Only a small proportion of patients with cancer show lasting responses to immune checkpoint blockade (ICB)-based monotherapies. The RNA-editing enzyme ADAR1 is an emerging determinant of resistance to ICB therapy and prevents ICB responsiveness by repressing immunogenic double-stranded RNAs (dsRNAs), such as those arising from the dysregulated expression of endogenous retroviral elements (EREs)1,2,3,4. These dsRNAs trigger an interferon-dependent antitumour response by activating A-form dsRNA (A-RNA)-sensing proteins such as MDA-5 and PKR5. Here we show that ADAR1 also prevents the accrual of endogenous Z-form dsRNA elements (Z-RNAs), which were enriched in the 3′ untranslated regions of interferon-stimulated mRNAs. Depletion or mutation of ADAR1 resulted in Z-RNA accumulation and activation of the Z-RNA sensor ZBP1, which culminated in RIPK3-mediated necroptosis. As no clinically viable ADAR1 inhibitors currently exist, we searched for a compound that can override the requirement for ADAR1 inhibition and directly activate ZBP1. We identified a small molecule, the curaxin CBL0137, which potently activates ZBP1 by triggering Z-DNA formation in cells. CBL0137 induced ZBP1-dependent necroptosis in cancer-associated fibroblasts and reversed ICB unresponsiveness in mouse models of melanoma. Collectively, these results demonstrate that ADAR1 represses endogenous Z-RNAs and identifies ZBP1-mediated necroptosis as a new determinant of tumour immunogenicity masked by ADAR1. Therapeutic activation of ZBP1-induced necroptosis provides a readily translatable avenue for rekindling the immune responsiveness of ICB-resistant human cancers.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Epigenetic regulation in the tumor microenvironment: molecular mechanisms and therapeutic targets
Signal Transduction and Targeted Therapy Open Access 22 May 2023
A bibliometric analysis of ferroptosis, necroptosis, pyroptosis, and cuproptosis in cancer from 2012 to 2022
Cell Death Discovery Open Access 15 April 2023
ADAR1 has an oncogenic function and can be a prognostic factor in cervical cancer
Scientific Reports Open Access 23 March 2023
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
Rent or buy this article
Get just this article for as long as you need it
Prices may be subject to local taxes which are calculated during checkout
The RNA-seq, ChIP-seq and RIP-seq experimental data are available from the Gene Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo) under the accession number GSE184966. The genomic annotations used in the DeepZ feature analysis were obtained from the ChIP-Atlas public resource (https://chip-atlas.org). Source data are provided with this paper.
Detailed commands and scripts to reproduce the analysis are available online at GitHub: https://github.com/alnfedorov/MEF-CBL0137. The data preprocessing pipeline for the DeepZ model can be found at https://github.com/Nazar1997/DeepZ_data_creation.
Liu, H. et al. Tumor-derived IFN triggers chronic pathway agonism and sensitivity to ADAR loss. Nat. Med. 25, 95–102 (2019).
Ishizuka, J. J. et al. Loss of ADAR1 in tumours overcomes resistance to immune checkpoint blockade. Nature 565, 43–48 (2019).
Mehdipour, P. et al. Epigenetic therapy induces transcription of inverted SINEs and ADAR1 dependency. Nature 588, 169–173 (2020).
Gannon, H. S. et al. Identification of ADAR1 adenosine deaminase dependency in a subset of cancer cells. Nat. Commun. 9, 5450 (2018).
Chen, R., Ishak, C. A. & De Carvalho, D. D. Endogenous retroelements and the viral mimicry response in cancer therapy and cellular homeostasis. Cancer Discov. 11, 2707–2725 (2021).
Loo Yau, H., Ettayebi, I. & De Carvalho, D. D. The cancer epigenome: exploiting its vulnerabilities for immunotherapy. Trends Cell Biol. 29, 31–43 (2019).
Heraud-Farlow, J. E., Chalk, A. M. & Walkley, C. R. Defining the functions of adenosine-to-inosine RNA editing through hematology. Curr. Opin. Hematol. 26, 241–248 (2019).
Eisenberg, E. & Levanon, E. Y. A-to-I RNA editing—immune protector and transcriptome diversifier. Nat. Rev. Genet. 19, 473–490 (2018).
Samuel, C. E. Adenosine deaminase acting on RNA (ADAR1), a suppressor of double-stranded RNA-triggered innate immune responses. J. Biol. Chem. 294, 1710–1720 (2019).
Herbert, A. ALU non-B-DNA conformations, flipons, binary codes and evolution. R. Soc. Open Sci. 7, 200222 (2020).
