Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Inhibition of LTβR signalling activates WNT-induced regeneration in lung

A Publisher Correction to this article was published on 22 December 2020

This article has been updated

Abstract

Lymphotoxin β-receptor (LTβR) signalling promotes lymphoid neogenesis and the development of tertiary lymphoid structures1,2, which are associated with severe chronic inflammatory diseases that span several organ systems3,4,5,6. How LTβR signalling drives chronic tissue damage particularly in the lung, the mechanism(s) that regulate this process, and whether LTβR blockade might be of therapeutic value have remained unclear. Here we demonstrate increased expression of LTβR ligands in adaptive and innate immune cells, enhanced non-canonical NF-κB signalling, and enriched LTβR target gene expression in lung epithelial cells from patients with smoking-associated chronic obstructive pulmonary disease (COPD) and from mice chronically exposed to cigarette smoke. Therapeutic inhibition of LTβR signalling in young and aged mice disrupted smoking-related inducible bronchus-associated lymphoid tissue, induced regeneration of lung tissue, and reverted airway fibrosis and systemic muscle wasting. Mechanistically, blockade of LTβR signalling dampened epithelial non-canonical activation of NF-κB, reduced TGFβ signalling in airways, and induced regeneration by preventing epithelial cell death and activating WNT/β-catenin signalling in alveolar epithelial progenitor cells. These findings suggest that inhibition of LTβR signalling represents a viable therapeutic option that combines prevention of tertiary lymphoid structures1 and inhibition of apoptosis with tissue-regenerative strategies.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: LTβR-signalling is activated in COPD and inhibition disrupts iBALT in the lungs of mice exposed to cigarette smoke.
Fig. 2: LTβR–Ig reverses emphysema in young and aged mice chronically exposed to cigarette smoke.
Fig. 3: Blocking LTβR induces WNT/β-catenin signalling in alveolar epithelial cells.
Fig. 4: Blocking WNT/β-catenin signalling reverses LTβR–Ig-induced regeneration.

Data availability

Microarray data were submitted to the NCBI Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) accession number GSE125521. scRNA-seq data were submitted to the NCBI GEO database accession number GSE151674. scRNA-seq metadata can be found in Supplementary Table 4. Proteomics data can be found in Supplementary Table 5. Series matrix files were also downloaded from the NCBI GEO databases: GSE47460–GPL14550, GSE37768, GSE56768 and GSE52509. Proteomic peak lists were searched against the mouse Uniprot FASTA database (version November 2016) https://www.uniprot.org/proteomes/UP000000589. All other data supporting the findings of this study are available within the Article and Supplementary Information. All data are available from the corresponding authors upon reasonable request. Source data are provided with this paper.

Code availability

All code used for data visualization of the scRNA-seq data can be found at https://github.com/theislab/2020_Inhibition_LTbetaR-signalling.

Change history

  • 22 December 2020

    A Correction to this paper has been published: https://doi.org/10.1038/s41586-020-03087-6

References

  1. 1.

    Kratz, A., Campos-Neto, A., Hanson, M. S. & Ruddle, N. H. Chronic inflammation caused by lymphotoxin is lymphoid neogenesis. J. Exp. Med. 183, 1461–1472 (1996).

    CAS  PubMed  Google Scholar 

  2. 2.

    Drayton, D. L., Liao, S., Mounzer, R. H. & Ruddle, N. H. Lymphoid organ development: from ontogeny to neogenesis. Nat. Immunol. 7, 344–353 (2006).

    CAS  PubMed  Google Scholar 

  3. 3.

    Hogg, J. C. et al. The nature of small-airway obstruction in chronic obstructive pulmonary disease. N. Engl. J. Med. 350, 2645–2653 (2004).

    CAS  PubMed  Google Scholar 

  4. 4.

    Galkina, E. & Ley, K. Immune and inflammatory mechanisms of atherosclerosis*. Annu. Rev. Immunol. 27, 165–197 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Pitzalis, C., Jones, G. W., Bombardieri, M. & Jones, S. A. Ectopic lymphoid-like structures in infection, cancer and autoimmunity. Nat. Rev. Immunol. 14, 447–462 (2014).

    CAS  PubMed  Google Scholar 

  6. 6.

    Senda, T. et al. Microanatomical dissection of human intestinal T-cell immunity reveals site-specific changes in gut-associated lymphoid tissues over life. Mucosal Immunol. 12, 378–389 (2019).

    CAS  PubMed  Google Scholar 

  7. 7.

    Lozano, R. et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380, 2095–2128 (2012).

    PubMed  Google Scholar 

  8. 8.

    Vogelmeier, C. F. et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary. Am. J. Respir. Crit. Care Med. 195, 557–582 (2017).

    CAS  PubMed  Google Scholar 

  9. 9.

    Baarsma, H. A. & Königshoff, M. ‘WNT-er is coming’: WNT signalling in chronic lung diseases. Thorax 72, 746–759 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Polverino, F. et al. B cell-activating factor. An orchestrator of lymphoid follicles in severe chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 192, 695–705 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11.

    Faner, R. et al. Network analysis of lung transcriptomics reveals a distinct B-cell signature in emphysema. Am. J. Respir. Crit. Care Med. 193, 1242–1253 (2016).

    CAS  PubMed  Google Scholar 

  12. 12.

    Sullivan, J. L. et al. B cell adaptive immune profile in emphysema-predominant COPD. Am. J. Respir. Crit. Care Med200, 1434–1439 (2019).

  13. 13.

    Bracke, K. R. et al. Role of CXCL13 in cigarette smoke-induced lymphoid follicle formation and chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 188, 343–355 (2013).

    CAS  PubMed  Google Scholar 

  14. 14.

    Jia, J. et al. Cholesterol metabolism promotes B-cell positioning during immune pathogenesis of chronic obstructive pulmonary disease. EMBO Mol. Med. 10, e8349 (2018).

    PubMed  PubMed Central  Google Scholar 

  15. 15.

    Wolf, M. J., Seleznik, G. M., Zeller, N. & Heikenwalder, M. The unexpected role of lymphotoxin beta receptor signaling in carcinogenesis: from lymphoid tissue formation to liver and prostate cancer development. Oncogene 29, 5006–5018 (2010).

    CAS  PubMed  Google Scholar 

  16. 16.

