The volume-regulated anion channel LRRC8C suppresses T cell function by regulating cyclic dinucleotide transport and STING–p53 signaling (original) (raw)
Data availability
The RNA-Seq data generated in this study have been deposited in the GEO database under accession number GSE163679. Additional available gene expression datasets and ChiP–Seq data used in this study were downloaded from GEO using the following accession numbers: GSE127267 for mouse RNA-seq data from the Immunological Genome Project (www.immgen.org)17. GSE116177 for mouse RNA-Seq data from Haemopedia (www.haemosphere.org)18. GSE10246 for mouse microarray data from BioGPS (www.biogps.org)20. GSE49834 for human CAGE–Seq data from Fantom5 (https://fantom.gsc.riken.jp/5/)19. GSE36890 for mouse RNA-Seq data and STAT5 ChIP–Seq data using T cells from _Stat5a–Stat5b_-DKI N-domain mutant mice in which STAT5 proteins form dimers but not tetramers23. GSE110370 for gene expression analysis and p53 ChIP–Seq data from PHA-stimulated human T-lymphocytes treated with nutlin-3 (ref. 62). Source data for each figure, Extended Data Figure and supplemental figures are provided with this paper. Source data are provided with this paper.
References
- Qiu, Z. et al. SWELL1, a plasma membrane protein, is an essential component of volume-regulated anion channel. Cell 157, 447–458 (2014).
Article CAS PubMed PubMed Central Google Scholar - Voss, F. K. et al. Identification of LRRC8 heteromers as an essential component of the volume-regulated anion channel VRAC. Science 344, 634–638 (2014).
Article CAS PubMed Google Scholar - Deneka, D., Sawicka, M., Lam, A. K. M., Paulino, C. & Dutzler, R. Structure of a volume-regulated anion channel of the LRRC8 family. Nature 558, 254–259 (2018).
Article CAS PubMed Google Scholar - Syeda, R. et al. LRRC8 proteins form volume-regulated anion channels that sense ionic strength. Cell 164, 499–511 (2016).
Article CAS PubMed PubMed Central Google Scholar - Kumar, L. et al. Leucine-rich repeat containing 8A (LRRC8A) is essential for T lymphocyte development and function. J. Exp. Med. 211, 929–942 (2014).
Article CAS PubMed PubMed Central Google Scholar - Luck, J. C., Puchkov, D., Ullrich, F. & Jentsch, T. J. LRRC8/VRAC anion channels are required for late stages of spermatid development in mice. J. Biol. Chem. 293, 11796–11808 (2018).
Article PubMed PubMed Central Google Scholar - Stuhlmann, T., Planells-Cases, R. & Jentsch, T. J. LRRC8/VRAC anion channels enhance β-cell glucose sensing and insulin secretion. Nat. Commun. 9, 1974 (2018).
Article PubMed PubMed Central Google Scholar - Zhang, Y. et al. SWELL1 is a regulator of adipocyte size, insulin signalling and glucose homeostasis. Nat. Cell Biol. 19, 504–517 (2017).
Article CAS PubMed PubMed Central Google Scholar - Sawada, A. et al. A congenital mutation of the novel gene LRRC8 causes agammaglobulinemia in humans. J. Clin. Invest. 112, 1707–1713 (2003).
Article CAS PubMed PubMed Central Google Scholar - Platt, C. D. et al. Leucine-rich repeat containing 8A (LRRC8A)-dependent volume-regulated anion channel activity is dispensable for T-cell development and function. J. Allergy Clin. Immunol. 140, 1651–1659 e1651 (2017).
Article CAS PubMed PubMed Central Google Scholar - Burow, P., Klapperstuck, M. & Markwardt, F. Activation of ATP secretion via volume-regulated anion channels by sphingosine-1-phosphate in RAW macrophages. Pflug. Arch. 467, 1215–1226 (2015).
Article CAS Google Scholar - Lee, C. C., Freinkman, E., Sabatini, D. M. & Ploegh, H. L. The protein synthesis inhibitor blasticidin S enters mammalian cells via leucine-rich repeat-containing protein 8D. J. Biol. Chem. 289, 17124–17131 (2014).
Article CAS PubMed PubMed Central Google Scholar - Lutter, D., Ullrich, F., Lueck, J. C., Kempa, S. & Jentsch, T. J. Selective transport of neurotransmitters and modulators by distinct volume-regulated LRRC8 anion channels. J. Cell Sci. 130, 1122–1133 (2017).
