IL-10- and TGF-β-driven BCL6 expression suppresses antiviral defenses and renders lymph node T follicular helper cells permissive to HIV infection

IL-10- and TGF-β-driven BCL6 expression suppresses antiviral defenses and renders lymph node T follicular helper cells permissive to HIV infection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article IL-10- and TGF-β-driven BCL6 expression suppresses antiviral defenses and renders lymph node T follicular helper cells permissive to HIV infection Susan Pereira Ribeiro, Perla Mariana Del Rio Estrada, Spiros Georgakis, and 36 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8919968/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Lymph nodes (LNs) constitute a key anatomical sanctuary for HIV. Follicular helper T (Tfh) cells expand early upon infection and represent a principal cellular target for initial viral seeding. Here, we identified the transcription factor BCL6, a Tfh-lineage defining marker, as central in favoring the infection of Tfh cells in LNs during the untreated phase in humans, and for the persistence of the reservoir during ART in non-human primates. In situ and ex vivo analyses of LN from people with HIV (PWH) in absence of antiretroviral therapy (ART) revealed preferential enrichment of viral RNA, total HIV DNA, and intact proviruses within BCL6hi Tfh cells, which also presented significantly lower expression of proteins with antiviral functions (IRF7, MX1, APOBEC3G, pSTAT1). In vitro genetic (genome-wide CRISPR knockouts) and pharmacologic perturbations confirmed that BCL6 enhances the cellular permissiveness of Tfh cells to HIV infection. IL-10 and TGF-β were enriched in LNs from people without HIV (PWoH), and cooperatively induced bona fide BCL6hi Tfh differentiation in vitro, with repressed antiviral pathways. IL-10 and TGF-β blockade limited Tfh differentiation, confirming their contribution to Tfh and LN biology. Human Single Nucleotide Polymorphisms (SNPs) in proximity to genes of the IL-10 and TGF-β pathways were enriched in PWH who controls viremia spontaneously (HIV elite controllers). Importantly, in vivo downmodulation of IL-10 and TGF-β signaling pathways in ART-treated SIV-infected macaques, by using anti–IL-10 and anti–PD-1 therapy, led to reduced frequencies of LN BCL6+ Tfh cells. These Tfh cells expressed significantly higher expression of antiviral machineries, similar to gene signatures found in HIV elite controllers, and resulted in significantly lower SIV reservoir size in LNs. This data highlights that the modulation of the IL-10/TGF-β/BCL6 axis is relevant at early stages upon infection, but also during ART, after the HIV reservoir is already established. In both scenarios it results in higher antiviral machinery and lower HIV seeding and reservoir sizes. Thus, the modulation of these pathways in vivo has potential to alter Tfh biology in LNs leading to HIV reservoir decay, contributing to HIV cure strategies. Biological sciences/Immunology/Infectious diseases/HIV infections Biological sciences/Immunology/Lymphoid tissues/Lymph node Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Despite sustained antiretroviral therapy (ART), HIV persists in long-lived cellular reservoirs representing a major barrier to cure 1 . Lymphoid tissues, particularly lymph nodes (LNs), constitute key anatomical sites for HIV infection, dissemination and persistence due to their unique immunological architecture and cellular composition 2-7 . Within LNs, CD4⁺ T follicular helper (Tfh) cells are disproportionately enriched for HIV DNA and transcriptionally active virus 8-10 . However, the intrinsic and extrinsic mechanisms that render Tfh cells highly permissive to HIV infection remain incompletely understood. Tfh cell differentiation and maintenance are orchestrated by the transcriptional repressor B cell lymphoma 6 (BCL6), a lineage-defining factor that programs Tfh identity and function 11-14 . While BCL6 is essential for germinal center formation and humoral immunity, emerging evidence suggests that this transcriptional program may come at the cost of impaired antiviral defense. Indeed, Tfh cells display reduced expression of antiviral restriction factors and interferon-stimulated genes compared with other CD4⁺ T cell subsets 10 , yet whether this phenotype is causally linked to BCL6 expression or reflects the LN microenvironment remains unclear. Moreover, immunoregulatory cytokines such as interleukin-10 (IL-10) and transforming growth factor-β (TGF-β) 15-17 , which are abundant in lymphoid tissues and known to promote Tfh differentiation 18-21 , may further shape this permissive state, but their contribution to HIV reservoir seeding in tissues has not been systematically examined. Here, we investigated how BCL6 and its upstream regulatory network influence HIV susceptibility, antiviral immunity, and reservoir seeding within lymphoid tissues. By integrating analyses of human LN biopsies, functional perturbation studies, genome-scale CRISPR screens, genetic association data in HIV Elite controllers, and in vivo non-human primate intervention models on ART, we identify an IL-10/TGF-β/BCL6 axis that actively suppresses antiviral machinery and promotes the susceptibility of Tfh cells to HIV infection. Our findings reveal that modulation of these pathways reprograms Tfh biology, restricts HIV infection, and reduces tissue reservoir size, highlighting a previously underappreciated immunoregulatory circuit that can be therapeutically targeted to destabilize HIV reservoirs and advance cure strategies. Results BCL6 hi CD4 follicular helper T (Tfh) cells are highly susceptible to HIV infection. BCL6 is known as a lineage-defining transcriptional factor (TF) for Tfh cells and is associated with lower expression of antiviral protiens 10,22,23 . To assess whether BCL6 contributes to the heightened susceptibility of Tfh cells to HIV early upon infection, we examined LN biopsies from chronically infected people with HIV (PWH, n=5) ( Fig. 1A, Extended Data Fig. 1A-B ). Quantitative analysis of LN sections revealed a significant enrichment of vRNA hi CD4⁺ T cells within the BCL6 hi compartment when compared with the BCL6 lo CD4 T cell counterpart (p = 0.001; Fig. 1B ). These findings suggest preferential infection and/or enhanced viral transcriptional activity in BCL6 hi T cells during untreated HIV infection. Consistent with these in situ observations, Tfh cells sorted from LN cell suspensions during the untreated phase ( Fig. 1C ), were also preferentially infected, with significantly higher amounts per million CD4 T cells of total HIV DNA and intact proviruses compared with other LN CD4⁺ T-cell subsets (CXCR5 mid PDPD1 mid , p = 0.0319; CXCR5+PD1-, p<0.0001/p<0.0001; CXCR5-PD1- p<0.0001/p=0.0013) and peripheral blood CD4 T cells (p= 0.0169, Fig. 1C-E , respectively). To confirm the heightened susceptibility of BCL6 hi CD4 T cells to HIV infection rather than preferential death of other CD4⁺ T-cell subsets, we isolated CD4⁺ T cells from tonsillar tissue of PWoH (n=5) and infected them with HIV in vitro by spinoculation ( Fig. 1F ). BCL6 hi cells presented significantly higher HIV p24 protein expression per cell than BCL6 lo CD4 T cells at day 4 post-HIV infection (p = 0.0006; Fig. 1G ). Of note, the frequencies of the BCL6 hi and BCL6 lo CD4 T cell subsets were maintained throughout the assay ( Extended Data Fig1C ). These data confirmthe higher susceptibility of BCL6 hi CD4 T cells to HIV infection rather than preferential survival over other subsets. To validate the role of BCL6 in regulating Tfh susceptibility to HIV infection, we used Bl-3802, a compound known to inhibit and degrade BCL6 24 . Bl-3802 dose was selected based on cell viability and efficiency of BCL6 degradation ( Extended Data Fig. 1D-E ). CD4⁺ T cells from tonsils of PWoH were pre-treated with the selected dose of BI-3802 for 24 hours before HIV infection, and the inhibitor was maintained throughout the assay. No changes in Tfh frequencies or cell viability were observed ( Extended Data Fig. 1F-G, respectively). BI-3802 reduced BCL6 protein expression by 10% after 24 hours (pre-infection timepoint), reaching around 50% decay, 4 days post-infection (median fold change (FC) BI-3802/untreated: 0.9197 and 0.5156, for pre-infection and day 4 post-infection, respectively; p = 0.0057; Fig. 1H ). The treatment with BI-3802 alone showed a trend towards reduction in HIV infection as compared with the untreated condition (p = 0.0602, Fig. 1I , 2 left bars). The combination with IFN-b reduced further the infection rates as compared to IFN-b alone (p = 0.0398, Fig. 1I , 2 right bars). This data confirms that the BCL6 degradation boosts antiviral immunity leading to reduced infection rates. To further validate the role of BCL6 in supporting virus infection (thereafter referred as proviral factor), we analyzed data from our recent pooled genome-scale CRISPRn (knockout) screens performed in CD4+ T cells isolated from peripheral blood mononuclear cells (PBMCs) of PWoH (n=3), in which perturbed cells were infected with a GFP-tagged HIV (NL4-3) to identify pro- and anti-HIV host factors ( Cell , in press). Consistent with a proviral role for endogenous BCL6, its knockout reduced HIV infection (log₂FC = −0.122, false discovery rate (FDR) = 0.135, rank 131), placing BCL6 among the top proviral factors identified in the screen ( Supplementary Table 1 ). Altogether, these findings support the role of BCL6 as a proviral factor contributing to the relevance of Tfh cells for the initial HIV seeding and possibly contributing to the establishment of tissue reservoir sanctuaries. BCL6 suppresses interferon-driven antiviral programs in germinal center Tfh cells. We next evaluated the interplay between BCL6 and antiviral proteins expression. We assessed the transcription factor IRF7, the phosphorylated form of STAT1 (pSTAT1), and downstream antiviral proteins such as MX1 and APOBEC3G by histo-cytometry 25 ( Fig. 2A, Extended Data Fig. 1H ). This analysis was initially performed on LNs from PWoH (n=5) to eliminate the virus as an upstream trigger of the targeted antiviral molecules. In average 5-12% and 4-13% of Tfh and B cells expressed any of these antiviral proteins in the follicles from PWoH, respectively ( Fig. 2B ). Notably, the expression of these antiviral proteins was found mostly on the BCL6 lo compartment, with more than 85% of the BCL6 lo cells expressing all the measured antiviral proteins (p<0.0001; Fig. 2C ). Next, we evaluated additional lymphoid tissues to confirm the persistence of these opposing signatures (BCL6 expression vs . antivirals signatures). We analyzed single cell RNA-Seq (scRNA-Seq) data performed in cell suspensions isolated from different compartments of the gastrointestinal (GI) tract (colon (CO), ileum (IL) and rectum (RE)) in PWH on ART (n=8), when viremia is suppressed ( Extended Data Fig. 2A) . Using single-sample gene set enrichment (ssGSEA) analysis 26 , we evaluated type I, II and III IFN pathways and the HIV restriction factors pathway 27 in Tfh vs. non-Tfh cells (T CM and Th17 cells) in these different compartments. We used Cliff’s Δ non-parametric effect size to quantify the difference between 2 cell subsets, Tfh vs T CM or Tfh vs Th17 cells ( Supplementary Table 2 ). Across all GI sites, Tfh cells presented significantly lower expression of signaling pathways for type I, II and III IFNs, and HIV restriction factors when compared to central memory (T CM ) CD4 T cells (Cliff’s Δ: IFN-I = 0.41, IFN-II = 0.27, IFN-III = 0.37, restriction factors = 0.12 - Extended Data Fig. 2B and D ) and Th17 cells (Cliff’s Δ: IFN-I = 0.66, IFN-II = 0.62, IFN-III = 0.54, restriction factors = 0.24 - Extended Data Fig. 2C and E ). Together, this data supports the generalizability and persistent dampening of interferon signaling and HIV restriction factors signatures in Tfh cells in different lymphoid tissues, including in PWH on ART. To confirm the role of BCL6 in dampening antiviral responses, we assessed the IRF7 protein levels in CD4 T cells isolated from tonsils from PWoH from the experiment shown in Fig. 1F. Following treatment with the BCL6 inhibitor, Bl-3802, for 24 hours, a significant increase in the total IRF7 protein expression was observed (p=0.0384; Extended Data Fig. 2F ). Importantly, the pre-infection IRF7 expression levels (24 hours post-Bl-3802 treatment) was inversely correlated with the levels of HIV protein expression (HIV-p24 MFI) 4 days post-infection (p = 0.0214/rho = 0.7667; Extended Data Fig. 2G ). This data confirms the role of BCL6 in suppressing Tfh antiviral responses promoting their higher susceptibility to HIV infection. IL-10 and TGF-β lead to BCL6 expression and Tfh differentiation. Given the importance of IL-10 and TGF-βcytokines for the LN microenvironment, and their previously reported role on Tfh differentiation 18,19 we quantified their mRNA expression in LNs from PWoH in situ (n=5) ( Fig. 3A ). IL-10 and TGF-β mRNA expression was detected in both Follicular (F) and Extra Follicular (EF) areas, with significantly higher expression in the EF areas (IL-10 + p= 0.0625; TGF-β + p= 0.0312; Fig. 3B ). The expression levels of these two cytokines were positively correlated with one another (p= 0.0316, rho = 0.2957; Fig. 3C ), suggesting coordinated regulation within the tissue microenvironment. Of note, the total number of cells expressing mRNA for IL-10 or TGF-β was significantly correlated with the numbers of follicular Tfh cells as defined by CD3 + PD1 hi cells inside the follicles (p = 0.0497/rho= 0.2684 and p = 0.0400/rho = 0.2804, respectively; Fig. 3D-E ), consistent with the role of these cytokines in supporting Tfh differentiation or maintenance in vivo as previously shown 18,19 . To confirm the role of IL-10 and TGF-β for the differentiation of Tfh cells, naïve CD4 T cells isolated from PBMCs from PWoH (n=5), were cultured in the presence of either IL-10, TGF-β, or the combination of both cytokines (hereafter called combo condition ( Fig. 3F ). After 3 days in culture, all conditions promoted differentiation of naïve CD4 T cells towards several subsets ( Extended Data Fig. 3A-B ). A significant enrichment in the absolute numbers of Tfh cells, characterized by the simultaneous increased expression of CXCR5, PD1, and ICOS ( Fig. 3G ) was observed in the combo condition (p =0.007, p = 0.0185, and p= 0.0342 for combo treatment vs. TCR stimulation, IL-10, and TGF-β, respectively; Fig. 3H). Tfh cells induced under this condition exhibited significantly higher per-cell levels of BCL6 protein expression, as assessed by median fluorescence intensity (MFI), compared with non-Tfh memory CD4⁺ T cell subsets (p= 0.0011, Extended Data Fig. 3C ). Furthermore, the combo-differentiated Tfh cells also presented the appropriate transcription factor profile, evidenced by higher expression of BCL6 and reduced expression of BLIMP-1 ( Extended Data Fig. 3D ). The balance between these two TFs is critical for Tfh cell fate, with BCL6 functioning as a master regulator for Tfh differentiation supporting B cell help, while BLIMP-1 antagonizes this process 28,29 . In addition to the induction of the Tfh phenotype (CXCR5, PD1, ICOS) and its proper transcriptional factor machinery (BCL6 hi BLIMP-1 lo ), the combo condition also induced Tfh cells capable of producing IL-21 ( Extended Data Fig. 3E-F ), a cytokine that depicts Tfh functionality 30,31 . In the other cytokine treatment groups, IL-21 was produced mainly by effector memory CD4 T cells (T EM ) known as well to produce IL-21 32 ( Extended Data Fig. 3G ). To further confirm the contribution of IL-10 and TGF-β for Tfh differentiation, we used titrated anti-IL-10 or Galunisertib to block IL-10 (pSTAT3) or TGF-β (pSMAD2/3) signaling respectively ( Extended Data Fig. 4A-C ). Blockade of these signaling pathways markedly impaired Tfh differentiation, resulting in substantially lower numbers of Tfh cells. The dual blockade was detrimental for Tfh differentiation in our in vitro model ( Fig. 3I ). To validate that the combo condition induced the whole transcriptional profile of bona fide Tfh cells, we performed bulk RNA-Seq in cells from the different culture conditions. Gene Set Enrichment Analysis (GSEA) showed that Tfh cells generated in vitro by the combo condition were enriched in signatures found in ex vivo isolated bona fide Tfh cells 33 (Fig. 3J, top panel), even though other cell types were also present ( Extended Data Fig. 3B ). The high-confidence interaction network using leading-edge genes (LEGs) enriched in the combo condition (STRING database (score ≥ 0.7)) showed PDCD1, CTLA4, TIGIT, CD27 and CXCR5 were the key drivers of the induced gene signature ( Fig. 3J, bottom panel). Of note, the combo condition led to the differentiation of bona fide Tfh cells with significantly lower expression of antiviral pathways, including the downstream signaling of type I, II and -III IFNs 34 , and HIV restriction factors 27 ( Fig. 3J, top panel , Supplementary Table 3 ) when compared to TCR stim only (p = 0.006, 1.76 x10 -30 , 6.33 x 10 -17 , 5.84 x 10 -7 ,0.0004, for Tfh, type I, II, III and HIV restriction factors, respectively). Of note, the Tfh cells differentiated by combo condition were also more infected than the non-Tfh cells (p = 0.0625, Extended Dat Fig. 5A ) and the BCL6 protein levels were significantly associated with the infection rates (p =0.0345 /rho = 0.953; Extended Data Fig. 5B ). Altogether, this data confirms the role of IL-10 and TGF-β to induce the differentiation of bonafide Tfh cells with higher expression of BCL6 and lower expression of antiviral machineries. Genetic gain- and loss-of-function CRISPR screens support the proviral role of BCL6 and Tfh-related genes. To genetically validate the role of Tfh-related genes as proviral factors, we performed targeted analyses within genome-wide CRISPRn (knockout) and CRISPRa (activation) screens to mimic loss and gain of function, respectively ( Fig. 4A ). A curated set of candidate genes ( Supplementary Table 1 ) was evaluated for their proviral/antiviral properties at 96 hours post-HIV infection. Genes were classified as proviral or antiviral based on direction-matched false-discovery rates (FDR <0.3). In the CRISPRn library, among the selected genes, knockdown of BCL2, BCL6, EZH2 and HIF1a reduced HIV infection rates, highlighting their proviral activity ( BCL2, log2-fold changes (HIV-GFP+/HIV-GFP-) (LFC) = −0.423, FDR = 1.5×10 −4 , rank: 34; HIF1A , LFC = −0.265, FDR = 0.0092, rank: 60; BCL6 , LFC = −0.122 FDR = 0.135, rank: 131; EZH2, LFC = −0.171, FDR = 0.187, rank: 149) ( Fig. 4B ). Additionally, the CRISPRa mediated overexpression of TGFBR2 and FOS led to increased HIV infection rates, further highlighting their proviral properties ( FOS , LFC = 0.025, FDR = 0.213, rank: 733; TGFBR2, LFC = 0.231, FDR = 0.240, rank: 831) ( Fig. 4C ). These are key transcriptional, signaling, metabolic, and epigenetic regulators that collectively shape Tfh cell differentiation while simultaneously creating a cellular environment permissive to HIV infection. On the other hand, CRISPRn for EP300 , and CRISPRa for MYC, NR4A1, TRIM5, BACH2 and CEBPB confirmed their antiviral roles, with the knockout of EP300 increasing infection, and the overexpression of MYC, NR4A1, TRIM5, BACH2 and CEBPB decreasing HIV infection in vitro ( MYC , LFC = −0.527, FDR = 8.8×10 −5 , rank: 10; NR4A1 , LFC = −0.222, FDR = 7.7×10 −4 , rank: 70; TRIM5, LFC = −0.334, FDR = 0.0086, rank:107; BACH2 , LFC = −0.183, FDR = 0.113, rank: 242; and CEBPB , LFC = −0.472; FDR = 0.179, rank: 318). Together, these findings support a model in which the IL-10/TGF-b/BCL6 axis, important for Tfh differentiation, promotes early HIV permissiveness linking LN cytokine signaling to Tfh biology and HIV seeding. Single-nucleotide polymorphisms in proximity to IL-10 and TGF-β genes are associated with HIV elite control. To establish the clinical relevance of these pathways in HIV control, we analyzed genome-wide association study (GWAS) data from spontaneous HIV controllers (HIC), including HIV elite controllers (ECs), who maintain durable suppression of viremia in the absence of ART and typically exhibit reduced HIV reservoir sizes, including within lymph nodes 35-39 . We leveraged GWAS data from “The 2000HIV study” 40 and performed an hypothesis-driven candidate gene association study (CGAS) 41 to identify single-nucleotide polymorphisms (SNPs) defining spontaneous HIV control, based on our experimental data ( Fig. 4D ). We selected 60 genes associated with Tfh signatures and/or part of the IL-10 and TGF-b signaling pathways ( Supplementary Table 4 ). Although the selected genes have not emerged as genome-wide significant loci in unbiased GWAS (typically p<10⁻⁸), we hypothesized that genetic variants within or proximal to these loci could nonetheless modulate LN biology and, in turn, influence HIV seeding into Tfh cells, thereby contributing to viral control in these individuals. We extracted GWAS summary statistics for all SNPs within a 150 kb window from these genes (n = 45,202) and pruned for independence using a 500 kb window, retaining one independent SNP per gene. More than 45,000 SNPs were found for the selected genes in a populational level ( Supplementary Table 5A ). Of these, eight independent SNPs passed the threshold for suggestive association (p<10 -3 , Supplementary Table 5B ). Of note, the most significant association with the HIC phenotype was found for rs885334, located ~15kb upstream of the IL10 gene (C allele, odds ratio (OR) = 2.18, p = 1.56 × 10 -5 , Fig. 4E ). We also observed association with the HIC phenotype for rs6503691, a SNP in proximity to the STAT3 gene (the transcription factor downstream of IL-10 signaling) which falls within an intron of the STATB5B gene, (T allele, OR = 2.26, p = 6.17× 10 -4 , Fig. 4F ). In the TGF-β signaling pathway, rs4955304 near TGFBR2 gene, was similarly associated with the HIV elite control phenotype (T allele, OR = 2.615; p = 1.67 ∙ 10 -4 , Fig. 4G ). In addition, SNPs proximity to genes KIFC3 (rs11866228, T allele, OR = 2.52, p = 2.3 ∙10 -5 ), HDAC4 (rs79547584, T allele, OR = 2.78, p = 5.60 ∙10 -5 ), E2F4 (rs76230685, A allele, OR = 2.24, p = 6.77 ∙10 -5 ), E2F8 (rs6483581, C allele, OR = 2.10, p = 0.0009) were all also suggestively associated with the HIC phenotype, while the SNP rs12443672, close to the gene SIAH1 (T allele, OR = 0.18, p = 0.0008) was associated with the progressor phenotype ( Supplementary Table 5B ). SNPs in proximity to BCL6 (rs55813711, p = 0.00856, OR = 1.653) and the pro-survival molecule BCL2 genes (rs524916, p = 0.0012, OR = 1.809) were also identified and associated with the HIC phenotype, however with lower levels of confidence. Together, these findings suggest that genetic variation in IL-10 and TGF-β related pathways are associated with spontaneous HIV control and may influence reservoir establishment within lymphoid tissues. Therapeutic down-modulation of IL-10 and TGF-β signaling in vivo in rhesus macaques enhances antiviral programs and decreases frequencies of BCL6 + Tfh cells in LNs. In our prior work using rhesus macaques (RMs) infected with SIV and maintained on ART for long-term, we demonstrated that in vivo immune intervention with de-immunized monoclonal antibodies targeting IL-10 and PD-1 signaling (combination therapy, “combo”) resulted in unprecedented control of viral rebound following analytical treatment interruption (ATI) 42 . Notably, a subset of combo-treated RMs also exhibited a significant reduction in the size of the SIV reservoir within LNs 43 , allowing for discrimination of two distinct groups based on LN cell-associated viral DNA levels: animals with low reservoir size (CA-vDNA lo , n=4) and those with higher reservoirs (CA-vDNA hi , n=6) ( Fig. 5A ). Importantly, CA-vDNA lo combo-treated RMs also displayed a marked attenuation of TGF-β signaling 43 . Thus, in this in vivo study, CA-vDNA lo RMs represent a setting of effective in vivo blockade of key pathways involved in Tfh differentiation, including IL-10, PD-1, and TGF-β signaling. Leveraging publicly available multiome datasets (scRNA-seq+scATAC-seq) from this study 42,43 , we assessed the impact of the combo therapy on the induction of antiviral programs within LN Tfh cells prior to ATI comparing CA-vDNA lo vs CA-vDNA hi RMs, and evaluated, by flow cytometry, the frequency of BCL6-expressing Tfh cells at ATI. Pathway over-representation analysis revealed a dominant enrichment of interferon-associated pathways in Tfh cells from CA-vDNA lo RMs (FDR < 0.1) as compared to CA-vDNA hi . These included Hallmark interferon-α and interferon-γ responses, Reactome interferon signaling pathways (including interferon-α/β signaling), and Gene Ontology terms related to type I interferon responses and negative regulation of viral genome replication ( Fig. 5B ; Supplementary Table 6 ). Of relevance, gene signatures enriched in HIV ECs as compared to non-controllers 42,44 (pathway named EC vs non-HIC up) were also significantly elevated in Tfh cells from CA-vDNA lo RMs, highlighting that the down modulation of these pathways is critical for the EC phenotype, resulting in a smaller reservoir sized in