁺ gut CD4⁺ T cells, cells were stained with surface protein markers CD4 (BUV737, clone SK3, BD Biosciences catalog no. 612748), CD69 (BV605, clone FN50, BioLegend catalog no. 310938), CD49a (PE/Cyanine7, clone TS2/7, BioLegend catalog no. 328312), CD161 (BV711, clone HP-3G10, BioLegend catalog no. 339958), CD194 (APC ,clone L291H4, BioLegend catalog no. 359408), CD195 (APC/Cyanine7, clone J418F1, BioLegend catalog no. 359110), CD196 (BV510, clone G034E3, BioLegend catalog no. 353424), CD183 (BUV395, clone 1C6, BD Biosciences catalog no. 565223) for 30 minutes at 37 °C and 40 minutes at 4 °C. Flow cytometry data was acquired at Beckman CytoFLEX and analyzed using FlowJo v10.9.0. UltraComp eBeads Plus Compensation Beads (Invitrogen, catalog no. 01-3333-42) were used for setting up compensation control.

Single-cell analyses

DOGMA-seq data pre-processing

Raw fastq sequences from Chromium Single Cell Multiome ATAC + Gene Expression sequencing (ATAC & RNA) were demultiplexed with CellRanger-Arc v2 (10x Genomics) mkfastq and sequences were aligned to the hg38 reference genome using CellRanger-Arc count. Raw fastq sequences from Single Cell Gene Expression with Feature Barcoding (RNA + ADT/HTO) were demultiplexed with CellRanger v5 (10x Genomics) mkfastq and reads were aligned to the hg38 reference genome using CellRanger count. Cell barcodes that passed knee call by CellRanger-Arc and CellRanger were used for downstream analyses.

Removal of low-quality cells

Cells that passed knee call were used to construct filtered feature count matrices to initialize independent RNA, surface protein (ADT), and ATAC Seurat Objects^86,87 in Seurat v5⁸⁸. For ATAC objects, a unified set of ATAC peaks was created across datasets using Signac v1.7⁸⁹ and used as peak features to generate the count matrix to initialize ATAC Seurat objects for each sample. Cell barcodes in RNA Seurat objects were filtered to remove cells with ≥ 25% mitochondrial gene content, ≤ 200 genes, and RNA UMI counts ≤ 500 or ≥ 25,000. Of note, single-cells were prepared for DOGMA-seq, hence mitochondrial DNA and RNA contents were higher than 10x Multiome (RNA + ATAC) where single nuclei were prepared. Cell barcodes in ATAC Seurat objects were filtered to remove cells with ≤ 200 unique ATAC fragments, nucleosome signal strength ≥ 1, TSS enrichment score ≤ 2, and ATAC UMI counts ≤ 350 or ≥ 20,000.

Removal of doublets and demultiplexing

Three methods were applied to remove doublets. First, hashtag oligo (HTOs) counts data was normalized across cells (margin = 2) using NormalizeData in Seurat v5 with centered log-ratio (CLR) transformation followed by scaling (transforming normalized expression to standard deviations from zero-centered mean). Cells from individual sequencing runs were demultiplexed based on enrichment of normalized and scaled HTO expression using MULTIseqDemux⁹⁰ implemented in Seurat v5, with automated threshold finding set to TRUE, range of quantile values from 0.1 to 0.999, and maximum number of iterations set to 10. Second, we applied scDblFinder⁹¹, which calculated expected doublet rates to identify heterotypic doublets from cellular RNA content by comparing cellular transcriptome against artificially simulated doublets. A third method was applied to demultiplex pooled samples, by using the genotype-free SNP-genotyping method Freemuxlet implemented in Demuxafy⁹². For each pooled sample, Freemuxlet was performed with ‘--nsamples’ equals to the number of participant samples pooled, using publicly available SNP genotype vcf file (genome GRCh38, Region filtering = Genes, Chr Encoding). Doublets determined by either MULTIseqDemux, or ScDblFInder, or Freemuxlet (in the case of pooled samples) were removed. Outputs from MULTIseqDemux and Freemuxlet were also used to demultiplex pooled samples, and cells were removed when predicted outcomes from MULTIseqDemux and Freemuxlet were contradicted. After filtering for poor quality cells and doublets, RNA, ADT, and ATAC Seurat objects from individual sequencing runs (independent and pooled PLWH and HIV⁻ individuals (healthy donor) datasets altogether) were merged by shared GEX barcode and 10x run IDs, to create a combined Seurat object containing all three assays (ATAC, RNA, and protein). Cell barcodes that do not pass QC filter for either RNA or ATAC were discarded.

ATAC peak re-call and normalization

For combined Seurat object, ATAC peaks called by CellRanger-Arc were recalled using MACS2 v2.2.7.1⁹³ with default parameters in Signac v1.7. MACS2 recalled ATAC peaks were normalized by latent semantic indexing [LSI: term frequency-inverse document frequency (TF-IDF) followed by singular value decomposition (SVD)] in Signac v1.7 to correct for differences in cellular sequencing depth and across all peaks and to assign higher values to rare peaks. Gene accessibility was measured in Signac v1.7 (determined as number of fragments mapping to each gene locus extended by 2kb upstream region to include promoter).

Gene loci and transcription factor motif accessibility

Gene accessibility data was normalized by LogNormalize in Seurat v5 to adjust for gene accessibility measurements by the total accessibility in each cell multiplied by median UMI (scale factor = median UMI count), and then scaled by ScaleData (expression distribution of each gene feature across cells is transformed to a centered mean of 0 and feature’s scaled expression in each cell is represented as standard deviations from the mean). Chromatin accessibility at gene locus was visualized by CoveragePlot in Signac v1.9. Peak coordinates were assigned to genes by ClosestFeature in Signac v1.9 with reference genome hg38. Transcription factor motif accessibility was measured in bias-corrected deviations computed using chromVAR v1.20⁹⁴ with human genome reference hg38 and transcription factor binding motif references from the JASPAR2022 Core Vertebrates database⁹⁵.