CAS PubMed Google Scholar - Planells-Cases, R. et al. Subunit composition of VRAC channels determines substrate specificity and cellular resistance to Pt-based anti-cancer drugs. EMBO J. 34, 2993–3008 (2015).
Article CAS PubMed PubMed Central Google Scholar - Zhou, C. et al. Transfer of cGAMP into bystander cells via LRRC8 volume-regulated anion channels augments STING-mediated interferon responses and anti-viral immunity. Immunity 52, 767–781 e766 (2020).
Article CAS PubMed Google Scholar - Lahey, L. J. et al. LRRC8A:C/E heteromeric channels are ubiquitous transporters of cGAMP. Mol. Cell 80, 578–591 e575 (2020).
Article CAS PubMed Google Scholar - Heng, T. S., Painter, M. W. & Immunological Genome Project, C. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).
Article CAS PubMed Google Scholar - Choi, J. et al. Haemopedia RNA-seq: a database of gene expression during haematopoiesis in mice and humans. Nucleic Acids Res. 47, D780–D785 (2019).
Article CAS PubMed Google Scholar - Consortium, F. et al. A promoter-level mammalian expression atlas. Nature 507, 462–470 (2014).
Article Google Scholar - Wu, C. et al. BioGPS: an extensible and customizable portal for querying and organizing gene annotation resources. Genome Biol. 10, R130 (2009).
Article PubMed PubMed Central Google Scholar - Liberzon, A. et al. The molecular signatures database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).
Article CAS PubMed PubMed Central Google Scholar - 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).
Article CAS PubMed PubMed Central Google Scholar - Lin, J. X. et al. Critical Role of STAT5 transcription factor tetramerization for cytokine responses and normal immune function. Immunity 36, 586–599 (2012).
Article CAS PubMed PubMed Central Google Scholar - Hayashi, T. et al. Factor for adipocyte differentiation 158 gene disruption prevents the body weight gain and insulin resistance induced by a high-fat diet. Biol. Pharm. Bull. 34, 1257–1263 (2011).
Article CAS PubMed Google Scholar - Joerger, A. C. & Fersht, A. R. The p53 pathway: origins, inactivation in cancer, and emerging therapeutic approaches. Annu. Rev. Biochem. 85, 375–404 (2016).
Article CAS PubMed Google Scholar - Watanabe, M., Moon, K. D., Vacchio, M. S., Hathcock, K. S. & Hodes, R. J. Downmodulation of tumor suppressor p53 by T cell receptor signaling is critical for antigen-specific CD4+ T cell responses. Immunity 40, 681–691 (2014).
Article CAS PubMed PubMed Central Google Scholar - Gulen, M. F. et al. Signalling strength determines proapoptotic functions of STING. Nat. Commun. 8, 427 (2017).
Article PubMed PubMed Central Google Scholar - Madapura, H. S. et al. p53 contributes to T cell homeostasis through the induction of pro-apoptotic SAP. Cell Cycle 11, 4563–4569 (2012).
Article CAS PubMed PubMed Central Google Scholar - Kern, D. M., Oh, S., Hite, R. K. & Brohawn, S. G. Cryo-EM structures of the DCPIB-inhibited volume-regulated anion channel LRRC8A in lipid nanodiscs. eLife 8, e42636 (2019).
Article PubMed PubMed Central Google Scholar - Konig, B. & Stauber, T. Biophysics and structure-function relationships of LRRC8-formed volume-regulated anion channels. Biophys. J. 116, 1185–1193 (2019).
Article PubMed PubMed Central Google Scholar - Ma, Z., Ni, G. & Damania, B. Innate sensing of DNA virus genomes. Annu Rev. Virol. 5, 341–362 (2018).
Article CAS PubMed PubMed Central Google Scholar - Abe, T. & Barber, G. N. Cytosolic-DNA-mediated, STING-dependent proinflammatory gene induction necessitates canonical NF-κB activation through TBK1. J. Virol. 88, 5328–5341 (2014).
Article PubMed PubMed Central Google Scholar - Balka, K. R. et al. TBK1 and IKKε act redundantly to mediate STING-induced NF-κB responses in myeloid cells. Cell Rep. 31, 107492 (2020).
Article CAS PubMed Google Scholar - Cerboni, S. et al. Intrinsic antiproliferative activity of the innate sensor STING in T lymphocytes. J. Exp. Med. 214, 1769–1785 (2017).
Article CAS PubMed PubMed Central Google Scholar - Larkin, B. et al. Cutting edge: activation of STING in T cells induces type I IFN responses and cell death. J. Immunol. 199, 397–402 (2017).
