ZFP148 is a transcriptional repressor of cytolytic effector CD8+ T cell differentiation
Tong Xiao, Xingyu Chen, No-Joon Song, Ryan J. Brown, Anjun Ma, Jay K. Mandula, Amir Yousif, Yi Wang, Minh Quynh May Le, Jianying Li, Fengxia Gao, Bella Weaver, Heng-Yi Chen, Fang-Yun Lay, Debasish Sundi, Maria Velegraki, Payton Weltge, Juanita L. Merchant, Mark P. Rubinstein
TL;DR
This study shows that ZFP148 limits the development of CD8+ T cells into cytolytic effectors and that reducing ZFP148 improves antitumor immunity.
Contribution
The study identifies ZFP148 as a novel transcriptional repressor of CD8+ T cell effector differentiation and a potential therapeutic target.
Findings
ZFP148-deficient CD8+ T cells show increased cytolytic effector cells and reduced exhaustion during chronic viral infection.
ZFP148 represses KLF2 expression and limits chromatin accessibility of effector-driving transcription factors.
ZFP148 ablation in CD8+ T cells synergizes with PD-1 blockade to improve tumor control in mouse models.
Abstract
Progenitor CD8+ T cells differentiate into effector and exhausted progenies during chronic antigen stimulation; however, mechanisms that restrain exhaustion and sustain effector differentiation remain incompletely defined. Here we identified the transcription factor ZFP148 as a repressor of CD8+ T cell effector differentiation. ZFP148-deficient CD8+ T cells displayed increased frequency of cytolytic effector cells and reduced frequency of exhausted cells compared with Zfp148fl/fl controls during chronic viral infection. Mechanistically, ZFP148 limited the chromatin accessibility of effector-driving transcription factor motifs and directly repressed expression of the transcription factor KLF2. Furthermore, conditional ZFP148 ablation in CD8+ T cells synergized with programmed cell death-1 blockade to improve tumor control in syngeneic mouse models. Consistently, cancer patients with…
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Figure 9- —https://doi.org/10.13039/100000002U.S. Department of Health & Human Services | National Institutes of Health (NIH)
- —https://doi.org/10.13039/100000054U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
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Taxonomy
TopicsCancer Immunotherapy and Biomarkers · CAR-T cell therapy research · Immunotherapy and Immune Responses
Main
During persistent viral infection and cancer, antigen-specific CD8^+^ T cells undergo T cell exhaustion, marked by progressive loss of effector function and proliferative capacity^1,2^. Within this heterogeneous compartment, a small subset of self-renewing progenitor CD8^+^ T cells (T_PRO_ cells), defined by high expression of stemness-associated transcription factor TCF1 and chemokine receptor CXCR5, sustains antigen-driven responses by generating more differentiated progenies, including terminally exhausted (T_EX_) cells^3,4^. Although the exhaustion program limits immunopathology, it also compromises viral clearance and tumor control^5,6^. Notably, CD8^+^ T_PRO_ cells can alternatively give rise to cytolytic effectors that highly express the effector marker CX3CR1 and cytolytic molecule granzyme B (GZMB) (hereafter T_EFF_ cells), which are critical for viral and tumor control and response to programmed cell death 1 (PD-1) blockade^7–9^. Several transcriptional and epigenetic programs regulating these fate decisions have been described. Sustained TCF1 expression maintains the CD8^+^ T_PRO_ cell pool, whereas TCF1 downregulation is associated with terminal exhaustion^10,11^. Transcription factors reinforcing exhaustion include TOX^12–14^, IRF2^15^, IRF4^16^, the NR4A family^17^ and NFAT proteins^18^, whereas differentiation toward CX3CR1^+^CD8^+^ T_EFF_ cells is driven by transcription factors BATF^19^, KLF2^20^ and ZEB2^21^. Thus, defining mechanisms that enhance cytolytic CD8^+^ T_EFF_ cell differentiation while limiting exhaustion is key to improving immune control of chronic infections and cancer.
ZFP148 (encoded by Zfp148 in mouse and ZNF148 in human) is a Krüppel family transcription factor that regulates proliferation and differentiation in nonimmune cells^22^ and promotes T helper 2 function in CD4^+^ T cells^23^. However, the role of ZFP148 in CD8^+^ T cells remains unknown. Here we identify ZFP148 as a negative regulator that restrains CD8^+^ T_EFF_ cell differentiation and promotes exhaustion through repressing KLF2. Conditional deletion of ZFP148 in CD8^+^ T cells enhanced their cytolytic capacity, costimulatory signaling and proliferative potential, synergizing with PD-1 blockade to control syngeneic tumor growth; concordantly, lower ZNF148 expression was associated with improved immunotherapy responses in cancer patients, implicating a conserved ZFP148–KLF2 axis. Together, these findings establish ZFP148 as a transcriptional repressor of cytolytic CD8^+^ T_EFF_ cell differentiation and a potential target to enhance responses to immunotherapies.
Results
ZFP148 is enriched in CD8+ TPRO cells
Published computational inferences identified ZFP148 as a potential transcriptional regulator in CD8^+^ T cells differentiation during chronic viral infection and cancer^10,24^. We analyzed published single-cell (sc) RNA sequencing (RNA-seq) datasets of adoptively transferred P14 CD8^+^ T cells from spleens of C57BL/6 wild-type (WT) recipient mice infected with acute (Armstrong) or chronic (Cl13) lymphocytic choriomeningitis virus (LCMV) at days 8, 15 and 30 postinfection (p.i.)^21^. Zfp148 expression was reduced in P14 CD8^+^ T cells during chronic compared to acute infection (Fig. 1a), suggesting potential antigen load-dependent regulation. Flow cytometry showed that ZFP148 mean fluorescence intensity (MFI) was upregulated rapidly in splenic CD44^hi^GP_33–41_ tetramer (Tet)^+^CD8^+^ T cells of C57BL/6 WT mice at day 8 post-LCMV Cl13 infection versus naive CD44^−^CD62L^+^CD8^+^ T cells (hereafter CD8^+^ T_N_ cells; Fig. 1b and Extended Data Fig. 1a). In vitro TCR engagement through anti-CD3 (aCD3) antibody (Ab) stimulation also induced ZFP148 protein expression in splenic CD8^+^ T_N_ cells with minimal dependence on CD28 costimulatory signaling (Fig. 1c,d). Furthermore, cyclosporin A (CsA) abrogated this induction (Extended Data Fig. 1b), indicating calcineurin–NFAT-dependent regulation.Fig. 1ZFP148 is enriched in CD8^+^ T_PRO_ cells and induced by TCR signaling.a, Expression of Zfp148, Pdcd1, Tcf7 and Gzmb mRNA in splenic P14 CD8^+^ T cells transferred to C57BL/6 WT mice infected with either LCMV Armstrong (Arm) or Cl13 at day 1 post-T cell transfer and analyzed at day (D) 0, 8, 15 and 30 p.i., generated by reanalysis of published scRNA-seq datasets^21^. b, MFI of ZFP148 in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in C57BL/6 WT mice at day 8 (n = 8), 16 (n = 6), 22 (n = 8) and 30 (n = 6) post-LCMV Cl13 infection and splenic CD8^+^ T_N_ cells in noninfected C57BL/6 WT mice as a control (n = 29). c,d, Density plots showing ZFP148 protein expression (left) and MFI of ZFP148 (right) in splenic CD8^+^ T_N_ cells of C57BL/6 WT mice stimulated with 5 μg ml^−1^ aCD3 Ab with or without 2 μg ml^−1^ aCD28 Ab for 0, 10, 24 and 48 h (c) or with 0.1, 0.5, 1, 2 or 5 μg ml^−1^ of aCD3 Ab and 2 μg ml^−1^ aCD28 Ab for 48 h (d) (n = 4). e, Scatter plots showing CX3CR1 versus Ly108 protein expression in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells (left) and density plots showing expression of ZFP148 protein (middle) and MFI of ZFP148 (right) in splenic CX3CR1^−^Ly108^+^CD8^+^ T_PRO_ cells, CX3CR1^−^Ly108^−^CD8^+^ T_EX_ cells and CX3CR1^+^Ly108^−^CD8^+^ T_EFF_ cells in C57BL/6 WT mice at day 22 post-LCMV Cl13 infection (n = 10) and splenic CD8^+^ T_N_ cells in noninfected C57BL/6 WT mice as a control (n = 5). FMO, fluorescence minus one. Data are representative of two to three independent experiments (b–e). Data are presented as mean ± s.d. Statistical analysis was performed using one-way ANOVA followed by Šídák’s multiple comparisons test (b); two-way ANOVA (c); one-way ANOVA followed by Tukey’s multiple comparisons test (e).Source data
ZFP148 protein expression declined by ~50% between days 8 and 16 and stabilized through day 30 in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells p.i. (Fig. 1b). Using established effector markers (CX3CR1, GZMB) and progenitor markers (Ly108, TCF1)^19^, we detected higher expression of ZFP148 protein in CX3CR1^−^Ly108^+^ or GZMB^−^TCF1^+^CD8^+^ T_PRO_ cells versus their CD8^+^ T_EFF_ or CD8^+^ T_EX_ counterparts in both splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells and CD44^hi^GP_276–286_ Tet^+^CD8^+^ T cells at day 22 p.i. (Fig. 1e and Extended Data Fig. 1c,d). Similar enrichment of ZFP148 in CD8^+^ T_PRO_ cells was also observed in CD8^+^ tumor-infiltrating lymphocytes (CD8^+^ TILs) in C57BL/6 WT mice at day 14 postsubcutaneous injection with the colon adenocarcinoma MC38 and human muscle-invasive bladder tumors (Extended Data Fig. 1e,f). Collectively, these data indicated that ZFP148 was enriched in CD8^+^ T_PRO_ cells and declined during differentiation into CD8^+^ T_EFF_ and CD8^+^ T_EX_ cells.
ZFP148 inhibits CD8+ TEFF cell differentiation
To assess the role of ZFP148 in antigen-specific CD8^+^ T cell differentiation during chronic viral infection, we crossed E8i^Cre^ mice with Zfp148^fl/fl^ mice to generate CD8^+^ T cell-specific ZFP148-knockout (KO) mice (hereafter ZFP148 cKO mice). Flow cytometry confirmed efficient deletion of ZFP148 in splenic CD8^+^ T cells and comparable frequencies of principal immune cell lineages in spleens of ZFP148 cKO mice compared to Zfp148^fl/fl^ mice (Extended Data Fig. 2a–c).
