Ankrd11 deficiency reverses the dysfunction of chronic HBV-specific T-cell

Ankrd11 deficiency reverses the dysfunction of chronic HBV-specific T-cell | 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 Letter Ankrd11 deficiency reverses the dysfunction of chronic HBV-specific T-cell Xuyu Zhou, Wei Xu, Jie Guo, Xue Cao, Liping Li, Qiuzhu Jin, Fuping Zhang, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6785364/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 CD8 + T cell dysfunction, driven by intricate molecular mechanisms, poses a major obstacle to hepatitis B virus (HBV) clearance in chronic infection. Using a highly humanized mouse model, we identified a T cell receptor (TCR) targeting a dominant epitope essential for HBV clearance in clinical settings. We further uncovered Ankrd11, an epigenetic regulator critical for sustaining CD8 + T cell dysfunction during chronic infection. Strikingly, Ankrd11 knockout in CD8 + T cells markedly enhanced HBV-specific T cell proliferation, particularly in immunosuppressive environments, through upregulated AP-1 family gene expression. Additionally, Ankrd11-deficient T cells exhibited robust granzymes production and superior effector functions, resulting in potent antiviral and anti-tumor activity. These findings open new avenues for immunology and virology research, offering promising therapeutic strategies against chronic HBV infection. Biological sciences/Immunology Biological sciences/Immunology/Adaptive immunity/Cellular immunity/Lymphocyte activation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction HBV infection is a severe liver disorder with a significant global health burden. Chronic HBV infection is a leading cause of cirrhosis, hepatocellular carcinoma (HCC), and liver failure, contributing to substantial morbidity and mortality worldwide 1 . Despite the availability of an effective prophylactic vaccine, chronic hepatitis B remains incurable, and the global prevalence continues to rise alarmingly 2 . Recent estimates indicate that approximately 296 million individuals are living with chronic HBV infection, with 1.5 million new infections reported annually 2 . These staggering figures underscore the urgent need for intensified research efforts to develop curative therapies and improve clinical outcomes for affected individuals. The pathogenesis of HBV infection is closely linked to the host immune response, particularly the role of HBV-specific CD8 + T cells 3, 4 . In acute HBV infection, these cytotoxic T lymphocytes (CTLs) play a pivotal role in viral clearance by directly eliminating infected hepatocytes and secreting antiviral cytokines such as interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α) 3, 4, 5 . However, the functional impairment of HBV-specific CD8 + T cells in chronic HBV infection is a hallmark of disease persistence 6 . T cell exhaustion—characterized by the progressive loss of T cell effector functions, reduced proliferative capacity, and sustained expression of inhibitory receptors such as programmed cell death protein 1 (PD-1)—has been widely recognized as a primary cause of this dysfunction 7 . This concept, initially identified in mouse models of chronic Lymphocytic Choriomeningitis Virus (LCMV) infection, has been extended to various cancers and chronic diseases, including HIV, HCV, and HBV 7, 8, 9, 10 . However, the mechanisms underlying T cell dysfunction in HBV are more complex than those observed in LCMV models, underscoring the need for further research to understand and address this unique state of immune dysregulation 11, 12 . Mounting evidence underscores the pivotal role of the liver microenvironment in determining the functional fate of HBV-specific CD8 + T cells. Priming of CD8 + T cells within the liver—particularly in the absence of adequate co-stimulation—coupled with immunosuppressive signaling pathways, such as those driven by transforming growth factor-β (TGF-β), may trigger a unique program of T cell dysfunction 6, 11, 13 . While immune checkpoint blockade targeting PD-1 has demonstrated potential in reversing T cell exhaustion in preclinical LCMV models, its therapeutic impact in chronic HBV infection remains limited 11 . This disparity emphasizes the necessity for deeper mechanistic insights into the molecular and cellular drivers of T cell dysfunction in HBV, as well as the discovery of novel therapeutic targets. Our research has focused on developing advanced animal models that recapitulate key aspects of human HBV infection. In a previous study, we generated a humanized mouse model expressing HLA-A11 and the human antigen processing and presentation machinery, including transporters associated with antigen processing (TAP1 and TAP2) and proteasome subunits (LMP2 and LMP7) 14 . Using this model, we identified the HBc 141–151 (STLPETTVVRR) epitope (also referred to as HBVcore169) as an immunodominant epitope capable of eliciting a robust CTL response and promoting HBV clearance 14 . Notably, this epitope—processed by IFN-γ–inducible immunoproteasomes, in which three constitutive 20S proteasome subunits are replaced by the inducible subunits LMP2, LMP7, and MECL-1—is closely linked to disease progression in clinical settings 12, 15 . Robust expansion of HBc 141–151 -specific CD8 + T cells is observed in acute HBV patients, whereas severe functional impairment is seen in chronic infection 12 . These findings raised critical questions about the mechanisms underlying the dysfunction of HBc 141–151 -specific CD8 + T cells in chronic HBV infection. This study investigated the molecular mechanisms underlying T cell dysfunction in chronic HBV infection by developing a novel TCR transgenic mouse model specific for the HBc 141–151 epitope. Genome-wide CRISPR/Cas9 screening identified the Ankrd11 gene as a critical regulator of HBV-specific T cell exhaustion. Genetic ablation of Ankrd11 in HBV-reactive CD8 + T cells restored effector function, enhancing granzyme B production and proliferative capacity. Mechanistically, Ankrd11 deficiency augmented CD8 + T cell responsiveness to interleukin-2 (IL-2) signaling—a pivotal pathway for T cell survival and expansion—while conferring resistance to TGF-β-mediated suppression via upregulation of AP-1 family transcription factors. Therapeutic targeting of Ankrd11 in a chronic recombinant adeno-associated virus (rAAV)-HBV mouse model significantly reduced hepatitis B surface antigen (HBsAg) levels, a key indicator of viral persistence. Our research clearly highlights that inhibiting Ankrd11 presents a powerful strategy to significantly boost antiviral and anti-tumor immunity, paving the way for effective functional cures for chronic HBV infection. Results Target a human immune dominant epitope HBc 141–151 reverse chronic HBV infection The HBc 141–151 epitope is an immunodominant T-cell epitope of hepatitis B virus (HBV), and functional responses of CD8 + T cells targeting this epitope strongly correlate with viral control in HLA-A11-positive patients 12 . However, studying HBc 141–151 -specific CD8 + T cells in chronic HBV infection is challenging due to their exceptionally low frequency in vivo 12 . To overcome this limitation and investigate the functional dynamics of these epitope-specific T cells, we immunized HLA-A11/hTAP transgenic mice—which express human HLA-A11 and the human antigen-processing machinery, including transporters associated with antigen processing (TAP1/2) and proteasome subunits (LMP2/7)—with the pAAV-HBV1.2 plasmid via hydrodynamic injection, followed by a subcutaneous boost with the HBc 141–151 peptide (STLPETTVVRR) (Fig. 1a). Peripheral blood cells from immunized mice exhibited robust responses to the HBc 141–151 peptide (Fig. 1b). We isolated HBc 141–151 -specific CD8 + T cells using an HBc 141–151 /HLA-A*11:01 tetramer and performed single-cell T-cell receptor (TCR) sequencing to identify HBc 141–151 -specific TCRs (Fig. 1a and Extended Data Fig. 1a). A unique HBc 141–151 -specific TCR was identified (Extended Data Fig. 1b, c), and we developed a novel TCR transgenic mouse model expressing the rearranged TCR α (TRAV3-3) and β (TRBV4) chains (Extended Data Fig. 1b). As shown in Fig. 1c, nearly 99% of these T cells expressed the Vβ4 chain, confirming successful generation of a monoclonal T-cell population specific to the HBc 141–151 epitope. CD8 + T cells from HBc 141–151 TCR transgenic mice exhibited a highly specific immune response: they strongly responded to HBc 141–151 but showed no reactivity to the non-specific peptide NP 91-99 (an HLA-A11-restricted influenza epitope) (Fig. 1d), underscoring the precision of this model (hereafter HB-I Tg). Furthermore, activated HB-I Tg CD8 + T cells displayed potent cytotoxic activity against peptide-pulsed target cells in vitro and in vivo (Fig. 1e, f). Importantly, this killing activity was strictly HLA-A11-dependent, as the T cells failed to recognize HLA-A2 or HLA-A33 (despite HLA-A33 belonging to the same A3 superfamily and sharing anchor residues with HLA-A11) (Fig. 1g). Thus, the HB-I Tg model provides a valuable tool for elucidating HBc 141–151 -specific T-cell responses and their role in HBV immunity. To evaluate the therapeutic potential of HBc 141–151 -specific CD8 + T cells in chronic HBV infection, we employed a recombinant adeno-associated virus (rAAV)-HBV1.3 mouse model, which recapitulates key features of human chronic HBV infection, including persistent HBsAg and HBV DNA levels, and the absence of anti-HBsAg antibodies 16 . Six weeks post-infection, HBsAg levels stabilize, mimicking the chronic phase of human HBV infection. We adoptively transferred 10 million activated HBc 141–151 -specific CD8 + T cells from TCR transgenic mice into HLA-A11/hTAP transgenic mice and control C57BL/6 (B6) mice (Fig. 1h). Compared to B6 controls, HLA-A11/hTAP recipients exhibited significant reductions in HBsAg and HBV DNA (Fig. 1i, j), accompanied by a transient increase in alanine aminotransferase (ALT) (Fig. 1k), indicative of hepatocyte damage during viral clearance. Remarkably, after two rounds of treatment, HBsAg levels dropped below the detection threshold in five of seven mice, with some animals developing anti-HBsAg antibodies (Fig. 1l), suggesting functional cure 17 . These results demonstrate that HBc 141–151 -specific CD8 + T cells can mediate viral clearance and restore immune control in chronic HBV infection. However, the liver microenvironment in chronic HBV infection poses challenges to adoptive T-cell therapy. To assess the impact of this suppressive environment on HBc 141–151 -specific CD8 + T cells, we evaluated their cytotoxic activity in vivo . Peptide-reloaded splenocytes were introduced as target cells into HLA-A11/hTAP mice that had received HB-I CD8 + T cells two weeks prior (Fig. 1m). Compared to B6 controls, HLA-A11/hTAP recipients exhibited substantially reduced killing activity (Fig. 1n), partly due to significant loss of HBc 141–151 -specific CD8 + T cells in the liver (Fig. 1o), highlighting the immunosuppressive nature of chronic HBV infection. These findings suggest that while HBc 141–151 -specific TCR transgenic CD8 + T cells represent a promising therapeutic strategy, their efficacy depends on a delicate balance between effector function and liver immunosuppression. Whole genome CRISPR-Cas9 library screening identifies the key in control T cells dysfunction in chronic HBV infection To investigate the molecular mechanisms underlying liver-mediated immune suppression in chronic HBV infection, we crossed HB-I transgenic mice with CD4 Cre - Rosa26 LSL-Cas9-GFP strains and conducted a whole-genome CRISPR/Cas9 screening 18 . We hypothesized that targeted gene knockouts mediated by CRISPR-Cas9 and guide RNAs (gRNAs) could rescue the diminished abundance of HBc 141-151 -specific CD8 + T cells observed during chronic HBV infection. To test this, CD8 + T cells from HB-I transgenic mice were activated for two days and subsequently transfected with a whole-genome CRISPR lentivirus library containing 90,230 single-guide RNAs (sgRNAs) targeting 18,424 genes 19 . To ensure experimental precision, the virus library titer was carefully controlled to guarantee single-virus infection per T cell. Following puromycin selection to enrich transfected T cells, the HBc 141-151 -specific T cell pool with diverse gene knockouts was adoptively transferred into HLA-A11/hTAP mice infected with recombinant adeno-associated virus carrying HBV1.3 (rAAV-HBV1.3) for six weeks. After nine days of in vivo selection, HB-I TCR transgenic CD8 + T cells were isolated from the liver via fluorescence-activated cell sorting (FACS), and their genomic DNA was extracted and subjected to deep sequencing (Fig. 2a). The MAGeCK algorithm was employed to analyze the genome-scale CRISPR/Cas9 knockout screening data, identifying essential genes with a stringent cutoff score of <0.001 20 . This analysis revealed over 50 gene knockouts that were significantly enriched compared to the input DNA (Fig. 2b), highlighting their potential roles in modulating CD8+ T cell responses during chronic HBV infection. Among the top hits were several key regulators of CD8 + T cell function, including Zc3h12a , Rc3h1 , and Fli1 (Fig. 2b,c), which are implicated in the context of chronic HBV infection, where T cell dysfunction and dysregulated immune responses facilitate viral persistence. Zc3h12a , an RNA-binding protein with endoribonuclease activity, modulates CD8 + T cell activation by degrading mRNAs encoding inflammatory cytokines, thereby fine-tuning immune responses 21, 22 . Rc3h1 , another RNA-binding protein, regulates mRNA stability and translation of genes critical for T cell receptor signaling and cytokine production, suggesting its role in maintaining T cell functionality 23 . Fli1 , an ETS family transcription factor, suppresses CD8 + T cell effector differentiation by binding effector-associated gene regulatory elements. Its deletion enhances effector T (Teff) cytotoxicity and anti-tumor/infectious immunity (Fig. 2d) 24 . Gene Ontology analysis of positively selected genes revealed significant enrichment in genes regulating T cell activation (e.g. , Rc3h1, Zc3h12a, Peli1, Ctla2a , Mdk , and Bad ) and pathways associated with T cell differentiation and cellular response to chemical stress (Fig. 2e). Among downregulated genes, we identified significant enrichment in biological processes governing cell migration and calcium ion transport, processes essential for CD8 + T cell tissue infiltration, metabolic reprogramming, and cytotoxic effector function (Fig. 2e). These genes represent potential therapeutic targets for transcriptional activation or pharmacological agonists to enhance the efficacy of T cell-based immunotherapies. Together, these findings provide first unbiased, genome-wide perspective on the molecular mechanisms driving immune suppression in chronic HBV infection, offering potential therapeutic targets to restore CD8 + T cell function and enhance viral control. Ankrd11 deficiency enhances effector function of CD8 + T cells Ankrd11, a chromatin regulator, emerged as a focus of interest among the top candidates identified. Ankrd11 modulates histone acetylation by recruiting HDACs and is known to play a critical role in the proliferation and development of cortical neural precursors 25, 26 . However, its function in the immune system remains unexplored. To investigate the role of Ankrd11 in T cells, we generated T cell-specific Ankrd11 knockout mice (CKO) by crossing CD4-Cre mice with a floxed Ankrd11 strain. Some of these mice were further crossed with HB-I transgenic mice to enable antigen-specific analysis of Ankrd11 function. Phenotypically, the T cell-specific Ankrd11 knockout mice exhibited no obvious abnormalities. T cell development in the thymus appeared normal, with the ratios and cell numbers of double-positive (DP) and single-positive (SP) CD4 and CD8 + T cells comparable to those of littermate controls (Fig. 3a). In the periphery, a slight reduction in the CD8 + T cell population was observed (Fig. 3b); however, these cells showed no signs of spontaneous activation, as the majority of CD8 + T cells remained CD44 low and CD62L high (Fig. 3c). These findings suggest that Ankrd11 deficiency has a minimal impact on T cell development and peripheral homeostasis. To evaluate the antigen-specific CD8 + T cell response, we stimulated CTV-labeled CD8 + T cells isolated from HB-I transgenic mice in vitro with the HBc 141–151 peptide. While Ankrd11-deficient CD8 + T cells exhibited a slightly weaker response to HBc 141–151 peptide stimulation than wild-type controls within the first three days (Fig. 3d and Extended Data Fig. 2a), they displayed elevated production of effector molecules, including Granzyme B, IFNγ, TNFα, T-bet, and PD-1, alongside reduced CD62L expression (Fig. 3e,f). This result indicates that Ankrd11 may not be essential for initiating T cell activation but plays a critical role in regulating the quality of CD8 + T cell effector function. To ensure the validity of our findings, we rigorously ruled out any potential influence of Ankrd11 on T cell development, differentiation, and activation. We employed a CRISPR-Cas9-based gene-editing strategy to specifically delete Ankrd11 in activated CD8 + T cells. These cells were derived from CD4-Cre-Rosa26 LSL-Cas9-GFP mice and were activated for two days in vitro . Retroviral transfection was used to deliver sgRNA for Ankrd11 deletion, enabling targeted gene knockout