Abstract
Metastasis constitutes the primary cause of cancer-related deaths, with the lung being a commonly affected organ. We found that activation of lung-resident group 2 innate lymphoid cells (ILC2s) orchestrated suppression of natural killer (NK) cell-mediated innate antitumor immunity, leading to increased lung metastases and mortality. Using multiple models of lung metastasis, we show that interleukin (IL)-33-dependent ILC2 activation in the lung is involved centrally in promoting tumor burden. ILC2-driven innate type 2 inflammation is accompanied by profound local suppression of interferon-γ production and cytotoxic function of lung NK cells. ILC2-dependent suppression of NK cells is elaborated via an innate regulatory mechanism, which is reliant on IL-5-induced lung eosinophilia, ultimately limiting the metabolic fitness of NK cells. Therapeutic targeting of IL-33 or IL-5 reversed NK cell suppression and alleviated cancer burden. Thus, we reveal an important function of IL-33 and ILC2s in promoting tumor metastasis via their capacity to suppress innate type 1 immunity.
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Data availability
The transcriptomic data used in Fig. 6 and Extended Data Fig. 7 are available at the SRA under the BioProject no. PRJNA637311: lung NK cell transcriptomic. Bulk RNA-seq data are available under BioSample accession nos. SAMN15099850 (bulk PBS versus IL-33; the sample names are A1, D1, B2, C2, F2, E1 and H1 for the PBS-treated condition and D2, B1, F1, E2, A2 and G1 for the IL-33-treated condition) and SAMN15099866 (time course) with the following triplicate sample names: E4, A4, C3 (WT PBS day 2); H3, F4, D3 (WT IL-33 day 2); C4, G4, A3 (WT IL-33 day 7); B4, E3, G3 (WT IL-33 day 14); and D4, F3, B3 (ILC2 knockout IL-33 day 2).
References
Steeg, P. S. Targeting metastasis. Nat. Rev. Cancer 16, 201–218 (2016).
López-Soto, A., Gonzalez, S., Smyth, M. J. & Galluzzi, L. Control of metastasis by NK cells. Cancer Cell 32, 135–154 (2017).
Monticelli, L. A. et al. Innate lymphoid cells promote lung-tissue homeostasis after infection with influenza virus. Nat. Immunol. 12, 1045–1054 (2011).
Schuijs, M. J. & Halim, T. Y. F. Group 2 innate lymphocytes at the interface between innate and adaptive immunity. Ann. N. Y. Acad. Sci. 1417, 87–103 (2018).
Halim, T. Y. F. et al. Tissue-restricted adaptive type 2 immunity is orchestrated by expression of the costimulatory molecule OX40L on group 2 innate lymphoid cells. Immunity 48, 1195–1207.e6 (2018).
Molofsky, A. B. et al. Interleukin-33 and interferon-γ counter-regulate group 2 innate lymphoid cell activation during immune perturbation. Immunity 43, 161–174 (2015).
Crome, S. Q. et al. A distinct innate lymphoid cell population regulates tumor-associated T cells. Nat. Med. 23, 368–375 (2017).
Wang, S. et al. Regulatory innate lymphoid cells control innate intestinal inflammation. Cell 171, 201–216.e18 (2017).
Seehus, C. R. et al. Alternative activation generates IL-10 producing type 2 innate lymphoid cells. Nat. Commun. 8, 1900 (2017).
Fournie, J.-J. & Poupot, M. The pro-tumorigenic IL-33 involved in antitumor immunity: a yin and yang cytokine. Front. Immunol. 9, 2506 (2018).
Chevalier, M. F. et al. ILC2-modulated T cell-to-MDSC balance is associated with bladder cancer recurrence. J. Clin. Invest. 127, 2916–2929 (2017).
Jovanovic, I. P. et al. Interleukin-33/ST2 axis promotes breast cancer growth and metastases by facilitating intratumoral accumulation of immunosuppressive and innate lymphoid cells. Int. J. Cancer 134, 1669–1682 (2014).
Saranchova, I. et al. Type 2 innate lymphocytes actuate immunity against tumours and limit cancer metastasis. Sci. Rep. 8, 2924 (2018).
DeNardo, D. G. et al. CD4+ T cells regulate pulmonary metastasis of mammary carcinomas by enhancing protumor properties of macrophages. Cancer Cell 16, 91–102 (2009).