RNA and ADT data normalization

RNA data was normalized by LogNormalize (feature expression measurement divided by the total expression in each cell multiplied by a scale factor of 10,000 then natural-log transformed) in Seurat v5. Top 2000 variable genes were determined using FindVariableFeatures with ‘vst’ selection method, and the normalized RNA data was scaled for variable genes using ScaleData. The ADT (protein) data was normalized across cells (margin = 2) using NormalizeData in Seurat v5 with centered-log-ratio (CLR) transformation method and scaled for all protein features.

Batch effect correction

We corrected for batch effect for each ATAC, RNA, and ADT data to account for technical variation from separate 10x runs. To correct for batch effects between ATAC datasets, we performed integration by identifying pairwise anchors (pairwise correspondences between single cells across datasets) using FindIntegrationAnchors in Seurat v5 with reciprocal LSI reduction method. The reciprocal LSI coordinates were then integrated across ATAC dataset using IntegrateEmbeddings. To correct for batch effects between RNA datasets, we performed PCA dimensionality reduction followed by integration by Harmony⁹⁶. To correct for batch effects between ADT datasets, we performed integration by identifying pairwise anchors with reciprocal PCA reduction method in Seurat v5.

Integration, clustering, and visualization

To identify and visualize cell subsets, we integrated both ATAC and RNA modalities that were independently preprocessed and batch-effect corrected by performing weighted-nearest neighbor (WNN) analysis⁸⁶. Twenty nearest neighbors for each cell were calculated based on the weighted combination of ATAC and RNA profile similarities using FindMultiModalNeighbors in Seurat v5, with integrated ATAC LSI embeddings and harmony as dimensional reductions (2:20, 1:20 numbers of components, respectively). Next, we used the WNN knn outputs in RunUMAP in Seurat v5 to visualize cells in 2D space by Uniform Manifold Approximation and Projection (UMAP)⁴⁸. Then, the weighted nearest neighbors dimension reduction was used in cluster determination in Seurat v5 with the SLM algorithm at an optimal resolution parameter of 0.5 as determined by Clustree evaluation⁹⁷ and visualized by UMAP.

Cell subset identification

CD4⁺ T cells and CD8⁺ T cells were determined from the combined dataset by expressions of RNA markers (CD3E, CD4, and CD8A) and ADT markers (CD3, CD4, CD8) and subsetted out as independent Seurat objects. Next, for each CD4⁺ T cell and CD8⁺ T cell datasets, and for combined CD4⁺ T cell and CD8⁺ T cell dataset (for cell-cell communication), we re-processed the Seurat Objects by re-performing MACS2 peak call, determination of transcription factor motif accessibility and chromatin accessibility at gene locus, counts data normalization and scaling, batch effect correction, integration, data visualization by WNN UMAP, and cell cluster generation, to generate unique WNN UMAP for each CD4⁺ T cells, CD8⁺ T cells, and combined T cell dataset. For CD4⁺ T cells, 2:50 and 1:30 numbers of integrated ATAC LSI embeddings and harmony components, respectively, were used to find multimodal neighbors, and clusters were generated at an optimal resolution parameter of 1 as determined by Clustree evaluation. For CD8⁺ T cells, 2:40 and 1:50 numbers of integrated ATAC LSI embeddings and harmony components, respectively, were used to find multimodal neighbors, and clusters were generated at an optimal resolution parameter of 1 as determined by Clustree evaluation. Cell clusters were visualized by UMAP.

Next, for each total gut, CD4⁺ T cell, and CD8⁺ T cell combined datasets, we manually annotated each determined cell clusters to specific cell types and sub-types by their overall expression of key cell type defining RNA and protein markers and chromatin accessibility at gene locus of key transcription factor genes. For combined CD4⁺ T cell and CD8⁺ T cell dataset, cell subset annotation labels were transferred over from independent CD4⁺ T and CD8⁺ T cell datasets.

HIV-1⁺ cell identification

To identify HIV-1 RNA⁺ cells, HIV-1 transcripts were identified per cell barcode by mapping RNA reads to HXB2 reference sequence and compendium of Clade B sequences (including HXB2), independently, using STAR v2.7⁹⁸ with a pipeline that we have previously optimized for ECCITE-seq¹². Briefly, STAR was run in two pass modes: first pass to identify and annotate splice sites in input HIV-1 reference genomes, and second pass to realign reads to annotated references. The maximum number of multiple alignments allowed (outFilterMultimapNmax) was set to 100 and maximum number of multi-mapping loci for anchors seeds (winAnchorMultimapNmax) was set to 200. Reads that mapped to multiple HIV-1 references were deduplicated. Barcodes and UMIs from associated Read 1 file were extracted and matched to HIV-1 mapped reads. To guard against index hopping and sequencing artifacts, cells having a minimum of 2 deduplicated HIV-1 reads per barcode were considered positive. Cell barcodes identified as HIV-1 RNA⁺ from mapping to either HXB2 or clade B compendium were considered as HIV-1 RNA⁺.

To identify HIV-1 DNA⁺ cells, HIV-1 DNA fragments were identified per cell barcode by mapping adapter-trimmed mate-pair ATAC reads against HXB2 reference sequence and compendium of Clade B sequences (including HXB2), independently, using Bowtie 2 v2.4.2⁹⁹ with a pipeline that we have previously optimized for DOGMA-seq. Bowtie 2 was run with local alignment and -k 2 (search for at most 2 distinct reference alignment) modes. The --very-sensitive pre-set was used with modifications. Specifically, to account for in vivo hypermutation events, we allowed for 1 mismatch (-N 1) per seed length of 10 (-L 10) during multi-seed alignment. All other parameters were kept at --very-sensitive pre-set (-D 20; -R 3; -i S,1,0.50). Reads that mapped to multiple HIV-1 references were deduplicated and reads with match length < 30 were removed. Barcodes and UMIs from associated I1 file were extracted and matched with mapped reads. To guard against index hopping and sequencing artifacts, cells with a minimum of 2 deduplicated HIV-1 reads per barcode were considered positive. Cell barcodes identified as HIV-1 DNA⁺ from mapping to either HXB2 or clade B compendium were considered as HIV-1 DNA⁺.