Article CAS PubMed Google Scholar - Haag, S. M. et al. Targeting STING with covalent small-molecule inhibitors. Nature 559, 269–273 (2018).
Article CAS PubMed Google Scholar - Wu, J. et al. STING-mediated disruption of calcium homeostasis chronically activates ER stress and primes T cell death. J. Exp. Med. 216, 867–883 (2019).
Article CAS PubMed PubMed Central Google Scholar - Prakriya, M. & Lewis, R. S. Store-operated calcium channels. Physiol. Rev. 95, 1383–1436 (2015).
Article CAS PubMed PubMed Central Google Scholar - Feske, S., Wulff, H. & Skolnik, E. Y. Ion channels in innate and adaptive immunity. Annu Rev. Immunol. 33, 291–353 (2015).
Article CAS PubMed PubMed Central Google Scholar - Vaeth, M., Kahlfuss, S. & Feske, S. CRAC channels and calcium signaling in T cell-mediated immunity. Trends Immunol. 41, 878–901 (2020).
Article CAS PubMed PubMed Central Google Scholar - Shaw, P. J., Qu, B., Hoth, M. & Feske, S. Molecular regulation of CRAC channels and their role in lymphocyte function. Cell. Mol. Life Sci. 70, 2637–2656 (2013).
Article CAS PubMed Google Scholar - Li, W. et al. cGAS-STING-mediated DNA sensing maintains CD8+ T cell stemness and promotes antitumor T cell therapy. Sci. Transl. Med. 12, eaay9013 (2020).
Article CAS PubMed Google Scholar - & Diercks, B. P. ORAI1, STIM1/2, and RYR1 shape subsecond Ca2+ microdomains upon T cell activation. Sci. Signal. 11, eaat0358 (2018).
Article CAS PubMed PubMed Central Google Scholar - Kar, P. et al. Dynamic assembly of a membrane signaling complex enables selective activation of NFAT by Orai1. Curr. Biol. 24, 1361–1368 (2014).
Article CAS PubMed PubMed Central Google Scholar - Chen, X. et al. Regulation of anion channel LRRC8 volume-regulated anion channels in transport of 2′3′-Cyclic GMP-AMP and cisplatin under steady state and inflammation. J. Immunol. 206, 2061–2074 (2021).
Article CAS PubMed Google Scholar - Luteijn, R. D. et al. SLC19A1 transports immunoreactive cyclic dinucleotides. Nature 573, 434–438 (2019).
Article CAS PubMed PubMed Central Google Scholar - Bouis, D. et al. Severe combined immunodeficiency in stimulator of interferon genes (STING) V154M/wild-type mice. J. Allergy Clin. Immunol. 143, 712–725 e715 (2019).
Article CAS PubMed Google Scholar - Liu, Y. et al. Activated STING in a vascular and pulmonary syndrome. N. Engl. J. Med. 371, 507–518 (2014).
Article CAS PubMed PubMed Central Google Scholar - Motwani, M. et al. Hierarchy of clinical manifestations in SAVI N153S and V154M mouse models. Proc. Natl Acad. Sci. USA 116, 7941–7950 (2019).
Article CAS PubMed PubMed Central Google Scholar - Warner, J. D. et al. STING-associated vasculopathy develops independently of IRF3 in mice. J. Exp. Med. 214, 3279–3292 (2017).
Article CAS PubMed PubMed Central Google Scholar - Imanishi, T. et al. Reciprocal regulation of STING and TCR signaling by mTORC1 for T-cell activation and function. Life Sci. Alliance 2, e201800282 (2019).
Article PubMed PubMed Central Google Scholar - Wu, J., Dobbs, N., Yang, K. & Yan, N. Interferon-Independent activities of mammalian STING mediate antiviral response and tumor immune evasion. Immunity 53, 115–126 e115 (2020).
Article CAS PubMed PubMed Central Google Scholar - Srikanth, S. et al. The Ca2+ sensor STIM1 regulates the type I interferon response by retaining the signaling adaptor STING at the endoplasmic reticulum. Nat. Immunol. 20, 152–162 (2019).
Article CAS PubMed PubMed Central Google Scholar - Giorgi, C. et al. p53 at the endoplasmic reticulum regulates apoptosis in a Ca2+-dependent manner. Proc. Natl Acad. Sci. USA 112, 1779–1784 (2015).
Article CAS PubMed PubMed Central Google Scholar - Oh-Hora, M. et al. Dual functions for the endoplasmic reticulum calcium sensors STIM1 and STIM2 in T cell activation and tolerance. Nat. Immunol. 9, 432–443 (2008).