Taranova, A. G. et al. Allergic pulmonary inflammation promotes the recruitment of circulating tumor cells to the lung. Cancer Res. 68, 8582–8589 (2008).
Halim, T. Y. F., Krauss, R. H., Sun, A. C. & Takei, F. Lung natural helper cells are a critical source of Th2 cell-type cytokines in protease allergen-induced airway inflammation. Immunity 36, 451–463 (2012).
Street, S. E., Cretney, E. & Smyth, M. J. Perforin and interferon-γ activities independently control tumor initiation, growth, and metastasis. Blood 97, 192–197 (2001).
Long, A. et al. Type 2 innate lymphoid cells impede IL-33-mediated tumor suppression. J. Immunol. 201, 3456–3464 (2018).
Halim, T. Y. F. et al. Group 2 innate lymphoid cells license dendritic cells to potentiate memory TH2 cell responses. Nat. Immunol. 17, 57–64 (2016).
Halim, T. Y. F. et al. Group 2 innate lymphoid cells are critical for the initiation of adaptive T helper 2 cell-mediated allergic lung inflammation. Immunity 40, 425–435 (2014).
Altorki, N. K. et al. The lung microenvironment: an important regulator of tumour growth and metastasis. Nat. Rev. Cancer 19, 9–31 (2019).
Coffelt, S. B. et al. IL-17-producing γδ T cells and neutrophils conspire to promote breast cancer metastasis. Nature 522, 345–348 (2015).
Gabrilovich, D. I. Myeloid-derived suppressor cells. Cancer Immunol. Res. 5, 3–8 (2017).
Albrengues, J. et al. Neutrophil extracellular traps produced during inflammation awaken dormant cancer cells in mice. Science 361, eaao4227 (2018).
Quail, D. F. et al. Obesity alters the lung myeloid cell landscape to enhance breast cancer metastasis through IL5 and GM-CSF. Nat. Cell Biol. 19, 974–987 (2017).
Weller, P. F. & Spencer, L. A. Functions of tissue-resident eosinophils. Nat. Rev. Immunol. 17, 746–760 (2017).
Zaynagetdinov, R. et al. Interleukin-5 facilitates lung metastasis by modulating the immune microenvironment. Cancer Res. 75, 1624–1634 (2015).
O’Brien, K. L. & Finlay, D. K. Immunometabolism and natural killer cell responses. Nat. Rev. Immunol. 19, 282–290 (2019).
Donnelly, R. P. et al. mTORC1-dependent metabolic reprogramming is a prerequisite for NK cell effector function. J. Immunol. 193, 4477–4484 (2014).
Porter, L. et al. Metabolic profiling of human eosinophils. Front. Immunol. 9, 1404 (2018).
Fairfax, K. A. et al. Transcriptional profiling of eosinophil subsets in interleukin-5 transgenic mice. J. Leukoc. Biol. 104, 195–204 (2018).
Lawrence, M. G., Steinke, J. W. & Borish, L. Cytokine-targeting biologics for allergic diseases. Ann. Allergy Asthma Immunol. 120, 376–381 (2018).
Hsieh, C. S., Macatonia, S. E., O’Garra, A. & Murphy, K. M. T cell genetic background determines default T helper phenotype development in vitro. J. Exp. Med. 181, 713–721 (1995).
Wculek, S. K. & Malanchi, I. Neutrophils support lung colonization of metastasis-initiating breast cancer cells. Nature 528, 413–417 (2015).
Lucarini, V. et al. IL-33 restricts tumor growth and inhibits pulmonary metastasis in melanoma-bearing mice through eosinophils. Oncoimmunology 6, e1317420 (2017).
Iwamoto, I., Nakajima, H., Endo, H. & Yoshida, S. Interferon γ regulates antigen-induced eosinophil recruitment into the mouse airways by inhibiting the infiltration of CD4+ T cells. J. Exp. Med. 177, 573–576 (1993).
Tanaka, T., Hu-Li, J., Seder, R. A., Fazekas de St Groth, B. & Paul, W. E. Interleukin 4 suppresses interleukin 2 and interferon γ production by naive T cells stimulated by accessory cell-dependent receptor engagement. Proc. Natl Acad. Sci. USA 90, 5914–5918 (1993).
Li, M. O., Wan, Y. Y. & Flavell, R. A. T cell-produced transforming growth factor-β1 controls T cell tolerance and regulates Th1- and Th17-cell differentiation. Immunity 26, 579–591 (2007).
