Abstract
Adoptive cell therapy represents a new paradigm in cancer immunotherapy, but it can be limited by the poor persistence and function of transferred T cells1. Here we use an in vivo pooled CRISPR–Cas9 mutagenesis screening approach to demonstrate that, by targeting REGNASE-1, CD8+ T cells are reprogrammed to long-lived effector cells with extensive accumulation, better persistence and robust effector function in tumours. REGNASE-1-deficient CD8+ T cells show markedly improved therapeutic efficacy against mouse models of melanoma and leukaemia. By using a secondary genome-scale CRISPR–Cas9 screening, we identify BATF as the key target of REGNASE-1 and as a rheostat that shapes antitumour responses. Loss of BATF suppresses the increased accumulation and mitochondrial fitness of REGNASE-1-deficient CD8+ T cells. By contrast, the targeting of additional signalling factors—including PTPN2 and SOCS1—improves the therapeutic efficacy of REGNASE-1-deficient CD8+ T cells. Our findings suggest that T cell persistence and effector function can be coordinated in tumour immunity and point to avenues for improving the efficacy of adoptive cell therapy for cancer.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
Microarray, RNA-seq, ATAC-seq and scRNA-seq data have been deposited in the NCBI Gene Expression Omnibus (GEO) database and are accessible through the GEO SuperSeries accession number GSE126072. Source Data for Figs. 1–5 and Extended Data Figs. 1–9 are provided with the paper. All other relevant data are available from the corresponding author upon reasonable request.
References
Lim, W. A. & June, C. H. The principles of engineering immune cells to treat cancer. Cell 168, 724–740 (2017).
Gattinoni, L. et al. Acquisition of full effector function in vitro paradoxically impairs the in vivo antitumor efficacy of adoptively transferred CD8+ T cells. J. Clin. Invest. 115, 1616–1626 (2005).
Kishton, R. J., Sukumar, M. & Restifo, N. P. Metabolic regulation of T cell longevity and function in tumor immunotherapy. Cell Metab. 26, 94–109 (2017).
Muri, J. et al. The thioredoxin-1 system is essential for fueling DNA synthesis during T-cell metabolic reprogramming and proliferation. Nat. Commun. 9, 1851 (2018).
Peng, M. et al. Aerobic glycolysis promotes T helper 1 cell differentiation through an epigenetic mechanism. Science 354, 481–484 (2016).
Vanoaica, L. et al. Conditional deletion of ferritin H in mice reduces B and T lymphocyte populations. PLoS ONE 9, e89270 (2014).
Ouyang, W., Beckett, O., Flavell, R. A. & Li, M. O. An essential role of the Forkhead-box transcription factor Foxo1 in control of T cell homeostasis and tolerance. Immunity 30, 358–371 (2009).
Matsushita, K. et al. Zc3h12a is an RNase essential for controlling immune responses by regulating mRNA decay. Nature 458, 1185–1190 (2009).
Uehata, T. et al. Malt1-induced cleavage of regnase-1 in CD4+ helper T cells regulates immune activation. Cell 153, 1036–1049 (2013).
Churchman, M. L. et al. Synergism of FAK and tyrosine kinase inhibition in Ph+ B-ALL. JCI Insight 1, 86082 (2016).
Singer, M. et al. A distinct gene module for dysfunction uncoupled from activation in tumor-infiltrating T cells. Cell 166, 1500–1511 (2016).
Im, S. J. et al. Defining CD8+ T cells that provide the proliferative burst after PD-1 therapy. Nature 537, 417–421 (2016).
Leong, Y. A. et al. CXCR5+ follicular cytotoxic T cells control viral infection in B cell follicles. Nat. Immunol. 17, 1187–1196 (2016).
Zhou, X. et al. Differentiation and persistence of memory CD8+ T cells depend on T cell factor 1. Immunity 33, 229–240 (2010).
Hurton, L. V. et al. Tethered IL-15 augments antitumor activity and promotes a stem-cell memory subset in tumor-specific T cells. Proc. Natl Acad. Sci. USA 113, E7788–E7797 (2016).
Roychoudhuri, R. et al. BACH2 regulates CD8+ T cell differentiation by controlling access of AP-1 factors to enhancers. Nat. Immunol. 17, 851–860 (2016).
Ichii, H., Sakamoto, A., Kuroda, Y. & Tokuhisa, T. Bcl6 acts as an amplifier for the generation and proliferative capacity of central memory CD8+ T cells. J. Immunol. 173, 883–891 (2004).
Man, K. et al. Transcription factor IRF4 promotes CD8+ T cell exhaustion and limits the development of memory-like T cells during chronic infection. Immunity 47, 1129–1141 (2017).
Sade-Feldman, M. et al. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175, 998–1013 (2018).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Khan, O. et al. TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion. Nature 571, 211–218 (2019).
Miller, B. C. et al. Subsets of exhausted CD8+ T cells differentially mediate tumor control and respond to checkpoint blockade. Nat. Immunol. 20, 326–336 (2019).
Utzschneider, D. T. et al. T cell factor 1-expressing memory-like CD8+ T cells sustain the immune response to chronic viral infections. Immunity 45, 415–427 (2016).
Ciofani, M. et al. A validated regulatory network for Th17 cell specification. Cell 151, 289–303 (2012).
Kurachi, M. et al. The transcription factor BATF operates as an essential differentiation checkpoint in early effector CD8+ T cells. Nat. Immunol. 15, 373–383 (2014).
Yang, C. Y. et al. The transcriptional regulators Id2 and Id3 control the formation of distinct memory CD8+ T cell subsets. Nat. Immunol. 12, 1221–1229 (2011).