To detect and remove false positive HIV-1 DNA, we mapped all detected HIV-1 DNA sequences against human reference Hg38 using Bowtie 2 with the same parameter settings and against all organisms with NCBI MegaBLAST¹⁰⁰. All reads that do not map uniquely to HIV-1 references were removed. To guard against false positive HIV-1 RNA, mapped read was mapped against human reference Hg38 using Bowtie 2 with the same parameter settings and against Los Alamos HIV Sequence Database. No false positive HIV-1 RNA was detected.

To guard against false negative due to cell barcode sequencing errors, we corrected for hamming errors in cell barcodes using PacBio Iso-Seq v3.8.2, for all cell barcodes in which HIV-1 RNA or DNA was detected from each 10x run independently. 10x GEX whitelist barcodes from each 10x run were used as reference, and cell barcode corrections were performed with a tolerance of single-base edit distance (corrected to the whitelist barcode with the lowest edit distance and lowest hamming distance).

Analyses of differential feature expressions

Differential expression analyses were performed using FindMarkers (comparisons of 2 groups) or FindAllMarkers (comparisons of > 2 groups) in Seurat v5 with test.use set to “wilcox_limma” unless otherwise stated and the following feature selection filters: 1) minimum percent of cells expressing the feature threshold (min.pct) and 2) log2 fold change or average difference threshold (logfc.threshold), as specified in figure legends for each comparison. As intended by Seurat V5, only features that met ‘min.pct’ and ‘logfc.threshold’ cutoffs were retained for comparisons, so that rarely expressed genes or genes having low differential expression (low effect size) do not skew the differential expression results (i.e., the FDR-adjusted P value). Of note, no min.pct values were set for differential chromVAR scores since they are Z-scores.

For differential gene accessibility, normalized and scaled data was used to determine fold change significance, with Wilcoxon Rank-sum test as method for fold change significance. For differential peak accessibility, normalized and scaled data was used to determine fold change significance, with logistic regression as method for fold change significance. For differential peak accessibility, gene expression, and protein expression, normalized and scaled data was used to determine fold change significance, with Wilcoxon Rank-sum test as method for fold change significance. Specifically for ADT data, a protein feature was kept for comparison if its mean expression is greater than the mean expression of its specific isotype control with Z > 2 (2 standard deviation difference) by Two-Sample Z-test. For differential transcription factor motif accessibility analyses, Z-score data was used to determine mean differences in accessibility deviations, with Wilcoxon Rank-sum test as method for significant difference. To adjust for sample size for a fair comparison between groups that have drastically different cell population size, groups were first down-sampled to match cell population sizes with 1,000 bootstrap replicates followed by test for differential expression. The 1,000 P values were transformed to follow a standard Cauchy distribution and a combined P value is calculated as the weighted sum of Cauchy transformed P values¹⁰¹. For all comparisons, P values were corrected for multiple comparisons using the Benjamini-Hochberg (FDR) procedure¹⁰².

For heatmaps and dot plot representations of averaged feature expression of significantly differentially expressed features, averaged feature expression were calculated using normalized and scaled values so that all feature expressions can be shown on the same Z-score scale. This scaling is applied for all average feature expressions shown in heatmaps and dot plots (RNA, ADT, accessibility), except for transcription factor motif accessibility (which were already in Z-score).

Constructing gene regulatory network

Gene regulatory network was constructed by linking gene expression – peak accessibility associations to gene expression – transcription factor (TF) motif accessibility associations for each PLWH CD4⁺ T cell and PLWH CD8⁺ T cell datasets using FigR¹⁰³. First, Spearman correlation between gene expression and chromatin accessibility of gene’s transcriptional start site (TSS) was computed for each gene and compared against correlations between the same gene to 100 background peaks with matching GC content and accessibility by permutation test. Then, genes with at least 2 significant peak – gene associations (by correlation between gene expression and TSS chromatin accessibility) were determined as domains of regulatory chromatin (DORCs). Next, we performed cisTopic¹⁰⁴ to determine optimal numbers of cis-regulatory topics (by log-likelihood) from ATAC counts data, which represents sets of chromatin regions that contribute to cell and cell cluster identity. For gene accessibility and gene expression of DORC genes, smoothing was applied to sparse counts data per feature per cell using K nearest neighbors (KNNs) derived from cell – cisTopic probability distributions.

To determine TF to gene associations for DORC genes, the Spearman correlation coefficient between TF gene expression and DORC gene expression was computed for all TFs whose binding motifs were enriched in DORC accessible peaks by runFigRGRN, using built-in reference TF motif database and smoothed DORC gene accessibility and RNA count matrix. Mean regulation scores (signed, −log10 scaled TF-gene Spearman correlation) were computed to predict activator (positive regulation score) and repressor (negative regulation score) TFs on DORC gene expression. Finally, pair-wise TF–DORC regulation scores were visualized on heatmap by plotfigRHeatmap with score.cut set to 1.5 (absolute regulation score of 1.5 to filter poor TF–DORC connections).

Gene Ontology, GSEA, and ModuleScore analyses

For Gene Ontology and KEGG Pathway analysis, we used all significantly upregulated expressed genes to test for significant GO with Biology Process (GO:BP) subontology¹⁰⁵ and KEGG terms using clusterProfiler v4.6.2^106,107 and org.Hs.eg.db v3.8.2 (human genome wide annotation primarily based on mapping using Entrez Gene identifiers)¹⁰⁸. GO and KEGG terms were considered significant if Benjamini-Hochberg adjusted P < 0.05 as determined by enrichGO and enrichKEGG in clusterProfiler.

For GSEA, all 36,601 genes were ranked by log₂ fold change in gene expression comparisons between in-group and out-group. Gene ranks were used to determine significantly enriched gene sets in in-group and to build enrichment plot using fGSEA v1.22 with default settings¹⁰⁹. Reference gene sets were retrieved from MSigDB v2022.1^110,111 using msigdbr v7.5.1¹¹². Specifically, a total of 111,635 reference gene sets from the H (hallmark), C2 (curated), and C7 (immunologic signature) collections were retrieved from MSigDB. Gene sets were considered significantly enriched with P < 0.05.

