Abstract
The formation of an immunological synapse (IS) is essential for natural killer (NK) cells to eliminate target cells. Despite an advanced understanding of the characteristics of the IS and its formation processes, the mechanisms that regulate its stability via the cytoskeleton are unclear. Here, we show that Nogo receptor 1 (NgR1) has an important function in modulating NK cell-mediated killing by destabilization of IS formation. NgR1 deficiency or blockade resulted in improved tumor control of NK cells by enhancing NK-to-target cell contact stability and regulating F-actin dynamics during IS formation. Patients with tumors expressing abundant NgR1 ligand had poor prognosis despite high levels of NK cell infiltration. Thus, our study identifies NgR1 as an immune checkpoint in IS formation and indicates a potential approach to improve the cytolytic function of NK cells in cancer immunotherapy.
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Data availability
The data supporting the findings of this study are available within the paper and from the corresponding authors upon request. Patient samples, including gene expression and clinical information, were accessed from the GDC data portal (https://portal.gdc.cancer.gov). Primary tumor data for survival analysis were from TCGA. Mathematical data for pan-cancer gene expression were analyzed using CIBERSORT (https://cibersortx.stanford.edu). Source data are provided with this paper.
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Acknowledgements
We thank Jong Bae Park (National Cancer Center) and Si Rim Lee (KRIBB) for expert technical assistance and experimental trials, respectively. This work was supported by KRIBB Research Initiative Program, the National Research Council of Science & Technology (NST) grant (CAP-18-02-KRIBB to T.-D.K.), the National Research Foundation grant (2022M3E5F1016693 to T.-D.K. and 2020R1A2B5B03001747 to J.D.), and Korea Drug Development Fund (HN21C0117 to T.-D.K.) by the Korea government.
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T.-D.K. and J.D. conceived and designed the study. S.-C.O., S.-E.K. and I.-H.J. aquired, analyzed and interpreted the data. J.D. and S.-E.K. designed the live image data. I.-H.J. and I.-S.C. designed and analyzed bioinformatic data. S.-M.K., S.Y.L., S.L., I.C., S.R.Y. and H.J. provided discussions and advice. S.-C.O., S.-E.K., T.-D.K. and J.D. wrote, reviewed and revised the manuscript. T.-D.K. and J.D. supervised the study and acquired funding.
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Nature Immunology thanks Daniel Billadeau and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: N. Bernard in collaboration with the Nature Immunology team. Peer reviewer reports are available.
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Extended data
Extended Data Fig. 1 Identification of NgR1 in NK cells.
a, Representative flow cytometric histograms and folds of MFI for NgR1 expression in splenic CD8 T cells from WT mouse and EL4 cell line (n = 1 each). b, Representative flow cytometric histograms and folds of MFI for LINGO1, TROY and p75NTR expression in mouse splenic NK cells and EL4 cell line (n = 3 each). c, Representative flow cytometric histograms and folds of MFI for NogoA expression in CT26, YAC-1, 4T1 and B16F10 cell lines (n = 3 each). d, Cytotoxicity analysis on CT26, YAC-1, and 4T1 cells of splenocytes from WT mice with or without NEP1-40 treatment (n = 5 each). In b–d, the data represent mean ± s.e.m. Data are representative of two independent experiments (d). Statistical significance was calculated by unpaired two-tailed Student’s t-test (d).
Extended Data Fig. 2 Characterization of immune composition in NgR1 deficiency.
a,b, Representative flow cytometric plots and frequencies of total NK cells (CD3−NK1.1+) and classified NK cells (CD27−CD11b−, CD27+CD11b−, CD27+CD11b+, and CD27−CD11b+) with or without IL-2 stimulation in WT and KO mice (n = 3 each). c, Representative flow cytometric histograms and folds of MFI for intracellular IFNγ expression of total NK cells in WT and KO mice (n = 3 each). d–f, Frequencies of resting or IL-2 stimulated CD4 T cells (CD3+CD4+) and CD8 T cells (CD3+CD8+) (n = 3 each) (d), B cells (B220+) (n = 3 each) (e), and myeloid cells (CD11b+Gr1+), neutrophils (CD11b+Gr1high), monocytes (CD11b+Gr1low), and macrophages (CD11b+F4/80high) (f) in WT and KO mice. The data represent mean ± s.e.m. Data are representative of one independent experiment using biologically independent samples (a–f). Statistical significance was calculated by one-way ANOVA with Tukey’s multiple comparisons test (a,c), two-way ANOVA with Tukey’s multiple comparisons test (b,d,f) or unpaired two-tailed Student’s t-test (c). NS, not significant (P > 0.05).