Doench, J. G. et al. Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR–Cas9. Nat. Biotechnol. 34, 184–191 (2016).
Sinclair, L. V. et al. Control of amino-acid transport by antigen receptors coordinates the metabolic reprogramming essential for T cell differentiation. Nat. Immunol. 14, 500–508 (2013).
Atherly, L. O., Brehm, M. A., Welsh, R. M. & Berg, L. J. Tec kinases Itk and Rlk are required for CD8+ T cell responses to virus infection independent of their role in CD4+ T cell help. J. Immunol. 176, 1571–1581 (2006).
Blagih, J. et al. The energy sensor AMPK regulates T cell metabolic adaptation and effector responses in vivo. Immunity 42, 41–54 (2015).
D’Souza, W. N., Chang, C. F., Fischer, A. M., Li, M. & Hedrick, S. M. The Erk2 MAPK regulates CD8 T cell proliferation and survival. J. Immunol. 181, 7617–7629 (2008).
Sullivan, B. M., Juedes, A., Szabo, S. J., von Herrath, M. & Glimcher, L. H. Antigen-driven effector CD8 T cell function regulated by T-bet. Proc. Natl Acad. Sci. USA 100, 15818–15823 (2003).
Geiger, R. et al. l-Arginine modulates T cell metabolism and enhances survival and anti-tumor activity. Cell 167, 829–842 (2016).
Kawalekar, O. U. et al. Distinct signaling of coreceptors regulates specific metabolism pathways and impacts memory development in CAR T cells. Immunity 44, 380–390 (2016).
Sharma, P. & Allison, J. P. The future of immune checkpoint therapy. Science 348, 56–61 (2015).
Shifrut, E. et al. Genome-wide CRISPR screens in primary human T cells reveal key regulators of immune function. Cell 175, 1958–1971 (2018).
Manguso, R. T. et al. In vivo CRISPR screening identifies Ptpn2 as a cancer immunotherapy target. Nature 547, 413–418 (2017).
Quigley, M. et al. Transcriptional analysis of HIV-specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nat. Med. 16, 1147–1151 (2010).
Platt, R. J. et al. CRISPR–Cas9 knockin mice for genome editing and cancer modeling. Cell 159, 440–455 (2014).
Hogquist, K. A. et al. T cell receptor antagonist peptides induce positive selection. Cell 76, 17–27 (1994).
Overwijk, W. W. et al. Tumor regression and autoimmunity after reversal of a functionally tolerant state of self-reactive CD8+ T cells. J. Exp. Med. 198, 569–580 (2003).
Chen, R. et al. In vivo RNA interference screens identify regulators of antiviral CD4+ and CD8+ T cell differentiation. Immunity 41, 325–338 (2014).
Birsoy, K. et al. An essential role of the mitochondrial electron transport chain in cell proliferation is to enable aspartate synthesis. Cell 162, 540–551 (2015).
Sanson, K. R. et al. Optimized libraries for CRISPR–Cas9 genetic screens with multiple modalities. Nat. Commun. 9, 5416 (2018).
Sukumar, M. et al. Mitochondrial membrane potential identifies cells with enhanced stemness for cellular therapy. Cell Metab. 23, 63–76 (2016).
Wang, W. et al. Effector T cells abrogate stroma-mediated chemoresistance in ovarian cancer. Cell 165, 1092–1105 (2016).
Wei, J. et al. Autophagy enforces functional integrity of regulatory T cells by coupling environmental cues and metabolic homeostasis. Nat. Immunol. 17, 277–285 (2016).
Zeng, H. et al. mTORC1 couples immune signals and metabolic programming to establish Treg-cell function. Nature 499, 485–490 (2013).
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Karmaus, P. W. F. et al. Metabolic heterogeneity underlies reciprocal fates of TH17 cell stemness and plasticity. Nature 565, 101–105 (2019).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Robinson, J. T. et al. Integrative genomics viewer. Nat. Biotechnol. 29, 24–26 (2011).
Zhang, Y. et al. Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137 (2008).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
Calvo, S. E., Clauser, K. R. & Mootha, V. K. MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins. Nucleic Acids Res. 44, D1251–D1257 (2016).
Bailey, T. L. et al. MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res. 37, W202–W208 (2009).
Ramírez, F., Dündar, F., Diehl, S., Grüning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
Cuellar-Partida, G. et al. Epigenetic priors for identifying active transcription factor binding sites. Bioinformatics 28, 56–62 (2012).
Krishnamoorthy, V. et al. The IRF4 gene regulatory module functions as a read-write integrator to dynamically coordinate T helper cell fate. Immunity 47, 481–497 (2017).
Acknowledgements
The authors acknowledge M. Hendren for animal colony management, C. Li for help with plasmids, G. Neale and S. Olsen for assistance with sequencing and St. Jude Immunology FACS core facility for cell sorting. This work was supported by NIH AI105887, AI131703, AI140761, AI150241, AI150514, and CA221290 (to H.C.).
Author information
Authors and Affiliations
Contributions
J.W. conceived the project, designed and performed in vitro and in vivo experiments, analysed data and wrote the manuscript; L.L. performed molecular experiments and analysed data; W.Z. performed CAR T-cell-related experiments and analysed data, with guidance from T.L.G., who also provided CAR transgenic mice and human CD19-Ph+ B-ALL cell line; Y.D. performed bioinformatic analyses; S.A.L. helped to perform cellular experiments; C.G. performed imaging experiments; Y.W. performed Seahorse experiments; Y.-D.W. and J.Y. analysed CRISPR–Cas9 screening data; C.Q. performed scRNA-seq data analyses, with guidance from J.Y.; B.X. helped with ATAC-seq analysis; A.K. helped with molecular cloning; J.S. helped with ATAC-seq sample preparation; H.H. helped to perform scRNA-seq experiments; J.G.D. designed and generated the lentiviral sgRNA metabolic library and provided guidance for CRISPR–Cas9 screening data analyses; and H.C. helped to conceive and design experiments, co-wrote the manuscript and provided overall direction.