Asseman, C., Mauze, S., Leach, M. W., Coffman, R. L. & Powrie, F. An essential role for interleukin 10 in the function of regulatory T cells that inhibit intestinal inflammation. J. Exp. Med. 190, 995–1004 (1999).
Korman, A. J., Peggs, K. S. & Allison, J. P. Checkpoint blockade in cancer immunotherapy. Adv. Immunol. 90, 297–339 (2006).
Bonilla, W. V. et al. The alarmin interleukin-33 drives protective antiviral CD8+ T cell responses. Science 335, 984–989 (2012).
Bourgeois, E. et al. The pro-Th2 cytokine IL-33 directly interacts with invariant NKT and NK cells to induce IFN-γ production. Eur. J. Immunol. 39, 1046–1055 (2009).
Qi, L. et al. Interleukin-33 activates and recruits natural killer cells to inhibit pulmonary metastatic cancer development. Int. J. Cancer 146, 1421–1434 (2020).
Moral, J. A. et al. ILC2s amplify PD-1 blockade by activating tissue-specific cancer immunity. Nature 579, 130–135 (2020).
Dougan, M., Dranoff, G. & Dougan, S. K. GM-CSF, IL-3, and IL-5 family of cytokines: regulators of inflammation. Immunity 50, 796–811 (2019).
Mishra, A. & Rothenberg, M. E. Intratracheal IL-13 induces eosinophilic esophagitis by an IL-5, eotaxin-1, and STAT6-dependent mechanism. Gastroenterology 125, 1419–1427 (2003).
Keppel, M. P., Saucier, N., Mah, A. Y., Vogel, T. P. & Cooper, M. A. Activation-specific metabolic requirements for NK cell IFN-γ production. J. Immunol. 194, 1954–1962 (2015).
Brand, A. et al. LDHA-associated lactic acid production blunts tumor immunosurveillance by T and NK cells. Cell Metab. 24, 657–671 (2016).
Kedia-Mehta, N. & Finlay, D. K. Competition for nutrients and its role in controlling immune responses. Nat. Commun. 10, 2123 (2019).
Rolf, J. et al. AMPKα1: a glucose sensor that controls CD8 T-cell memory. Eur. J. Immunol. 43, 889–896 (2013).
Schlenner, S. M. et al. Fate mapping reveals separate origins of T cells and myeloid lineages in the thymus. Immunity 32, 426–436 (2010).
Mack, M. et al. Expression and characterization of the chemokine receptors CCR2 and CCR5 in mice. J. Immunol. 166, 4697–4704 (2001).
Wagenblast, E. et al. A model of breast cancer heterogeneity reveals vascular mimicry as a driver of metastasis. Nature 520, 358–362 (2015).
Iwano, S. et al. Single-cell bioluminescence imaging of deep tissue in freely moving animals. Science 359, 935–939 (2018).
Lu, T. X. & Rothenberg, M. E. Bone marrow derived eosinophil cultures. Bio Protoc. 4, e1161 (2014).
Marin-Valencia, I. et al. Analysis of tumor metabolism reveals mitochondrial glucose oxidation in genetically diverse human glioblastomas in the mouse brain in vivo. Cell Metab. 15, 827–837 (2012).
Takáts, Z., Wiseman, J. M., Gologan, B. & Cooks, R. G. Mass spectrometry sampling under ambient conditions with desorption electrospray ionization. Science 306, 471–473 (2004).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
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).
Wu, H., Wang, C. & Wu, Z. PROPER: comprehensive power evaluation for differential expression using RNA-seq. Bioinformatics 31, 233–241 (2015).
Lun, A. T. L. et al. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 20, 63 (2019).
Griffiths, J. A., Richard, A. C., Bach, K., Lun, A. T. L. & Marioni, J. C. Detection and removal of barcode swapping in single-cell RNA-seq data. Nat. Commun. 9, 2667 (2018).
Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Res. 5, 2122 (2016).
McCarthy, D. J., Campbell, K. R., Lun, A. T. L. & Wills, Q. F. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics 33, 1179–1186 (2017).
Lun, A. T. L., Bach, K. & Marioni, J. C. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol. 17, 75 (2016).
Heng, T. S. P. et al. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).
Holtzman, M. J., Byers, D. E., Alexander-Brett, J. & Wang, X. The role of airway epithelial cells and innate immune cells in chronic respiratory disease. Nat. Rev. Immunol. 14, 686–698 (2014).