Chung, H. et al. Human ADAR1 prevents endogenous RNA from triggering translational shutdown. Cell 172, 811–824.e14 (2018).
George, C. X., Ramaswami, G., Li, J. B. & Samuel, C. E. Editing of cellular self-RNAs by adenosine deaminase ADAR1 suppresses innate immune stress responses. J. Biol. Chem. 291, 6158–6168 (2016).
Liddicoat, B. J. et al. RNA editing by ADAR1 prevents MDA5 sensing of endogenous dsRNA as nonself. Science 349, 1115–1120 (2015).
Herbert, A. et al. A Z-DNA binding domain present in the human editing enzyme, double-stranded RNA adenosine deaminase. Proc. Natl Acad. Sci. USA 94, 8421–8426 (1997).
Herbert, A. Mendelian disease caused by variants affecting recognition of Z-DNA and Z-RNA by the Zα domain of the double-stranded RNA editing enzyme ADAR. Eur. J. Hum. Genet. 28, 114–117 (2020).
Zhang, T. et al. Influenza virus Z-RNAs induce ZBP1-mediated necroptosis. Cell 180, 1115–1129.e13 (2020).
Hardin, C. C. et al. Stabilization of Z-RNA by chemical bromination and its recognition by anti-Z-DNA antibodies. Biochemistry 26, 5191–5199 (1987).
Schade, M., Turner, C. J., Lowenhaupt, K., Rich, A. & Herbert, A. Structure–function analysis of the Z-DNA-binding domain Zα of dsRNA adenosine deaminase type I reveals similarity to the (α + β) family of helix–turn–helix proteins. EMBO J. 18, 470–479 (1999).
Bazak, L., Levanon, E. Y. & Eisenberg, E. Genome-wide analysis of Alu editability. Nucleic Acids Res. 42, 6876–6884 (2014).
Nichols, P. J. et al. Recognition of non-CpG repeats in Alu and ribosomal RNAs by the Z-RNA binding domain of ADAR1 induces A-Z junctions. Nat. Commun. 12, 793 (2021).
Balasubramaniyam, T., Ishizuka, T., Xiao, C. D., Bao, H. L. & Xu, Y. 2′-O-Methyl-8-methylguanosine as a Z-form RNA stabilizer for structural and functional study of Z-RNA. Molecules 23, 2572–2579 (2018).
Brown, B. A. 2nd, Lowenhaupt, K., Wilbert, C. M., Hanlon, E. B. & Rich, A. The Zα domain of the editing enzyme dsRNA adenosine deaminase binds left-handed Z-RNA as well as Z-DNA. Proc. Natl Acad. Sci. USA 97, 13532–13536 (2000).
Kim, K. et al. Solution structure of the Zβ domain of human DNA-dependent activator of IFN-regulatory factors and its binding modes to B- and Z-DNAs. Proc. Natl Acad. Sci. USA 108, 6921–6926 (2011).
Peck, L. J., Nordheim, A., Rich, A. & Wang, J. C. Flipping of cloned d(pCpG)n.d(pCpG)n DNA sequences from right- to left-handed helical structure by salt, Co(III), or negative supercoiling. Proc. Natl Acad. Sci. USA 79, 4560–4564 (1982).
Chang, H. W. et al. Histone chaperone FACT and curaxins: effects on genome structure and function. J. Cancer Metastasis Treat. 5, 78 (2019).
Safina, A. et al. FACT is a sensor of DNA torsional stress in eukaryotic cells. Nucleic Acids Res. 45, 1925–1945 (2017).
Sookdeo, A., Hepp, C. M., McClure, M. A. & Boissinot, S. Revisiting the evolution of mouse LINE-1 in the genomic era. Mob. DNA 4, 3 (2013).
Beknazarov, N., Jin, S. & Poptsova, M. Deep learning approach for predicting functional Z-DNA regions using omics data. Sci Rep. 10, 19134 (2020).
Bao, H. L. & Xu, Y. Observation of Z-DNA structure via the synthesis of oligonucleotide DNA containing 8-trifluoromethyl-2-deoxyguanosine. Curr. Protoc. 1, e28 (2021).
Jeronimo, C., Watanabe, S., Kaplan, C. D., Peterson, C. L. & Robert, F. The histone chaperones FACT and Spt6 restrict H2A.Z from intragenic locations. Mol. Cell 58, 1113–1123 (2015).
Denli, A. M. et al. Primate-specific ORF0 contributes to retrotransposon-mediated diversity. Cell 163, 583–593 (2015).
Somers, K. et al. Potent antileukemic activity of curaxin CBL0137 against MLL-rearranged leukemia. Int. J. Cancer 146, 1902–1916 (2020).