Article CAS PubMed PubMed Central Google Scholar - Kaufmann, U. et al. Selective ORAI1 Inhibition ameliorates autoimmune central nervous system inflammation by suppressing effector but not regulatory T cell function. J. Immunol. 196, 573–585 (2016).
Article CAS PubMed Google Scholar - Fellmann, C. et al. An optimized microRNA backbone for effective single-copy RNAi. Cell Rep. 5, 1704–1713 (2013).
Article CAS PubMed Google Scholar - Chen, R. et al. In vivo RNA interference screens identify regulators of antiviral CD4(+) and CD8(+) T cell differentiation. Immunity 41, 325–338 (2014).
Article CAS PubMed PubMed Central Google Scholar - 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).
Article CAS PubMed PubMed Central Google Scholar - Huang, B., Johansen, K. H. & Schwartzberg, P. L. Efficient CRISPR/Cas9-mediated mutagenesis in primary murine T lymphocytes. Curr. Protoc. Immunol. 124, e62 (2019).
Article PubMed Google Scholar - 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).
Article PubMed PubMed Central Google Scholar - Nguyen, T. T. et al. Revealing a human p53 universe. Nucleic Acids Res. 46, 8153–8167 (2018).
Article CAS PubMed PubMed Central Google Scholar - Vaeth, M. et al. Store-operated Ca2+ entry in follicular T cells controls humoral immune responses and autoimmunity. Immunity 44, 1350–1364 (2016).
Article CAS PubMed PubMed Central Google Scholar - Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Article CAS PubMed Google Scholar - Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).
Article CAS PubMed Google Scholar - Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Article CAS PubMed PubMed Central Google Scholar
Acknowledgements
This work was funded by National Institutes of Health (NIH) grant nos. AI097302, AI130143, AI137004 and AI125997 to S.F., and R01DE014756 to D.I.Y, an Irma T. Hirsch career development grant to S.F., a postdoctoral fellowship from the Alfonso Martin Escudero Foundation and the Bernard Levine postdoctoral fellowship in Immunology to A.R.C. and a predoctoral fellowship by the T32 training program in Immunology and Inflammation (AI100853) to A.Y.T. We thank M. Imagawa (Nagoya City University, Japan) for providing Lrrc8c −/− mice and P.G. Thomas (St. Jude Children’s Research Hospital) for the gift of influenza A/PR8 viral stocks. We thank P. Schwarzberg (NIH) and M. Pipkin (Scripps Research) for providing us with plasmids for expression of sgRNA and shRNA, respectively. We thank B. Desai (University of Virginia School of Medicine) for critical reading of the manuscript and members of the Feske laboratory for helpful discussions.
Author information
Authors and Affiliations
- Department of Pathology, New York University Grossman School of Medicine, New York, NY, USA
Axel R. Concepcion, Jingjie Zhu, Anthony Y. Tao, Jun Yang, Yin-Hu Wang, Menghan Liu, William A. Coetzee & Stefan Feske - Department of Pharmacology and Physiology, University of Rochester, Rochester, NY, USA
Larry E. Wagner II & David I. Yule - Applied Bioinformatics Laboratories, New York University Grossman School of Medicine, New York, NY, USA
Alireza Khodadadi-Jamayran - Metabolomics Core Resource Laboratory, New York University Grossman School of Medicine, New York, NY, USA
Rebecca E. Rose & Drew R. Jones
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Contributions
A.R.C. and S.F. designed the research; A.R.C., L.E.W.II, J.Z., J.Y., Y-H.W. and R.E.R. performed experiments; A.R.C., A.Y.T., A.K.-J. and M.L. performed bioinformatic analyses. A.R.C., L.E.W.II, D.R.J., W.A.C., D.I.Y. and S.F. analyzed data and interpreted the results; A.R.C. and S.F. wrote the manuscript.
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Correspondence toStefan Feske.
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S.F. is a cofounder of CalciMedica; the other coauthors declare no conflict of interest.
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Nature Immunology thanks Zhaozhu Qiu, Jeroen Roose and Mohamed Trebak for their contribution to the peer review of this work. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. Peer reviewer reports are available.
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Extended data
Extended Data Fig. 1 Transcriptomic analysis of the ion channelome in immune cells identifies LRRC8C and other ICTs specific to T cells.