Misharin, A. V., Morales-Nebreda, L., Mutlu, G. M., Budinger, G. R. S. & Perlman, H. Flow cytometric analysis of macrophages and dendritic cell subsets in the mouse lung. Am. J. Respir. Cell Mol. Biol. 49, 503–510 (2013).
Crinier, A. et al. High-dimensional single-cell analysis identifies organ-specific signatures and conserved NK cell subsets in humans and mice. Immunity 49, 971–986.e5 (2018).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Lun, A. T. L., Chen, Y. & Smyth, G. K. It’s DE-licious: a recipe for differential expression analyses of RNA-seq experiments using quasi-likelihood methods in edgeR. Methods Mol. Biol. 1418, 391–416 (2016).
McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).
Acknowledgements
We thank the following funding sources: EMBO long-term post-doctoral fellowship (no. ALTF 423-2017 to M.J.S.); MRC Skills Development Fellowship (no. MR/P014178/1 to A.C.R.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement (Pan-ILC no. 840501 to J.S.); Wallonie-Bruxelles International Grant of Excellence WBI.WORLD (SUB/2019/441873 to J.S.); the Royal Society and Wellcome Trust (no. 204622/Z/16/Z to T.Y.H.); Cancer Research UK (CRUK) core award (no. A24995 to T.Y.H.); and the CRUK Grand Challenge Rosetta Consortium (no. C197/A25040 to K.M.B., R.J.A.G., A.T. and G.H.). DESI–MSI optimization was supported by N. Strittmatter. We thank the CRUK Cambridge Institute Research Instrumentation, flow cytometry, genomics, bioinformatics, histopathology, imaging and BRU cores for their expertise and help.
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M.J.S. designed and conducted the experiments and wrote the manuscript. S. Png, A.C.R., A.T., G.J.H., S. Pinaud, C.G., A.N., J. Stockis, J. Su, A.R. and E.M.S. assisted with the experiments or analysis. J.D.S., M.D.E., A.N.J.M., H.-R.R., M.M., G.J.H., X.R.R., E.S.C., R.J.A.G., K.M.B. and J.C.M. provided reagents and/or advice. T.Y.F.H. supervised the study, designed and conducted experiments and wrote the manuscript.
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G.H., X.R.R., R.J.A.G. and E.S.C. are employees of AstraZeneca and have stock/stock options in AstraZeneca.
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Extended data
Extended Data Fig. 1 Pre-existing innate type-2 inflammation promotes lung metastasis formation.
a, WT mice were treated as in (Fig. 1a) and tumor burden was assessed by histological staining for Ki67+ tumor foci and quantification of tumor area over total lung area by automated image analysis (n = 10). b, Il33cit/+ reporter mice were treated intranasally with PBS or IL-33 on days 0 and 1, and citrine+ lung cells were assessed on day 3. c, WT mice were treated intranasally with IL-33 on days 0 and 1 (or PBS injection for day 0), followed by quantification of total lung eosinophils by flow cytometry (see Extended Data Fig. 4a for gating strategy) at the indicated time-points (n = 3). d, WT mice were treated as indicated, followed by visual quantification of lung metastases on day 28 (n = 9). e, WT mice were treated as indicated, followed by visual quantification of lung metastases on day 21 (n = 6). f, WT mice were treated intranasally with LPS, CpG or PBS on days 0 and 1, followed by intravenous transfer of B16.F10 cells on day 7, and sacrifice on day 21, followed by visual quantification of lung metastases (n = 10). g, 4T1 (n = 10) or 4T1-T (n = 13,14,10) breast cancer cells were implanted in the mammary fat pad (see Methods), primary tumors were dissected and weighed. h, B6.MMTV-PyMT mice were randomized, treated intranasally with PBS, IL-33, or Asp (from week 12 to 20, see Methods), followed by measurement of total primary breast tumor(s) weight at 20 weeks of age (n = 10,10,11). i, Representative flow cytometry gating strategy of lung lymphocytes and innate lymphocyte populations from WT mice treated with PBS or IL-33 on day 0 and 1, followed by sacrifice on day 3 description of gated cells is listed above the dot plot, cell exclusion performed by Boolean-gating. j, WT mice were treated as in (c), followed by quantification of total lung ILC2 (Live CD45+CD3–B220–NK1.1–Lineage–CD127+RORγt–GATA3+) at the indicated time points (n = 3). k, WT and Il33–/– mice were treated with PBS or Asp on day 0 and 1, followed by quantification of total lung eosinophils at day 3 (n = 10,10,5,10). Bar graphs indicate mean (±SEM) and show combined data of two (d-f, k, g) or three (a, h), or representative of three independent experiments (b, c, i, j c). Statistical analyses were calculated using one-way ANOVA with **** = p ≤ 0.0001.