LNs. Core genes shared across these pathways included canonical interferon-stimulated genes and antiviral effectors (MX1, ISG15, OAS1, DDX60, IFI6, IFI27, IFI44, HERC6), together with activation-associated transcripts (CD38, SELL, ST8SIA4, FKBP5). Of note, the frequency of BCL6⁺ Tfh cells (CD4⁺CD200⁺cMAF⁺BCL6⁺) was significantly higher in LNs from CA-vDNA hi RMs, which maintained active TGF-β signaling, compared with CA-vDNA lo RMs (p = 0.0253; Fig. 5C-D ). Collectively, these in vivo data support that the downmodulation of these pathways reprograms LN Tfh cells toward an elite-controller-like antiviral state, reduced BCL6-dependent Tfh maintenance, resulting in low SIV reservoir sizes. Discussion In this study, we show that IL-10 and TGF-β cooperate to induce a BCL6-dependent Tfh transcriptional program that suppresses interferon signaling and antiviral restriction factors, rendering these cells highly permissive to HIV infection. We confirmed that i) BCL6 hi CD4 T cells are preferentially infected and pharmacological or genetic BCL6 degradation limits HIV infection which is further synergized by IFNs ; ii) BCL6 hi CD4 T cells present significantly lower expression of antiviral machineries than other CD4 T cell subsets; iii) IL-10 and TGF-β induce the differentiation of bona fide Tfh cells with higher expression of BCL6 and lower expression of antiviral genes, iv) SNPs in genes associated with IL-10 and TGF-β signaling contribute to the phenotype of HIV elite controllers, and v) in vivo in ART-treated SIV-infected RMs , the blockade of IL-10, PD-1 and TGF-b signaling pathways induces antiviral machineries in Tfh cells, decreasing the frequencies of BCL6+Tfh cells, and result in lower reservoir size in LNs post-ATI, mimicking HIV ECs. To our knowledge, this is the first comprehensive investigation on the combined actions of IL-10 and TGF-β on modulating the intrinsic antiviral properties of Tfh cells during chronic and ART-treated infection, thereby enhancing their susceptibility to HIV infection leading to the persistence of the HIV reservoir in LNs on ART. Interventions capable of modulating these pathways may contribute to HIV cure strategies by reducing the size of the HIV reservoir in LNs, similarly to the observed in our pre-clinical study. IL-10 and TGF-β decrease antiviral signatures in Tfh cells. Lymph nodes are key sites for initiating adaptive immune responses by facilitating antigen presentation to T and B cells, resulting in their activation, proliferation, and differentiation 45,46 . This environment is enriched in cytokines that modulate immune responses, including IL-10 and TGF-b, making this a well-regulated site for the generation of proper immune responses 16,47 . In the periphery, these cytokines are well known for suppressing immune responses 48,49 , important in the context of viral infections 50,51 . IL-10 decreases the expression of MHC molecules on antigen presenting cells 17,52 and the effector function of T cells by decreasing their cytokine production capability, proliferation and differentiation 48 . TGF-β is also known for decreasing immune function and for antagonizing antiviral machineries 43,53,54 . Furthermore, TGF-β reduces T cell proliferation 55,56 and plays major roles in tumorigenesis. In the context of LNs, these cytokines counter-regulate the hyper immune reaction in situ and lead to a controlled homeostatic environment for T and B cell interactions, priming and maturation 47,57 . While IL-10 and TGF-β broadly constrain immune activation, our data demonstrate that within LN these cytokines suppress the intrinsic antiviral defenses of Tfh cells, rendering them susceptible to infection 8,10 , which could support the role of these cells as a source of virions in the absence of ART 5 . BCL6 is the upstream TF dampening antiviral machineries in Tfh cells. BCL6 is one of the master TFs of Tfh cells 13,14 which was reported to directly repress IRF7 transcription 22,23 and down-modulate RIG-I driven antiviral immunity. By interacting with the nuclear co-repressor complex (NCOR1) and histone deacetylase 3 (HDAC3), BCL6 promotes repressive histone modifications at the IRF7 locus 23 . This BCL6-mediated suppression of key antiviral genes likely disrupts the IRF7-IFN axis in Tfh cells, limiting the expression of antiviral and HIV restriction factors, creating a permissive environment that facilitates HIV intactness, transcription and persistence in tissues. The use of BCL6 inhibitor FX1, which binds the lateral side of the BTB domain of BCL6, reduces T cell activation, proliferation, and the phosphorylation of SAMHD1 (pSAMHD1, Thr592), thereby creating a cellular environment permissive to HIV replication 58 , similar to other BCL6 peptide inhibitors 22 . Together, the LN microenvironment per se (IL-10/TGF-β), the high BCL6 expression, and the decreased expression of antivirals facilitate the seeding of HIV-1 in Tfh cells. APOBEC3G, an IFN-induced protein, is an HIV restriction factor and part of the cytidine deaminase family 59-61 . It causes G-to-A hypermutations, rendering the viral genome nonfunctional. APOBECs were shown to be decreased in Tfh cells in this work and in our previous work 10 . The downmodulation of antivirals and HIV restriction factors could be the major upstream mechanisms for the higher intactness of provirus in these cells. The development of compounds that elevate the cellular levels of APOBEC3G and APOBEC3F proteins led to reduction in HIV infectivity ex vivo by interfering with cell-intrinsic degradation pathways 62 . HIV elite controllers exhibit SNPs in genes involved in IL-10 and TGF-β signaling pathways. Natural HIV controllers ( i.e., HIV ECs) present with decreased reservoir size 63 . The presence of protective HLA alleles (i.e., HLA-B57:01-03, HLA-B27:05) have been reported to be enriched in this population 64 . It was demonstrated that HLA influence on EC likely extends beyond traditional HLA class I or class II allele associations, encompassing other HLA SNPs with various biological impacts (e.g., psoriasis) 65 . Allelic variations at immune loci, including variants in or near IL7RA 66 , IRF5-TNOP3 67 , TRIM5a 68 , BST2 69,70 , TNF-α-238 and PDCD1-7209 71 , have been previously reported promote HIV control in the absence of ART. In contrast, specific alleles/genotypes at variants of the IL6 -174G/C, FASL -124A/G, FAS -670A/G 72 , vitamin D-binding protein (DBP) 73 , APOBEC3H (A3H) 74 , and the allelic frequency of CCR5 59353C 75 , have been associated with faster disease progression. Across all these studies, none of them reported SNPs on Tfh-related genes associated with the HIC/EC phenotype. In our hypothesis-driven analysis, intronic variants in IL10 and STAT3, together with an ncRNA-exonic variant at the TGFBR2 locus, may impact gene regulatory programs in a cell-state dependent manner. Such effects could shape Tfh biology and antiviral gene signatures (e.g., IRF3, IRF7, IRF9, APOBEC3G), with implications for tissue HIV control in HIC individuals. Indeed, our genome-wide CRISPR screening confirmed that loss-of-function and gain-of-function mutations in these genes modulate pro- or antiviral roles in HIV infection. Additionally, the EC signatures were also enriched in Tfh cells from LNs of the CA-vDNA lo RMs, highlighting the importance of this higher antiviral machinery for the natural or induced HIV EC phenotype . Interventions to modulate Tfh/antiviral related pathways to improve HIV cure strategies . Modulation of Tfh-related and antiviral pathways with antibodies or small molecules could alter the Tfh biology and contribute to HIV cure strategies by modulating the tissue reservoir. The induction of antiviral immunity by infusing a pegylated IFN form in Rhesus macaques led to protection against infection 76 . However, the chronic activation of this pathway can be detrimental in the context of HIV 77 , but also in several other comorbidities 78-81 . The direct induction of APOBECs by small molecules, or the blockade of their degradation by HIV-Vif 82 could be another way to impair Tfh cells as reservoirs. The blockade of IL-10 in vivo in SIV infected RMs 21 decreased the Tfh signatures in the periphery and could further be used as an intervention to modulate the HIV reservoir in tissues. Of note, TGF-b blockade in vivo led to significant decay of LN Tfh cells and in the reservoir size, while increasing the expression of IFN-signaling 83 . BCL6 blockade by PROTAC 84,85 has been evaluated in cancer clinical trials and shows promise in degrading the BCL6 protein 86 , thereby impeding tumor growth. A possible combination of these molecules, along with interventions to boost the immune system 43 , could be used temporarily in PWH on ART to induce antiviral responses and reduce the tissue reservoir, leading to sustainable EC phenotype and possibly a cure. In summary, our work defines a mechanistic framework by which IL-10 and TGF-β signaling, through the induction of BCL6, programs Tfh cells to downregulate intrinsic antiviral defenses, thereby fostering a cellular state that is highly permissive to HIV persistence. By integrating transcriptional, functional, and genetic evidence, we demonstrate that this cytokine-transcription factor axis is not only central for the Tfh reservoir biology but also amenable to therapeutic intervention. SNPs in these genes in HIV ECs, highlight the clinical relevance of such pathways. These insights open avenues for cure-directed strategies that combine targeted modulation of Tfh differentiation or BCL6 activity with established interventions such as latency reversal, immune checkpoint blockade, disruption of the IL-10 and TGF-β signaling pathways, and other agents reversing immune dysfunction in PWH. Ultimately, disrupting the protective niche provided by BCL6 hi Tfh cells represents a promising strategy to diminish the size and resilience of the HIV reservoir in LNs, potentially informing future cure-directed strategies. Methods Ethic Statement. All tissue samples from PWH were procured with explicit written informed consent from participants prior to donation, adhering strictly to the principles outlined in the Declaration of Helsinki. The utilization of remnant samples was formally sanctioned by both the Research Committee and the Ethics in Research Committee of the National Institute of Respiratory Diseases "Ismael Cosío Villegas" (INER), Mexico City as part of the “C71-18” protocol. Under a material transfer agreement this project was conducted at Emory University with ethical approval STUDY00006200. Participants recruitment . The volunteers to participate in the LN protocol were under HIV diagnostic assessment or under follow-up for virological control. Each volunteer underwent a comprehensive clinical evaluation by a specialist in General Surgery and/or Otorhinolaryngology to detect palpable superficial lymph nodes suspected of malignancy or chronic infection. Tonsil donors were under evaluation for sleep apnea and tonsillectomy was performed when clinically indicated. Following clinical assessments, volunteers attended a recruitment consultation where they were informed about the study objectives and the significance of their lymph node/tonsil donation for diagnostic and research purposes and were provided with informed consent documents to review and discuss any questions. Once written consent was obtained, preoperative laboratory tests were performed, followed by a pre-anesthetic assessment using the American Society of Anesthesiologists Physical Status (ASA PS) scale, with individuals in groups 3 and 4 excluded prior to scheduling the outpatient procedure. Cervical LN extraction was limited to superficial, low-risk nodes to avoid vascular or nerve injury, performed under local anesthesia (5 cc of 2% lidocaine + 1:10000 epinephrine) with a precise skin incision, and closed with layered absorbable sutures (Vicryl 4-0, Monocryl 5-0). In the case of inguinal LN acquisition, individuals underwent evaluation through palpation or percutaneous ultrasound (Fujifilm Sonosite, M turbo, Transducer 13-16MHz) of the inguinal region. LN biopsies were performed under local anesthesia (5 cc 2% lidocaine + 1:10000 epinephrine and 5 cc 7.5% ropivacaine) with a ~1.5 cm ultrasound-guided incision, followed by closure with absorbable sutures. Nine PWH receiving suppressive antiretroviral therapy (ART) for over one year donate gut tissue biopsies. Ileal, colonic, and rectal samples were collected during lower endoscopy procedures (Olympus CF-HQ190). Twenty tissue snips per anatomical site were collected using standard biopsy forceps (Boston Scientific, Radial Jaw), immediately transferred into complete RPMI medium (cRPMI, RPMI 1640 [Cytiva, Marlborough, Massachusetts] supplemented with 10% fetal bovine serum [Biowest, Bradenton, Florida], 2mM L-glutamine [Cytiva], and 100 U/ml penicillin and 100ug/ml of streptomycin [Cytiva]), and transported to the laboratory within 15 minutes of collection. No adverse events were reported during the procedures. All participants were scheduled for a follow-up consultation one week after the surgical procedure. None of the participants of this study had opportunistic infections, and all were HBV- and HCV-negative. Clinical and demographic data of all donors is shown in Supplementary Table 7. Lymph node and tonsil sample processing. All LNs and tonsils were divided into three parts: the first placed in saline solution, used in microbiological studies to rule out bacterial, fungal, and Mycobacterium tuberculosis presence; the second, placed in paraformaldehyde 4%, used for histopathological examination to rule out neoplasms; and the third part was used for cell dissociation to be used in experiments of this study. For paraffin-embedded tissue, fresh tissues were fixed as soon as possible after biopsy for 24 hours in freshly prepared paraformaldehyde in PBS (4%) followed by preparation of formalin-fixed paraffin-embedded (FFPE) blocks using standard procedures from Roche Diagnostics (Ventana Medical Systems, Tucson, AZ, USA). To obtain cell suspension from the tissues, biopsy samples were placed in cRPMI and immediately transfer to the laboratory for further processing. Lymphoid tissues were cut into small pieces with a scalpel and manually dissociated using a 70mm mesh and a syringe embolus. Erythrocytes were removed by incubating the cells with ACK (Ammonium-Chloride-Potassium, Lonza, Houston, Tx, USA) Lysing Buffer for 5 minutes. Cells were counted and cryopreserved until further use at a density of 10 million cells per ml of freezing media (FBS [Corning, Cat no 35-016 CV] with 10% DMSO [Millipore-Sigma, cat # D2650-100ml]). Microbiological tests and histopathological studies were conducted in INER laboratories, with results recorded in the participants’ medical records. Additionally, peripheral blood was collected for plasma viral load determination and T lymphocyte counts at the virological diagnosis laboratory and the cytometry laboratory of CIENI. None of the microbiological or histopathological tests returned positive for bacterial or fungal culture, nor for neoplasia. Gut sample processing. Intestinal tissue biopsies were processed to obtain single-cell suspensions for downstream immunological and transcriptomic analyses. Samples were transferred into Petri dishes containing 2 mL of digestion medium composed of RPMI (Cytiva), 10% FBS, 1% HEPES (Gibco, cat # 15630080), 0.05 mM 2-mercaptoethanol (Millipore-Sigma, cat # M3148), 0.5 mg/mL Liberase (Roche, cat # 5401020001), and 10 U/mL DNase I (Invitrogen, cat # 18047019). Tissues were finely minced with sterile disposable scalpels and transferred to 15 mL conical tubes containing an additional 3 mL of digestion medium. Samples were incubated at 37 °C for 1 hour under continuous agitation. After enzymatic digestion, mechanical disaggregation was performed, followed by filtration through a 70 μm cell strainer (Falcon, cat # 352350). Freshly isolated gut cells were maintained in cRPMI medium at 37 °C prior to staining. Fc receptor blocking was performed using Human TruStain FcX™ (BioLegend, cat # 422302) for 10 minutes at room temperature. Cells were subsequently stained for 30 minutes at 37 °C in cRPMI with CD45-BV570 antibody (clone HI30, BioLegend, Cat # 304034) and viability marker (LIVE/DEAD Fixable Aqua Dead Cell Stain Kit, for 405 nm excitation, Invitrogen, cat. No L34966). After staining, cells were washed and maintained in cRPMI until sorting. Cell sorting was performed on a BD FACSAria Fusion cytometer. Following standard gating strategies to exclude doublets and dead cells, and to isolate viable CD45⁺ singlet leukocytes. CD45 + sorted cells were used to prepare single-cell RNA libraries using the 10x Genomics platform. Multiplex immunofluorescence imaging. Before staining, the slides were heated on a metal hotplate (Stretching Table, Medite, Burgdorf, OTS 40.2025, Ref. 9064740715) at 65 °C for 30 min. Tissue sections were stained with titrated antibodies ( Supplementary Table 8 ) using a Ventana Discovery Ultra Autostainer (Roche Diagnostics, Ventana Medical Systems, Tucson, AZ, 85755, USA). Tissues were deparaffinized, hydrated and the protein epitopes were retrieved by applying the standard Ventana Discovery’s protocols. Before all antibody incubation steps, tissues were blocked using Antibody Diluent/Block from Akoya (ARD1001EA, Akoya Biosciences, Marlborough, MA 01752, USA). Opal dyes (Opal 7-color Automation IHC kit, from Akoya, Ref. NEL821001KT and Opal650 reagent pack FP1496001KT) were used to amplify the signal of primary antibodies ( Supplementary Table 8 ). More specifically, tissue sections were sequentially subjected to antibody blocking, staining with primary antibodies, incubation with secondary HRP-conjugated antibodies (DISCOVERY OmniMap anti-Ms HRP/ 760-4310, DISCOVERY OmniMap anti-Rb HRP/ 760-4311) for 16 min, detection with optimized fluorescent Opal tyramide signal amplification (TSA) dyes and repeated antibody denaturation cycles. RNAscope in situ hybridization for TGF-b, IL-10, BCL6 and HIV RNA visualization was performed according to the manufacturer’s instructions using the RNA probes ( Supplementary Table 8 - Advanced Cell Diagnostics, Hayward CA) and the RNAscope Multiplex Fluorescent Reagent Kit v2 (Advanced Cell Diagnostics, Hayward CA) with small modifications. Briefly, tissue samples were deparaffinized by applying the standard Ventana Discovery’s protocols. Then, we treated the sections with RNAscope Hydrogen Peroxide for 10min at RT, followed by an antigen retrieval step at 100°C for 15min. Subsequently, sections were incubated with Protease III for 15min at 40°C in a HybEz hybridization oven (ACD). Sections were incubated either with the TGF-b, IL-10 or with Bcl6 and HIV specific probes at 40°C for 2 hours, and then proceeded with 3 signal amplification cycles using the RNAscope amplification reagents. To visualize the RNA signals, we used the tyramide based detection system by Akoya as mentioned above (Opal 7-color Automation IHC kit, from Akoya, Ref. NEL821001KT). Following the RNAscope protocol, we subjected the tissue sections to antibody blocking for 30min and then we proceeded with the protein marker staining ( Supplementary Table 8 ). Applied antibodies were tested for their compatibility with the RNAscope protocol. Both unconjugated and conjugated antibodies were diluted in Antibody Diluent/Blocker and incubated for 90min RT. Alexa Fluor conjugated secondary antibodies were diluted in Antibody Diluent/Blocker and incubated for 45min at RT. All of the stained slides were counterstained with with SYTO45 (1/10 000 dilution in TBS‐T, Cat. No. 10297192, ThermoFisher Scientific for 23 mins, rinsed in soapy water and mounted using DAKO mounting medium (Dako/Agilent, Santa Clara, CA, USA, Ref. S302380-2). A representative individual cell positive for both HIV and Bcl6 mRNAs in the context of B and T cells is shown ( Fig. 1A, lower panel). The representative image was included to show the close proximity of T and B cells in the germinal center, which could lead to signal spillover between the two cell types 87 , in addition to possible cells sharing interconnected membrane structures 88 . Confocal data acquisition. Images were acquired using a Leica Stellaris 8 SP8 confocal system, equipped with Leica Application Suite X (LAS-X)-4.6.1.27508 software, at 512 × 512-pixel density, 0.75× optical zoom and a z-step of 0,69 to 0,8(max) in order to have a much detail as possible, using a 20× objective (0.75 NA). High-resolution images were also captured at 1024x1024 -pixel density, 1× optical zoom and a z-step of 0.8 μm using a 40× objective (1.4 NA). For RNAscope data acquisition, a 40x (0.95 NA) and a 63x (1.4 NA) objective was used. Frame averaging or summing was never used while obtaining the images. At least 70% of each section was imaged to ensure an accurate representation and minimize selection bias. Tissues stained with a single antibody fluorophore combination were used to create a compensation matrix via the Leica LAS-AF Channel Dye Separation module (Leica Microsystems), which was used to correct fluorophore spillover (when present), as per the user’s manual. When the dye separation results were not optimal, the manual LAS-AF Channel Dye Separation module was employed. Quantitative Imaging Analysis (Histo-Cytometry). The Surface Creation module of Imaris (v 9.9.0) was used to generate 3-dimensional segmented surfaces (based on the nuclear signal) of spillover-corrected images. Segmented cells were then processed with the filtering Imaris module using different combinations of filtering types based on the mean and median intensities of channels to exclude artifacts that are characterized by a uniform/background-like staining across the segmented area. Areas with uniform staining were excluded among the different tissues. Data generated, such as average voxel intensities across all channels, in addition to the volume and sphericity of the 3-dimensional surfaces, were exported in Microsoft Excel format. The Excel files obtained from cell segmentation were converted to comma separated value (.CSV) files, and data were imported into FlowJo (version 10) for further analysis. Well-defined follicular areas were included in the analysis of follicular immune landscape, and the data were quantified as relative frequencies (%) or as absolute counts. For the analysis of individual samples, hand gating was performed for the identification of relevant cell subsets and the intensities of individual biomarkers used in gated populations were presented as 2D histo plots. Follicular areas were identified based on the density of CD20hi/dim, a biomarker specific for B cells. The cut-off values for the identification of cells expressing ‘high’ profile for a given biomarker (e.g., CD3, PD1) was determined based on the 2D plot expression profile for this biomarker on relevant cells (e.g., PD1 expression on CD3 vs CD20 cells), using Histocytometry 25 analysis and the inspection of its intensity in the raw mIF image. Histocytometry analyzed cells of interest were exported and imported into Imaris raw mIF image as segmented spots for the comparison/validation of these cells to their original counterparts. Integrated and intact HIV DNA. Cell-associated total and intact HIV-1 DNA analyses were performed on isolated CD4 T cells from blood or sorted LN-derived live CD3+CD8- T cells from untreated people with HIV-1 (PWH; Fiebig IV/V and chronic infection). Sorting of LN cells was based on CXCR5 and PD-1 expression into four subsets: CXCR5negPD-1neg double negatives (DN), CXCR5+PD-1neg, CXCR5midPD-1mid, and CXCR5hiPD-1hi Tfh cells, all at >95% purity. Immediately after sorting, cells were centrifuged and lysed in Direct PCR Lysis Reagent (DLR; Viagen, cat. #301-C) containing Proteinase K (Fisher Scientific, cat. #10181030) and XHoI restriction enzyme (Fisher Scientific, cat. #10880041). Cell pellets were stored at −20 °C until further processing. Sufficient material was recovered from each subset to support downstream virological assays. Cell-associated total and intact HIV-1 DNA analyses were performed on these lysates using the Rainbow proviral HIV-1 DNA dPCR assay, a multiplex digital PCR approach that simultaneously quantifies total and intact HIV-1 DNA, as previously reported 89 . Total HIV-1 DNA was defined by the detection of the repeated unique 5′ (RU5) region, and intact proviruses were classified according to three predefined combinations of HIV-1 sub genomic targets from the Rainbow assay. Reactions were run on a QIAcuity Four digital PCR platform (Qiagen, Germany) as described previously 89 . Briefly, 8 µL of sorted cells from blood or lymph nodes (32,000–120,000 cells per reaction; mean 72,200) were used per replicate (triplicates). Each 40 µL reaction contained 10 µL 4× QIAcuity Probe Master Mix (Qiagen, #250102), 2 µL of each primer/probe set (final concentrations: RU5, 0.675 µM primers/0.187 µM probe; PSI, 0.675 µM primers/0.187 µM probe; env, 0.5 µM primers/0.25 µM probe) 89 , 0.3 µL XbaI restriction enzyme (100,000 U/mL), lysate (5–8 µL) and nuclease-free water. Reaction mixes were loaded onto 26k 24-well nanoplates (Qiagen, #250001), partitioned and