Per-cell gene expression enrichment of a gene set was scored as previously described¹¹³ using AddModuleScore in Seurat v5, which calculates the per-cell averaged expression of all genes in gene list subtracted by the aggregated expression of a set of 100 randomly selected genes in the same cells. Significance of module score differences between cell groups were tested using Wilcoxon rank-sum test.

T cell clone determination

Reads from Index 1 of Trek-seq raw data (which reads the TCR region) were adapter-trimmed using Cutadapt v4.2¹¹⁴ and filtered to remove poly-G and N repeat sequences using FilterSeq.py and then split into FASTQ files having 50 million reads each using SplitSeq.py in pRESTO v0.7.1¹¹⁵. Reads were then aligned to the human TRA and TRB VDJ reference database in IMGT¹¹⁶ using AssignGenes.py (--organism human; --loci tr) in Change-O v1.2¹¹⁷, a wrapper function for IgBLAST v1.17¹¹⁸. IgBLAST annotated reads were filtered for productive sequences (TCR sequences that are in frame but do not contain in-frame stop codon, contain complementarity-determining region 3 (CDR3)) using ParseDb.py in Change-O, and reads were discarded with a threshold of E-value of > 1E-06 for V and J region assignments. Cell barcodes and UMIs from associated Read 1 file (same barcodes and UMI as cells from DOGMA-seq as they are the same cDNA library) were extracted and matched to IgBLAST annotated reads. TCRβ of each cell is called if a cell barcode is associated with at least 5 UMIs having identical TCRβ CDR3 junction sequences (104-cysteine and 118-phenylalanine and translating nucleotide sequences in between). If in a given cell barcode multiple CDR3 junction sequence meets the 5 UMI threshold for more than a unique CDR3 junction sequence, the CDR3 junction sequence with the highest number of duplicate copies was called.

Clones were determined if at-least two cell barcodes in the same participants share the same TCRβ VDJ calls and the same TCRβ CDR3 junction sequence. Each clone was assigned with a unique ID, by distinct CDR3 junction sequence and participant. In CD4⁺ T cells, we identified the TCRβ CDR3 junction sequence for 13,012/43,113 cells in PLWH and 1,104/6,092 cells in HIV⁻ individuals. Out of CD4⁺ T cells with determined TCRβ CDR3 junction sequence, we captured 1,485/13,012 cells in clones in PLWH and 138/1,104 cells in clones in HIV⁻ individuals. In CD8⁺ T cells, we identified the TCRβ CDR3 junction sequence for 10,868/45,475 cells in PLWH and 875/6064 cells in HIV⁻ individuals. Out of CD8⁺ T cells with determined TCRβ CDR3 junction sequence, we captured 5,358/10,868 cells in clones in PLWH and 308/875 cells in clones in HIV⁻ individuals.

Determining CD8⁺ T cell antigen-specificity

CD8⁺ T cell antigen-specificity was determined using methods described in Gantner et al.¹¹⁹ and criteria described in Meysman et al.¹²⁰. Briefly, to predict antigen specificity, TCRβ CDR3 junction amino acid sequences from all cells were compared against the McPAS-TCR database of TCRs of known HIV-1 and CMV antigen specificity⁶⁰ for sequence similarity. Next, we applied the three criteria in predicting TCR antigen-specificity as described in Meysman et al.¹²⁰: 1) CDR3 sequences are not different by more than one amino acid from predicted match in McPAS-TCR database, 2) CDR3 sequences have the same amino acid length as their predicted match, and 3) CDR3 sequences are long enough (≥ 10 amino acids). All CDR3 sequences that fulfilled these three criteria with matching CDR3 sequences from the McPAS-TCR database were considered as highly likely of sharing the same antigen specificity.

To determine the false positive rate of inferring antigen specificity by near-match TCRβ CDR3 amino acid sequences, we retrieved all non-HIV-1-specific and non-CMV-specific TCRβ CDR3 sequences collected by the McPAS-TCR database and applied the above ‘near-match’ computational approach against HIV-1-specific TCR sequences in the database. Out of 23,271 unique non-HIV-1-specific TCRβ CDR3 sequences, 182 were identified as a near match to a HIV-1-specifc TCR, a false positive rate of 0.78%. Out of 25,696 unique non-CMV-specfic TCRβ CDR3 sequences, 845 were identified as a near match to a CMV-specific TCR, a false positive rate of 3.78%. Additionally, all CDR3 sequences that are predicted to be cross-reactive (specific to more than one pathogen) were removed from antigen specificity assignment, to further mitigate false positive detection. Of note, as public TCR repositories remain limited in the collection of known HIV- and CMV-specific TCRβ CDR3 sequences, the false negative rate is likely high.

Cell-cell communication determination

Scriabin⁵⁵ Interaction Program was performed to determine significant cell–cell ligand–receptor pairs between CD4⁺ T cell and CD8⁺ T cell subsets. After significant ligand–receptor interactions were determined for specific T cell subsets, Scriabin’s implementation of NicheNet⁵⁶ was performed to determine ligand activity in receptor expressing cell types by gene expression of predicted gene responses due to ligand–receptor interactions.

First, the sparse RNA count matrix of combined T cell Seurat object was denoised by adaptively thresholded low-rank approximation (ALRA¹²¹). Then, we performed differential ligand–receptor interaction analysis between PLWH and HIV⁻ individuals samples across all CD4⁺ T cell and CD8⁺ T cell subsets: we identified interaction programs (modules of co-expressed ligand–receptor pairs) by FindAllInteractionPrograms in Scriabin, with assay set to ‘ALRA’, Jaccard overlap index threshold set to ‘0.4’ for interaction program merging, species set to ‘human’, ligand and receptor database set to “OmniPath’, group.by set to PLWH and HIV⁻ individuals conditions and cell_types set to T cell subsets, such that cells from PLWH and HIV⁻ individual cell subsets are equally represented in the subsamples used for iterative approximations of ligand–receptor pair topological overlap matrix. Next, for all interaction programs determined, significance of each interaction program was tested by InteractionProgramSignificance, which compares the ligand–receptor co-expression in each program against 10,000 randomly generated interaction programs (Mann-Whitney U test, less than 500 P > 0.05 for significant programs).