Extended Data Fig. 2 IL-33 influences lung NK cells.
a, WT mice were treated as indicated, followed by visual quantification of lung metastases on day 21. b, WT mice were treated with anti-IFNγ or controlmAb similar to (a); Tumor burden was assessed on day 21 by visual quantification of lung metastases (n = 10). c, Mice were treated intranasally with PBS or IL-33 on days 0 and 1, followed by quantification of total lung NK cells by flow cytometry on day 3 (n = 3). d, WT mice were treated as in (c), followed by flow cytometric detection of Ki-67+ lung NK cells and ILC2 (n = 5). e, Representative flow cytometry gating strategy of lung NK cells and T lymphocytes from WT mice treated with PBS or IL-33 on day 0 and 1, followed by sacrifice on day 3. f,g, WT mice were treated as in (c), followed by quantification of IFNγ+ NK cells (Live CD45+NK1.1+/lowCD49b+) after 3 hr stimulation of total lung cells with plate bound anti-NK1.1 (f) (n = 3); or PI (g) (n = 3). h, Total WT mouse lung cells were stimulated for 3hrs ex vivo with a combination of IL-12 and IL-18, followed by quantification of IFNγ+ positive NK cells (n = 10). i, WT mice were treated as in (c) and cardiac WAT NK cell were ana lyzed for intracellular IFNγ (n = 9,6) and GzmB (n = 9,6) after 3 hr stimulation with PI. j, Lung CD4 and CD8 T cells from PBS or IL-33 treated WT mice were analyzed for intracellular IFNγ after 3 hr stimulation with PI or anti-NK1.1 (representative gating shown in (e)). k, WT mice were treated as in (c), followed by lung NK cells purification and co-cultured with CFSE labelled whole lung homogenates from PBS or IL-33 treated WT mice (12 h), followed by 3 hr PI stimulation and detection of GzmB positive NK cells; grey bars indicate CFSE-labelled NK cells present in whole lung homogenates (n = 6,11,10,11,2,8 biologically independent samples). l-m, WT mice housed at the MRC ARES facility were treated as in (c), followed by quantification of percent of IFNγ+ NK cells (after 3hrs of PI) (l) (n = 5), or treated as in Fig. 1a (50 K B16.F10) followed by visual quantification of lung metastases (m) (n = 5). Bar graphs indicate mean (±SEM) and show combined data of two (b, i) or three (h, k) independent experiments. (e, j) show representative flow cytometry plots of three independent experiments, whereas (c, d, f, g, I, and m) show a representative bar graph of two independent experiments. Statistical analyses were calculated using one-way ANOVA or unpaired two-tailed Student’s t-test (c, f-i, l, m) **** = p ≤ 0.0001.
Extended Data Fig. 3 ILC2 suppress NK cells via an indirect innate immune mechanism.
a, Il7raCre/+ or Il7raCre/+Rorafl/fl mice were treated with PBS or Asp on day 0 and 1, followed by quantification of IFNγ+ lung NKcells on day 3 (n = 12,13,11,12). b,c, WT mice were treated as in (a), followed by purification of lung ILC2 (flow cytometry) and lung NK cells (magnetic bead, see Methods) on day 3. Lung NK were cultured alone or with ILC2 and analyzed for intracellular IFNγ and GzmB (c) (n = 8). d, WT mice were treated as in (e) with PBS or IL-33 and A2AR antagonist or DMSO followed by quantification of IFNγ+ lung NK cells on day 3 (n = 6,7,6). e, WT mice were treated as indicated, followed by quantification of GzmB+ lung NK cells (n = 3, representative gating on right). f, WT and Foxp3DTR mice were treated with PBS or IL-33 and DTx, followed by quantification of percent IFNγ+ lung NK cells on day 3 (n = 3,3,4,5). g, WT and Rag2–/– mice were treated with PBS or IL-33 on days 0 and 1, and given adoptive transfer of LL/2 cells (i.v.) on day 7. Tumor burden was assessed on day 21 by visual quantification of lung metastases (n = 10,8,8,10). h, WT, Rag2–/– and Rag2–/–Il7raCre/+Rorafl/fl mice were treated with PBS or IL-33 on day 0 and 1, followed by flow cytometry analysis for the indicated lung lymphoid cells on day 3 (n = 5). Bar graphs indicate mean (±S EM) and show combined data of two (c, d, and g) or three (a) independent experiments. (e, f, and h) shows a representative bar graph of two independent experiments. Statistical analyses were calculated using one-way ANOVA with ns = not significant, and **** = p ≤ 0.0001.