(a) Correlation analysis of ICT genes in CD4+ T cells from mouse ImmGen RNA-Seq and human Fantom5 CAGE-Seq databases. 148 ICTs are highly expressed in both human and mouse T cells (red quadrant). (b) Correlation analysis of 148 highly expressed ICT genes in CD4+ T cells and other immune cells from mouse ImmGen RNA-Seq. ICT genes on the bottom right (grey triangle) are 2-fold higher expressed in CD4+ T cells compared to other immune cells. CD4-specific ICT genes highlighted in blue are shared in both mouse ImmGen and Haemopedia datasets, and red are shared in ImmGen, Haemopedia, and human Fantom5 datasets. (c) Correlation analysis of ICT genes in mouse CD4+ T cells from ImmGen RNA-Seq and Haemopedia RNA-Seq databases. Most specific ICTs are highlighted in red and known ICTs to play a role in T cells shared in both mouse ImmGen and Haemopedia datasets are highlighted in blue. (d,e) Expression profile of differentially expressed ICTs in CD4+ T cells from (d) Haemopedia RNA-Seq and (e) Fantom5 CAGE-Seq databases. Color coding: high (red) and low (blue) relative mRNA expression per row.
Extended Data Fig. 2 VRAC channels in mouse T cells are composed of LRRC8C and LRRC8A.
(a) Relative expression of Lrrc8a and Lrrc8c mRNA from CD4+ T cells transduced with shRNAs targeting Lrrc8a, Lrrc8c, or both Lrrc8a/c, and measured by RT–qPCR 3 days after transduction. shRNA targeting non-mammalian gene Renilla Luciferase (Ren.713) was used as shControl. Gapdh mRNA was used as housekeeping control and relative expression was normalized to shControl. Data are the mean ± s.e.m. from 6 independent experiments and mice (representing 15, 6, 6 and 7 transductions of T cells with shControl, shLrrc8a, shLrrc8c, and shLrrc8a/c, respectively). (b-e) Volume-regulated anion currents (IVRAC) from CD4+ T cells transduced with shRNAs. (b) _I_VRAC from T cells shown in (a) and measured by patch-clamping in whole-cell configuration (representative traces from at least 6 cells per shRNA from 2 independent experiments). Recording protocol (top): T cells were held at −70 mV and were depolarized to +80 mV every 5 s in hypotonic solution (∼215 mOsm). Current densities as a function of voltage (c) and extracted at +80 mV (d) at the end of each test pulse from experiment shown in (b). (e) Average current densities at −70 mV and +80 mV over time induced by hypotonic (Hypo) solution (applied at the arrow) in shRNA transduced CD4+ T cells (mean ± s.e.m., 6-10 cells per shRNA from 2 independent experiments). Unapparent error bars are smaller than symbols. (f,g) RVD traces (f) and quantification of peak, area under the curve (AUC) and time to RVD50 (g) in CD4+ T cells transduced with shRNAs and subjected to hypoosmotic swelling. Data are the mean ± s.e.m. from 23, 18, 16 and 7 traces for shControl, shLrrc8a, shLrrc8c, and shLrrc8a/c, respectively, and pooled from 3 independent experiments. Statistical analysis by two-tailed, unpaired Student’s t-test in (a and g), and one-way ANOVA with Dunnett’s multiple comparisons test in (d). Not significant (n.s.). P > 0.05, **P < 0.01 and ***P < 0.001.
Extended Data Fig. 3 _Lrrc8c_−/− mice have normal T cell development.
(a-d) Cell numbers and proportion of thymocytes from WT and _Lrrc8c_−/− mice. (a) Absolute cell numbers, (b) representative flow cytometry plots showing the proportion (c) and numbers (d) of thymocyte subsets DN1, DN2, DN3, DN4, DP, SP4+ and SP8+ cells. (e-j) Cell numbers and proportion of splenocytes from WT and _Lrrc8c_−/− mice. (e) Absolute cell numbers, (f) representative flow cytometry plots showing the gating strategy to identify the different subsets of T cells in the spleen from WT and _Lrrc8c_−/− mice. Proportion and absolute cell numbers of (g) CD3+ T cells, (h) CD4+ and CD8+ T cells, and (i,j) naïve CD44loCD62Lhi, T central memory (TCM) CD44hiCD62Lhi, and T effector memory (TEM) CD44hiCD62Llo T cell subsets in both CD4+ and CD8+ T cells. (k-m) Cell number and proportion of T cells in the lymph nodes (LNs) from WT and _Lrrc8c_−/− mice. (k) Absolute cell numbers, and proportion and absolute cell numbers of (l) CD3+ T cells, and (m) CD4+ and CD8+ T cells. Data represent mean ± s.e.m. of 9 mice per genotype (a-j) and 7 mice per genotype (k-m), pooled from at least 4 independent experiments.