Extended Data Fig. 3 NgR1 signals in NK cells.
a,b, Representative flow cytometric histograms and folds of MFI for NgR1 expression in human UCB-CD8 T and Jurkat cell line (n = 3 each) (a), and for LINGO1, TROY and p75NTR expression in human UCB-NK, PB-NK, mNK, NK92 and Jurkat cell lines (n = 3 each) (b). c, Representative immunoblots and quantification analysis of lysate from human UCB-NK cells treated with Nogo-P4 during indicated time (n = 3 each). The data represent mean ± s.e.m. (a,b) or mean ± s.d. (c). Data are representative of two independent experiments (a,b,c). Statistical significance was calculated by one-way ANOVA with Tukey’s multiple comparisons test (c). NS, not significant (P > 0.05).
Extended Data Fig. 4 Characterization of F-actin dynamics by NgR1.
a, Time-lapse images for fluorescent intensity of phalloidin-stained NK92 cells with untreated (Ctrl), scrambled peptide (Scram) or Nogo-P4 treatment using fluorescence microscopy. Scale bar, 5 μm. b, Single-cell analysis for phalloidin intensity of NK92 cells (n = 26 each). c, Time-lapse images of NK92 cells with untreated (Ctrl), scrambled peptide (Scram) or Nogo-P4 treatment using video microscopy. Scale bar, 5 μm. d, Single-cell analysis for protrusion frequency of NK92 cells (n = 66 Ctrl; n = 63 Scram; n = 53 Nogo-P4). e, Time-lapse images for fluorescent intensity of phalloidin-stained NK92 cells expressing Lifeact-GFP with untreated (Ctrl), scrambled peptide (Scram) or Nogo-P4 treatment using fluorescence microscopy. Scale bar, 5 μm. f, Single-cell analysis for phalloidin intensity of NK92 cells expressing Lifeact-GFP (n = 26 each). The data represent mean ± s.e.m. (b,d,f). Data are representative of one independent experiment (b,d,f). Statistical significance was calculated by one-way ANOVA with Tukey’s multiple comparisons test (b,d,f). NS, not significant (P > 0.05).
Extended Data Fig. 5 NgR1 function in NK cell killing.
a, Folds of MFI for NogoA expression in K562, HEK293T, U87MG and AU565 cell lines (n = 3 each). b,c, Representative immunoblots of lysate from K562 or HEK293T cells overexpressed NogoA. d, Representative immunoblots and quantification analysis of lysate from U87MG cells transfected scrambled siRNA (Scram) or NogoA siRNA (siNogoA) (n = 3 each). e, Representative immunoblots and quantification analysis of lysate from NK92 cells transfected scrambled siRNA (Scram) or NgR1 siRNA #1 and #3 (n = 3 each). f, Representative flow cytometric histograms and folds of MFI for NgR1 expression in NK92 cells transfected scrambled siRNA (Scram), siNgR1 #1 and siNgR1 #3 (n = 3 each). g, Folds of MFI for ULBP1, ULBP2, ULBP3, MIC-A/B, and HLA-A/B/C expression in K562, HEK293T, U87MG, and AU565 cell lines (n = 3 each). h, Cytotoxicity analysis of human UCB-NK cells to AU565 cells with or without NEP1-40 treatment (n = 3 each). i-l, Cytotoxicity analysis of human PB-NK cells to K562 cells (n = 7 each) (i), NogoA overexpressed K562 cells (n = 3 each) (j), U87MG cells (n = 7) (k), or AU565 cells (n = 7) (l) with and without NEP1-40 treatment. The data represent mean ± s.e.m. (a,f–l) or mean ± s.d. (d,e). Data are representative of five independent experiments (k,l), three independent experiments (h,i), two independent experiments (a,d,e,j). Statistical significance was calculated by one-way ANOVA with Tukey’s multiple comparisons test (d,e,j) or unpaired two-tailed Student’s t-test (g,i,k,l). NS, not significant (P > 0.05).
Extended Data Fig. 6 Interference of NgR1 in the NK-to-target cell contact.
a, Representative time-lapse images of interaction between human PB-NK cells (green border line) and U87MG cells (white border line) with and without NEP1-40. Scale bar, 10 μm. b-d, Effects of NEP1-40 on NK transient interaction frequencies (n = 12 each) (b), contact duration time (n = 49 Ctrl, n = 45 NEP1-40) (c) and killing probability (n = 12 each) (d). e, Representative time-lapse images of interaction between mouse NK cells (green border line) and B16F10 cells (white border line) with and without NEP1-40. Scale bar, 10 μm. f–h, Effects of NEP1-40 on NK transient interaction frequencies (n = 15 each) (f), contact duration time (n = 32 Ctrl, n = 20 NEP1-40) (g) and killing probability (n = 6 each) (h). i, Representative time-lapse images of interaction between NK cells from WT and KO mice (green border line) and B16F10 cells (white border line). Scale bar, 10 μm. j–l, Effects of KO on NK transient interaction frequencies (n = 10 each) (j), contact duration time (n = 31 each) (k) and killing probability (n = 8 each) (l). In b–d,f–h,j–l the data represent mean ± s.e.m. Data are representative of one independent experiments (b–d,f-h,j–l). Statistical significance was calculated by unpaired two-tailed Student’s t-test.
Extended Data Fig. 7 Regulation of LIMK–Cofilin signals by NgR1.
a, Representative immunoblots of lysate from NK92 cells with siCofilin transfection. b, Representative immunoblots and quantification analysis of lysate from NK92 cells with and without Nogo-P4 or LIMKi3 treatment during indicated time (n = 3 each). The data represent mean ± s.d. (b). Data are representative of two independent experiments (b). Statistical significance was calculated by two-way ANOVA with Sidak’s multiple comparisons test (b). NS, not significant (P > 0.05).
Extended Data Fig. 8 NgR1 serves as a novel immune checkpoint.
a, Cox hazard ratio of RTN4 expression level stratified by the quantity of infiltrated NK cells at the top 10% to 50% and the bottom 50% to 90% in TCGA pan-cancer. b, Cox hazard ratio of RTN4 expression level stratified by the quantity of infiltrated CD8 T cells at the top 20% in TCGA pan-cancer. c, Kaplan–Meir plot of RTN4 expression level on top 20% CD8 T-rich and bottom 80% CD8 T-poor groups. d, Cox hazard ratio of RTN4 expression level stratified by the quantity of infiltrated CD8 T cells at the top 10% to 50% and the bottom 50% to 90% in TCGA pan-cancer. In c, the data represent mean ± s.e.m. Statistical significance was calculated by Wald test (a,b,d) or log-rank test (c).
Supplementary information
Supplementary Information
Supplementary Tables 1–3.
Supplementary Video 1
Live imaging of NK92 cells expressing Lifeact-GFP incubated with untreated (Ctrl), scrambled peptide (Scram) or Nogo-P4 for indicated time using video microscopy.
Supplementary Video 2
Live imaging of NK92 cells incubated with untreated (Ctrl), scrambled peptide (Scram) or Nogo-P4 for indicated time using video microscopy.
Supplementary Video 3
Live imaging of coculture of NK92 (green border line) and U87MG cells (white border line) with or without NEP1-40 treatment.
Supplementary Video 4
Live imaging of coculture of human PB-NK cells (green border line) and U87MG cell line (white border line) with or without NEP1-40 treatment.
Supplementary Video 5
Live imaging of coculture of NK cells (green border line) from WT mice and B16F10 cell line (white border line) with or without NEP1-40 treatment.
Supplementary Video 6
Live imaging of coculture of NK cells (green border line) from WT or KO mice and B16F10 cell line (white border line).
Supplementary Video 7
Live imaging of coculture of NK92 cells (green border line) pretreated with or withoqut LIMKi3 and U87MG cells (white border line).
Supplementary Video 8
Live imaging of coculture of NK92 cells expressing Lifeact-GFP (green) and U87MG cells (white border line) with or without NEP1-40 treatment.
Supplementary Video 9
Live imaging of coculture of lysosensor (green) stained NK92 cells and U87MG cells (white border line) with or without NEP1-40 treatment.
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Oh, SC., Kim, SE., Jang, IH. et al. NgR1 is an NK cell inhibitory receptor that destabilizes the immunological synapse. Nat Immunol 24, 463–473 (2023). https://doi.org/10.1038/s41590-022-01394-w
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DOI: https://doi.org/10.1038/s41590-022-01394-w