Longitudinal flow cytometric analysis showed similar numbers and frequencies of CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in spleens of Zfp148^fl/fl^ and ZFP148 cKO mice at days 16, 22 and 30 post-LCMV Cl13 infection, albeit a reduction in number at day 8 p.i. in ZFP148 cKO mice compared to Zfp148^fl/fl^ mice (Extended Data Fig. 3a,b). ZFP148 cKO mice exhibited increased frequencies of CX3CR1^+^Ly108^−^CD8^+^ T_EFF_ cells compared to Zfp148^fl/fl^ mice in spleens at days 16, 22 and 30 p.i. (Fig. 2a and Extended Data Fig. 3c), accompanied by reduced frequency and number of CX3CR1^−^Ly108^+^CD8^+^ T_PRO_ cells at days 8 and 16 p.i. (Extended Data Fig. 3d) and CX3CR1^−^Ly108^−^CD8^+^ T_EX_ cells and Ly108^−^CD69^+^CD8^+^ T_EX_ cells at day 22 p.i. (Extended Data Fig. 3e,f). Expression of inhibitory receptors (PD-1, TIM-3, LAG-3, CTLA-4, CD39 and CD101) in total CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells or CD8^+^ T_EX_ cells in spleens at days 22 and 30 p.i. was largely comparable between genotypes (Fig. 2b and Extended Data Fig. 3g). We observed increased production of GZMB but not pro-inflammatory cytokines (interferon gamma (IFNγ), tumor necrosis factor (TNF) and interleukin 2 (IL-2)) in ZFP148 cKO versus Zfp148^fl/fl^ CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in spleens at days 8, 16 and 22 p.i. (Fig. 2c and Extended Data Fig. 3h). Increased frequencies of CX3CR1^+^Ly108^−^CD8^+^ T_EFF_ cells were also detected in livers and lungs of ZFP148 cKO versus Zfp148^fl/fl^ mice at day 22 p.i. (Extended Data Fig. 3i).Fig. 2ZFP148 restrains cytolytic effector differentiation of antigen-specific CD8^+^ T cells during chronic LCMV infection.a, Scatter plots showing CX3CR1 versus Ly108 protein expression (left) and frequency of CX3CR1^+^Ly108^−^CD8^+^ T_EFF_ cells (right) in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in Zfp148^fl/fl^ or ZFP148 cKO mice at day 8 (Zfp148^fl/fl^, n = 8; ZFP148 cKO, n = 9), 16 (Zfp148^fl/fl^, n = 8; ZFP148 cKO, n = 8), 22 (Zfp148^fl/fl^, n = 8; ZFP148 cKO, n = 9) and 30 (Zfp148^fl/fl^, n = 7; ZFP148 cKO, n = 10) post-LCMV Cl13 infection. b, MFI of PD-1, TIM-3, LAG-3, CTLA-4, CD39 and CD101 in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in Zfp148^fl/fl^ or ZFP148 cKO mice at day 22 (Zfp148^fl/fl^, n = 8; ZFP148 cKO, n = 9) and 30 (Zfp148^fl/fl^, n = 7; ZFP148 cKO, n = 10) post-LCMV Cl13 infection. c, Density plots showing expression of GZMB protein (left) and MFI of GZMB (right) in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in Zfp148^fl/fl^ and ZFP148 cKO mice as in a. d, Frequency of Ki-67^+^CX3CR1^+^CD8^+^ T cells in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in Zfp148^fl/fl^ or ZFP148 cKO mice as in a. e,f, Kinetics of Annexin V^+^ total area in B16-GP cells cocultured with mixed splenic CD44^hi^GP_33–41_ Tet^+^ and CD44^hi^GP_276–286_ Tet^+^CD8^+^ T cells sorted from ZFP148 cKO or Zfp148^fl/fl^ mice at day 22 post-LCMV Cl13 infection (n = 4) (e) or P14 CD8^+^ T cells activated and CRISPR-edited with either an NT control or Zfp148-targeting sgRNAs (n = 3) (f). Fold changes in a–d were calculated as mean (ZFP148 cKO) divided by mean (Zfp148^fl/fl^). Data are representative of two to three independent experiments (a–f). Data are presented as mean ± s.d. (a–d) or ± s.e.m. (e,f). Statistical analysis was performed using multiple unpaired two-sided t-tests (a, c and d); an unpaired two-sided t-test (b) and two-way ANOVA (e and f).Source data
ZFP148 cKO mice displayed increased frequencies of subset expressing both CX3CR1 and the proliferation marker Ki-67 in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells compared to Zfp148^fl/fl^ mice (Fig. 2d), together with elevated Ki-67 MFI in splenic CD8⁺ T_PRO_ cells, but not CD8^+^ T_EX_ cells (Extended Data Fig. 3j). Despite enhanced proliferation, neither the frequency nor number of splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells (Extended Data Fig. 3b), nor their apoptosis, as assessed by Annexin V and propidium iodide (PI) staining, differed in ZFP148 cKO versus Zfp148^fl/fl^ mice (Extended Data Fig. 4a,b). Instead, increased frequency and number of CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells were detected in the blood of ZFP148 cKO mice (Extended Data Fig. 4c), accompanied by increased lymphoid-homing marker CD62L and reduced tissue-resident marker CD69 expression in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells, compared to Zfp148^fl/fl^ mice (Extended Data Fig. 4d). These features were consistent with increased circulation and reduced tissue retention of CX3CR1^+^Ly108^−^CD8^+^ T_EFF_ cells^25–28^.
To assess cytolytic function, mixed splenic CD44^hi^GP_33–41_ Tet^+^ and CD44^hi^GP_276–286_ Tet^+^CD8^+^ T cells sorted from ZFP148 cKO or Zfp148^fl/fl^ mice at day 22 post-LCMV Cl13 infection were cocultured with B16F10 cells expressing LCMV glycoprotein (hereafter B16-GP cells). Sorted CD8^+^ T cells from ZFP148 cKO mice exhibited enhanced killing of B16-GP cells compared to those from Zfp148^fl/fl^ mice, as indicated by increased Annexin V staining (Fig. 2e). Similarly, increased cytotoxicity against B16-GP cells was observed in activated P14 CD8^+^ T cells CRISPR-edited with Zfp148-targeting sgRNAs (hereafter P14 CD8^+^ T_ZFP148-null_ cells) versus a nontargeting (NT) control sgRNA (hereafter P14 CD8^+^ T_NT_ cells) (Fig. 2f). Serum LCMV Cl13 titers were comparable between ZFP148 cKO and Zfp148^fl/fl^ mice at days 8, 21 and 28 p.i. (Extended Data Fig. 4e).
During acute LCMV Armstrong infection, both frequencies and numbers of CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in inguinal lymph nodes (iLNs) and spleens were comparable between ZFP148 cKO and Zfp148^fl/fl^ mice at day 9 p.i. (Extended Data Fig. 5a). We observed increased frequency of short-lived effector CD8^+^ T cells (T_SLEC_) expressing the effector marker KLRG1 but not the memory marker CD127 and reduced frequency and number of memory precursor KLRG1^−^CD127^+^CD8^+^ T cells (T_MPEC_) (Extended Data Fig. 5b–d), accompanied by increased expression of cytolytic molecule GZMA and GZMB and unchanged IFNγ and TNF production, in CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in iLNs and spleens of ZFP148 cKO versus Zfp148^fl/fl^ mice at day 9 p.i. (Extended Data Fig. 5e). Together, these results indicated that CD8^+^ T cell-intrinsic loss of ZFP148 promoted cytolytic CD8^+^ T_EFF_ differentiation during both chronic and acute viral infection.
ZFP148 loss promotes CD8+ TEFF cell programming
To define transcriptional and epigenetic reprogramming induced by ZFP148 deficiency, we performed paired scRNA-seq and scATAC-seq (assay for transposase-accessible chromatin using sequencing) on CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells sorted from spleens of ZFP148 cKO or Zfp148^fl/fl^ mice at day 21 post-LCMV Cl13 infection (Extended Data Fig. 6a). Joint analysis identified six clusters (Fig. 3a). After examining expression of key CD8^+^ T cell marker genes (Fig. 3b) and gene signatures from a published LCMV Cl13 scRNA-seq dataset^29^ (Fig. 3c), we annotated five distinct CD8^+^ T cell subsets, including T_P1_ (progenitor CD8^+^ T cells group 1, expressing Sell, Tcf7, Slamf6 and Pdcd1), T_P2_ (progenitor CD8^+^ T cells group 2; Tcf7, Slamf6 and Pdcd1), T_EFF_ (effector CD8^+^ T cells, Cx3cr1, Gzmb), prolif T (proliferative CD8^+^ T cells; mKi67) and T_EX_ (exhausted CD8^+^ T cells; Pdcd1, Havcr2, Cd244a) (Fig. 3d and Extended Data Fig. 6b).Fig. 3ZFP148 deficiency transcriptionally reprograms antigen-specific CD8^+^ T cells toward cytolytic effectors.a, RNA, ATAC and weighted-nearest neighbor (WNN) Uniform Manifold Approximation and Projection (UMAP) visualization of splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in Zfp148^fl/fl^ (pooled from 10 mice; 5,669 cells) or ZFP148 cKO mice (pooled from 12 mice; 8,567 cells) at day 21 post-LCMV Cl13 infection, annotated by clusters identified by the Seurat R package. b,c, UMAP visualization of Cx3cr1, Gzmb, Pdcd1, Slamf6, Sell, Havcr2, Cd244a and Mki67 mRNA expression (b) and published ‘Progenitor exhausted,’ ‘Effector,’ and ‘Terminally exhausted’ gene signatures^29^ (c) in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in Zfp148^fl/fl^ or ZFP148 cKO mice as in a. d, UMAP visualization of splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells annotated by subsets (CD8^+^ T_P1_, CD8^+^ T_P2_, CD8^+^ T_EFF_, CD8^+^ prolif T and CD8^+^ T_EX_) (top) and pie charts showing frequency of subsets in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in Zfp148^fl/fl^ or ZFP148 cKO mice as in a. e, ‘Effector’ and ‘exhausted’ cell-state trajectories inferred using the Slingshot R package in splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in Zfp148^fl/fl^ or ZFP148 cKO mice as in a (top) and pie charts showing proportion of Zfp148^fl/fl^ or ZFP148 cKO CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells assigned to each lineage (bottom). f, Heatmap showing subset-specific DEGs in Zfp148^fl/fl^ versus ZFP148 cKO splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells as in a. g,h, Volcano plot showing DEGs (g) and GO enrichment analysis of DEGs (h) in splenic CD8^+^ T_EFF_ cells of Zfp148^fl/fl^ versus ZFP148 cKO mice as in a. NS, nonsignificant. Statistical analysis was performed using a two-sided Wilcoxon rank-sum test with Benjamini–Hochberg correction for multiple comparisons (g) or a one-sided hypergeometric test with Benjamini–Hochberg correction for multiple comparisons (h).
Compared to splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in Zfp148^fl/fl^ mice, the same cells in ZFP148 cKO mice contained increased frequency of CD8^+^ T_EFF_ and CD8^+^ prolif T cells (35.2% and 13.5% versus 13.8% and 6.5%, respectively), accompanied by reduced frequency of CD8^+^ T_P2_ and CD8^+^ T_EX_ cells and minimal changes in CD8^+^ T_P1_ cells (Fig. 3d). Slingshot pseudotime analysis identified two trajectories: an ‘effector lineage’ progressing from CD8^+^ T_P1_ through CD8^+^ T_P2_ to CD8^+^ T_EFF_ and CD8^+^ prolif T cells, and an ‘exhausted lineage’ progressing from CD8^+^ T_P1_ through CD8^+^ T_P2_ and CD8^+^ T_EFF_ to CD8^+^ T_EX_ cells (Fig. 3e). Consistently, CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in ZFP148 cKO mice were enriched in the ‘effector lineage’ compared to same cells in Zfp148^fl/fl^ mice (38.2% versus 23.8%) (Fig. 3e).
Differential gene expression analysis revealed reduced expression of stemness genes (Il7r, Tcf7 and Slamf6) in ZFP148 cKO versus Zfp148^fl/fl^ CD8^+^ T_P2_ cells, increased expression of effector genes (Klrk1, Klrd1, Cx3cr1, Zeb2 and Klf2) in ZFP148 cKO versus Zfp148^fl/fl^ CD8^+^ T_EFF_ cells, increased expression of proliferation-associated genes (Cdca8, Cdc20 and E2f7) in ZFP148 cKO versus Zfp148^fl/fl^ CD8^+^ prolif T cells and reduced expression of inhibitory receptor genes (Cd160 and Cd244a) in ZFP148 cKO versus Zfp148^fl/fl^ CD8^+^ T_EX_ cells (Fig. 3f). Subset-specific transcriptional differences were also pronounced in CD8^+^ T_EFF_ cells, with ZFP148 cKO CD8^+^ T_EFF_ cells showing increased expression of effector genes (Zeb2, Klrc2, Klf2 and S1pr5) compared to Zfp148^fl/fl^ CD8^+^ T_EFF_ cells (Fig. 3g and Extended Data Fig. 6c). Gene ontology (GO) enrichment analysis revealed enrichment of ‘Cell killing’ and ‘Stimulatory C-type lectin receptor signaling’ pathways and natural killer (NK) cell-related cell identities in ZFP148 cKO versus Zfp148^fl/fl^ CD8^+^ T_EFF_ cells (Fig. 3h and Extended Data Fig. 6d). Collectively, these data indicated that ZFP148 deficiency redirected CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells differentiation toward CD8^+^ T_EFF_ cells with NK cell-like transcriptional features over CD8^+^ T_EX_ cells.
ZFP148 loss remodels transcriptional regulatory programs
We analyzed differentially accessible chromatin regions (DACRs) and observed widespread changes in chromatin accessibility in ZFP148 cKO versus Zfp148^fl/fl^ splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells at day 21 post-LCMV Cl13 infection in scATAC-seq data described above (Fig. 4a). The highest proportion of gene-linked DACRs was observed in the CD8^+^ T_EFF_ cells (Extended Data Fig. 7a) and expression of DACR-linked effector genes (Klf2, Rap1gap2, S1pr5, Zeb2 and Klrg1) was increased in ZFP148 cKO versus Zfp148^fl/fl^ CD8^+^ T_EFF_ cells (Extended Data Fig. 7b). Consistently, Zeb2 and S1pr5 loci displayed increased promoter accessibility in ZFP148 cKO versus Zfp148^fl/fl^ CD8^+^ T_P2_ and CD8^+^ T_EFF_ cells (Fig. 4b), indicating enhanced accessibility at key CD8^+^ T_EFF_-associated gene loci.Fig. 4ZFP148 limits effector-driving transcription factor motif accessibility in antigen-specific CD8^+^ T cells.a, Heatmap showing subset-specific DACRs in Zfp148^fl/fl^ versus ZFP148 cKO splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells at day 21 post-LCMV Cl13 infection. Genes linked to specific peaks identified by the Link() function of the Signac R package are displayed at the bottom of the heatmap. b, Genomic tracks showing subset-specific open chromatin regions at Zeb2 and S1pr5 loci in Zfp148^fl/fl^ or ZFP148 cKO CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells as in a. DACRs (adjusted P < 0.05) are highlighted in orange. mRNA expression of Zeb2 or S1pr5 are displayed on the right. Gene-peak links, identified using the LinkPeaks() function of the Signac R package, are displayed at the bottom. c, Heatmap showing subset-specific differentially accessible transcription factor motifs between Zfp148^fl/fl^ or ZFP148 cKO CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells as in a. d, Volcano plot showing differentially accessible transcription factor motifs in Zfp148^fl/fl^ versus ZFP148 cKO splenic CD8^+^ T_P1_ cells (top) and CD8^+^ T_EFF_ cells (bottom) as in a. e, Violin plots showing motif accessibility of TCF1, T-bet and NFATc4 in Zfp148^fl/fl^ and ZFP148 cKO CD8^+^ T_P1_, CD8^+^ T_P2_, CD8^+^ T_EFF_, CD8^+^ prolif T and CD8^+^ T_EX_ cells as in a. Data are pooled from 10 mice for Zfp148^fl/fl^ and 12 mice for ZFP148 cKO. Statistical analysis was performed using a two-sided Wilcoxon rank-sum test with Benjamini–Hochberg correction for multiple comparisons (d) or a two-sided Wilcoxon rank-sum test (e).
ZFP148 deficiency altered accessibility of several lineage-determining transcription factor motifs across subsets of CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells (Fig. 4c). We observed reduced accessibility of progenitor-associated TCF1 and LEF1 motifs in ZFP148 cKO CD8^+^ T_P2_ cells, increased accessibility of effector-associated KLF2, T-bet, STAT5A and RUNX3 motifs in ZFP148 cKO CD8^+^ T_EFF_ cells and reduced accessibility of exhaustion-associated IRF2, IRF4, IKZF1, FLI1 and NFAT family motifs in ZFP148 cKO CD8^+^ T_EX_ cells, compared to their Zfp148^fl/fl^ counterparts (Fig. 4c).
Through subset-specific differential transcription factor motif accessibility analysis, we observed increased accessibility of effector-driving T-bet motif and reduced accessibility of LEF1 and TCF1 motifs in ZFP148 cKO versus Zfp148^fl/fl^ CD8^+^ T_P2_ cells (Fig. 4d). Concordantly, we also detected increased accessibility of effector-driving AP-1 family (FOS, JUND) motifs and reduced accessibility of exhaustion-associated NFATc2, NFATc4, IKZF1 and NR4A1 motifs in ZFP148 cKO versus Zfp148^fl/fl^ CD8^+^ T_EFF_ cells (Fig. 4d). These changes were initiated early in ZFP148 cKO CD8^+^ T_P2_ cells and were sustained in CD8^+^ T_EFF_, CD8^+^ prolif T and CD8^+^ T_EX_ progenies compared to Zfp148^fl/fl^ counterparts (Fig. 4e and Extended Data Fig. 7c). Collectively, ZFP148 deficiency reduced accessibility of CD8^+^ T_PRO_ or CD8^+^ T_EX_ cell-associated transcription factor motifs, while enhancing accessibility of CD8^+^ T_EFF_ cell-associated transcription factor motifs.
ZFP148 restrains KLF2-driving effector differentiation
To identify ZFP148 target genes, we focused on genes exhibiting both differential expression and altered promoter chromatin accessibility in ZFP148 cKO versus Zfp148^fl/fl^ splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in scRNA-seq data described above and identified Klf2 (encoding KLF2) among the top three upregulated genes in the absence of ZFP148 (Fig. 5a). Increased Klf2 mRNA expression, promoter chromatin accessibility and KLF2 motif accessibility were detected across subsets of ZFP148 cKO versus Zfp148^fl/fl^ splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells (Fig. 5b,c and Extended Data Fig. 8a). To assess KLF2 protein expression, we used P14 mice expressing a KLF2–EGFP fusion protein (hereafter P14 KLF2–EGFP mice). Increased Klf2 mRNA and KLF2–EGFP protein were detected in P14 KLF2–EGFP CD8^+^ T cells activated in vitro and CRISPR-edited with Zfp148-targeting sgRNAs versus an NT sgRNA (Fig. 5d,e). Concordantly, increased expression of the KLF2 gene signatures were observed in ZFP148 cKO versus Zfp148^fl/fl^ splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cell (Fig. 5f and Extended Data Fig. 8b), indicating enhanced KLF2 downstream effector-driving transcriptional programs^20,30^.Fig. 5ZFP148 represses KLF2 to inhibit CD8^+^ T_EFF_ differentiation.a, log_2_ fold changes of mRNA expression versus promoter chromatin accessibility in genes showing both differential expression and differential promoter chromatin accessibility in Zfp148^fl/fl^ versus ZFP148 cKO splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells at day 21 post-LCMV Cl13 infection. R and P values are calculated by Spearman correlation. b,c, Violin plots showing Klf2 mRNA expression (b) and KLF2 motif accessibility (c) in Zfp148^fl/fl^ and ZFP148 cKO CD8^+^ T_P1_, CD8^+^ T_P2_, CD8^+^ T_EFF_, CD8^+^ prolif T and CD8^+^ T_EX_ cells as in a. d,e, expression of Klf2 mRNA (n = 3) (d) and density plots showing expression of KLF2–EGFP fusion protein (e) in P14 KLF2–EGFP CD8^+^ T_NT_, P14 KLF2–EGFP CD8^+^ T_ZFP148-null_ and P14 KLF2–EGFP CD8^+^ T_KLF2-null_ cells activated in vitro. f, Violin plots showing KLF2 motif accessibility in Zfp148^fl/fl^ and ZFP148 cKO CD8^+^ T_P1_, CD8^+^ T_P2_, CD8^+^ T_EFF_, CD8^+^ prolif T and CD8^+^ T_EX_ cells as in a. g,h, Scatter plots showing CX3CR1 versus Ly108 protein expression (g) and frequency of CX3CR1^+^Ly108^−^CD8^+^ T_EFF_ cells (h) in transferred P14 CD8^+^ T_NT_ cells (n = 6), P14 CD8^+^ T_ZFP148-null_ cells (n = 8), P14 CD8^+^ T_KLF2-null_ cells (n = 7) and P14 CD8^+^ T_ZFP148+KLF2-null_ cells (n = 8) in spleens of C57BL/6 WT recipient mice at day 22 post-LCMV Cl13 infection. i, Overlaid genome tracks showing chromatin accessibility of the Klf2 loci in Zfp148^fl/fl^ and ZFP148 cKO CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells as in a (top) and binding activity of ZFP148 measured by CUT&Tag-seq using splenic CD8^+^ T cells from C57BL/6 WT mice activated for 24 h in vitro with IgG Ab as a negative control (bottom). j,k, Expression of Klf2 mRNA (n = 3) (j) and density plots showing expression of KLF2–EGFP fusion protein (k) in P14 KLF2–EGFP CD8^+^ T_NT_, P14 KLF2–EGFP Klf2^+10.9kb/element^ CD8^+^ T and P14 KLF2–EGFP CD8^+^ T_KLF2-null_ cells activated in vitro. l, Frequency of CX3CR1^+^Ly108^−^CD8^+^ T_EFF_ cells and GZMB^+^TCF1^−^CD8^+^ T_EFF_ cells in transferred P14 KLF2–EGFP CD8^+^ T_NT_ (n = 10) and P14 KLF2–EGFP Klf2^+10.9kb/element^ CD8^+^ T cells (n = 5) in spleens of C57BL/6 WT recipient mice at day 22 post-LCMV Cl13 infection. m, Relative luciferase activity in EL4_ZFP148-null_ versus EL4_NT_ cells transfected with the pGL3-Klf2^+10.9kb/element^ luciferase vector (n = 3). Data are pooled from 10 mice for Zfp148^fl/fl^ and 12 mice for ZFP148 cKO (a–c, f and i); pooled from 5 mice for CUT&Tag-seq (i); representative of two to three independent experiments (d–g, h, j and k); pooled from two to three independent experiments (l and m). Data are presented as mean ± s.d. Statistical analysis was performed using a two-sided Wilcoxon rank-sum test (b, c and f); one-way ANOVA followed by Šídák’s multiple comparisons test (d and j); one-way ANOVA followed by Tukey’s multiple comparisons test (h) or an unpaired two-sided t-test (l and m).Source data
To test whether KLF2 mediated ZFP148-dependent CD8^+^ T_EFF_ differentiation, we CRISPR-edited splenic P14 CD8^+^ T cells activated in vitro with an NT sgRNA (P14 CD8⁺ T_NT_ cells), Zfp148-targeting sgRNAs (P14 CD8⁺ T_ZFP148-null_ cells), Klf2-targeting sgRNAs (hereafter P14 CD8^+^ T_KLF2-null_ cells) and mixed Zfp148-targeting and Klf2-targeting sgRNAs (hereafter P14 CD8⁺ T_ZFP148+KLF2-null_ cells) and transferred equal numbers of CRISPR-edited cells into C57BL/6 WT recipient mice followed by LCMV Cl13 infection on the same day. Splenic CX3CR1^+^Ly108^−^CD8^+^ T_EFF_ cells were enriched in transferred P14 CD8^+^ T_ZFP148-null_ cells but nearly absent in P14 CD8^+^ T_KLF2-null_ cells and P14 CD8^+^ T_ZFP148+KLF2-null_ cells compared to P14 CD8^+^ T_NT_ cells at day 22 p.i. (Fig. 5g,h). To enable direct comparison and priming of transferred P14 CD8^+^ T cells in vivo, we CRISPR knocked out both Zfp148 and Klf2 in naive splenic P14 CD8^+^ T cells and transferred mixtures containing equal numbers of naive P14 CD8^+^ T_NT_ cells and P14 CD8^+^ T_ZFP148+KLF2-null_ cells into C57BL/6 WT recipient mice followed by LCMV Cl13 infection on the same day. We observed decreased frequency of CX3CR1^+^Ly108^−^CD8^+^ T_EFF_ cells, increased frequency of CD8^+^ T_EX_ cells expressing exhaustion marker CD39 and 2B4 and increased MFI of exhaustion marker PD-1 and TOX in transferred P14 CD8^+^ T_ZFP148+KLF2-null_ cells compared to P14 CD8^+^ T_NT_ cells at day 22 p.i. (Extended Data Fig. 8c,d). Overall, these data indicated that KLF2 is required for the augmented CD8^+^ T_EFF_ differentiation post-ZFP148 deletion.
To determine whether ZFP148 bound the Klf2 locus directly, we performed ZFP148 CUT&Tag-seq in splenic CD8^+^ T cells from C57BL/6 WT mice activated for 24 h in vitro. Overlay of scATAC-seq and CUT&Tag-seq revealed increased chromatin accessibility in ZFP148 cKO versus Zfp148^fl/fl^ splenic CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cell and direct ZFP148 binding at the same region 10.9 kb downstream of the Klf2 transcription start site (hereafter Klf2^+10.9kb/element^) previously reported in the CD8^+^ T_PRO_-to-T_EFF_ cell transition during LCMV Cl13 infection^19^(Fig. 5i). To assess the role of Klf2^+10.9kb/element^ in CD8^+^ T_EFF_ cell differentiation, we used CRISPR ribonucleoprotein (RNP) to delete this region in splenic P14 KLF2–EGFP CD8^+^ T cells (hereafter P14 KLF2–EGFP Klf2^+10.9kb/element^ CD8^+^ T cells) activated in vitro and transferred equal numbers of P14 CD8^+^ T_NT_ cells, P14 CD8^+^ T_ZFP148-null_ cells and P14 KLF2–EGFP Klf2^+10.9kb/element^ CD8^+^ T cells separately into C57BL/6 WT recipient mice followed by LCMV Cl13 infection on the same day. We detected increased expression of Klf2 mRNA expression and KLF2–EGFP protein in P14 KLF2–EGFP Klf2^+10.9kb/element^ CD8^+^ T cells versus P14 CD8⁺ T_NT_ cells before adoptive transferring (Fig. 5j,k) and increased frequencies of CX3CR1^+^Ly108^−^ and GZMB^+^TCF1^−^CD8^+^ T_EFF_ cells in transferred P14 KLF2–EGFP Klf2^+10.9kb/element^ CD8^+^ T cells versus P14 CD8^+^ T_NT_ cells at day 22 p.i. (Fig. 5l), phenocopying P14 CD8^+^ T_ZFP148-null_ cells (Extended Data Fig. 8e). To test Klf2^+10.9kb/element^-mediated transcriptional repression of Klf2 directly, EL4 cells were CRISPR-edited with Zfp148-targeting (hereafter EL4_ZFP148-null_) or NT (hereafter EL4_NT_) sgRNAs and transduced with a pGL3 luciferase vector containing the Klf2^+10.9kb/element^. Both endogenous ZFP148 and KLF2 were detected by flow cytometry in WT EL4 and EL4_NT_ cells (Extended Data Fig. 8f,g). Through efficient ZFP148 deletion in EL4_ZFP148-null_ cells (Extended Data Fig. 8g), we observed increased luciferase activity in EL4_ZFP148-null_ cells versus EL4_NT_ cells (Fig. 5m), demonstrating direct trans-repression of ZFP148 on Klf2 through the Klf2^+10.9kb/element^. In summary, ZFP148 repressed Klf2 through a distal regulatory element (Klf2^+10.9kb/element^) and restrained KLF2-driven CD8^+^ T_EFF_ cell differentiation.
ZFP148 loss synergizes with PD-1 blockade
Next, we analyzed CD8^+^ TILs in ZFP148 cKO versus Zfp148^fl/fl^ mice at day 16 postsubcutaneous injection of MC38 tumors and observed increased expression of activation marker CD44, costimulatory marker ICOS, GZMB and inhibitory receptors (PD-1, TIM-3, LAG-3, TIGIT and CD39) in ZFP148 cKO CD8^+^ TILs and a comparable frequency and number of CD8^+^ TILs in ZFP148 cKO versus Zfp148^fl/fl^ mice (Fig. 6a and Extended Data Fig. 9a,b). We also detected an increased frequency of GZMB⁺TCF1^−^CD8^+^ T_EFF_ cells and unchanged frequency of GZMB^−^TCF1^+^CD8^+^ T_PRO_ cells in ZFP148 cKO versus Zfp148^fl/fl^ mice at days 10, 14, 16 and 18 postsubcutaneous injection of MC38 tumors (Extended Data Fig. 9c). These data suggest increases in both CD8^+^ T_EX_ and CD8^+^ T_EFF_ cell differentiation in ZFP148 cKO versus Zfp148^fl/fl^ CD8^+^ TILs.Fig. 6ZFP148 deficiency in CD8^+^ TILs enhance PD-1 blockade efficacy.a, MFI of CD44, ICOS, GZMB and PD-1 in CD8^+^ TILs in Zfp148^fl/fl^ (n = 16) and ZFP148 cKO (n = 11) mice at day 16 postsubcutaneous injection of MC38 tumors. b, PDCD1 mRNA expression in ZNF148^hi^ (n = 51) versus ZNF148^lo^ (n = 123) colorectal adenocarcinoma patients from the TCGA database. c, Tumor growth curves in Iso-Zfp148^fl/fl^ (n = 10), aPD-1-Zfp148^fl/fl^ (n = 11), Iso-cKO (n = 5) and aPD-1-cKO (n = 8) mice injected subcutaneously with MC38 tumors. Mice received intraperitoneal injection of aPD-1 or IgG Ab at days 9, 12 and 15 after tumor implantation. d, OS of Iso-Zfp148^fl/fl^ (n = 10), aPD-1-Zfp148^fl/fl^ (n = 10), Iso-cKO (n = 6) and aPD-1-cKO (n = 8) mice as in c. e,f, UMAP visualization of clustering (e) and CX3CR1, GZMB, Ki-67, TCF1, ICOS, CD27, CD25 and TIM-3 protein expression (f) in all CD8^+^ TILs in Iso-Zfp148^fl/fl^ (n = 13), aPD-1-Zfp148^fl/fl^ (n = 18), Iso-cKO (n = 20) and aPD-1-cKO (n = 17) mice combined together at day 13 postsubcutaneous injection of MC38 tumors. g, Contour plots showing distribution of CD8^+^ TILs in Iso-Zfp148^fl/fl^, aPD-1-Zfp148^fl/fl^, Iso-cKO and aPD-1-cKO mice as in e. h,i, Scatter plots showing GZMB versus CX3CR1 (h, left) or GZMB versus Perforin protein expression (i, left) and frequency of CX3CR1^+^GZMB^+^CD8^+^ T_EFF_ cells (h, right) or GZMB^+^Perforin^+^CD8^+^ T_EFF_ cells (i, right) in CD8^+^ TILs as in e. j,k, MFI of GZMB (left) and Perforin (right) (j) and frequency of proliferating BCL2^−^Ki-67^+^CD8^+^ T cells in CD8^+^ TILs (k) as in e. Data are pooled from two to three independent experiments (a, and c–k). Data are presented as mean ± s.d (a, and h–k); mean tumor areas ± s.e.m (c). Statistical analysis was performed using an unpaired two-sided t-test (a); a two-sided Wilcoxon rank-sum test (b); two-way ANOVA with Bonferroni correction for multiple comparisons (c); a log-rank test with Bonferroni correction for multiple comparisons (d) or a one-way ANOVA followed by Holm–Šídák’s multiple comparisons test (h–k).Source data
Because PD-1 signaling constrains CD8^+^ T_EFF_ cell functions^31^, we examined the relationship between ZNF148 and PDCD1 mRNA expression in TCGA colorectal adenocarcinoma patients and revealed higher PDCD1 mRNA in ZNF148^lo^ versus ZNF148^hi^ patients (Fig. 6b). To test whether ZFP148 loss sensitized CD8^+^ TILs to anti-PD-1 (aPD-1) Ab, ZFP148 cKO and Zfp148^fl/fl^ mice were treated with aPD-1 or isotype Abs at days 9, 12 and 15 postsubcutaneous injection of MC38 tumors (hereafter Iso-Zfp148^fl/fl^, aPD-1-Zfp148^fl/fl^, Iso-cKO and aPD-1-cKO mice). Improved tumor control and prolonged overall survival (OS) were detected in aPD-1-cKO versus aPD-1-Zfp148^fl/fl^ mice (median survival >48 versus 29 days) (Fig. 6c,d).
At day 13 postsubcutaneous injection of MC38 tumors, flow cytometry detected increased frequency and number of total CD8^+^ TILs in aPD-1-cKO versus Iso-cKO mice (Extended Data Fig. 9d,e). Both uniform manifold approximation and projection (UMAP) analysis and conventional gating further identified increased frequencies of CX3CR1^+^GZMB^+^CD8^+^ T_EFF_ and GZMB^+^Perforin^+^ CD8^+^ T_EFF_ cells and higher production of cytolytic molecule GZMB and Perforin in aPD-1-cKO versus aPD-1-Zfp148^fl/fl^ CD8^+^ TILs (Fig. 6e–j). Production of IFNγ and TNF was unchanged, whereas IL-2 production was reduced in aPD-1-cKO versus aPD-1-Zfp148^fl/fl^ CD8^+^ TILs (Extended Data Fig. 9f). aPD-1-cKO CD8^+^ TILs also showed increased expression of costimulatory marker CD27, ICOS and IL-2 receptor α chain CD25, indicating reinforced costimulation and IL-2 signaling (Fig. 6f,g and Extended Data Fig. 9g,h), and increased frequency of proliferating BCL2^−^Ki-67^+^CD8^+^ T cells (Fig. 6k), whereas minor and nondirectional differences in the expression of inhibitory receptors (TIM-3, CTLA-4, CD39 and VISTA) (Extended Data Fig. 9i), compared with aPD-1-Zfp148^fl/fl^ CD8^+^ TILs. Collectively, ZFP148 deficiency synergized with aPD-1 Ab to preferentially expand a proliferative, cytolytic CD8^+^ T_EFF_ population, resulting in superior tumor control.
Low ZNF148 associates with immunotherapy response
To examine the role of ZFP148 in human CD8^+^ T cells, we annotated CD8^+^ TILs from published scRNA-seq datasets of treatment-naive patients across 17 cancer types (hereafter integrated human cancer scRNA-seq dataset) with a published classification scheme^32^ and stratified them into ZNF148^lo^, ZNF148^int^ and ZNF148^hi^ groups (Extended Data Fig. 10a,b). We detected increased expression of effector genes (GZMA, GZMB, PRF1) and NK cell receptors (KLRD1, GNLY) in ZNF148^lo^ versus ZNF148^hi^ total CD8^+^ TILs (Fig. 7a) and CD8^+^ T_EFF_ subsets (‘CD8_c2_Teff’ and ‘CD8_c8_Teff_KLRG1’) (Extended Data Fig. 10c). We also observed enhanced expression of ‘Positive regulation of T cell-mediated cytotoxicity,’ ‘CD8 cytotoxicity,’ ‘CD8 TCR signaling’ and ‘Effector LCMV Cl13’ signatures, and reduced expression of a ‘CD8 naive’ signature, in ZNF148^lo^ versus ZNF148^hi^ CD8^+^ TILs (Fig. 7b,c), indicating an inverse relationship between ZNF148 expression and human CD8^+^ T_EFF_ cell differentiation.Fig. 7. Low ZNF148 in human CD8^+^ TILs associates with improved immunotherapy response.a, Dotplots showing mean expression of genes in ZNF148^hi^ and ZNF148^lo^ CD8^+^ TILs in the integrated human cancer scRNA-seq dataset. b, Gene set enrichment analysis plot showing ‘Positive regulation of T cell mediated cytotoxicity’ pathway in ZNF148^hi^ (n = 895 cells) and ZNF148^lo^ CD8^+^ TILs (n = 7,415 cells) in the integrated human cancer scRNA-seq dataset. NES, normalized enrichment score. c, Functional signature scores of ‘CD8 cytotoxicity,’ ‘CD8 TCR signaling,’ ‘Effector LCMV Cl13’ and ‘CD8 naive’ pathways in ZNF148^hi^ and ZNF148^lo^ CD8^+^ TILs in the integrated human cancer scRNA-seq dataset. d, Density plots showing expression of ZFP148, TCF1, GZMB, IFNγ, TNF and IL-2 protein in healthy donor PBMCs-derived human CD8^+^ T cells activated and CRISPR-edited with an NT control or ZNF148-targeting sgRNAs in vitro. Cytokine production was assessed following restimulation. e, Kaplan–Meier survival curves showing OS of colon cancer (left) or gastric cancer (right) patients, stratified into ZNF148^hi^ (colon, n = 787; gastric, n = 577) and ZNF148^lo^ groups (colon, n = 274; gastric, n = 298). f, Kaplan–Meier survival curves showing OS of melanoma patients treated with aPD-1 or aCTLA-4 Ab, stratified into ZNF148^hi^ (aPD-1, n = 83; aCTLA-4, n = 30) and ZNF148^lo^ groups (aPD-1, n = 274; aCTLA-4, n = 61). g,h, Expression of ZNF148 mRNA (g) or the ZFP148 KO signature (h) in CD8^+^ TILs in responders (R) versus nonresponders (NR) of advanced ccRCC patients treated with aPD-1 + aCTLAT-4 Abs^37^ (left, NR: 5,150 cells from five patients, R: 12,561 cells from eight patients) and DLBCL patients treated with anti-CD19 CAR-T cells^38^ (right, NR: n = 63,482 cells from 57 patients; R: n = 59,351 cells from 52 patients). Data are representative of two independent experiments (d). Data are presented as mean ± s.d. Statistical analysis was performed using a two-sided permutation-based enrichment test with false discovery rate (FDR) adjustment (b); two-sided Wilcoxon rank-sum test (c, g and h) or a log-rank test (e and f).
We next CRISPR-edited peripheral blood mononuclear cells (PBMCs)-derived human CD8^+^ T cells activated in vitro with ZNF148-targeting or NT sgRNAs. We detected reduced expression of TCF1 and increased expression of GZMB, TNF and IL-2 in ZNF148-targeting sgRNA-edited versus NT sgRNA-edited human CD8^+^ T cells (Fig. 7d). By using the Kaplan–Meier plotter webserver, we observed improved OS in ZNF148^lo^ versus ZNF148^hi^ cancer patients^33^ (Fig. 7e and Supplementary Table 1). We also generated a ZFP148 KO signature from genes upregulated in ZFP148 cKO versus Zfp148^fl/fl^ CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in our in-house scRNA-seq dataset and stratified a published liver cancer patient cohort^34^ based on ZFP148 KO signature enrichment in the CD8^+^ T_EFF_ subset of CD8^+^ TILs (‘CD8T_04_GNLY’). Consistently, we detected prolonged OS in ZFP148 KO signature^hi^ versus ZFP148 KO signature^lo^ patients (Extended Data Fig. 10d). Relevant to immunotherapies, we observed prolonged OS in ZNF148^lo^ versus ZNF148^hi^ melanoma patients stratified by ZNF148 expression pre-aPD-1 or pre-aCTLA-4 Ab treatments^35^ (Fig. 7f). In separate cohorts, we also observed prolonged OS in ZFP148 KO signature^hi^ versus ZFP148 KO signature^lo^ melanoma patients treated with aPD-1 or aCTLA-4 Ab^36^ (Extended Data Fig. 10e). Consistently, by reanalyzing scRNA-seq datasets of patients with clear cell renal cell carcinoma (ccRCC) treated with aPD-1 plus aCTLA-4 Abs^37^ or diffuse large B-cell lymphoma (DLBCL) treated with anti-CD19 CAR-T cells^38^, we found lower ZNF148 expression or higher ZFP148 KO signature scores in responder (R) versus nonresponder (NR) CD8^+^ TILs (Fig. 7g,h). Thus, reduced ZNF148 or elevated ZFP148 KO signature might predict improved responsiveness to immunotherapies.
Finally, higher expression of KLF2 mRNA was found in ZNF148^lo^ versus ZNF148^hi^ colorectal adenocarcinoma patients from the TCGA database (Extended Data Fig. 10f). Analysis of the integrated human cancer scRNA-seq dataset showed higher expression of KLF2 mRNA and KLF2 gene signature^30^ in ZNF148^lo^ versus ZNF148^hi^ CD8^+^ TILs (Extended Data Fig. 10g). We also detected improved OS in KLF2^hi^ or KLF2 gene signature^hi^ versus to KLF2^lo^ or KLF2 signature^lo^ patients, respectively, in melanoma patients treated with aPD-1 or aCTLA-4 Abs^36^ (Extended Data Fig. 10h,i). Collectively, these results establish ZFP148 as a conserved negative checkpoint that restrains CD8^+^ T_EFF_ differentiation in humans, with direct implications for cancer immunotherapy.
Discussion
Here we showed that ZFP148 restrained antigen-specific CD8^+^ T cell cytolytic effector differentiation by repressing KLF2. Conditional deletion of ZFP148 enriched frequency of CD8^+^ T_EFF_ cells and synergized with PD-1 blockade, resulting in superior tumor control.
We found that ZFP148 modulated chromatin accessibility at binding motifs for several lineage-determining transcription factors (TCF1, T-bet, KLF2, BATF, IRFs and IKZF1) in a subset-specific manner and repressed KLF2 directly through a distal regulatory element within the Klf2 loci in CD8^+^ T cells. In mouse embryonic fibroblasts, ZFP148 deficiency induces p53-dependent proliferative arrest partially through regulating other transcription factors^39^. Furthermore, ZFP148 can act as a binding partner for transcription factors NF-1^40^, GATA1^41^ and ZFP281^23^ or compete with SP1^42^ in various types of cells other than CD8^+^ T cells. Thus, future studies are needed to define the binding motifs and cofactors of ZFP148 to elucidate its precise downstream regulations in CD8^+^ T cells.
Our data did not exclude the possibility that ZFP148 may alternatively promote the formation or maintenance of KLF2^−^CD8^+^ T_PRO_ and CD8^+^ T_EX_ cells instead of driving KLF2^+^CD8^+^ T_EFF_ cell differentiation. In the absence of ZFP148, both the frequency and number of CD8^+^ T_PRO_ and CD8^+^ T_EX_ cells were reduced, potentially shifting the differentiation of CD8^+^ T cells toward CD8^+^ T_EFF_ cells. This model requires further validation using lineage-tracing approaches and temporal deletion of ZFP148.