Extended Data Fig. 4 p53 controls the proliferation and survival of T cells.
(a-c) Protein expression of p53 (a), Ki67 (b), and apoptosis determined by annexin V staining (c) in WT and _Lrrc8c_−/− CD4+ T cells after 3 days of retroviral transduction. Representative flow cytometry plots (left) and quantification (right). Data represent the mean ± s.e.m. of 8 mice per genotype, pooled from 2 independent experiments. (d,e) Protein expression of p53 (d) and Ki67 (e) in CD4+Cas9+ T cells transduced with sgRNAs targeting p53 (Trp53) after 3 days of retroviral infection. Representative flow cytometry plots (left) and quantification (right). Data represent the mean ± s.e.m. of 3 (in d) and 5 (in e) mice, pooled from 2 independent experiments. (f,g) Protein expression of p53 (f) and Ki67 (g) in T cells shown in (d,e) after restimulation for additional 3 days with anti-CD3 + 28 dynabeads and treated or not with idasanutlin (nutlin). Representative flow cytometry plots (left) and quantification (right). Data represent the mean ± s.e.m. of 3 and 5 mouse donors for sgControl and sgTrp53, respectively, pooled from 2 independent experiments. Statistical analysis by two-tailed, unpaired Student’s t test. *P < 0.05 and ***P < 0.001.
Extended Data Fig. 5 VRAC inhibitor DCPIB suppresses p53 signaling in activated T cells.
(a) Averaged traces (left) and quantification (right) of RVD (AUC) in CD4+ T cells stimulated with anti-CD3 + 28 for at least 3 days and subjected to hypotonic solution after treatment for 30 min with 20 μM DCPIB (data are the mean ± s.e.m. of 12 and 20 traces for vehicle and DCPIB treated cells, respectively, pooled from at least 5 independent experiments). (b) _I_VRAC traces from CD4+ T cells stimulated for 2d with anti-CD3 + 28 and pre-treated or not with 20 μM DCPIB, measured by patch clamping in whole-cell configuration. Representative traces from 10 cells (vehicle) and 5 cells (DCPIB) from at least 3 independent experiments. Recording protocol (top): T cells were held at −70 mV and were depolarized to +80 mV every 5 s in hypotonic solution (∼215 mOsm). (c) GSEA of RNA-Seq data from WT CD4+ T cells treated or not with 20 μM DCPIB identifies DEGs associated with p53 pathway after anti-CD3 + 28 stimulation. (d) Venn-diagram showing number of DEGs related to the p53 pathway between WT _vs Lrrc8c_−/− and WT vs WT + DCPIB CD4+ T cells after anti-CD3 + 28 stimulation for 1 and 2 days**. (e)** Heat map of DEGs associated with p53 pathway identified by GSEA in (c). Color coding: high (red) and low (blue) relative mRNA expression per row. DEGs highlighted in red are shared between WT vs WT + DCPIB and WT _vs Lrrc8c_−/− CD4+ T cells. Statistical analysis in (a) by two-tailed, unpaired Student’s t test. ***P < 0.001.
Extended Data Fig. 6 p53 controls CD80 expression in T cells.
(a) CD80 mRNA expression in human T cells from healthy volunteers stimulated with phytohemagglutinin for 3 days and treated with nutlin-3 for 24 h. CD80 mRNA expression based on microarray data from GSE110369 (ref. 62). (b) p53 binding to the CD80 gene locus in human T cells treated as in (a) and analyzed by ChIP-Seq (data source: GSE110368)62. Representative binding peaks in the CD80 promoter (left) and quantification (right). (c) CD80 expression in CD4+Cas9+ T cells transduced with sgRNAs targeting p53 (Trp53) and restimulated for 3 days with anti-CD3 + 28 and treated or not with nutlin. Representative flow cytometry plots (left) and quantification (right) of mean fluorescence intensities (MFI) of CD80. Data are from 3 and 5 mice for sgControl and sgTrp53, respectively, pooled from 2 independent experiments. (d) Cd80 mRNA expression in CD4+ T cells of WT or _Lrrc8c_−/− mice stimulated with anti-CD3/28 for 1 and 2 days. T cells from WT mice were treated or not with DCPIB for the duration of T cell stimulation. mRNA expression based on RNA-Seq data (compare with Fig. 3). Data are from 3 mice per genotype and treatment. (e) CD80 expression in WT and _Lrrc8c_−/− CD4+ T cells before and after stimulation with anti-CD3 + 28 for 3 days. Representative flow cytometry plots (left) and quantification (right) from 11 mice per genotype, pooled from 4 independent experiments. (f) CD80 expression in WT CD4+ T cells treated with 20 μM DCPIB for 3 days following anti-CD3 + 28 stimulation. Representative overlay histograms (left) and quantification (right) of CD80 expression from 6 mice per condition. (g,h) CD80 cell expression in WT and _Lrrc8c_−/− CD4+ T cells upon stimulation with anti-CD3 + 28 for 3 days and treated or not with nutlin (in g), or after 3 days of retroviral transduction with empty vector or p53 (in h). Representative flow cytometry plots (left) and quantification (right) of 8 mice per genotype, pooled from 4 and 2 independent experiments in (g) and (h), respectively. All data are mean ± s.e.m. and were analyzed by two-tailed, unpaired Student’s t test (a-c,e,g,h) and by two-tailed, paired t test (f). Not significant (n.s.) P > 0.05, *P < 0.05, **P < 0.01 and ***P < 0.001.