Corresponding author
Ethics declarations
Competing interests
H.C. and J.W. are authors of a patent application related to REGNASE-1 and BATF.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
Extended Data Fig. 1 Validation of the effect of REGNASE-1 deletion on CD8+ T cell accumulation in tumour immunity using the in vivo dual transfer system.
a, Diagram of the in vivo dual transfer system. OT-I cells transduced with sgRNA viral vectors expressing distinct fluorescent proteins were mixed and transferred into the same tumour-bearing hosts, in which further analyses were performed. b, Gating strategy for sgRNA-transduced OT-I cell analysis. c, d, OT-I cells transduced with non-targeting control sgRNA (mCherry+) were mixed at a 1:1 ratio with cells either transduced with control sgRNA (ametrine+) (c (n = 2), d (n = 5), left, top) or two different sgRNAs targeting Regnase-1 (Regnase-1 sgRNA, ametrine+, c (n = 4), left, bottom; or Regnase-1 sgRNA no. 2, ametrine+, d (n = 5), left, bottom), and transferred into tumour-bearing hosts. Mice were analysed at 7 days after adoptive transfer for the proportion of OT-I cells in CD8α+ cells (c, d, left), and the quantification of relative OT-I cell percentages in CD8α+ cells (normalized to input) in the spleen and TILs (c, d, right). Numbers in plots indicate the frequencies of OT-I cells. e, OT-I cells transduced with control sgRNA (ametrine+) were mixed at a 1:1 ratio with cells transduced with Regnase-1 sgRNA (mCherry+) and transferred into tumour-bearing hosts (n = 5). Mice were analysed at 7 days after adoptive transfer for the proportion of OT-I cells in CD8α+ cells (left), and the quantification of relative OT-I cell percentage in CD8α+ cells (normalized to input) in the spleen and TILs (right). Numbers in plots indicate the frequencies of OT-I cells. f, Insertion and deletion (indel) mutations after CRISPR targeted disruption in OT-I cells transduced with either control sgRNA or Regnase-1 sgRNA, via deep sequencing analysis of indels generated at the exonic target site of the Regnase-1 gene, including 97.3% of indel events in Regnase-1-sgRNA-transduced cells isolated from tumours compared to 1.3% in control-sgRNA-transduced cells. Mean ± s.e.m. (c–e). ***P < 0.001. Two-tailed unpaired Student’s t-test (d, e). Data are representative of two independent experiments (e).
Extended Data Fig. 2 Tumour-infiltrating and peripheral REGNASE-1-null CD8+ T cells show distinct immune signatures.
a, b, GSEA enrichment plots of antigen-specific CXCR5+ and CXCR5− exhausted CD8+ T cells from chronic infection using gene targets repressed by REGNASE-1 (that is, the top 100 upregulated genes in TIL Regnase-1-sgRNA- compared to control-sgRNA-transduced OT-I cells, as identified using RNA-seq). c, Representative images (left) and quantification of MFI (right) of TCF-1 expression (pink) in control-sgRNA- (mCherry+; red) and Regnase-1-sgRNA-transduced OT-I cells (ametrine+; green) in the whole-tumour section (n = 4 mice). Scale bars, 20 μm. d, e, Gene-expression heat maps normalized by row (z-score) for the naive- or memory-T-cell-associated transcription factors (d) or effector- or exhausted-T-cell-associated transcription factors (e) in control-sgRNA- (n = 4) and Regnase-1-sgRNA (n = 5)-transduced OT-I cells isolated from TILs. Specifically, control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells were mixed and transferred into tumour-bearing mice, and tumour-infiltrating OT-I cells were isolated at day 7 for transcriptional profiling by RNA-seq. f, Real-time PCR analysis of Irf4 mRNA expression in control-sgRNA- (n = 4 samples) and Regnase-1-sgRNA (n = 5 samples)-transduced OT-I cells isolated from TILs. g, Summary of ATAC-seq motif enrichment data showing log2(odds ratio) and −log10(FDR) of cells from control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells isolated from TILs (n = 4 samples per group). Specifically, control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells were mixed and transferred into tumour-bearing mice, and tumour-infiltrating OT-I cells were isolated at day 7 for ATAC-seq analysis. h, Tn5 insert sites from ATAC-seq analysis were aligned to motifs for transcription factors from the TRANSFAC database, and the binding profiles of TCF-1, BACH2, BCL6 and IRF4 are shown. i, Venn diagram showing the overlap of significantly upregulated (left, Regnase-1-sgRNA- (n = 5 samples) versus control-sgRNA-transduced OT-I cells (n = 4 samples)) or downregulated genes (right, Regnase-1-sgRNA- versus control-sgRNA-transduced OT-I cells) by RNA-seq profiling between TIL and PLN OT-I cells. Specifically, control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells were mixed and transferred into tumour-bearing mice, and OT-I cells were isolated at day 7 for transcriptional profiling by RNA-seq. j, GSEA enrichment plots of PLN Regnase-1-sgRNA- (n = 5) versus control-sgRNA (n = 4)-transduced OT-I cells using gene sets of four different tumour-infiltrating CD8 T cell activation states11. Specifically, control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells were mixed and transferred into tumour-bearing mice, and PLN OT-I cells were isolated at day 7 for transcriptional profiling by RNA-seq. k, OT-I cells transduced with control sgRNA (mCherry+) and Regnase-1 sgRNA (ametrine+) were mixed and transferred into tumour-bearing mice (n = 5 mice), and OT-I cells in the spleen were analysed at day 7 for expression of TCF-1 (top), and quantification of the frequency of TCF-1+ cells (bottom). Numbers in graphs indicate the frequencies of cells in gates. Mean ± s.e.m. (c, f, k). *P < 0.05, **P < 0.01, ***P < 0.001. Kolmogorov–Smirnov test followed by Benjamini–Hochberg correction (a, b, j), two-tailed unpaired Student’s t-test (c, f, k), two-sided Fisher’s exact test followed by Benjamini–Hochberg correction (g) or two-sided Fisher’s exact test (i). Data are representative of two independent experiments (c, f, k).