sealed on the QIAcuity system, and amplified with 2 min at 95 °C followed by 40 cycles of 94 °C for 30 s and 56 °C for 60 s. Imaging times were 500 ms, 500 ms, 400 ms, 200 ms and 400 ms in the green, yellow, orange, red and crimson channels, respectively. Tfh differentiation in vitro model. Human naïve CD4 T cells were enriched from PBMCs of PWoH, by magnetic bead negative selection with the EasySep Naive CD4+ T Cell Isolation Kit (StemCell Technologies, cat. # 17555), according to the manufacturer’s instructions. Purity (CD4+CD45RA+) was routinely confirmed to exceed 90% or higher by flow cytometry. Purified naïve CD4 T cells (7.5 × 104 cells/well) were resuspended in cRPMI medium supplemented with 10% of fetal bovine serum (FBS). Naïve CD4 T cells were activated in 96-well plates (70,000 cells per well) with T Cell TransAct, a polymeric nanomatrix conjugated to humanized recombinant CD3 and CD28 agonists (1:500, Miltenyi, Cat #130-128-758), in the presence of recombinant human IL-7 (4 ng/ml, R&D, Cat. # 207-IL) and anti-IL-2 (1 μg/ml, clone 5334, R&D, Cat # MAB202100) for blockade of IL-2 signaling. To induce Tfh cell differentiation, cultures were supplemented with recombinant human TGF-β (20 ng/ml; Peprotech, Cat # 100-21C-100ug), recombinant human IL-10 (10 ng/ml; Preprotech Cat # 200-10-100UG), or both cytokines and left in culture for 3 days (5% CO2, 37 o C). At the endpoint, the cells were counted using Countess and harvested for flow cytometric analysis or processed for RNA isolation and downstream gene expression profiling (see below). Differentiated cells were also infected in vitro with HIV for the analysis of susceptibility to infection. The flow cytometry panel included markers to recognize memory CD4 cell subsets (e.g., CD45RA, CCR7, CD27) including Tfh markers (PD1, ICOS, CXCR5, BCL6, c-maf, and IL21) (details of reagents and antibodies are in the Reagents, antibodies, and virus section below). For Infection rates, antibody for HIV-p24 was used. For inhibition experiments, cells were cultured with either Galunisertib (20ug/ml, LY2157299, Selleck Chemicals) or anti-IL-10 (10ug/ml, clone 3F9, Biolegend) and kept in culture for 3 days. Galunisertib was pre-incubated for one hour before the addition of stimulation media containing TGF-β. Flow cytometry. We used flow cytometry panels to evaluate Naïve CD4 cells differentiation, including Tfh cells, and IL-21 intracellular expression (details of reagents and antibodies are in the Reagents, antibodies, and virus section below). Intracellular cytokine production was assessed in unstimulated cells or following a 12 hours stimulation with phorbol myristate acetate (50ng/ml) and ionomycin (1mg/ml) as a positive control. Brefeldin A (1ml/ml, BD Biosciences, Cat # 555029) and monensin (1.4ml/ml, BD Bioscienes, Cat No 554724) were added at 1 hour post stimulation (total of 11 h). The unstimulated cells served to define cytokine production induced by differentiated cells under the different stimulation media. Surface staining was performed at room temperature for 20 min. Samples underwent fixation and permeabilization with eBioscience™ Foxp3 / Transcription Factor Staining Buffer Set (Invitrogen, Cat # 00-5523-00) for 45 min at 4 °C. Intracellular staining was performed for 45 min at 4 °C. Acquisition was performed on a minimum of 70,000 live cells on a A5 Symphony flow cytometer (BD Biosciences) driven by BD FACSDiva software. Acquired data was analyzed using FlowJo v.10.8.1. Representative cytograms are shown for each panel in the respective figures. Bulk RNA. Ten thousand cells were used for the gene expression profile of the differentiated cells. RNA was used as input for cDNA synthesis using using the Illumina® Stranded Total RNA Prep, Ligation with Ribo-Zero Plus kit (Illumina, cat # 20040529), according to the manufacturer’s instructions. Libraries were validated by capillary electrophoresis on an Agilent 4200 Tape Station, pooled at equimolar concentrations, and sequenced on an Illumina NovaSeq6000 at 100SR, yielding 25-30 million reads per sample. After sequencing, reads were processed using Cutadapt 90 (v4.4) to remove Illumina adapters and low-quality bases from the 3' end. Trimmed reads were then aligned to the human reference genome (GRCh38) using the STAR aligner 91 (v2.7.10a). Alignment parameters were set as follows: --outFilterScoreMinOverLread 0.3; --outFilterMatchNminOverLread 0.3 to improve alignment rates; and --quantMode GeneCounts to obtain read counts per gene. All other parameters were kept at their default values. Quantified gene counts of protein-coding genes in the GRCh38 were further screened for low-quality genes. Specifically, we excluded genes with fewer than 0.5 counts per million in more than 15% of the samples using the EdgeR package 92 in R (version 4.3.2). 92 in R (version 4.3.2). After filtering, 12,804 genes remained for downstream differential gene expression analysis. Differential gene expression analysis was performed using the DESeq2 package 93 (version 1.44.0) in R with raw read counts and a biological condition vector as input to identify differentially expressed genes between biological conditions. Package-recommended default parameters were used unless otherwise specified. Genes were considered differentially expressed based on a nominal p-value threshold. Estimated log2 fold-changes were subsequently used as input for gene set enrichment analysis via the fgsea 94 package in R with a collection of pathways for Tfh signatures, host antiviral restriction factors, and IFN signaling extracted from Locci et al., 2013 33 (GSE50391), Abdel-Mohsen et al., 2015 27 , and the Interferome database 34 , respectively. BCL6 inhibition assays in tonsil CD4 T cells. We performed HIV infection of CD4 T cells isolated from tonsils of PWoH. Tonsil cells were thawed, and total CD4 T cells were isolated by negative selection according to the manufacturer’s protocol and using the EasySep Human CD4+ T Cell Isolation Kit (StemCell Technologies, cat # 17952). Purified CD4 T cells (95%) were allowed to rest in cRPMI at 37°C in 5% CO2 for 6 hours. Cells were then plated at 2x10 6 cells/ml and treated with the BCL6 inhibitor (0.5nM, BI-3802, Selleckchem, cat # S6937) with or without IFN-b (2ng/ml, ProSci, cat # 40-278) for 24 hours. An unstimulated condition was included as a control. Cells were then infected by spinoculation as previously described and cells were kept in cRPMI supplemented with 30 U/ml IL-2 (R&D Systems; 202-IL) and 5mM saquinavir in the presence or absence of BCL6 inhibitor and/or IFN-b. The presence of HIV-p24+ cells was assessed by flow cytometry four days after infection. Gut biopsies Single-Cell RNA analysis. Gut single-cell emulsions were prepared within 30 minutes after cell sorting. Reactions were carried out using the Chromium Next GEM Single Cell 3' GEM, Library & Gel Bead Kit v3.1 and the Chromium Next GEM Chip G Single Cell Kit (10x Genomics, cat # 1000127), along with the i7 Multiplex Kit for samples 1–9 and the Dual Index Kit TT Set A for samples 10–11, following the manufacturer’s protocol. Each library was prepared using 10,000 cells per gut tissue. Sequencing was performed on the Illumina NextSeq 500 platform using paired-end reads and the NextSeq 500/550 High Output Kit v2.5 (150 cycles). Libraries prepared with the i7 Multiplex Kit were sequenced using 28 cycles for Read 1, 8 cycles for the i7 index, and 91 cycles for Read 2. Libraries prepared with the Dual Index Kit TT used 28 cycles for Read 1, 10 cycles for i7 and i5 indices, and 90 cycles for Read 2. Fastq files were processed in 10x Genomics Cell Ranger v5.0.1 using 10x Genomics Cloud Analysis 95,96 . Reads were mapped to the GRCh38 human reference genome and counted without depth normalization. The filtered count matrix was then analyzed using the Seurat (v5.1.0) 97 in R. Low-quality cells were identified and removed based on the following criteria: more than 100 RNA unique molecular identifiers, less than 25% mitochondrial read fraction. Doublet cells were identified using the DoubletFinder package in R 98 . Only CD4+ population cells were analyzed further. We performed single-cell RNA-seq analysis using the Seurat (v5.0.0) 99 package in R (v4.4.0), followed by batch correction with Harmony to integrate data across experimental conditions. Outlier cells were removed prior to analysis to ensure high-quality clustering. Genes associated with mitochondrial content, ribosomal components, hemoglobin, sex chromosomes, and the surfactant protein family were excluded to reduce technical and tissue-specific bias. Gene expression values were then normalized. We identified the top 5,000 highly variable features using the variance-stabilizing transformation (VST) method, and scaled gene expression values across cells. Principal component analysis (PCA) was performed using these variable genes, and the resulting principal components were corrected for batch effects using the Harmony algorithm. An elbow plot of the Harmony components was used to determine the number of informative dimensions, and the first 20 components were selected for downstream analysis. Clustering was performed using shared nearest neighbor (SNN) graph construction and the Louvain algorithm, with parameters k.param= 50 and resolution = 0.7 based on the Harmony-reduced space. We identified Tfh cells by using a combination of a composite expression score of Tfh markers (BCL6, CXCR5, CXCL13, CD200, PDCD1) and Azimuth Human Tonsil v2 100 (GC-Tfh-SAP or Tfh T:B border) labels. We also identified 2 non-Tfh populations for comparison: Th17 and Central Memory (CM) CD4+. A composite score of Th17 markers (CCR6, IL23R, and RORC) was used to select the Th17 cells. For the CM non-Tfh CD4+ group, the dataset was re-clustered using the Azimuth Human PBMC reference 101 . The cells labeled central memory and effector memory were selected. For all groups, only cells from PWH were kept for analysis. First, gene sets related to Tfh markers, viral machinery, and restriction factors were acquired. Single-sample gene set enrichment analysis (ssGSEA) was performed using the escape R package 102 to identify the enrichment score for each gene set of interest and cell type. The difference between 2 cell populations at a time was calculated. Statistical significance was determined using p-values using the Wilcoxon rank-sum test (<0.05) and effect sizes were quantified with Cliff's Δ (≥ abs(0.1). We then visualized the data using boxplots with a dot overlay. Only 70% of the population was represented by dots and was randomly sampled. In the second analysis we separated the cells based on tissue location. ssGSEA was performed using the escape R package to identify the cell enrichment score for each pathway in each tissue. Genetic analyses. The GWAS on HIV controllers from European ancestry in the 2000HIV cohort was performed as previously described 103 . In short, genotypes were associated with the spontaneous HIV controllers (HIC) phenotype in 67 HICs compared to 1,179 non-HICs. The logistic regression was performed in PLINK v1.90b 103 and the model was corrected for age, sex and the first five genetic principal components (PCs). We extracted summary statistics for all SNPs in a +-150 kb window from the 60 genes of interest. SNPs were greedily clumped based on a 500kb window. SNPs with a P-value below 1 ∙ 10 -3 were regarded as suggestively associated with the phenotype. Gene-based annotation was performed using ANNOVAR 104 using the RefGenewithVer protocol. Functional element-based annotation was performed by intersecting the SNP locations with the ENSEMBL regulatory features (v115 on GRCh38) using bedtools 105 . Expression Quantitative trait locus mapping was performed as described by Botey-Bataller et al 106 . Summary statistics were extracted for the suggestive SNPs, and those with < 1 ∙ 10 -5 in the discovery cohort and p < 0.05 in the validation cohort were regarded as significant. Analysis of pooled genome-wide CRISPR perturbation screens. Genome-scale pooled CRISPR activation (CRISPRa) and CRISPR knockout (CRISPRn) screening data from our previous study in stimulated primary human CD4⁺ T cells were analyzed as previously described (Cell, in press). In these screens, genetic perturbations were introduced into primary CD4⁺ T cells via lentiviral delivery of pooled sgRNA libraries, together with dCas9 (CRISPRa) or Cas9 (CRISPRn), followed by challenge with replication-competent, GFP-tagged HIV. Cells were sorted into GFP⁺ and GFP⁻ bins, and gene-level effects on HIV infection were computed from guide-level enrichment analyses using MAGeCK (v0.5.9.5). For the present study, we focused on a curated set of 60 genes associated with Tfh cell differentiation and IL-10/TGF-β signaling. Gene-level log₂ fold changes (GFP⁺/GFP⁻), ranks, and direction-matched false discovery rates (FDRs) were extracted from the pooled screening datasets and used to classify genes as proviral or antiviral (FDR < 0.3). Comparisons were performed between the target gene vs. overall distribution of all the guides in the screen. Full experimental procedures and data processing steps are described in the referenced study (Cell, in press). Non-human primate study. Publicly available datasets from Ribeiro et al., was used 42,43 . Briefly, 10 RMs received a combination therapy using de-immunized anti-IL-10 and anti-PD-1 antibodies during ART and early after ATI. This intervention led to 9/10 RMs to controll viremia post-ATI, and to 4 combo-treated RMs to decay the amount of cell associated viral DNA (CA-vDNA) in LNs (so called, CA-vDNA lo RMs). scRNA-Seq/ATAC-Seq and high dimensional flow cytometry were performed at pre-ATI and 24 weeks post-ATI, respectively ( Fig. 5A ). In this study we focused our analysis on TFh cells gene-signatures comparing CA-vDNA lo vs CA-vDNA hi RMs in LNs pre-ATI, and on the frequencies of Tfh cells expressing BCL6 post-ATI. Pathway over-representation analysis was performed with clusterProfiler R package (v. 4.18.2) using genes upregulated (FDR < 0.2) in TFH cells from RMs with low CA-vDNA compared to those with high CA-vDNA. Gene sets from the Human Molecular Signatures Database (MSigDB) Hallmark, C2, and C5 collections were included, along with gene sets containing genes up- and downregulated in CD4⁺ and CD8⁺ T cells and monocytes from Elite Controllers 106 . Enriched pathways (FDR < 0.1) were clustered using the vissE R package (v. 1.18.0), which computes gene set overlap via the Jaccard index and identifies highly connected clusters of pathways using the Walktrap community detection algorithm. The cluster primarily associated with interferon response was selected for further exploration. Statistical analysis. Correlation analyses were done using a Spearman’s correlation test. Rho and p values are shown. Differences between follicular and extra-follicular areas, Tfh and non-Tfh cells were analyzed using the Paired t-test or Wilcoxon signed-rank test. Differences among treatments were analyzed using the Friedman test followed by Dunn’s test to correct for multiple comparisons, and one-way ANOVA followed by Tukey’s post hoc test. p < 0.05 is reported as significant. Statistical analyses were performed using R 4.2.2 with rstatix package version 0.7.2 and GraphPad Prism 9.4.0 (GraphPad Software, Boston, MA). Plots were generated with the R package ggplo2 or GraphPad Prism. To construct a gene network of Tfh signature leading genes, leading-edge genes were identified from the enrichment analysis of Tfh-associated transcriptional signatures comparing the combo and TCR conditions (combo/TCR). The resulting gene list was mapped to the STRING database (version 12; species Homo sapiens) using the R package STRINGdb, with a minimum interaction score threshold of 0.7 (high confidence). The network was built in R using the igraph package and visualized with ggraph . Reagents, antibodies, and virus. IL-10 from Peprotech was used at 10ng/mL; IL-7 from R&D Systems was used at 40ng/mL; TGF-b1 from Peprotech was used at 20ng/mL; anti-IL-10 from Biolegend was used at 10ug/ml; Galunisertib (Selleckem, cat # S2230). Antibodies for flow cytometry were acquired from BD Biosciences (BCL-6-PE-CF594, clone K112-91, cat. No562401; BCL6-BUV737, clone K112-91, cat. No 567412; BLIMP-1-Alexa Fluor 700, clone 6D3, cat. No567764; BLIMP-1-PE, clone 6D3, cat. No564702; CCR7-BUV395, clone 3D12, cat. No740267; CCR7-PE-Cy7, clone 3D12, cat. No557648; CD27-APCCy7, clone M-T271, cat. No560222; CD3 -BUV805, clone UCHT1, cat. No612895; CD3-BUV737, clone UCHT1, cat. No612750; CD3-BV750, clone UCHT1, cat. No747177; CD45RA-BUV563, clone HI100, cat. No612926; CXCR5 -BUV496, clone RF8B2, cat. No741115; FOXP3-BB700, clone 236A/E7, cat. No566526; ICOS-BUV805, clone DX29, cat. No748903; IFN-g-BB700, clone B27, cat. No566394; IL-10R-Alexa Fluor 647, clone 3F9, cat. No556013; IL-2-BV750, clone MQ1-17H12, cat. No566361; IL-10R-Alexa Fluor 647, clone 3F9, cat. No565255; IL-10R-BV421, clone 3F9, cat. No742942; IL-10R-PE, clone 3F9, cat. No556013; IRF7-Alexa Fluor 488, clone K40-321, cat. No558707; pIRF7 (pS477/pS479)-PE, clone K47-671, cat. No558621; pSTAT1 (pY710)-BB515, clone 4a, cat. No612596; PSTAT5 (pY694)-PE-CF594, clone 47/STAT5(pY694), cat. No562501; Smad2 (pS465/pS467)-PE-CF594, clone O72-670, cat. No562697; Tbet-BV711, clone O4-46, cat. No563320; TNF-Alexa Fluor 488, clone MAb11, cat. No557722); BioLegend (CD4-BV605, clone OKT4, cat. No317438; CD4-BV605, clone OKT4, cat. No317438; CD4-BV650, clone OKT4, cat. No317436; CD45RA-BV605, clone HI100, cat. No304134; CD45RA-BV650, clone HI100, cat. No304136; ICOS-BV421, clone DX29, cat. No313524; IL-10R-PECY7, clone 3f9, cat. No308814; IL-10R-PECy7, clone 3F9, cat. No308814; PD1-BV786, clone EH12.2H7, cat. No329930; PD1-BV786, clone EH12.2H7, cat. No329930; pSTAT3 (pS727)-APC, clone A16089B, cat. No698914; pSTAT3 (pY705)-Percp Cy5.5, clone 13A3-1, cat. No651022; TCF7-Alexa Fluor 647, clone 7F11A10, cat. No655204); Beckman Coulter (HIV-1 core antigen-RD1, (clone KC57, cat. No6604667; HIV-1 core antigen-FITC, clone KC57, cat. No6604665), Invitrogen (CD27-APCeFluor780, clone O323, cat. No47-0279-42; IL-21-PE, clone eBio3A3-N2, cat. No12-7219-42), Thermo Fisher Scientific (c-maf-PE-eFluor610, clone sym0F1, cat. No61-9855-42), and LSBio (NLRX1-BIOTIN, clone Polyclonal, cat. NoLS-C499438). Streptavidin-BUV737 was used to detect NLRX1 signal (BD, cat. No612775). All antibodies were titrated for best performance in each flow cytometry panel. All flow cytometry panels included viability markers: Live/Dead-AF700, BD, cat. No564997; LIVE/DEAD™ Fixable Aqua Dead Cell Stain Kit, for 405 nm excitation, Invitrogen, cat. No L34966. Infection experiments used HIV (clone 89.6) acquired from the NIH reagents program. Declarations Data availability. Extended data Tables. Code availability. No new code was generated for this manuscript. Acknowledgements. We would like to thank the participants from CIENI for the generous gift for this study. We also would like to thank the CIENI team for their commitment and ethics for the recruiting these participants. Funding. NIH funding R01AI179476 (SPR), R37AI141258 (RPS). Authors' contributions. PMDRE, SG, and SPR conceptualized, planned, interpreted the results, and contributed to the writing of the manuscript. PMDRE, SG, MO, CB, FCC, executed the in vitro and imaging experiments. FRB, GXM have supported experiment execution and logistics. 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A review of signaling and transcriptional control in T follicular helper cell differentiation. J Leukoc Biol 111 , 173-195 (2022). https://doi.org:10.1002/JLB.1RI0121-066R Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations There is NO Competing Interest. Supplementary Files Extendeddata1.pdf H) Sequential gating strategy used to identify CD4⁺ BCL6⁺ GC‑T cells, CD4⁺ BCL6⁻ extra-follicular (EF) T cells, and B cells (CD20⁺ BCL6⁺) within follicular and EF zones from LNs from PWoH. Sequential two parameter plots, histograms, and positional density maps illustrating how CD4⁺ BCL6⁺ and CD4⁺ BCL6⁻ T cells were segregated and interrogated for IRF7, pSTAT1, MX1 and APOBEC3G expression. Extended Data Figure 1. Histo-cytometry gate analysis for BCL6 , HIV mRNA and antivirals. A) Representative image for a confocal panel from LN section showing nuclei (DAPI, blue), HIV gag mRNA (green), BCL6 mRNA (red), CD3 (yellow), PD 1 (cyan), and CD20 (light grey). Scale bars, 5 µm. B) Histocytometry gating scheme for the identification of relevant cell subsets. Segmented events were first plotted by CD20 intensity to delineate follicles. Follicular events/areas were then gated based on BCL6 and PD-1 expression and followed by identification of cells stratified by HIV mRNA signal. Relevant subsets:CD3 hi PD1 hi HIV hi , BCL6 hi PD1 hi CD3 hi HIV hi , and BCL6 Lo PD1 hi CD3 hi HIV hi . C) Proportions of tonsil Tfh and non-Tfh cells at pre-infection and day 4 post-infection (from Fig. 1F) (n=5). Bars represent cell proportions. Colors indicate cell subset (non-Tfh, blue; Tfh, red). Comparisons of Tfh or non-Tfh cell proportions between time points were performed using a paired Wilcoxon matched-pairs signed-rank test. No significant differences were observed between time points. D-E) Bl-3802 titration in isolated CD4 T cells from PBMCs of PWoH. BCL6 levels (D) and cell viability (E) (n=5). F-G) Frequencies of Tfh cells (F) and live cells (G) at pre-infection and at Day 4 (post-HIV infection) of tonsil CD4 T cells across different treatments (from Fig. 1F) (n=5). Each dot represents an individual donor, with dots colored by donor. No statistical differences (ns). One Way ANOVA, Kruskal Wallis post-test. Extendeddata2.pdf Extended Data Figure 2. Antiviral expression on Tfh cells from the gut. A) Schematic representation of the processing of gut tissue biopsies to perform sc-RNAseq analysis. Intestinal tissue biopsies were finely minced, single-cell suspension was stained with anti-CD45 (clone HI30) and a viability dye to perform cell sorting of CD45+ cells. Single cell 10x genomics libraries of sorted cells were prepared, sequenced, and the output was used for single cell GSEA analysis. B-C) Single-sample GSEA analysis. scRNA enrichment for central memory (CM) (B) or Th17 (C) non-Tfh CD4 T cells and Tfh cell populations for PPI prioritized, GRN prioritized, Vinuesa et al., 2016 107 and the Hart and Laufer, 2022 107,108 published reference gene sets. Significance by effect size (Cliffs Δ) ≥ 0.1 and p-value < 0.0001. Each dot represents an individual cell, with boxplots summarizing the median and interquartile range for each subset. The Cliffs Δ value is shown. D-E) Single-cell GSEA analysis of distinct gut regions. scGSEA enrichment was performed for Tfh cells vs. CM (D) or Th17 (E) cell populations, evaluating pathway signatures associated with Tfh identity, host antiviral restriction factors, and interferon (IFN) signaling. Gene sets were derived from Locci et al., 2013 33 , Abdel-Mohsen et al., 2015 27,33 , and the Interferome database 34 , respectively. CO: colon; IL: Illeum; RE: rectum. F) Pre-infection levels (MFI) of IRF7 in Tfh cells isolated from tonsils untreated or treated for 24h with BCL6 inhibitor (BI-3802) (from Fig. 1F, n=5). Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Paired two‑tailed t-test; exact p value is shown. G) Negative correlation between the expression of IRF7 at pre-infection timepoint and the expression of HIV p24 protein 4 days post-infection (from Fig. 1F, n=5). Each dot represents an individual donor. Red dots = Tfh cells without BCL6 inhibitor BI-3802. Blue dots = Tfh cells treated with BCL6 inhibitor BI-3802. Spearman rank correlations were performed. Correlation coefficients and p values are indicated on each plot. Extendeddata3.pdf Extended Data Figure 3. In vitro Tfh differentiation. A) Representative dot plots showing the gating strategy to recognize different CD4 T cell subsets based on the expression of CD45RA, CCR7, CD27, CXCR5 and PD-1. Single cell, live CD3+ CD4+ cells were pre-gated. Identified CD4 T cell subsets: from CD45RA+ we identified naïve CD4 T cells (CD45RA+, CCR7+, CD27+) and TD (CD45RA+, CCR7, CD27-). From CD45RA- CD4 T cells we gated Tfh (CD45RA-, CXCR5hi, PD1hi); from non-Tfh cells we further identified TCM (CD45RA-, CCR7+, CD27+), TEM (CD45RA-, CCR7, CD27-), TTM (CD45RA-, CCR7-, CD27+), other subsets (CD45RA-, CCR7+, CD27-). B) Proportions of CD4⁺ T cell subsets (expressed as a percentage of total CD4⁺ T cells. Naïve CD4⁺ T cells were cultured for 3 days in stimulation media supplemented with TransAct (1:500), anti-IL-2 antibody (1 μg/mL), and IL-7 (4 ng/mL), in the presence of IL-10 (10 ng/mL), TGF-β (20 ng/mL), or both cytokines (n=5). Naïve (green), TCM (dark blue), TTM (brown), TEM (dark grey), TD (light grey), other phenotype (CD45RA⁻ CXCR5 lo PD1 lo CCR7⁺ CD27⁻, wine), and Tfh (red). C) BCL6 expression in Tfh and non-Tfh cells after 3 days in supplemented with IL-10 and TGF-β (n=5). Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Paired two‑tailed t-test; exact p value is shown. D) Longitudinal expression of BCL6 and Blimp-1 in Tfh cells. Expression of BCL6 and Blimp-1 was evaluated by flow cytometry on days 0, 1 and 3 in the in vitro differentiation in the presence of IL-10, TGF-β, or both cytokines. Differences among treatments were analyzed using one-way ANOVA followed by Tukey’s post hoc test. Exact p values are shown. E) Representative cytometry plots for IL-21+ Tfh cells after three days of Tfh differentiation in media containing either IL-10, TGF-b, or both cytokines. Red dots indicate IL-21+ cells. F) Frequency of IL-21+ Tfh cells after 3 days of Tfh differentiation in media containing either IL-10, TGF-b, or both cytokines (n=5). Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Differences among treatments were analyzed using one-way ANOVA followed by Tukey’s post hoc test. Exact p values are shown. G) Frequency of IL-21+ cells in different CD4 T cell subsets defined as in (A). Bars denote mean ± SEM. Each dot represents an individual donor (n=5), with dots colored by donor. Differences among treatments were analyzed using Friedman test followed by Dunn’s test to correct for multiple comparisons. No statistical significances were found. Extendeddata4.pdf Extended Data Figure 4. SMAD2/3 and STAT3 phosphorylation upon TGF-b and IL-10 blockade. A) Dose-response of the TGF-βR1 inhibitor Galunisertib. Peripheral blood mononuclear cells (PBMCs) were pre-treated for 1 hour with increasing concentrations of Galunisertib (2, 20, and 50 μg/mL), followed by stimulation with TGF-β (20 ng/mL) for 30 minutes. The inhibitory effect of Galunisertib was evaluated by flow cytometric analysis of pSMAD2/3 expression (n=5). Bars denote mean ± SEM of the expression of pSMAD2/3 in naïve CD4 T cells. Each dot represents an individual donor, with dots colored by donor. Differences among treatments were analyzed using Friedman test followed by Dunn’s test to correct for multiple comparisons. Exact p values are shown. B-C) Dose-response of IL-10 blocking antibody. Peripheral blood mononuclear cells (PBMCs) were treated for 30 minutes with IL-10 (10 ng/mL) in the presence of increasing concentrations of anti-IL-10 antibody (clone3F9) (1, 10, and 20 μg/mL) (n=5). The blocking efficacy was assessed by flow cytometric analysis of STAT3 phosphorylation at two sites: S727 and Y705. Bars denote mean ± SEM of the expression of STAT3pS727 or pY705 in naïve CD4 T cells. Each dot represents an individual donor, with dots colored by donor. Differences among treatments were analyzed using Friedman test followed by Dunn’s test to correct for multiple comparisons. Exact p values are shown. Extendeddata5.pdf Extended Data Figure 5. HIV infection of Tfh cells. A) Expression of HIV p24 protein in BCL6Lo and BCL6Hi p24+ cells. Naïve CD4 T cells were differentiated in the presences of IL-10 and TGF-b for 3 days (from Fig. 3F) then infected with a dual tropic HIV (strain 89.6) and left in culture for 4 days in the presence of Saquinavir (5mM), IL-2 (30 U/ml). HIV p24 protein expression in infected cells (HIV p24+) was assessed by flow cytometry using an anti–HIV p24 antibody (clone 28B7) four days post-infection. Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Paired two‑tailed t-test; exact p value is shown. B) Correlation between the expression of BCL6 and the expression of HIV p24 protein 4 days post-infection. Each dot represents an individual donor with dots colored by donor. Spearman rank correlations were performed. Correlation coefficients and p values are indicated on each plot. Table1.pdf Table 1. Demographic and clinical information of sample donors Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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en Enfermedades Infecciosas, Instituto Nacional de Enfermedades Respiratorias \"Ismael Cosio Villegas\"","correspondingAuthor":false,"prefix":"","firstName":"Lady","middleName":"","lastName":"Ruiz-Carbajal","suffix":""},{"id":599868136,"identity":"fbad4416-e7da-4d76-a4ff-673d9c04bcd9","order_by":27,"name":"Dafne Díaz-Rivera","email":"","orcid":"","institution":"Centro de Investigación en Enfermedades Infecciosas, Instituto Nacional de Enfermedades Respiratorias \"Ismael Cosio Villegas\"","correspondingAuthor":false,"prefix":"","firstName":"Dafne","middleName":"","lastName":"Díaz-Rivera","suffix":""},{"id":599868137,"identity":"142c4865-067f-4325-8e1a-ab02ac302d78","order_by":28,"name":"Olivia Briceño","email":"","orcid":"","institution":"Centro de Investigación en Enfermedades Infecciosas, Instituto Nacional de Enfermedades Respiratorias \"Ismael Cosio Villegas\"","correspondingAuthor":false,"prefix":"","firstName":"Olivia","middleName":"","lastName":"Briceño","suffix":""},{"id":599868138,"identity":"54e61637-f338-4d1e-987b-ebad7b710bd6","order_by":29,"name":"Karla Ordaz-Candelario","email":"","orcid":"","institution":"Centro de Investigación en Enfermedades Infecciosas, Instituto Nacional de Enfermedades Respiratorias \"Ismael Cosio Villegas\"","correspondingAuthor":false,"prefix":"","firstName":"Karla","middleName":"","lastName":"Ordaz-Candelario","suffix":""},{"id":599868139,"identity":"b0e9a52b-dd16-4b16-8dab-c95aa74a0e57","order_by":30,"name":"Santiago Ávila-Ríos","email":"","orcid":"","institution":"Centro de Investigación en Enfermedades Infecciosas, Instituto Nacional de Enfermedades Respiratorias \"Ismael Cosio Villegas\"","correspondingAuthor":false,"prefix":"","firstName":"Santiago","middleName":"","lastName":"Ávila-Ríos","suffix":""},{"id":599868140,"identity":"f9c623f0-21b0-4ff5-816d-cc9ab442dd57","order_by":31,"name":"Robert Balderas","email":"","orcid":"https://orcid.org/0000-0002-8734-8629","institution":"BD Biosciences","correspondingAuthor":false,"prefix":"","firstName":"Robert","middleName":"","lastName":"Balderas","suffix":""},{"id":599868141,"identity":"83af9359-8029-476c-8a0c-a246881a1dfb","order_by":32,"name":"Alexander Marson","email":"","orcid":"","institution":"Gladstone Institutes","correspondingAuthor":false,"prefix":"","firstName":"Alexander","middleName":"","lastName":"Marson","suffix":""},{"id":599868142,"identity":"69a2061e-cbd2-4539-9be6-180f0bbc65d5","order_by":33,"name":"Rafick-Pierre Sekaly","email":"","orcid":"https://orcid.org/0000-0002-7816-4828","institution":"Emory University","correspondingAuthor":false,"prefix":"","firstName":"Rafick-Pierre","middleName":"","lastName":"Sekaly","suffix":""},{"id":599868143,"identity":"3fa6323a-fa33-4c1e-b345-987aad0772ca","order_by":34,"name":"Linos Vandekerckhove","email":"","orcid":"","institution":"Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Linos","middleName":"","lastName":"Vandekerckhove","suffix":""},{"id":599868144,"identity":"aab03717-7d56-4c94-a32f-ae687b05bb98","order_by":35,"name":"André van der Ven","email":"","orcid":"https://orcid.org/0000-0003-1833-3391","institution":"Radboud University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"André","middleName":"van der","lastName":"Ven","suffix":""},{"id":599868145,"identity":"087d8423-b912-4c5c-881e-b7fc31188a29","order_by":36,"name":"Mihai Netea","email":"","orcid":"https://orcid.org/0000-0003-2421-6052","institution":"Radboud University Nijmegen Medical Centre","correspondingAuthor":false,"prefix":"","firstName":"Mihai","middleName":"","lastName":"Netea","suffix":""},{"id":599868146,"identity":"40b87f4d-5c23-4cc9-955f-02f5d9ba221b","order_by":37,"name":"Jishnu Das","email":"","orcid":"https://orcid.org/0000-0002-5747-064X","institution":"University of Pittsburgh School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jishnu","middleName":"","lastName":"Das","suffix":""},{"id":599868147,"identity":"1bf53ec7-220e-4680-bf66-a1c2a32edfe4","order_by":38,"name":"Constantinos Petrovas","email":"","orcid":"","institution":"Department of Laboratory Medicine and Pathology, Institute of Pathology, Lausanne University Hospital and Lausanne University","correspondingAuthor":false,"prefix":"","firstName":"Constantinos","middleName":"","lastName":"Petrovas","suffix":""}],"badges":[],"createdAt":"2026-02-19 18:06:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8919968/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8919968/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104540629,"identity":"c5a7208b-1ffc-4834-84c1-6aa10ea1ece2","added_by":"auto","created_at":"2026-03-13 05:43:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":670277,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBCL6\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cstrong\u003ehi\u003c/strong\u003e\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e\u003cstrong\u003e CD4 follicular helper T (Tfh) cells are highly susceptible to HIV infection.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Representative confocal images of a human lymph node (LN) obtained from an HIV viremic donor. Representative channels showing nuclei (DAPI, grey), CD20 (blue), CD3 (yellow), CD57 (red), PD‑1 (cyan), BCL6 mRNA (magenta), and HIV gag mRNA (green) positive cells. Scale bars are shown in each section. Multiplex immunofluorescence (mIF) staining composite images showing the co-expression of BCL6 and HIV mRNA in CD3 or PD1+ cells within the follicles. The bottom part shows\u003cstrong\u003e \u003c/strong\u003e‘Isothermic’ surfaces generated with Imaris module (v9.9) for the visualization of BCL6 and HIV mRNAs in accordance with CD3 (yellow) membrane. This helps to improve cell segmentation and better visualization of BCL6, PD1, CD20, and CD3 expression. n = 5 donors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB)\u003c/strong\u003e Top Plot: Relative frequency of HIV\u003csup\u003ehi\u003c/sup\u003e in the BCL6\u003csup\u003elo\u003c/sup\u003e (grey) or BCL6\u003csup\u003ehi\u003c/sup\u003e (dark red) CD4 T cells quantified per follicle. Each bar represents one follicle (F). Numbers above each bar are the total number of HIV\u003csup\u003ehi\u003c/sup\u003e cells per follicle. A total of 3 donors and 12 follicles were quantified. Bottom plot: Total relative frequencies of HIV\u003csup\u003ehi\u003c/sup\u003e CD4 T cells across all follicles. Bars denote mean\u0026nbsp;± SEM. Paired Wilcoxon test; exact p value is shown. n = 5 donors\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC)\u003c/strong\u003e Lymph node cells sorting strategy from untreated PWH (Fiebig IV–V, n=5; chronic untreated, n=9). Single cells, lymphocytes (SSC-A vs FSC-A), Live, CD3+CD8- cells were pre-gated and cells were sorted according to the expression of CXCR5 and PD-1: 1) CXCR5\u003csup\u003ehi\u003c/sup\u003ePD1\u003csup\u003ehi\u003c/sup\u003e (Tfh cells), 2) CXCR5\u003csup\u003emid\u003c/sup\u003ePD1\u003csup\u003emid\u003c/sup\u003e, 3) CXCR5\u003csup\u003e+\u003c/sup\u003ePD1\u003csup\u003eneg\u003c/sup\u003e, and 4) CXCR5\u003csup\u003eneg\u003c/sup\u003ePD1\u003csup\u003eneg\u003c/sup\u003e. Sorted cells were lysed in Direct Lysis Buffer and used for Total HIV-DNA and Intact HIV-1 DNA provirus quantifications. Histograms represent the expression of BCL6 (MFI) of each sorted population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD)\u003c/strong\u003e Total HIV-DNA quantification in isolated CD4 T cells from blood and sorted populations from LN of untreated PWH. Red dots represent samples from the acute phase (Fiebig IV–V), while blue dots represent samples from the chronic phase without ART. Lines denote mean. Differences among CD4 cell subsets were analyzed using Kruskal Wallis test followed by Dunn’s test to correct for multiple comparisons. Exact p values are shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE)\u003c/strong\u003e Intact HIV-DNA proviral quantification in Isolated CD4 T cells from blood and sorted populations from LN of untreated PWH. Red dots represent samples from the acute phase (Fiebig IV–V), while blue dots represent samples from the chronic phase without ART. Lines denote mean. Differences among CD4 cell subsets were analyzed using Kruskal Wallis test followed by Dunn’s test to correct for multiple comparisons. Exact p values are shown. Missing dots reflect lack of input sample due to the low number of cell numbers or mismatched probes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF)\u003c/strong\u003e Experimental design. Tonsils CD4 T cells (n=5) were isolated from PWoH and infected \u003cem\u003ein vitro \u003c/em\u003ewith HIV in the presence or absence of the BCL6 inhibitor, +/- IFN-b. Briefly, isolated CD4 T cells were treated for 24h with BCL6 inhibitor (BI-3802, 5nM) or left in media. Tonsil cells were then infected with a dual tropic HIV (strain 89.6) and left in culture for 4 days in the presence of Saquinavir (5mM), IL-2 (30 U/ml), and the BCL6 inhibitor (BI-3802, 5nM). HIV p24 protein expression in infected cells (HIV p24+) was assessed by flow cytometry using an anti-HIV p24 antibody (clone 28B7) four days post-infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG)\u003c/strong\u003e Expression of HIV p24 protein in non-Tfh (BCL6\u003csup\u003elo\u003c/sup\u003e) or Tfh (BCL6\u003csup\u003ehi\u003c/sup\u003e) cells after 4 days of infection. Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Paired two‑tailed t-test; exact p value is shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH)\u003c/strong\u003e Fold change of the BCL6 expression in isolated tonsil CD4 T cells treated with the BCL6 inhibitor (BI-3802, 0.5nM) at pre-infection timepoint (24h) and four days after HIV infection over untreated conditions. Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Paired two‑tailed t-test; exact p value is shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI)\u003c/strong\u003e Expression of HIV p24 protein in Tfh cells after 4 days of infection in the presence or absence of BCL6 inhibitor and/or IFN-b (2ng/ml) or the combination of both. Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Comparisons among treatments were performed using the ANOVA test followed by Tukey’s post hoc test for multiple comparisons.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/00269db259cd2202569ea210.jpg"},{"id":104540621,"identity":"5206cf26-cd20-4cf3-babb-08422a09ce8f","added_by":"auto","created_at":"2026-03-13 05:43:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":678754,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eBCL6 suppresses interferon-driven antiviral programs in germinal center Tfh cells.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eRepresentative confocal images of paraffin‑embedded LN sections from PWoH. Single channels: nuclei staining (DAPI, blue), CD20 (green), BCL6 (light grey), CD4 (yellow), MX1 (cyan), pSTAT1 (magenta), APOBEC3G (red), and IRF7 (blue). White circles enclose regions with high expression of BCL6 in the follicles. Scale bar, 50 µm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB) \u003c/strong\u003eQuantification of antiviral proteins in the LN tissues sections from PWoH (n=5). Frequencies of follicular cells positive for IRF7, pSTAT1, APOBEC3G, or MX1 within CD4⁺ T and B cells. Each dot corresponds to a different donor, with dots colored by donor [1–10 Regions of Interest (ROIs) per donor, five donors total]. Bars denote mean ± SE.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC)\u003c/strong\u003e Percentage of BCL6\u003csup\u003ehi\u003c/sup\u003e or BCL6\u003csup\u003elo\u003c/sup\u003e cells expressing all the antiviral proteins evaluated on CD4+ and CD20+ cells. The identification of single antiviral positive cells per follicle was performed, followed by Boolean gating including all cells positive for each antiviral protein. Red and grey bars denote mean\u0026nbsp;± SEM. Paired Wilcoxon test between BCL6\u003csup\u003ehi\u003c/sup\u003e and BCL6\u003csup\u003elo\u003c/sup\u003e; exact p values are shown.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/c0026eada2ffb85a9769a1d9.jpg"},{"id":104540622,"identity":"07ae4453-559a-41b5-96d3-492646d12b7b","added_by":"auto","created_at":"2026-03-13 05:43:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":567382,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eIL-10 and TGF-β lead to BCL6 expression and Tfh differentiation.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Representative confocal images of a paraffin‑embedded LN from PWoH. Single channels: DAPI (grey), CD3 (yellow), CD20 (blue, delineating B cell follicles), IL‑10 mRNA (green), and TGF-β mRNA (magenta). Magnification of the circled area show a composite overlay highlighting CD3+ T cells positive to either IL-10 or TGF-b. Scale bars are shown in each image.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB)\u003c/strong\u003e Quantification of IL‑10\u003csup\u003ehi\u003c/sup\u003e (left) and TGF-b\u003csup\u003ehi\u003c/sup\u003e (right) bulk cells per mm² in follicles (F) or extra‑follicular (EF) regions of interest (ROIs). N=5. Each dot is one ROI. Bars denote mean ± SD. Paired two‑tailed Wilcoxon signed‑rank test; exact p values shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC)\u003c/strong\u003e Spearman rank correlations between the cell counts expressing mRNA IL‑10\u003csup\u003ehi\u003c/sup\u003e (x‑axis) and mRNA TGF-β\u003csup\u003ehi\u003c/sup\u003e (y‑axis). Rho and p values are indicated on each plot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD-E)\u003c/strong\u003e Spearman rank correlations between the cell counts expressing mRNA IL‑10\u003csup\u003ehi\u003c/sup\u003e (\u003cstrong\u003eD\u003c/strong\u003e) or mRNA TGF-β\u003csup\u003ehi\u003c/sup\u003e (\u003cstrong\u003eE\u003c/strong\u003e) cells (x‑axis) and the number of CD3\u003csup\u003ehi\u003c/sup\u003ePD‑1\u003csup\u003ehi\u003c/sup\u003e cells (Tfh cells - y‑axis). Rho and p values are indicated on each plot.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF)\u003c/strong\u003e Experimental design for the \u003cem\u003ein vitro\u003c/em\u003e Tfh differentiation assay. Purified naïve CD4 T cells from PBMC of PWoH (n=5) were stimulated with TCR (Trans Act 1:500) in the presence of anti-IL-2 (1mg/ml), and IL-7 (4ng/ml). Stimulation media was supplemented with IL-10 (10 ng/ml), TGF-β (20 ng/ml), or IL-10 and TGF-β. Cells were maintained in culture for 3 days at 37\u003csup\u003eo\u003c/sup\u003eC and 5% CO\u003csub\u003e2\u003c/sub\u003e. Flow cytometry was used to determine the presence and function of Tfh cells, bulk RNAseq performed for gene expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG)\u003c/strong\u003e Representative dot lots showing the identification of Tfh cells co-expressing the surface markers CXCR5, PD1, and ICOS in media only (left) and after 3 days in differentiation media containing IL-10 (10ng/ml) and TGF-b (20ng/ml) (right). X-axis: CXCR5, Y-axis PD-1; ICOS is shown as a gradient from blue to red, representing low to higher expression of the MFI (median fluorescence intensity - bottom, Comp-BUV805).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eH)\u003c/strong\u003e Absolute numbers of differentiated Tfh cells after 3 days in each culture condition (n=5). Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Comparisons among treatments were performed using the ANOVA test followed by Tukey’s post hoc test for multiple comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eI)\u003c/strong\u003e Absolute numbers of differentiated Tfh cells after 3 days in each culture condition culture in the presence of IL-10 blocking antibody (aIL-10, 10ug/ml), TGF-β inhibitor Galunisertib (Gal, 1uM), or both inhibitors. Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Comparisons among treatments were performed using the ANOVA test followed by Tukey’s post hoc test for multiple comparisons.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJ)\u003c/strong\u003e Top panel: Bulk RNA-sequencing in cells after 3 days in culture with media or IL-10 (10ng/ml) + TGF-b (20ng/ml). Gene set enrichment analysis of Tfh (GEO GSE50391, p = 0.0064, IL-10 + TGF-β vs. TCR), IFN-related pathways (gene set from the interefome database\u003csup\u003e34.\u003c/sup\u003e, p = 1.75616E-30, p = 6.33051E-17, p= 5.83511E-07, for IFNI, IFNII, and IFNII, respectively; IL-10 + TGF-β vs. TCR), and HIV restriction factors (gene set from Abdel-Mohsen et al.,\u003csup\u003e27\u003c/sup\u003e p = 0.0004, IL-10 + TGF-β vs. TCR) comparing IL-10 + TGF-b vs media is shown. Bottom panel: gene network of Tfh signature leading genes from GSEA analysis in the IL-10 + TGF-β condition compared to TCR only. The gene network was generated using leading genes of the Tfh signature gene set used for GSEA analysis, using the R package STRINGdb, with a minimum interaction score threshold of 0.7 (high confidence).\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/c546a2c0ff4447f684be6788.jpg"},{"id":104540628,"identity":"7dbfcd1f-0a3a-423d-ae2c-02be35399d71","added_by":"auto","created_at":"2026-03-13 05:43:34","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":530822,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenetic gain- and loss-of-function CRISPR screens support the proviral role of BCL6 and Tfh-related genes; Single-nucleotide polymorphisms in proximity to IL-10 and TGF-β genes are associated with HIV elite control.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Schematic of a pooled CRISPR screening pipeline for the identification of host genes regulating HIV infection in stimulated primary human CD4+ T cells (n=3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB-C)\u003c/strong\u003e Volcano plots for HIV-GFP LFC (log2-fold changes (HIV-GFP+/HIV-GFP- sorting bins)) summarizing gene-level effects of perturbing 60 pre-selected genes related to Tfh differentiation and IL-10/TGF-β signaling (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e) on HIV infection, derived from pooled CRISPR knockout (CRISPRn) (\u003cstrong\u003eB\u003c/strong\u003e) and CRISPR activation (CRISPRa) (n=3) (\u003cem\u003eCell\u003c/em\u003e, in press) (\u003cstrong\u003eC\u003c/strong\u003e). Points are colored as antiviral hits (blue), proviral hits (red), or non-significant (gray).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD)\u003c/strong\u003e Schematic overview of the Candidate Gene Association Studies (CGAS) study in the 2000HIV cohort\u003csup\u003e40\u003c/sup\u003e. The GWAS included a discovery cohort and an independent validation cohort of PWH on ART\u003csup\u003e40\u003c/sup\u003e. From this framework, 60 genes mapping to GWAS loci were selected for downstream CGAS analyses, focusing on pathways linked to the IL-10/TGF-β/Tfh axis and antiviral gene programs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE-G) \u003c/strong\u003eRegional association (locus) plots for CGAS analysis associated with spontaneous HIV control at SNPs in close proximity to the IL-10 (\u003cstrong\u003eE\u003c/strong\u003e; lead variant rs885334), STAT3 (\u003cstrong\u003eF\u003c/strong\u003e; lead variant rs6503691), and TGFBR2 (\u003cstrong\u003eG\u003c/strong\u003e; lead variant rs4955304) genes. Each point represents a variant plotted by genomic position (x-axis) and association strength (−log10(p value), y-axis). Point colors denote linkage disequilibrium (r²) with the lead variant. Gene models are shown below each locus.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/884092fe4a4890da7957dd4d.jpg"},{"id":104540627,"identity":"222b576a-fe91-4e94-987e-3f65ccfc2d1c","added_by":"auto","created_at":"2026-03-13 05:43:34","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":450043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eD)\u003c/strong\u003e Frequency of BCL6⁺ Tfh cells in LN of CA-vDNA\u003csup\u003elo\u003c/sup\u003e and Ca-vDNA\u003csup\u003ehi\u003c/sup\u003e combo-treated RMs. Bars denote mean ± SEM. Each dot represents an individual RM. Paired two‑tailed t-test; exact p value is shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTherapeutic down-modulation of IL-10 and TGF-β signaling \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in rhesus macaques enhances antiviral programs and decreases frequencies of BCL6\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e Tfh cells in LNs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Study design. Rhesus macaques (RMs, n = 10) were infected (i.v.) with SIVmac239, initiated on ART (dolutegravir, tenofovir disoproxil fumarate and emtricitabine) 6 weeks p.i.\u003csup\u003e42\u003c/sup\u003e. Combined anti–IL-10 and anti–PD-1 (aIL-10 + aPD-1) started 12 weeks before analytical treatment interruption (ATI; week 0) and continued through early ATI (dotted lines indicate dosing time points). Cell-associated viral DNA (CA-vDNA) and flow cytometry from LN cell suspensions were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB)\u003c/strong\u003e ScRNA-seq in LNs at pre-ATI. Comparison on antiviral pathways in Tfh cells from CA-vDNA\u003csup\u003elo\u003c/sup\u003e (n=4) vs. CA-vDNA\u003csup\u003ehi\u003c/sup\u003e (n=6) combo-treated RMs is shown. Pathway over-representation analysis (ORA; clusterProfiler v4.18.2 - differential expression significance threshold: FDR \u0026lt; 0.2, Benjamini–Hochberg). Each circle represents an enriched pathway (FDR \u0026lt; 0.1; Benjamini–Hochberg), while diamonds indicate the upregulated genes within each pathway. Edges denote genes shared between pathways. Abbreviations: HALLMARK: Hallmark gene sets from the Human Molecular Signatures Database; REACTOME: Reactome Pathway Database; GOBP: Gene Ontology Biological Process; WP: WikiPathways; EC_vs_nonHIC up: pathway including genes upregulated in CD4⁺ and CD8⁺ T cells and monocytes from Elite Controllers\u003csup\u003e44\u003c/sup\u003e. Cell-associated viral DNA (CA-vDNA) in lymph node (LN) CD4 T cells, plotted as log10 CA-vDNA per 10\u003csup\u003e6\u003c/sup\u003e CD4 T cells, with animals stratified into vDNA\u003csup\u003elo\u003c/sup\u003e and vDNA\u003csup\u003ehi\u003c/sup\u003e groups. Each dot denotes one animal. p value is shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC)\u003c/strong\u003e Representative dot plots for the quantification of Tfh cells in LNs: Singlets were first selected, followed by lymphocytes and live cells. CD3⁺ T cells were identified and gated on CD4⁺ T cells. Tfh cells were subsequently defined based on high expression of CD200 and CMAF (CD200⁺CMAF⁺) within the CD4⁺ T-cell compartment. Histograms illustrate BCL6 expression of Tfh cells.