Extended Data Fig. 7 Activation of STING in T cells depends on the channel function of LRRC8C.
(a,b) Compound screening to identify substrates for LRRC8C in T cells. Flow cytometry analysis showing (a) the CFSE dilution and (b) CD80 surface expression of CD4+ T cells stimulated for 2 days with anti-CD3 + 28 dynabeads and treated with 19 different substrates known to be transported by VRAC channels. The concentration of tested compounds is: 3 and 10 μg/ml for 2′3′cGAMP, 3′3′cGAMP, c-di-AMP, and c-di-GMP; 5 and 10 μg/ml Blasticidin S, 0.1 and 1 μM sphingosine 1-phosphate (S1P), 1 and 10 μM bradykinin acetate, 50 and 100 μM cisplatin, 100 and 200 μM folic acid, 100 and 500 μM GABA, _myo_-inositol and thiamine hydrochloride; 250μM and 1 mM ATP, D-glutamic acid, taurine, D-lysine, D-aspartic acid, D-serine; 500μM and 1 mM D-sorbitol. The concentration of positive controls that lack requirement for VRAC transport are: 3 μg/ml DMXAA, 1 μM staurosporine (STS), and 5 μM idasanutlin (Nutlin). Data are from 4 mice per genotype, pooled from 2 independent experiments. Area highlighted in light grey represents unstimulated conditions. (c,d) Dose-dependent effects of CDNs on (c) proliferation measured by CFSE dilution and (d) CD80 expression in WT and _Lrrc8c_−/− CD4+ T cells stimulated for 2 days with anti-CD3 + 28. Data are from 6 mice per genotype and treatment, pooled from 3 independent experiments. (e) Immunoblots of total and p-STING (S365) protein in WT and _Lrrc8c_−/− CD4+ T cells activated for 2 days with anti-CD3 + 28 and treated with 3 μg/ml DMXAA at the indicated time points. Actin was used as loading control. Representative blot (top) and quantification (bottom) from at least 2 independent experiments. (f) Intracellular concentration of 2′3′ cGAMP in T cells stimulated with anti-CD3 + 28 for 12-72 hours and measured by ELISA. Data are from 6 (unstimulated) and 12 (stimulated) T cell samples, pooled from 12 mice per genotype and 2 independent experiments. (g) GSEA of RNA-Seq data from WT, _Lrrc8c_−/−, and WT + DCPIB-treated CD4+ T cells identifies DEGs associated with TNF-α signaling via NFκB pathway after anti-CD3 + 28 stimulation. (h) Venn-diagram showing number of DEGs related to the TNF-α signaling via NFκB pathway between WT _vs. Lrrc8c_−/− and WT vs. WT + DCPIB CD4+ T cells after anti-CD3 + 28 stimulation for 1 and 2 days. All data are the mean ± s.e.m. and were analyzed by two-tailed, unpaired Student’s t test. Not significant (n.s.) P > 0.05, *P < 0.05, **P < 0.01 and ***P < 0.001.
Extended Data Fig. 8 Inhibition of STING increases proliferation and decreases apoptosis, p53 and CD80 expression in T cells.