Gasparian, A. V. et al. Curaxins: anticancer compounds that simultaneously suppress NF-κB and activate p53 by targeting FACT. Sci. Transl. Med. 3, 95ra74 (2011).
Carter, D. R. et al. Therapeutic targeting of the MYC signal by inhibition of histone chaperone FACT in neuroblastoma. Sci. Transl. Med. 7, 312ra176 (2015).
Koo, G. B. et al. Methylation-dependent loss of RIP3 expression in cancer represses programmed necrosis in response to chemotherapeutics. Cell Res. 25, 707–725 (2015).
Gabrilovich, D. I. & Nagaraj, S. Myeloid-derived suppressor cells as regulators of the immune system. Nat. Rev. Immunol. 9, 162–174 (2009).
Weiss, S. A., Wolchok, J. D. & Sznol, M. Immunotherapy of melanoma: facts and hopes. Clin. Cancer Res. 25, 5191–5201 (2019).
Wang, J. et al. UV-induced somatic mutations elicit a functional T cell response in the YUMMER1.7 mouse melanoma model. Pigment Cell Melanoma Res. 30, 428–435 (2017).
Nirschl, C. J. et al. IFNγ-dependent tissue-immune homeostasis is co-opted in the tumor microenvironment. Cell 170, 127–141.e15 (2017).
Benci, J. L. et al. Tumor interferon signaling regulates a multigenic resistance program to immune checkpoint blockade. Cell 167, 1540–1554.e12 (2016).
Ishii, K. J. et al. TANK-binding kinase-1 delineates innate and adaptive immune responses to DNA vaccines. Nature 451, 725–729 (2008).
Chen, P. et al. Anti-CD70 immunocytokines for exploitation of interferon-γ-induced RIP1-dependent necrosis in renal cell carcinoma. PLoS ONE 8, e61446 (2013).
Cancer Genome Atlas Network. Genomic classification of cutaneous melanoma. Cell 161, 1681–1696 (2015).
Becht, E. et al. Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression. Genome Biol. 17, 218 (2016).
Gabitova-Cornell, L. et al. Cholesterol pathway inhibition induces TGF-β signaling to promote basal differentiation in pancreatic cancer. Cancer Cell 38, 567–583.e11 (2020).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Bachu, M. et al. A versatile mouse model of epitope-tagged histone H3.3 to study epigenome dynamics. J. Biol. Chem. 294, 1904–1914 (2019).
Roth, S. H., Levanon, E. Y. & Eisenberg, E. Genome-wide quantification of ADAR adenosine-to-inosine RNA editing activity. Nat. Methods 16, 1131–1138 (2019).
Gruber, A. R., Lorenz, R., Bernhart, S. H., Neubock, R. & Hofacker, I. L. The Vienna RNA websuite. Nucleic Acids Res. 36, W70–W74 (2008).
Lo Giudice, C., Tangaro, M. A., Pesole, G. & Picardi, E. Investigating RNA editing in deep transcriptome datasets with REDItools and REDIportal. Nat. Protoc. 15, 1098–1131 (2020).
Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Kechin, A., Boyarskikh, U., Kel, A. & Filipenko, M. cutPrimers: a new tool for accurate cutting of primers from reads of targeted next generation sequencing. J. Comput. Biol. 24, 1138–1143 (2017).
Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Zhu, L. J. et al. ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data. BMC Bioinformatics 11, 237 (2010).
Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).
Penzkofer, T. et al. L1Base 2: more retrotransposition-active LINE-1s, more mammalian genomes. Nucleic Acids Res. 45, D68–D73 (2017).
Sievers, F. et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol. Syst. Biol. 7, 539 (2011).
Ho, P. S., Ellison, M. J., Quigley, G. J. & Rich, A. A computer aided thermodynamic approach for predicting the formation of Z-DNA in naturally occurring sequences. EMBO J. 5, 2737–2744 (1986).
Oki, S. et al. ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep. 19, e46255 (2018).
Rosenberger, C. M. Characterization of innate responses to influenza virus infection in a novel lung type I epithelial cell model. J. Gen. Virol. 95, 350–362 (2014).
Brown, D. M., Fisher, T. L., Wei, C., Frelinger, J. G. & Lord, E. M. Tumours can act as adjuvants for humoral immunity. Immunology 102, 486–497 (2001).
Rodriguez, D. A. Characterization of RIPK3-mediated phosphorylation of the activation loop of MLKL during necroptosis. Cell Death Differ. 23, 76–88 (2016).