Next, per cell scores were calculated for all significant interaction programs using ScoreInteractionPrograms in Scriabin. For each significant interaction program, sender (ligand expressing, blue) and receiver (receptor expressing, red) cells were visualized in WNN UMAP plot by IPFeaturePlot. Lastly, we identified upregulated interaction programs in PLWH over HIV⁻ individuals by FindMarkers in Seurat with default parameter settings (log₂ Fold change = 0.1, percentage of cells expressing = 0.01).

Scriabin implementation of NicheNet⁵⁶ was performed to determine ligand activity in receiver cells by gene expression of predicted gene responses, using NicheNet database of predicted gene expression changes due to specific ligand–receptor interactions. To determine most active ligands in receiver cell types by gene expression of response genes, cellular gene expression data was used to first determine variable genes in PLWH and HIV⁻ individuals (IDVariantGenes, group.by set to PLWH vs HIV⁻ individuals conditions), generate gene signature for ligand activity ranking (GenerateCellSignature, default settings), and predict ligand activity using cell-resolved gene signatures (RankActiveLigands, default settings).

QUANTIFICATION AND STATISTICAL ANALYSIS

Unless otherwise stated, statistical analysis was performed using R (version 4.2.0). For all analyses, the statistical tests used and significance are reported in the results, figures, and figure legends when applicable. Sample size n, mean, and standard deviation are described in results and figure legends when applicable. For additional details of the statistical tests used for each type of analysis, see also relevant Methods sections. Unless otherwise stated, P values were corrected for multiple comparisons using the Benjamini-Hochberg (FDR) procedure when applicable, and significance was defined as FDR-adjusted P < 0.05. Poor quality cells were excluded from analysis (see Methods for quality control metrics). No data or subjects were excluded from analysis.

Supplementary Material

Data S1. DOGMA-seq protocol modifications and primers for DOGMA-seq and TREK-seq, related to STAR Methods.

NIHMS2105715-supplement-1.pdf^{(174.2KB, pdf)}

Document S1. Supplemental Figures S1 – S7, Table S1.

NIHMS2105715-supplement-2.pdf^{(3.3MB, pdf)}

Table S2. Cell subset annotation and proportions, related to Figures 1, 2, 5, and 6.

NIHMS2105715-supplement-3.xlsx^{(42.7KB, xlsx)}

Table S3. Figure source data, related to Figures 1, 2, 3, 5, 6.

NIHMS2105715-supplement-4.xlsx^{(901.1KB, xlsx)}

Table S4. T cell clone and antigen-specificity determination, related to Figure 5.

NIHMS2105715-supplement-5.xlsx^{(1.6MB, xlsx)}

Table S5. HIV-1 DNA and HIV-1 RNA mapping to clade B reference genomes, related to Figure 6.

NIHMS2105715-supplement-6.xlsx^{(304.6KB, xlsx)}

Highlights.

BACH2 restrains immune effectors, drives long-lived memory, and shapes tissue residence
BACH2-shaped Trm T cells are a major HIV reservoir in the human gut
HIV-specific CD8⁺ T cells in the gut are Trms with epigenetic scar of immune exhaustion
Mechanisms of HIV persistence in the gut are distinct from that in the blood

ACKNOWLEDGEMENTS

We thank all study participants. We thank Kenneth Lynn and Beth Peterson for supporting study participant recruitment. We thank Guilin Wang and Yale Center for Genome Analysis. We thank Matthew Fair and Penn Immunology Core for study sample processing. We thank Yalai Bai and Dongqing Liu from Yale Tissue Services and Oluwabunmi Olaloye for tissue processing guidance. We thank Joseph Craft for immunology insight and Cara Wilson, Mario Santiago, Stephanie Dillon, and Marcus Buggert for gut sample processing advice. We thank Caterina Prelli Bozzo for guidance on CRISPR ablation design in primary cells. We thank Jack A. Collora for the concept of combining DOGMA-seq and TREK-seq. This work is supported by NIH R01 AI174863, R01 AI183430, R01 AI141009, P01 AI169768, BEAT-HIV Martin Delaney Collaboratory UM1 AI164570, REACH UM1 AI164565, CHEETAH U54 AI170856, R01 AI176601, R33 DA047037, UM1 Y-SCORCH DA051410, U01 M-SCORCH DA053628, R01 DA051906 (Y.-C.H.), Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellowship (PDF557234) and AIDS and Cancer Specimen Resource Pilot Grant (Y.W.), Yale Gruber Fellowship (H.K.M.), U01AI110434, UM1AI191272, the Robert I. Jacobs Fund of the Philadelphia Foundation, and the Herbert Kean, M.D., Family Professorship (L.J.M.).

Footnotes

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DECLARATION OF INTERESTS

The authors have no competing interests to declare.

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Associated Data

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

Supplementary Materials

Data S1. DOGMA-seq protocol modifications and primers for DOGMA-seq and TREK-seq, related to STAR Methods.

NIHMS2105715-supplement-1.pdf^{(174.2KB, pdf)}

Document S1. Supplemental Figures S1 – S7, Table S1.

NIHMS2105715-supplement-2.pdf^{(3.3MB, pdf)}

Table S2. Cell subset annotation and proportions, related to Figures 1, 2, 5, and 6.

NIHMS2105715-supplement-3.xlsx^{(42.7KB, xlsx)}

Table S3. Figure source data, related to Figures 1, 2, 3, 5, 6.

NIHMS2105715-supplement-4.xlsx^{(901.1KB, xlsx)}

Table S4. T cell clone and antigen-specificity determination, related to Figure 5.