    Finkin, S. et al. Ectopic lymphoid structures function as microniches for tumor progenitor cells in hepatocellular carcinoma. Nat. Immunol. 16, 1235–1244 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Xiao, G., Harhaj, E. W. & Sun, S. C. NF-κB-inducing kinase regulates the processing of NF-κB2 p100. Mol. Cell 7, 401–409 (2001).

    CAS  PubMed  Google Scholar 

  18. 18.

    Dejardin, E. et al. The lymphotoxin-β receptor induces different patterns of gene expression via two NF-κB pathways. Immunity 17, 525–535 (2002).

    CAS  PubMed  Google Scholar 

  19. 19.

    Fava, R. A. et al. A role for the lymphotoxin/LIGHT axis in the pathogenesis of murine collagen-induced arthritis. J. Immunol. 171, 115–126 (2003).

    CAS  PubMed  Google Scholar 

  20. 20.

    Haybaeck, J. et al. A lymphotoxin-driven pathway to hepatocellular carcinoma. Cancer Cell 16, 295–308 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Uhl, F. E. et al. Preclinical validation and imaging of Wnt-induced repair in human 3D lung tissue cultures. Eur. Respir. J. 46, 1150–1166 (2015).

    CAS  PubMed  Google Scholar 

  22. 22.

    Alsafadi, H. N. et al. Applications and approaches for 3D precision-cut lung slices: disease modeling and drug discovery. Am. J. Respir. Cell Mol. Biol. 62, 681–691 (2020).

  23. 23.

    Verhamme, F. M., Bracke, K. R., Joos, G. F. & Brusselle, G. G. Transforming growth factor-β superfamily in obstructive lung diseases. more suspects than TGF-β alone. Am. J. Respir. Cell Mol. Biol. 52, 653–662 (2015).

    CAS  PubMed  Google Scholar 

  24. 24.

    Rabe, K. F. & Watz, H. Chronic obstructive pulmonary disease. Lancet 389, 1931–1940 (2017).

    PubMed  Google Scholar 

  25. 25.

    Sandri, M. et al. PGC-1α protects skeletal muscle from atrophy by suppressing FoxO3 action and atrophy-specific gene transcription. Proc. Natl Acad. Sci. USA 103, 16260–16265 (2006).

    ADS  CAS  PubMed  Google Scholar 

  26. 26.

    Lee, H. Y. et al. Targeted expression of catalase to mitochondria prevents age-associated reductions in mitochondrial function and insulin resistance. Cell Metab. 12, 668–674 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    John-Schuster, G. et al. Inflammaging increases susceptibility to cigarette smoke-induced COPD. Oncotarget 7, 30068–30083 (2016).

    PubMed  Google Scholar 

  28. 28.

    Boutaffala, L. et al. NIK promotes tissue destruction independently of the alternative NF-κB pathway through TNFR1/RIP1-induced apoptosis. Cell Death Differ. 22, 2020–2033 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Nabhan, A. N., Brownfield, D. G., Harbury, P. B., Krasnow, M. A. & Desai, T. J. Single-cell Wnt signaling niches maintain stemness of alveolar type 2 cells. Science 359, 1118–1123 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  30. 30.

    Zacharias, W. J. et al. Regeneration of the lung alveolus by an evolutionarily conserved epithelial progenitor. Nature 555, 251–255 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Kneidinger, N. et al. Activation of the WNT/β-catenin pathway attenuates experimental emphysema. Am. J. Respir. Crit. Care Med. 183, 723–733 (2011).

    CAS  PubMed  Google Scholar 

  32. 32.

    de Leon-Boenig, G. et al. The crystal structure of the catalytic domain of the NF-κB inducing kinase reveals a narrow but flexible active site. Structure 20, 1704–1714 (2012).

    PubMed  Google Scholar 

  33. 33.

    Tokunaga, Y. et al. Selective inhibitor of Wnt/β-catenin/CBP signaling ameliorates hepatitis C virus-induced liver fibrosis in mouse model. Sci. Rep. 7, 325 (2017).

    ADS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Ying, Q. L. et al. The ground state of embryonic stem cell self-renewal. Nature 453, 519–523 (2008).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Hu, Y. et al. Wnt/β-catenin signaling is critical for regenerative potential of distal lung epithelial progenitor cells in homeostasis and emphysema. Stem Cells https://doi.org/10.1002/stem.3241 (2020).

  36. 36.

    Baarsma, H. A. et al. Noncanonical WNT-5A signaling impairs endogenous lung repair in COPD. J. Exp. Med. 214, 143–163 (2017).

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Barkauskas, C. E. et al. Type 2 alveolar cells are stem cells in adult lung. J. Clin. Invest. 123, 3025–3036 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Ng-Blichfeldt, J. P. et al. Retinoic acid signaling balances adult distal lung epithelial progenitor cell growth and differentiation. EBioMedicine 36, 461–474 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    John, G. et al. The composition of cigarette smoke determines inflammatory cell recruitment to the lung in COPD mouse models. Clin. Sci. (Lond.) 126, 207–221 (2014).

    CAS  Google Scholar 

  40. 40.

    Yildirim, A. O. et al. Palifermin induces alveolar maintenance programs in emphysematous mice. Am. J. Respir. Crit. Care Med. 181, 705–717 (2010).

    CAS  PubMed  Google Scholar 

  41. 41.

    Fuchs, H. et al. Mouse phenotyping. Methods 53, 120–135 (2011).

    CAS  PubMed  Google Scholar 

  42. 42.

    Fallica, J., Das, S., Horton, M. & Mitzner, W. Application of carbon monoxide diffusing capacity in the mouse lung. J. Appl. Physiol. 110, 1455–1459 (2011).

    PubMed  PubMed Central  Google Scholar 

  43. 43.

    Strunz, M. et al. Longitudinal single cell transcriptomics reveals Krt8+ alveolar epithelial progenitors in lung regeneration. Preprint at https://doi.org/10.1101/705244 (2019).

  44. 44.

    Angelidis, I. et al. An atlas of the aging lung mapped by single cell transcriptomics and deep tissue proteomics. Nat. Commun. 10, 963 (2019).

    ADS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Hughes, T. K. et al. Highly efficient, massively-parallel single-cell RNA-seq reveals cellular states and molecular features of human skin pathology. Preprint at https://doi.org/10.1101/689273 (2019).

  47. 47.

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Luecken, M. D. & Theis, F. J. Current best practices in single-cell RNA-seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Lun, A. T., 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).

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Shevchenko, A., Tomas, H., Havlis, J., Olsen, J. V. & Mann, M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat. Protoc. 1, 2856–2860 (2006).