We are grateful to E. Gurova for the CBL0137 analogues; M. Bosenberg, J. Upton, A. Degterev and D.R. Green for providing cell lines or antibodies; M. Andrake for help with molecular modelling; and A. Gupte and Z. Liang for technical assistance. This work was supported by a gift from David Wiest and the Seeds of Hope foundation of the Bucks County Board of Associates to S.B., NIH grants CA168621, CA190542, AI135025 and AI144400 to S.B., and NIH grant GM119398 to V.S. C.W. was supported by the National Health and Medical Research Council, Australia (NHMRC; APP1144049 and APP1183553). J.C.C. and P.G.T. were supported by the Mark Foundation, NIH grant U01AI150747, and the American Lebanese Syrian Associated Charities at St Jude Children’s Hospital. Additional funds were provided by NIH Cancer Center Support grant P30CA006927 to S.B. A.F., N.B. and M.P. are supported by the Basic Research Program of the National Research University Higher School of Economics, for which A.H. is an International Supervisor.
T.Z., C.Y. and S.B. are co-inventors on a provisional patent application ‘Combination of curaxins and immune checkpoint inhibitors for treating cancer’ filed by the Fox Chase Cancer Center.
Peer review information
Nature thanks William Haining, Jan Rehwinkel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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 Z-RNA accumulation upon Adar ablation.
a, Time course of Z-RNA and A-RNA formation in ADAR1 WT, ADAR1 KO, ADAR1/MAVS double KO MEFs, or ADAR1 KO MEFs treated with anti-IFNAR1 neutralizing antibody (20 µg/mL). b, Quantification of fluorescence intensity of Z-RNA and A-RNA signals in a. c, MAVS protein levels in WT MEFs following CRISPR-based murine Mavs (MAVS KO), and exposure to IFNβ (100 ng/mL, 24 h). Mavs was ablated using lentiviruses expressing ‘all-in-one’ sgRNA. d, Whole-cell extracts from ADAR1 WT or ADAR1 KO treated with IFNβ for 6 h were examined for phosphorylated STAT1, total STAT1 and ADAR1 by immunoblot analysis. e, Isg15 mRNA levels were examined by RT-qPCR in ADAR1 WT or ADAR1 KO treated with or without IFNβ for 6 h. f, Detection of Z-RNA and A-RNA accumulation in primary Adar+/+Ifih1−/− or Adar−/−Ifih1−/− MEFs in the presence or absence IFNβ (100 ng/mL). g, Quantification of fluorescence intensity of Z-RNA and A-RNA signals in f. h, Detection of Z-RNA and A-RNA accumulation in immortalized BMDMs in the presence or absence of IFNβ (100 ng/mL), following ADAR1 ablation by CRISPR-based approaches. i, Protein levels of ADAR1 p150 in immortalized BMDMs were detected by immunoblot (upper panel). Quantification of fluorescence intensity of Z-RNA and A-RNA signals in h (bottom panel). j, Detection of Z-RNA and A-RNA accumulation in airway epithelium-derived immortalized LET1 cells in the presence or absence of IFNβ (100 ng/mL), following ADAR1 ablation by CRISPR-based approaches. k, Protein levels of ADAR1 p150 in LET1 cells were detected by immunoblot (upper panel). Quantification of fluorescence intensity of Z-RNA and A-RNA signals in j (bottom panel). l, Detection of Z-RNA and A-RNA accumulation in ADAR1 WT or KO immortalized endothelial SVEC4-10 cells in the presence or absence of IFNβ (100 ng/mL). m, Protein levels of ADAR1 p150 in SVEC4-10 cells were detected by immunoblot (upper panel). Quantification of fluorescence intensity of Z-RNA and A-RNA signals in l (bottom panel). n, Schematic of FLAG-tagged ADAR1 p150, E861A and Zα (N175A/Y179A) mutants used in this experiment. o, Equivalent expression levels of ADAR1 constructs in stably-reconstituted Adar−/− MEFs were confirmed by immunoblotting. p, Z-RNA accumulation in immortalized Adar−/− MEFs stably reconstituted with empty vector (Vec), or with ADAR1 p150, Zα N175A/Y179A (Zα mut) or editing-deficient (E861A) mutants without IFNβ treatment. q, Detection of Z-RNA accumulation in ADAR1 p150 or E861A mutant cells following IFNβ (100 ng/mL) treatment. r, Quantification of fluorescence intensity of Z-RNA signals in q. s, Detection of Z-RNA accumulation in ADAR1 p150 or E861A mutant cells after proteinase treatment. Fixed cells were treated with proteinase K (0.008 U/ml) for 30 min before staining. t, Quantification of fluorescence intensity of Z-RNA signals in s. Data are mean ± s.d. (n = 30 in b, g, i, k, m, r, t or n = 3 in e per group). One-way ANOVA test (b, g, i, k, m), two-tailed unpaired t-test with Welch’s correction (e, r) or two-tailed unpaired Student’s t-test (t). ***P < 0.0005 (P < 0.0001 in b, g, i, k, m, r). NS, no significance. Data are representative of at least three independent experiments. Adar was ablated by CRISPR-based approaches, as detailed in the manuscript.