(a-c) Immunoblots of total and phospho-STING (S365) protein expression in WT CD4+ T cells activated for 2 days with anti-CD3 + 28 and treated or not with the STING inhibitor H-151. Cell were stimulated with 10 μg/ml 2′3′cGAMP (a), 5 μg/ml c-di-AMP (b) or 3 μg/ml DMXAA (c) for 3-6 hours. Actin was used as loading control. Representative blots (left) and quantification (right) of at least 3 independent experiments and 4, 3, and 5 mice for 2′3′cGAMP, c-di-AMP, and DMXAA treatment, respectively. (d-g) Flow cytometry analysis of Ki67 expression (d), apoptosis measured by annexin V and active caspase (e), p53 (f), and CD80 expression (g) in WT CD4+ T cells stimulated for 3 days with anti-CD3 + 28 and treated or not with STING agonists and pre-treated or not with the STING inhibitor H-151. Representative flow cytometry plots (left) and quantification (right) of at least 6 mice per treatment, pooled from 4-6 independent experiments and shown as mean ± s.e.m. (h) Correlation analysis of CD80 and p53 expression in T cells stimulated and treated as shown in (f,g). Statistical analysis in (a-g) by two-tailed, unpaired Student’s t test. **P < 0.01 and ***P < 0.001. ##P < 0.01 and #P < 0.001 between STING agonists vs. vehicle untreated.
Extended Data Fig. 9 Deletion of p53 protects T cells from STING-mediated proliferation arrest.
(a) Immunoblots of STING in CD4+Cas9+ T cells transduced with sgRNAs targeting STING and p53 at 4 days after retroviral infection. Actin was used as loading control. Representative blots (left) and quantification (right) represented as the mean ± s.e.m. of n = 3 mice and 2 independent experiments. (b) CD80 expression in CD4+Cas9+ T cells transduced with sgRNAs targeting STING and p53, restimulated for 2 days with anti-CD3 + 28 dynabeads and treated or not (vehicle) with 3 μg/ml DMXAA. Representative flow cytometry plots (left) and quantification (right). Data represent the mean ± s.e.m. of at least 3 mice, pooled from 2 independent experiments. (c) CFSE dilution in CD4+Cas9+ T cells transduced with sgRNAs targeting STING and p53, restimulated for 3 days with anti-CD3 + 28 dynabeads and treated or not (vehicle) with STING agonists and idasanutlin (nutlin). Representative flow cytometry plots (left) and quantification (right) of the frequency of proliferating cells. Data represent the mean ± s.e.m. of 3-5 mice, pooled from 2 independent experiments. Statistical analysis by two-tailed, unpaired Student’s t test (a,b) and 2-way ANOVA with Dunnett’s multiple comparisons test (c). Not significant (n.s.) P > 0.05, *P < 0.05, **P < 0.01 and ***P < 0.001. #P < 0.001 between treatment vs vehicle untreated.
Extended Data Fig. 10 LRRC8C-STING-p53 signaling modulates Ca2+ influx in T cells.
(a,b) Cytosolic Ca2+ signals in WT T cells stimulated for 3 days with anti-CD3 + 28 and treated or not with the STING inhibitor H-151 and STING agonists. T cells were stimulated with thapsigargin (TG) in Ca2+-containing Ringer buffer. Averaged Ca2+ traces (a) and area under the curve (AUC) following TG treatment (b). Data are the mean ± s.e.m. of 6 mice (vehicle, DMXAA, 2′3′cGAMP, 3′3cGAMP) or 4 mice (c-di-AMP, c-di-GMP) pooled from 2-3 independent experiments. (c) Cytosolic Ca2+ signals in WT and _Lrrc8c_−/− T cells 3 days after retroviral transduction with p53 using a similar protocol as in (a). Averaged Ca2+ traces (left) and quantification of the AUC (right) following TG treatment. Data are the mean ± s.e.m. of 4 mice per genotype and treatment, pooled from 2 independent experiments**. (d)** Cytosolic Ca2+ signals in CD4+Cas9+ T cells transduced with sgRNAs targeting STING and p53 three days after retroviral transduction using a similar protocol as in (a). Averaged Ca2+ traces (left) and quantification of the AUC (right) after TG treatment. Data are the mean ± s.e.m. of 3 mice pooled from 2 independent experiments. Statistical analysis by two-tailed, unpaired Student’s t test. Not significant (n.s.) P > 0.05, **P < 0.01 and ***P < 0.001. #P < 0.001 between treatment vs vehicle untreated.
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Concepcion, A.R., Wagner, L.E., Zhu, J. et al. The volume-regulated anion channel LRRC8C suppresses T cell function by regulating cyclic dinucleotide transport and STING–p53 signaling.Nat Immunol 23, 287–302 (2022). https://doi.org/10.1038/s41590-021-01105-x
- Received: 22 February 2021
- Accepted: 01 December 2021
- Published: 01 February 2022
- Issue Date: February 2022
- DOI: https://doi.org/10.1038/s41590-021-01105-x