following initial T cell activation. Consistent with previous findings, Ankrd11 knockout cells, identified by NGFR expression, displayed elevated levels of effector molecules and surface markers, including Granzyme B and ICOS, alongside reduced CD62L expression compared to control cells treated with non-targeting sgRNA (Fig. 3g). Functional assays demonstrated that Ankrd11-deficient CD8 + T cells exhibited significantly enhanced cytotoxic activity in vitro (Fig. 3h,i). These cells were more efficient at killing target cells compared to wild-type controls (Fig. 3i), without compromising their own viability (Fig. 3j). These findings underscore the critical role of Ankrd11 in modulating CD8 + T cell function and suggest that its deletion promotes a more potent effector phenotype. Loss of Ankrd11 confers resistance to immunosuppression and augments cytotoxic function in CD8 + T cells To further investigate the molecular and functional consequences of Ankrd11 deletion, we isolated and purified Ankrd11 knockout (KO) (NGFR + ) and wild-type (WT) control cells for bulk RNA sequencing (RNA-seq) (Fig. 4a). Transcriptomic analysis revealed a distinct gene expression signature in Ankrd11 KO cells, characterized by significant upregulation of genes associated with CD8 + T cell activation, effector function, and cytotoxicity (Fig. 4a). Notably, cytotoxic effector molecules such as Gzmb, Gzmf, Gzmg, Gzmd, Gzme, Prf1 and the proinflammatory cytokine IFNγ, were markedly elevated in Ankrd11-deficient cells (Fig. 4a). These findings align with the enhanced killing activity observed in Ankrd11 KO CD8 + T cells (Fig. 3i). Gene ontology enrichment (GO) analysis further highlighted the upregulation of pathways related to post-transcriptional regulation of gene expression, T cell activation and leukocyte differentiation in Ankrd11-deficient CD8 + T cells (Fig. 4b). Intriguingly, AP-1 family genes ( Fos, Fosb, Fosl1 , etc.) emerged as potential central hubs mediating these transcriptional changes (Fig. 4a). Given that Ankrd11 may modulate epigenetic regulation by recruiting histone deacetylases (HDACs) 25 , we performed ATAC-seq and CUT&Tag with an anti-histone H3 acetylation antibody. Although Ankrd11 deficiency did not significantly alter global chromatin accessibility (Fig. 4c), we observed enhanced histone H3 modifications at the Fos and Fosb loci—but not at the Sell locus—in Ankrd11 KO CD8 + T cells (Fig. 4c). These results support the hypothesis that Ankrd11 suppresses AP-1 family protein expression by regulating site-specific histone modifications. Notably, activated Aknrd11-deficient CD8+ T cells exhibited significant upregulation of the high-affinity IL-2 receptor subunit, IL-2Rα (CD25) (Fig. 4a,d), a well-established downstream target of AP-1. To determine whether this altered receptor expression affected downstream signaling, we restimulated activated CD8 + T cells with varying IL-2 concentrations and assessed STAT5 activation by phospho-STAT5 (pSTAT5) staining. At a standard IL-2 concentration (50 U/mL), Aknrd11 knockout and wild-type CD8 + T cells showed comparable pSTAT5 levels (Fig. 4e). However, under IL-2-limiting conditions (2.5 U/mL and 10 U/mL), Aknrd11-deficient cells exhibited a significantly stronger pSTAT5 response than WT controls (Fig. 4e). Furthermore, activated Aknrd11-deficient CD8 + T cells produced substantially more IFNγ and TNFα in vitro under low IL-2 conditions (Extended Data Fig. 2b). Given the critical role of STAT5 in CD8 + T cell homeostasis and cytotoxicity, these findings suggest that Aknrd11-deficient cells may possess a survival and proliferative advantage in IL-2-restricted environments. AP-1 is a critical regulator of CD8 + T cell function, and its impairment in tumors promotes exhaustion 27, 28 . Hypoxia, TGF-β, and chronic antigen exposure in the tumor microenvironment suppress AP-1 activity, further driving T cell dysfunction. Interestingly, while initial Ki67 staining did not reveal a proliferative advantage in Ankrd11 KO cells in vitro (Fig. 4f and Extended Data Fig. 2c), these cells exhibited a unique resistance to immunosuppressive mediators. Specifically, they were refractory to the inhibitory effects of TGF-β, lactic acid, and PGE2—key components of the immunosuppressive liver microenvironment (Fig. 4f) 6, 29, 30 . Moreover, Ankrd11 KO cells rescued TGF-β-mediated suppression of IFNγ and TNF-α production (Fig. 4g), suggesting that Ankrd11 deficiency not only enhances effector function but also overcomes microenvironmental suppression. Collectively, these findings demonstrate that Ankrd11 deficiency endows CD8 + T cells with enhanced effector function, improved cytokine sensitivity, and resistance to immunosuppression. Given the critical role of AP-1 in sustaining T cell responses—and the observed upregulation of AP-1 targets (e.g., Fos/Fosb) in Ankrd11 KO cells—these results suggest that Ankrd11 may normally act as a brake on CD8 + T cell activation by suppressing AP-1 activity. Targeting Ankrd11 could thus represent a novel therapeutic strategy to reinvigorate CD8 + T cell responses in chronic viral infections, such as hepatitis B virus (HBV), or in tumors where AP-1 dysfunction contributes to T cell exhaustion. Ankrd11 is an essential gatekeeper to prevent the conduction of effector function in CD8 + T cells To evaluate the in vivo activity of Ankrd11 knockout (KO) CD8 + T cells, we utilized HB-I CD4-Cre Ankrd11 fl/fl Rosa-stop-YFP mice. CD8 + T cells from these mice were activated in vitro with the HBc 141–151 peptide for three days and subsequently co-transferred at a 1:1 ratio with wild-type T cells (YFP - ) into HLA-A11/hTAP mice (Fig. 5a). These recipient mice had been immunized with the pAAV-HB1.2 plasmid via hydrodynamic injection (Fig. 5a), a method that mimics acute HBV infection without inducing persistent viral replication, closely resembling clinical acute infection scenarios 31 . Consistent with the results from our Cas9 library screening, we observed significant enrichment of YFP + Ankrd11 KO cells in both the spleen and liver of recipient mice (Fig. 5b). As shown in the figure, more than 80% of the cells in these organs were YFP + Ankrd11 KO cells (Fig. 5b), indicating a robust expansion and survival advantage of Ankrd11-deficient CD8 + T cells in vivo . To further validate these findings, we employed a CRISPR-Cas9-based approach to knock out Ankrd11 in activated CD8 + T cells using sgRNA. These KO cells, along with control sgRNA-transfected cells, were transferred into HLA-A11/hTAP mice that had been injected with the AAV-HBv2.0 plasmid to induce a liver-specific immune response (Fig. 5c). In this setup, NGFR + cells (representing Ankrd11 KO cells) also exhibited dramatic expansion in the liver, with a less pronounced but still significant presence in the spleen compared to control cells (Fig. 5d). Notably, a large proportion of NGFR + cells in the liver displayed a bona fide effector phenotype, characterized by high expression of KLRG1 and CX3CR1 and downregulation of CD62L and CXCR3 (Fig. 5e,f). These markers are indicative of terminally differentiated effector cells, suggesting that Ankrd11 deficiency promotes the acquisition of a highly functional effector state 32 . These findings strongly suggest that Ankrd11 acts as a critical gatekeeper in restraining the differentiation and functional capacity of CD8 + T cells. In its absence, CD8 + T cells exhibit enhanced effector function, as evidenced by their increased expansion, survival, and tissue infiltration in vivo . This aligns with our earlier observations that Ankrd11 deficiency enhances cytotoxic activity and resistance to immune suppression in vitro . Expanded Ankrd11 deficient CD8 + T cells reverse chronic HBV infection in vivo The ultimate value of TCR-T cell therapy lies in its potential to treat individuals with chronic hepatitis B virus (HBV) infection. To evaluate whether Ankrd11 knockout (KO) could enhance the efficacy of antigen-specific CD8 + T cells in clearing HBV during chronic infection, we established a chronic HBV infection model using adeno-associated virus (AAV)-HBV-infected mice. We then transferred Ankrd11 KO CD8 + T cells into these recipient mice to assess their therapeutic potential (Fig. 6a). In this model, AAV-HBV infection mimics the persistent viral replication and immune tolerance characteristic of chronic HBV infection in humans 16 . Upon transferring Ankrd11 KO HB-I cells, we observed a marked increase in the frequency of HB-I cells in both the peripheral blood and spleen of recipient mice (Fig. 6b,c). In comparison to control cells, the Ankrd11 KO cells demonstrated substantially greater expansion within the liver, accounting for up to 40% of the total CD8 + T cell population in this organ (Fig. 6c). These cells also exhibited a bona fide effector phenotype, characterized by high expression of CX3CR1, Granzyme B and Ki67 (Fig. 6d,e), markers indicative of terminally differentiated effector T cells. This phenotypic shift suggests that Ankrd11 deficiency promotes the acquisition of a highly functional effector state, enabling the cells to mount a more potent antiviral response. Notably, while the transfer of one million HB-I cells had no significant effect on viral clearance in this model, Ankrd11 Cas9-deficient HB-I cells elicited a robust and uniform reduction in serum HBsAg levels across all treated mice, indicating a reversal of chronic HBV infection progression. (Fig. 6f). This striking difference underscores the critical role of Ankrd11 in regulating CD8 + T cell function and highlights the therapeutic potential of targeting Ankrd11 to enhance T cell-mediated immunity against chronic viral infections. The enhanced efficacy of Ankrd11-deficient CD8 + T cells can be attributed to their improved survival, expansion, and functional capacity in the immunosuppressive microenvironment characteristic of chronic HBV infection. These findings align with our earlier observations that Ankrd11 deficiency enhances cytotoxic activity, resistance to immune suppression, and the acquisition of an effector phenotype in vitro and in vivo . Together, these results demonstrate that Ankrd11 acts as a key regulator of CD8 + T cell exhaustion and dysfunction during chronic viral infection. Ankrd11 deficiency promoter cancer rejection Given the role of Ankrd11 in modulating CD8 + T cell responses, we hypothesized that Ankrd11 could similarly influence antitumor immunity, particularly in tumors resistant to PD-1 blockade. To test whether Ankrd11 knockout (KO) enhances tumor rejection, we employed the B16-OVA melanoma model, in which ovalbumin (OVA)-transfected B16-F10 cells exhibit resistance to PD-1/PD-L1 inhibition, as demonstrated in prior studies 33, 34 . Using this model, we evaluated the antitumor capacity of Cas9-mediated Ankrd11-deficient OT-I CD8 + T cells (Fig. 7a). Our findings revealed that adoptive transfer of 1 million wild-type (WT) OT-I T cells had minimal effect on controlling B16-OVA tumor growth (Fig. 7b,c), consistent with their poor expansion in secondary lymphoid organs and limited tumor infiltration (Fig. 7d,e). In stark contrast, Ankrd11 deficiency significantly enhanced the antitumor activity of OT-I T cells (Fig. 7b,c), correlating with robust expansion in draining lymph nodes and markedly increased tumor infiltration (Fig. 7d,e). Furthermore, Ankrd11 KO CD8 + T cells exhibited heightened effector function, as evidenced by increased Ki67 expression and elevated Granzyme B production (Fig. 7f). These results demonstrate that Ankrd11 deficiency augments the antitumor efficacy of CD8 + T cells, even in the context of PD-1 immunotherapy-resistant tumors. Our data suggest that targeting Ankrd11 may represent a promising strategy to overcome resistance to immune checkpoint blockade, potentially improving T cell-mediated tumor control. Further investigation is warranted to elucidate the precise mechanisms by which Ankrd11 regulates T cell exhaustion and effector function in the tumor microenvironment. Discussion Our study elucidates a critical role for the transcriptional regulator Ankrd11 in constraining CD8 + T cell responses during chronic viral infection and cancer. By combining TCR transgenic models with genome-wide CRISPR screening, we demonstrate that Ankrd11 deficiency unlocks potent antiviral and antitumor immunity, overcoming key barriers in the immunosuppressive liver microenvironment and checkpoint-resistant tumors. These findings establish Ankrd11 as a central regulator of T cell exhaustion and identify actionable targets for immunotherapy. The immunodominant HBc 141–151 epitope represents a vulnerable target in HBV infection, with clinical studies correlating HBc 141–151 -specific responses with viral control in HLA-A11 positive individuals 12 . Our development of HBc 141–151 -specific TCR transgenic mice provided a powerful tool to dissect these responses mechanistically. These T cells exhibited exquisite specificity for HLA-A11-HBc 141–151 complexes while remaining unresponsive to other HLA-A11-restricted epitopes, demonstrating their potential for targeted immunotherapy. In adoptive transfer experiments, HBc 141–151 -specific CD8 + T cells effectively reduced viral loads in chronically infected mice, with some animals achieving seroconversion - a hallmark of functional cure. These data reinforce the clinical relevance of HBc 141–151 -directed immunity while highlighting the need to overcome microenvironmental suppression. To systematically identify barriers to T cell function in chronic HBV infection, we employed genome-wide CRISPR screening in HBc 141–151 -specific CD8 + T cells. This approach revealed Ankrd11 as a top hit whose deletion enhanced T cell persistence and effector function. Mechanistically, Ankrd11 deficiency remodeled the transcriptional landscape of activated CD8 + T cells, upregulating critical cytotoxic mediators such as granzymes (Gzms) and perforin (Prf1) associated with AP-1 activation. Notably, Ankrd11 KO cells exhibited heightened IL-2 sensitivity through enhanced STAT5 signaling, particularly under cytokine-limited conditions mimicking the liver microenvironment 13 . These findings position Ankrd11 as a rheostat controlling the balance between T cell activation and exhaustion. The liver presents a formidable barrier to effective immunity, with multiple immunosuppressive mechanisms converging to limit T cell function 6 . Our studies revealed that Ankrd11-deficient CD8 + T cells acquired resistance to key suppressive factors including TGF-β, PGE2, and metabolic stressors. This resistance translated to superior expansion and persistence in vivo , with Ankrd11 KO cells dominating the T cell compartment in both lymphoid organs and the liver. Importantly, these cells maintained a terminally differentiated effector phenotype (KLRG1 hi CX3CR1 hi ) associated with potent antiviral activity. The ability of Ankrd11-deficient HBc 141–151 -specific T cells to reverse established chronic infection underscores the therapeutic potential of targeting this pathway. More importantly, the conserved role of Ankrd11 in limiting CD8 + T cell responses extended beyond viral infection to cancer immunity. In PD-1-resistant tumors, Ankrd11 deficiency rescued T cell expansion, tumor infiltration, and effector function, mirroring our observations in HBV infection. This suggests that Ankrd11 operates as a fundamental checkpoint across diverse chronic disease contexts. The convergence of HBV and cancer models on Ankrd11 highlights its importance as a key regulator of T cell dysfunction. Our work suggests two complementary therapeutic strategies—adoptive transfer of epitope-specific T cells with engineered Ankrd11 deficiency and pharmacological targeting of the Ankrd11 pathway to enhance endogenous immunity—though several key considerations require further investigation, including the tissue-specific effects of Ankrd11 modulation, the potential risk with systemic Ankrd11 inhibition, and the optimization of combination therapies such as with checkpoint blockade. Future studies should explore the molecular mechanisms by which Ankrd11 coordinates the exhaustion program, including its interaction with other epigenetic regulators. By integrating TCR engineering with functional genomics, we have identified Ankrd11 as a master regulator of CD8 + T cell exhaustion in chronic infection and cancer. Our findings provide a roadmap for developing novel immunotherapies that target this pathway to achieve durable immune control. More broadly, this work advances our understanding of T cell dysfunction and reveals new opportunities to reinvigorate exhausted immune responses across disease states. EXPERIMENTAL MODEL AND SUBJECT DETAILS Mice Ankrd11 fl/f l was purchased from GemPharmatech. Cd4-Cre transgenic mice were generously provided by Qibin Leng (Guangzhou Medical University). Rosa26 LSL-Cas9-GFP were generously provided by Pengyuan Yang (Institute of Biophysics，Chinese Academy of Sciences). C57BL/6J mice were purchased from GemPharmatech. HLA-A11/hTAP transgenic mice have been described 14 . For HB-I TCR-transgenic mouse generation, the TCR α chain sequence was cloned into the phCD2 expression vector at the EcoR I restriction site, while the TCR β chain sequence was cloned into the p428 expression vector via SalI sites. Subsequently, the two recombinant plasmids, phCD2-TCR α and p428-TCR β, were mixed in equal proportions to achieve a final concentration of 3 ng/μL. This mixture was injected into fertilized