Extended Data Fig. 2 Z-forming mRNAs suppressed by ADAR1.
a, Origin of sequenced RNA fragments in Z22 RIP-seq for ADAR1 KO Zbp1−/− MEFs with IFNβ (100 ng/mL). b, A→I Editing Index of RNAs in Z22 pull-downs from ADAR1 WT and ADAR1 Zbp1−/− KO cells before and after IFNβ treatment (100 ng/mL). c, Expression levels of ADAR2 in ADAR1 WT or ADAR1 KO MEFs in the presence or absence of IFNβ (100 ng/mL, 24 h). d, Distribution of repeats in Z22 enriched ISG mRNAs from ADAR1 KO Zbp1−/− MEFs. Localization and the total number of repeats that were also edited in Z22 pulldowns from IFNβ-treated ADAR1 WT cells are provided in red (A→I Editing Index > 0.1%; mean coverage per adenosine > 5). e, 3’UTRs of inverted SINE-containing mRNAs showing Z22 enrichment (blue peaks), IgG (grey peaks), input (orange peaks), editing sites (vertical red bars), location of SINEs, and location of qPCR primers (red arrows). qPCR quantitation of the indicated inverted SINE-containing mRNAs (Xrn1 and Knl1) 3’UTRs following immunoprecipitation with Z22 or control IgG antibodies from ADAR1 MEFs stimulated with or without IFNβ (100 ng/mL, 48 h) is shown to the right. f, qPCR analysis of the indicated GU-type simple repeat containing mRNAs (Eif2ak2 and Ifih1) following immunoprecipitation with Z22 or control IgG antibodies from ADAR1 KO MEFs. Potential bipartite (dumbbell) Z-forming RNA structures (RNA-fold) are shown to the right of each graph, and putative Zα binding sites are outlined in pink boxes. Data are mean ± s.d. (n = 3 in e, f per group). Two-tailed unpaired t-test with Welch’s correction (e, f). *P < 0.05 [P = 0.005 in e (Xrn1)], **P < 0.005 [P = 0.003 in e (Knl1), P = 0.001 in f (Eif2ak2), P = 0.004 in f (Ifih1)]. Data are representative of at least three independent experiments.
Extended Data Fig. 3 ZBP1-activated cell death following Adar ablation.
a, Photomicrographs of ADAR1 WT or ADAR1 KO following treatment with IFNβ (100 ng/mL, 48 h). b, Zbp1−/− MEFs stably expressing FLAG-ZBP1, in which ADAR1 was present (ADAR1 WT) or ablated by CRISPR/Cas9 (ADAR1 KO), were treated with the indicated cytokines (100 ng/mL). Cell viability was determined at 48 h post treatment. (c, d), MEFs produced as in b were treated with IFNβ (c) or IFNγ (100 ng/mL) (d) in the presence or absence of zVAD (50 mM) and RIPK3 inhibitor (R3i) GSK’843 (5 µM). Viability was examined at 48 h post treatment. e, Adar was ablated by CRISPR-based approaches in Mlkl−/− or Mlkl−/−Casp8−/− MEFs. Cells were treated IFNβ (100 ng/mL) in the presence or absence of zVAD (50 mM). Viability was examined at 48 h post treatment. f, IFNβ (100 ng/mL)-treated ADAR1 WT and ADAR1 KO MEFs were lysed at 24 h post treatment and examined for RIPK1, FADD, Caspase 8 (Casp8), and ADAR1 by immunoblotting. g, Schematic of FLAG-tagged murine ZBP1 and its mutants. h, Equivalent expression levels of FLAG-ZBP1 constructs in retrovirally-reconstitued Zbp1−/− MEFs were confirmed by immunoblotting for the FLAG tag. i, Cell viability of Zbp1−/− MEFs stably expressing FLAG-ZBP1 or its mutants was determined at 48 h post IFNβ (100 ng/mL) treatment. j, Immortalized Zbp1−/− MEFs stably reconstituted with either FLAG-ZBP1 or FLAG-ZBP1ΔZα mutant were ablated for ADAR1 expression by a CRISPR-based approach, treated with IFNβ (100 ng/mL, 48 h). Following cell lysis, anti-FLAG immunoprecipitates were examined for FLAG. Whole-cell extract (5% input) was