Extended Data Fig. 4 Myeloid cell profiling after IL-33 administration and correlation with NK cell function.
a,b, WT mice were treated with PBS or IL-33 on day 0 and 1, followed by (a) flow cytometry analysis and (b) quantification for the indicated lung myeloid cellson day 3 (n = 6). c, Representative flow cytometry plot of eosinophils from mice injected and gated as in (a) assessed for expression of Ly-6G (left) Ly-6C (right). d, The correlation between percent IFNγ+ NK cells and total numbers of the indicated myeloid cells in the lung of PBS or IL-33 injected WT mice (day 0 and 1, sacrificed on day 3) was analyzed on pooled results (n = 125). Bar graphs indicate mean (±SEM) and show representative data from three independent experiments (a, b, and c) and (d) representing Pearson r.
Extended Data Fig. 5 IL-33-mediated suppression of NK cells is not dependent on neutrophils or alveolar macrophages.
a-e, WT mice were treated with PBS or IL-33 on day 0 and 1, and the indicated mAb (or clodronate liposomes, C.L.) on day -1 and 1 followed by quantification of percent IFNγ+ or GzmB+ lung NK cells (after PI stimulation) and quantification for the indicated lung myeloid cells on day 3 (a; GzmB n = 10,9,9,10, and depicted myeloid cells n = 15 ,14,14,15, b; n = 8,8,8,9, c; n = 7, d,e, n = 6). Bar graphs indicate mean (±S EM) and show combined data of two (b-e) or three (a) independent experiments. Statistical analyses were calculated using one-way ANOVA with ns = not significant, and **** = p ≤ 0.0001.
Extended Data Fig. 6 IL-5 and eosinophils mediate IL-33-driven suppression of NK cells.
a, WT mice were treated with PBS or IL-33 on day 0 and 1, followed by quantification of IL-5+ ILC2 (CD45+B220–Lineage–), or CD45+B220–lineage+ cells in the lungs on day 3 (n = 5); the identity of IL-5+ ILC2 was further confirmed by ICOS expression. b-d, WT mice were treated with PBS or IL-33 on day 0 and 1, and anti-IL-5 or control on day -6, -3 and -1 followed by quantification of the total numbers of the indicated myeloid cells in the lung (c) (n = 10), and the percent IFNγ+ and GzmB+ lung NK cells (after anti-NK1.1 stimulation) on day 3 (d) (n = 5). e, WT mice were treated intranasally with PBS or the indicated cytokines on day 0 and 1, followed by quantification of percent IFNγ+ NK cells (after PI stimulation), or lung eosinophil numbers on day 3 (n = 3). f, Mice of the indicated genotypes were treated intranasally Asp or PBS on days 0 and 1, followed by intravenous transfer of B16.F10 cells on day 7 and subsequent determination of lung metastases-related mortality by Kaplan-Meier survival curve (n = 15,14,15,9). g, Purified WT mouse lung NK cells were cultured alone or with ex vivo bone marrow derived eosinophils at the indicated ratios for 18 hours, followed by a 3 hr re-stimulation with PI and quantification of IFNγ+ and GzmB+ NK cells by flow cytometry (n = 6,6,9). Bar graphs indicate mean (±SEM) of combined data of two (c, g) or three (f) independent experiments. (d and e) show representative data of three independently performed experiments and (a) depicts representative flow cytometry plots. Statistical analyses were calculated using one-way ANOVA or Log-rank (Mantel-Cox) test (f) with **** = p ≤ 0.0001.