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/48b6ad1f3c9188d2874e6b6d.jpg"},{"id":104785004,"identity":"d5bfa208-30e3-447d-beaf-f0665a7d632f","added_by":"auto","created_at":"2026-03-17 08:09:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5692634,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/afe59d80-7651-4676-8bce-fe941c731e48.pdf"},{"id":104540631,"identity":"6b995f0e-79d3-4ecc-93b9-37a554d3f99e","added_by":"auto","created_at":"2026-03-13 05:43:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1716863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eH)\u003c/strong\u003e Sequential gating strategy used to identify CD4⁺ BCL6⁺ GC‑T cells, CD4⁺ BCL6⁻ extra-follicular (EF) T cells, and B cells (CD20⁺ BCL6⁺) within follicular and EF zones from LNs from PWoH. Sequential two parameter plots, histograms, and positional density maps illustrating how CD4⁺ BCL6⁺ and CD4⁺ BCL6⁻ T cells were segregated and interrogated for IRF7, pSTAT1, MX1 and APOBEC3G expression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended Data Figure 1. Histo-cytometry gate analysis for \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eBCL6\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e, HIV mRNA and antivirals.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Representative image for a confocal panel from LN section showing nuclei (DAPI, blue), HIV gag mRNA (green), \u003cem\u003eBCL6\u003c/em\u003e mRNA (red), CD3 (yellow), PD 1 (cyan), and CD20 (light grey). Scale bars, 5 µm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB)\u003c/strong\u003e Histocytometry gating scheme for the identification of relevant cell subsets. Segmented events were first plotted by CD20 intensity to delineate follicles. Follicular events/areas were then gated based on BCL6 and PD-1 expression and followed by identification of cells stratified by HIV mRNA signal. Relevant subsets:CD3\u003csup\u003ehi\u003c/sup\u003ePD1\u003csup\u003ehi\u003c/sup\u003eHIV\u003csup\u003ehi\u003c/sup\u003e, BCL6\u003csup\u003ehi\u003c/sup\u003ePD1\u003csup\u003ehi\u003c/sup\u003eCD3\u003csup\u003ehi\u003c/sup\u003eHIV\u003csup\u003ehi\u003c/sup\u003e, and BCL6\u003csup\u003eLo\u003c/sup\u003ePD1\u003csup\u003ehi\u003c/sup\u003eCD3\u003csup\u003ehi\u003c/sup\u003eHIV\u003csup\u003ehi\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC)\u003c/strong\u003e Proportions of tonsil Tfh and non-Tfh cells at pre-infection and day 4 post-infection (from \u003cstrong\u003eFig. 1F\u003c/strong\u003e) (n=5). Bars represent cell proportions. Colors indicate cell subset (non-Tfh, blue; Tfh, red). Comparisons of Tfh or non-Tfh cell proportions between time points were performed using a paired Wilcoxon matched-pairs signed-rank test. No significant differences were observed between time points.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD-E) \u003c/strong\u003eBl-3802 titration in isolated CD4 T cells from PBMCs of PWoH. BCL6 levels (D) and cell viability (E) (n=5).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF-G)\u003c/strong\u003e Frequencies of Tfh cells (F) and live cells (G) at pre-infection and at Day 4 (post-HIV infection) of tonsil CD4 T cells across different treatments (from \u003cstrong\u003eFig. 1F\u003c/strong\u003e) (n=5). Each dot represents an individual donor, with dots colored by donor. No statistical differences (ns). One Way ANOVA, Kruskal Wallis post-test\u003cstrong\u003e.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Extendeddata1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/00aedabdf5cf53cfab027bc6.pdf"},{"id":104540626,"identity":"a0c08e79-6ffe-4829-ab54-cb4de329de60","added_by":"auto","created_at":"2026-03-13 05:43:34","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3456126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Figure 2. Antiviral expression on Tfh cells from the gut.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Schematic representation of the processing of gut tissue biopsies to perform sc-RNAseq analysis. Intestinal tissue biopsies were finely minced, single-cell suspension was stained with anti-CD45 (clone HI30) and a viability dye to perform cell sorting of CD45+ cells. Single cell 10x genomics libraries of sorted cells were prepared, sequenced, and the output was used for single cell GSEA analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB-C)\u003c/strong\u003e Single-sample GSEA analysis. scRNA enrichment for central memory (CM) (B) or Th17 (C) non-Tfh CD4 T cells and Tfh cell populations for PPI prioritized, GRN prioritized, Vinuesa et al., 2016\u003csup\u003e107\u003c/sup\u003e and the Hart and Laufer, 2022\u003csup\u003e107,108\u003c/sup\u003e published reference gene sets. Significance by effect size (Cliffs Δ) ≥ 0.1 and p-value \u0026lt; 0.0001. Each dot represents an individual cell, with boxplots summarizing the median and interquartile range for each subset. The Cliffs Δ value is shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD-E)\u003c/strong\u003e Single-cell GSEA analysis of distinct gut regions. scGSEA enrichment was performed for Tfh cells vs. CM (D) or Th17 (E) cell populations, evaluating pathway signatures associated with Tfh identity, host antiviral restriction factors, and interferon (IFN) signaling. Gene sets were derived from Locci et al., 2013\u003csup\u003e33\u003c/sup\u003e, Abdel-Mohsen et al., 2015\u003csup\u003e27,33\u003c/sup\u003e, and the Interferome database\u003csup\u003e34\u003c/sup\u003e, respectively. CO: colon; IL: Illeum; RE: rectum.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF)\u003c/strong\u003e Pre-infection levels (MFI) of IRF7 in Tfh cells isolated from tonsils untreated or treated for 24h with BCL6 inhibitor (BI-3802) (from \u003cstrong\u003eFig. 1F\u003c/strong\u003e, n=5). Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Paired two‑tailed t-test; exact p value is shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG)\u003c/strong\u003e Negative correlation between the expression of IRF7 at pre-infection timepoint and the expression of HIV p24 protein 4 days post-infection (from \u003cstrong\u003eFig. 1F\u003c/strong\u003e, n=5). Each dot represents an individual donor. Red dots = Tfh cells without BCL6 inhibitor BI-3802. Blue dots = Tfh cells treated with BCL6 inhibitor BI-3802. Spearman rank correlations were performed. Correlation coefficients and p values are indicated on each plot.\u003c/p\u003e","description":"","filename":"Extendeddata2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/924df8d03aea75e2a81b5085.pdf"},{"id":104781249,"identity":"b1e75f22-0c16-41cc-b536-cd34c2c22190","added_by":"auto","created_at":"2026-03-17 07:55:12","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":584823,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Figure 3. \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eIn vitro\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Tfh differentiation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Representative dot plots showing the gating strategy to recognize different CD4 T cell subsets based on the expression of CD45RA, CCR7, CD27, CXCR5 and PD-1. Single cell, live CD3+ CD4+ cells were pre-gated. Identified CD4 T cell subsets: from CD45RA+ we identified naïve CD4 T cells (CD45RA+, CCR7+, CD27+) and TD (CD45RA+, CCR7, CD27-). From CD45RA- CD4 T cells we gated Tfh (CD45RA-, CXCR5hi, PD1hi); from non-Tfh cells we further identified TCM (CD45RA-, CCR7+, CD27+), TEM (CD45RA-, CCR7, CD27-), TTM (CD45RA-, CCR7-, CD27+), other subsets (CD45RA-, CCR7+, CD27-).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB)\u003c/strong\u003e Proportions of CD4⁺ T cell subsets (expressed as a percentage of total CD4⁺ T cells. Naïve CD4⁺ T cells were cultured for 3 days in stimulation media supplemented with TransAct (1:500), anti-IL-2 antibody (1 μg/mL), and IL-7 (4 ng/mL), in the presence of IL-10 (10 ng/mL), TGF-β (20 ng/mL), or both cytokines (n=5). Naïve (green), TCM (dark blue), TTM (brown), TEM (dark grey), TD (light grey), other phenotype (CD45RA⁻ CXCR5\u003csup\u003elo\u003c/sup\u003e PD1\u003csup\u003elo\u003c/sup\u003e CCR7⁺ CD27⁻, wine), and Tfh (red).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC)\u003c/strong\u003e BCL6 expression in Tfh and non-Tfh cells after 3 days in supplemented with IL-10 and TGF-β (n=5). Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Paired two‑tailed t-test; exact p value is shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eD)\u003c/strong\u003e Longitudinal expression of BCL6 and Blimp-1 in Tfh cells. Expression of BCL6 and Blimp-1 was evaluated by flow cytometry on days 0, 1 and 3 in the \u003cem\u003ein vitro\u003c/em\u003e differentiation in the presence of IL-10, TGF-β, or both cytokines. Differences among treatments were analyzed using one-way ANOVA followed by Tukey’s post hoc test. Exact p values are shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE)\u003c/strong\u003e Representative cytometry plots for IL-21+ Tfh cells after three days of Tfh differentiation in media containing either IL-10, TGF-b, or both cytokines. Red dots indicate IL-21+ cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eF)\u003c/strong\u003e Frequency of IL-21+ Tfh cells after 3 days of Tfh differentiation in media containing either IL-10, TGF-b, or both cytokines (n=5). Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Differences among treatments were analyzed using one-way ANOVA followed by Tukey’s post hoc test. Exact p values are shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eG)\u003c/strong\u003e Frequency of IL-21+ cells in different CD4 T cell subsets defined as in (A). Bars denote mean ± SEM. Each dot represents an individual donor (n=5), with dots colored by donor. Differences among treatments were analyzed using Friedman test followed by Dunn’s test to correct for multiple comparisons. No statistical significances were found.\u003c/p\u003e","description":"","filename":"Extendeddata3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/2c3a79c94f8e69a659e60732.pdf"},{"id":104782105,"identity":"19ca1904-281a-4762-a843-49440a69107e","added_by":"auto","created_at":"2026-03-17 07:56:50","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":138452,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Figure 4. SMAD2/3 and STAT3 phosphorylation upon TGF-b and IL-10 blockade.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Dose-response of the TGF-βR1 inhibitor Galunisertib. Peripheral blood mononuclear cells (PBMCs) were pre-treated for 1 hour with increasing concentrations of Galunisertib (2, 20, and 50 μg/mL), followed by stimulation with TGF-β (20 ng/mL) for 30 minutes. The inhibitory effect of Galunisertib was evaluated by flow cytometric analysis of pSMAD2/3 expression (n=5). Bars denote mean ± SEM of the expression of pSMAD2/3 in naïve CD4 T cells. Each dot represents an individual donor, with dots colored by donor. Differences among treatments were analyzed using Friedman test followed by Dunn’s test to correct for multiple comparisons. Exact p values are shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB-C)\u003c/strong\u003e Dose-response of IL-10 blocking antibody. Peripheral blood mononuclear cells (PBMCs) were treated for 30 minutes with IL-10 (10 ng/mL) in the presence of increasing concentrations of anti-IL-10 antibody (clone3F9) (1, 10, and 20 μg/mL) (n=5). The blocking efficacy was assessed by flow cytometric analysis of STAT3 phosphorylation at two sites: S727 and Y705. Bars denote mean ± SEM of the expression of STAT3pS727 or pY705 in naïve CD4 T cells. Each dot represents an individual donor, with dots colored by donor. Differences among treatments were analyzed using Friedman test followed by Dunn’s test to correct for multiple comparisons. Exact p values are shown.\u003c/p\u003e","description":"","filename":"Extendeddata4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/64a0445a507c1e6f4c4157e3.pdf"},{"id":104540630,"identity":"db0f1734-9e3f-4a7e-9a8c-962a0a5e3d6e","added_by":"auto","created_at":"2026-03-13 05:43:34","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":97738,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExtended Data Figure 5. HIV infection of Tfh cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA)\u003c/strong\u003e Expression of HIV p24 protein in BCL6Lo and BCL6Hi p24+ cells. Naïve CD4 T cells were differentiated in the presences of IL-10 and TGF-b for 3 days (from \u003cstrong\u003eFig. 3F\u003c/strong\u003e) then infected with a dual tropic HIV (strain 89.6) and left in culture for 4 days in the presence of Saquinavir (5mM), IL-2 (30 U/ml). HIV p24 protein expression in infected cells (HIV p24+) was assessed by flow cytometry using an anti–HIV p24 antibody (clone 28B7) four days post-infection. Bars denote mean ± SEM. Each dot represents an individual donor, with dots colored by donor. Paired two‑tailed t-test; exact p value is shown.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB) \u003c/strong\u003eCorrelation between the expression of BCL6 and the expression of HIV p24 protein 4 days post-infection. Each dot represents an individual donor with dots colored by donor. Spearman rank correlations were performed. Correlation coefficients and p values are indicated on each plot.\u003c/p\u003e","description":"","filename":"Extendeddata5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/28fd74a5381f2970716b0dd6.pdf"},{"id":104781221,"identity":"759876e2-ebf4-44a4-a4b2-27b7cdb22488","added_by":"auto","created_at":"2026-03-17 07:55:10","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":39956,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Demographic and clinical information of sample donors\u003c/p\u003e","description":"","filename":"Table1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8919968/v1/4bce607a71420f2ce8c39204.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"IL-10- and TGF-β-driven BCL6 expression suppresses antiviral defenses and renders lymph node T follicular helper cells permissive to HIV infection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDespite sustained antiretroviral therapy (ART), HIV persists in long-lived cellular reservoirs representing a major barrier to cure\u003csup\u003e1\u003c/sup\u003e. Lymphoid tissues, particularly lymph nodes (LNs), constitute key anatomical sites for HIV infection, dissemination and persistence due to their unique immunological architecture and cellular composition\u003csup\u003e2-7\u003c/sup\u003e. Within LNs, CD4⁺\u0026nbsp;T follicular helper (Tfh) cells are disproportionately enriched for HIV DNA and transcriptionally active virus\u003csup\u003e8-10\u003c/sup\u003e. However, the intrinsic and extrinsic mechanisms that render Tfh cells highly permissive to HIV infection remain incompletely understood.\u003c/p\u003e\n\u003cp\u003eTfh cell differentiation and maintenance are orchestrated by the transcriptional repressor B cell lymphoma 6 (BCL6), a lineage-defining factor that programs Tfh identity and function\u003csup\u003e11-14\u003c/sup\u003e. While BCL6 is essential for germinal center formation and humoral immunity, emerging evidence suggests that this transcriptional program may come at the cost of impaired antiviral defense. Indeed, Tfh cells display reduced expression of antiviral restriction factors and interferon-stimulated genes compared with other CD4⁺\u0026nbsp;T cell subsets\u003csup\u003e10\u003c/sup\u003e, yet whether this phenotype is causally linked to BCL6 expression or reflects the LN microenvironment remains unclear. Moreover, immunoregulatory cytokines such as interleukin-10 (IL-10) and transforming growth factor-\u0026beta; (TGF-\u0026beta;)\u003csup\u003e15-17\u003c/sup\u003e, which are abundant in lymphoid tissues and known to promote Tfh differentiation\u003csup\u003e18-21\u003c/sup\u003e, may further shape this permissive state, but their contribution to HIV reservoir seeding in tissues has not been systematically examined.\u003c/p\u003e\n\u003cp\u003eHere, we investigated how BCL6 and its upstream regulatory network influence HIV susceptibility, antiviral immunity, and reservoir seeding within lymphoid tissues. By integrating analyses of human LN biopsies, functional perturbation studies, genome-scale CRISPR screens, genetic association data in HIV Elite controllers, and \u003cem\u003ein vivo\u003c/em\u003e non-human primate intervention models on ART, we identify an IL-10/TGF-\u0026beta;/BCL6 axis that actively suppresses antiviral machinery and promotes the susceptibility of Tfh cells to HIV infection. Our findings reveal that modulation of these pathways reprograms Tfh biology, restricts HIV infection, and reduces tissue reservoir size, highlighting a previously underappreciated immunoregulatory circuit that can be therapeutically targeted to destabilize HIV reservoirs and advance cure strategies.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCL6\u003csup\u003ehi\u003c/sup\u003e CD4 follicular helper T (Tfh) cells are highly susceptible to HIV infection.\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBCL6 is known as a lineage-defining transcriptional factor (TF) for Tfh cells and is associated with lower expression of antiviral protiens\u003csup\u003e10,22,23\u003c/sup\u003e. To assess whether BCL6 contributes to the heightened susceptibility of Tfh cells to HIV early upon infection, we examined LN biopsies from chronically infected people with HIV (PWH, n=5) (\u003cstrong\u003eFig. 1A, Extended Data Fig. 1A-B\u003c/strong\u003e). Quantitative analysis of LN sections revealed a significant enrichment of vRNA\u003csup\u003ehi\u003c/sup\u003e CD4⁺\u0026nbsp;T cells within the BCL6\u003csup\u003ehi\u003c/sup\u003e compartment when compared with the BCL6\u003csup\u003elo\u003c/sup\u003e CD4 T cell counterpart (p = 0.001; \u003cstrong\u003eFig. 1B\u003c/strong\u003e). These findings suggest preferential infection and/or enhanced viral transcriptional activity in BCL6\u003csup\u003ehi\u003c/sup\u003e T cells during untreated HIV infection. Consistent with these \u003cem\u003ein situ\u003c/em\u003e observations, Tfh cells sorted from LN cell suspensions during the untreated phase (\u003cstrong\u003eFig. 1C\u003c/strong\u003e), were also preferentially infected, with significantly higher amounts per million CD4 T cells of total HIV DNA and intact proviruses compared with other LN CD4⁺\u0026nbsp;T-cell subsets (CXCR5\u003csup\u003emid\u003c/sup\u003ePDPD1\u003csup\u003emid\u003c/sup\u003e, p = 0.0319; CXCR5+PD1-, p\u0026lt;0.0001/p\u0026lt;0.0001; CXCR5-PD1- p\u0026lt;0.0001/p=0.0013) and peripheral blood CD4 T cells (p= 0.0169, \u003cstrong\u003eFig. 1C-E\u003c/strong\u003e, respectively). To confirm the heightened susceptibility of BCL6\u003csup\u003ehi\u003c/sup\u003e CD4 T cells to HIV infection rather than preferential death of other CD4⁺\u0026nbsp;T-cell subsets, we isolated CD4⁺\u0026nbsp;T cells from tonsillar tissue of PWoH (n=5) and infected them with HIV \u003cem\u003ein vitro\u003c/em\u003e by spinoculation (\u003cstrong\u003eFig. 1F\u003c/strong\u003e). BCL6\u003csup\u003ehi\u003c/sup\u003e cells presented significantly higher HIV p24 protein expression per cell than BCL6\u003csup\u003elo\u003c/sup\u003e CD4 T cells at day 4 post-HIV infection (p = 0.0006; \u003cstrong\u003eFig. 1G\u003c/strong\u003e). Of note, the frequencies of the BCL6\u003csup\u003ehi\u0026nbsp;\u003c/sup\u003eand BCL6\u003csup\u003elo\u003c/sup\u003e CD4 T cell subsets were maintained throughout the assay (\u003cstrong\u003eExtended Data Fig1C\u003c/strong\u003e). These data confirmthe higher susceptibility of BCL6\u003csup\u003ehi\u003c/sup\u003e CD4 T cells to HIV infection rather than preferential survival over other subsets. To validate the role of BCL6 in regulating Tfh susceptibility to HIV infection, we used Bl-3802, a compound known to inhibit and degrade BCL6\u003csup\u003e24\u003c/sup\u003e. Bl-3802 dose was selected based on cell viability and efficiency of BCL6 degradation (\u003cstrong\u003eExtended Data Fig. 1D-E\u003c/strong\u003e). CD4⁺\u0026nbsp;T cells from tonsils of PWoH were pre-treated with the selected dose of BI-3802 for 24 hours before HIV infection, and the inhibitor was maintained throughout the assay. No changes in Tfh frequencies or cell viability were observed (\u003cstrong\u003eExtended Data Fig. 1F-G,\u003c/strong\u003e respectively). BI-3802 reduced BCL6 protein expression by 10% after 24 hours (pre-infection timepoint), reaching around 50% decay, 4 days post-infection (median fold change (FC) BI-3802/untreated: 0.9197 and 0.5156, for pre-infection and day 4 post-infection, respectively; p = 0.0057; \u003cstrong\u003eFig. 1H\u003c/strong\u003e). The treatment with BI-3802 alone showed a trend towards reduction in HIV infection as compared with the untreated condition (p = 0.0602, \u003cstrong\u003eFig. 1I\u003c/strong\u003e, 2 left bars). The combination with IFN-b\u0026nbsp;reduced further the infection rates as compared to IFN-b\u0026nbsp;alone (p = 0.0398, \u003cstrong\u003eFig. 1I\u003c/strong\u003e, 2 right bars). This data confirms that the BCL6 degradation boosts antiviral immunity leading to reduced infection rates. To further validate the role of BCL6 in supporting virus infection (thereafter referred as proviral factor), we analyzed data from our recent pooled genome-scale CRISPRn (knockout) screens performed in CD4+ T cells isolated from peripheral blood mononuclear cells (PBMCs) of PWoH (n=3), in which perturbed cells were infected with a GFP-tagged HIV (NL4-3) to identify pro- and anti-HIV host factors (\u003cem\u003eCell\u003c/em\u003e, in press). Consistent with a proviral role for endogenous BCL6, its knockout reduced HIV infection (log₂FC = −0.122, false discovery rate (FDR) = 0.135, rank 131), placing BCL6 among the top proviral factors identified in the screen (\u003cstrong\u003eSupplementary Table 1\u003c/strong\u003e). Altogether, these findings support the role of BCL6 as a proviral factor contributing to the relevance of Tfh cells for the initial HIV seeding and possibly contributing to the establishment of tissue reservoir sanctuaries.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCL6 suppresses interferon-driven antiviral programs in germinal center Tfh cells.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe next evaluated the interplay between BCL6 and antiviral proteins expression. We assessed the transcription factor IRF7, the phosphorylated form of STAT1 (pSTAT1), and downstream antiviral proteins such as MX1 and APOBEC3G by histo-cytometry\u003csup\u003e25\u003c/sup\u003e (\u003cstrong\u003eFig. 2A, Extended Data Fig. 1H\u003c/strong\u003e). This analysis was initially performed on LNs from PWoH (n=5) to eliminate the virus as an upstream trigger of the targeted antiviral molecules. In average 5-12% and 4-13% of Tfh and B cells expressed any of these antiviral proteins in the follicles from PWoH, respectively (\u003cstrong\u003eFig. 2B\u003c/strong\u003e). Notably, the expression of these antiviral proteins was found mostly on the BCL6\u003csup\u003elo\u0026nbsp;\u003c/sup\u003ecompartment, with more than 85% of the BCL6\u003csup\u003elo\u0026nbsp;\u003c/sup\u003ecells expressing all the measured antiviral proteins (p\u0026lt;0.0001; \u003cstrong\u003eFig. 2C\u003c/strong\u003e). Next, we evaluated additional lymphoid tissues to confirm the persistence of these opposing signatures (BCL6 expression \u003cem\u003evs\u003c/em\u003e. antivirals signatures). We analyzed single cell RNA-Seq (scRNA-Seq) data performed in cell suspensions isolated from different compartments of the gastrointestinal (GI) tract (colon (CO), ileum (IL) and rectum (RE)) in PWH on ART (n=8), when viremia is suppressed (\u003cstrong\u003eExtended Data Fig. 2A)\u003c/strong\u003e. Using single-sample gene set enrichment (ssGSEA) analysis\u003csup\u003e26\u003c/sup\u003e, we evaluated type I, II and III IFN pathways and the HIV restriction factors pathway\u003csup\u003e27\u003c/sup\u003e in Tfh \u003cem\u003evs.