examined in parallel for FLAG. GAPDH was used as a loading control. k, Zbp1−/− MEFs stably expressing FLAG-ZBP1 or FLAG-ZBP1ΔZα mutant were ablated for ADAR1 expression, treated with or without IFNβ (100 ng/mL). RNA present in anti-FLAG or control IgG immunoprecipitates was subjected to RT-qPCR using primers for the 3’UTRs of Eif2ak2, Ddx58, or Ifih1. l, Zbp1−/− MEFs reconstituted with FLAG-ZBP1 or FLAG-ZBP1ΔZα were ablated for ADAR1 expression, treated with or without IFNβ (100 ng/mL). FLAG or control IgG immunoprecipitates from cell lysates were subjected to RT-qPCR using primers for the 3’UTRs of Xrn1, Knl1 or Slfn5. m, Immunofluorescence staining for pMLKL (green) in Zbp1−/− MEFs stably expressing FLAG-ZBP1 and ablated for Adar (ADAR1 KO) at the indicated time point after treatment with IFNβ (100 ng/mL). Nuclei are stained with DAPI (blue) and outlined with dashed white lines. pMLKL is seen in the nucleus and cytoplasm of cells after IFNβ exposure. n, Line graph depicts the kinetics of pMLKL positivity, and bars show the localization of the pMLKL signal. o, Lamin B1 (green) staining for nuclear envelope integrity of ADAR1 WT or ADAR1 KO MEFs at 48 h post treatment with IFNβ (100 ng/mL). p, Kinetics of nuclear envelope breakdown in ADAR1 WT or ADAR1 KO MEFs after IFNβ (100 ng/mL) treatment. Data are mean ± s.d. (n = 4 in b, c, d, e, i, n, p or n = 3 in k, l per group). Two-tailed unpaired t-test with Welch’s correction (b, e, i, k, l), one-way ANOVA test (c, d) or two-way ANOVA test (p). *P < 0.05 [P = 0.0178 in k (Ifih1), P = 0.026 in l (Slfn5)], **P < 0.005 [P = 0.0007 in b (IFNγ), P = 0.001 in e, P = 0.0044 in k (Eif2ak2), P = 0.002 in k (Ddx58), P = 0.0018 in l (Knl1)], ***P < 0.0005 [P < 0.0001 in b (IFNβ), c, d, i, P = 0.0002 in l (Xrn1)]. Data are representative of at least three independent experiments.
Extended Data Fig. 4 CBL0137 induced Z-DNA formation.
a, Structures of A-RNA, Z-RNA, Z-DNA and B-DNA. b, Z-DNA inducing activity of CBL0137 analogs. c, Quantification of fluorescence intensity of Z-DNA signal in b. d, Genomic distribution of L1Md A and L1Md T repeats overlapping with Z22 peaks in CBL0137-treated FLAG-ZBP1 MEFs. e, Quantile-quantile plot of the linear model weights for the features analyzed by DeepZ. The graph shows that the values for the upper and lower bounds of the plot differ from the normal distribution expected under the null hypothesis. f, Comparative plot of the importance of the features evaluated in datasets Z22 and FLAG-ZBP1 shows the reproducibility of the DeepZ analysis when each independently derived data set was analyzed separately. g, The importance of features based on the weights of linear regression trained on the combined Z22 and FLAG-ZBP1 dataset are scored using a normalized scale of 1 to −1. Epigenetic marks, transcription factor binding sites and other elements that predict pull-down by Z22 and FLAG-ZBP1 of the LINE 5' UTR region have positive values and identify features that are associated with Z-DNA formation in cells. h, MEFs treated with or without CBL0137 (5µM) for 12 h were stained for Z-DNA (red) and PML (green). i, Quantification of PML foci in CBL0137 untreated or treated cells. Data are mean ± s.d. (n = 30 in c or n = 5 in i per group). Two-tailed unpaired t-test with Welch’s correction (i). ***P < 0.0005 (P < 0.0001 in i). Data are representative of at least three independent experiments.