Extended Data Fig. 7 Single-cell and bulk-RNA-seq of naive and IL-33-inflammed lung NK cells.
a, Mice were treated with PBS or IL-33 on day 0 and 1, followed by FACS purification of lung NK cells at the indicated time-points for either scRNA-seq or bulk-RNA-seq analysis. b, Post FACS purity was assessed for all sorts. c, Clusters from (Fig. 6a) were annotated based on their gene expression patterns. Heatmap of genes significantly (FDR < 0.05) upregulated or downregulated in one cluster versusall others. Where more than 15 genes were significantly differentially expressed, only the 15 with the greatest average log-fold changes in each direction were included. Blue-to-yellow color gradient indicates log2 (normalized gene expression). d, scRNA-seq expression of Cd27 and Itgam (encoding CD11b) separated by cluster. Specific clusters were annotated as follows (where individual genes were significantly upregulated in one cluster compared to all others, the 5 with greate st average log-fold-change are listed as marker genes): Cluster 2: signaling/inflammatory-chemokine-expressing NK cells (Pim1, Nfkbia, Gadd45b, Ccl4, Icam1); Cluster 3: (Kcnj8, Ly6c2); Cluster 4: (Hsp90ab1, Hspe1, Nme1, Ptma, Rps2); Cluster 6: (Ccl5, Cma1, Klrg1, Itm2b); Cluster 7: immature NK cells (Ctla2a, Emb, Ccr2, Rps15a, Rpl10a). Cluster 2 was most similar to the previously identified splenic murine NK cell cluster 3, and Cluster 7 was most similar to the previously identified splenic and blood murine NK cell cluster 2 as identified by Crinier et al.32. e, Results of a differential abundance analysis comparing the abundance of cells in each cluster after IL-33 versus PBS treatment. P-values were calculated using empirical Bayes quasi-likelihood F-tests in a negative binomial GLM (as described in Methods). f,g, Expression of NK cell consensus32, effector, and both activating and inhibitory receptor transcripts from bulk-RNA-seq analysis of sorted lung NK cells. Data are represented as a heatmap of log2-transformed normalized read counts of individual genes, grouped by category (f), or z-scaled expression values for genes within the 4 gene lists. Each point represents the expression value obtained by one replicate for a given gene at a given time point (g). Box plots represent mean (black line), first and third quartiles (box) and range within 1.5 times the interquartile range from the box (whiskers). Violin plots represent median (black line), interquartile ranges (box) and a kernel density plot.
Extended Data Fig. 8 IL-33 increases glucose flux in the lung environment via ILC2 and IL-5.
a, BALB/c mice were treated intranasally with IL-33 or PBS on days 0 and 1, and anti-IL-5 or control antibody (i.p.) on day -6, -3, and 0 and sacrificed on day 3. Lung homogenates were cultured for 18 hours and glucose (Glu) and lactate (Lac) concentrations were measured in the supernatant by NMR analysis (n = 10,9,9). b, Spatial resolving glycolytic activity in lung by MSI. WT or Il7raCre/+Rorafl/fl (KO) mice were dosed with PBS or IL-33 on day 0 and 1, and sacrificed on day 3 and infused or not with [U-13C] glucose (as described in Methods). (Right to left) H&E stained lungs and post DESI-MSI molecular images of lactate, [U-13C] lactate, glucose, [U-13C] glucose, normal and [U-13C] lactate to glucose ratio (pixel per pixel). Intensity scale is fixed for each molecular species independently, and monochromatic lighter colors correspond to higher relative abundance. c, d, Bar graphs indicate mean relative abundances of [U-13C] glucose or [U-13C] lactate (c) (n = 4), or the ratio of [U-12C] lactate over glucose (d) (n = 4). e, BALB/c were treated intranasally with Asp or PBS on days 0 and 1, and anti-IL-5 or control antibody (i.p.) on day -6, -3, 0 and 3, followed by injected with 4T1-T breast cancer cells in the mammary fat pad on day 7, and sacrifice on day 21. f, Tumor burden of mice treated as in (e) with 4T1-T cells was quantified by visual examination and primary tumor weight was recorded (n = 9,10,9). g, Graphical abstract. Bar graphs indicate mean (±SEM) of combined data of two (a, c, d, and f) independent experiments. (b) depicts representative MSI images of two independent experiments. Statistical analyses were calculated using one-way ANOVA with **** = p ≤ 0.0001.
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Schuijs, M.J., Png, S., Richard, A.C. et al. ILC2-driven innate immune checkpoint mechanism antagonizes NK cell antimetastatic function in the lung. Nat Immunol 21, 998–1009 (2020). https://doi.org/10.1038/s41590-020-0745-y
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DOI: https://doi.org/10.1038/s41590-020-0745-y
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