    CAS  PubMed  Google Scholar 

  51. 51.

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  PubMed  Google Scholar 

  52. 52.

    Schiller, H. B. et al. Deep proteome profiling reveals common prevalence of MZB1-positive plasma B cells in human lung and skin fibrosis. Am. J. Respir. Crit. Care Med. 196, 1298–1310 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53.

    Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    CAS  PubMed  Google Scholar 

  54. 54.

    Rainer, J., Sanchez-Cabo, F., Stocker, G., Sturn, A. & Trajanoski, Z. CARMAweb: comprehensive R- and bioconductor-based web service for microarray data analysis. Nucleic Acids Res. 34, W498–W503(2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Malehmir, M. et al. Platelet GPIbQα is a mediator and potential interventional target for NASH and subsequent liver cancer. Nat. Med. 25, 641–655 (2019).

    CAS  PubMed  Google Scholar 

  56. 56.

    Wang, X., Spandidos, A., Wang, H. & Seed, B. PrimerBank: a PCR primer database for quantitative gene expression analysis, 2012 update. Nucleic Acids Res. 40, D1144–D1149 (2012).

    CAS  PubMed  Google Scholar 

  57. 57.

    Gendusa, R., Scalia, C. R., Buscone, S. & Cattoretti, G. Elution of high-affinity (>10-9 KD) antibodies from tissue sections: clues to the molecular mechanism and use in sequential immunostaining. J. Histochem. Cytochem. 62, 519–531 (2014).

    PubMed  PubMed Central  Google Scholar 

  58. 58.

    Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Berg, S. et al. ilastik: interactive machine learning for (bio)image analysis. Nat. Methods 16, 1226–1232 (2019).

    CAS  PubMed  Google Scholar 

  60. 60.

    Wählby, C. et al. An image analysis toolbox for high-throughput C. elegans assays. Nat. Methods 9, 714–716 (2012).

    PubMed  PubMed Central  Google Scholar 

  61. 61.

    John-Schuster, G. et al. Cigarette smoke-induced iBALT mediates macrophage activation in a B cell-dependent manner in COPD. Am. J. Physiol. Lung Cell. Mol. Physiol. 307, L692–L706 (2014).

    CAS  PubMed  Google Scholar 

  62. 62.

    Jenkins, R. G. et al. An official american thoracic society workshop report: use of animal models for the preclinical assessment of potential therapies for pulmonary fibrosis. Am. J. Respir. Cell Mol. Biol. 56, 667–679 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63.

    Corti, M., Brody, A. R. & Harrison, J. H. Isolation and primary culture of murine alveolar type II cells. Am. J. Respir. Cell Mol. Biol. 14, 309–315 (1996).

    CAS  PubMed  Google Scholar 

  64. 64.

    Königshoff, M. et al. WNT1-inducible signaling protein-1 mediates pulmonary fibrosis in mice and is upregulated in humans with idiopathic pulmonary fibrosis. J. Clin. Invest. 119, 772–787 (2009).

    PubMed  PubMed Central  Google Scholar 

  65. 65.

    Mutze, K., Vierkotten, S., Milosevic, J., Eickelberg, O. & Königshoff, M. Enolase 1 (ENO1) and protein disulfide-isomerase associated 3 (PDIA3) regulate Wnt/β-catenin-driven trans-differentiation of murine alveolar epithelial cells. Dis. Model. Mech. 8, 877–890 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66.

    Lehmann, M. et al. Senolytic drugs target alveolar epithelial cell function and attenuate experimental lung fibrosis ex vivo. Eur. Respir. J. 50, 1602367 (2017).

    PubMed  PubMed Central  Google Scholar 

  67. 67.

    Caporale, A., Tartaggia, S., Castellin, A. & De Lucchi, O. Practical synthesis of aryl-2-methyl-3-butyn-2-ols from aryl bromides via conventional and decarboxylative copper-free Sonogashira coupling reactions. Beilstein J. Org. Chem. 10, 384–393 (2014).

    PubMed  PubMed Central  Google Scholar 

  68. 68.

    Veeman, M. T., Slusarski, D. C., Kaykas, A., Louie, S. H. & Moon, R. T. Zebrafish prickle, a modulator of noncanonical Wnt/Fz signaling, regulates gastrulation movements. Curr. Biol. 13, 680–685 (2003).

    CAS  PubMed  Google Scholar 

  69. 69.

    Mootha, V. K. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat. Genet. 34, 267–273 (2003).

    CAS  PubMed  Google Scholar 

  70. 70.

    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).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Kim, S. et al. Integrative phenotyping framework (iPF): integrative clustering of multiple omics data identifies novel lung disease subphenotypes. BMC Genomics 16, 924 (2015).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors acknowledge the help of C. Hollauer, M. Pankla, R. Pineda, M. Neumann and K. Hafner. We gratefully acknowledge the provision of human biomaterial and clinical data from the CPC-M bioArchive and its partners at the Asklepios Biobank Gauting, the Klinikum der Universität München and the Ludwig-Maximilians-Universität München. We would like to thank all the members of the Theis laboratory for valuable input and discussion regarding the analysis of single cell RNA-seq data. We thank J. Browning for providing LTβR–Ig. We thank the Flowcytometry Core facility of the TranslaTUM, TUM Munich for technical support. We thank the Mass Spectrometry-based Protein Analysis Unit of the DKFZ, Heidelberg for technical support. We are most thankful to Z. Ertuz for the art work  and  M. Gerckens for the precision cut lung image. M.H. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project ID 272983813-SFBTR 179, project ID 360372040-SFB 1335 and project ID 314905040 -SFBTR 209, the ERC CoG (HepatoMetabopath), the ERC POC (Faith), the Helmholtz Future topic Inflammation and Immunology, an EOS grant from the FNRS (MODEL-IDI 30826052), the Rainer Hoenig foundation and the Horizon 2020 program HEPCAR. D.P. was supported by the Helmholtz Future topic Inflammation and Immunology. M.K. was funded by grant R01HL141380. M.K., R.G. and C.C. by a grant from Longfonds, project no. 5.1.17.166. Y.H. was funded by grant F32HL149290-01. M.A.L. is a Marie-Curie COFUND postdoctoral fellow at the University of Liege co-funded by the European Union. E.D. is supported by an EOS grant from the FNRS (MODEL-IDI 30826052). This work was supported by the Helmholtz Alliance ‘Aging and Metabolic Programming, AMPro’ (J.B., M.C.F., M.B.). Work in the laboratory of M.B. was supported by the SFB 1324. M.H.dA. was supported by the German Federal Ministry of Education and Research (Infrafrontier grant 01KX1012). H.B.S. is supported by grants from the German Center for Lung Research (DZL) and the Helmholtz Association.