Extended Data Fig. 3 Upstream signals regulate REGNASE-1 expression and REGNASE-1-null cell phenotypes.
a, Immunoblot analysis of REGNASE-1 expression in control-sgRNA-transduced OT-I cells isolated from PLN and TILs at 7 days after adoptive transfer (n = 4 samples per group) (top). Quantification of the relative intensity of REGNASE-1 expression (bottom). β-Actin is used as a loading control. b, GSEA enrichment plots of PLN and TIL control-sgRNA-transduced OT-I cells (n = 4) used in a, by using gene targets repressed by REGNASE-1 (that is, the top 100 upregulated genes in TIL Regnase-1-sgRNA- compared to control-sgRNA-transduced cells, as identified using RNA-seq). c, OT-I cells were stimulated with anti-CD3 and anti-CD28 overnight before viral transduction, and then cultured in IL-7- and IL-15-containing medium for another 3 days in vitro. Pre-activated OT-I cells were then stimulated with anti-CD3, IL-2 or IL-21 for 0, 1 and 4 h (n = 5 samples per group) for immunoblot analysis of full-length and cleaved REGNASE-1 (top), and quantification of the relative intensity of cleaved REGNASE-1 expression (bottom). β-Actin is used as a loading control. d, e, OT-I cells transduced with control sgRNA (mCherry+) and Regnase-1 sgRNA (ametrine+) were mixed at a 1:1 ratio and transferred into mice bearing B16 Ova (n = 6 mice) or B16 F10 (n = 6 mice) tumours. Mice were analysed at day 7 after adoptive transfer for quantification of relative OT-I cell percentage in total cells (normalized to input) in the TILs (d) and expression of TCF-1 (e, left), and quantification of the frequency of TCF-1+ cells (e, right) in tumour-infiltrating OT-I cells. f, g, OT-I cells were stimulated with anti-CD3 and anti-CD28 overnight before viral transduction, and then cultured in IL-2-, IL-7- and IL-15-containing medium for another 3 days in vitro. Pre-activated OT-I cells were then continuously cultured in normoxia (21% O2) or hypoxia (1% O2) conditions for 48 h for immunoblot analysis of expression of HIF1α, REGNASE-1 and BATF (f), and for flow cytometry analysis of the expression of BATF, CD69, GZMB, CD25 and TCF-1 (g). Numbers in graphs indicate MFI (g). β-Actin is used as a loading control. Mean ± s.e.m. (a, c–e). *P < 0.05, **P < 0.01, ***P < 0.001. Two-tailed unpaired Student’s t-test (a), Kolmogorov–Smirnov test followed by Benjamini–Hochberg correction (b) or one-way ANOVA (c–e). Data are representative of two (c, f, g) independent experiments, or pooled from two (d, e) independent experiments.
Extended Data Fig. 4 Proliferation and survival analyses of REGNASE-1-null CD8+ T cells in tumour immunity.
a, List of the top-10 significantly (FDR < 0.05) upregulated and downregulated pathways in TIL Regnase-1-sgRNA-transduced OT-I cells, as revealed by performing GSEA using Hallmark gene sets. Specifically, control-sgRNA- (n = 4) and Regnase-1-sgRNA (n = 5)-transduced OT-I cells were mixed and transferred into tumour-bearing mice, and tumour-infiltrating OT-I cells were isolated at day 7 for transcriptional profiling by RNA-seq. b, GSEA enrichment plots of TIL Regnase-1-sgRNA-transduced OT-I cells using cell-cycling-associated gene sets, including E2F targets (top), G2M checkpoint (middle) and mitotic spindle (bottom). c–g, OT-I cells transduced with control sgRNA (mCherry+) and Regnase-1 sgRNA (ametrine+) were mixed and transferred into tumour-bearing mice, and tumour-infiltrating OT-I cells were analysed at day 7 (d–g) (n = 6 mice) and day 14 (c) (n = 5 mice) by flow cytometry for Ki-67 expression (c, left; e, left), BrdU incorporation (d, top; pulse for 18 h), active caspase-3 expression (f, left), Ser139 phosphorylation of histone variant H2A.X (g, top), and quantification of MFI of Ki-67 (c, right; e, right), frequency of BrdU+ cells (d, bottom), frequency of active caspase-3+ cells (f, right) and the frequency of the Ser139-phosphorylated histone variant H2A.X+ cells (g, bottom). Numbers in graphs indicate the MFI of Ki-67 (c, left; e, left). Numbers in plots indicate the frequencies of BrdU+ cells (d, top), active caspase-3+ cells (f, left) and Ser139-phosphorylated histone variant H2A.X+ cells (g, top). h, List of the top-15 significantly (FDR < 0.05) upregulated and top-4 significantly downregulated pathways in PLN Regnase-1-sgRNA-transduced OT-I cells, as revealed by performing GSEA using Hallmark gene sets. Specifically, control-sgRNA- (n = 4) and Regnase-1-sgRNA (n = 5)-transduced OT-I cells were mixed and transferred into tumour-bearing mice, and PLN OT-I cells were isolated at day 7 for transcriptional profiling by RNA-seq. i, j, OT-I cells transduced with control sgRNA (mCherry+) and Regnase-1 sgRNA (ametrine+) were mixed and transferred into tumour-bearing mice, and OT-I cells in the spleen were analysed at day 7 (i, j) (n = 6 mice) by flow cytometry for BrdU incorporation (i, top; pulse for 18 h) and active caspase-3 expression (j, top), and quantification of frequencies of BrdU+ cells (i, bottom) and active caspase-3+ cells (j, bottom). Numbers in plots indicate the frequencies of BrdU+ cells (i, top) and active caspase-3+ cells (j, top). Mean ± s.e.m. (c–g, i, j). *P < 0.05, **P < 0.01, ***P < 0.001. Kolmogorov–Smirnov test followed by Benjamini–Hochberg correction (a, b, h) or two-tailed unpaired Student’s t-test (c–g, i, j). Data are representative of two (c) independent experiments, or pooled from two (d–g, i, j) independent experiments.