Extended Data Fig. 5 ZBP1-dependent cell death induced by CBL0137.
a, Mlkl−/− or Mlkl−/−Casp8−/− MEFs were treated or untreated with CBL0137 (5 µM) in the presence or absence of zVAD (50 mM) and viability was examined at 18 h post treatment. b, Z-DNA formation in primary early-passage (p<5) Zbp1+/+ and littermate control Zbp1−/− MEFs treated with CBL0137 (5 µM). c, Quantification of fluorescence intensity of Z-DNA signals in b. d, Zbp1+/+ and Zbp1−/− MEFs were treated with CBL0137 (5 µM) in the presence or absence of zVAD (50 mM) and RIPK3 inhibitor (R3i) GSK’843 (5 µM) and viability was examined at 18 h post treatment. e, Immunoblot analysis of ZBP1-dependent MLKL activation in primary MEFs. f, Immunoblots showing levels of ZBP1, RIPK3 and MLKL in the human fibroblast cell line HS68 in the presence or absence of hIFNβ (100 ng/mL, 6 h). g, Z-DNA formation in HS68 cells treated with CBL0137 (5 µM, 12 h). h, Quantification of fluorescence intensity of Z-DNA signals in g. i, hIFNβ pretreated (100 ng/mL, 6 h) HS68 cells were exposed to CBL0137 in the presence or absence of zVAD (50 mM) and RIPK3 inhibitor (R3i, GSK’872, 5 µM) and viability was examined at 36 h post treatment. j, MLKL activation in hIFNβ pretreated (100 ng/mL, 6 h) HS68 cells treated with CBL0137 in the presence or absence of zVAD (50 mM) and RIPK3 inhibitor (R3i, GSK’872, 5 µM) was examined by immunoblot analysis 30 h post-CBL0137 treatment. k, CBL0137-induced cell death kinetics in Zbp1−/− MEFs stably reconstituted with empty vector (Vec), FLAG-ZBP1, or its mutants. l, Immunoblot analysis of MLKL activation in Zbp1−/− MEFs reconstituted with empty vector (Vec), FLAG-ZBP1, or FLAG-ZBP1 mutants after CBL0137 treatment. m, Primary Zbp1+/+ and littermate-matched Zbp1−/− BMDMs were treated with CBL0137 (5 µM, 18 h) and stained for Z-DNA (red) and the macrophage marker F4/80 (green). n, Primary Zbp1+/+ and Zbp1−/− BMDMs were treated with CBL0137 (5 µM) and viability was examined at 24 h post treatment. o, Distribution of FLAG-enriched peaks following treatment with CBL0137 (5µM) for 14 h. p, Immortalized Zbp1−/− MEFs stably reconstituted with either FLAG-ZBP1 or FLAG- ΔZα mutant were treated with CBL0137 (1.5 µM, 14 h), and anti-FLAG immunoprecipitates were examined for FLAG. Whole-cell extract (5% input) was examined in parallel for FLAG. GAPDH was used as a loading control. Data are mean ± s.d. (n = 4 in a, d, i, k, n or n = 20 in c, h per group). Two-tailed unpaired t-test with Welch’s correction (a, c, n), one-way ANOVA test (d, i) or two-way ANOVA test (k). ***P < 0.0005 (P = 0.0002 in a, P < 0.0001 in c, d, i, k, n). Data are representative of at least three independent experiments.
Extended Data Fig. 6 CBL0137 induced Z-DNA and immunogenicity.
a, HepG2, MCF7, A549, HT-29, HeLa, SK-MEL-2 or A375 human cell lines were treated with CBL0137 (5 µM) for 12 h and stained for Z-DNA. b, Quantification of fluorescence intensity of Z-DNA signal in a. c, Cell lines deficient in necroptosis effector expression (HepG2, MCF7, A549, HeLa or SK-MEL-2) or those with intact/reconstituted necroptosis signaling (HT-29 cells reconstituted with FLAG-hZBP1, HS68 cells pretreated with hIFNβ (100 ng/mL) for 6 h, WT MEFs, LET1 murine airway epithelial cells, or SVEC4-10 murine endothelial cells stably reconstituted with FLAG-mZBP1) were treated with CBL0137 (5µM), and cell viability was examined 24 h (or 36 h for HS68 cells) after CBL0137 treatment. d, CBL0137-induced cell death kinetics in HT-29 cells reconstituted with empty vector (Vec) or FLAG-hZBP1. e, Whole-cell extracts from human HT-29 cells stably expressing empty vector or FLAG-hZBP1 and treated with CBL0137 were examined for phosphorylated MLKL and FLAG-hZBP1 by immunoblot analysis. f, Mice bearing subcutaneous B16-F10 melanoma tumors were treated as shown in Figure 5g. Tumors were collected following four cycles of CBL0137 (20 µM, intratumoral injection) and anti-PD1 antibody (200 µg/mouse, i.p. injection). Frozen sections prepared from these tumors were co-stained for immunofluorescence detection of CD8+ CD44+, CD8+ GzmB+ or CD8+ Ki67+ T cells, or CD11c+ DCs. g, Treatment schedule of mice bearing syngeneic subcutaneous B16-OVA melanomas. Treatments were initiated at 8 days post inoculation of tumor cells, on mice with similar tumor volumes (~100 mm3). h, Gating strategy for identification of OVA-specific (SIINFEKL H-2Kb+) CD8+ T cells. i, Total numbers of OVA-specific CD8+ T cells isolated from tumor-draining inguinal lymph node (left panel) and frequencies of OVA-specific CD8+ T cells in the tumor-draining inguinal lymph node, as a fraction of total CD8+ T cells (right panel) are shown. j, Treatment schedule of mice bearing bilateral B16-OVA melanomas. Treatments were initiated at 8 days post inoculation of tumor cells, on mice with similar tumor volumes (~100 mm3). k, Tumor growth curves of CBL0137-treated (ipsilateral) and untreated (contralateral) B16-OVA tumors following the indicated treatments. l, Immunofluorescence staining for fibroblasts (PDGFRα, green) and Z-DNA (red) in YUMMER1.7 melanoma sections from CBL0137 (intra-tumoral, 20 µM)-treated or untreated WT mice. m, Quantification of the proportion of Z-DNA positive fibroblasts in l. n, Vehicle or CBL0137 injected YUMMER1.7 tumors in WT or Zbp1−/− mice were stained for PDGFRα (green) or pMLKL (red). Nuclei are stained with DAPI (blue). o. Quantification of pMLKL+ fibroblasts in n. p, Immunofluorescence staining for Z-DNA (red) and DAPI (blue) in B16-F10 melanoma tumor sections from CBL0137 treated or untreated mice, 24 h after intravenous administration of drug at 50 mg/kg. q, Quantification of Z-DNA positive cells in p. Data are mean ± s.d. in c, d, m, o, q or s.e.m. in f, i, k (n = 20 in b, n = 4 in c, d, n = 6 in f, i, n = 3 in m, q or n = 5 in o per group). Two-tailed unpaired t-test with Welch’s correction (c), one-way ANOVA test (f, i, o) or two-way ANOVA test (d, k). **P < 0.005 (P = 0.0015 in i), ***P < 0.0005 (P < 0.0001 in d, f, i, k, o). Data are representative of at least three independent experimentsSource data.
Extended Data Fig. 7 Flow chart showing the algorithm used to construct enrichment profiles for L1Md A and L1Md T repeats in Z22 and FLAG-ZBP1 ChIP-Seq datasets.
Following mapping to L1 elements, reads were normalized and aligned to consensus L1 sequences derived from the UCSC genome browser RepeatMasker tracks for the mouse assembly mm10. The enrichment values at each position were calculated and smoothed as described in Supplementary Methods and aligned with the NCBI annotation of LI open reading frames.
Supplementary Fig. 1
Uncropped immunoblots used to prepare the main and extended data figures.
Supplementary Table 1
Z-RNAs enriched in Z22 RIP-seq. Z-forming features of Z22-enriched RNAs from ADAR1 KO Zbp1–/– MEFs, following IFNβ treatment (48 h). Location of repeats edited in Z22 pulldowns from IFNβ-treated ADAR1 WT MEFs is also shown. Enrichment was measured by comparing RIP-seq with matched control total RNA-seq experiments. P values were derived using a one-sided negative binomial test implemented in the DeSeq2 package (pvalue column), and the Benjamini–Hochberg procedure was used to account for multiple comparisons (padj column).
Supplementary Table 2
DeepZ features of Z-DNA-forming sequences identified by ChIP-seq in CBL0137-treated cells. The 874 features derived from ENCODE data were evaluated using the DeepZ model to identify their positive or negative importance in predicting the regions of Z-DNA formation identified by Z22 ChIP-seq in CBL0137-treated cells and validated by Flag–ZBP1 ChIP-seq. The weights assigned are listed from negative to positive importance. Features use the same colour scheme for the labels as shown in Extended Data Fig. 4g.
Rights and permissions
About this article
Cite this article
Zhang, T., Yin, C., Fedorov, A. et al. ADAR1 masks the cancer immunotherapeutic promise of ZBP1-driven necroptosis. Nature 606, 594–602 (2022). https://doi.org/10.1038/s41586-022-04753-7
This article is cited by
ADAR1: a mast regulator of aging and immunity
Signal Transduction and Targeted Therapy (2023)
Epigenetic regulation in the tumor microenvironment: molecular mechanisms and therapeutic targets
Signal Transduction and Targeted Therapy (2023)
RNA modifications in cardiovascular health and disease
Nature Reviews Cardiology (2023)
The double-edged functions of necroptosis
Cell Death & Disease (2023)
A bibliometric analysis of ferroptosis, necroptosis, pyroptosis, and cuproptosis in cancer from 2012 to 2022
Cell Death Discovery (2023)
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.