NIHMS2105715-supplement-5.xlsx^{(1.6MB, xlsx)}

Table S5. HIV-1 DNA and HIV-1 RNA mapping to clade B reference genomes, related to Figure 6.

NIHMS2105715-supplement-6.xlsx^{(304.6KB, xlsx)}

Data Availability Statement

Single-cell DOGMA-seq and TREK-seq data have been deposited in the Gene Expression Omnibus (GEO) and are publicly available from the date of publication. The accession number is listed in the Key Resource Table.
Original western blot images have been deposited at Mendeley Data and are publicly available from the date of publication. The DOI is listed in the Key Resources Table.
All codes used to generate results have been deposited at Zenodo and are publicly available from the date of publication. The DOI is listed in the Key Resources Table.
Any additional information required to reanalyze the data reported in this paper is available from the lead contacts upon request.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
TotalSeq-A Human Universal Cocktail V1.0	BioLegend	CAT # 399907; RRID: AB_2888692
TotalSeq-A CD197, clone G043H7	BioLegend	CAT # 353247; RRID: AB_2750357
TotalSeq-A anti-human Hashtag 1	BioLegend	CAT # 394601; RRID: AB_2750015
TotalSeq-A anti-human Hashtag 2	BioLegend	CAT # 394603; RRID: AB_2750016
TotalSeq-A anti-human Hashtag 3	BioLegend	CAT # 394605; RRID: AB_2750017
TotalSeq-A anti-human Hashtag 4	BioLegend	CAT # 394607; RRID: AB_2750018
TotalSeq-A anti-human Hashtag 5	BioLegend	CAT # 394609; RRID: AB_2750019
TotalSeq-A anti-human Hashtag 6	BioLegend	CAT # 394611; RRID: AB_2750020
Human TruStain FcX	BioLegend	CAT # 422302; RRID: AB_2818986
Mouse Anti-Human CD4, clone SK3	BD Biosciences	CAT # 612748
Anti-human CD69, clone FN50	BioLegend	CAT # 310938; RRID: AB_2562306
Anti-human CD49a, clone TS2/7	BioLegend	CAT # 328312; RRID: AB_2566271
Anti-human CD161, clone HP-3G10	BioLegend	CAT # 339958; RRID: AB_2941516
Anti-human CD194, clone L291H4	BioLegend	CAT # 359408; RRID: AB_2562428
Anti-human CD195, clone J418F1	BioLegend	CAT # 359110; RRID: AB_2562653
Anti-human CD196, clone G034E3	BioLegend	CAT # 353424; RRID: AB_2563868
Mouse Anti-Human CD183, clone 1C6	BD Biosciences	CAT # 565223
Anti-human CD3, clone SK7	BioLegend	CAT # 344818 RRID: AB_10644011
Mouse Anti-Human CD4, clone SK3	BD Biosciences	CAT # 612748
Anti-human CD8, clone SK1	BioLegend	CAT # 344748 RRID: AB_2629583
Anti-human CD103, clone Ber-ACT8	BioLegend	CAT # 350222 RRID: AB_2629650
Mouse Anti-Human CD45RO, clone UCHL1	BD Biosciences	CAT # 564291
Anti-human CD127, clone A019D5	BioLegend	CAT # 351336 RRID: AB_2563636
Anti-human CD197, clone G043H7	BioLegend	CAT # 353214 RRID: AB_10915474
BACH2 (D3T3G) Rabbit mAb	Cell Signaling	CAT# 80775
β-Actin (D6A8) Rabbit mAb	Cell Signaling	CAT# 8457
Anti-rabbit IgG, HRP-linked Antibody	Cell Signaling	CAT# 7074
Bacterial and virus strains
HIV-GKO	Addgene	CAT # 112234
R5-tropic JR-FL envelope	NIH HIV Reagents Program	CAT # ARP-4598
Biological samples
Colon biopsy samples from HIV-BEAT Cohort (Table S1)	This study and Papasavvas et al.	N/A
PBMC from New York Blood Center	This study	N/A
Colon biopsy samples from Yale Pathology (Table S1)	This study	N/A
Chemicals, peptides, and recombinant proteins
Digitonin 5%	ThermoFisher	CAT # BN2006
xGen Lockdown Reagents	IDT	CAT # 1072281
Protector RNase Inhibitor	Sigma-Aldrich	PN-3335399001
Human Cot-1 DNA	Invitrogen	CAT # 15279011
Dynabeads M-270 Streptavidin	Invitrogen	CAT # 65306
Collagenase II	Sigma-Aldrich	CAT # C6885-5G
Collagenase IV	Sigma-Aldrich	CAT # C5138-1G
DNase I	Sigma-Aldrich	CAT # DN25-1G
Ca2⁺/Mg2⁺ free PBS	Corning	CAT # 21-040-CV
HiFBS	Gibco	CAT # A5670801
DDT	Sigma-Aldrich	CAT # D9779-1G
EDTA	Invitrogen	CAT # 15575-038
Penicillin-Streptomycin	Gibco	CAT # 15140122
Zosyn	Novaplus	CAT # 44567-801-10
Fungizone	Corning	CAT # 30-003-CF
HEPES	Gibco	CAT # 15630080
CLSPA Collagenase	Worthington	CAT # LK003240
Human AB serum	Gemini Bioproducts	CAT # 100-512
Recombinant human interleukin-2	Conn Stem	CAT # C1002
Dynabeads Human T-activator CD3/CD28	Gibco	CAT # 11131D
UltraComp eBeads Plus Compensation Beads	Invitrogen	CAT # 01-3333-42
Critical commercial assays
Chromium Next GEM Single Cell Multiome ATAC + Gene Expression Reagent Bundle	10x Genomics	PN-1000283
Chromium Nuclei Isolation Kit with RNase Inhibitor	10x Genomics	PN-1000494
Chromium Next GEM Chip J Single Cell	10x Genomics	PN-1000230
Dual Index Kit TT Set A	10x Genomics	PN-1000215
Single Index Kit N Set A	10x Genomics	PN-1000212
3ʹ Feature Barcode Kit	10x Genomics	PN-1000262
EasySeq Dead Cell Removal Annexin V kit	STEMCELL	CAT # 17899
EasySep Release Human CD3 Positive Seleciton Kit	STEMCELL	CAT # 17751
Live/Dead Fixable Blue Dead Cell Stain Kit	Invitrogen	CAT # L23105
Kapa Hifi Hotstart Readymix	Kapa Biosystems	CAT # KK2602
SPRIselect 5 mL reagent kit	Beckman Coulter	CAT # B23317
Alt-R S.p. Cas9 Nuclease V3	IDT	CAT # 1081059
P3 Primary Cell 4D-Nucleofector X Kit S	Lonza	CAT # V4XP-3032
4D-Nucleofector X Unit	Lonza	CAT # AAF-1003X
Deposited data
DOGMA-seq and TREK-seq sequencing data	This study	GEO: GSE299348
Original western blot images	This study	doi: 10.17632/4g722x4jfg.1
Oligonucleotides
ADT: * indicates phosphorothioate: CCTTGGCACCCGAGAATTCC	Mimitou et al.⁴⁴	N/A
HTO: * indicates phosphorothioate: GTGACTGGAGTTCAGACGTGTGCTC	Mimitou et al.⁴⁴	N/A
SIPCR: Dual index common primer for ADT/HTO	This study, see Data S1	N/A
RPX: Dual index common primer for ADT	This study, see Data S1	N/A
D7X: Dual index common primer for HTO	This study, see Data S1	N/A
TREK-seq sample indexing primers	This study, see Data S1	N/A
TREK-seq primers	This study and Miller et al.⁴⁵, see Data S1	N/A
BACH2 Alt-R CRISPR-Cas9 sgRNA: 5’-ACGTGACTTTGATCGTGGAG-3’	IDT	N/A
Non-targeting Alt-R CRISPR-Cas9 sgRNA: 5’-ACGGAGGCTAAGCGTCGCAA-3’	Prelli Bozzo et al.¹²² IDT	N/A
Software and algorithms
CellRanger-Arc v2.0	10x Genomics	https://support.10xgenomics.com/single-cell-multiome-atac-gex/software/pipelines/latest/installation#download
CellRanger v5.0.1	10x Genomics	https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest
STAR v2.7	Dobin et al.⁹⁸	https://github.com/alexdobin/STAR
Bowtie 2 v2.4.2	Langmead and Salzberg⁹⁹	https://github.com/BenLangmead/bowtie2
Cutadapt v4.2	Martin¹¹⁴	https://cutadapt.readthedocs.io/en/stable/installation.html
Seqtk v1.3	https://github.com/lh3/seqtk	https://anaconda.org/bioconda/seqtk
Iso-Seq v3.8.2	PacBio	https://github.com/PacificBiosciences/pbbioconda
SAMtools v1.16.1	Li et al.¹²³	https://anaconda.org/bioconda/samtools
IGV v2.16	Robinson et al.¹²⁴	https://software.broadinstitute.org/software/igv/download
Seurat v5.0.0	Hao et al.⁸⁸	https://cran.r-project.org/web/packages/Seurat/index.html
Signac v1.9.0	Stuart et al.⁸⁹	https://cran.r-project.org/web/packages/Signac/index.html
Harmony v1.1.0	Korsunsky et al.⁹⁶	https://portals.broadinstitute.org/harmony/articles/quickstart.html
Clustree v0.5.0	Zappia and Oshlack⁹⁷	https://cran.r-project.org/web/packages/clustree/index.html
MACS2 v2.2.7.1	Zhang et al.⁹³	https://github.com/macs3-project/MACS
scDblFinder v1.12.0	Germain et al.⁹¹	https://bioconductor.org/packages/release/bioc/html/scDblFinder.html
Demuxafy v3.0.0	Neavin et al.⁹²	https://demultiplexing-doublet-detecting-docs.readthedocs.io/en/latest/Installation.html
EnsDb.Hsapiens.v86 v2.99.0	Rainer¹²⁵	https://bioconductor.org/packages/release/data/annotation/html/EnsDb.Hsapiens.v86.html
BSgenome.Hsapiens.UCSC.hg38 v1.4.5	Team TBD¹²⁶	https://bioconductor.org/packages/release/data/annotation/html/BSgenome.Hsapiens.UCSC.hg38.html
org.Hs.eg.db v3.16.0	Carlson et al.¹⁰⁸	https://bioconductor.org/packages/release/data/annotation/html/org.Hs.eg.db.html
TFBSTools v1.36.0	Tan and Lenhard¹²⁷	https://bioconductor.org/packages/release/bioc/html/TFBSTools.html
chromVAR v1.20.2	Schep et al.⁹⁴	http://bioconductor.org/packages/release/bioc/html/chromVAR.html
JASPAR2022 V0.99.7	Castro-Mondragon et al.⁹⁵	https://bioconductor.org/packages/release/data/annotation/html/JASPAR2022.html
Scriabin v0.0.0.9	Wilk et al.⁵⁵	https://github.com/BlishLab/scriabin
NicheNetr v1.1.1	Browaeys et al.⁵⁶	https://github.com/saeyslab/nichenetr
ComplexHeatmap v2.14.0	Gu et al.¹²⁸	https://bioconductor.org/packages/release/bioc/html/ComplexHeatmap.html
ACAT v0.91	Liu et al.¹⁰¹	https://github.com/yaowuliu/ACAT
FigR v0.1.0	Kartha et al.¹⁰³	https://buenrostrolab.github.io/FigR/
cisTopic v0.3.0	Gonzales-Blas et al.¹⁰⁴	https://github.com/aertslab/cisTopic
clusterProfiler v4.6.2	Xu et al.¹⁰⁶	https://bioconductor.org/packages/release/bioc/html/clusterProfiler.html
fGSEA v1.22	Korotkevich et al.¹⁰⁹	https://bioconductor.org/packages/release/bioc/html/fgsea.html
msigdbr v7.5.1	Dolgalev¹¹²	https://cran.r-project.org/web/packages/msigdbr/vignettes/msigdbr-intro.html
pRESTO v0.7.1	Heiden et al.¹¹⁵	https://presto.readthedocs.io/en/stable/install.html
Change-O v1.2	Gupta et al.¹¹⁷	https://changeo.readthedocs.io/en/stable/install.html#
R version 4.2.0	R Core Team	https://www.r-project.org/
FlowJo V10.9.0	FlowJo	https://www.flowjo.com/solutions/flowjo/downloads
Analysis scripts	This study	https://doi.org/10.5281/zenodo.15793852

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PERMALINK

BACH2 shapes tissue resident memory T cell programs to promote HIV-1 persistence

Yulong Wei

Haocong Katherine Ma

Michelle E Wong

Hyein Back

Emmanouil Papasavvas

Karam Mounzer

Fatema Aberra

Ricardo Morgenstern

Pablo Tebas

Liza Konnikova

Luis J Montaner

Ya-Chi Ho

SUMMARY

Graphical Abstract

eTOC blurb

INTRODUCTION

RESULTS

Single-cell DOGMA-seq identifies integrative single-cell epigenetic, transcriptional, and protein profiles of human gut

Figure 1. BACH2 shapes tissue resident memory programs in CD4+ T cells in human gut.

BACH2 shapes tissue resident memory programs in gut CD4+ T cells

BACH2-high CD4-Trms exhibit long-lived memory programs

BACH2 shapes tissue resident memory programs in gut CD8+ T cells

Figure 2. BACH2 shapes tissue resident memory programs in CD8+ T cells in human gut while interferon regulatory factors (IRF) drive effector programs.

BACH2-high CD8-Trms exhibit long-lived memory programs

BACH2-shaped tissue resident memory program restrains terminal effector function and promotes long-lived memory

Figure 3. BACH2 restrains terminal effector programs and drives increased long-lived memory in tissue resident CD4+ and CD8+ T cells.

TNF and IFN-γ ligand–receptor interactions from BACH2-low effectors to BACH2-high Trms shape long-lived memory

CD4+ T cells are less proliferative than CD8+ T cells in human gut

Figure 4. Interferon responses drive clonal expansion of gut effector CD8+ T cells but not CD4+ T cells.

Interferon responses promote clonal expansion of gut CD8+ T cell effectors

Clonally expanded gut CD4+ T cells exhibit Trm-Th1 and Trm-Th17 phenotypes

HIV-1-specific CD8+ T cells exhibit tissue residency, lower proliferation capacity, and epigenetic scar of exhaustion

Figure 5. HIV-1-specific CD8+ T cells in the gut exhibit tissue residency and epigenetic scars of exhaustion and are less proliferative than CMV-specific CD8+ T cells.

The majority of HIV-1-infected cells in the gut are Trms

Figure 6. BACH2 shapes HIV-1-infected cells, mainly Trm, in the gut but not in the blood.

HIV-1 persists in BACH2-shaped, long-lived, tissue resident memory cells

BACH2 ablation reduces of long-lived memory CD4+ and CD8+ T cells

Figure 7. BACH2-driven long-lived memory allows HIV-1 preferential infection and persistence in Trms.

HIV-1 preferentially infects and persists in Trms in the gut, particularly CCR6+ CD161+ Th17 Trm

DISCUSSION

Limitations of the Study

RESOURCE AVAILABILITY

Lead contact

Materials availability

Data and code availability

STAR METHODS

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Human subjects

Primary cell cultures

METHOD DETAILS

Sample preparation for DOGMA-seq

DOGMA-seq library preparation and sequencing

Comparing collagenase treatment in PBMC

Enrichment of TCR transcripts by TREK-seq

Constructing and sequencing TCR libraries

Gut biopsy lamina propria cell isolation

R5-tropic HIV-1 production and infection

BACH2 ablation by CRISPR-Cas9

Western blot for BACH2 ablation efficiency

Flow cytometry

Single-cell analyses

DOGMA-seq data pre-processing

Removal of low-quality cells

Removal of doublets and demultiplexing

ATAC peak re-call and normalization

Gene loci and transcription factor motif accessibility

RNA and ADT data normalization

Batch effect correction

Integration, clustering, and visualization

Cell subset identification

HIV-1+ cell identification

Analyses of differential feature expressions

Constructing gene regulatory network

Gene Ontology, GSEA, and ModuleScore analyses

T cell clone determination

Determining CD8+ T cell antigen-specificity

Cell-cell communication determination

QUANTIFICATION AND STATISTICAL ANALYSIS

Figure 1. BACH2 shapes tissue resident memory programs in CD4⁺ T cells in human gut.

BACH2 shapes tissue resident memory programs in gut CD4⁺ T cells

BACH2 shapes tissue resident memory programs in gut CD8⁺ T cells

Figure 2. BACH2 shapes tissue resident memory programs in CD8⁺ T cells in human gut while interferon regulatory factors (IRF) drive effector programs.

Figure 3. BACH2 restrains terminal effector programs and drives increased long-lived memory in tissue resident CD4⁺ and CD8⁺ T cells.

CD4⁺ T cells are less proliferative than CD8⁺ T cells in human gut

Figure 4. Interferon responses drive clonal expansion of gut effector CD8⁺ T cells but not CD4⁺ T cells.

Interferon responses promote clonal expansion of gut CD8⁺ T cell effectors

Clonally expanded gut CD4⁺ T cells exhibit Trm-Th1 and Trm-Th17 phenotypes

HIV-1-specific CD8⁺ T cells exhibit tissue residency, lower proliferation capacity, and epigenetic scar of exhaustion

Figure 5. HIV-1-specific CD8⁺ T cells in the gut exhibit tissue residency and epigenetic scars of exhaustion and are less proliferative than CMV-specific CD8⁺ T cells.

BACH2 ablation reduces of long-lived memory CD4⁺ and CD8⁺ T cells

HIV-1 preferentially infects and persists in Trms in the gut, particularly CCR6⁺ CD161⁺ Th17 Trm

HIV-1⁺ cell identification

Determining CD8⁺ T cell antigen-specificity