Extended Data Fig. 5 Effector molecule expression of tumour-infiltrating REGNASE-1-null CD8+ T cells.
a, b, OT-I cells transduced with control sgRNA (mCherry+) or Regnase-1 sgRNA (ametrine+) were mixed at a 5:1 ratio and transferred into tumour-bearing mice (n = 5 mice), and tumour-infiltrating OT-I cells were analysed at day 7 for the expression of CD69, CD25, CD49a, KLRG1, ICOS, LAG3, PD-1 and CTLA4 (a, top) and CD44 and CD62L (b, top), and quantification of MFI of CD69, CD25, CD49a, KLRG1, ICOS, LAG3, PD-1 and CTLA4 (a, bottom) and frequency of CD44+CD62L− cells (b, bottom). The numbers in graphs indicate the MFI (a, top). The numbers in plots indicate the frequency of CD44+CD62L− cells (b, top). c–f, OT-I cells transduced with control sgRNA (mCherry+) or Regnase-1 sgRNA (ametrine+) were mixed at a 5:1 ratio and transferred into tumour-bearing mice, and analysed at day 7 (n = 10 mice) or day 14 (n = 10 mice). Flow cytometry analysis of expression of IFNγ (c, top), GZMB (c, bottom), TNF (e, top) and IL-2 (e, bottom) in TIL OT-I cells, and quantification of the numbers of IFNγ+ cells (d, top), GZMB+ cells (d, bottom), TNF+ cells (f, left) and IL-2+ cells (f, right) per gram of tumour (normalized to input). The numbers adjacent to outlined areas indicate the frequencies of IFNγ+ cells and the MFI of IFNγ in IFNγ+ cells (c, top), and the frequency of GZMB+ cells and the MFI of GZMB in GZMB+ cells (c, bottom), and the frequencies of TNF+ cells (e, top) or IL-2+ cells (e, bottom). Mean ± s.e.m. (a, b, d, f). *P < 0.05, **P < 0.01, ***P < 0.001. Two-tailed unpaired Student’s t-test (a, b) or two-tailed paired Student’s t-test (d, f). Data are representative of two (a–c, e) independent experiments, or pooled from two (d, f) independent experiments.
Extended Data Fig. 6 scRNA-seq and flow cytometry analyses of tumour-infiltrating REGNASE-1-null OT-I cells.
a–e, scRNA-seq analysis of control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells isolated from TILs. Specifically, control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells were mixed and transferred into tumour-bearing mice, and tumour-infiltrating OT-I cells were isolated at day 7 for transcriptional profiling by scRNA-seq. t-SNE visualization of Pdcd1 (a, top), Havcr2 (a, bottom), Ifng (c, top), Gzmb (c, bottom), Batf (d) and Id2 (e) gene expression, and ‘CXCR5+ exhausted CD8 (Ahmed)12’ (b, top) and ‘CXCR5+ exhausted CD8 (Yu)13’ (b, bottom) gene signatures in individual cells. f, OT-I cells transduced with control sgRNA and Regnase-1 sgRNA were mixed and transferred into tumour-bearing mice (n = 5 mice; data from 1 representative mouse are shown), and tumour-infiltrating OT-I cells were analysed at day 7 for the expression of TOX, SLAMF6, CD127, KLRG1, TIM3 and PD-1 in TCF-1+ and TCF-1− cells of control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells. Numbers in graphs indicate the mean ± s.e.m. of MFI of markers on the x-axis after gating on TCF-1+ or TCF-1− subsets. Data are representative of two independent experiments (f).
Extended Data Fig. 7 Genome-scale CRISPR screening identifies BATF as an important REGNASE-1 functional target in tumour immunity.
a, Scatter plot of the enrichment of each gene versus its adjusted P value in genome-scale CRISPR screening. Gene enrichment was calculated by averaging the enrichment of the corresponding sgRNAs (n = 4 for each gene) in tumour-infiltrating OT-I cells relative to input (log2(TIL/input ratio)), with the most extensively enriched (red) and selectively depleted (blue) genes (adjusted P < 0.05), as well as dummy genes (green, generated by random combinations of 4 out of 1,000 non-targeting control sgRNAs per dummy gene). b, Venn diagram showing the overlap of genes between top-depleted genes in genome-scale CRISPR screening (by less than −3.5 log2(TIL/input ratio); adjusted P < 0.05) and top-upregulated genes in TIL Regnase-1-sgRNA- versus control-sgRNA-transduced OT-I cells as identified by RNA-seq (by greater than 1.5 fold change (log2-transformed ratio); adjusted P < 0.05). c, Tn5 insert sites from ATAC-seq analysis were aligned to motifs for transcription factors from the TRANSFAC database, and the binding profiles of BATF are shown. d, Enrichment of BATF-binding motifs in the genomic regions with upregulated accessibility in REGNASE-1-null cells. First, we analysed common regions in our REGNASE-1-null ATAC-seq data and published BATF ChIP-seq peaks (GSE5419126). Next, we scanned these common regions with TRANSFAC motifs for BATF, and numbers of motif matches and associated Fisher’s exact test P values and log2(odds ratios) are shown (a positive log2(odds ratio) value indicates that a motif is more likely to occur in REGNASE-1-null cells than in wild-type samples; ‘E−x’ denotes ‘ × 10−x’). e, Luciferase activity of HEK293T cells measured at 48 h after transfection with Il2 mRNA 3′ UTR (top) or Il4 mRNA 3′ UTR (bottom) luciferase reporter plasmid, together with control (mock), wild-type REGNASE-1- or REGNASE-1(D141N)-expressing plasmid (n = 3 samples per group). f, OT-I cells transduced with control sgRNA (mCherry+; spike) were mixed at a 1:1 ratio with cells transduced with control sgRNA (ametrine+), Regnase-1 sgRNA (ametrine+), Batf sgRNA (GFP+) or Batf and Regnase-1 sgRNAs (GFP+ and ametrine+), and transferred into tumour-bearing hosts individually (n = 4 mice per group). Mice were analysed at 5 days after adoptive transfer for quantification of relative MFI of BATF normalized to spike in the tumour-infiltrating OT-I cells (f). g, Immunoblot analysis of REGNASE-1 and BATF expression in in vitro cultured OT-I cells 3 days after transduction with control sgRNA or Batf and Regnase-1 sgRNAs. HSP90 is used as a loading control. h–k, The same transfer system as in f was used. Five days after adoptive transfer, mice were analysed for the quantification of relative OT-I cell percentage in CD8α+ cells normalized to spike in the spleen (h, left, n = 4) and TILs (h, right, n = 4). Tumour-infiltrating OT-I cells were analysed at day 5 (n = 4 mice per group) for the quantification of the relative frequency of active caspase-3+ cells normalized to spike (i), and the quantification of the relative frequency of TCF-1+ cells normalized to spike (j), or at day 7 (n = 6 mice per group) for quantification of the relative frequency of IFNγ+ cells normalized to spike (k). l, Four million pmel-1 cells transduced with Regnase-1 sgRNA (ametrine+) (n = 10 recipients) or Batf and Regnase-1 sgRNAs (GFP+ and ametrine+) (n = 10 recipients) were transferred into mice at day 12 after B16 F10 melanoma engraftment, followed by analysis of tumour size. Mean ± s.d. (e). Mean ± s.e.m. (f, h–k). *P < 0.05, **P < 0.01, ***P < 0.001. Two-tailed unpaired Student’s paired t-test followed by Bonferroni correction (a), two-sided Fisher’s exact test (d), one-way ANOVA (e, f, h–k) or two-way ANOVA (l). Data are representative of two (e) or three (g) independent experiments, or pooled from two (f, h–l) independent experiments.
Extended Data Fig. 8 BATF overexpression markedly enhances CD8+ T cell antitumour responses.
a, OT-I cells were stimulated with anti-CD3 and anti-CD28 overnight before viral transduction, and then cultured in IL-7- and IL-15-containing medium for another 3 days in vitro. Control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells were then stimulated with anti-CD3, IL-2 or IL-21 overnight for flow cytometry analysis of BATF expression (top), and quantification of the MFI of BATF (bottom) (n = 6 samples per group). Numbers in graphs indicate the MFI (top) and fold change between comparisons (bottom). b–h, OT-I cells transduced with control retrovirus (RV; mCherry+) were mixed at a 1:1 ratio with cells transduced with Batf-overexpressing retrovirus (GFP+), and transferred into tumour-bearing hosts. Mice were analysed at day 4 (e) (n = 4 mice), day 5 (b, h) (n = 4 mice), day 7 (c, d, f, g) (n = 6–8 mice) or day 14 (c, d) (n = 6 mice) for the expression of BATF (b, left), active caspase-3 (f, left), IFNγ, GZMB, TNF and IL-2 (g, left) and TCF-1 (h, top) in TIL OT-I cells; the quantification of the MFI of BATF in TIL OT-I cells (b, right); quantification of the frequencies of active caspase-3+ cells (f, right), IFNγ+, GZMB+, TNF+ and IL-2+ cells (g, right) and TCF-1+ cells (h, bottom) in TIL OT-I cells; analysis of the proportion of donor-derived OT-I cells in total CD8α+ cells in TILs and spleen (c); the quantification of the relative OT-I cell percentage in CD8α+ cells in the spleen (normalized to input) (d); the dilution of CellTrace Violet (CTV) in TIL OT-I cells (e, left); and the quantification of the MFI of CTV in TIL OT-I cells (e, right). The numbers in graphs indicate the MFI (b, left; e, left), the frequencies of OT-I cells in gates (c), the frequency of active caspase-3+ cells (f, left), the frequencies of IFNγ+, GZMB+, TNF+ or IL-2+ cells (g, left), and the frequency of TCF-1+ cells (h, top). Mean ± s.e.m. (a, b, d–h). *P < 0.05, **P < 0.01, ***P < 0.001. Two-tailed unpaired Student’s t-test (a, b, d–h). Data are representative of two (a, c) independent experiments, or pooled from two (b, d–h) independent experiments.
Extended Data Fig. 9 Genome-scale CRISPR screening identifies mitochondrial metabolism as an important downstream pathway of REGNASE-1 and BATF.
a, Chromatin accessibility heat maps normalized by row (z-score) for 7,480 genes with significantly increased chromatin accessibility (by |fold change (log2-transformed ratio)| > 0.5; P < 0.05) in Regnase-1-sgRNA-transduced OT-I cells as compared to control-sgRNA-transduced cells. Specifically, OT-I cells transduced with control sgRNA (mCherry+) (n = 4), Regnase-1 sgRNA (ametrine+) (n = 4), Batf sgRNA (GFP+) (n = 2) or Batf and Regnase-1 sgRNAs (GFP+ and ametrine+) (n = 4) were transferred into tumour-bearing hosts individually. OT-I cells were isolated from TILs at day 7 for ATAC-seq analysis. We annotated the differential accessibility regions in ATAC-seq for the nearest genes, and identified 7,480 genes with significantly increased chromatin accessibility in REGNASE-1-null cells as compared to wild-type cells. BATF co-deletion reversed the upregulated chromatin accessibility for a large proportion of these genes (5,052 in total). Also, 2,527 of these 5,052 genes showed significantly downregulated chromatin accessibility in BATF-null cells as compared to wild-type cells. b, Functional enrichment plots of the top-10 significantly (FDR < 0.05) enriched pathways in top-ranking depleted genes (n = 4 sgRNAs for each gene) identified in the genome-scale CRISPR screening (by less than −3.5 log2(TIL/input ratio); adjusted P < 0.05). c, GSEA enrichment plots of TIL Regnase-1-sgRNA-transduced OT-I cells using the OXPHOS Hallmark gene set. Specifically, control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells were mixed and transferred into tumour-bearing mice, and tumour-infiltrating OT-I cells were isolated at day 7 for transcriptional profiling by RNA-seq. d, Representative images (top) and quantification of mitochondrial volume (stained with TOM20, white) per cell (bottom) in control-sgRNA- (mCherry+; red) and Regnase-1-sgRNA-transduced OT-I cells (ametrine+; green) in tumours at 7 days after adoptive transfer (n = 4 mice). e, Oxygen consumption rate (OCR) bioenergetic profiling of control-sgRNA- and Regnase-1-sgRNA-transduced OT-I cells cultured in vitro for basal (left) and maximal (right) OCR (n = 9 samples per group). f, List of the top-2 significantly (FDR < 0.05) upregulated and top-8 significantly downregulated pathways in TIL Batf-and-Regnase-1-sgRNAs- versus Regnase-1-sgRNA-transduced OT-I cells (n = 3 samples per group) isolated from TILs, as revealed by performing GSEA using the Hallmark gene sets. Specifically, Regnase-1-sgRNA- and Batf-and-Regnase-1-sgRNA-transduced OT-I cells were mixed and transferred into tumour-bearing mice, and tumour-infiltrating OT-I cells were isolated at day 7 for transcriptional profiling by microarray. g, GSEA enrichment plots of TIL Batf-and-Regnase-1-sgRNAs- versus Regnase-1-sgRNA-transduced OT-I cells (n = 3 samples per group) using the OXPHOS gene set. h, OT-I cells transduced with control sgRNA (mCherry+; spike) were mixed at a 1:1 ratio with cells transduced with control sgRNA (ametrine+), Regnase-1 sgRNA (ametrine+), Batf sgRNA (GFP+) or Batf and Regnase-1 sgRNA (GFP+ and ametrine+), and transferred into tumour-bearing hosts individually (n = 4 mice per group). Mice were analysed at 5 days after adoptive transfer for quantification of the relative MFI of TMRM (left) and Mitotracker (right), normalized to spike in tumour-infiltrating OT-I cells. i, Chromatin accessibility heat maps normalized by row (z-score) for mitochondrial genes with significantly increased chromatin accessibility (by |fold change (log2-transformed ratio)| > 0.5; P < 0.05) in Regnase-1-sgRNA-transduced OT-I cells compared to control-sgRNA-transduced cells, determined by ATAC-seq as described in a. We annotated the differential accessibility regions in ATAC-seq for the nearest genes, and superimposed these genes with 1,158 mitochondrial genes defined in the MitoCarta 2.0 database. A total of 341 mitochondrial genes showed significantly upregulated chromatin accessibility in the absence of REGNASE-1, 214 of which were blocked by BATF co-deletion in BATF-null REGNASE-1-null cells. Moreover, 96 of these 214 genes showed significantly downregulated chromatin accessibility in BATF-null cells as compared to wild-type cells. Mean ± s.e.m. (d, e, h). *P < 0.05, **P < 0.01, ***P < 0.001. Two-sided Fisher’s exact test (a, i), right-tailed Fisher’s exact test (b), Kolmogorov–Smirnov test followed by Benjamini–Hochberg correction (c, f, g), two-tailed unpaired Student’s t-test (d, e) or one-way ANOVA (h). Data are representative of two (d, e) independent experiments, or pooled from two (h) independent experiments.
Extended Data Fig. 10 Targeting PTPN2 and SOCS1 and model of REGNASE-1 functions in tumour-specific CD8+ T cells.
a, Immunoblot analysis of REGNASE-1, PTPN2 and SOCS1 expression in OT-I cells cultured in vitro, 3 days after transduction with control sgRNA, Ptpn2 and Regnase-1 sgRNAs (left) or Socs1 and Regnase-1 sgRNAs (right). HSP90 is used as a loading control. b, Immunoblot analysis of REGNASE-1, BATF, SOCS1 and PTPN2 expression in OT-I cells transduced with control sgRNA or Regnase-1 sgRNA, cultured in vitro for 3 days after viral transduction. β-Actin is used as a loading control. c, REGNASE-1 is a major negative regulator of CD8+ T cell antitumour responses, and TCR and IL-2 inhibit its expression and activity. Deletion of REGNASE-1 unleashes a potent therapeutic efficacy of engineered tumour-specific CD8+ T cells against cancers, by coordinating transcriptional and metabolic programs to achieve greatly improved cell accumulation and function. As a key functional target of REGNASE-1, excessive BATF drives robust cell accumulation and effector function—in part through enhancing mitochondrial metabolism—in REGNASE-1-null CD8+ T cells. REGNASE-1 deletion also reprograms cells to acquire increased gene signatures associated with naive or memory cells and to gain survival advantage, which contribute to the improved persistence of REGNASE-1-null effector CD8+ T cells. Targeting PTPN2 and SOCS1 (not depicted here) acts in coordination with REGNASE-1 inhibition to promote CD8+ T cell antitumour responses. Data are representative of three independent experiments (a, b).
Supplementary information
Supplementary Figure
Supplementary Figure 1: Source western blot images. This file contains uncropped western blot scans with size marker indications.
41586_2019_1821_MOESM3_ESM.xlsx
Supplementary Table 1 Mouse sgRNA metabolic library. This file contains all sgRNAs in the mouse sgRNA metabolic sub-libraries in two tabs, AAAQ05 and AAAR07. Each sub-library consists of 9,051 sgRNAs targeting 3,017 cell metabolism-related genes (3 sgRNAs per gene per sub-library) encoding metabolic enzymes, small molecule transporters, and metabolism-related transcriptional regulators, as well as 500 non-targeting control sgRNAs. The first column lists the sgRNA sequence, the second column is the sgRNA target gene ID and the third column lists the sgRNA target gene symbol.
41586_2019_1821_MOESM4_ESM.xlsx
Supplementary Table 2 Analysis of mouse sgRNA metabolic library CRISPR screening data. This file contains the output of the analysis of mouse metabolic library CRISPR screening data in both gene level and sgRNA level in two separate tabs. For data analysis, FastQ files obtained after sequencing were demultiplexed using the HiSeq Analysis software (Illumina). Single-end reads were trimmed and quality-filtered using the CLC Genomics Workbench v11 (Qiagen) and matched against sgRNA sequences from the sgRNA metabolic library. Read counts for sgRNAs were normalized against total read counts across all samples. For each sgRNA, the fold change (log2-transformed ratio) for enrichment was calculated between each of the biological replicates and the input experiment. After merging the quantification results from two sub-libraries, candidate genes were ranked based on the average enrichment of their six gene-specific sgRNAs in tumour-infiltrating OT-I cells relative to input (log2(TIL/input ratio); adjusted P < 0.05). The gene level false discovery rate-adjusted P value was calculated among multiple sgRNAs (n = 6) of each gene, using a two-tailed paired Student’s t-test between log2-transformed average normalized read counts of tumor samples and those of input samples, and the P value was further adjusted using Bonferroni correction with gene size.
41586_2019_1821_MOESM5_ESM.xlsx
Supplementary Table 3 Analysis of mouse Brie sgRNA genome-scale library CRISPR screening data. This file contains the output of the analysis of mouse Brie genome-scale library CRISPR screening data in both gene level and sgRNA level in two separate tabs. For data analysis, FastQ files obtained after sequencing were demultiplexed using the HiSeq Analysis software (Illumina). Regnase-1 sgRNA (GGAGTGGAAACGCTTCATCG) reads were removed, and single-end reads were trimmed and quality-filtered using the CLC Genomics Workbench v11 (Qiagen) and matched against sgRNA sequences from the genome-scale sgRNA Brie library. Read counts for sgRNAs were normalized against total read counts across all samples. For each sgRNA, the fold change (log2-transformed ratio) for enrichment was calculated between each of the biological replicates and the input experiment. Gene ranking was based on the average enrichment (log2(TIL/input ratio)) among replicates in representation of four individual corresponding sgRNAs in the genome-scale sgRNA Brie library. The gene level false discovery rate-adjusted P value was calculated among multiple sgRNAs (n = 4) of each gene, using a two-tailed paired Student’s t-test between log2-transformed average normalized read counts of tumor samples and those of input samples, and the P value was further adjusted using Bonferroni correction with gene size.
Source data
Rights and permissions
About this article
Cite this article
Wei, J., Long, L., Zheng, W. et al. Targeting REGNASE-1 programs long-lived effector T cells for cancer therapy. Nature 576, 471–476 (2019). https://doi.org/10.1038/s41586-019-1821-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41586-019-1821-z
This article is cited by
-
CRISPR–Cas9 applications in T cells and adoptive T cell therapies
Cellular & Molecular Biology Letters (2024)
-
Functional CRISPR screens in T cells reveal new opportunities for cancer immunotherapies
Molecular Cancer (2024)
-
Regulation of inflammatory diseases via the control of mRNA decay
Inflammation and Regeneration (2024)
-
CRISPR-Cas gene knockouts to optimize engineered T cells for cancer immunotherapy
Cancer Gene Therapy (2024)
-
Efficient engineering of human and mouse primary cells using peptide-assisted genome editing
Nature Biotechnology (2024)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