eggs of C57BL/6J by the microinjection technique. Surviving embryos were then transplanted into the uteri of pseudopregnant female mice to establish the HB-I TCR-transgenic founder line. Mice were generally housed under specific pathogen-free conditions at the Institute of Microbiology, Chinese Academy of Sciences, in compliance with the guidelines for the care and use of laboratory animals established by the Beijing Association for Laboratory Animal Science. The protocol was approved by the Research Ethics Committee of the Institute of Microbiology, Chinese Academy of Sciences (permit number APIMCA2021104). Not specifically specified, the mice used in the experiment were male, 6–8 weeks old. HBV-carrier mouse models HBV carrier mouse models were established through two approaches: hydrodynamic injection of 10 μg pAAV-HBV1.2 plasmid dissolved in normal saline equivalent to 10% of mouse body weight for acute HBV expression modeling 14 ; and intravenous injection of 1×10 10 vector genome equivalents of rAAV8-HBV1.3 (FivePlus Molecular Medicine Institute, China) following established protocols for chronic HBV infection modeling 16, 35 . B16-OVA tumor model B16-OVA melanoma cells cultured for 3–4 passages were trypsinized, washed once with 1× PBS, and resuspended in 1× PBS. A suspension containing 8 × 10 5 cells in 100 μL was subcutaneously injected into the right flank of wild-type C57BL/6 mice. Seven days post-tumor inoculation, tumor-bearing mice were randomly allocated into three groups and received tail vein adoptive transfer of PBS, 1 × 10 6 sgNTC OT-I cells, or sgAnkrd11 OT-I cells. Tumor dimensions were measured every 2–4 days, with volumes calculated using the formula (length × width 2 )/2 (mm 3 ). Mice exhibiting tumor volumes exceeding 1000 mm³ were humanely euthanized as experimental endpoints. METHOD DETAILS Primary murine lymphocyte isolation Lymphocytes from lymph nodes and spleen were isolated by mechanical trituration in DMEM supplemented with 2% (v/v) fetal bovine serum (FBS), then filtered through 75-µm nylon mesh to generate lymphocyte suspensions. For splenic lymphocyte isolation, red blood cells in cell suspensions were subsequently lysed using 1× ACK lysis buffer following mechanical disaggregation. To isolate hepatic lymphocytes, minced liver tissues underwent enzymatic digestion with 50 µg/mL DNase I (Worthington, USA) and 200 U/mL collagenase IV (Worthington) at 37°C for 15 min under continuous gentle agitation (60 rpm). The digested tissue was mechanically dissociated and filtered through a 75-µm nylon mesh to obtain single-cell suspensions. These hepatic cell suspensions were then subjected to density gradient centrifugation through 40%:80% Percoll (GE Healthcare, USA) to deplete hepatocytes and achieve leukocyte enrichment. Lymphocyte populations were ultimately harvested from the interphase layer. Cell purification and sorting CD8 + T cells were enriched from the spleen and peripheral lymph nodes using anti-mouse CD4 mAb (BioXcell) and anti-mouse CD19 mAb (BioXcell) with BioMAG goat anti-rat IgG (QIAGEN) according to the manufacturers’ instructions. The CD8 + T cells enriched from the spleen and peripheral lymph nodes or lymphocytes isolated from the liver were stained with relevant surface markers and subsequently sorted using a FACSAria II cell sorter (BD Biosciences) In vitro killing assay 1×10 4 target cells were plated in U-bottom 96-well plates followed by addition of effector cells at effector-to-target (E: T) ratios of 10:1, 5:1, and 1:1, with total reaction volumes adjusted to 100 μL/well using phenol red-free RPMI 1640 medium supplemented with 5% (v/v) FBS. The mixtures were cultured for 4 h and analyzed with the CytoTox 96® Non-Radioactive Cytotoxicity Assay Kit (Promage) to quantify lactate dehydrogenase (LDH) release, with control wells established according to the manufacturer’s instructions for cytotoxicity calculation In vivo killing assay Splenocytes from HLA-A11/hTAP transgenic mice were aliquoted into two tubes and incubated in the presence or absence of 10 μg/mL HBc 141-151 (37°C, 5% CO2, 1 h). Unloaded cells were labeled with 5 μM CFSE (CFSE high ) and peptide-loaded cells with 0.5 μM CFSE (CFSE lo ). Cells were then counted, mixed 1:1 in PBS, and 5×10 6 target splenocytes were injected intravenously into C57BL/6 mice. After 2 h, 1×10 7 TCR-T effectors or PBS were infused. Mice were euthanized 24-h post-transfer, and CTL activity was quantified via flow cytometry using the formula: [1 - (%CFSE low ctrl / %CFSE high ctrl) ÷ (%CFSE low exp /%CFSE high exp)] × 100. Vector construction and sgRNA cloning To generate a retroviral vector for co-expression of single guide RNA (sgRNA) and NGFR marker, plasmid pSIN-U6-EF1a-Thy1.1-Neo (Addgene #191397) was engineered by replacing both the Thy1.1 expression reporter and Neo resistance gene with NGFR. sgRNAs were cloned by annealing two DNA oligos and T4 DNA ligation into a Bbs1- digested pSIN-U6-EF1a-NGFR vector. Genome-wide sgRNA library The Retroviral Mouse Genome-wide CRISPR Knockout Library (Addgene #104861) was amplified via electroporation into Endura™ Electrocompetent Cells (Lucigen, UK), which were plated onto fifteen 15-cm plates. Plates were incubated at 30°C for 14 hours, and bacterial colonies were harvested by plate scraping for plasmid extraction. Library coverage exceeded 1,000 colonies per sgRNA. Retrovirus production and transduction Retrovirus was packaged by co-transfecting Plat-E cells with the designated plasmid and helper plasmid pCL-Eco (Addgene #12371) using calcium phosphate precipitation-mediated transfection. Viral supernatants were collected at 24- and 48-hours post-transfection, filtered through 0.45 μm filters (Millipore, Germany). CD8 + T cells were isolated, activated for 36 hours, and then transduced with retrovirus. Polybrene (8 μg/mL) was added to each well. Sealed plates were centrifuged at 1000 × g for 90 min at 32°C. Infection efficiency was assessed by flow cytometry 24 hours post-transduction. Following sgRNA library transduction of T cells, puromycin (2 mg/mL) was added to the culture medium for positive cell selection. Genome-wide CRISPR screens of T cell fitness genes For genome-wide in vivo CRISPR screening in an HBV chronic infection model, 200 million Cas9 + HB-I cells were activated with HBc 141-151 peptide. Retroviral transduction was performed as described previously, with infection efficiency maintained at ~30% to ensure single-viral integration per cell and achieve >500 cells per sgRNA coverage. Puromycin selection (2 mg/mL) was applied post-transduction. At 5 days post-transduction, 10% of cells were cryopreserved at −80°C as input samples, while the remaining 90% were adoptively transferred into rAAV-HBV1.3-infected HLA-A11/hTAP transgenic mice (maximum 1×10 7 cells/recipient). BFP + hepatic CD8 + T cells were isolated from liver tissues using BD FACSAria II cell sorting 9 days post-transfer and cryopreserved at −80°C. sgRNA library preparation and sequencing To quantify sgRNA enrichment in the samples, genomic DNA (gDNA) was extracted using a Genomic DNA Extraction Kit (TIANGEN BIOTECH) according to the manufacturer’s protocol. sgRNA was amplified from gDNA by 16 cycles of PCR using specific primers targeting the pMSCV vector. Each reaction (100 μL) contained up to 1 mg of gDNA, and reactions were performed until all gDNA was depleted. PCR fragments containing sgRNA were purified using the Monarch® PCR & DNA Cleanup Kit (NEB T1030). The sgRNA sequencing library was prepared with the NEBNext® Ultra™ II DNA Library Prep Kit (NEB E7103) as described previously 36 . Briefly, 1 μg of fragmented DNA was subjected to end repair, 5' phosphorylation, and dA-tailing in a single reaction using the End Prep Enzyme Mix. Following ligation to a circular adapter with a "T" overhang, Uracil-Specific Excision Reagent (USER) enzyme was employed to excise uracil bases in the adapter, generating a "Y"-shaped adapter structure. Size selection was performed using Clean Up beads. Finally, barcoded Illumina paired-end sequencing adapters were attached to the "Y"-shaped adapter via three cycles of PCR with high-fidelity polymerase, followed by Illumina NovaSeq 6000-based deep sequencing performed by Novogene. Screen hits were identified using MAGeCK v0.5.9 with paired analysis under default parameters 20 . The analytical workflow initiates with the preparation of an sgRNA sequencing read count matrix file that maps sgRNAs to their corresponding genes. Raw data processing and normalization are performed through the mageck count pipeline. Differential analysis between control and treatment groups is conducted via the mageck test command, which implements statistical models such as negative binomial regression to determine gene-level significance (false discovery rate, FDR < 0.05, as the significance threshold). Final output files contain results at both gene and sgRNA levels. Serum Biomarker Quantification HBsAg and anti-HBs levels were quantified with commercial ELISA kits (Kehua Bio-Engineering, China) following the manufacturer's protocols, while HBV DNA levels were quantified via quantitative real-time PCR (qPCR) using the HBV DNA Quantitative Fluorescence Diagnostic Kit (Sansure Biotech, China). Hepatic function was assessed through alanine aminotransferase (ALT) activity measurements using the Roche cobas® 8000 system (Roche Diagnostics GmbH, Switzerland) following the manufacturer's standardized operating protocols. Isolation of tumor infiltrating lymphocytes For tumor-infiltrating lymphocyte (TIL) analysis, excised tumors were minced, enzymatically digested with 50 µg/mL DNase I (Worthington, USA) and 200 U/mL collagenase IV (Worthington) at 37°C for 15 minutes, then mechanically dissociated through a 75-µm cell strainer. Single-cell suspensions were isolated from tumor homogenates via density gradient centrifugation using Lympholyte-M (Cedarlane, Canada), followed by collection of the interphase lymphocyte layer. TIL immunophenotyping was performed using an LSRFortessa flow cytometer (BD Biosciences, USA) with appropriate gating strategies. In vitro suppressive culture assay HB1 cells were equally divided into four aliquots 24 hours post gene knockout. Three aliquots were cultured with lactic acid (10 mM), TGF-β1 (2 ng/mL), or prostaglandin E2 (PGE2; 100 ng/mL), respectively, while the remaining aliquot served as a control maintained in normal culture medium. Half-medium replacement and controlled cell dilution were performed every 24 hours during the 5-day culture period, followed by flow cytometric analysis. Single cell nested multiplex PCR and TCR sequencing Individual HBc 141-151 /HLA-A*11:01 tetramer + lymphocytes, initially isolated via FACS sorting, were manually selected under microscopic visualization. Each cell was directly lysed in a reverse transcription mixture containing SuperScript III Reverse Transcriptase (Invitrogen, USA) and subjected to cDNA synthesis according to the manufacturer’s thermal cycling parameters. First-strand cDNA served as template for TCR amplification via multiplex PCR using published primer sets and cycling conditions 37, 38 . Amplified products were electrophoresed on 1.5% agarose gels, purified using Agarose Gel DNA Purification Kits (Biomed, China), and Sanger-sequenced. TCRα/β chain sequences were annotated through IMGT/V-QUEST analysis 39, 40 . RNA-seq sgNTC + and sgAnkrd11 + HB-I cells were generated via retroviral transduction. NGFR + cells were sorted at 5 days post-infection with typical purity >95% for RNA sequencing. Total RNA was extracted using TRIzol reagent (Thermo Fisher Scientific): briefly, resuspended cells were lysed in 1 mL TRIzol, followed by addition of 200 μL ice-cold chloroform and vigorous vortexing. After phase separation via centrifugation, the aqueous layer was collected and mixed with an equal volume of isopropanol for RNA precipitation at room temperature. The pellet obtained by centrifugation was washed twice with 75% ethanol and finally dissolved in RNase-free water. RNA sequencing and bioinformatic analyses were performed by Novogene using established protocols 41 . Differential expression analysis was conducted with the DEGSeq R package (v1.16.1), with adjusted p-values calculated via the Benjamini-Hochberg method. Significance thresholds were set at adjusted p-value ≤0.05 and absolute log2(fold change) ≥1. ATAC-Seq sgNTC + and sgAnkrd11 + HB-I cells were generated via retroviral transduction. NGFR+ cells were sorted at 5 days post-infection with typical purity >95% for ATAC-seq. ATAC-seq was performed according to the manufacturer’s protocol of the Hyperactive ATAC-Seq Library Prep Kit for Illumina (Vazyme). Briefly, 50,000 cells per library were incubated with Lysis Buffer to lyse cellular membranes. A tagmentation mix containing Tn5 transposome was added and incubated at 37°C for 30 min, followed by Stop Buffer treatment to terminate the reaction. Fragmented DNA was purified using ATAC DNA Extract Beads. PCR amplification (12 cycles) with Illumina sequencing adapters was performed, and amplified libraries were size-selected using ATAC DNA Clean Beads. ATAC-seq librarys were sequenced on an Illumina NovaSeq 6000 platform (150-bp paired-end) by Novogene, with sequencing depth averaging 40 million reads per sample. CUT&Tag sgNTC + and sgAnkrd11 + HB-I cells were generated via retroviral infection. NGFR + cells were sorted at 5 days post-infection with typical purity >95% for CUT&Tag analysis. CUT&Tag libraries were prepared according to the manufacturer's protocol for the Hyperactive Universal CUT&Tag Assay Kit for Illumina Pro (Vazyme). Briefly, 100,000 cells per library were incubated with concanavalin A-coated magnetic beads at room temperature (RT) for 10 min. Bead-bound cells were resuspended in antibody buffer and incubated with 1 μg anti-H3K27ac antibody (Abcam, ab4729) overnight at 4°C. After discarding unbound antibodies, the secondary antibody (goat anti-rabbit IgG, Vazyme) diluted in Dig-wash buffer was added and incubated for 1 h at RT. Following gentle washing, samples were treated with 2 μL pA/G-Tn5 transposase and 98 μL Dig-300 buffer for 1 h at RT. Tagmentation was initiated by adding 50 μL tagmentation buffer and incubating at 37°C for 1 h. Reactions were quenched with 2 μL 10% SDS at 55°C for 10 min. DNA fragments were purified using DNA Extract Beads Pro. Libraries were amplified by PCR, size-selected with VAHTS DNA Clean Beads, and sequenced on an Illumina NovaSeq 6000 platform (150-bp paired-end) by Novogene, with an average sequencing depth of 40 million reads per sample. Flow cytometry For surface staining, cells were incubated with designated antibodies at 4°C for 30 minutes. For intracellular staining, cells were fixed and permeabilized using the Intracellular Fixation & Permeabilization Buffer Set (eBioscience) following the manufacturer’s instructions, followed by intracellular staining with antibodies at 4°C for 30 minutes. For intracellular cytokine staining, cells were stimulated with 0.5 mM ionomycin and 10 ng/mL PMA at 37°C under 5% CO₂ for 4 hours, with 3 mM monensin added to block intracellular protein transport, prior to intracellular antibody staining. For T-Bet and Ki67 expression analysis, cells were fixed and permeabilized using the Foxp3 Staining Buffer Kit (eBioscience) as instructed, followed by intracellular staining with anti-Ki67 or anti-T-Bet antibodies at 4°C for 1 hour. Phosphorylated STAT5 (pSTAT5 Y694) detection was performed as described previously 42 : briefly, cells were stimulated with indicated concentrations of rhIL-2 (PeproTech) in prewarmed complete RPMI medium at 37°C under 5% CO₂ for 30 minutes. STAT5 phosphorylation at Tyr694 was detected using BD Phosflow™ Lyse/Fix Buffer and BD Phosflow™ Perm Buffer III (BD Biosciences) according to the manufacturer’s protocol. Data were acquired on an LSRFortessa flow cytometer (BD Biosciences) and analyzed using FlowJo software. QUANTIFICATION AND STATISTICAL ANALYSIS Flow cytometry (FACS) data were analyzed using FlowJo 10 software. Statistical analyses of quantitative results were performed in GraphPad Prism 9.0. For comparisons between two groups, a two-tailed Student’s t-test was employed. For comparisons involving multiple groups, one-way analysis of variance (ANOVA) followed by Bonferroni post hoc testing was applied. Data are expressed as mean ± standard deviation (SD). Declarations Acknowledgments We thank Dr. Mingzhao Zhu and Dr. Zhaolin Hua for helpful suggestions. We are grateful to the Pathogenic Microbiology and Immunology Public Technology Service Center for their support and to all members of our laboratory for productive discussions. This work was supported by the National Natural Science Foundation of China (U23A20464) ; The National Key Research and Development Program (2023YFC2306901)；Beijing Municipal Health Commission high-level public health technical personnel construction project (discipline leader-03-26) and Beijing Hospitals Authority "peak" talent training program (DFL20241803). Some graphs were created with BioRender (https://biorender.com). Author contributions W.X., conducted experiments., J.G., X.C., L.L., X.Z., Q.J., B.H., and F.Z. provided experimental assistance. W.X. and X.Z. designed the study and wrote the manuscript. M.L. and X.Z. supervised the project. Declaration of interests Authors declare no competing interests. Declaration of generative AI and AI-assisted technologies in the writing process During the preparation of this work the author(s) used DeepSeek in order to improve the readability and language of the manuscript. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6785364","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Letter","associatedPublications":[],"authors":[{"id":470846007,"identity":"3049fa70-f712-481e-bd06-ef440d57461e","order_by":0,"name":"Xuyu Zhou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqUlEQVRIiWNgGAWjYDACCTBpw0OyljTStRwmQYf87OZj0rxt52V0G5gffmCouUNYC+OcY2mSM9tu85gdYDOWYDj2jLAWZokcM4mPYC0MZgyMDUS4kA2kJbHtHFAL+zfitPBAbDkA1MJDpC0SEmnJljPOJfOYHeYplkg4RoQW+RnJB2/zlNnZmx1v3/jhQw2xoc3IBiSYgTiBSA1A8Id4paNgFIyCUTACAQDa4jIVndN7VQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-6626-0898","institution":"Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chi-nese Academy of Sciences","correspondingAuthor":true,"prefix":"","firstName":"Xuyu","middleName":"","lastName":"Zhou","suffix":""},{"id":470846008,"identity":"6298dd2f-3991-4340-8c54-be314d8a8338","order_by":1,"name":"Wei Xu","email":"","orcid":"","institution":"CAS Key Laboratory of Pathogenic Microbiology and Immunology, Institute of Microbiology, Chi-nese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Xu","suffix":""},{"id":470846009,"identity":"c9849465-b20d-4ab8-8aef-569cde5546e7","order_by":2,"name":"Jie Guo","email":"","orcid":"","institution":"Institute of Microbiology, Chinese Academy of Science","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Guo","suffix":""},{"id":470846010,"identity":"105d6031-b831-4c28-b535-6e8664d29283","order_by":3,"name":"Xue Cao","email":"","orcid":"","institution":"Institute of Microbiology Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Cao","suffix":""},{"id":470846011,"identity":"31e868c6-674e-4750-9165-14c4ebd7a2c3","order_by":4,"name":"Liping Li","email":"","orcid":"","institution":"Institute of Microbiology, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Liping","middleName":"","lastName":"Li","suffix":""},{"id":470846012,"identity":"008d6694-519f-4542-a159-440a821502ef","order_by":5,"name":"Qiuzhu Jin","email":"","orcid":"","institution":"Institute of Microbiology Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Qiuzhu","middleName":"","lastName":"Jin","suffix":""},{"id":470846013,"identity":"881953da-16db-4796-b561-728fcea68276","order_by":6,"name":"Fuping Zhang","email":"","orcid":"https://orcid.org/0000-0001-8511-2885","institution":"Institute of Microbiology, Chinese Academy of Sciences (CAS)","correspondingAuthor":false,"prefix":"","firstName":"Fuping","middleName":"","lastName":"Zhang","suffix":""},{"id":470846014,"identity":"867b029d-6df0-4746-b376-b0ba301b28a2","order_by":7,"name":"Baidong Hou","email":"","orcid":"https://orcid.org/0000-0002-0892-0181","institution":"Institute of Biophysics, Chinese Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Baidong","middleName":"","lastName":"Hou","suffix":""},{"id":470846015,"identity":"3c400091-c49d-4fb0-a4f5-94c464eda904","order_by":8,"name":"Minghui Li","email":"","orcid":"","institution":"Beijing Ditan Hospital, Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Minghui","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-05-30 13:55:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6785364/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6785364/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84679097,"identity":"ee2ca839-321a-495f-935b-deac64438430","added_by":"auto","created_at":"2025-06-16 08:06:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1028944,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTarget a human immune dominant epitope HBc\u003c/strong\u003e\u003csub\u003e\u003cstrong\u003e141-151\u003c/strong\u003e\u003c/sub\u003e\u003cstrong\u003e reverse chronic HBV infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea, \u003c/strong\u003eHBc\u003csub\u003e141-151\u003c/sub\u003e-specific CTL induction in HLA-A11/hTAP mice: Day 0 hydrodynamic pAAV-HBV1.2 injection; days 14 and 28 subcutaneous HBc\u003csub\u003e141-151\u003c/sub\u003e peptide boosts. CTL response assessed 1-week post-final immunization via HBc\u003csub\u003e141-151\u003c/sub\u003e/HLA-A11 tetramer staining, flow sorting, and single-cell TCR sequencing. \u003cstrong\u003eb,\u003c/strong\u003e Flow cytometry analysis of IFNγ expression and the frequency of tetramer positive CD8\u003csup\u003e+\u003c/sup\u003e T cells in peripheral blood. \u003cstrong\u003ec, \u003c/strong\u003eFlow cytometry analysis of the percentage of TCRvβ10 expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells from HB-I TCR transgenic mice. \u003cstrong\u003ed, \u003c/strong\u003eHB-I CD8\u003csup\u003e+\u003c/sup\u003e T cells stimulated with NP91 (Control), HBc\u003csub\u003e141-151\u003c/sub\u003e, or anti-CD3/CD28. CPD dilution analyzed by flow cytometry (days 1-3). n=4/group. \u003cstrong\u003ee,\u003c/strong\u003e \u003cem\u003eIn vitro\u003c/em\u003e HB-I CD8\u003csup\u003e+\u003c/sup\u003e T cell cytotoxicity assay (E: T=1:1,5:1,10:1). n=3/group.\u003cstrong\u003e f,\u003c/strong\u003e \u003cem\u003eIn vivo\u003c/em\u003e cytotoxicity: HBc\u003csub\u003e141-151\u003c/sub\u003e-pulsed CFSE\u003csup\u003elo\u003c/sup\u003e splenocytes (HLA-A11 Tg) quantified via flow cytometry. n=6 or 7/group. \u003cstrong\u003eg,\u003c/strong\u003e Flow cytometry analysis of HBc\u003csub\u003e141-151\u003c/sub\u003e-pulsed splenocytes (CFSE\u003csup\u003elo\u003c/sup\u003e) from different HLA subtypes transgenic mice. n=6-8/group. \u003cstrong\u003eh,\u003c/strong\u003e Schematic treatment schema for therapeutic evaluation of HB-I CD8\u003csup\u003e+\u003c/sup\u003e T cells (5-day HBc\u003csub\u003e141-151\u003c/sub\u003e stimulation) in rAAV-HBV1.3-infected C57BL/6 or HLA-A11/hTAP transgenic mice. \u003cstrong\u003ei-l,\u003c/strong\u003e The levels of HBsAg (\u003cstrong\u003ei\u003c/strong\u003e), HBV DNA (\u003cstrong\u003ej\u003c/strong\u003e), ALT activity (\u003cstrong\u003ek\u003c/strong\u003e) and HBsAb (\u003cstrong\u003el\u003c/strong\u003e) in mouse serum were quantitatively measured at the indicated time points. n=7-10/group.\u003cstrong\u003e m,\u003c/strong\u003e Experimental design schematic for evaluating the cytotoxic activity and hepatic infiltration of HB-I cells at 14 days post-adoptive transfer in chronic hepatitis B mouse models. \u003cstrong\u003en,o,\u003c/strong\u003e Quantification of cytotoxic activity(\u003cstrong\u003en\u003c/strong\u003e) and the frequency(\u003cstrong\u003eo\u003c/strong\u003e) of HB-I cells in the liver. n=7 or 10/group. Each symbol represents an individual sample. two-tailed unpaired t test (\u003cstrong\u003ee\u003c/strong\u003e,\u003cstrong\u003ef\u003c/strong\u003e,\u003cstrong\u003ei\u003c/strong\u003e,\u003cstrong\u003ej\u003c/strong\u003e,\u003cstrong\u003ek\u003c/strong\u003e,\u003cstrong\u003el\u003c/strong\u003e,\u003cstrong\u003en\u003c/strong\u003e,\u003cstrong\u003eo\u003c/strong\u003e), two-tailed paired t-test (\u003cstrong\u003ej\u003c/strong\u003e,\u003cstrong\u003el\u003c/strong\u003e), one-way ANOVA followed by Bonferroni post hoc test (\u003cstrong\u003ed\u003c/strong\u003e,\u003cstrong\u003eg\u003c/strong\u003e) were performed. Data are shown as the mean ± SD.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/8ea61309b117c4044d94ab74.png"},{"id":84679101,"identity":"46e61ac0-705b-4450-87a3-df84ecb41501","added_by":"auto","created_at":"2025-06-16 08:06:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1061569,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWhole genome CRISPR-Cas9 library screening identifies the key in control T cells dysfunction in chronic HBV infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Experimental design. Genome-wide CRISPR screening of CD8\u003csup\u003e+\u003c/sup\u003e T cell fitness genes \u003cem\u003ein vivo\u003c/em\u003e. HLA-A11/hTAP transgenic mice infected with rAAV-HBV1.3 six weeks before the experiment were used as recipients. HB-I CD8\u003csup\u003e+\u003c/sup\u003e T cells (CD45.2\u003csup\u003e+\u003c/sup\u003e) were ex vivo-activated, transduced with retroviral CRISPR library, then puromycin-selected for 3 days. 10% frozen as baseline; remaining cells transferred to rAAV-HBV1.3 mice. Cas9\u003csup\u003e+\u003c/sup\u003esgRNA\u003csup\u003e+\u003c/sup\u003e HB-I cells FACS-sorted from liver (Day 9 post-transfer) underwent sgRNA cassette PCR/sequencing. \u003cstrong\u003eb,\u003c/strong\u003e The plots show the ranking of all genes from \u003cem\u003ein vivo\u003c/em\u003e screens based on sgRNA enrichment, with the top 10 hits labeled. \u003cstrong\u003ec,\u003c/strong\u003e The sgRNA distribution for the top 10 positively enriched and the top 10 negatively enriched genes is shown. The x-axis indicates log2 FC. Red bars denote enriched targets, blue bars depleted targets, and gray bars other sgRNAs. \u003cstrong\u003ed,\u003c/strong\u003e The top 10 enriched genes identified through \u003cem\u003ein vivo\u003c/em\u003e genome-wide positive-selection CRISPR screening, along with their p-values and the known functions of the corresponding proteins in T cells. \u003cstrong\u003ee,\u003c/strong\u003e Gene Ontology analysis of positive-selection (top) and negative-selection (bottom) genes\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/ffd613f60af9d30c23219b4f.png"},{"id":84680223,"identity":"56a0cac5-ac32-47bb-92e9-dc6674e6ad56","added_by":"auto","created_at":"2025-06-16 08:14:06","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1593717,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnkrd11 deficiency enhances effector function of CD8\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea, \u003c/strong\u003eFlow cytometry analysis identifying CD4SP, CD8SP, CD4\u003csup\u003e-\u003c/sup\u003eCD8\u003csup\u003e-\u003c/sup\u003e (DN), CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003e+\u003c/sup\u003e (DP) T cell populations, frequencies, and cell numbers in the thymus of 8-week-old WT (n=6) and cKO (n=5) mice. \u003cstrong\u003eb,\u003c/strong\u003e Flow cytometry of CD4\u003csup\u003e+\u003c/sup\u003e/CD8\u003csup\u003e+\u003c/sup\u003e T cell populations in LN/SP from WT (n=7) and cKO (n=8). \u003cstrong\u003ec,\u003c/strong\u003e FACS analysis of CD44 and CD62L expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells from LN and SP of WT and cKO mice. n=4/group. \u003cstrong\u003ed,\u003c/strong\u003e Flow cytometry analysis of FSC-A and SSC-A, with statistics on the ratio of activated to resting cells. n=4/group. \u003cstrong\u003ee,\u003c/strong\u003e Flow cytometry analysis of Granzyme B expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells from HB-I \u003cem\u003eCD4\u003c/em\u003e\u003csup\u003e\u003cem\u003eCre\u003c/em\u003e\u003c/sup\u003e and HB-I \u003cem\u003eCD4\u003c/em\u003e\u003csup\u003e\u003cem\u003eCre\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e Ankrd11\u003c/em\u003e\u003csup\u003e\u003cem\u003efl/fl\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003emice after 3 days of HBc\u003csub\u003e141-151\u003c/sub\u003e peptide activation. Relative levels were quantified and normalized to resting controls. n=4/group. \u003cstrong\u003ef,\u003c/strong\u003e Flow cytometry analysis of IFNγ, TNFα, T-Bet, PD1 and CD62L expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells from mice as in \u003cstrong\u003ed\u003c/strong\u003e after 3 days of HBc\u003csub\u003e141-151\u003c/sub\u003e peptide activation. n=4/group. \u003cstrong\u003eg,\u003c/strong\u003e Flow cytometry analysis of Granzyme B, CD62L and ICOS expression in HB-I Cas9\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells transduced with sgNTC (non-targeting control gRNAs) or sgAnkrd11 at day 5 post-infection (p.i.). n=3/group. \u003cstrong\u003eh,\u003c/strong\u003e Schema for \u003cem\u003ein vitro\u003c/em\u003e cytotoxicity of Ankrd11-deficient HB-I CD8\u003csup\u003e+\u003c/sup\u003e T cells: Cas9\u003csup\u003e+\u003c/sup\u003e HB-I cells transduced with sgRNA-retrovirus. HLA-A11/hTAP transgenic splenocytes pulsed with HBc\u003csub\u003e141-151\u003c/sub\u003e (CTVlo) or PBS (CTVhi) mixed 1:1 as targets; co-cultured with effectors (E:T=1:5) for 24h. \u003cstrong\u003ei,\u003c/strong\u003e The statistical summary of the cytotoxic capability of Ankrd11 deficient CD8\u003csup\u003e+\u003c/sup\u003e T cells against target cells \u003cem\u003ein vitro\u003c/em\u003e. n=3/group. \u003cstrong\u003ej,\u003c/strong\u003e Flow cytometry analysis of Annexin V\u003csup\u003e-\u003c/sup\u003e 7AAD\u003csup\u003e-\u003c/sup\u003e Live cells frequencies in HB-I Cas9\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells transduced with sgNTC or sgAnkrd11 at day 5 p.i. n=3/group. Each symbol represents an individual sample. two-tailed unpaired t test was performed. Data are shown as the mean ± SD.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/00c994466ddea335baa96066.png"},{"id":84679100,"identity":"0527eaed-7301-4998-9b37-68346a208070","added_by":"auto","created_at":"2025-06-16 08:06:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1215750,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLoss of Ankrd11 confers resistance to immunosuppression and augments cytotoxic function in CD8\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Heatmap showing the relative accumulation of selected genes in HB-I Cas9\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells transduced with sgNTC or sgAnkrd11 at day 5 p.i. \u003cstrong\u003eb,\u003c/strong\u003e Gene set enrichment analysis identified upregulated Hallmark pathways in sgAnkrd11 CD8\u003csup\u003e+\u003c/sup\u003e T cells. \u003cstrong\u003ec,\u003c/strong\u003e Integrative Genomics Viewer showing ATAC-seq and CUT\u0026amp;TAG-seq tracks of H3K27ac for Sell, Fos, Fosb loci in cells as in \u003cstrong\u003ea\u003c/strong\u003e. \u003cstrong\u003ed,\u003c/strong\u003e Flow cytometry analysis of CD25 expression in CD8\u003csup\u003e+\u003c/sup\u003e T cells from HB-I \u003cem\u003eCD4\u003c/em\u003e\u003csup\u003e\u003cem\u003eCre\u003c/em\u003e\u003c/sup\u003e and HB-I \u003cem\u003eCD4\u003c/em\u003e\u003csup\u003e\u003cem\u003eCre\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e Ankrd11\u003c/em\u003e\u003csup\u003e\u003cem\u003efl/fl\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e \u003c/em\u003emice after 3 days of HBc\u003csub\u003e141-151\u003c/sub\u003e peptide activation. n=4/group. \u003cstrong\u003ee,\u003c/strong\u003e Phosphorylation of STAT5 at Try694 (p-STAT5(Y694)) in CD8\u003csup\u003e+\u003c/sup\u003e T cells stimulated with 0, 0.5, 2.5, 10, 50 IU/mL rhIL-2 for 30 min at 37°c from mice as in \u003cstrong\u003ed\u003c/strong\u003e after 3 days of HBc\u003csub\u003e141-151\u003c/sub\u003e peptide activation. n=4/group.\u003cstrong\u003e f,\u003c/strong\u003e Histogram of Ki67 staining (left) and statistical analyses (right) of Cas9\u003csup\u003e+\u003c/sup\u003e HB-I cells transduced with sgNTC or sgAnkrd11 and cultured in medium with lactic acid, PGE2, TGFβ, or normal 1640 medium. n=4/group.\u003cstrong\u003e g,\u003c/strong\u003e Flow cytometry analysis of IFNγ\u003csup\u003e+\u003c/sup\u003e TNFα\u003csup\u003e+\u003c/sup\u003e cells frequencies in HB-I Cas9\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003e T cells transduced with sgNTC or sgAnkrd11 and cultured in medium with lactic acid, PGE2, TGF-β1, or normal RPMI 1640 medium. n=3/group. Each symbol represents an individual sample. two-tailed unpaired t test was performed. Data are shown as the mean ± SD.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/edca71f6f058dc03beb83d1d.png"},{"id":84679099,"identity":"901c15a9-89c4-4221-afe1-3c16507c1631","added_by":"auto","created_at":"2025-06-16 08:06:06","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":937410,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnkrd11 is an essential gatekeeper to prevent the conduction of effector function in CD8\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Experimental design. CD8\u003csup\u003e+\u003c/sup\u003e T cells from CD45.2\u003csup\u003e+\u003c/sup\u003e HB-I CD4\u003csup\u003eCre\u003c/sup\u003e and CD45.2\u003csup\u003e+\u003c/sup\u003e HB-I CD4\u003csup\u003eCre\u003c/sup\u003e \u003cem\u003eAnkrd11\u003c/em\u003e\u003csup\u003e\u003cem\u003efl/fl\u003c/em\u003e\u003c/sup\u003e R26YFP mice were activated with the HBc\u003csub\u003e141-151\u003c/sub\u003e peptide for 3 days, subsequently sorted, mixed at a 1:1 ratio, and transferred into CD45.1\u003csup\u003e+\u003c/sup\u003e HLA-A11/hTAP mice pre-injected with pAAV-HBV1.2 plasmid via hydrodynamic injection two days prior. \u003cstrong\u003eb,\u003c/strong\u003e Flow cytometry analysis of CD45.1\u003csup\u003e-\u003c/sup\u003eYFP\u003csup\u003e+\u003c/sup\u003e and CD45.1\u003csup\u003e-\u003c/sup\u003eYFP\u003csup\u003e-\u003c/sup\u003e cells frequencies in spleen and liver at Day 9 post-transfer. n=4/group\u003cstrong\u003e c,\u003c/strong\u003e Experimental design. Activated Cas9\u003csup\u003e+\u003c/sup\u003e HB-I cells were infected with retrovirus expressing indicated sgRNAs, were transferred into CD45.1\u003csup\u003e+\u003c/sup\u003e HLA-A11/hTAP mice pre-injected with pAAV-HBV1.2 plasmid via hydrodynamic injection two days prior. \u003cstrong\u003ed-f,\u003c/strong\u003e Flow cytometry analysis of NGFR\u003csup\u003e+\u003c/sup\u003e donor cell frequencies (\u003cstrong\u003ed\u003c/strong\u003e), surface expression of KLRG1/CD62L (\u003cstrong\u003ee\u003c/strong\u003e), and CX3CR1/CXCR3 (\u003cstrong\u003ef\u003c/strong\u003e) in spleen and liver at Day 9 post-transfer. n=3/group. Each symbol represents an individual sample. two-tailed unpaired t test was performed. Data are shown as the mean ± SD.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/4241efdd7810bee21b88a18a.png"},{"id":84680224,"identity":"b9166ca4-d138-4d5e-b947-48a65b387b75","added_by":"auto","created_at":"2025-06-16 08:14:06","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":690885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eExpanded Ankrd11 deficient CD8\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e+\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e T cells reverse chronic HBV infection \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Experimental design schematic. Naïve HB-I CD8\u003csup\u003e+\u003c/sup\u003e T cells or HB-I Ankrd11 cKO cells were transferred into rAAV-HBV1.3-infected HLA-A11/hTAP recipients 6 weeks post-infection. \u003cstrong\u003eb,\u003c/strong\u003e Flow cytometry analysis of donor cell frequencies in blood at Day 7 and Day 14 post-transfer. n=5 or 6/group. \u003cstrong\u003ec,\u003c/strong\u003e Flow cytometry analysis of donor cell frequencies in spleen and liver at Day 9 post-transfer. n=4/group. \u003cstrong\u003ed,e,\u003c/strong\u003e Flow cytometry analysis of CX3CR1, Granzyme B, and Ki67 expression in spleen and liver at Day 9 post-transfer. n=4/group. \u003cstrong\u003ef,\u003c/strong\u003e Serum HBsAg levels were measured at indicated time points following adoptive transfer of HB-I (n=8) or HB-I Ankrd11 cKO (n=5) cells. Each symbol represents an individual sample. two-tailed unpaired t test was performed. Data are shown as the mean ± SD.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/b6bc5905fb166974c38bba8d.png"},{"id":84679103,"identity":"8143696c-7a4d-49b6-98f1-0a2772d64eb3","added_by":"auto","created_at":"2025-06-16 08:06:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":821707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnkrd11 deficiency promoter cancer rejection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea,\u003c/strong\u003e Experimental design schematic. B16-OVA tumor-bearing C57BL/6 mice received adoptive transfer of PBS, sgNTC OT-I cells, or sgAnkrd11 OT-I cells at 7 days post-tumor-inoculation. \u003cstrong\u003eb,c,\u003c/strong\u003e The volume changes (mm3) (\u003cstrong\u003eb\u003c/strong\u003e) and representative images (\u003cstrong\u003ec\u003c/strong\u003e) of subcutaneous B16-OVA tumors from mice receiving PBS (n=4), sgNTC (n=7) or sgAnkrd11 (n=7) OT-I cells. \u003cstrong\u003ed,e,\u003c/strong\u003e Total counts (d) and flow cytometry plots and quantification of percentage (e) of live CD8\u003csup\u003e+\u003c/sup\u003e T cells infiltrating B16 tumors from mice receiving sgNTC or sgAnkrd11 OT-I cells. n=7/group. \u003cstrong\u003ef,\u003c/strong\u003e Flow cytometry analysis of Ki67 and Granzyme B expression in B16-OVA infiltrating donor cells from mice receiving sgNTC or sgAnkrd11 OT-I cells on D17 p.t. n=7/group. Each symbol represents an individual sample. two-tailed unpaired t test was performed. Data are shown as the mean ± SD.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/b1225503bee3000e02196ccf.png"},{"id":84680909,"identity":"2aca3c5d-8c63-4b0b-ab44-d0d0ee66e982","added_by":"auto","created_at":"2025-06-16 08:22:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7203824,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/314a1762-161f-4932-b6c3-421e20c744ed.pdf"},{"id":84679096,"identity":"3f633aba-a6da-4809-a5d4-5840bb2114e3","added_by":"auto","created_at":"2025-06-16 08:06:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16496,"visible":true,"origin":"","legend":"Extended Figure legend","description":"","filename":"ExtendedDatafigurelegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/4ecd8e25ca024777beb82ac8.docx"},{"id":84679098,"identity":"8de494e5-9ed1-48c4-864d-6ec9f21f8876","added_by":"auto","created_at":"2025-06-16 08:06:06","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":584545,"visible":true,"origin":"","legend":"Extended Figure 1","description":"","filename":"ExtendedDataFig.1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/374c64b5eda2a7fb46a65179.pdf"},{"id":84679105,"identity":"4a33abc7-e225-4a6a-a615-6209b59d946b","added_by":"auto","created_at":"2025-06-16 08:06:06","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":710891,"visible":true,"origin":"","legend":"Extended Figure 2","description":"","filename":"ExtendedDataFig.2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6785364/v1/dadbd95ad0361304634ca626.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Ankrd11 deficiency reverses the dysfunction of chronic HBV-specific T-cell","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHBV infection is a severe liver disorder with a significant global health burden. Chronic HBV infection is a leading cause of cirrhosis, hepatocellular carcinoma (HCC), and liver failure, contributing to substantial morbidity and mortality worldwide\u003csup\u003e1\u003c/sup\u003e. Despite the availability of an effective prophylactic vaccine, chronic hepatitis B remains incurable, and the global prevalence continues to rise alarmingly\u003csup\u003e2\u003c/sup\u003e. Recent estimates indicate that approximately 296 million individuals are living with chronic HBV infection, with 1.5 million new infections reported annually \u003csup\u003e2\u003c/sup\u003e. These staggering figures underscore the urgent need for intensified research efforts to develop curative therapies and improve clinical outcomes for affected individuals.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The pathogenesis of HBV infection is closely linked to the host immune response, particularly the role of HBV-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003csup\u003e3, 4\u003c/sup\u003e. In acute HBV infection, these cytotoxic T lymphocytes (CTLs) play a pivotal role in viral clearance by directly eliminating infected hepatocytes and secreting antiviral cytokines such as interferon-gamma (IFN-\u0026gamma;) and tumor necrosis factor-alpha (TNF-\u0026alpha;)\u003csup\u003e3, 4, 5\u003c/sup\u003e. However, the functional impairment of HBV-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells in chronic HBV infection is a hallmark of disease persistence\u003csup\u003e6\u003c/sup\u003e. T cell exhaustion\u0026mdash;characterized by the progressive loss of T cell effector functions, reduced proliferative capacity, and sustained expression of inhibitory receptors such as programmed cell death protein 1 (PD-1)\u0026mdash;has been widely recognized as a primary cause of this dysfunction\u003csup\u003e7\u003c/sup\u003e. This concept, initially identified in mouse models of chronic Lymphocytic Choriomeningitis Virus (LCMV) infection, has been extended to various cancers and chronic diseases, including HIV, HCV, and HBV\u003csup\u003e7, 8, 9, 10\u003c/sup\u003e. However, the mechanisms underlying T cell dysfunction in HBV are more complex than those observed in LCMV models, underscoring the need for further research to understand and address this unique state of immune dysregulation\u003csup\u003e11, 12\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Mounting evidence underscores the pivotal role of the liver microenvironment in determining the functional fate of HBV-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells. Priming of CD8\u003csup\u003e+\u003c/sup\u003e T cells within the liver\u0026mdash;particularly in the absence of adequate co-stimulation\u0026mdash;coupled with immunosuppressive signaling pathways, such as those driven by transforming growth factor-\u0026beta; (TGF-\u0026beta;), may trigger a unique program of T cell dysfunction\u003csup\u003e6, 11, 13\u003c/sup\u003e. While immune checkpoint blockade targeting PD-1 has demonstrated potential in reversing T cell exhaustion in preclinical LCMV models, its therapeutic impact in chronic HBV infection remains limited\u003csup\u003e11\u003c/sup\u003e. This disparity emphasizes the necessity for deeper mechanistic insights into the molecular and cellular drivers of T cell dysfunction in HBV, as well as the discovery of novel therapeutic targets.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our research has focused on developing advanced animal models that recapitulate key aspects of human HBV infection. In a previous study, we generated a humanized mouse model expressing HLA-A11 and the human antigen processing and presentation machinery, including transporters associated with antigen processing (TAP1 and TAP2) and proteasome subunits (LMP2 and LMP7)\u003csup\u003e14\u003c/sup\u003e. Using this model, we identified the HBc\u003csub\u003e141\u0026ndash;151\u003c/sub\u003e (STLPETTVVRR) epitope (also referred to as HBVcore169) as an immunodominant epitope capable of eliciting a robust CTL response and promoting HBV clearance\u003csup\u003e14\u003c/sup\u003e. Notably, this epitope\u0026mdash;processed by IFN-\u0026gamma;\u0026ndash;inducible immunoproteasomes, in which three constitutive 20S proteasome subunits are replaced by the inducible subunits LMP2, LMP7, and MECL-1\u0026mdash;is closely linked to disease progression in clinical settings\u003csup\u003e12, 15\u003c/sup\u003e. Robust expansion of HBc\u003csub\u003e141\u0026ndash;151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells is observed in acute HBV patients, whereas severe functional impairment is seen in chronic infection\u003csup\u003e12\u003c/sup\u003e. These findings raised critical questions about the mechanisms underlying the dysfunction of HBc\u003csub\u003e141\u0026ndash;151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells in chronic HBV infection.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study investigated the molecular mechanisms underlying T cell dysfunction in chronic HBV infection by developing a novel TCR transgenic mouse model specific for the HBc\u003csub\u003e141\u0026ndash;151\u003c/sub\u003e epitope. Genome-wide CRISPR/Cas9 screening identified the Ankrd11 gene as a critical regulator of HBV-specific T cell exhaustion. Genetic ablation of Ankrd11 in HBV-reactive CD8\u003csup\u003e+\u003c/sup\u003e T cells restored effector function, enhancing granzyme B production and proliferative capacity. Mechanistically, Ankrd11 deficiency augmented CD8\u003csup\u003e+\u003c/sup\u003e T cell responsiveness to interleukin-2 (IL-2) signaling\u0026mdash;a pivotal pathway for T cell survival and expansion\u0026mdash;while conferring resistance to TGF-\u0026beta;-mediated suppression via upregulation of AP-1 family transcription factors. Therapeutic targeting of Ankrd11 in a chronic recombinant adeno-associated virus (rAAV)-HBV mouse model significantly reduced hepatitis B surface antigen (HBsAg) levels, a key indicator of viral persistence. Our research clearly highlights that inhibiting Ankrd11 presents a powerful strategy to significantly boost antiviral and anti-tumor immunity, paving the way for effective functional cures for chronic HBV infection.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eTarget a human immune dominant epitope HBc\u003csub\u003e141–151\u003c/sub\u003e reverse chronic HBV infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe HBc\u003csub\u003e141–151\u003c/sub\u003e epitope is an immunodominant T-cell epitope of hepatitis B virus (HBV), and functional responses of CD8\u003csup\u003e+\u003c/sup\u003e T cells targeting this epitope strongly correlate with viral control in HLA-A11-positive patients\u003csup\u003e12\u003c/sup\u003e. However, studying HBc\u003csub\u003e141–151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells in chronic HBV infection is challenging due to their exceptionally low frequency \u003cem\u003ein vivo\u003c/em\u003e\u003csup\u003e12\u003c/sup\u003e. To overcome this limitation and investigate the functional dynamics of these epitope-specific T cells, we immunized HLA-A11/hTAP transgenic mice—which express human HLA-A11 and the human antigen-processing machinery, including transporters associated with antigen processing (TAP1/2) and proteasome subunits (LMP2/7)—with the pAAV-HBV1.2 plasmid via hydrodynamic injection, followed by a subcutaneous boost with the HBc\u003csub\u003e141–151\u003c/sub\u003e peptide (STLPETTVVRR) (Fig. 1a). Peripheral blood cells from immunized mice exhibited robust responses to the HBc\u003csub\u003e141–151\u003c/sub\u003e peptide (Fig. 1b).\u003c/p\u003e\n\u003cp\u003eWe isolated HBc\u003csub\u003e141–151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells using an HBc\u003csub\u003e141–151\u003c/sub\u003e/HLA-A*11:01 tetramer and performed single-cell T-cell receptor (TCR) sequencing to identify HBc\u003csub\u003e141–151\u003c/sub\u003e-specific TCRs (Fig. 1a and Extended Data Fig. 1a). A unique HBc\u003csub\u003e141–151\u003c/sub\u003e-specific TCR was identified (Extended Data Fig. 1b, c), and we developed a novel TCR transgenic mouse model expressing the rearranged TCR α (TRAV3-3) and β (TRBV4) chains (Extended Data Fig. 1b). As shown in Fig. 1c, nearly 99% of these T cells expressed the Vβ4 chain, confirming successful generation of a monoclonal T-cell population specific to the HBc\u003csub\u003e141–151\u003c/sub\u003e epitope. CD8\u003csup\u003e+\u003c/sup\u003e T cells from HBc\u003csub\u003e141–151\u003c/sub\u003e TCR transgenic mice exhibited a highly specific immune response: they strongly responded to HBc\u003csub\u003e141–151\u003c/sub\u003e but showed no reactivity to the non-specific peptide NP\u003csub\u003e91-99\u003c/sub\u003e (an HLA-A11-restricted influenza epitope) (Fig. 1d), underscoring the precision of this model (hereafter HB-I Tg). Furthermore, activated HB-I Tg CD8\u003csup\u003e+\u003c/sup\u003e T cells displayed potent cytotoxic activity against peptide-pulsed target cells \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e (Fig. 1e, f). Importantly, this killing activity was strictly HLA-A11-dependent, as the T cells failed to recognize HLA-A2 or HLA-A33 (despite HLA-A33 belonging to the same A3 superfamily and sharing anchor residues with HLA-A11) (Fig. 1g). Thus, the HB-I Tg model provides a valuable tool for elucidating HBc\u003csub\u003e141–151\u003c/sub\u003e-specific T-cell responses and their role in HBV immunity.\u003c/p\u003e\n\u003cp\u003eTo evaluate the therapeutic potential of HBc\u003csub\u003e141–151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells in chronic HBV infection, we employed a recombinant adeno-associated virus (rAAV)-HBV1.3 mouse model, which recapitulates key features of human chronic HBV infection, including persistent HBsAg and HBV DNA levels, and the absence of anti-HBsAg antibodies\u003csup\u003e16\u003c/sup\u003e. Six weeks post-infection, HBsAg levels stabilize, mimicking the chronic phase of human HBV infection. We adoptively transferred 10 million activated HBc\u003csub\u003e141–151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells from TCR transgenic mice into HLA-A11/hTAP transgenic mice and control C57BL/6 (B6) mice (Fig. 1h). Compared to B6 controls, HLA-A11/hTAP recipients exhibited significant reductions in HBsAg and HBV DNA (Fig. 1i, j), accompanied by a transient increase in alanine aminotransferase (ALT) (Fig. 1k), indicative of hepatocyte damage during viral clearance. Remarkably, after two rounds of treatment, HBsAg levels dropped below the detection threshold in five of seven mice, with some animals developing anti-HBsAg antibodies (Fig. 1l), suggesting functional cure\u003csup\u003e17\u003c/sup\u003e. These results demonstrate that HBc\u003csub\u003e141–151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells can mediate viral clearance and restore immune control in chronic HBV infection.\u003c/p\u003e\n\u003cp\u003eHowever, the liver microenvironment in chronic HBV infection poses challenges to adoptive T-cell therapy. To assess the impact of this suppressive environment on HBc\u003csub\u003e141–151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells, we evaluated their cytotoxic activity \u003cem\u003ein vivo\u003c/em\u003e. Peptide-reloaded splenocytes were introduced as target cells into HLA-A11/hTAP mice that had received HB-I CD8\u003csup\u003e+\u003c/sup\u003e T cells two weeks prior (Fig. 1m). Compared to B6 controls, HLA-A11/hTAP recipients exhibited substantially reduced killing activity (Fig. 1n), partly due to significant loss of HBc\u003csub\u003e141–151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eT cells in the liver (Fig. 1o), highlighting the immunosuppressive nature of chronic HBV infection. These findings suggest that while HBc\u003csub\u003e141–151\u003c/sub\u003e-specific TCR transgenic CD8\u003csup\u003e+\u003c/sup\u003e T cells represent a promising therapeutic strategy, their efficacy depends on a delicate balance between effector function and liver immunosuppression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhole genome CRISPR-Cas9 library screening identifies the key in control T cells dysfunction in chronic HBV infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the molecular mechanisms underlying liver-mediated immune suppression in chronic HBV infection, we crossed HB-I transgenic mice with \u003cem\u003eCD4 Cre\u003c/em\u003e-\u003cem\u003eRosa26\u003csup\u003eLSL-Cas9-GFP\u003c/sup\u003e\u003c/em\u003e strains and conducted a whole-genome CRISPR/Cas9 screening\u003csup\u003e18\u003c/sup\u003e. We hypothesized that targeted gene knockouts mediated by CRISPR-Cas9 and guide RNAs (gRNAs) could rescue the diminished abundance of HBc\u003csub\u003e141-151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells observed during chronic HBV infection. To test this, CD8\u003csup\u003e+\u003c/sup\u003e T cells from HB-I transgenic mice were activated for two days and subsequently transfected with a whole-genome CRISPR lentivirus library containing 90,230 single-guide RNAs (sgRNAs) targeting 18,424 genes\u003csup\u003e19\u003c/sup\u003e. To ensure experimental precision, the virus library titer was carefully controlled to guarantee single-virus infection per T cell. Following puromycin selection to enrich transfected T cells, the HBc\u003csub\u003e141-151\u003c/sub\u003e-specific T cell pool with diverse gene knockouts was adoptively transferred into HLA-A11/hTAP mice infected with recombinant adeno-associated virus carrying HBV1.3 (rAAV-HBV1.3) for six weeks. After nine days of \u003cem\u003ein vivo\u003c/em\u003e selection, HB-I TCR transgenic CD8\u003csup\u003e+\u003c/sup\u003e T cells were isolated from the liver via fluorescence-activated cell sorting (FACS), and their genomic DNA was extracted and subjected to deep sequencing (Fig. 2a). The MAGeCK algorithm was employed to analyze the genome-scale CRISPR/Cas9 knockout screening data, identifying essential genes with a stringent cutoff score of \u0026lt;0.001\u003csup\u003e20\u003c/sup\u003e. This analysis revealed over 50 gene knockouts that were significantly enriched compared to the input DNA (Fig. 2b), highlighting their potential roles in modulating CD8+ T cell responses during chronic HBV infection.\u003c/p\u003e\n\u003cp\u003eAmong the top hits were several key regulators of CD8\u003csup\u003e+\u003c/sup\u003e T cell function, including \u003cem\u003eZc3h12a\u003c/em\u003e, \u003cem\u003eRc3h1\u003c/em\u003e, and \u003cem\u003eFli1\u003c/em\u003e (Fig. 2b,c), which are implicated in the context of chronic HBV infection, where T cell dysfunction and dysregulated immune responses facilitate viral persistence. \u003cem\u003eZc3h12a\u003c/em\u003e, an RNA-binding protein with endoribonuclease activity, modulates CD8\u003csup\u003e+\u003c/sup\u003e T cell activation by degrading mRNAs encoding inflammatory cytokines, thereby fine-tuning immune responses\u003csup\u003e21, 22\u003c/sup\u003e. \u003cem\u003eRc3h1\u003c/em\u003e, another RNA-binding protein, regulates mRNA stability and translation of genes critical for T cell receptor signaling and cytokine production, suggesting its role in maintaining T cell functionality \u003csup\u003e23\u003c/sup\u003e. \u003cem\u003eFli1\u003c/em\u003e, an ETS family transcription factor, suppresses CD8\u003csup\u003e+\u003c/sup\u003e T cell effector differentiation by binding effector-associated gene regulatory elements. Its deletion enhances effector T (Teff) cytotoxicity and anti-tumor/infectious immunity (Fig. 2d)\u003csup\u003e24\u003c/sup\u003e. Gene Ontology analysis of positively selected genes revealed significant enrichment in genes regulating T cell activation (e.g.\u003cem\u003e, Rc3h1, Zc3h12a, Peli1, Ctla2a\u003c/em\u003e, \u003cem\u003eMdk\u003c/em\u003e, and \u003cem\u003eBad\u003c/em\u003e) and pathways associated with T cell differentiation and cellular response to chemical stress (Fig. 2e). Among downregulated genes, we identified significant enrichment in biological processes governing cell migration and calcium ion transport, processes essential for CD8\u003csup\u003e+\u003c/sup\u003e T cell tissue infiltration, metabolic reprogramming, and cytotoxic effector function (Fig. 2e). These genes represent potential therapeutic targets for transcriptional activation or pharmacological agonists to enhance the efficacy of T cell-based immunotherapies. Together, these findings provide first unbiased, genome-wide perspective on the molecular mechanisms driving immune suppression in chronic HBV infection, offering potential therapeutic targets to restore CD8\u003csup\u003e+\u003c/sup\u003e T cell function and enhance viral control.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnkrd11 deficiency enhances effector function of CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnkrd11, a chromatin regulator, emerged as a focus of interest among the top candidates identified. Ankrd11 modulates histone acetylation by recruiting HDACs and is known to play a critical role in the proliferation and development of cortical neural precursors \u003csup\u003e25, 26\u003c/sup\u003e. However, its function in the immune system remains unexplored. To investigate the role of Ankrd11 in T cells, we generated T cell-specific Ankrd11 knockout mice (CKO) by crossing \u003cem\u003eCD4-Cre\u0026nbsp;\u003c/em\u003emice with a floxed\u003cem\u003e\u0026nbsp;Ankrd11\u0026nbsp;\u003c/em\u003estrain. Some of these mice were further crossed with HB-I transgenic mice to enable antigen-specific analysis of Ankrd11 function.\u003c/p\u003e\n\u003cp\u003ePhenotypically, the T cell-specific Ankrd11 knockout mice exhibited no obvious abnormalities. T cell development in the thymus appeared normal, with the ratios and cell numbers of double-positive (DP) and single-positive (SP) CD4 and CD8\u003csup\u003e+\u003c/sup\u003e T cells comparable to those of littermate controls (Fig. 3a). In the periphery, a slight reduction in the CD8\u003csup\u003e+\u003c/sup\u003e T cell population was observed (Fig. 3b); however, these cells showed no signs of spontaneous activation, as the majority of CD8\u003csup\u003e+\u003c/sup\u003e T cells remained CD44\u003csup\u003elow\u003c/sup\u003e and CD62L\u003csup\u003ehigh\u0026nbsp;\u003c/sup\u003e(Fig. 3c). These findings suggest that Ankrd11 deficiency has a minimal impact on T cell development and peripheral homeostasis.\u003c/p\u003e\n\u003cp\u003eTo evaluate the antigen-specific CD8\u003csup\u003e+\u003c/sup\u003e T cell response, we stimulated CTV-labeled CD8\u003csup\u003e+\u003c/sup\u003e T cells isolated from HB-I transgenic mice \u003cem\u003ein vitro\u003c/em\u003e with the HBc\u003csub\u003e141–151\u003c/sub\u003e peptide. While Ankrd11-deficient CD8\u003csup\u003e+\u003c/sup\u003e T cells exhibited a slightly weaker response to HBc\u003csub\u003e141–151\u003c/sub\u003e peptide stimulation than wild-type controls within the first three days (Fig. 3d and Extended Data Fig. 2a), they displayed elevated production of effector molecules, including Granzyme B, IFNγ, TNFα, T-bet, and PD-1, alongside reduced CD62L expression (Fig. 3e,f). This result indicates that Ankrd11 may not be essential for initiating T cell activation but plays a critical role in regulating the quality of CD8\u003csup\u003e+\u003c/sup\u003e T cell effector function.\u003c/p\u003e\n\u003cp\u003eTo ensure the validity of our findings, we rigorously ruled out any potential influence of Ankrd11 on T cell development, differentiation, and activation. We employed a CRISPR-Cas9-based gene-editing strategy to specifically delete Ankrd11 in activated CD8\u003csup\u003e+\u003c/sup\u003e T cells. These cells were derived from \u003cem\u003eCD4-Cre-Rosa26\u003csup\u003eLSL-Cas9-GFP\u003c/sup\u003e\u003c/em\u003e mice and were activated for two days \u003cem\u003ein vitro\u003c/em\u003e. Retroviral transfection was used to deliver sgRNA for Ankrd11 deletion, enabling targeted gene knockout following initial T cell activation. Consistent with previous findings, Ankrd11 knockout cells, identified by NGFR expression, displayed elevated levels of effector molecules and surface markers, including Granzyme B and ICOS, alongside reduced CD62L expression compared to control cells treated with non-targeting sgRNA (Fig. 3g). Functional assays demonstrated that Ankrd11-deficient CD8\u003csup\u003e+\u003c/sup\u003e T cells exhibited significantly enhanced cytotoxic activity \u003cem\u003ein vitro\u003c/em\u003e (Fig. 3h,i). These cells were more efficient at killing target cells compared to wild-type controls (Fig. 3i), without compromising their own viability (Fig. 3j). These findings underscore the critical role of Ankrd11 in modulating CD8\u003csup\u003e+\u003c/sup\u003e T cell function and suggest that its deletion promotes a more potent effector phenotype.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLoss of Ankrd11 confers resistance to immunosuppression and augments cytotoxic function in CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further investigate the molecular and functional consequences of Ankrd11 deletion, we isolated and purified Ankrd11 knockout (KO) (NGFR\u003csup\u003e+\u003c/sup\u003e) and wild-type (WT) control cells for bulk RNA sequencing (RNA-seq) (Fig. 4a). Transcriptomic analysis revealed a distinct gene expression signature in Ankrd11 KO cells, characterized by significant upregulation of genes associated with CD8\u003csup\u003e+\u003c/sup\u003e T cell activation, effector function, and cytotoxicity (Fig. 4a). Notably, cytotoxic effector molecules such as \u003cem\u003eGzmb, Gzmf, Gzmg, Gzmd, Gzme, Prf1\u0026nbsp;\u003c/em\u003eand the proinflammatory cytokine IFNγ, were markedly elevated in Ankrd11-deficient cells (Fig. 4a). These findings align with the enhanced killing activity observed in Ankrd11 KO CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig. 3i).\u003c/p\u003e\n\u003cp\u003eGene ontology enrichment (GO) analysis further highlighted the upregulation of pathways related to post-transcriptional regulation of gene expression, T cell activation and leukocyte differentiation in Ankrd11-deficient CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig. 4b). Intriguingly, AP-1 family genes (\u003cem\u003eFos, Fosb, Fosl1\u003c/em\u003e, etc.) emerged as potential central hubs mediating these transcriptional changes (Fig. 4a). Given that Ankrd11 may modulate epigenetic regulation by recruiting histone deacetylases (HDACs)\u003csup\u003e25\u003c/sup\u003e, we performed ATAC-seq and CUT\u0026amp;Tag with an anti-histone H3 acetylation antibody. Although Ankrd11 deficiency did not significantly alter global chromatin accessibility (Fig. 4c), we observed enhanced histone H3 modifications at the Fos and Fosb loci—but not at the Sell locus—in Ankrd11 KO CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig. 4c). These results support the hypothesis that Ankrd11 suppresses AP-1 family protein expression by regulating site-specific histone modifications.\u003c/p\u003e\n\u003cp\u003eNotably, activated Aknrd11-deficient CD8+ T cells exhibited significant upregulation of the high-affinity IL-2 receptor subunit, IL-2Rα (CD25) (Fig. 4a,d), a well-established downstream target of AP-1. To determine whether this altered receptor expression affected downstream signaling, we restimulated activated CD8\u003csup\u003e+\u003c/sup\u003e T cells with varying IL-2 concentrations and assessed STAT5 activation by phospho-STAT5 (pSTAT5) staining. At a standard IL-2 concentration (50 U/mL), Aknrd11 knockout and wild-type CD8\u003csup\u003e+\u003c/sup\u003e T cells showed comparable pSTAT5 levels (Fig. 4e). However, under IL-2-limiting conditions (2.5 U/mL and 10 U/mL), Aknrd11-deficient cells exhibited a significantly stronger pSTAT5 response than WT controls (Fig. 4e). Furthermore, activated Aknrd11-deficient CD8\u003csup\u003e+\u003c/sup\u003e T cells produced substantially more IFNγ and TNFα \u003cem\u003ein vitro\u003c/em\u003e under low IL-2 conditions (Extended Data Fig. 2b). Given the critical role of STAT5 in CD8\u003csup\u003e+\u003c/sup\u003e T cell homeostasis and cytotoxicity, these findings suggest that Aknrd11-deficient cells may possess a survival and proliferative advantage in IL-2-restricted environments.\u003c/p\u003e\n\u003cp\u003eAP-1 is a critical regulator of CD8\u003csup\u003e+\u003c/sup\u003e T cell function, and its impairment in tumors promotes exhaustion\u003csup\u003e27, 28\u003c/sup\u003e. Hypoxia, TGF-β, and chronic antigen exposure in the tumor microenvironment suppress AP-1 activity, further driving T cell dysfunction. Interestingly, while initial Ki67 staining did not reveal a proliferative advantage in Ankrd11 KO cells \u003cem\u003ein vitro\u003c/em\u003e (Fig. 4f and Extended Data Fig. 2c), these cells exhibited a unique resistance to immunosuppressive mediators. Specifically, they were refractory to the inhibitory effects of TGF-β, lactic acid, and PGE2—key components of the immunosuppressive liver microenvironment (Fig. 4f)\u003csup\u003e6, 29, 30\u003c/sup\u003e. Moreover, Ankrd11 KO cells rescued TGF-β-mediated suppression of IFNγ and TNF-α production (Fig. 4g), suggesting that Ankrd11 deficiency not only enhances effector function but also overcomes microenvironmental suppression. Collectively, these findings demonstrate that Ankrd11 deficiency endows CD8\u003csup\u003e+\u003c/sup\u003e T cells with enhanced effector function, improved cytokine sensitivity, and resistance to immunosuppression. Given the critical role of AP-1 in sustaining T cell responses—and the observed upregulation of AP-1 targets (e.g., Fos/Fosb) in Ankrd11 KO cells—these results suggest that Ankrd11 may normally act as a brake on CD8\u003csup\u003e+\u003c/sup\u003e T cell activation by suppressing AP-1 activity. Targeting Ankrd11 could thus represent a novel therapeutic strategy to reinvigorate CD8\u003csup\u003e+\u003c/sup\u003e T cell responses in chronic viral infections, such as hepatitis B virus (HBV), or in tumors where AP-1 dysfunction contributes to T cell exhaustion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnkrd11\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;is an essential gatekeeper to prevent the conduction of effector function in CD8\u003csup\u003e+\u003c/sup\u003e T cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the \u003cem\u003ein vivo\u003c/em\u003e activity of Ankrd11 knockout (KO) CD8\u003csup\u003e+\u003c/sup\u003e T cells, we utilized HB-I \u003cem\u003eCD4-Cre\u003c/em\u003e \u003cem\u003eAnkrd11\u003csup\u003efl/fl\u0026nbsp;\u003c/sup\u003eRosa-stop-YFP\u003c/em\u003e mice. CD8\u003csup\u003e+\u003c/sup\u003e T cells from these mice were activated \u003cem\u003ein vitro\u003c/em\u003e with the HBc\u003csub\u003e141–151\u003c/sub\u003e peptide for three days and subsequently co-transferred at a 1:1 ratio with wild-type T cells (YFP\u003csup\u003e-\u003c/sup\u003e) into HLA-A11/hTAP mice (Fig. 5a). These recipient mice had been immunized with the pAAV-HB1.2 plasmid via hydrodynamic injection (Fig. 5a), a method that mimics acute HBV infection without inducing persistent viral replication, closely resembling clinical acute infection scenarios \u003csup\u003e31\u003c/sup\u003e. Consistent with the results from our Cas9 library screening, we observed significant enrichment of YFP\u003csup\u003e+\u003c/sup\u003e Ankrd11 KO cells in both the spleen and liver of recipient mice (Fig. 5b). As shown in the figure, more than 80% of the cells in these organs were YFP\u003csup\u003e+\u003c/sup\u003e Ankrd11 KO cells (Fig. 5b), indicating a robust expansion and survival advantage of Ankrd11-deficient CD8\u003csup\u003e+\u003c/sup\u003e T cells \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003eTo further validate these findings, we employed a CRISPR-Cas9-based approach to knock out Ankrd11 in activated CD8\u003csup\u003e+\u003c/sup\u003e T cells using sgRNA. These KO cells, along with control sgRNA-transfected cells, were transferred into HLA-A11/hTAP mice that had been injected with the AAV-HBv2.0 plasmid to induce a liver-specific immune response (Fig. 5c). In this setup, NGFR\u003csup\u003e+\u003c/sup\u003e cells (representing Ankrd11 KO cells) also exhibited dramatic expansion in the liver, with a less pronounced but still significant presence in the spleen compared to control cells (Fig. 5d). Notably, a large proportion of NGFR\u003csup\u003e+\u003c/sup\u003e cells in the liver displayed a bona fide effector phenotype, characterized by high expression of KLRG1 and CX3CR1 and downregulation of CD62L and CXCR3 (Fig. 5e,f). These markers are indicative of terminally differentiated effector cells, suggesting that Ankrd11 deficiency promotes the acquisition of a highly functional effector state\u003csup\u003e32\u003c/sup\u003e. These findings strongly suggest that Ankrd11 acts as a critical gatekeeper in restraining the differentiation and functional capacity of CD8\u003csup\u003e+\u003c/sup\u003e T cells. In its absence, CD8\u003csup\u003e+\u003c/sup\u003e T cells exhibit enhanced effector function, as evidenced by their increased expansion, survival, and tissue infiltration \u003cem\u003ein vivo\u003c/em\u003e. This aligns with our earlier observations that Ankrd11 deficiency enhances cytotoxic activity and resistance to immune suppression \u003cem\u003ein vitro\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExpanded Ankrd11 deficient CD8\u003csup\u003e+\u003c/sup\u003e T cells reverse chronic HBV infection \u003cem\u003ein vivo\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe ultimate value of TCR-T cell therapy lies in its potential to treat individuals with chronic hepatitis B virus (HBV) infection. To evaluate whether Ankrd11 knockout (KO) could enhance the efficacy of antigen-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells in clearing HBV during chronic infection, we established a chronic HBV infection model using adeno-associated virus (AAV)-HBV-infected mice. We then transferred Ankrd11 KO CD8\u003csup\u003e+\u003c/sup\u003e T cells into these recipient mice to assess their therapeutic potential (Fig. 6a). In this model, AAV-HBV infection mimics the persistent viral replication and immune tolerance characteristic of chronic HBV infection in humans\u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eUpon transferring Ankrd11 KO HB-I cells, we observed a marked increase in the frequency of HB-I cells in both the peripheral blood and spleen of recipient mice (Fig. 6b,c). In comparison to control cells, the Ankrd11 KO cells demonstrated substantially greater expansion within the liver, accounting for up to 40% of the total CD8\u003csup\u003e+\u003c/sup\u003e T cell population in this organ (Fig. 6c). These cells also exhibited a bona fide effector phenotype, characterized by high expression of CX3CR1, Granzyme B and Ki67 (Fig. 6d,e), markers indicative of terminally differentiated effector T cells. This phenotypic shift suggests that Ankrd11 deficiency promotes the acquisition of a highly functional effector state, enabling the cells to mount a more potent antiviral response. Notably, while the transfer of one million HB-I cells had no significant effect on viral clearance in this model, Ankrd11 Cas9-deficient HB-I cells elicited a robust and uniform reduction in serum HBsAg levels across all treated mice, indicating a reversal of chronic HBV infection progression. (Fig. 6f). This striking difference underscores the critical role of Ankrd11 in regulating CD8\u003csup\u003e+\u003c/sup\u003e T cell function and highlights the therapeutic potential of targeting Ankrd11 to enhance T cell-mediated immunity against chronic viral infections. The enhanced efficacy of Ankrd11-deficient CD8\u003csup\u003e+\u003c/sup\u003e T cells can be attributed to their improved survival, expansion, and functional capacity in the immunosuppressive microenvironment characteristic of chronic HBV infection. These findings align with our earlier observations that Ankrd11 deficiency enhances cytotoxic activity, resistance to immune suppression, and the acquisition of an effector phenotype \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e. Together, these results demonstrate that Ankrd11 acts as a key regulator of CD8\u003csup\u003e+\u003c/sup\u003e T cell exhaustion and dysfunction during chronic viral infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnkrd11 deficiency promoter cancer rejection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven the role of Ankrd11 in modulating CD8\u003csup\u003e+\u003c/sup\u003e T cell responses, we hypothesized that Ankrd11 could similarly influence antitumor immunity, particularly in tumors resistant to PD-1 blockade. To test whether Ankrd11 knockout (KO) enhances tumor rejection, we employed the B16-OVA melanoma model, in which ovalbumin (OVA)-transfected B16-F10 cells exhibit resistance to PD-1/PD-L1 inhibition, as demonstrated in prior studies\u003csup\u003e33, 34\u003c/sup\u003e. Using this model, we evaluated the antitumor capacity of Cas9-mediated Ankrd11-deficient OT-I CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig. 7a).\u003c/p\u003e\n\u003cp\u003eOur findings revealed that adoptive transfer of 1 million wild-type (WT) OT-I T cells had minimal effect on controlling B16-OVA tumor growth (Fig. 7b,c), consistent with their poor expansion in secondary lymphoid organs and limited tumor infiltration (Fig. 7d,e). In stark contrast, Ankrd11 deficiency significantly enhanced the antitumor activity of OT-I T cells (Fig. 7b,c), correlating with robust expansion in draining lymph nodes and markedly increased tumor infiltration (Fig. 7d,e). Furthermore, Ankrd11 KO CD8\u003csup\u003e+\u003c/sup\u003e T cells exhibited heightened effector function, as evidenced by increased Ki67 expression and elevated Granzyme B production (Fig. 7f).\u003c/p\u003e\n\u003cp\u003eThese results demonstrate that Ankrd11 deficiency augments the antitumor efficacy of CD8\u003csup\u003e+\u003c/sup\u003e T cells, even in the context of PD-1 immunotherapy-resistant tumors. Our data suggest that targeting Ankrd11 may represent a promising strategy to overcome resistance to immune checkpoint blockade, potentially improving T cell-mediated tumor control. Further investigation is warranted to elucidate the precise mechanisms by which Ankrd11 regulates T cell exhaustion and effector function in the tumor microenvironment.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study elucidates a critical role for the transcriptional regulator Ankrd11 in constraining CD8\u003csup\u003e+\u003c/sup\u003e T cell responses during chronic viral infection and cancer. By combining TCR transgenic models with genome-wide CRISPR screening, we demonstrate that Ankrd11 deficiency unlocks potent antiviral and antitumor immunity, overcoming key barriers in the immunosuppressive liver microenvironment and checkpoint-resistant tumors. These findings establish Ankrd11 as a central regulator of T cell exhaustion and identify actionable targets for immunotherapy.\u003c/p\u003e\n\u003cp\u003eThe immunodominant HBc\u003csub\u003e141–151\u003c/sub\u003e epitope represents a vulnerable target in HBV infection, with clinical studies correlating HBc\u003csub\u003e141–151\u003c/sub\u003e-specific responses with viral control in HLA-A11 positive individuals \u003csup\u003e12\u003c/sup\u003e. Our development of HBc\u003csub\u003e141–151\u003c/sub\u003e-specific TCR transgenic mice provided a powerful tool to dissect these responses mechanistically. These T cells exhibited exquisite specificity for HLA-A11-HBc\u003csub\u003e141–151\u003c/sub\u003e complexes while remaining unresponsive to other HLA-A11-restricted epitopes, demonstrating their potential for targeted immunotherapy. In adoptive transfer experiments, HBc\u003csub\u003e141–151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells effectively reduced viral loads in chronically infected mice, with some animals achieving seroconversion - a hallmark of functional cure. These data reinforce the clinical relevance of HBc\u003csub\u003e141–151\u003c/sub\u003e-directed immunity while highlighting the need to overcome microenvironmental suppression.\u003c/p\u003e\n\u003cp\u003eTo systematically identify barriers to T cell function in chronic HBV infection, we employed genome-wide CRISPR screening in HBc\u003csub\u003e141–151\u003c/sub\u003e-specific CD8\u003csup\u003e+\u003c/sup\u003e T cells. This approach revealed Ankrd11 as a top hit whose deletion enhanced T cell persistence and effector function. Mechanistically, Ankrd11 deficiency remodeled the transcriptional landscape of activated CD8\u003csup\u003e+\u003c/sup\u003e T cells, upregulating critical cytotoxic mediators such as granzymes (Gzms) and perforin (Prf1) associated with AP-1 activation. Notably, Ankrd11 KO cells exhibited heightened IL-2 sensitivity through enhanced STAT5 signaling, particularly under cytokine-limited conditions mimicking the liver microenvironment\u0026nbsp;\u003csup\u003e13\u003c/sup\u003e. These findings position Ankrd11 as a rheostat controlling the balance between T cell activation and exhaustion.\u003c/p\u003e\n\u003cp\u003eThe liver presents a formidable barrier to effective immunity, with multiple immunosuppressive mechanisms converging to limit T cell function\u003csup\u003e6\u003c/sup\u003e. Our studies revealed that Ankrd11-deficient CD8\u003csup\u003e+\u003c/sup\u003e T cells acquired resistance to key suppressive factors including TGF-β, PGE2, and metabolic stressors. This resistance translated to superior expansion and persistence \u003cem\u003ein vivo\u003c/em\u003e, with Ankrd11 KO cells dominating the T cell compartment in both lymphoid organs and the liver. Importantly, these cells maintained a terminally differentiated effector phenotype (KLRG1\u003csup\u003ehi\u003c/sup\u003eCX3CR1\u003csup\u003ehi\u003c/sup\u003e) associated with potent antiviral activity. The ability of Ankrd11-deficient HBc\u003csub\u003e141–151\u003c/sub\u003e-specific T cells to reverse established chronic infection underscores the therapeutic potential of targeting this pathway.\u003c/p\u003e\n\u003cp\u003eMore importantly, the conserved role of Ankrd11 in limiting CD8\u003csup\u003e+\u003c/sup\u003e T cell responses extended beyond viral infection to cancer immunity. In PD-1-resistant tumors, Ankrd11 deficiency rescued T cell expansion, tumor infiltration, and effector function, mirroring our observations in HBV infection. This suggests that Ankrd11 operates as a fundamental checkpoint across diverse chronic disease contexts. The convergence of HBV and cancer models on Ankrd11 highlights its importance as a key regulator of T cell dysfunction.\u003c/p\u003e\n\u003cp\u003eOur work suggests two complementary therapeutic strategies—adoptive transfer of epitope-specific T cells with engineered Ankrd11 deficiency and pharmacological targeting of the Ankrd11 pathway to enhance endogenous immunity—though several key considerations require further investigation, including the tissue-specific effects of Ankrd11 modulation, the potential risk with systemic Ankrd11 inhibition, and the optimization of combination therapies such as with checkpoint blockade. Future studies should explore the molecular mechanisms by which Ankrd11 coordinates the exhaustion program, including its interaction with other epigenetic regulators.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy integrating TCR engineering with functional genomics, we have identified Ankrd11 as a master regulator of CD8\u003csup\u003e+\u003c/sup\u003e T cell exhaustion in chronic infection and cancer. Our findings provide a roadmap for developing novel immunotherapies that target this pathway to achieve durable immune control. More broadly, this work advances our understanding of T cell dysfunction and reveals new opportunities to reinvigorate exhausted immune responses across disease states.\u003c/p\u003e"},{"header":"EXPERIMENTAL MODEL AND SUBJECT DETAILS","content":"\u003cp\u003e\u003cstrong\u003eMice\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAnkrd11\u003csup\u003efl/f\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u003csup\u003el\u003c/sup\u003e\u003c/em\u003e was purchased from GemPharmatech. Cd4-Cre transgenic mice were generously provided by Qibin Leng (Guangzhou Medical University). \u003cem\u003eRosa26\u003csup\u003eLSL-Cas9-GFP\u003c/sup\u003e\u003c/em\u003e were generously provided by Pengyuan Yang (Institute of Biophysics，Chinese Academy of Sciences). C57BL/6J mice were purchased from GemPharmatech. HLA-A11/hTAP transgenic mice have been described\u003csup\u003e14\u003c/sup\u003e. For HB-I TCR-transgenic mouse generation, the TCR \u0026alpha; chain sequence was cloned into the phCD2 expression vector at the EcoR I restriction site, while the TCR \u0026beta; chain sequence was cloned into the p428 expression vector via SalI sites. Subsequently, the two recombinant plasmids, phCD2-TCR \u0026alpha; and p428-TCR \u0026beta;, were mixed in equal proportions to achieve a final concentration of 3 ng/\u0026mu;L. This mixture was injected into fertilized eggs of C57BL/6J by the microinjection technique. Surviving embryos were then transplanted into the uteri of pseudopregnant female mice to establish the HB-I TCR-transgenic founder line. Mice were generally housed under specific pathogen-free conditions at the Institute of Microbiology, Chinese Academy of Sciences, in compliance with the guidelines for the care and use of laboratory animals established by the Beijing Association for Laboratory Animal Science. The protocol was approved by the Research Ethics Committee of the Institute of Microbiology, Chinese Academy of Sciences (permit number APIMCA2021104). Not specifically specified, the mice used in the experiment were male, 6\u0026ndash;8 weeks old.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHBV-carrier mouse models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHBV carrier mouse models were established through two approaches: hydrodynamic injection of 10 \u0026mu;g pAAV-HBV1.2 plasmid dissolved in normal saline equivalent to 10% of mouse body weight for acute HBV expression modeling\u003csup\u003e14\u003c/sup\u003e; and intravenous injection of 1\u0026times;10\u003csup\u003e10\u003c/sup\u003e vector genome equivalents of rAAV8-HBV1.3 (FivePlus Molecular Medicine Institute, China) following established protocols for chronic HBV infection modeling\u003csup\u003e16, 35\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eB16-OVA tumor model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eB16-OVA melanoma cells cultured for 3\u0026ndash;4 passages were trypsinized, washed once with 1\u0026times; PBS, and resuspended in 1\u0026times; PBS. A suspension containing 8 \u0026times; 10\u003csup\u003e5\u003c/sup\u003e cells in 100 \u0026mu;L was subcutaneously injected into the right flank of wild-type C57BL/6 mice. Seven days post-tumor inoculation, tumor-bearing mice were randomly allocated into three groups and received tail vein adoptive transfer of PBS, 1 \u0026times; 10\u003csup\u003e6\u003c/sup\u003e sgNTC OT-I cells, or sgAnkrd11 OT-I cells. Tumor dimensions were measured every 2\u0026ndash;4 days, with volumes calculated using the formula (length \u0026times; width\u003csup\u003e2\u003c/sup\u003e)/2 (mm\u003csup\u003e3\u003c/sup\u003e). Mice exhibiting tumor volumes exceeding 1000 mm\u0026sup3; were humanely euthanized as experimental endpoints.\u0026nbsp;\u003c/p\u003e"},{"header":"METHOD DETAILS","content":"\u003cp\u003e\u003cstrong\u003ePrimary murine lymphocyte isolation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLymphocytes from lymph nodes and spleen were isolated by mechanical trituration in DMEM supplemented with 2% (v/v) fetal bovine serum (FBS), then filtered through 75-\u0026micro;m nylon mesh to generate lymphocyte suspensions. For splenic lymphocyte isolation, red blood cells in cell suspensions were subsequently lysed using 1\u0026times; ACK lysis buffer following mechanical disaggregation. To isolate hepatic lymphocytes, minced liver tissues underwent enzymatic digestion with 50 \u0026micro;g/mL DNase I (Worthington, USA) and 200 U/mL collagenase IV (Worthington) at 37\u0026deg;C for 15 min under continuous gentle agitation (60 rpm). The digested tissue was mechanically dissociated and filtered through a 75-\u0026micro;m nylon mesh to obtain single-cell suspensions. These hepatic cell suspensions were then subjected to density gradient centrifugation through 40%:80% Percoll (GE Healthcare, USA) to deplete hepatocytes and achieve leukocyte enrichment. Lymphocyte populations were ultimately harvested from the interphase layer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell purification and sorting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCD8\u003csup\u003e+\u003c/sup\u003e T cells were enriched from the spleen and peripheral lymph nodes using anti-mouse CD4 mAb (BioXcell) and anti-mouse CD19 mAb (BioXcell) with BioMAG goat anti-rat IgG (QIAGEN) according to the manufacturers\u0026rsquo; instructions. The CD8\u003csup\u003e+\u003c/sup\u003e T cells enriched from the spleen and peripheral lymph nodes or lymphocytes isolated from the liver were stained with relevant surface markers and subsequently sorted using a FACSAria II cell sorter (BD Biosciences)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn vitro\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ekilling assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1\u0026times;10\u003csup\u003e4\u003c/sup\u003e target cells were plated in U-bottom 96-well plates followed by addition of effector cells at effector-to-target (E: T) ratios of 10:1, 5:1, and 1:1, with total reaction volumes adjusted to 100 \u0026mu;L/well using phenol red-free RPMI 1640 medium supplemented with 5% (v/v) FBS. The mixtures were cultured for 4 h and analyzed with the CytoTox 96\u0026reg; Non-Radioactive Cytotoxicity Assay Kit (Promage) to quantify lactate dehydrogenase (LDH) release, with control wells established according to the manufacturer\u0026rsquo;s instructions for cytotoxicity calculation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn vivo\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003ekilling assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSplenocytes from HLA-A11/hTAP transgenic mice were aliquoted into two tubes and incubated in the presence or absence of 10 \u0026mu;g/mL HBc\u003csub\u003e141-151\u003c/sub\u003e (37\u0026deg;C, 5% CO2, 1 h). Unloaded cells were labeled with 5 \u0026mu;M CFSE (CFSE\u003csup\u003ehigh\u003c/sup\u003e) and peptide-loaded cells with 0.5 \u0026mu;M CFSE (CFSE\u003csup\u003elo\u003c/sup\u003e). Cells were then counted, mixed 1:1 in PBS, and 5\u0026times;10\u003csup\u003e6\u003c/sup\u003e target splenocytes were injected intravenously into C57BL/6 mice. After 2 h, 1\u0026times;10\u003csup\u003e7\u003c/sup\u003e TCR-T effectors or PBS were infused. Mice were euthanized 24-h post-transfer, and CTL activity was quantified via flow cytometry using the formula: [1 - (%CFSE\u003csup\u003elow\u003c/sup\u003e ctrl / %CFSE\u003csup\u003ehigh\u003c/sup\u003e ctrl) \u0026divide; (%CFSE\u003csup\u003elow\u003c/sup\u003e exp /%CFSE\u003csup\u003ehigh\u003c/sup\u003e exp)] \u0026times; 100.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVector construction and sgRNA cloning\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo generate a retroviral vector for co-expression of single guide RNA (sgRNA) and NGFR marker, plasmid pSIN-U6-EF1a-Thy1.1-Neo (Addgene #191397) was engineered by replacing both the Thy1.1 expression reporter and Neo resistance gene with NGFR. sgRNAs were cloned by annealing two DNA oligos and T4 DNA ligation into a Bbs1- digested pSIN-U6-EF1a-NGFR vector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide sgRNA library\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Retroviral Mouse Genome-wide CRISPR Knockout Library (Addgene #104861) was amplified via electroporation into Endura\u0026trade; Electrocompetent Cells (Lucigen, UK), which were plated onto fifteen 15-cm plates. Plates were incubated at 30\u0026deg;C for 14 hours, and bacterial colonies were harvested by plate scraping for plasmid extraction. Library coverage exceeded 1,000 colonies per sgRNA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRetrovirus production and transduction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRetrovirus was packaged by co-transfecting Plat-E cells with the designated plasmid and helper plasmid pCL-Eco (Addgene #12371) using calcium phosphate precipitation-mediated transfection. Viral supernatants were collected at 24- and 48-hours post-transfection, filtered through 0.45 \u0026mu;m filters (Millipore, Germany). CD8\u003csup\u003e+\u003c/sup\u003e T cells were isolated, activated for 36 hours, and then transduced with retrovirus. Polybrene (8 \u0026mu;g/mL) was added to each well. Sealed plates were centrifuged at 1000 \u0026times; g for 90 min at 32\u0026deg;C. Infection efficiency was assessed by flow cytometry 24 hours post-transduction. Following sgRNA library transduction of T cells, puromycin (2 mg/mL) was added to the culture medium for positive cell selection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGenome-wide CRISPR screens of T cell fitness genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor genome-wide \u003cem\u003ein vivo\u003c/em\u003e CRISPR screening in an HBV chronic infection model, 200 million Cas9\u003csup\u003e+\u003c/sup\u003e HB-I cells were activated with HBc\u003csub\u003e141-151\u003c/sub\u003e peptide. Retroviral transduction was performed as described previously, with infection efficiency maintained at ~30% to ensure single-viral integration per cell and achieve \u0026gt;500 cells per sgRNA coverage. Puromycin selection (2 mg/mL) was applied post-transduction. At 5 days post-transduction, 10% of cells were cryopreserved at \u0026minus;80\u0026deg;C as input samples, while the remaining 90% were adoptively transferred into rAAV-HBV1.3-infected HLA-A11/hTAP transgenic mice (maximum 1\u0026times;10\u003csup\u003e7\u003c/sup\u003e cells/recipient). BFP\u003csup\u003e+\u003c/sup\u003e hepatic CD8\u003csup\u003e+\u003c/sup\u003e T cells were isolated from liver tissues using BD FACSAria II cell sorting 9 days post-transfer and cryopreserved at \u0026minus;80\u0026deg;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003esgRNA library preparation and sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo quantify sgRNA enrichment in the samples, genomic DNA (gDNA) was extracted using a Genomic DNA Extraction Kit (TIANGEN BIOTECH) according to the manufacturer\u0026rsquo;s protocol. sgRNA was amplified from gDNA by 16 cycles of PCR using specific primers targeting the pMSCV vector. Each reaction (100 \u0026mu;L) contained up to 1 mg of gDNA, and reactions were performed until all gDNA was depleted. PCR fragments containing sgRNA were purified using the Monarch\u0026reg; PCR \u0026amp; DNA Cleanup Kit (NEB T1030). The sgRNA sequencing library was prepared with the NEBNext\u0026reg; Ultra\u0026trade; II DNA Library Prep Kit (NEB E7103) as described previously\u003csup\u003e36\u003c/sup\u003e. Briefly, 1 \u0026mu;g of fragmented DNA was subjected to end repair, 5\u0026apos; phosphorylation, and dA-tailing in a single reaction using the End Prep Enzyme Mix. Following ligation to a circular adapter with a \u0026quot;T\u0026quot; overhang, Uracil-Specific Excision Reagent (USER) enzyme was employed to excise uracil bases in the adapter, generating a \u0026quot;Y\u0026quot;-shaped adapter structure. Size selection was performed using Clean Up beads. Finally, barcoded Illumina paired-end sequencing adapters were attached to the \u0026quot;Y\u0026quot;-shaped adapter via three cycles of PCR with high-fidelity polymerase, followed by Illumina NovaSeq 6000-based deep sequencing performed by Novogene. Screen hits were identified using MAGeCK v0.5.9 with paired analysis under default parameters\u003csup\u003e20\u003c/sup\u003e. The analytical workflow initiates with the preparation of an sgRNA sequencing read count matrix file that maps sgRNAs to their corresponding genes. Raw data processing and normalization are performed through the mageck count pipeline. Differential analysis between control and treatment groups is conducted via the mageck test command, which implements statistical models such as negative binomial regression to determine gene-level significance (false discovery rate, FDR \u0026lt; 0.05, as the significance threshold). Final output files contain results at both gene and sgRNA levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSerum Biomarker Quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHBsAg and anti-HBs levels were quantified with commercial ELISA kits (Kehua Bio-Engineering, China) following the manufacturer\u0026apos;s protocols, while HBV DNA levels were quantified via quantitative real-time PCR (qPCR) using the HBV DNA Quantitative Fluorescence Diagnostic Kit (Sansure Biotech, China). Hepatic function was assessed through alanine aminotransferase (ALT) activity measurements using the Roche cobas\u0026reg; 8000 system (Roche Diagnostics GmbH, Switzerland) following the manufacturer\u0026apos;s standardized operating protocols.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIsolation of tumor infiltrating lymphocytes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor tumor-infiltrating lymphocyte (TIL) analysis, excised tumors were minced, enzymatically digested with 50 \u0026micro;g/mL DNase I (Worthington, USA) and 200 U/mL collagenase IV (Worthington) at 37\u0026deg;C for 15 minutes, then mechanically dissociated through a 75-\u0026micro;m cell strainer. Single-cell suspensions were isolated from tumor homogenates via density gradient centrifugation using Lympholyte-M (Cedarlane, Canada), followed by collection of the interphase lymphocyte layer. TIL immunophenotyping was performed using an LSRFortessa flow cytometer (BD Biosciences, USA) with appropriate gating strategies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIn vitro\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;suppressive culture assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHB1 cells were equally divided into four aliquots 24 hours post gene knockout. Three aliquots were cultured with lactic acid (10 mM), TGF-\u0026beta;1 (2 ng/mL), or prostaglandin E2 (PGE2; 100 ng/mL), respectively, while the remaining aliquot served as a control maintained in normal culture medium. Half-medium replacement and controlled cell dilution were performed every 24 hours during the 5-day culture period, followed by flow cytometric analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle cell nested multiplex PCR and TCR sequencing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividual HBc\u003csub\u003e141-151\u003c/sub\u003e/HLA-A*11:01 tetramer\u003csup\u003e+\u003c/sup\u003e lymphocytes, initially isolated via FACS sorting, were manually selected under microscopic visualization. Each cell was directly lysed in a reverse transcription mixture containing SuperScript III Reverse Transcriptase (Invitrogen, USA) and subjected to cDNA synthesis according to the manufacturer\u0026rsquo;s thermal cycling parameters. First-strand cDNA served as template for TCR amplification via multiplex PCR using published primer sets and cycling conditions\u003csup\u003e37, 38\u003c/sup\u003e. Amplified products were electrophoresed on 1.5% agarose gels, purified using Agarose Gel DNA Purification Kits (Biomed, China), and Sanger-sequenced. TCR\u0026alpha;/\u0026beta; chain sequences were annotated through IMGT/V-QUEST analysis\u003csup\u003e39, 40\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA-seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003esgNTC\u003csup\u003e+\u003c/sup\u003e and sgAnkrd11\u003csup\u003e+\u003c/sup\u003e HB-I cells were generated via retroviral transduction. NGFR\u003csup\u003e+\u003c/sup\u003e cells were sorted at 5 days post-infection with typical purity \u0026gt;95% for RNA sequencing. Total RNA was extracted using TRIzol reagent (Thermo Fisher Scientific): briefly, resuspended cells were lysed in 1 mL TRIzol, followed by addition of 200 \u0026mu;L ice-cold chloroform and vigorous vortexing. After phase separation via centrifugation, the aqueous layer was collected and mixed with an equal volume of isopropanol for RNA precipitation at room temperature. The pellet obtained by centrifugation was washed twice with 75% ethanol and finally dissolved in RNase-free water. RNA sequencing and bioinformatic analyses were performed by Novogene using established protocols\u003csup\u003e41\u003c/sup\u003e. Differential expression analysis was conducted with the DEGSeq R package (v1.16.1), with adjusted p-values calculated via the Benjamini-Hochberg method. Significance thresholds were set at adjusted p-value \u0026le;0.05 and absolute log2(fold change) \u0026ge;1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eATAC-Seq\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003esgNTC\u003csup\u003e+\u003c/sup\u003e and sgAnkrd11\u003csup\u003e+\u003c/sup\u003e HB-I cells were generated via retroviral transduction. NGFR+ cells were sorted at 5 days post-infection with typical purity \u0026gt;95% for ATAC-seq. ATAC-seq was performed according to the manufacturer\u0026rsquo;s protocol of the Hyperactive ATAC-Seq Library Prep Kit for Illumina (Vazyme). Briefly, 50,000 cells per library were incubated with Lysis Buffer to lyse cellular membranes. A tagmentation mix containing Tn5 transposome was added and incubated at 37\u0026deg;C for 30 min, followed by Stop Buffer treatment to terminate the reaction. Fragmented DNA was purified using ATAC DNA Extract Beads. PCR amplification (12 cycles) with Illumina sequencing adapters was performed, and amplified libraries were size-selected using ATAC DNA Clean Beads. ATAC-seq librarys were sequenced on an Illumina NovaSeq 6000 platform (150-bp paired-end) by Novogene, with sequencing depth averaging 40 million reads per sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCUT\u0026amp;Tag\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003esgNTC\u003csup\u003e+\u003c/sup\u003e and sgAnkrd11\u003csup\u003e+\u003c/sup\u003e HB-I cells were generated via retroviral infection. NGFR\u003csup\u003e+\u003c/sup\u003e cells were sorted at 5 days post-infection with typical purity \u0026gt;95% for CUT\u0026amp;Tag analysis. CUT\u0026amp;Tag libraries were prepared according to the manufacturer\u0026apos;s protocol for the Hyperactive Universal CUT\u0026amp;Tag Assay Kit for Illumina Pro (Vazyme). Briefly, 100,000 cells per library were incubated with concanavalin A-coated magnetic beads at room temperature (RT) for 10 min. Bead-bound cells were resuspended in antibody buffer and incubated with 1 \u0026mu;g anti-H3K27ac antibody (Abcam, ab4729) overnight at 4\u0026deg;C. After discarding unbound antibodies, the secondary antibody (goat anti-rabbit IgG, Vazyme) diluted in Dig-wash buffer was added and incubated for 1 h at RT. Following gentle washing, samples were treated with 2 \u0026mu;L pA/G-Tn5 transposase and 98 \u0026mu;L Dig-300 buffer for 1 h at RT. Tagmentation was initiated by adding 50 \u0026mu;L tagmentation buffer and incubating at 37\u0026deg;C for 1 h. Reactions were quenched with 2 \u0026mu;L 10% SDS at 55\u0026deg;C for 10 min. DNA fragments were purified using DNA Extract Beads Pro. Libraries were amplified by PCR, size-selected with VAHTS DNA Clean Beads, and sequenced on an Illumina NovaSeq 6000 platform (150-bp paired-end) by Novogene, with an average sequencing depth of 40 million reads per sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFlow cytometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor surface staining, cells were incubated with designated antibodies at 4\u0026deg;C for 30 minutes. For intracellular staining, cells were fixed and permeabilized using the Intracellular Fixation \u0026amp; Permeabilization Buffer Set (eBioscience) following the manufacturer\u0026rsquo;s instructions, followed by intracellular staining with antibodies at 4\u0026deg;C for 30 minutes. For intracellular cytokine staining, cells were stimulated with 0.5 mM ionomycin and 10 ng/mL PMA at 37\u0026deg;C under 5% CO₂ for 4 hours, with 3 mM monensin added to block intracellular protein transport, prior to intracellular antibody staining. For T-Bet and Ki67 expression analysis, cells were fixed and permeabilized using the Foxp3 Staining Buffer Kit (eBioscience) as instructed, followed by intracellular staining with anti-Ki67 or anti-T-Bet antibodies at 4\u0026deg;C for 1 hour. Phosphorylated STAT5 (pSTAT5 Y694) detection was performed as described previously\u003csup\u003e42\u003c/sup\u003e: briefly, cells were stimulated with indicated concentrations of rhIL-2 (PeproTech) in prewarmed complete RPMI medium at 37\u0026deg;C under 5% CO₂ for 30 minutes. STAT5 phosphorylation at Tyr694 was detected using BD Phosflow\u0026trade; Lyse/Fix Buffer and BD Phosflow\u0026trade; Perm Buffer III (BD Biosciences) according to the manufacturer\u0026rsquo;s protocol. Data were acquired on an LSRFortessa flow cytometer (BD Biosciences) and analyzed using FlowJo software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQUANTIFICATION AND STATISTICAL ANALYSIS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFlow cytometry (FACS) data were analyzed using FlowJo 10 software. Statistical analyses of quantitative results were performed in GraphPad Prism 9.0. For comparisons between two groups, a two-tailed Student\u0026rsquo;s t-test was employed. For comparisons involving multiple groups, one-way analysis of variance (ANOVA) followed by Bonferroni post hoc testing was applied. Data are expressed as mean \u0026plusmn; standard deviation (SD).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Mingzhao Zhu and Dr. Zhaolin Hua for helpful suggestions. We are grateful to the Pathogenic Microbiology and Immunology Public Technology Service Center for their support and to all members of our laboratory for productive discussions. This work was supported by the National Natural Science Foundation of China (U23A20464) ; The National Key Research and Development Program (2023YFC2306901)；Beijing Municipal Health Commission high-level public health technical personnel construction project (discipline leader-03-26) and Beijing Hospitals Authority \u0026quot;peak\u0026quot; talent training program (DFL20241803). Some graphs were created with BioRender (https://biorender.com).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.X., conducted experiments., J.G., X.C., L.L., X.Z., Q.J., B.H., and F.Z. provided experimental assistance. W.X. and X.Z. designed the study and wrote the manuscript. M.L. and X.Z. supervised the project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used DeepSeek in order to improve the readability and language of the manuscript. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eIannacone, M. \u0026amp; Guidotti, L.G. 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Using a highly humanized mouse model, we identified a T cell receptor (TCR) targeting a dominant epitope essential for HBV clearance in clinical settings. We further uncovered Ankrd11, an epigenetic regulator critical for sustaining CD8\u003csup\u003e+\u003c/sup\u003e T cell dysfunction during chronic infection. Strikingly, Ankrd11 knockout in CD8\u003csup\u003e+\u003c/sup\u003e T cells markedly enhanced HBV-specific T cell proliferation, particularly in immunosuppressive environments, through upregulated AP-1 family gene expression. Additionally, Ankrd11-deficient T cells exhibited robust granzymes production and superior effector functions, resulting in potent antiviral and anti-tumor activity. These findings open new avenues for immunology and virology research, offering promising therapeutic strategies against chronic HBV infection.\u003c/p\u003e","manuscriptTitle":"Ankrd11 deficiency reverses the dysfunction of chronic HBV-specific T-cell","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 08:06:01","doi":"10.21203/rs.3.rs-6785364/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-immunology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ni","sideBox":"Learn more about [Nature Immunology](http://www.nature.com/ni/)","snPcode":"","submissionUrl":"","title":"Nature Immunology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2b43e395-c3cc-4d61-9d8b-00b5af35aa1c","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":50003770,"name":"Biological sciences/Immunology"},{"id":50003771,"name":"Biological sciences/Immunology/Adaptive immunity/Cellular immunity/Lymphocyte activation"}],"tags":[],"updatedAt":"2026-05-12T08:35:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 08:06:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6785364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6785364","identity":"rs-6785364","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}