Author information

Affiliations

Authors

Contributions

T.M.C., G.J.S., O.E., M.K., M.H. and A.Ö.Y. conceived the study and experimental design. T.M.C., G.J.S., D.H., M.A.L., R.C., Y.H., Z.E., C.C., S.P., J.H., H.A., G.G., M.Z.J., L.B., D.P., M.S.K., A.G., A.J., G. Beroshvili, M.C.F., M.D., I.S., J.J. and D.W. performed experiments. M.H., F.T., D.P. and M.S.K. designed, undertook, and analysed flow cytometry experiments. A.G. designed, undertook, and analysed multiplex immunofluorescence, supervised by F.T. E.G. and B.P. prepared NIK inhibitor. S.E.V. prepared patient lung core samples. M.I. and J.B. contributed to microarray analysis. M.H., D.H. designed and executed the immunohistochemistry and RNA in situ hybridization analyses. M.H.dA. supervised microarray experiments. M.A., M.S. and I.A. designed, undertook, and analysed scRNA-seq experiments, supervised by H.B.S. and F.J.T. I.S. undertook proteomics analysis. C.M. analysed proteomic datasets. M. Lindner supplied human lung tissue for lung slices. D.W. and M.K. established the 3D human lung slice model. Y.H., C.C. and M.K. developed and undertook human lung organoid experiments. T.M.C., G.J.S., D.P., M. Lehmann, M.A., M.S., I.A., C.M., Y.H., T.S., P.K., C.C., G. Burgstaller, R.G., R.C., M.S.K., A.G., M.C.F., F.J.T., F.T., M.B., E.D., H.B.S., M.K., M.H. and A.Ö.Y. analysed and interpreted data. T.M.C., G.J.S., T.O.C., M.H. and A.Ö.Y. wrote the manuscript. All authors read and edited the manuscript.

Corresponding authors

Correspondence to Mathias Heikenwalder or Ali Önder Yildirim.

Ethics declarations

Competing interests

G.J.S. is currently employed as an editor at Genome Medicine, a Springer Nature journal. He joined the company after his participation in the study and was not involved in the editorial process at Nature. All other authors declare no competing interests.

Additional information

Peer review information Nature thanks the anonymous reviewers 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 Canonical and non-canonical NF-κB signalling pathways are activated in the lungs of patients with COPD and mice exposed to cigarette smoke.

a, b, Representative images of immunohistochemical analysis for RELA (a) and RELB (b) (brown signal, indicated by arrows, nuclei counterstained with haematoxylin) in lung core biopsy sections from healthy participants (n = 3) patients with COPD (n = 4), with the quantification of RELA and RELB-positive alveolar epithelial nuclei shown as mean ± s.d. Scale bar, 50 μm and 25 μm (inset). ce, GSEA of the LTβR signalling, NF-κB signalling (gene lists from IPA software, Qiagen), TNFR-mediated signalling (GO:0033209), positive regulation of I-κB kinase NF-κB signalling (GO:0043123) and NIK NF-κB signalling (GO:0038061) pathways in publicly available array data from lung tissue (GSE47460–GPL14550) of healthy (n = 91) versus patients with COPD (n = 145) (c), from lung tissue (GSE37768) of healthy (n = 9) versus patients with COPD (n = 18) (d) and from PBMCs (GSE56768) of healthy (n = 5) versus patients with COPD (n = 49) (e). f, mRNA expression levels of Lta, Ltbr, Tnfsf14, Tnf, Ccl2 and Cxcl13 determined by qPCR in whole lung from B6 mice exposed to filtered air (n = 6) or cigarette smoke (n = 8) for 6 months; individual mice are shown. g, GSEA of the pathways described in ce in the publicly available array data (GSE52509) of lungs from our mice exposed to filtered air (n = 3) and cigarette smoke (n = 6) for 4 and 6 months. h, Western blot analysis for RELB, p100 and p52 in total lung homogenate from the mice described in f. Quantification relative to vinculin of individual mice shown (n = 3). For gel source data, see Supplementary Fig. 1. i, Schematic representation of the LTβR–Ig treatment protocol. j, Representative low and high magnification overlay images of Multiplex immunofluorescence staining to identify CD4 (red), CD8 (green), B220 (turquoise) and DAPI (blue) counterstained lung sections (n = 4) from B6 mice exposed to cigarette smoke for 6 months, plus LTβR–Ig fusion protein or control Ig (80 μg intraperitoneally, weekly) therapeutically from 4 to 6 months, and analysed at 6 months. Scale bars, 100 μm k, mRNA expression levels of Cxcl13 and Ccl19 determined by qPCR in whole lung from B6 mice exposed to filtered air or cigarette smoke for 4 and 6 months, plus LTβR–Ig fusion or control Ig (80 μg intraperitoneally, weekly) prophylactically from 2 to 4 months and analysed at 4 months, and therapeutically from 4 to 6 months and analysed at 6m (n = 4 mice/group, repeated twice, pooled data shown). P values indicated, Mann–Whitney one-sided test (a, b), unpaired two-tailed Student’s t-test (f, h), one-way ANOVA multiple comparisons Bonferroni test (k).

Source data

Extended Data Fig. 2 Immune response in lungs of mice exposed to cigarette smoke and treated with LTβR–Ig.