\u003c/em\u003e non-Tfh cells (T\u003csub\u003eCM\u003c/sub\u003e and Th17 cells) in these different compartments. We used Cliff’s Δ non-parametric effect size to quantify the difference between 2 cell subsets, Tfh vs T\u003csub\u003eCM\u003c/sub\u003e or Tfh vs Th17 cells (\u003cstrong\u003eSupplementary Table 2\u003c/strong\u003e). Across all GI sites, Tfh cells presented significantly lower expression of signaling pathways for type I, II and III IFNs, and HIV restriction factors when compared to central memory (T\u003csub\u003eCM\u003c/sub\u003e) CD4 T cells (Cliff’s Δ: IFN-I = 0.41, IFN-II = 0.27, IFN-III = 0.37, restriction factors = 0.12 -\u003cstrong\u003eExtended Data Fig. 2B\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;D\u003c/strong\u003e) and Th17 cells (Cliff’s Δ: IFN-I = 0.66, IFN-II = 0.62, IFN-III = 0.54, restriction factors = 0.24 - \u003cstrong\u003eExtended Data Fig. 2C\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;E\u003c/strong\u003e). Together, this data supports the generalizability and persistent dampening of interferon signaling and HIV restriction factors signatures in Tfh cells in different lymphoid tissues, including in PWH on ART.\u003c/p\u003e\n\u003cp\u003eTo confirm the role of BCL6 in dampening antiviral responses, we assessed the IRF7 protein levels in CD4 T cells isolated from tonsils from PWoH from the experiment shown in \u003cstrong\u003eFig. 1F.\u0026nbsp;\u003c/strong\u003eFollowing treatment with the BCL6 inhibitor, Bl-3802, for 24 hours, a significant increase in the total IRF7 protein expression was observed (p=0.0384; \u003cstrong\u003eExtended Data Fig. 2F\u003c/strong\u003e). Importantly, the pre-infection IRF7 expression levels (24 hours post-Bl-3802 treatment) was inversely correlated with the levels of HIV protein expression (HIV-p24 MFI) 4 days post-infection (p = 0.0214/rho = 0.7667; \u003cstrong\u003eExtended Data Fig. 2G\u003c/strong\u003e). This data confirms the role of BCL6 in suppressing Tfh antiviral responses promoting their higher susceptibility to HIV infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIL-10 and TGF-β lead to BCL6 expression and Tfh differentiation.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the importance of IL-10 and TGF-βcytokines for the LN microenvironment, and their previously reported role on Tfh differentiation\u003csup\u003e18,19\u003c/sup\u003e we quantified their mRNA expression in LNs from PWoH \u003cem\u003ein situ\u003c/em\u003e (n=5) (\u003cstrong\u003eFig. 3A\u003c/strong\u003e). IL-10 and TGF-β mRNA expression was detected in both Follicular (F) and Extra Follicular (EF) areas, with significantly higher expression in the EF areas (IL-10\u003csup\u003e+\u003c/sup\u003e p= 0.0625; TGF-β\u003csup\u003e+\u003c/sup\u003ep= 0.0312;\u003cstrong\u003e\u0026nbsp;Fig. 3B\u003c/strong\u003e). The expression levels of these two cytokines were positively correlated with one another (p= 0.0316, rho = 0.2957; \u003cstrong\u003eFig. 3C\u003c/strong\u003e), suggesting coordinated regulation within the tissue microenvironment. Of note, the total number of cells expressing mRNA for IL-10 or TGF-β was significantly correlated with the numbers of follicular Tfh cells as defined by CD3\u003csup\u003e+\u003c/sup\u003ePD1\u003csup\u003ehi\u003c/sup\u003e cells inside the follicles (p = 0.0497/rho= 0.2684 and p = 0.0400/rho = 0.2804, respectively; \u003cstrong\u003eFig. 3D-E\u003c/strong\u003e), consistent with the role of these cytokines in supporting Tfh differentiation or maintenance \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003eas previously shown\u003csup\u003e18,19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo confirm the role of IL-10 and TGF-β for the differentiation of Tfh cells, naïve CD4 T cells isolated from PBMCs from PWoH (n=5), were cultured in the presence of either IL-10, TGF-β, or the combination of both cytokines (hereafter called combo condition (\u003cstrong\u003eFig. 3F\u003c/strong\u003e). After 3 days in culture, all conditions promoted differentiation of naïve CD4 T cells towards several subsets (\u003cstrong\u003eExtended Data Fig. 3A-B\u003c/strong\u003e). A significant enrichment in the absolute numbers of Tfh cells, characterized by the simultaneous increased expression of CXCR5, PD1, and ICOS (\u003cstrong\u003eFig. 3G\u003c/strong\u003e) was observed in the combo condition (p =0.007, p = 0.0185, and p= 0.0342 for combo treatment \u003cem\u003evs.\u003c/em\u003e TCR stimulation, IL-10, and TGF-β, respectively; \u003cstrong\u003eFig. 3H).\u003c/strong\u003e Tfh cells induced under this condition exhibited significantly higher per-cell levels of BCL6 protein expression, as assessed by median fluorescence intensity (MFI), compared with non-Tfh memory CD4⁺\u0026nbsp;T cell subsets (p= 0.0011, \u003cstrong\u003eExtended Data Fig. 3C\u003c/strong\u003e). Furthermore, the combo-differentiated Tfh cells also presented the appropriate transcription factor profile, evidenced by higher expression of BCL6 and reduced expression of BLIMP-1 (\u003cstrong\u003eExtended Data Fig. 3D\u003c/strong\u003e). The balance between these two TFs is critical for Tfh cell fate, with BCL6 functioning as a master regulator for Tfh differentiation supporting B cell help, while BLIMP-1 antagonizes this process\u003csup\u003e28,29\u003c/sup\u003e. In addition to the induction of the Tfh phenotype (CXCR5, PD1, ICOS) and its proper transcriptional factor machinery (BCL6\u003csup\u003ehi\u003c/sup\u003e BLIMP-1\u003csup\u003elo\u003c/sup\u003e), the combo condition also induced Tfh cells capable of producing IL-21 (\u003cstrong\u003eExtended Data Fig. 3E-F\u003c/strong\u003e), a cytokine that depicts Tfh functionality\u003csup\u003e30,31\u003c/sup\u003e. In the other cytokine treatment groups, IL-21 was produced mainly by effector memory CD4 T cells (T\u003csub\u003eEM\u003c/sub\u003e) known as well to produce IL-21\u003csup\u003e32\u003c/sup\u003e (\u003cstrong\u003eExtended Data Fig. 3G\u003c/strong\u003e). To further confirm the contribution of IL-10 and TGF-β for Tfh differentiation, we used titrated anti-IL-10 or Galunisertib to block IL-10 (pSTAT3) or TGF-β (pSMAD2/3) signaling respectively (\u003cstrong\u003eExtended Data Fig. 4A-C\u003c/strong\u003e). Blockade of these signaling pathways markedly impaired Tfh differentiation, resulting in substantially lower numbers of Tfh cells. The dual blockade was detrimental for Tfh differentiation in our \u003cem\u003ein vitro\u003c/em\u003e model (\u003cstrong\u003eFig. 3I\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo validate that the combo condition induced the whole transcriptional profile of \u003cem\u003ebona fide\u0026nbsp;\u003c/em\u003eTfh cells, we performed bulk RNA-Seq in cells from the different culture conditions. Gene Set Enrichment Analysis (GSEA) showed that Tfh cells generated \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003eby the combo condition were enriched in signatures found in \u003cem\u003eex vivo\u0026nbsp;\u003c/em\u003eisolated \u003cem\u003ebona fide\u003c/em\u003e Tfh cells\u003csup\u003e33\u003c/sup\u003e\u003cstrong\u003e\u0026nbsp;(Fig. 3J,\u0026nbsp;\u003c/strong\u003etop panel), even though other cell types were also present (\u003cstrong\u003eExtended Data Fig. 3B\u003c/strong\u003e). The high-confidence interaction network using leading-edge genes (LEGs) enriched in the combo condition (STRING database (score ≥ 0.7)) showed \u003cem\u003ePDCD1, CTLA4, TIGIT, CD27\u0026nbsp;\u003c/em\u003eand \u003cem\u003eCXCR5\u003c/em\u003e were the key drivers of the induced gene signature (\u003cstrong\u003eFig. 3J,\u0026nbsp;\u003c/strong\u003ebottom panel). Of note, the combo condition led to the differentiation of \u003cem\u003ebona fide\u003c/em\u003e Tfh cells with significantly lower expression of antiviral pathways, including the downstream signaling of type I, II and -III IFNs\u003csup\u003e34\u003c/sup\u003e, and HIV restriction factors\u003csup\u003e27\u003c/sup\u003e (\u003cstrong\u003eFig. 3J,\u0026nbsp;\u003c/strong\u003etop panel\u003cstrong\u003e,\u003c/strong\u003e \u003cstrong\u003eSupplementary Table 3\u003c/strong\u003e) when compared to TCR stim only (p = 0.006, 1.76 x10\u003csup\u003e-30\u003c/sup\u003e, 6.33 x 10\u003csup\u003e-17\u003c/sup\u003e, 5.84 x 10\u003csup\u003e-7\u003c/sup\u003e,0.0004, for Tfh, type I, II, III and HIV restriction factors, respectively). Of note, the Tfh cells differentiated by combo condition were also more infected than the non-Tfh cells (p = 0.0625, \u003cstrong\u003eExtended Dat Fig. 5A\u003c/strong\u003e) and the BCL6 protein levels were significantly associated with the infection rates (p =0.0345 /rho = 0.953; \u003cstrong\u003eExtended Data Fig. 5B\u003c/strong\u003e). Altogether, this data confirms the role of IL-10 and TGF-β to induce the differentiation of bonafide Tfh cells with higher expression of BCL6 and lower expression of antiviral machineries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic gain- and loss-of-function CRISPR screens support the proviral role of BCL6 and Tfh-related genes.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo genetically validate the role of Tfh-related genes as proviral factors, we performed targeted analyses within genome-wide CRISPRn (knockout) and CRISPRa (activation) screens to mimic loss and gain of function, respectively (\u003cstrong\u003eFig. 4A\u003c/strong\u003e). A curated set of candidate genes (\u003cstrong\u003eSupplementary\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Table 1\u003c/strong\u003e) was evaluated for their proviral/antiviral properties at 96 hours post-HIV infection. Genes were classified as proviral or antiviral based on direction-matched false-discovery rates (FDR \u0026lt;0.3). In the CRISPRn library, among the selected genes, knockdown of \u003cem\u003eBCL2, BCL6, EZH2\u003c/em\u003e and HIF1a reduced HIV infection rates, highlighting their proviral activity (\u003cem\u003eBCL2,\u0026nbsp;\u003c/em\u003elog2-fold changes (HIV-GFP+/HIV-GFP-) (LFC) = −0.423, FDR = 1.5×10\u003csup\u003e−4\u003c/sup\u003e, \u0026nbsp;rank: 34; \u003cem\u003eHIF1A\u003c/em\u003e, LFC = −0.265, FDR = 0.0092, rank: 60; \u003cem\u003eBCL6\u003c/em\u003e, LFC = −0.122 FDR = 0.135, rank: 131; \u003cem\u003eEZH2,\u0026nbsp;\u003c/em\u003eLFC = −0.171, FDR = 0.187, rank: 149) (\u003cstrong\u003eFig. 4B\u003c/strong\u003e). Additionally, the CRISPRa mediated overexpression of \u003cem\u003eTGFBR2 and FOS\u0026nbsp;\u003c/em\u003eled to increased HIV infection rates, further highlighting their proviral properties (\u003cem\u003eFOS\u003c/em\u003e, LFC = 0.025, FDR = 0.213, rank: 733; \u003cem\u003eTGFBR2,\u0026nbsp;\u003c/em\u003eLFC = 0.231, FDR = 0.240, rank: 831) (\u003cstrong\u003eFig. 4C\u003c/strong\u003e). These are key transcriptional, signaling, metabolic, and epigenetic regulators that collectively shape Tfh cell differentiation while simultaneously creating a cellular environment permissive to HIV infection. On the other hand, CRISPRn for \u003cem\u003eEP300\u003c/em\u003e, and CRISPRa for MYC, NR4A1, TRIM5, BACH2 and CEBPB confirmed their antiviral roles, with the knockout of EP300 increasing infection, and the overexpression of MYC, NR4A1, TRIM5, BACH2 and CEBPB decreasing HIV infection \u003cem\u003ein vitro\u003c/em\u003e (\u003cem\u003eMYC\u003c/em\u003e, LFC = −0.527, FDR = 8.8×10\u003csup\u003e−5\u003c/sup\u003e, rank: 10; \u003cem\u003eNR4A1\u003c/em\u003e, LFC = −0.222, FDR = 7.7×10\u003csup\u003e−4\u003c/sup\u003e, rank: 70; \u003cem\u003eTRIM5,\u003c/em\u003e LFC = −0.334, FDR = 0.0086, rank:107; \u0026nbsp;\u003cem\u003eBACH2\u003c/em\u003e, LFC = −0.183, FDR = 0.113, rank: 242; and \u003cem\u003eCEBPB\u003c/em\u003e, LFC = −0.472; FDR = 0.179, rank: 318). Together, these findings support a model in which the IL-10/TGF-b/BCL6 axis, important for Tfh differentiation, promotes early HIV permissiveness linking LN cytokine signaling to Tfh biology and HIV seeding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-nucleotide polymorphisms in proximity to IL-10 and TGF-β genes are associated with HIV elite control.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo establish the clinical relevance of these pathways in HIV control, we analyzed genome-wide association study (GWAS) data from spontaneous HIV controllers (HIC), including HIV elite controllers (ECs), who maintain durable suppression of viremia in the absence of ART and typically exhibit reduced HIV reservoir sizes, including within lymph nodes\u003csup\u003e35-39\u003c/sup\u003e. We leveraged GWAS data from “The 2000HIV study”\u003csup\u003e40\u003c/sup\u003e and performed an hypothesis-driven candidate gene association study (CGAS)\u003csup\u003e41\u003c/sup\u003e to identify single-nucleotide polymorphisms (SNPs) defining spontaneous HIV control, based on our experimental data (\u003cstrong\u003eFig. 4D\u003c/strong\u003e). We selected 60 genes associated with Tfh signatures and/or part of the IL-10 and TGF-b\u0026nbsp;signaling pathways (\u003cstrong\u003eSupplementary Table 4\u003c/strong\u003e). Although the selected genes have not emerged as genome-wide significant loci in unbiased GWAS (typically p\u0026lt;10⁻⁸), we hypothesized that genetic variants within or proximal to these loci could nonetheless modulate LN biology and, in turn, influence HIV seeding into Tfh cells, thereby contributing to viral control in these individuals.\u0026nbsp;We\u0026nbsp;extracted GWAS summary statistics for all SNPs within a 150 kb window from these genes (n = 45,202) and pruned for independence using a 500 kb window, retaining one independent SNP per gene. More than 45,000 SNPs were found for the selected genes in a populational level (\u003cstrong\u003eSupplementary Table 5A\u003c/strong\u003e). Of these, eight independent SNPs passed the threshold for suggestive association (p\u0026lt;10\u003csup\u003e-3\u003c/sup\u003e, \u003cstrong\u003eSupplementary Table 5B\u003c/strong\u003e). Of note, the most significant association with the HIC phenotype was found for rs885334, located ~15kb upstream of the \u003cem\u003eIL10\u003c/em\u003e gene (C allele, odds ratio (OR) = 2.18, p = 1.56\u0026nbsp;×\u0026nbsp;10\u003csup\u003e-5\u003c/sup\u003e, \u003cstrong\u003eFig. 4E\u003c/strong\u003e). We also observed association with the HIC phenotype for rs6503691, a SNP in proximity to the \u003cem\u003eSTAT3\u003c/em\u003e gene (the transcription factor downstream of IL-10 signaling) which falls within an intron of the \u003cem\u003eSTATB5B\u003c/em\u003e gene, (T allele, OR = 2.26, p = 6.17×\u0026nbsp;10\u003csup\u003e-4\u003c/sup\u003e, \u003cstrong\u003eFig. 4F\u003c/strong\u003e). In the TGF-β\u0026nbsp;signaling pathway, rs4955304 near \u003cem\u003eTGFBR2\u0026nbsp;\u003c/em\u003egene, was similarly associated with the HIV elite control phenotype (T allele, OR = 2.615; p = 1.67 ∙ 10\u003csup\u003e-4\u003c/sup\u003e, \u003cstrong\u003eFig. 4G\u003c/strong\u003e). In addition, SNPs proximity to genes \u003cem\u003eKIFC3\u003c/em\u003e (rs11866228, T allele, OR = 2.52, p = 2.3\u0026nbsp;∙10\u003csup\u003e-5\u003c/sup\u003e), \u003cem\u003eHDAC4\u003c/em\u003e (rs79547584, T allele, OR = 2.78, p = 5.60\u0026nbsp;∙10\u003csup\u003e-5\u003c/sup\u003e), \u003cem\u003eE2F4\u003c/em\u003e (rs76230685, A allele, OR = 2.24, p = 6.77\u0026nbsp;∙10\u003csup\u003e-5\u003c/sup\u003e), \u003cem\u003eE2F8\u003c/em\u003e (rs6483581, C allele, OR = 2.10, p = 0.0009) were all also\u0026nbsp;suggestively associated with the HIC phenotype, while the SNP rs12443672, close to the gene \u003cem\u003eSIAH1\u003c/em\u003e (T allele, OR = 0.18, p = 0.0008) was associated with the progressor phenotype (\u003cstrong\u003eSupplementary Table 5B\u003c/strong\u003e). \u003cstrong\u003eSNPs\u003c/strong\u003ein proximity to \u003cem\u003eBCL6\u003c/em\u003e (rs55813711, p = 0.00856, OR = 1.653) and the pro-survival molecule \u003cem\u003eBCL2\u003c/em\u003e genes (rs524916, p = 0.0012, OR = 1.809) were also identified and associated with the HIC phenotype, however with lower levels of confidence. Together, these findings suggest that genetic variation in IL-10 and TGF-β\u0026nbsp;related pathways are associated with spontaneous HIV control and may influence reservoir establishment within lymphoid tissues.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTherapeutic down-modulation of IL-10 and TGF-β signaling \u003cem\u003ein vivo\u003c/em\u003e in rhesus macaques enhances antiviral programs and decreases frequencies of BCL6\u003csup\u003e+\u003c/sup\u003e Tfh cells in LNs.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn our prior work using rhesus macaques (RMs) infected with SIV and maintained on ART for long-term, we demonstrated that\u0026nbsp;\u003cem\u003ein vivo\u003c/em\u003e immune intervention with de-immunized monoclonal antibodies targeting IL-10 and PD-1 signaling (combination therapy, “combo”) resulted in unprecedented control of viral rebound following analytical treatment interruption (ATI)\u003csup\u003e42\u003c/sup\u003e. Notably, a subset of combo-treated RMs also exhibited a significant reduction in the size of the SIV reservoir within LNs\u003csup\u003e43\u003c/sup\u003e, allowing for discrimination of two distinct groups based on LN cell-associated viral DNA levels: animals with low reservoir size (CA-vDNA\u003csup\u003elo\u003c/sup\u003e, n=4) and those with higher reservoirs (CA-vDNA\u003csup\u003ehi\u003c/sup\u003e, n=6) (\u003cstrong\u003eFig. 5A\u003c/strong\u003e). Importantly, CA-vDNA\u003csup\u003elo\u003c/sup\u003e combo-treated RMs also displayed a marked attenuation of TGF-β signaling\u003csup\u003e43\u003c/sup\u003e. Thus, in this \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003estudy, CA-vDNA\u003csup\u003elo\u003c/sup\u003e RMs represent a setting of effective \u003cem\u003ein vivo\u003c/em\u003e blockade of key pathways involved in Tfh differentiation, including IL-10, PD-1, and TGF-β signaling. Leveraging publicly available multiome datasets (scRNA-seq+scATAC-seq) from this study\u003csup\u003e42,43\u003c/sup\u003e, we assessed the impact of the combo therapy on the induction of antiviral programs within LN Tfh cells prior to ATI comparing CA-vDNA\u003csup\u003elo\u003c/sup\u003e vs CA-vDNA\u003csup\u003ehi\u0026nbsp;\u003c/sup\u003eRMs, and evaluated, by flow cytometry, the frequency of BCL6-expressing Tfh cells at ATI. Pathway over-representation analysis revealed a dominant enrichment of interferon-associated pathways in Tfh cells from CA-vDNA\u003csup\u003elo\u003c/sup\u003e RMs (FDR \u0026lt; 0.1) as compared to CA-vDNA\u003csup\u003ehi\u003c/sup\u003e. These included Hallmark interferon-α and interferon-γ responses, Reactome interferon signaling pathways (including interferon-α/β signaling), and Gene Ontology terms related to type I interferon responses and negative regulation of viral genome replication (\u003cstrong\u003eFig. 5B\u003c/strong\u003e; \u003cstrong\u003eSupplementary Table 6\u003c/strong\u003e). Of relevance, gene signatures enriched in HIV ECs as compared to non-controllers\u003csup\u003e42,44\u003c/sup\u003e (pathway named EC vs non-HIC up) were also significantly elevated in Tfh cells from CA-vDNA\u003csup\u003elo\u003c/sup\u003e RMs, highlighting that the down modulation of these pathways is critical for the EC phenotype, resulting in a smaller reservoir sized in LNs. Core genes shared across these pathways included canonical interferon-stimulated genes and antiviral effectors (MX1, ISG15, OAS1, DDX60, IFI6, IFI27, IFI44, HERC6), together with activation-associated transcripts (CD38, SELL, ST8SIA4, FKBP5). Of note, the frequency of BCL6⁺\u0026nbsp;Tfh cells (CD4⁺CD200⁺cMAF⁺BCL6⁺) was significantly higher in LNs from CA-vDNA\u003csup\u003ehi\u003c/sup\u003e RMs, which maintained active TGF-β signaling, compared with CA-vDNA\u003csup\u003elo\u003c/sup\u003e RMs (p = 0.0253; \u003cstrong\u003eFig. 5C-D\u003c/strong\u003e). Collectively, these \u003cem\u003ein vivo\u003c/em\u003e data support that the downmodulation of these pathways reprograms LN Tfh cells toward an elite-controller-like antiviral state, reduced BCL6-dependent Tfh maintenance, resulting in low SIV reservoir sizes.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we show that IL-10 and TGF-β cooperate to induce a BCL6-dependent Tfh transcriptional program that suppresses interferon signaling and antiviral restriction factors, rendering these cells highly permissive to HIV infection. We confirmed that \u003cstrong\u003ei)\u003c/strong\u003e BCL6\u003csup\u003ehi\u0026nbsp;\u003c/sup\u003eCD4 T cells are preferentially infected and pharmacological or genetic BCL6 degradation limits HIV infection which is further synergized by IFNs\u003cstrong\u003e; ii)\u003c/strong\u003e BCL6\u003csup\u003ehi\u003c/sup\u003e CD4 T cells present significantly lower expression of antiviral machineries than other CD4 T cell subsets; \u003cstrong\u003eiii)\u003c/strong\u003e IL-10 and TGF-β induce the differentiation of \u003cem\u003ebona fide\u003c/em\u003e Tfh cells with higher expression of BCL6 and lower expression of antiviral genes, \u003cstrong\u003eiv)\u003c/strong\u003e SNPs in genes associated with IL-10 and TGF-β signaling contribute to the phenotype of HIV elite controllers, and \u003cstrong\u003ev)\u003c/strong\u003e \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003ein ART-treated SIV-infected RMs\u003cem\u003e,\u003c/em\u003e the blockade of IL-10, PD-1 and TGF-b\u0026nbsp;signaling pathways induces antiviral machineries in Tfh cells, decreasing the frequencies of BCL6+Tfh cells, and result in lower reservoir size in LNs post-ATI, mimicking HIV ECs. To our knowledge, this is the first comprehensive investigation on the combined actions of IL-10 and TGF-β on modulating the intrinsic antiviral properties of Tfh cells during chronic and ART-treated infection, thereby enhancing their susceptibility to HIV infection leading to the persistence of the HIV reservoir in LNs on ART. Interventions capable of modulating these pathways may contribute to HIV cure strategies by reducing the size of the HIV reservoir in LNs, similarly to the observed in our pre-clinical study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIL-10 and TGF-β decrease antiviral signatures in Tfh cells.\u003c/em\u003e\u003c/strong\u003e Lymph nodes are key sites for initiating adaptive immune responses by facilitating antigen presentation to T and B cells, resulting in their activation, proliferation, and differentiation\u003csup\u003e45,46\u003c/sup\u003e. This environment is enriched in cytokines that modulate immune responses, including IL-10 and TGF-b, making this a well-regulated site for the generation of proper immune responses\u003csup\u003e16,47\u003c/sup\u003e. In the periphery, these cytokines are well known for suppressing immune responses\u003csup\u003e48,49\u003c/sup\u003e, important in the context of viral infections\u003csup\u003e50,51\u003c/sup\u003e. IL-10 decreases the expression of MHC molecules on antigen presenting cells\u003csup\u003e17,52\u003c/sup\u003e and the effector function of T cells by decreasing their cytokine production capability, proliferation and differentiation\u003csup\u003e48\u003c/sup\u003e. TGF-β is also known for decreasing immune function and for antagonizing antiviral machineries\u003csup\u003e43,53,54\u003c/sup\u003e. Furthermore, TGF-β reduces T cell proliferation\u003csup\u003e55,56\u003c/sup\u003e and plays major roles in tumorigenesis. In the context of LNs, these cytokines counter-regulate the hyper immune reaction \u003cem\u003ein situ\u003c/em\u003e and lead to a controlled homeostatic environment for T and B cell interactions, priming and maturation\u003csup\u003e47,57\u003c/sup\u003e. While IL-10 and TGF-β broadly constrain immune activation, our data demonstrate that within LN these cytokines suppress the intrinsic antiviral defenses of Tfh cells, rendering them susceptible to infection\u0026nbsp;\u003csup\u003e8,10\u003c/sup\u003e, which could support the role of these cells as a source of virions in the absence of ART\u003csup\u003e5\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eBCL6 is the upstream TF dampening antiviral machineries in Tfh cells.\u003c/em\u003e\u003c/strong\u003e BCL6 is one of the master TFs of Tfh cells\u003csup\u003e13,14\u003c/sup\u003e which was reported to directly repress IRF7 transcription\u003csup\u003e22,23\u003c/sup\u003e and down-modulate RIG-I driven antiviral immunity. By interacting with the nuclear co-repressor complex (NCOR1) and histone deacetylase 3 (HDAC3), BCL6 promotes repressive histone modifications at the IRF7 locus\u003csup\u003e23\u003c/sup\u003e. This BCL6-mediated suppression of key antiviral genes likely disrupts the IRF7-IFN axis in Tfh cells, limiting the expression of antiviral and HIV restriction factors, creating a permissive environment that facilitates HIV intactness, transcription and persistence in tissues. The use of BCL6 inhibitor FX1, which binds the lateral side of the BTB domain of BCL6, reduces T cell activation, proliferation, and the phosphorylation of SAMHD1 (pSAMHD1, Thr592), thereby creating a cellular environment permissive to HIV replication\u003csup\u003e58\u003c/sup\u003e, similar to other BCL6 peptide inhibitors\u003csup\u003e22\u003c/sup\u003e. Together, the LN microenvironment \u003cem\u003eper se\u0026nbsp;\u003c/em\u003e(IL-10/TGF-β), the high BCL6 expression, and the decreased expression of antivirals facilitate the seeding of HIV-1 in Tfh cells. APOBEC3G, an IFN-induced protein, is an HIV restriction factor and part of the cytidine deaminase family\u003csup\u003e59-61\u003c/sup\u003e. It causes G-to-A hypermutations, rendering the viral genome nonfunctional. APOBECs were shown to be decreased in Tfh cells in this work and in our previous work\u003csup\u003e10\u003c/sup\u003e. The downmodulation of antivirals and HIV restriction factors could be the major upstream mechanisms for the higher intactness of provirus in these cells. The development of compounds that elevate the cellular levels of APOBEC3G and APOBEC3F proteins led to reduction in HIV infectivity \u003cem\u003eex vivo\u003c/em\u003e by interfering with cell-intrinsic degradation pathways\u003csup\u003e62\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHIV elite controllers exhibit SNPs in genes involved in IL-10 and TGF-β signaling pathways.\u003c/em\u003e\u003c/strong\u003e Natural HIV controllers (\u003cem\u003ei.e.,\u003c/em\u003e HIV ECs) present with decreased reservoir size\u003csup\u003e63\u003c/sup\u003e. The presence of protective HLA alleles (i.e., HLA-B57:01-03, HLA-B27:05) have been reported to be enriched in this population\u003csup\u003e64\u003c/sup\u003e. It was \u003cstrong\u003edemonstrated that\u0026nbsp;\u003c/strong\u003eHLA influence on EC likely extends beyond traditional HLA class I or class II allele associations, encompassing other HLA SNPs with various biological impacts (e.g., psoriasis)\u003csup\u003e65\u003c/sup\u003e. Allelic variations at immune loci, including variants in or near IL7RA\u003csup\u003e66\u003c/sup\u003e, IRF5-TNOP3\u003csup\u003e67\u003c/sup\u003e, TRIM5a\u003csup\u003e68\u003c/sup\u003e, BST2\u003csup\u003e69,70\u003c/sup\u003e,\u0026nbsp;TNF-α-238 and PDCD1-7209\u003cstrong\u003e\u003csup\u003e71\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e, have been previously reported\u0026nbsp;\u003c/strong\u003epromote HIV control in the absence of ART. In contrast, specific alleles/genotypes at variants\u0026nbsp;of the IL6 -174G/C, FASL -124A/G, FAS -670A/G\u003cstrong\u003e\u003csup\u003e72\u003c/sup\u003e\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003evitamin D-binding protein (DBP)\u003csup\u003e73\u003c/sup\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eAPOBEC3H (A3H)\u003csup\u003e74\u003c/sup\u003e, and the allelic frequency of CCR5 59353C\u003csup\u003e75\u003c/sup\u003e, have been\u003cstrong\u003e\u0026nbsp;associated with faster disease progression. Across all these studies, none of them reported SNPs on Tfh-related genes associated with the HIC/EC phenotype. In our hypothesis-driven analysis, intronic variants in IL10 and STAT3, together with an ncRNA-exonic variant at the TGFBR2 locus, may impact gene regulatory programs in a cell-state dependent manner. Such effects could shape Tfh biology and antiviral gene signatures (e.g., IRF3, IRF7, IRF9, APOBEC3G), with implications for tissue HIV control in HIC individuals.\u0026nbsp;\u003c/strong\u003eIndeed, our genome-wide CRISPR screening confirmed that loss-of-function and gain-of-function mutations in these genes modulate pro- or antiviral roles in HIV infection. Additionally, \u003cstrong\u003ethe EC signatures\u0026nbsp;\u003c/strong\u003ewere also enriched in Tfh cells from LNs of the CA-vDNA\u003csup\u003elo\u003c/sup\u003e RMs, highlighting the importance of this higher antiviral machinery for the natural or induced HIV EC phenotype\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterventions to modulate Tfh/antiviral related pathways to improve HIV cure strategies\u003cem\u003e.\u003c/em\u003e\u003c/strong\u003e Modulation of Tfh-related and antiviral pathways with antibodies or small molecules could alter the Tfh biology and contribute to HIV cure strategies by modulating the tissue reservoir. The induction of antiviral immunity by infusing a pegylated IFN form in Rhesus macaques led to protection against infection\u003csup\u003e76\u003c/sup\u003e. However, the chronic activation of this pathway can be detrimental in the context of HIV\u003csup\u003e77\u003c/sup\u003e, but also in several other comorbidities\u003csup\u003e78-81\u003c/sup\u003e. The direct induction of APOBECs by small molecules, or the blockade of their degradation by HIV-Vif\u003csup\u003e82\u003c/sup\u003e could be another way to impair Tfh cells as reservoirs. The blockade of IL-10 \u003cem\u003ein vivo\u003c/em\u003e in SIV infected RMs\u003csup\u003e21\u003c/sup\u003e decreased the Tfh signatures in the periphery and could further be used as an intervention to modulate the HIV reservoir in tissues. Of note, TGF-b\u0026nbsp;blockade \u003cem\u003ein vivo\u0026nbsp;\u003c/em\u003eled to significant decay of LN Tfh cells and in the reservoir size, while increasing the expression of IFN-signaling\u003csup\u003e83\u003c/sup\u003e. BCL6 blockade by PROTAC\u003csup\u003e84,85\u003c/sup\u003e has been evaluated in cancer clinical trials and shows promise in degrading the BCL6 protein\u003csup\u003e86\u003c/sup\u003e, thereby impeding tumor growth. A possible combination of these molecules, along with interventions to boost the immune system\u003csup\u003e43\u003c/sup\u003e, could be used temporarily in PWH on ART to induce antiviral responses and reduce the tissue reservoir, leading to sustainable EC phenotype and possibly a cure.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, our work defines a mechanistic framework by which IL-10 and TGF-β signaling, through the induction of BCL6, programs Tfh cells to downregulate intrinsic antiviral defenses, thereby fostering a cellular state that is highly permissive to HIV persistence. By integrating transcriptional, functional, and genetic evidence, we demonstrate that this cytokine-transcription factor axis is not only central for the Tfh reservoir biology but also amenable to therapeutic intervention. SNPs in these genes in HIV ECs, highlight the clinical relevance of such pathways. These insights open avenues for cure-directed strategies that combine targeted modulation of Tfh differentiation or BCL6 activity with established interventions such as latency reversal, immune checkpoint blockade, disruption of the IL-10 and TGF-β signaling pathways, and other agents reversing immune dysfunction in PWH. Ultimately, disrupting the protective niche provided by BCL6\u003csup\u003ehi\u003c/sup\u003e Tfh cells represents a promising strategy to diminish the size and resilience of the HIV reservoir in LNs, potentially informing future cure-directed strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthic Statement.\u003c/em\u003e\u003c/strong\u003e All tissue samples from PWH were procured with explicit written informed consent from participants prior to donation, adhering strictly to the principles outlined in the Declaration of Helsinki. The utilization of remnant samples was formally sanctioned by both the Research Committee and the Ethics in Research Committee of the National Institute of Respiratory Diseases \"Ismael Cosío Villegas\" (INER), Mexico City as part of the “C71-18” protocol. Under a material transfer agreement this project was conducted at Emory University with ethical approval STUDY00006200.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eParticipants recruitment\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e.\u0026nbsp;\u003c/em\u003eThe volunteers to participate in the LN protocol were under HIV diagnostic assessment or under follow-up for virological control. Each volunteer underwent a comprehensive clinical evaluation by a specialist in General Surgery and/or Otorhinolaryngology to detect palpable superficial lymph nodes suspected of malignancy or chronic infection. Tonsil donors were under evaluation for sleep apnea and tonsillectomy was performed when clinically indicated. Following clinical assessments, volunteers attended a recruitment consultation where they were informed about the study objectives and the significance of their lymph node/tonsil donation for diagnostic and research purposes and were provided with informed consent documents to review and discuss any questions. Once written consent was obtained, preoperative laboratory tests were performed, followed by a pre-anesthetic assessment using the American Society of Anesthesiologists Physical Status (ASA PS) scale, with individuals in groups 3 and 4 excluded prior to scheduling the outpatient procedure. Cervical LN extraction was limited to superficial, low-risk nodes to avoid vascular or nerve injury, performed under local anesthesia (5 cc of 2% lidocaine + 1:10000 epinephrine) with a precise skin incision, and closed with layered absorbable sutures (Vicryl 4-0, Monocryl 5-0). In the case of inguinal LN acquisition, individuals underwent evaluation through palpation or percutaneous ultrasound (Fujifilm Sonosite, M turbo, Transducer 13-16MHz) of the inguinal region. LN biopsies were performed under local anesthesia (5 cc 2% lidocaine + 1:10000 epinephrine and 5 cc 7.5% ropivacaine) with a ~1.5 cm ultrasound-guided incision, followed by closure with absorbable sutures. Nine PWH receiving suppressive antiretroviral therapy (ART) for over one year donate gut tissue biopsies. Ileal, colonic, and rectal samples were collected during lower endoscopy procedures (Olympus CF-HQ190). Twenty tissue snips per anatomical site were collected using standard biopsy forceps (Boston Scientific, Radial Jaw), immediately transferred into complete RPMI medium (cRPMI, RPMI 1640 [Cytiva, Marlborough, Massachusetts] supplemented with 10% fetal bovine serum [Biowest, Bradenton, Florida], 2mM L-glutamine [Cytiva], and 100 U/ml penicillin and 100ug/ml of streptomycin [Cytiva]), and transported to the laboratory within 15 minutes of collection. No adverse events were reported during the procedures. All participants were scheduled for a follow-up consultation one week after the surgical procedure. None of the participants of this study had opportunistic infections, and all were HBV- and HCV-negative. Clinical and demographic data of all donors is shown in \u003cstrong\u003eSupplementary Table 7.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLymph node and tonsil sample processing.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eAll LNs and tonsils were divided into three parts: the first placed in saline solution, used in microbiological studies to rule out bacterial, fungal, and Mycobacterium tuberculosis presence; the second, placed in paraformaldehyde 4%, used for histopathological examination to rule out neoplasms; and the third part was used for cell dissociation to be used in experiments of this study. For paraffin-embedded tissue, fresh tissues were fixed as soon as possible after biopsy for 24 hours in freshly prepared paraformaldehyde in PBS (4%) followed by preparation of formalin-fixed paraffin-embedded (FFPE) blocks using standard procedures from Roche Diagnostics (Ventana Medical Systems, Tucson, AZ, USA). To obtain cell suspension from the tissues, biopsy samples were placed in cRPMI and immediately transfer to the laboratory for further processing. Lymphoid tissues were cut into small pieces with a scalpel and manually dissociated using a 70mm mesh and a syringe embolus. Erythrocytes were removed by incubating the cells with ACK (Ammonium-Chloride-Potassium, Lonza, Houston, Tx, USA) Lysing Buffer for 5 minutes. Cells were counted and cryopreserved until further use at a density of 10 million cells per ml of freezing media (FBS [Corning, Cat no 35-016 CV] with 10% DMSO [Millipore-Sigma, cat # D2650-100ml]). Microbiological tests and histopathological studies were conducted in INER laboratories, with results recorded in the participants’ medical records. Additionally, peripheral blood was collected for plasma viral load determination and T lymphocyte counts at the virological diagnosis laboratory and the cytometry laboratory of CIENI. None of the microbiological or histopathological tests returned positive for bacterial or fungal culture, nor for neoplasia.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGut sample processing.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eIntestinal tissue biopsies were processed to obtain single-cell suspensions for downstream immunological and transcriptomic analyses. Samples were transferred into Petri dishes containing 2 mL of digestion medium composed of RPMI (Cytiva), 10% FBS, 1% HEPES (Gibco, cat # 15630080), 0.05 mM 2-mercaptoethanol (Millipore-Sigma, cat # M3148), 0.5 mg/mL Liberase (Roche, cat # 5401020001), and 10 U/mL DNase I (Invitrogen, cat # 18047019). Tissues were finely minced with sterile disposable scalpels and transferred to 15 mL conical tubes containing an additional 3 mL of digestion medium. Samples were incubated at 37 °C for 1 hour under continuous agitation. After enzymatic digestion, mechanical disaggregation was performed, followed by filtration through a 70 μm cell strainer (Falcon, cat # 352350). Freshly isolated gut cells were maintained in cRPMI medium at 37 °C prior to staining. Fc receptor blocking was performed using Human TruStain FcX™ (BioLegend, cat # 422302) for 10 minutes at room temperature. Cells were subsequently stained for 30 minutes at 37 °C in cRPMI with CD45-BV570 antibody (clone HI30, BioLegend, Cat # 304034) and viability marker (LIVE/DEAD Fixable Aqua Dead Cell Stain Kit, for 405 nm excitation, Invitrogen, cat. No L34966). After staining, cells were washed and maintained in cRPMI until sorting. Cell sorting was performed on a BD FACSAria Fusion cytometer. Following standard gating strategies to exclude doublets and dead cells, and to isolate viable CD45⁺\u0026nbsp;singlet leukocytes. CD45\u003csup\u003e+\u003c/sup\u003e sorted cells were used to prepare single-cell RNA libraries using the 10x Genomics platform.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultiplex immunofluorescence imaging.\u0026nbsp;\u003c/strong\u003eBefore staining, the slides were heated on a metal hotplate (Stretching Table, Medite, Burgdorf, OTS 40.2025, Ref. 9064740715) at 65 °C for 30 min. Tissue sections were stained with titrated antibodies (\u003cstrong\u003eSupplementary Table 8\u003c/strong\u003e) using a Ventana Discovery Ultra Autostainer (Roche Diagnostics, Ventana Medical Systems, Tucson, AZ, 85755, USA). Tissues were deparaffinized, hydrated and the protein epitopes were retrieved by applying the standard Ventana Discovery’s protocols. Before all antibody incubation steps, tissues were blocked using Antibody Diluent/Block from Akoya (ARD1001EA, Akoya Biosciences, Marlborough, MA 01752, USA). Opal dyes (Opal 7-color Automation IHC kit, from Akoya, Ref. NEL821001KT and Opal650 reagent pack FP1496001KT) were used to amplify the signal of primary antibodies (\u003cstrong\u003eSupplementary Table 8\u003c/strong\u003e). More specifically, tissue sections were sequentially subjected to antibody blocking, staining with primary antibodies, incubation with secondary HRP-conjugated antibodies (DISCOVERY OmniMap anti-Ms HRP/ 760-4310, DISCOVERY OmniMap anti-Rb HRP/ 760-4311) for 16 min, detection with optimized fluorescent Opal tyramide signal amplification (TSA) dyes and repeated antibody denaturation cycles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRNAscope \u003cem\u003ein situ\u003c/em\u003e hybridization for TGF-b, IL-10, BCL6 and HIV RNA visualization was performed according to the manufacturer’s instructions using the RNA probes (\u003cstrong\u003eSupplementary Table 8\u003c/strong\u003e - Advanced Cell Diagnostics, Hayward CA) and the RNAscope Multiplex Fluorescent Reagent Kit v2 (Advanced Cell Diagnostics, Hayward CA) with small modifications. Briefly, tissue samples were deparaffinized by applying the standard Ventana Discovery’s protocols. Then, we treated the sections with RNAscope Hydrogen Peroxide for 10min at RT, followed by an antigen retrieval step at 100°C for 15min. Subsequently, sections were incubated with Protease III for 15min at 40°C in a HybEz hybridization oven (ACD). Sections were incubated either with the TGF-b, IL-10 or with \u003cem\u003eBcl6\u0026nbsp;\u003c/em\u003eand HIV specific probes at 40°C for 2 hours, and then proceeded with 3 signal amplification cycles using the RNAscope amplification reagents. To visualize the RNA signals, we used the tyramide based detection system by Akoya as mentioned above (Opal 7-color Automation IHC kit, from Akoya, Ref. NEL821001KT). Following the RNAscope protocol, we subjected the tissue sections to antibody blocking for 30min and then we proceeded with the protein marker staining (\u003cstrong\u003eSupplementary Table 8\u003c/strong\u003e). Applied antibodies were tested for their compatibility with the RNAscope protocol. Both unconjugated and conjugated antibodies were diluted in Antibody Diluent/Blocker and incubated for 90min RT. Alexa Fluor conjugated secondary antibodies were diluted in Antibody Diluent/Blocker and incubated for 45min at RT.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll of the stained slides were counterstained with with SYTO45 (1/10 000 dilution in TBS‐T, Cat. No. 10297192, ThermoFisher Scientific for\u0026nbsp;23 mins,\u0026nbsp;rinsed in soapy water and mounted using DAKO mounting medium (Dako/Agilent, Santa Clara, CA, USA, Ref. S302380-2).\u003c/p\u003e\n\u003cp\u003eA representative individual cell positive for both \u003cem\u003eHIV\u003c/em\u003e and \u003cem\u003eBcl6\u003c/em\u003e mRNAs in the context of B and T cells is shown (\u003cstrong\u003eFig. 1A,\u003c/strong\u003e lower panel). The representative image was included to show the close proximity of T and B cells in the germinal center, which could lead to signal spillover between the two cell types\u003csup\u003e87\u003c/sup\u003e, in addition to possible cells sharing interconnected membrane structures\u003csup\u003e88\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConfocal data acquisition.\u0026nbsp;\u003c/strong\u003eImages were acquired using a Leica Stellaris 8 SP8 confocal system, equipped with Leica Application Suite X (LAS-X)-4.6.1.27508 software, at 512 × 512-pixel density, 0.75× optical zoom and a z-step of\u0026nbsp;0,69 to 0,8(max) in order to have a much detail as possible,\u0026nbsp;using a 20× objective (0.75 NA). High-resolution images were also captured at 1024x1024 -pixel density, 1× optical zoom and a z-step of 0.8 μm using a 40× objective (1.4 NA). For RNAscope data acquisition, a 40x (0.95 NA) and a 63x (1.4 NA) objective was used. Frame averaging or summing was never used while obtaining the images. At least 70% of each section was imaged to ensure an accurate representation and minimize selection bias. Tissues stained with a single antibody fluorophore combination were used to create a compensation matrix via the Leica LAS-AF Channel Dye Separation module (Leica Microsystems), which was used to correct fluorophore spillover (when present), as per the user’s manual. When the dye separation results were not optimal, the manual LAS-AF Channel Dye Separation module was employed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative Imaging Analysis (Histo-Cytometry).\u0026nbsp;\u003c/strong\u003eThe Surface Creation module of Imaris (v 9.9.0) was used to generate 3-dimensional segmented surfaces (based on the nuclear signal) of spillover-corrected images. Segmented cells were then processed with the filtering Imaris module using different combinations of filtering types based on the mean and median intensities of channels to exclude artifacts that are characterized by a uniform/background-like staining across the segmented area. Areas with uniform staining were excluded among the different tissues. Data generated, such as average voxel intensities across all channels, in addition to the volume and sphericity of the 3-dimensional surfaces, were exported in Microsoft Excel format. The Excel files obtained from cell segmentation were converted to comma separated value (.CSV) files, and data were imported into FlowJo (version 10) for further analysis. Well-defined follicular areas were included in the analysis of follicular immune landscape, and the data were quantified as relative frequencies (%) or as absolute counts. For the analysis of individual samples, hand gating was performed for the identification of relevant cell subsets and the intensities of individual biomarkers used in gated populations were presented as 2D histo plots. Follicular areas were identified based on the density of CD20hi/dim, a biomarker specific for B cells. The cut-off values for the identification of cells expressing ‘high’ profile for a given biomarker (e.g., CD3, PD1) was determined based on the 2D plot expression profile for this biomarker on relevant cells (e.g., PD1 expression on CD3 vs CD20 cells), using Histocytometry\u003csup\u003e25\u003c/sup\u003e analysis and the inspection of its intensity in the raw mIF image. Histocytometry analyzed cells of interest were exported and imported into Imaris raw mIF image as segmented spots for the comparison/validation of these cells to their original counterparts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIntegrated and intact HIV DNA.\u0026nbsp;\u003c/strong\u003eCell-associated total and intact HIV-1 DNA analyses were performed on isolated CD4 T cells from blood or sorted LN-derived live CD3+CD8- T cells from untreated people with HIV-1 (PWH; Fiebig IV/V and chronic infection). Sorting of LN cells was based on CXCR5 and PD-1 expression into four subsets: CXCR5negPD-1neg double negatives (DN), CXCR5+PD-1neg, CXCR5midPD-1mid, and CXCR5hiPD-1hi Tfh cells, all at \u0026gt;95% purity. Immediately after sorting, cells were centrifuged and lysed in Direct PCR Lysis Reagent (DLR; Viagen, cat. #301-C) containing Proteinase K (Fisher Scientific, cat. #10181030) and XHoI restriction enzyme (Fisher Scientific, cat. #10880041). Cell pellets were stored at −20 °C until further processing. Sufficient material was recovered from each subset to support downstream virological assays. Cell-associated total and intact HIV-1 DNA analyses were performed on these lysates using the Rainbow proviral HIV-1 DNA dPCR assay, a multiplex digital PCR approach that simultaneously quantifies total and intact HIV-1 DNA, as previously reported\u003csup\u003e89\u003c/sup\u003e. Total HIV-1 DNA was defined by the detection of the repeated unique 5′ (RU5) region, and intact proviruses were classified according to three predefined combinations of HIV-1 sub genomic targets from the Rainbow assay. Reactions were run on a QIAcuity Four digital PCR platform (Qiagen, Germany) as described previously\u003csup\u003e89\u003c/sup\u003e. Briefly, 8 µL of sorted cells from blood or lymph nodes (32,000–120,000 cells per reaction; mean 72,200) were used per replicate (triplicates). Each 40 µL reaction contained 10 µL 4× QIAcuity Probe Master Mix (Qiagen, #250102), 2 µL of each primer/probe set (final concentrations: RU5, 0.675 µM primers/0.187 µM probe; PSI, 0.675 µM primers/0.187 µM probe; env, 0.5 µM primers/0.25 µM probe)\u003csup\u003e89\u003c/sup\u003e, 0.3 µL XbaI restriction enzyme (100,000 U/mL), lysate (5–8 µL) and nuclease-free water. Reaction mixes were loaded onto 26k 24-well nanoplates (Qiagen, #250001), partitioned and sealed on the QIAcuity system, and amplified with 2 min at 95 °C followed by 40 cycles of 94 °C for 30 s and 56 °C for 60 s. Imaging times were 500 ms, 500 ms, 400 ms, 200 ms and 400 ms in the green, yellow, orange, red and crimson channels, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTfh differentiation \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003emodel.\u0026nbsp;\u003c/strong\u003eHuman naïve CD4 T cells were enriched from PBMCs of PWoH, by magnetic bead negative selection with the EasySep Naive CD4+ T Cell Isolation Kit (StemCell Technologies, cat. # 17555), according to the manufacturer’s instructions. Purity (CD4+CD45RA+) was routinely confirmed to exceed 90% or higher by flow cytometry. Purified naïve CD4 T cells (7.5 × 104 cells/well) were resuspended in cRPMI medium supplemented with 10% of fetal bovine serum (FBS). Naïve CD4 T cells were activated in 96-well plates (70,000 cells per well) with T Cell TransAct, a polymeric nanomatrix conjugated to humanized recombinant CD3 and CD28 agonists (1:500, Miltenyi, Cat #130-128-758), in the presence of recombinant human IL-7 (4 ng/ml, R\u0026amp;D, Cat. # 207-IL) and anti-IL-2 (1 μg/ml, clone 5334, R\u0026amp;D, Cat # MAB202100) for blockade of IL-2 signaling. To induce Tfh cell differentiation, cultures were supplemented with recombinant human TGF-β (20 ng/ml; Peprotech, Cat # 100-21C-100ug), recombinant human IL-10 (10 ng/ml; Preprotech Cat # 200-10-100UG), or both cytokines and left in culture for 3 days (5% CO2, 37\u003csup\u003eo\u003c/sup\u003eC). At the endpoint, the cells were counted using Countess and harvested for flow cytometric analysis or processed for RNA isolation and downstream gene expression profiling (see below). Differentiated cells were also infected \u003cem\u003ein vitro\u003c/em\u003e with HIV for the analysis of susceptibility to infection. The flow cytometry panel included markers to recognize memory CD4 cell subsets (e.g., CD45RA, CCR7, CD27) including Tfh markers (PD1, ICOS, CXCR5, BCL6, c-maf, and IL21) (details of reagents and antibodies are in the Reagents, antibodies, and virus section below). For Infection rates, antibody for HIV-p24 was used. For inhibition experiments, cells were cultured with either Galunisertib (20ug/ml, LY2157299, Selleck Chemicals) or anti-IL-10 (10ug/ml, clone 3F9, Biolegend) and kept in culture for 3 days. Galunisertib was pre-incubated for one hour before the addition of stimulation media containing TGF-β.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow cytometry.\u0026nbsp;\u003c/strong\u003eWe used flow cytometry panels to evaluate Naïve CD4 cells differentiation, including Tfh cells, and IL-21 intracellular expression (details of reagents and antibodies are in the Reagents, antibodies, and virus section below). Intracellular cytokine production was assessed in unstimulated cells or following a 12 hours stimulation with phorbol myristate acetate (50ng/ml) and ionomycin (1mg/ml) as a positive control. Brefeldin A (1ml/ml, BD Biosciences, Cat # 555029) and monensin (1.4ml/ml, BD Bioscienes, Cat No 554724) were added at 1 hour post stimulation (total of 11 h). The unstimulated cells served to define cytokine production induced by differentiated cells under the different stimulation media. Surface staining was performed at room temperature for 20 min. Samples underwent fixation and permeabilization with eBioscience™ Foxp3 / Transcription Factor Staining Buffer Set (Invitrogen, Cat # 00-5523-00) for 45 min at 4 °C. Intracellular staining was performed for 45 min at 4 °C. Acquisition was performed on a minimum of 70,000 live cells on a A5 Symphony flow cytometer (BD Biosciences) driven by BD FACSDiva software. Acquired data was analyzed using FlowJo v.10.8.1. Representative cytograms are shown for each panel in the respective figures.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBulk RNA.\u0026nbsp;\u003c/strong\u003eTen thousand cells were used for the gene expression profile of the differentiated cells. RNA was used as input for cDNA synthesis using using the Illumina® Stranded Total RNA Prep, Ligation with Ribo-Zero Plus kit (Illumina, cat # 20040529), according to the manufacturer’s instructions. Libraries were validated by capillary electrophoresis on an Agilent 4200 Tape Station, pooled at equimolar concentrations, and sequenced on an Illumina NovaSeq6000 at 100SR, yielding 25-30 million reads per sample. After sequencing, reads were processed using Cutadapt\u003csup\u003e90\u003c/sup\u003e (v4.4) to remove Illumina adapters and low-quality bases from the 3' end. Trimmed reads were then aligned to the human reference genome (GRCh38) using the STAR aligner\u003csup\u003e91\u003c/sup\u003e (v2.7.10a). Alignment parameters were set as follows: --outFilterScoreMinOverLread 0.3; --outFilterMatchNminOverLread 0.3 to improve alignment rates; and --quantMode GeneCounts to obtain read counts per gene. All other parameters were kept at their default values. Quantified gene counts of protein-coding genes in the GRCh38 were further screened for low-quality genes. Specifically, we excluded genes with fewer than 0.5 counts per million in more than 15% of the samples using the EdgeR package\u003csup\u003e92\u003c/sup\u003e in R (version 4.3.2).\u003csup\u003e92\u003c/sup\u003e in R (version 4.3.2). After filtering, 12,804 genes remained for downstream differential gene expression analysis. Differential gene expression analysis was performed using the DESeq2 package\u003csup\u003e93\u003c/sup\u003e (version 1.44.0) in R with raw read counts and a biological condition vector as input to identify differentially expressed genes between biological conditions. Package-recommended default parameters were used unless otherwise specified. Genes were considered differentially expressed based on a nominal p-value threshold. Estimated log2 fold-changes were subsequently used as input for gene set enrichment analysis via the fgsea\u003csup\u003e94\u003c/sup\u003e package in R with a collection of pathways for Tfh signatures, host antiviral restriction factors, and IFN signaling extracted from Locci et al., 2013\u003csup\u003e33\u003c/sup\u003e (GSE50391), Abdel-Mohsen et al., 2015\u003csup\u003e27\u003c/sup\u003e, and the Interferome database\u003csup\u003e34\u003c/sup\u003e, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBCL6 inhibition assays in tonsil CD4 T cells.\u0026nbsp;\u003c/strong\u003eWe performed HIV infection of CD4 T cells isolated from tonsils of PWoH. Tonsil cells were thawed, and total CD4 T cells were isolated by negative selection according to the manufacturer’s protocol and using the EasySep Human CD4+ T Cell Isolation Kit (StemCell Technologies, cat # 17952). Purified CD4 T cells (95%) were allowed to rest in cRPMI at 37°C in 5% CO2 for 6 hours. Cells were then plated at 2x10\u003csup\u003e6\u003c/sup\u003e cells/ml and treated with the BCL6 inhibitor (0.5nM, BI-3802, Selleckchem, cat # S6937) with or without IFN-b\u0026nbsp;(2ng/ml, ProSci, cat # 40-278) for 24 hours. An unstimulated condition was included as a control. Cells were then infected by spinoculation as previously described and cells were kept in cRPMI supplemented with 30 U/ml IL-2 (R\u0026amp;D Systems; 202-IL) and 5mM saquinavir in the presence or absence of BCL6 inhibitor and/or IFN-b. The presence of HIV-p24+ cells was assessed by flow cytometry four days after infection.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGut biopsies Single-Cell RNA analysis.\u0026nbsp;\u003c/strong\u003eGut single-cell emulsions were prepared within 30 minutes after cell sorting. Reactions were carried out using the Chromium Next GEM Single Cell 3' GEM, Library \u0026amp; Gel Bead Kit v3.1 and the Chromium Next GEM Chip G Single Cell Kit (10x Genomics, cat # 1000127), along with the i7 Multiplex Kit for samples 1–9 and the Dual Index Kit TT Set A for samples 10–11, following the manufacturer’s protocol. Each library was prepared using 10,000 cells per gut tissue. Sequencing was performed on the Illumina NextSeq 500 platform using paired-end reads and the NextSeq 500/550 High Output Kit v2.5 (150 cycles). Libraries prepared with the i7 Multiplex Kit were sequenced using 28 cycles for Read 1, 8 cycles for the i7 index, and 91 cycles for Read 2. Libraries prepared with the Dual Index Kit TT used 28 cycles for Read 1, 10 cycles for i7 and i5 indices, and 90 cycles for Read 2. Fastq files were processed in 10x Genomics Cell Ranger v5.0.1 using 10x Genomics Cloud Analysis\u003csup\u003e95,96\u003c/sup\u003e. Reads were mapped to the GRCh38 human reference genome and counted without depth normalization. The filtered count matrix was then analyzed using the Seurat (v5.1.0)\u003csup\u003e97\u003c/sup\u003e in R. Low-quality cells were identified and removed based on the following criteria: more than 100 RNA unique molecular identifiers, less than 25% mitochondrial read fraction. Doublet cells were identified using the DoubletFinder package in R\u003csup\u003e98\u003c/sup\u003e. Only CD4+ population cells were analyzed further. We performed single-cell RNA-seq analysis using the Seurat (v5.0.0)\u003csup\u003e99\u003c/sup\u003e package in R (v4.4.0), followed by batch correction with Harmony to integrate data across experimental conditions. Outlier cells were removed prior to analysis to ensure high-quality clustering. Genes associated with mitochondrial content, ribosomal components, hemoglobin, sex chromosomes, and the surfactant protein family were excluded to reduce technical and tissue-specific bias. Gene expression values were then normalized. We identified the top 5,000 highly variable features using the variance-stabilizing transformation (VST) method, and scaled gene expression values across cells. Principal component analysis (PCA) was performed using these variable genes, and the resulting principal components were corrected for batch effects using the Harmony algorithm. An elbow plot of the Harmony components was used to determine the number of informative dimensions, and the first 20 components were selected for downstream analysis. Clustering was performed using shared nearest neighbor (SNN) graph construction and the Louvain algorithm, with parameters k.param= 50 and resolution = 0.7 based on the Harmony-reduced space. We identified Tfh cells by using a combination of a composite expression score of Tfh markers (BCL6, CXCR5, CXCL13, CD200, PDCD1) and Azimuth Human Tonsil v2\u003csup\u003e100\u003c/sup\u003e (GC-Tfh-SAP or Tfh T:B border) labels. We also identified 2 non-Tfh populations for comparison: Th17 and Central Memory (CM) CD4+. A composite score of Th17 markers (CCR6, IL23R, and RORC) was used to select the Th17 cells. For the CM non-Tfh CD4+ group, the dataset was re-clustered using the Azimuth Human PBMC reference\u003csup\u003e101\u003c/sup\u003e. The cells labeled central memory and effector memory were selected. For all groups, only cells from PWH were kept for analysis. First, gene sets related to Tfh markers, viral machinery, and restriction factors were acquired. Single-sample gene set enrichment analysis (ssGSEA) was performed using the escape R package\u003csup\u003e102\u003c/sup\u003e to identify the enrichment score for each gene set of interest and cell type. The difference between 2 cell populations at a time was calculated. Statistical significance was determined using p-values using the Wilcoxon rank-sum test (\u0026lt;0.05) and effect sizes were quantified with Cliff's Δ (≥ abs(0.1). We then visualized the data using boxplots with a dot overlay. Only 70% of the population was represented by dots and was randomly sampled. In the second analysis we separated the cells based on tissue location. ssGSEA was performed using the escape R package to identify the cell enrichment score for each pathway in each tissue.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenetic analyses.\u0026nbsp;\u003c/strong\u003eThe GWAS on HIV controllers from European ancestry in the 2000HIV cohort was performed as previously described\u003csup\u003e103\u003c/sup\u003e. In short, genotypes were associated with the spontaneous HIV controllers (HIC) phenotype in 67 HICs compared to 1,179 non-HICs. The logistic regression was performed in PLINK v1.90b\u003csup\u003e103\u003c/sup\u003e and the model was corrected for age, sex and the first five genetic principal components (PCs). We extracted summary statistics for all SNPs in a +-150 kb window from the 60 genes of interest. SNPs were greedily clumped based on a 500kb window. SNPs with a P-value below 1\u0026nbsp;∙\u0026nbsp;10\u003csup\u003e-3\u003c/sup\u003e were regarded as suggestively associated with the phenotype. Gene-based annotation was performed using ANNOVAR\u003csup\u003e104\u003c/sup\u003e using the RefGenewithVer protocol. Functional element-based annotation was performed by intersecting the SNP locations with the ENSEMBL regulatory features (v115 on GRCh38) using bedtools\u003csup\u003e105\u003c/sup\u003e. Expression Quantitative trait locus mapping was performed as described by Botey-Bataller et al\u003csup\u003e106\u003c/sup\u003e. Summary statistics were extracted for the suggestive SNPs, and those with \u0026nbsp;\u0026lt; 1\u0026nbsp;∙\u0026nbsp;10\u003csup\u003e-5\u0026nbsp;\u003c/sup\u003ein the discovery cohort and p \u0026lt; 0.05 in the validation cohort were regarded as significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of pooled genome-wide CRISPR perturbation screens.\u0026nbsp;\u003c/strong\u003eGenome-scale pooled CRISPR activation (CRISPRa) and CRISPR knockout (CRISPRn) screening data from our previous study in stimulated primary human CD4⁺\u0026nbsp;T cells were analyzed as previously described (Cell, in press). In these screens, genetic perturbations were introduced into primary CD4⁺\u0026nbsp;T cells via lentiviral delivery of pooled sgRNA libraries, together with dCas9 (CRISPRa) or Cas9 (CRISPRn), followed by challenge with replication-competent, GFP-tagged HIV. Cells were sorted into GFP⁺\u0026nbsp;and GFP⁻\u0026nbsp;bins, and gene-level effects on HIV infection were computed from guide-level enrichment analyses using MAGeCK (v0.5.9.5). For the present study, we focused on a curated set of 60 genes associated with Tfh cell differentiation and IL-10/TGF-β signaling. Gene-level log₂\u0026nbsp;fold changes (GFP⁺/GFP⁻), ranks, and direction-matched false discovery rates (FDRs) were extracted from the pooled screening datasets and used to classify genes as proviral or antiviral (FDR \u0026lt; 0.3). Comparisons were performed between the target gene vs. overall distribution of all the guides in the screen. Full experimental procedures and data processing steps are described in the referenced study (Cell, in press).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-human primate study.\u0026nbsp;\u003c/strong\u003ePublicly available datasets from Ribeiro et al., was used\u003csup\u003e42,43\u003c/sup\u003e. Briefly, 10 RMs received a combination therapy using de-immunized anti-IL-10 and anti-PD-1 antibodies during ART and early after ATI. This intervention led to 9/10 RMs to controll viremia post-ATI, and to 4 combo-treated RMs to decay the amount of cell associated viral DNA (CA-vDNA) in LNs (so called, CA-vDNA\u003csup\u003elo\u003c/sup\u003e RMs). scRNA-Seq/ATAC-Seq and high dimensional flow cytometry were performed at pre-ATI and 24 weeks post-ATI, respectively (\u003cstrong\u003eFig. 5A\u003c/strong\u003e). In this study we focused our analysis on TFh cells gene-signatures comparing CA-vDNA\u003csup\u003elo\u003c/sup\u003e vs CA-vDNA\u003csup\u003ehi\u003c/sup\u003e RMs in LNs pre-ATI, and on the frequencies of Tfh cells expressing BCL6 post-ATI. Pathway over-representation analysis was performed with clusterProfiler R package (v. 4.18.2) using genes upregulated (FDR \u0026lt; 0.2) in TFH cells from RMs with low CA-vDNA compared to those with high CA-vDNA. Gene sets from the Human Molecular Signatures Database (MSigDB) Hallmark, C2, and C5 collections were included, along with gene sets containing genes up- and downregulated in CD4⁺\u0026nbsp;and CD8⁺\u0026nbsp;T cells and monocytes from Elite Controllers\u003csup\u003e106\u003c/sup\u003e. Enriched pathways (FDR \u0026lt; 0.1) were clustered using the vissE R package (v. 1.18.0), which computes gene set overlap via the Jaccard index and identifies highly connected clusters of pathways using the Walktrap community detection algorithm. The cluster primarily associated with interferon response was selected for further exploration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis.\u0026nbsp;\u003c/strong\u003eCorrelation analyses were done using a Spearman’s correlation test. Rho and p values are shown. Differences between follicular and extra-follicular areas, Tfh and non-Tfh cells were analyzed using the Paired t-test or Wilcoxon signed-rank test. Differences among treatments were analyzed using the Friedman test followed by Dunn’s test to correct for multiple comparisons, and one-way ANOVA followed by Tukey’s post hoc test. p \u0026lt; 0.05 is reported as significant. Statistical analyses were performed using R 4.2.2 with \u003cem\u003erstatix\u003c/em\u003e package version 0.7.2 and GraphPad Prism 9.4.0 (GraphPad Software, Boston, MA). Plots were generated with the R package ggplo2 or GraphPad Prism.\u003c/p\u003e\n\u003cp\u003eTo construct a gene network of Tfh signature leading genes, leading-edge genes were identified from the enrichment analysis of Tfh-associated transcriptional signatures comparing the combo and TCR conditions (combo/TCR). The resulting gene list was mapped to the STRING database (version 12; species Homo sapiens) using the R package STRINGdb, with a minimum interaction score threshold of 0.7 (high confidence). The network was built in R using the \u003cem\u003eigraph\u003c/em\u003e package and visualized with \u003cem\u003eggraph\u003c/em\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReagents, antibodies, and virus.\u0026nbsp;\u003c/strong\u003eIL-10 from Peprotech was used at 10ng/mL; IL-7 from R\u0026amp;D Systems was used at 40ng/mL; TGF-b1 from Peprotech was used at 20ng/mL; anti-IL-10 from Biolegend was used at 10ug/ml; Galunisertib (Selleckem, cat # S2230).\u0026nbsp;Antibodies for flow cytometry were acquired from BD Biosciences (BCL-6-PE-CF594, clone K112-91, cat. No562401; BCL6-BUV737, clone K112-91, cat. No 567412; BLIMP-1-Alexa Fluor 700, clone 6D3, cat. No567764; BLIMP-1-PE, clone 6D3, cat. No564702; CCR7-BUV395, clone 3D12, cat. No740267; CCR7-PE-Cy7, clone 3D12, cat. No557648; CD27-APCCy7, clone M-T271, cat. No560222; CD3 -BUV805, clone UCHT1, cat. No612895; CD3-BUV737, clone UCHT1, cat. No612750; CD3-BV750, clone UCHT1, cat. No747177; CD45RA-BUV563, clone HI100, cat. No612926; CXCR5 -BUV496, clone RF8B2, cat.\u0026nbsp;No741115; FOXP3-BB700, clone 236A/E7, cat. No566526; ICOS-BUV805, clone DX29, cat. No748903; IFN-g-BB700, clone B27, cat. No566394; IL-10R-Alexa Fluor 647, clone 3F9, cat. No556013; IL-2-BV750, clone MQ1-17H12, cat. No566361; IL-10R-Alexa Fluor 647, clone 3F9, cat. No565255; IL-10R-BV421, clone 3F9, cat. No742942; IL-10R-PE, clone 3F9, cat. No556013; IRF7-Alexa Fluor 488, clone K40-321, cat. No558707; pIRF7 (pS477/pS479)-PE, clone K47-671, cat. No558621; pSTAT1 (pY710)-BB515, clone 4a, cat. No612596; PSTAT5 (pY694)-PE-CF594, clone 47/STAT5(pY694), cat. No562501; Smad2 (pS465/pS467)-PE-CF594, clone O72-670, cat. No562697; Tbet-BV711, clone O4-46, cat. No563320; TNF-Alexa Fluor 488, clone MAb11, cat. No557722); BioLegend (CD4-BV605, clone OKT4, cat. No317438; CD4-BV605, clone OKT4, cat. No317438; CD4-BV650, clone OKT4, cat. No317436; CD45RA-BV605, clone HI100, cat. No304134; CD45RA-BV650, clone HI100, cat. No304136; ICOS-BV421, clone DX29, cat.\u0026nbsp;No313524; IL-10R-PECY7, clone 3f9, cat. No308814; IL-10R-PECy7, clone 3F9, cat. No308814; PD1-BV786, clone EH12.2H7, cat. No329930; PD1-BV786, clone EH12.2H7, cat. No329930; pSTAT3 (pS727)-APC, clone A16089B, cat. No698914; pSTAT3 (pY705)-Percp Cy5.5, clone 13A3-1, cat. No651022; TCF7-Alexa Fluor 647, clone 7F11A10, cat. No655204);\u0026nbsp;Beckman\u0026nbsp;Coulter (HIV-1 core antigen-RD1, (clone KC57, cat.\u0026nbsp;No6604667; HIV-1 core antigen-FITC, clone KC57, cat. No6604665), Invitrogen (CD27-APCeFluor780, clone O323, cat. No47-0279-42; IL-21-PE, clone eBio3A3-N2, cat. No12-7219-42),\u0026nbsp;Thermo Fisher Scientific (c-maf-PE-eFluor610, clone sym0F1, cat. No61-9855-42), and LSBio (NLRX1-BIOTIN, clone Polyclonal, cat.\u0026nbsp;NoLS-C499438).\u0026nbsp;\u0026nbsp;Streptavidin-BUV737 was used to detect NLRX1 signal (BD, cat. No612775).\u0026nbsp;All antibodies were titrated for best performance in each flow cytometry panel. All flow cytometry panels included viability markers:\u0026nbsp;Live/Dead-AF700, BD, cat. No564997; LIVE/DEAD™ Fixable Aqua Dead Cell Stain Kit, for 405 nm excitation, Invitrogen, cat. No L34966.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInfection experiments used HIV (clone 89.6) acquired from the NIH reagents program.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability.\u0026nbsp;\u003c/strong\u003eExtended data Tables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability.\u003c/strong\u003e No new code was generated for this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u0026nbsp;\u003c/strong\u003eWe would like to thank the participants from CIENI for the generous gift for this study. We also would like to thank the CIENI team for their commitment and ethics for the recruiting these participants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding.\u0026nbsp;\u003c/strong\u003eNIH funding R01AI179476 (SPR), R37AI141258 (RPS).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePMDRE, SG, and SPR conceptualized, planned, interpreted the results, and contributed to the writing of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePMDRE, SG, MO, CB, FCC, executed the \u003cem\u003ein vitro\u003c/em\u003e and imaging experiments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFRB, GXM have supported experiment execution and logistics.\u003c/p\u003e\n\u003cp\u003eLV, MD, YN, MP have generated the virological readouts in sorted cells from LNs and PBMCs.\u003c/p\u003e\n\u003cp\u003eJS, SR, AVDV, MGN have performed the CGAS analysis in HIV ECs.\u003c/p\u003e\n\u003cp\u003eUR, ED, and AM have conceptualized, performed, and analyzed the data from the CRISPRn and CRIPSPRa experiments.\u003c/p\u003e\n\u003cp\u003eAO, SK, VD, FTC have performed all the bioinformatic analysis, including bulkRNA-Seq and scRNA-Seq.\u003c/p\u003e\n\u003cp\u003eMGN, YALV, MFTR, EPI, GSMO, MSN, CRL, DDR, OB, KKOC, SAR have enrolled all participants of this study and processed all the samples including PBMCs, LNs and gut biopsies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRB has contributed to the design of the flow panels and contributed to the scientific discussions.\u003c/p\u003e\n\u003cp\u003ePMDRE, SG, RPS, JD, CP, and SPR have contributed to the conceptualization and scientific discussions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRPS and SPR have funded the execution of this Project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests.\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHarper, J.\u003cem\u003e et al.\u003c/em\u003e Progress Note 2024: Curing HIV; Not in My Lifetime or Just Around the Corner? \u003cem\u003ePathog Immun\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 115-157 (2023). https://doi.org:10.20411/pai.v8i2.665\u003c/li\u003e\n\u003cli\u003eParyad-Zanjani, S., Jagarapu, A., Piovoso, M. 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A review of signaling and transcriptional control in T follicular helper cell differentiation. \u003cem\u003eJ Leukoc Biol\u003c/em\u003e \u003cstrong\u003e111\u003c/strong\u003e, 173-195 (2022). https://doi.org:10.1002/JLB.1RI0121-066R\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8919968/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8919968/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Lymph nodes (LNs) constitute a key anatomical sanctuary for HIV. Follicular helper T (Tfh) cells expand early upon infection and represent a principal cellular target for initial viral seeding. Here, we identified the transcription factor BCL6, a Tfh-lineage defining marker, as central in favoring the infection of Tfh cells in LNs during the untreated phase in humans, and for the persistence of the reservoir during ART in non-human primates. In situ and ex vivo analyses of LN from people with HIV (PWH) in absence of antiretroviral therapy (ART) revealed preferential enrichment of viral RNA, total HIV DNA, and intact proviruses within BCL6hi Tfh cells, which also presented significantly lower expression of proteins with antiviral functions (IRF7, MX1, APOBEC3G, pSTAT1). In vitro genetic (genome-wide CRISPR knockouts) and pharmacologic perturbations confirmed that BCL6 enhances the cellular permissiveness of Tfh cells to HIV infection. IL-10 and TGF-β were enriched in LNs from people without HIV (PWoH), and cooperatively induced bona fide BCL6hi Tfh differentiation in vitro, with repressed antiviral pathways. IL-10 and TGF-β blockade limited Tfh differentiation, confirming their contribution to Tfh and LN biology. Human Single Nucleotide Polymorphisms (SNPs) in proximity to genes of the IL-10 and TGF-β pathways were enriched in PWH who controls viremia spontaneously (HIV elite controllers). Importantly, in vivo downmodulation of IL-10 and TGF-β signaling pathways in ART-treated SIV-infected macaques, by using anti–IL-10 and anti–PD-1 therapy, led to reduced frequencies of LN BCL6+ Tfh cells. These Tfh cells expressed significantly higher expression of antiviral machineries, similar to gene signatures found in HIV elite controllers, and resulted in significantly lower SIV reservoir size in LNs. This data highlights that the modulation of the IL-10/TGF-β/BCL6 axis is relevant at early stages upon infection, but also during ART, after the HIV reservoir is already established. In both scenarios it results in higher antiviral machinery and lower HIV seeding and reservoir sizes. Thus, the modulation of these pathways in vivo has potential to alter Tfh biology in LNs leading to HIV reservoir decay, contributing to HIV cure strategies.","manuscriptTitle":"IL-10- and TGF-β-driven BCL6 expression suppresses antiviral defenses and renders lymph node T follicular helper cells permissive to HIV infection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 05:43:29","doi":"10.21203/rs.3.rs-8919968/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"364f6827-64d9-4744-be97-4146af7eb1da","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63833762,"name":"Biological sciences/Immunology/Infectious diseases/HIV infections"},{"id":63833763,"name":"Biological sciences/Immunology/Lymphoid tissues/Lymph node"}],"tags":[],"updatedAt":"2026-03-13T05:43:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 05:43:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8919968","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8919968","identity":"rs-8919968","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}