Despite the transformative impact of PD-1/programmed death ligand 1 (PD-L1) blockade, durable response in cancer patients is limited^43^. Here we found synergy between conditional ZFP148 deletion and PD-1 blockade, paralleling observations from dual checkpoint blockade strategies^44,45^, These findings suggest complementary roles for ZFP148 and PD-1 that ZFP148 restrains primarily cytolytic effector differentiation, whereas PD-1 signaling limits proliferation^46,47^, of CD8^+^ T cells.
The effect of ZFP148 conditional deletion seems to be context dependent. ZFP148 deficiency led to reduction in CD8^+^ T_PRO_ cells during LCMV Cl13 infection but not in the MC38 tumor model, probably reflecting distinct antigenic landscapes, as chronic viral infection imposes uniform, high antigen load^48^, whereas tumors present heterogeneous and reduced antigen levels due to MHC-I downregulation^49^. As ZFP148 induction depends on TCR signal strength, these contextual differences probably shape the expression and function of ZFP148. Future studies are needed to examine how ZFP148 regulates CD8^+^ T cell differentiation across diverse human disease settings to determine the clinical application of targeting this pathway.
Methods
Ethics
All research conducted in this study complied with ethical regulations and was approved by The Ohio State University Institutional Animal Care and Use Committee (IACUC; protocol 2019A00000075), Institutional Biosafety Committee (IBC; protocol 2019R00000046), and Institutional Review Board (Buck-IRB; protocol 2018C0181).
Mice
C57BL/6J (strain 000664) mice were obtained from the Jackson Laboratory. CD8^+^ T cell-specific ZFP148-deficient mice were generated by crossing E8i^Cre^ (the Jackson Laboratory, strain 008766) mice with Zfp148^fl/fl^ mice kindly provided by J. L. Merchant at University of Arizona. P14 mice were kindly provided by S. M. Kaech at The Salk Institute for Biological Studies. KLF2^GFP^ reporter mice were kindly provided by S. C. Jameson at the University of Minnesota through W. Cui at Northwestern University and crossed with P14 mice at Northwestern University to generate P14 KLF2–EGFP mice. Mice were maintained in the animal facility at The Ohio State University under standard conditions (ambient temperature 20–24 °C, relative humidity 30–70%, 12 h dark–light cycle (lights on from 6:00 to 18:00)). Both male and female mice aged 8–10 weeks were used. All procedures were performed in strict accordance with the NIH Guide for the Care and Use of Laboratory Animals and approved by the Committee on the Ethics of Animal Experiments of The Ohio State University.
Cell lines
B16-GP cell line was kindly provided by A. Wieland at The Ohio State University. B16-GP cells were cultured in RPMI-1640 medium (Gibco, cat. no. 11875-093) with 10% heat-inactivated fetal bovine serum (FBS) (Gibco, cat. no. 10082-147) and 1% penicillin/streptomycin (Pen-Strep; Gibco, cat. no. 15140-122). The MC38 cell line was purchased from Kerafast (cat. no. ENH204-FP). MC38 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM; Gibco, cat. no. 11965-092) with 10% FBS and 1% Pen-Strep. EL4 cell line was kindly provided by K. Oestreich at The Ohio State University. EL4 cells were cultured in RPMI-1640 with 10% FBS and 1% Pen-Strep. Cell lines were tested regularly for mycoplasma contamination.
Tumor challenge
MC38 cells (1.5 × 10^6^) were resuspended in 100 μl of ice-cold phosphate-buffered saline (PBS) for subcutaneous injection into the right flanks of shaved mice. For experiments involving PD-1 blockade, mice were treated with aPD-1 Ab (BioXCell, 200 μg, clone 29F.1A12X) on days 9, 12 and 15 after tumor implantation. Tumors were monitored and measured daily using an electronic caliper starting on day 8 after tumor injection until day 20. Tumor surface area was calculated using the formula (width × length in square millimeters). Maximal tumor size cutoff was 16 mm in diameter for non-endpoint studies. For survival analysis, tumor-bearing mice were monitored daily and euthanized as nonsurvivors when the tumors sizes reached 16 mm in diameter.
LCMV infection models
For acute LCMV infection, 8- to 10-week-old mice were injected intraperitoneally with 2 × 10^5^ plaque-forming units of LCMV Armstrong. For chronic LCMV infection, 8- to 10-week-old mice were injected intravenously with 2 × 10^6^ plaque-forming units of LCMV Cl13. Viruses were prepared by a single passage on BHK21 cells and viral titers were determined by plaque formation assay on Vero cells. Serum virus titers were determined by the plaque assay performed using Vero cells as described previously^50^.
Tissue processing and single-cell suspension preparation
Mouse spleens were disrupted mechanically, washed once with ice-cold PBS, subjected to red blood cell lysis (BioLegend, cat. no. 420302), passed through 70-μm cell strainers, and resuspended as single-cell suspensions.
For liver lymphocyte isolation, mice were perfused with ice-cold PBS via the hepatic portal vein. Livers were dissociated mechanically through 100-μm strainers, followed by lymphocyte isolation using Percoll gradient centrifugation (44% Percoll in RPMI over 67% Percoll in PBS; 450g for 20 min at room temperature) and red blood cell lysis.
For lung lymphocyte isolation, mice were perfused with ice-cold PBS. Lungs were minced and digested with Collagenase Type III (Worthington Biochemical Corporation, cat. no. LS004182) for 90 min at 37 °C, passed through 70-μm strainers, and lymphocytes were isolated by Percoll gradient centrifugation (44% Percoll in RPMI over 67% Percoll in PBS; 500g for 20 min at room temperature).
Tumors were dissociated mechanically and digested with Collagenase Type I and Collagenase Type IV (Worthington Biochemical Corporation, cat. nos. LS004196 and LS004188) for 30 min at 37 °C with shaking at 125 rpm. Digestion was quenched with ice-cold PBS containing 2% bovine serum albumin (BSA), and red blood cell lysis (BioLegend) was performed as needed before filtration through 70-μm strainers.
Human bladder tumor and peripheral blood sample processing
Muscle-invasive bladder tumor samples were obtained from consenting treatment-naive patients (the patient cohort had a median age of 68 years) enrolled prospectively in The Ohio State University Comprehensive Cancer Center bladder cancer tissue registry (Buck-IRB 2018C0181). Clinical specimens were processed on the day of radical cystectomy. Tumor tissue was dissociated manually, centrifuged (600g for 5 min at 4 °C), resuspended in human tumor dissociation enzyme solution (Miltenyi Biotec, cat. no. 130-095-929) and homogenized using a gentleMACS semi-automated dissociator (Miltenyi Biotec). Homogenized tissue was incubated at 37 °C for 20 min under continuous rotation on a MACSmix Tube Rotator (Miltenyi Biotec). Following addition of 2% BSA, single-cell suspensions were filtered twice through a 70-µm cell strainer, washed with PBS, subjected to red blood cell lysis (BioLegend, cat. no. 420302), and resuspended in RPMI medium.
Peripheral blood was collected in heparin–EDTA tubes and processed by Ficoll–Hypaque (Sigma) density gradient centrifugation to isolate PBMCs.
Flow cytometry
Single-cell suspensions were stained at 4 °C with LIVE/DEAD Fixable Blue viability dye (Invitrogen, cat. no. L23105) for 15 min, followed by simultaneous Fc receptor (FcR) blocking and surface marker staining for 30 min at 4 °C. Intracellular staining was performed using the Foxp3 Transcription Factor Staining Kit (Invitrogen, cat. no. 00-5523-00) according to the manufacturer’s instructions. Data were acquired on a five-laser Cytek Aurora high-dimensional flow cytometer.
For cytokine detection in CD8^+^ TILs and human CD8^+^ T cells, cells were restimulated with Cell Stimulation Cocktail (Invitrogen, cat. no. 00-4970-03) containing PMA and ionomycin in the presence of brefeldin A (BioLegend, cat. no. 420601) for 2 h at 37 °C. For detection of cytokine production by GP_33–41_-specific CD8^+^ T cells from spleens of LCMV-infected mice, splenocytes were restimulated with GP_33–41_ peptide in the presence of brefeldin A for 5 h at 37 °C.
Apoptosis was assessed by Annexin V and PI staining using the fluorescein isothiocyanate Annexin V Apoptosis Detection Kit with PI (BioLegend, cat. no. 640914), followed by FcR blocking and surface marker staining. Data were analyzed using FlowJo software (v.10.7.1, Tree Star) or OMIQ Flow Cytometry software (Dotmatics). Antibodies used for multispectral flow cytometry are listed in Supplementary Table 3.
CD8+ T cell isolation and stimulation in vitro
Spleens from C57BL/6J WT mice were processed into single-cell suspensions, and untouched CD8^+^ T cells were purified by negative selection (STEMCELL, cat. no. 19853). For time-course TCR stimulation assays, CD8^+^ T cells were cultured in complete T cell medium (cTCM) (RPMI-1640 (Gibco, cat. no. 11875-093) with 10% FBS, 1% Pen-Strep, 1 mM sodium pyruvate (Gibco, cat. no. 11360-070), 1× MEM NEAA (Gibco, cat. no. 11140-050), 10 mM HEPES (Gibco, cat. no. 15630-080) and 50 μM 2-mercaptoethanol (Gibco, cat. no. 21985-023)) supplemented with 100 U ml^−1^ recombinant human IL-2 (rhIL-2, acquired from the Biological Resources Branch at the NIH) and stimulated with 5 μg ml^−1^ plate-bound aCD3 Ab (BioLegend, cat. no. 100359), with or without 2 μg ml^−1^ soluble aCD28 Ab (BioLegend, cat. no. 102121), at 1 × 10^6^ cells per well in 24-well plates for 0, 10, 24 or 48 h at 37 °C and 5% CO_2_. For dose–response assays, cells were stimulated with 0.1–5 μg ml^−1^ plate-bound aCD3 Ab plus 2 μg ml^−1^ soluble aCD28 Ab for 48 h under identical culture conditions. For CsA inhibition experiments, cells were stimulated with 5 μg ml^−1^ plate-bound aCD3 Ab and 2 μg ml^−1^ soluble aCD28 Ab in the presence of 0, 1, 10, 100, 1,000 or 10,000 nM cyclosporine A (Thermo Scientific Chemicals, cat. no. 457970010) for 48 h.
CRISPR–Cas9 RNP KO in activated P14 CD8+ T cells and adoptive transferring
sgRNAs targeting Klf2 were adapted from a published study^51^, whereas sgRNAs for other candidates were designed by Integrated DNA Technologies (IDT); all were purchased from IDT. Sequences are listed in Supplementary Table 2. For experiments using P14 or P14 KLF2–EGFP mice, on day 0, fresh splenocytes were isolated and stimulated with 1 μg ml^−1^ LCMV GP_33–41_ peptide (GenScript, cat. no. RP20257) in the presence of 100 U ml^−1^ rIL-2 (Biological Resources Branch at NIH) in cTCM at 1 × 10^6^ cells ml^−1^ in 24-well plates. After 3 days, cells were collected and counted using a Bio-Rad TC20 automated cell counter. Cas9 (IDT, cat. no. 1081059) and sgRNAs were combined and incubated at room temperature for 20 min. Two sgRNAs were used per target to increase KO efficiency. Electroporation was performed using the 4D-Nucleofector 4 Core Unit (Lonza) and P4 primary cell 4D-Nucleofector kit S (Lonza, cat. no. V4XP-4032) with program CM137. Following electroporation, cells were kept at room temperature for 10 min, after which 200 μl prewarmed cTCM was added to each well. Cells were then rested at 37 °C and 5% CO_2_ for 2 h, counted, resuspended in cTCM with 100 U ml^−1^ rhIL-2 at 0.5 × 10^6^ cells ml^−1^ in 24-well plates, and returned to the incubator. Two days postelectroporation, CRISPR-edited P14 or P14 KLF2–EGFP CD8^+^ T cells were collected for flow cytometry analysis or 5,000 cells from each condition were adoptively transferred into C57BL/6 WT recipient mice by intravenous injection, followed by LCMV Cl13 infection on the same day.
CRISPR–Cas9 RNP KO of genomic region in activated P14 KLF2–EGFP CD8+ T cells
sgRNAs targeting the distal regulatory element of Klf2 (Klf2^+10.9kb/element^) were designed and purchased from IDT (sequences in Supplementary Table 2). CRISPR–RNP transfection was performed as described in the ‘CRISPR–Cas9 RNP KO in activated P14 CD8^+^ T cells and adoptive transferring’ section.
Targeting efficiency on genomic DNA was assessed by PCR amplification of 50 ng genomic DNA (extracted using New England Biolabs, cat. no. T3010L) using Platinum SuperFi II PCR Master Mix (Thermo Fisher, cat. no. 12369010). PCR products were purified (Qiagen, cat. no. 28506) and Sanger sequenced (Azenta). Editing efficiency was assessed using the Inference of CRISPR edits tool (Synthego).
Two days postelectroporation, cells were subjected to RNA extraction and quantitative PCR analysis, flow cytometry, or adoptive transfer into C57BL/6J WT recipient mice as described above.
Human CD8+ T cell activation and CRISPR–Cas9 RNP KO
Naive human CD8^+^ T cells were isolated from cryopreserved healthy donor PBMCs using EasySep immunomagnetic negative selection kits (STEMCELL, cat. no. 17953). Cells were resuspended in cTCM with 100 U ml^−1^ rhIL-2 at 1 × 10^6^ cells ml^−1^ and stimulated with Dynabeads Human T-Activator CD3/CD28 (Gibco, cat. no. 11131D). On day 3 poststimulation, cells were collected and counted. sgRNAs were designed and purchased from IDT (sequences in Supplementary Table 2).
Electroporation was performed using the 4D-Nucleofector 4 Core Unit and P2 primary cell 4D-Nucleofector kit S (Lonza, cat. no. V4XP-2024) with program DN100. Cells were rested at room temperature for 10 min, recovered with 200 μl prewarmed cTCM per well, incubated at 37 °C and 5% CO_2_ for 2 h, then counted and replated in cTCM with 100 U ml^−1^ rhIL-2 at 0.5 × 10^6^ cells ml^−1^. Two days postelectroporation, CRISPR-edited human CD8^+^ T cells were collected for flow cytometry.
Quantitative PCR
Total RNA was extracted using Direct-zol RNA Microprep Kits (Zymo Research, cat. no. R2062) according to the manufacturer’s instructions. For each sample, 1 μg total RNA was reverse transcribed using the iScript cDNA Synthesis Kit (Bio-Rad, cat. no. 1708891) in a 20 μl reaction. qPCR was performed using SsoAdvanced Universal SYBR Green Supermix (Bio-Rad, cat. no. 1725272) on a StepOne Real-Time PCR System (Applied Biosystems). Primers were purchased from IDT: Klf2 (forward: 5′-ACCAACTGCGGCAAGACCTA-3′; reverse: 5′-CATCCTTCCCAGTTGCAATGA-3′)^51^; β-actin (forward: 5′-AGCTGAGAGGGAAATCGTGC-3′; reverse: 5′-TCCAGGGAGGAAGAGGATGC-3′)^24^.
Dual reporter luciferase assay
A 1,913-bp genomic region spanning Klf2^+10.9kb/element^ was cloned into the pGL3 Promoter Vector (Addgene, cat. no. 212939) 15 bp upstream of the SV40 minimal promoter by GenScript to generate the pGL3-Klf2^+10.9kb/element^ vector.
Two days before electroporation, EL4 cells were seeded at 3 × 10^5^ cells ml^−1^ in T-75 flasks. On the day of electroporation, EL4 cells were collected and counted. CRISPR–RNPs were prepared with an NT sgRNA or the same pair of Zfp148-targeting sgRNAs as in mouse CD8^+^ T cells (sequences in Supplementary Table 2). Electroporation was performed using the 4D-Nucleofector system and SF Cell Line 4D-Nucleofector X Kit S (Lonza, cat. no. V4XC-2032) with program CM120. Electroporated EL4 cells were rested at room temperature for 10 min and recovered in prewarmed electroporation medium (RPMI-1640 with 10% FBS).
For luciferase transfection, 2 days after CRISPR–RNP electroporation, 4 × 10^5^ EL4_NT_ or EL4_ZFP148-null_ cells were transfected with 3 μg pGL3-Klf2^+10.9kb/element^ and 20 ng SV40-Renilla vectors using program CM120. After 24 h, cells were collected and luciferase activity was measured using the Dual-Luciferase Reporter Assay System (Promega, cat. no. E1910) on a SpectraMax iD3 reader in technical duplicates.
Incucyte cytotoxicity assay
B16-GP cells were seeded at 2 × 10^3^ cells per well in flat-bottom 96-well plates and incubated at 37 °C for 30 min in the presence of Incucyte Annexin V Green Reagent (Sartoris, cat. no. 4642). Following initial imaging on the IncuCyte S3 Live-Cell Analysis System, CD8^+^ T cells were added: (1) mixed CD44^hi^GP_33–41_ Tet^+^ and CD44^hi^GP_276–286_ Tet^+^CD8^+^ T cells sorted from spleens of Zfp148^fl/fl^ or ZFP148 cKO mice at day 22 post-LCMV Cl13 infection (E:T = 25:1), or (2) P14 CD8^+^ T cells CRISPR-edited with NT or Zfp148-targeting sgRNAs (E:T = 5:1). Images were acquired every 2 h and analyzed using IncuCyte S3 software.
Comparison of KLF2 and PDCD1 mRNA expression in ZNF148hi versus ZNF148lo patient cohorts
mRNA expression of PDCD1 and KLF2 was compared between ZNF148^hi^ and ZNF148^lo^ patients from the TCGA colorectal adenocarcinoma dataset. ZNF148^hi^ was defined as ZNF148 mRNA expression z scores greater than 2 (n = 51) relative to normal and ZNF148^lo^ as z scores less than −2 (n = 123) relative to normal.
Single-cell multiome library preparation
CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells were FACs-sorted from the spleens of Zfp148^fl/fl^ or ZFP148 cKO mice at day 21 post-LCMV C13 infection. After sorting, cells were washed with PBS containing 0.04% BSA, then approximately 10,000 nuclei of either Zfp148^fl/fl^ or ZFP148 cKO sample were isolated and processed with the Chromium Single Cell Multiome ATAC+ Gene Expression Reagent Kit (10x genomics, cat. no. 1000283) following the manufacturer’s manual. GEX and ATAC libraries were generated per manufacturer instructions, quality controlled by TapeStation and sequenced on an Illumina NovaSeq X Plus platform (Azenta Life Sciences).
Single-cell multiomic sequencing alignment and downstream analysis
scRNA-seq and scATAC-seq data were processed using the 10x Genomics Cell Ranger ARC pipeline (v.2.0.2) and aligned to the mm10 reference genome. Downstream analyses were performed in R (v.4.4.0) using Seurat (v.5.1.0) and Signac (v.1.14.0) with default parameters unless otherwise specified. For quality control, high-quality cells were defined as those with ATAC counts between 5 × 10^3^ and 7 × 10^5^, RNA counts between 1,000 and 25,000, and mitochondrial gene expression <20%. For the RNA modality, standard Seurat preprocessing was performed, including SCTransform normalization, principal component analysis (RunPCA), and dimensionality reduction using UMAP (RunUMAP). For the ATAC modality, preprocessing included term frequency–inverse document frequency normalization (RunTFIDF), feature selection (FindTopFeatures), singular value decomposition (RunSVD) and UMAP projection. To integrate modalities, we applied the weighted nearest neighbor (WNN) algorithm using the FindMultiModalNeighbors function in Seurat to construct a joint neighbor graph representing weighted contributions of RNA and ATAC modalities. WNN clusters were identified at a resolution of 0.1. Cell types were annotated based on mRNA expression of canonical marker genes and signature scores derived from previously published genesets calculated using AUCell (v.1.26.0). Gene activity scores were computed from chromatin accessibility data using the GeneActivity function in Signac. Differential gene expression analyses were performed on RNA or gene activity matrices using FindAllMarkers or FindMarkers with min.pct = 0.25, filtered at log_2_ fold change ≥ 0.25 and adjusted P < 0.05. DACRs were identified using FindAllMarkers or FindMarkers with logistic regression testing, min.pct = 0.05, log_2_ fold change ≥ 0.25 and adjusted P < 0.05, including total counts as a latent variable. Genes linked to DACRs were identified using the Links() function in Signac. GO enrichment analysis of DEGs was performed using clusterProfiler (v.4.12.6). Developmental trajectories and pseudotime relationships were inferred using Slingshot (v.2.12.0). Motif deviation (accessibility) analysis was conducted using chromVAR (v.1.26.0) to quantify variability in transcription factor motif accessibility across single cells. Differential transcription factor motif enrichment was calculated using FindAllMarkers or FindMarkers for pairwise comparisons with logistic regression testing, min.pct = 0.05, log_2_ fold change threshold = 2 and adjusted P < 0.05, with total counts included as a latent variable. All heatmaps were generated using ComplexHeatmap (v.2.20.0).
CUT&Tag-seq
Naive CD8^+^ T cells were isolated from spleens of C57BL/6 WT mice and activated with 5 μg ml^−1^ plate-bound aCD3 Ab and 2 μg ml^−1^ soluble aCD28 Ab for 24 h in 24-well plates. A total of 1 × 10^6^ cells were used per target (ZFP148 or IgG control) for library construction using CUT&Tag (Epicypher, cat. no. 14-1102-48s1). Libraries were pooled at equimolar ratios and sequenced on an Illumina NovaSeq X Plus platform (Azenta Life Sciences), generating 5–10 million reads per sample.
CUT&Tag-seq analysis
CUT&Tag sequencing data were processed using the nf-core/cutandrun pipeline (v.3.0.0) (https://nf-co.re/cutandrun/3.2.2/)—a community-curated Nextflow pipeline. Raw sequencing reads were first subjected to adapter trimming using fastp (v.0.23.2), followed by alignment to the mouse reference genome (mm10) using Bowtie2 (v.2.4.4). Aligned reads were filtered to remove low-quality mappings, PCR duplicates (using Picard MarkDuplicates v.2.27.5) and mitochondrial reads. Peaks were called using SEACR (v.1.3) in ‘relaxed’ mode with appropriate IgG or input control normalization. Genome-wide signal tracks were generated using deepTools (v.3.5.1) and IGV (v.2.18.4) for visualization. Quality control metrics, including fragment size distribution, duplication rates and library complexity, were assessed and summarized using MultiQC (v.1.13). All steps were run with default settings unless otherwise specified.
Secondary analysis of scRNA-seq datasets of human cancer patients
Pan-cancer scRNA-seq data assembly
An extensive compendium of single-cell transcriptomes was constructed by aggregating profiles from 346 tumor specimens, representing 251 patients, across 20 publicly available scRNA-seq datasets^52–71^. To reduce platform-specific biases and ensure consistency, only datasets generated with the 10x Genomics droplet-based system was included.
Quality assessment and preprocessing
Raw single-cell data were filtered stringently using Scanpy (v.1.9.5). Cells were retained only if they expressed at least 200 genes and exhibited mitochondrial gene fractions below 20% of total counts. Additional filters eliminated barcodes suggestive of debris (fewer than 400 genes or 500 unique molecular identifiers) and excluded potential doublets (cells with more than 5,500 genes or 30,000 unique molecular identifiers). After these quality control steps, the raw count matrices and corresponding AnnData objects were merged. Data normalization to transcripts per million was performed using sc.pp.normalize_total, followed by a logarithmic transformation with sc.pp.log1p. Only tumor-derived cells were retained, resulting in a final dataset comprising 1,030,968 high-quality cells and 14,090 genes for further analyses.
Batch correction and integration
To reconcile differences between studies while preserving genuine biological variation, batch effects were mitigated using the scVI Python package (scvi-tools v.1.0.4). By incorporating sample identity as a covariate, the scVI model effectively reduced technical variability across samples. The performance of the batch correction was evaluated by measuring the reduction of study-specific biases while retaining critical biological signals. Subsequent analyses—including clustering, differential expression and trajectory inference—were conducted on the integrated dataset. Cellular heterogeneity was visualized using UMAP, which delineated variations across batches, datasets, sex, tissue origin and cancer type.
Cell type identification
Cell populations were classified using the scANVI algorithm (scvi-tools v.1.0.4), which leverages pre-annotated reference data for main immune cell lineages such as epithelial, endothelial, fibroblast, lymphoid, myeloid and plasma cells as well as subsets of CD8^+^ TILs. Initial clustering within the scANVI latent space was refined with Leiden clustering to assign discrete cell identities. The model was trained for 20 epochs with cell-type labels transferred using 100 samples per label. For a more detailed resolution of T cell subpopulations, corresponding AnnData objects were further integrated and subjected to scVI-based batch correction.
Functional signature score analysis
Functional states across individual cells were quantified by computing gene signature scores using the scanpy.tl.score_genes function (Scanpy v.1.9.5). This approach enabled the assessment of cellular functional signatures within the dataset.
Validation and prognostic evaluation of the ZFP148 KO gene signature in CD8+ TILs
The clinical relevance of the ZFP148 KO gene signature in CD8^+^ T cells was examined using processed scRNA-seq data from 116 liver cancer samples obtained from 94 male patients^34^. Analysis was confined to primary tumors and metastatic lesions. After applying the same rigorous quality control, batch correction, and cell-type annotation pipeline, CD8^+^ T cells were isolated and ZFP148 KO signature score was computed using scanpy.tl.score_genes.
Survival analysis using expression of ZFP148 KO gene signature in CD8+ TILs
To determine the prognostic impact of ZFP148 KO gene signature expression in CD8^+^ TILs, Kaplan–Meier survival curves were generated and differences assessed via the log-rank test and univariate Cox proportional hazards (Cox PH) models. Two additional multivariable Cox PH models were also employed to adjust for potential confounders, with hazard ratios and 95% confidence intervals reported accordingly. The optimal cutoff for stratifying ZFP148 KO gene signature expression levels was established using the surv_cutpoint function from the survminer R package, which applies maximally selected rank statistics from the maxstat^72^ package. Continuous covariates in the Cox PH models were examined for linearity to validate the model assumptions.
Expression of ZNF148 mRNA, ZFP148 KO gene signature, KLF2 mRNA and KLF2 gene signature in immunotherapy-treated cohorts
Expression of ZNF148 mRNA, ZFP148 KO gene signature, KLF2 mRNA and KLF2 gene signature was investigated further in independent scRNA-seq datasets derived from patients undergoing various immunotherapeutic regimens. These cohorts included patients receiving anti-CD19 CAR-T cells for DLBCL^38^ and aPD-1 + aCTLA-4 Abs for ccRCC^37^. For each dataset, the identical preprocessing pipeline—comprising quality filtering, batch correction and cell-type annotation—was applied.
OS analysis of human cancer patients using published datasets
OS analyses of human cancer patients were performed using webservers that have access to published datasets. Treatment-naive gastric cancer, colon cancer, ovarian cancer, pancreatic cancer, liver hepatocellular carcinoma, pancreatic ductal adenocarcinoma, sarcoma, thyroid carcinoma and uterine corpus endometrial carcinoma patients were stratified into ZNF148^hi^ and ZNF148^lo^ groups using the expression-based ‘best cutoff’ option of the Kaplan–Meier Plotter webserver^33^, and OS was visualized using Kaplan–Meier curves. Melanoma patients treated with aPD-1 or aCTLA-4 Ab were stratified into high- and low- expression groups based on pretreatment ZNF148 or KLF2 mRNA levels using the expression-based ‘best cutoff’ option of the Kaplan–Meier Plotter webserver^35^ or expression of the ZFP148 KO gene signature or KLF2 gene signature^30^ using the Tumor Immune Dysfunction and Exclusion (TIDE) webserver^36^; OS was visualized by Kaplan–Meier analysis.
Statistics and reproducibility
Statistical analyses for flow cytometry, tumor growth curves and mouse survival were performed using GraphPad Prism (v.10). Unpaired or paired two-sided t-tests were used for comparisons between two unpaired or paired groups, respectively. One-way analysis of variance (ANOVA) followed by Tukey’s multiple comparisons test was used for comparisons among three or more groups. One-way ANOVA followed by Holm–Šídák’s multiple comparisons test was used for comparisons between preselected pairs among three or more groups. Two-way ANOVA was used to compare time-course curves, with Bonferroni correction for multiple comparisons. The log-rank test was used to compare OS of mice across several treatment groups, with Bonferroni correction for multiple comparisons.
Analyses of mouse scRNA-seq and scATAC-seq data were performed using R (v.4.4.0) with the packages Seurat (v.5.1.0), Signac (v.1.14.0), AUCell (v.1.26.0), slingshot (v.2.12.0), chromVAR (v.1.26.0), clusterProfiler (v.4.12.6), ComplexHeatmap (v.2.20.0) and EnhancedVolcano (v.1.22.0). Differentially expressed genes (DEGs) were identified using the FindMarkers() or FindAllMarkers() functions in Seurat, with statistical significance assessed by a two-sided Wilcoxon rank-sum test, with Benjamini–Hochberg correction for multiple comparisons. DACRs and differential transcription factor motif accessibility were identified using FindMarkers() or FindAllMarkers() with statistical significance assessed by a two-sided logistic regression likelihood-ratio test, with Benjamini–Hochberg correction for multiple comparisons. GO enrichment analysis was performed using a one-sided hypergeometric test, with Benjamini–Hochberg correction for multiple comparisons. A two-sided Wilcoxon rank-sum test was used to compare mRNA expression, promoter chromatin accessibility, motif accessibility and gene signature scores between Zfp148^fl/fl^ and ZFP148 cKO CD8^+^ T cells. Analyses of the integrated human scRNA-seq dataset were performed using Python (v.3.10.9) packages Scanpy (v.1.9.5), Pandas (v.2.0.0), Statsmodels (v.0.14.0), NumPy (v.1.24.2), SciPy (v.1.10.1), Matplotlib (v.3.8.0), Seaborn (v.0.11.2) and scikit-learn (v.1.3.2), as well as R (v.4.3.1) packages Circlize (v.0.4.16), GseaVis (v.0.0.5), Enrichplot (v.1.22.0), GridExtra (v.2.3.0), pheatmap (v.1.0.12) and DEGreport (v.1.38.5). A two-sided Wilcoxon rank-sum test was used for comparisons between two groups. OS between two groups of patients was compared using the log-rank test. A P value ≤ 0.05 (or adjusted P value ≤ 0.05 after multiple-testing correction) was considered statistically significant.
No statistical methods were used to predetermine sample size, but sample sizes were similar to those reported in previous publications^24,73^. Data distribution was assumed to be normal but was not formally tested. Age- and sex-matched animals were assigned randomly to experimental conditions. Investigators were not blinded to group allocation during data collection and analysis. No data were excluded. All data were generated from at least two independent experiments with a minimum of three biological replicates, yielding consistent phenotypes to ensure reproducibility, with the exceptions of the paired scRNA-seq and scATAC-seq using CD44^hi^GP_33–41_ Tet^+^CD8^+^ T cells in spleens of Zfp148^fl/fl^ and ZFP148 cKO mice at day 21 post-LCMV Cl13 infection and the CUT&Tag-seq using activated splenic CD8^+^ T cells from C57BL/6 WT mice. To minimize intermouse variability and enhance reproducibility, cells used for the single-cell multiome experiment were pooled from 10 mice for Zfp148^fl/fl^ and 12 mice for ZFP148 cKO; cells used for the CUT&Tag-seq experiment were pooled from 5 mice.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Online content
Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41590-026-02461-2.
Supplementary information
Supplementary InformationSupplementary Fig. 1 gating strategy. Reporting Summary Peer Review File Supplementary Table 1OS of cancer patients stratified by ZNF148 mRNA expression (analyzed by the Kaplan–Meier plotter webserver). Supplementary Table 2sgRNA sequences. Supplementary Table 3. Antibody information for flow cytometry, CUT&Tag-seq and cell culture.
Source data
Source Data Fig. 1. Statistical source data. Source Data Fig. 2. Statistical source data. Source Data Fig. 5. Statistical source data. Source Data Fig. 6. Statistical source data. Source Data Extended Data Fig. 1. Statistical source data. Source Data Extended Data Fig. 2. Statistical source data. Source Data Extended Data Fig. 3. Statistical source data. Source Data Extended Data Fig. 4. Statistical source data. Source Data Extended Data Fig. 5. Statistical source data. Source Data Extended Data Fig. 8. Statistical source data. Source Data Extended Data Fig. 9. Statistical source data. Source Data Extended Data Fig. 10. Statistical source data.