ac, Flow cytometry analysis of single cell suspensions for adaptive immune cells from whole lung of B6 mice exposed to filtered air (n = 6) or cigarette smoke for 6 months, plus LTβR–Ig fusion (n = 5) or control Ig (n = 5) (80 μg intraperitoneally, weekly) from 4 to 6 months and analysed at 6 months. a, t-SNE plots showing the distribution and composition of CD4 and CD8 T cells as central memory T cells (Tcm) (CD62L+CD44+), effector memory T cells (Tem) (CD62LCD44+) and T memory stem cells (Tscm) (CD62L+CD44) (left) and t-SNE plots showing the distribution of the surface markers indicated (top right) and global changes in composition with treatment (bottom right). b, Abundance of the T cell populations indicated as a percentage of total CD45+ cells. c, Top, t-SNE plots showing the distribution of CD19-, IgG-, MHCII-, CD69- and GL7-positive cells. Bottom, the abundance of CD19+ B cells as a percentage of total CD45+ cells and the geometric mean fluorescence intensity of the expressed markers indicated on CD19+ B cells. dg, B6 mice were exposed to filtered air or cigarette smoke for 4 and 6 months, plus LTβR–Ig fusion protein or control Ig (80 μg i.p., weekly) prophylactically (Proph.) from 2 to 4 months and analysed at 4 months and therapeutically (Ther.) from 4 to 6 months, and analysed at 6 months. d, Representative images of immunohistochemical analysis for CD68 macrophages in lung sections from the mice (n = 4 mice per group, brown signal indicated by arrowheads, haematoxylin counter stained). Scale bar, 100 μm. e, Quantification of CD68 positive macrophages across 20 random fields of view from lung sections stained in d (n = 4 mice per group). f, Representative low and high magnification overlay images of Multiplex immunofluorescence staining to identify IBA1 (red), iNOS (green), CD206 (turquoise) and DAPI (blue) counterstained lung sections from mice at 6 months (n = 4 mice per group). Scale bars, 100 μm and 25 μm, respectively. g, iNOS and IBA1 double-positive macrophages from Multiplex immunofluorescence staining on lung sections from mice treated both prophylactically and therapeutically was quantified using Ilastik and CellProfiler (n = 4 mice per group). hl, Flow cytometry analysis of single cell suspensions for myeloid cells from whole lung of B6 mice exposed to filtered air (n = 6) or cigarette smoke for 6 months, plus LTβR–Ig fusion (n = 5) or control Ig (n = 5) (80 μg intraperitoneally, weekly) from 4 to 6 months and analysed at 6 months. h, t-SNE plots showing the distribution and composition of myeloid cells and surface markers indicated. i, t-SNE plots showing global changes in composition with treatment. j, Composition of CD45+LY6GF480+CD11c+ alveolar macrophages. k, Composition of CD45+LY6GF480+CD11cCD11b+ interstitial macrophages. l, Composition of CD45+LY6GF480+CD11cCD11b+LY6Chigh infiltrating macrophages. Data are mean ± s.d. P values determined by one-way ANOVA multiple comparisons Bonferroni test (b, c, e, g, jl).

Source data

Extended Data Fig. 3 scRNA-seq analysis of lungs from mice exposed to cigarette smoke and treated with LTβR–Ig.

Cells from whole lung suspensions of B6 mice exposed to filtered air (n = 3) or cigarette smoke for 6 months, plus LTβR–Ig fusion protein (n = 5) or control Ig (n = 5) therapeutically from 4 to 6 months, were analysed at 6 months by scRNA-seq (Drop-Seq). a, Heat map depicting the expression of key genes used in identifying the individual cell populations. b, UMAP of scRNA-seq profiles (dots) coloured by experimental group. c, UMAP plots showing expression of genes indicated in scRNA-seq profiles. d, Dot blot depicting the expression level (log-transformed, normalized UMI counts) and percentage of cells in a population positive for Ltb, Lta, Tnf, Tnfsf14, Ltbr, Tnfrsf1a and Tnfrsf1b. e, UMAP plot showing the relative intensity of the positive regulation of NIK (non-canonical) NF-κB signalling pathway (GO:1901224) across the scRNA-seq profiles. f, UMAP plot of scRNA-seq profiles (dots) of lung epithelial cells coloured by experimental group (left) and the relative intensity of the positive regulation of NIK (non-canonical) NF-κB signalling pathway (GO:1901224) (right). g, Box and whiskers plot (box representing 25th–75th percentile, median line indicated and Tukey whiskers representing ± 1.5× IQR) showing the relative score for the positive regulation of NIK (non-canonical) NF-κB signalling pathway in the cell types indicated across the three groups. Statistical significance was assessed using Wilcoxon rank-sum two-sided test on normalized, log-transformed count values and corrected with Benjamini–Hochberg.

Source data

Extended Data Fig. 4 Analysis of LTA and LTB expression in human and mouse lungs.

a, Representative images of in situ hybridization analysis for LTA and immunohistochemical analysis for LTB in lung sections from healthy participants and patients with COPD (n = 4, red signal indicated by arrowheads (LTA), brown signal (LTB) and nuclei counterstained with haematoxylin). Scale bar, 50 μm. b, Representative images of in situ hybridization analysis for Lta and Ltb in lung sections from B6 mice exposed to cigarette smoke for 6 months with LTβR–Ig fusion protein or control Ig (80 μg intraperitoneally, weekly) therapeutically for 4 to 6 months, and analysed at 6 months (brown positive staining (Lta) and red positive staining (Ltb) indicated by arrowheads, open arrowhead unstained cells, nuclei were counterstained with haematoxylin) (n = 4 mice per group, repeated twice). Scale bar, 20 μm. Non-staining with sense probe in cigarette smoke plus Ig sections shown as negative control. Representative images of immunohistochemical analysis identifying CD68-positive macrophages (brown staining indicated by arrowheads) also shown. c, Representative images of in situ hybridization analysis for Tnfsf14 in lung sections from mice described in b (brown positive staining indicated by arrowheads, open arrowhead unstained cells, nuclei counterstained with haematoxylin) (n = 4 mice per group). Scale bars, 20 μm. Spleen section shown as a positive control. d, Representative images of in situ hybridization analysis for Tnf in lung sections from mice described in b (brown positive macrophage indicated by arrowheads, open arrowhead unstained macrophage, nuclei counterstained with haematoxylin). Scale bars, 20 μm. Representative immunohistochemical analysis identifying CD68-positive macrophages (brown staining indicated by arrowheads, haematoxylin counterstained) also shown. Scale bars, 20 μm. e, Quantification of Tnf-positive macrophages across 20 random fields of view per lung (n = 4). Data are mean ± s.d. P values determined by one-way ANOVA multiple comparisons Bonferroni test.

Source data

Extended Data Fig. 5 Inhibition of LTβR signalling strongly reduces non-canonical but not canonical NF-κB signalling in lung.

a, Principal component analysis of microarray data, using Mouse Ref-8 v2.0 Expression BeadChips (Illumina), performed on lung tissue from mice exposed to filtered air or cigarette smoke for 6 months, plus LTβR–Ig fusion or control Ig (80 μg intraperitoneally, weekly) therapeutically from 4 to 6 months (n = 3 mice per group). b, Principal component analysis of normalized z-scored mass spectrometry intensities from proteomics of whole lung lysates from mice exposed to filtered air (n = 6) or cigarette smoke for 6 months, plus LTβR–Ig fusion (n = 7) or control Ig (n = 4) (80 μg intraperitoneally, weekly) from 4 to 6 months. c, Heat map depicting the top 20 up and down LTβR–Ig regulated genes presented as fold change (FDR < 10%) from the microarray data described in a. Left, expression in mice exposed to cigarette smoke plus Ig relative to mice exposed to filtered air. Right, expression in mice exposed to cigarette smoke plus LTβR–Ig relative to mice exposed to cigarette smoke plus Ig. d, GSEA of the NIK (non-canonical) NF-κB signalling (GO:0038061) pathway of the microarray data from a. e, Heat map of significantly regulated proteins from the NIK (non-canonical) NF-κB signalling (GO:0038061) pathway as determined by Student’s t-test statistic from the proteomics data described in b. f, GSEA of the NIK (non-canonical) NF-κB signalling (GO:0038061) pathway of the normalized proteome data described in b. g, Representative images of two independent experiments of immunohistochemical analysis for RELB in lung sections from B6 mice exposed to filtered air or cigarette smoke for 4 or 6 months, plus LTβR–Ig fusion or control Ig (80 μg intraperitoneally, weekly) prophylactically from 2 to 4 months and analysed at 4 months, and therapeutically from 4 to 6 months and analysed at 6 months (brown signal indicated by arrowheads, nuclei counterstained with haematoxylin). Scale bar, 25 μm. h, Quantification of RELB-positive alveolar epithelial nuclei from the immunohistochemistry sections in g (n = 3 mice per group). i, Representative images of two independent experiments of immunohistochemical analysis for RELA in lung sections from the mice described in g (brown signal indicated by arrowheads, nuclei counterstained with haematoxylin). Scale bar, 25 μm. j, Quantification of RELA-positive alveolar epithelial nuclei from the immunohistochemistry sections in i (n = 3 mice per group). k, mRNA expression levels of Ccl2, Ccl3, Cxcl1 and Tnf determined by qPCR in whole lung from the mice described in g (n = 4 mice per group, repeated twice, pooled data shown). l, mRNA expression levels of LTA, CXCL13 and TNF determined by qPCR in ex vivo human precision-cut lung slices stimulated for 24 h with LPS (10 μg ml−1) in the presence or absence of human LTβR–Ig fusion protein (1 μg ml−1) (n = 3 independent experiments from three separate lungs). Left image shows a representative picture of preparing a lung slice from the three independent experiments. Data are mean ± s.d. P values determined by one-way ANOVA multiple comparisons Bonferroni test.

Source data

Extended Data Fig. 6 LTβR–Ig treatment reverses airway remodelling and comorbidities in mice chronically exposed to cigarette smoke.

a, Representative images of Masson’s Trichrome stained lung sections from B6 mice exposed to filtered air or cigarette smoke for 4 or 6 months, plus LTβR–Ig fusion protein or control Ig (80 μg intraperitoneally, weekly) prophylactically from 2 to 4 months and analysed at 4 months, and therapeutically from 4 to 6 months and analysed at 6 months (n = 4 mice per group, repeated twice). Scale bar, 200 μm. These are low-magnification images of the sections depicted and quantified in Fig. 2c, d. b, Representative images of immunohistochemical analysis for collagen I (red signal, nuclei counterstained with haematoxylin) in lung sections from B6 mice described in a. Scale bar, 100 μm. c, Quantification of small airway collagen deposition normalized to the surface area of airway and vessel basement membrane from the sections in b (n = 7 mice FA, 7 mice CS+Ig, 7 mice CS+LTβR-Ig groups, from two independent experiments). d, Representative images of immunohistochemical analysis for phosphorylated SMAD2 in lung sections from mice described in a (red signal indicated by arrows, nuclei counterstained with haematoxylin) (n = 4 mice per group, repeated twice). Scale bar, 25 μm. e, mRNA expression levels of Ppargc1a and Mcat determined by qPCR in gastrocnemius muscle from 6-month mice described in a (n = 4 mice per group, repeated twice, pooled data shown). f, Four-paw muscle strength test in mice at 6 months treated as described in a (n = 8 mice per group). g, Schematic representation of the LTβR–Ig treatment protocol in aged mice. h, Representative images of lung sections stained with H&E and Masson’s Trichrome from 12-month-old B6 mice exposed to filtered air or cigarette smoke for 4 months, plus LTβR–Ig fusion protein or control Ig (80 μg i.p., weekly) from 2 to 4 months and analysed at 4 months (n = 5 mice FA, 5 mice CS+Ig, 7/8 mice CS+LTβR-Ig groups, repeated twice). Scale bar, 50 μm. These are low magnification images of the sections depicted and quantified in Fig. 2f, g.) Data are mean ± s.d. P values determined by one-way ANOVA multiple comparisons Bonferroni test (c) or Student’s two-tailed t-test (e, f).

Source data

Extended Data Fig. 7 Disease development is not attenuated by LTβR–Ig treatment in iBALT independent emphysema.

a, Schematic representation of the LTβR–Ig treatment protocol in mice exposed to a single oropharangeal application of PPE or PBS control. b, mRNA expression level fold changes (FC) of Lta, Tnfsf14, Ltbr and Tnf relative to Hprt, determined by qPCR in whole lung from B6 mice treated with a single oropharyngeal application of PBS (n = 8) or PPE (40 U kg−1 body weight) and analysed after 3 months (n = 7) or 4 months of chronic exposure to cigarette smoke (n = 8 mice per group). c, Representative images of immunohistochemical analysis for B220-positive B cells and CD3-positive T cells (brown signal, indicated by arrowheads, nuclei counterstained with haematoxylin) in lung sections from mice treated with PBS or PPE as described in b, plus mice treated with PPE followed by LTβR–Ig fusion protein (80 μg intraperitoneally, weekly) 28 days later for 2 months (n = 8 mice per group, repeated twice). Scale bar, 50 μm. d, Lymphocyte counts in the BAL fluid from the mice described in c plus mice exposed to cigarette smoke for 4 months (n = 8 mice per group). e, Representative images of in situ hybridization analysis for Lta and Ltb in lung sections from mice described in c, plus splenic positive controls (brown staining, nuclei counterstained with haematoxylin) (n = 4 mice per group, repeated twice). Scale bar, 50 μm. f, Representative images of immunohistochemical analysis for RELA and RELB in lung sections from B6 mice described in c (brown signal indicated by arrowheads, nuclei counterstained with haematoxylin) (n = 4 mice per group, repeated twice). Scale bar, 50 μm. g, Representative images of H&E-stained lung sections from mice described in c (n = 8 mice per group, repeated twice). Scale bars, 200 μm and 50 μm (inset). h, Emphysema scoring (1–5; 5 most severe) of lung sections from f (n = 5 mice PBS, 5 mice PPE, 7 mice PPE+LTβR-Ig groups). i, Diffusing capacity of carbon monoxide (DFCO) in the lungs of mice described in c (n = 8 mice PBS, 7 mice PPE, 8 mice PPE+LTβR-Ig groups). j, Dynamic compliance (Cdyn) pulmonary function data from the mice described in c (n = 8 mice PBS, 7 mice PPE, 8 mice PPE+LTβR-Ig groups). Data are mean ± s.d. P values determined by one-way ANOVA multiple comparisons Bonferroni test.

Source data

Extended Data Fig. 8 Inhibiting LTβR-signalling suppresses cigarette-smoke-induced apoptosis.

a, Representative images of immunohistochemical analysis for cleaved caspase-3 in lung sections from healthy participants and patients with COPD (n = 5, brown signal indicated by arrowheads, nuclei counterstained with haematoxylin). Scale bar, 50 μm. b, Quantification of alveolar epithelial cells positive for cleaved caspase-3 from the lung sections stained in a. Data are mean ± s.d. (n = 5 patients per group). P = 0.0079, Mann–Whitney two-sided test. c, d, GSEA of apoptosis (Hallmark collection) in transcriptomic array data from publicly available array data of lung tissue (GSE47460–GPL14550) from healthy participants (n = 91) versus patients with COPD (n = 145) (c) and the lungs of B6 mice after exposure for 6 months to filtered air, cigarette smoke plus Ig, or cigarette smoke plus LTβR–Ig fusion protein therapeutically (n = 3 mice per group) (d). e, Box and whiskers plot (box representing 25th–75th percentile, median line indicated and Tukey whiskers representing ± 1.5 × IQR) showing the relative score for apoptosis (Hallmark collection) in AT2 cells after scRNA-seq analysis of lungs from B6 mice after exposure for 6 months to filtered air (n = 3 mice per group), cigarette smoke plus Ig (n = 5 mice per group) or cigarette smoke plus LTβR–Ig fusion protein (n = 5 mice per group) therapeutically. Statistical significance was determined by Wilcoxon rank-sum two-sided test on normalized, log-transformed count values and corrected with Benjamini–Hochberg. f, g, Proteome analysis of whole lung lysates from mice exposed to filtered air (n = 6) or cigarette smoke for 6 months, plus LTβR–Ig fusion (n = 7) or control Ig (n = 4) (80 μg intraperitoneally, weekly) from 4 to 6 months was undertaken. f, Heat map of the significantly regulated proteins from the Hallmark apoptosis list as determined by Student’s two-sided t-test. g, GSEA of the Hallmark apoptosis list on the normalized proteome data. h, Representative images of immunohistochemical analysis for cleaved caspase-3 in lung sections from B6 mice exposed to filtered air or cigarette smoke for 4 and 6 months, plus LTβR–Ig fusion protein or control Ig (80 μg intraperitoneally, weekly) prophylactically from 2 to 4 months and analysed at 4 months, and therapeutically from 4 to 6 months and analysed at 6 months (n = 4 mice per group, brown signal indicated by arrowheads, nuclei counterstained with haematoxylin). Scale bar, 50 μm. Quantification of cleaved caspase-3-positive alveolar epithelial cells from the immunohistochemistry sections also shown. i, Western blot analysis for cleaved caspase-3 (c-Cas-3) in total lung homogenate from mice described in h, quantification relative to β-actin (prophylactic groups: FA n = 7, CS+Ig n = 7, CS+LTβR-Ig n = 6 mice per group, therapeutic groups: FA n = 6, CS+Ig n = 5, CS+LTβR-Ig n = 6 mice per group, pooled from two independent experiments), individual mice shown. For gel source data see Supplementary Fig. 1. jl, The mouse AT2-like cell line LA4 was stimulated with LTβR-Ag (2 μg ml−1), recombinant mouse TNF (1 ng ml−1) or a combination of both, in the presence or absence of necrostatin-1 (Nec1, 50 μM) (j) and (k) or Z-Val-Ala-DL-Asp-fluoromethylketone (z-VAD, 20 μM) (l). Apoptosis was assessed at 6 h (jl) and 24 h (k, l) by flow cytometric analysis of annexin V and propidium iodide (PI) staining (n = 2–3, repeated twice, pooled data shown in k). m, n, Wound-healing assay in LA4 cells grown to confluence, scratched and then incubated with LTβR-Ag (2 μg ml−1), recombinant mouse TNF (1 ng ml−1) or a combination of both, in the presence or absence of necrostatin-1 (50 μM). m, Representative images at 0 h and 56 h after scratch are shown (n = 4 from one experiment). Scale bar, 200 μm. n, Degree of wound closure (100% representing fully closed) at 56 h (n = 4). Data are mean ± s.d. P values determined by one-way ANOVA multiple comparisons Bonferroni test (h, i, k, l, n).

Source data

Extended Data Fig. 9 LTβR stimulation regulates WNT/β-catenin-signalling.

a, GSEA of canonical WNT signalling (GO: 0060070) and β-catenin/TCF transcription factor complex assembly (GO:1904837) in transcriptomic array data from the lungs of B6 mice after 6 months exposure to filtered air, cigarette smoke plus Ig, or cigarette smoke plus LTβR–Ig fusion protein therapeutically (n = 3 mice per group) and publicly available array data from lung tissue (GSE47460–GPL14550) of healthy participants (n = 91) versus patients with COPD (n = 145). b, Representative images of immunohistochemical analysis for AXIN2 in lung sections from healthy participants (n = 6) and patients with COPD (n = 8) (brown signal indicated by arrowheads, nuclei counterstained with haematoxylin). Scale bar, 50 μm. c, mRNA expression levels of Nkd1 and Lgr5 relative to Hprt in primary mouse AT2 cells treated with LTβR-Ag (2 μg ml−1) for 24 h, with or without mouse rWNT3A (100 ng ml−1) (n = 5 individual experiments). d, mRNA expression levels of Tcf4 relative to Hprt in the LA4 cells stimulated with LTβR-Ag (2 μg ml−1) or recombinant mouse TNF (1 ng ml−1) (n = 3, repeated three times). e, mRNA expression levels of Tcf4 relative to Hprt in LA4 cells stimulated with LTβR-Ag (2 μg ml−1) plus recombinant mouse TNF (1 ng ml−1) with or without necrostatin-1 (50 μM), and the IKK kinase inhibitors TPCA-1 (10 μM) or BAY 11-7082 (10 μM) (n = 2, repeated twice). f, mRNA expression levels of AXIN2 relative to HPRT and normalized to vehicle, in human A549 cells treated with human LTβR-Ag (0.5 μg ml−1) for 24 h with or without TPCA-1 (5 μM) (n = 3 independent experiments). g, WNT/β-catenin luciferase reporter activity in mouse MLE12 cells, activated by GSK-3β inhibitor (CHIR99021, 1 μM) and treated with LTβR-Ag at the concentrations indicated for 24 h (activity relative to CHIR alone, n = 2–9). h, Western blot analysis for β-catenin in MLE12 cells treated with LTβR-Ag (2 μg ml−1) for 24 h with or without mouse rWNT3A (100 ng ml−1) plus bortezomib (10 nM). Quantification relative to actin shown (n = 3 independent experiments). For gel source data, see Supplementary Fig. 1. i, mRNA expression levels of TCF4 relative to HPRT in ex vivo human precision-cut lung slices stimulated for 24 h with recombinant human TNF (20 ng ml−1) or LTβR-Ag (2 μg ml−1) for 24 h (n = 5 slices from individual lungs). j, Western blot analysis for β-catenin in MLE12 cells treated with mouse rWNT3A (200 ng ml−1) and TNFSF14 (200 ng ml−1) for 30 h. Quantification relative to vinculin shown (n = 3 independent experiments). For gel source data, see Supplementary Fig. 1. km, B6 mice were treated with a single oropharyngeal application of PBS (n = 8), PPE (40 U kg−1 body weight) (n = 7 mice per group) or PPE followed by LTβR–Ig fusion protein (80 μg intraperitoneally, weekly) 28 days later for 2 months and all analysed after 3 months (n = 8 mice per group); see Extended Data Fig. 7a. k, mRNA expression levels of Axin2, Bcl9l, Cdh1, Dvl1, Gsk3b, Rab5a, Tcf4, Wif1, Wnt2 and Wnt4 relative to Hprt, determined by qPCR in whole lung. l, Representative images of immunohistochemical analysis for TCF and AXIN2 in lung sections from the mice described (n = 4 mice per group, brown signal indicated by arrowheads, nuclei counterstained with haematoxylin). Scale bar, 25 μm. m, Quantification of alveolar epithelial cells positive for TCF4 and AXIN2 from l. Data shown as individual lungs (c, i) or mean ± s.d. (dh, j, k and m). P values determined by one-tailed (c) or two-tailed (i) paired Student’s t-test, two-tailed unpaired Student’s t-test (h, j) or one-way ANOVA multiple comparisons Bonferroni test (dg (compared to vehicle in g), k, m).

Source data

Extended Data Fig. 10 LTβR-stimulation regulates lung repair and regeneration by modulating WNT/β-catenin-signalling.

a, Schematic representation of the experiment in which B6 mice were exposed to filtered air (n = 5) or cigarette smoke for 6 months plus control Ig (n = 5), LTβR–Ig fusion protein (80 μg intraperitoneally, weekly, n = 5), LTβR–Ig fusion protein plus β-catenin/CBP inhibitor PRI-724 (0.6 mg intraperitoneally, twice weekly, n = 6) or CHIR99021 (0.75 mg intraperitoneally, weekly, n = 5) from 4 to 6 months, and analysed at 6 months. b, mRNA expression levels of Ltb, Tnfsf14 and Ltbr relative to Hprt, determined by qPCR in whole lung from the mice described in a (FA n = 5, CS plus control Ig n = 5, LTβR-Ig n = 5, LTβR-Ig + PRI-724 n = 6 and CHIR99021 n = 5 mice per group). c, Representative images of immunohistochemical analysis for CD3+ T cells and B220+ B cells (brown signal, nuclei counterstained with haematoxylin) in lung sections from the mice described in a. Scale bar, 100 μm. d, mRNA expression levels of Axin2 relative to Hprt, determined by qPCR in whole lung from the mice described in a (FA n = 5, CS plus control Ig n = 5, LTβR-Ig n = 5, LTβR-Ig + PRI-724 n = 6 and CHIR99021 n = 5 mice per group). e, Schematic representation of human lung organoid experiments. f, Representative images and quantification of lung organoids from primary human AT2 epithelial cells cultured for 14 days with or without human LTβR-Ag (2 μg ml−1) and LiCl (5 mM) (n = 2 replicates from 2 separate donors). Scale bar, 500 μm. g, Schematic representation of the re-ignition of repair and regeneration pathways in AT2 lung cells after LTβR-Ig therapy in both young and aged mice chronically exposed to cigarette smoke. Data are mean ± s.d. P values determined by two-tailed Student’s t-test (d) or one-way ANOVA multiple comparisons Bonferroni test (f).

Source data

Supplementary information

Supplementary Figure 1

This file contains gel source data, flow cytometry gating structure for Extended Data Fig 2 and the chemical structure of the NIK kinase inhibitor CMP1.

Reporting Summary

Supplementary Tables 1-3

This file contains Supplementary Table 1: Demographics and clinical characteristics of healthy and COPD patients; Supplementary Table 2: Antibodies used for flow cytometry; and Supplementary Table 3: Primer sequences used for the quantitative real time RT-PCR.

Supplementary Table 4

This file contains metadata from the single cell RNA-Seq analysis of lungs from mice chronically exposed to cigarette smoke for 6 months and treated therapeutically with LTβR-Ig from 4 to 6 months and filtered air controls.

Supplementary Table 5

This file contains proteomics data from the lungs of mice mice chronically exposed to cigarette smoke for 6 months and treated therapeutically with LTβR-Ig from 4 to 6 months and filtered air controls.

Source data

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Conlon, T.M., John-Schuster, G., Heide, D. et al. Inhibition of LTβR signalling activates WNT-induced regeneration in lung. Nature 588, 151–156 (2020). https://doi.org/10.1038/s41586-020-2882-8

Download citation

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing