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.

  • Article
  • Published:

Physiological microbial exposure transiently inhibits mouse lung ILC2 responses to allergens

Nature Immunology volume 23, pages 1703–1713 (2022)Cite this article

Subjects

Abstract

Lung group 2 innate lymphoid cells (ILC2s) control the nature of immune responses to airway allergens. Some microbial products, including those that stimulate interferons, block ILC2 activation, but whether this occurs after natural infections or causes durable ILC2 inhibition is unclear. In the present study, we cohoused laboratory and pet store mice as a model of physiological microbial exposure. Laboratory mice cohoused for 2 weeks had impaired ILC2 responses and reduced lung eosinophilia to intranasal allergens, whereas these responses were restored in mice cohoused for ≥2 months. ILC2 inhibition at 2 weeks correlated with increased interferon receptor signaling, which waned by 2 months of cohousing. Reinduction of interferons in 2-month cohoused mice blocked ILC2 activation. These findings suggest that ILC2s respond dynamically to environmental cues and that microbial exposures do not control long-term desensitization of innate type 2 responses to allergens.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Mouse lung immune cell populations are altered by cohousing.
Fig. 2: Short-term cohousing impairs eosinophil and ILC2 responses to intranasal allergens.
Fig. 3: Acute IL-33, IL-5 and IL-13 responses after Alt exposure are reduced in 2-month cohoused mice.
Fig. 4: Comparable immune infiltration in SPF and 2-month cohoused mice after repeated Alt exposure.
Fig. 5: Cohousing with pet store mice alters the fecal microbiota.
Fig. 6: Cohousing with pet store mice induces transient systemic and lung inflammation.
Fig. 7: Poly(I:C) suppresses ILC2 and eosinophil responses to Alt in SPF and 2-month cohoused mice.

Similar content being viewed by others

Data availability

The 16S rRNA amplicon sequence data are deposited in the SRA under the primary accession no. SRP371485. The SILVA rRNA database can be found at https://www.arb-silva.de/ (PMID: 1794732). THe UCHIME database can be found at https://drive5.com/uchime/uchime_download.html (PMID: 21700674). The Ribosomal Database Project can be found at http://rdp.cme.msu.edu/ (PMID: 19004872). Source data are provided with this paper.

Code availability

Code for 16S rRNA-seq-based microbiome analysis is available in PMID: 30227891 (ref. 69).

References

  1. Croisant, S. Epidemiology of asthma: prevalence and burden of disease. Adv. Exp. Med. Biol. 795, 17–29 (2014).

    Article  Google Scholar 

  2. Kim, B. J., Lee, S. Y., Kim, H. B., Lee, E. & Hong, S. J. Environmental changes, microbiota, and allergic diseases. Allergy Asthma Immunol. Res. 6, 389–400 (2014).

    Article  CAS  Google Scholar 

  3. Holgate, S. T. Innate and adaptive immune responses in asthma. Nat. Med. 18, 673–683 (2012).

    Article  CAS  Google Scholar 

  4. Kubo, M. Innate and adaptive type 2 immunity in lung allergic inflammation. Immunol. Rev. 278, 162–172 (2017).

    Article  CAS  Google Scholar 

  5. Vivier, E. et al. Innate lymphoid cells: 10 years on. Cell 174, 1054–1066 (2018).

    Article  CAS  Google Scholar 

  6. Halim, T. Y. 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).

    Article  CAS  Google Scholar 

  7. Halim, T. Y. et al. Group 2 innate lymphoid cells license dendritic cells to potentiate memory TH2 cell responses. Nat. Immunol. 17, 57–64 (2016).

    Article  CAS  Google Scholar 

  8. 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.e1196 (2018).

    Article  CAS  Google Scholar 

  9. Kato, A. Group 2 innate lymphoid cells in airway diseases. Chest 156, 141–149 (2019).

    Article  Google Scholar 

  10. Scadding, G. K. & Scadding, G. W. Innate and adaptive immunity: ILC2 and Th2 cells in upper and lower airway allergic diseases. J. Allergy Clin. Immunol. Pr. 9, 1851–1857 (2021).

    Article  CAS  Google Scholar 

  11. Pivniouk, V., Gimenes Junior, J. A., Honeker, L. K. & Vercelli, D. The role of innate immunity in asthma development and protection: lessons from the environment. Clin. Exp. Allergy 50, 282–290 (2020).

    Article  Google Scholar 

  12. Kaplan, B. A., Mascie-Taylor, C. G. & Boldsen, J. Birth order and health status in a British national sample. J. Biosoc. Sci. 24, 25–33 (1992).

    Article  CAS  Google Scholar 

  13. Ball, T. M. et al. Siblings, day-care attendance, and the risk of asthma and wheezing during childhood. N. Engl. J. Med. 343, 538–543 (2000).

    Article  CAS  Google Scholar 

  14. Kramer, U., Heinrich, J., Wjst, M. & Wichmann, H. E. Age of entry to day nursery and allergy in later childhood. Lancet 353, 450–454 (1999).

    Article  CAS  Google Scholar 

  15. Riedler, J., Eder, W., Oberfeld, G. & Schreuer, M. Austrian children living on a farm have less hay fever, asthma and allergic sensitization. Clin. Exp. Allergy 30, 194–200 (2000).

    Article  CAS  Google Scholar 

  16. Sjögren, Y. M., Jenmalm, M. C., Böttcher, M. F., Björkstén, B. & Sverremark-Ekström, E. Altered early infant gut microbiota in children developing allergy up to 5 years of age. Clin. Exp. Allergy 39, 518–526 (2009).

    Article  Google Scholar 

  17. Bisgaard, H. et al. Reduced diversity of the intestinal microbiota during infancy is associated with increased risk of allergic disease at school age. J. Allergy Clin. Immunol. 128, 646–652.e641-645 (2011).

    Article  Google Scholar 

  18. Stein, M. M. et al. Innate immunity and asthma risk in amish and hutterite farm children. N. Engl. J. Med. 375, 411–421 (2016).

    Article  CAS  Google Scholar 

  19. Marra, F. et al. Antibiotic use in children is associated with increased risk of asthma. Pediatrics 123, 1003–1010 (2009).

    Article  Google Scholar 

  20. Kline, J. N. et al. Modulation of airway inflammation by CpG oligodeoxynucleotides in a murine model of asthma. J. Immunol. 160, 2555–2559 (1998).

    CAS  Google Scholar 

  21. Sur, S. et al. Long term prevention of allergic lung inflammation in a mouse model of asthma by CpG oligodeoxynucleotides. J. Immunol. 162, 6284–6293 (1999).

    CAS  Google Scholar 

  22. Sabatel, C. et al. Exposure to bacterial CpG DNA protects from airway allergic inflammation by expanding regulatory lung interstitial macrophages. Immunity 46, 457–473 (2017).

    Article  CAS  Google Scholar 

  23. Schuijs, M. J. et al. Farm dust and endotoxin protect against allergy through A20 induction in lung epithelial cells. Science 349, 1106–1110 (2015).

    Article  CAS  Google Scholar 

  24. Tei, R. et al. TLR3-driven IFN-β antagonizes STAT5-activating cytokines and suppresses innate type 2 response in the lung. J. Allergy Clin. Immunol. 149, 1044–1059.e5 (2022).

    Article  CAS  Google Scholar 

  25. Pivniouk, V. et al. Airway administration of OM-85, a bacterial lysate, blocks experimental asthma by targeting dendritic cells and the epithelium/IL-33/ILC2 axis. J. Allergy Clin. Immunol. 149, 943–956 (2022).

    Article  CAS  Google Scholar 

  26. McSorley, H. J., Blair, N. F., Smith, K. A., McKenzie, A. N. & Maizels, R. M. Blockade of IL-33 release and suppression of type 2 innate lymphoid cell responses by helminth secreted products in airway allergy. Mucosal Immunol. 7, 1068–1078 (2014).

    Article  CAS  Google Scholar 

  27. Osbourn, M. et al. HpARI protein secreted by a helminth parasite suppresses Interleukin-33. Immunity 47, 739–751.e735 (2017).

    Article  CAS  Google Scholar 

  28. Machiels, B. et al. A gammaherpesvirus provides protection against allergic asthma by inducing the replacement of resident alveolar macrophages with regulatory monocytes. Nat. Immunol. 18, 1310–1320 (2017).

    Article  CAS  Google Scholar 

  29. 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).

    Article  CAS  Google Scholar 

  30. Moro, K. et al. Interferon and IL-27 antagonize the function of group 2 innate lymphoid cells and type 2 innate immune responses. Nat. Immunol. 17, 76–86 (2016).

    Article  CAS  Google Scholar 

  31. Duerr, C. U. et al. Type I interferon restricts type 2 immunopathology through the regulation of group 2 innate lymphoid cells. Nat. Immunol. 17, 65–75 (2016).

    Article  CAS  Google Scholar 

  32. Beura, L. K. et al. Normalizing the environment recapitulates adult human immune traits in laboratory mice. Nature 532, 512–516 (2016).

    Article  CAS  Google Scholar 

  33. Reese, T. A. et al. Sequential infection with common pathogens promotes human-like immune gene expression and altered vaccine response. Cell Host Microbe 19, 713–719 (2016).

    Article  CAS  Google Scholar 

  34. Rosshart, S. P. et al. Laboratory mice born to wild mice have natural microbiota and model human immune responses. Science 365, eaaw4361 (2019).

    Article  CAS  Google Scholar 

  35. Rosshart, S. P. et al. Wild mouse gut microbiota promotes host fitness and improves disease resistance. Cell 171, 1015–1028.e1013 (2017).

    Article  CAS  Google Scholar 

  36. Bartemes, K. R. et al. IL-33-responsive lineage- CD25+ CD44(hi) lymphoid cells mediate innate type 2 immunity and allergic inflammation in the lungs. J. Immunol. 188, 1503–1513 (2012).

    Article  CAS  Google Scholar 

  37. Milne, J. & Brand, S. Occupational asthma after inhalation of dust of the proteolytic enzyme, papain. Br. J. Ind. Med 32, 302–307 (1975).

    CAS  Google Scholar 

  38. Halim, T. Y., 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).

    Article  CAS  Google Scholar 

  39. Oboki, K. et al. IL-33 is a crucial amplifier of innate rather than acquired immunity. Proc. Natl Acad. Sci. USA 107, 18581–18586 (2010).

    Article  CAS  Google Scholar 

  40. Melo-González, F. et al. Distal consequences of mucosal infections in intestinal and lung inflammation. Front. Immunol. 13, 877533 (2022).

    Article  Google Scholar 

  41. Abt, M. C. et al. Commensal bacteria calibrate the activation threshold of innate antiviral immunity. Immunity 37, 158–170 (2012).

    Article  CAS  Google Scholar 

  42. Hild, B. et al. Neonatal exposure to a wild-derived microbiome protects mice against diet-induced obesity. Nat. Metab. 3, 1042–1057 (2021).

    Article  CAS  Google Scholar 

  43. Knights, D. et al. Bayesian community-wide culture-independent microbial source tracking. Nat. Methods 8, 761–763 (2011).

    Article  CAS  Google Scholar 

  44. Jiang, Z. et al. Poly(I-C)-induced Toll-like receptor 3 (TLR3)-mediated activation of NFkappa B and MAP kinase is through an interleukin-1 receptor-associated kinase (IRAK)-independent pathway employing the signaling components TLR3-TRAF6-TAK1-TAB2-PKR. J. Biol. Chem. 278, 16713–16719 (2003).

    Article  CAS  Google Scholar 

  45. Uccellini, M. B. & García-Sastre, A. ISRE-reporter mouse reveals high basal and induced type I IFN responses in inflammatory monocytes. Cell Rep. 25, 2784–2796.e2783 (2018).

    Article  CAS  Google Scholar 

  46. Staeheli, P., Grob, R., Meier, E., Sutcliffe, J. G. & Haller, O. Influenza virus-susceptible mice carry Mx genes with a large deletion or a nonsense mutation. Mol. Cell Biol. 8, 4518–4523 (1988).

    CAS  Google Scholar 

  47. Fay, E. J. et al. Natural rodent model of viral transmission reveals biological features of virus population dynamics. J. Exp. Med. 219, e20211220 (2022).

    Article  CAS  Google Scholar 

  48. Hamilton, S. E. et al. New insights into the immune system using dirty mice. J. Immunol. 205, 3–11 (2020).

    Article  CAS  Google Scholar 

  49. Masopust, D., Sivula, C. P. & Jameson, S. C. Of mice, dirty mice, and men: using mice to understand human immunology. J. Immunol. 199, 383–388 (2017).

    Article  CAS  Google Scholar 

  50. Fiege, J. K. et al. Mice with diverse microbial exposure histories as a model for preclinical vaccine testing. Cell Host Microbe 29, 1815–1827 (2021).

    Article  CAS  Google Scholar 

  51. Huggins, M. A. et al. Microbial exposure enhances immunity to pathogens recognized by TLR2 but increases susceptibility to cytokine storm through TLR4 sensitization. Cell Rep. 28, 1729–1743.e1725 (2019).

    Article  CAS  Google Scholar 

  52. Hartmann, S. et al. Gastrointestinal nematode infection interferes with experimental allergic airway inflammation but not atopic dermatitis. Clin. Exp. Allergy 39, 1585–1596 (2009).

    Article  CAS  Google Scholar 

  53. Maizels, R. M. Regulation of immunity and allergy by helminth parasites. Allergy 75, 524–534 (2020).

    Article  Google Scholar 

  54. Wark, P. A. B., Ramsahai, J. M., Pathinayake, P., Malik, B. & Bartlett, N. W. Respiratory viruses and asthma. Semin. Respir. Crit. Care Med 39, 45–55 (2018).

    Article  Google Scholar 

  55. Ikutani, M. et al. Identification of innate IL-5-producing cells and their role in lung eosinophil regulation and antitumor immunity. J. Immunol. 188, 703–713 (2012).

    Article  CAS  Google Scholar 

  56. Steinert, E. M. et al. Quantifying memory CD8 T cells reveals regionalization of immunosurveillance. Cell 161, 737–749 (2015).

    Article  CAS  Google Scholar 

  57. Borges da Silva, H. et al. The purinergic receptor P2RX7 directs metabolic fitness of long-lived memory CD8. Nature 559, 264–268 (2018).

    Article  CAS  Google Scholar 

  58. Skon, C. N. et al. Transcriptional downregulation of S1pr1 is required for the establishment of resident memory CD8+ T cells. Nat. Immunol. 14, 1285–1293 (2013).

    Article  CAS  Google Scholar 

  59. Anderson, K. G. et al. Intravascular staining for discrimination of vascular and tissue leukocytes. Nat. Protoc. 9, 209–222 (2014).

    Article  CAS  Google Scholar 

  60. Caporaso, J. G. et al. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 6, 1621–1624 (2012).

    Article  CAS  Google Scholar 

  61. Gohl, D. M. et al. Systematic improvement of amplicon marker gene methods for increased accuracy in microbiome studies. Nat. Biotechnol. 34, 942–949 (2016).

    Article  CAS  Google Scholar 

  62. Schloss, P. D. Reintroducing mothur: 10 years later. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.02343-19 (2020).

  63. Aronesty, E. Comparison of sequencing utility programs. The Open Bioinformatics J. 7, 1–8 (2013).

    Article  Google Scholar 

  64. Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007).

    Article  CAS  Google Scholar 

  65. Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).

    Article  CAS  Google Scholar 

  66. Cole, J. R. et al. The Ribosomal Database Project: improved alignments and new tools for rRNA analysis. Nucleic Acids Res. 37, D141–D145 (2009).

    Article  CAS  Google Scholar 

  67. Schloss, P. D. The effects of alignment quality, distance calculation method, sequence filtering, and region on the analysis of 16S rRNA gene-based studies. PLoS Comput. Biol. 6, e1000844 (2010).

    Article  Google Scholar 

  68. Anderson, M. J. & Willis, T. J. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84, 511–525 (2003).

    Article  Google Scholar 

  69. Staley, C. et al. Predicting recurrence of Clostridium difficile infection following encapsulated fecal microbiota transplantation. Microbiome 6, 166 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

We thank UMN Flow Cytometry Resource Facility, CFI Dirty Mouse Colony, UMN BSL-3 Program, Minnesota Supercomputing Institute, Cytokine Reference Laboratory and UMN Research Animal Resources for support. We thank Jamequist and Kita lab members for thoughtful discussion. This work was supported by the NIH (grant nos. R01HL117823 to H.K., T32HL007741 to K.E.B. and R35GM140881 to T.S.G.), Minnesota Partnership for Biotechnology and Medical Genomics (grant no. 16.48 to S.C.J. and H.K.) and UMN Medical School (grant no. AIRP-CP-21 to S.C.J.).

Author information

Authors and Affiliations

  1. Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA

    Katharine E. Block, Mark J. Pierson, Daniel A. Walsh & Stephen C. Jameson

  2. Center for Immunology, University of Minnesota, Minneapolis, MN, USA

    Katharine E. Block, Mark J. Pierson, Daniel A. Walsh, Thomas S. Griffith & Stephen C. Jameson

  3. Division of Allergy, Asthma and Clinical Immunology and Department of Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA

    Koji Iijima, Rinna Tei & Hirohito Kita

  4. Department of Pulmonary Medicine and Clinical Immunology, Dokkyo Medical University, Tochigi, Japan

    Rinna Tei

  5. Department of Urology, University of Minnesota, Minneapolis, MN, USA

    Tamara A. Kucaba, Julie Xu & Thomas S. Griffith

  6. Department of Surgery, University of Minnesota, Minneapolis, MN, USA

    Mohammad Haneef Khan & Christopher Staley

  7. Division of Cell signaling and Immunology, School of Life Sciences, University of Dundee, Dundee, UK

    Henry J. McSorley

Authors
  1. Katharine E. Block
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Koji Iijima
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mark J. Pierson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniel A. Walsh
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rinna Tei
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Tamara A. Kucaba
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Julie Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mohammad Haneef Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Christopher Staley
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Thomas S. Griffith
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Henry J. McSorley
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Hirohito Kita
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Stephen C. Jameson
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

K.E.B. designed and performed experiments and analyzed and interpreted the data. K.I., M.J.P., D.A.W., R.T., T.A.K., J.X. and T.S.G. performed experiments. M.H.K. and C.S. carried out 16S rRNA-seq-based microbiome analysis. H.J.M. provided the HpARI reagent. S.C.J. and H.K. supervised the project. K.E.B. and S.C.J. wrote the manuscript. H.K., T.S.G. and H.J.M. edited the manuscript.

Corresponding authors

Correspondence to Hirohito Kita or Stephen C. Jameson.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Immunology thanks Donata Vercelli and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. N. Bernard was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Serology screening for SPF pathogens in cohoused mice.

Blood samples were collected and tested for antibodies against common murine pathogens. Each column represents an animal. Each row indicates a pathogen. Filled boxes indicate positive results, lighter shaded boxes indicate equivocal (weak positive) results. White boxes indicate negative results. a, Serology results from representative pet store (n = 8), cohoused B6 (n = 26) and F1 IL-5v (n = 11) mice cohoused for approximately two weeks at the time of blood collection. b, Serology results from representative pet store and cohoused B6, F1 IL-5v, and BALB/c mice cohoused for at least two months at the time of blood collection (n = 26/group). CPIL = Clostridium piliforme, ECUN = Encephalitozoon cuniculi, EDIM = rotavirus, LCMV = lymphocytic choriomeningitis virus, MAV1 + 2 = mouse adenovirus 1 and 2, MCMV = murine cytomegalovirus, MHV = mouse hepatitis virus, MNV = murine norovirus, MPUL = Mycoplasma pulmonis, MPV1 + 2 = mouse parvovirus type 1 and type 2, MVM = minute virus of mice, POLY = polyoma virus, PVM = pneumonia virus of mice, REO = reovirus, SEND = murine respirovirus (Sendai virus), TCMV = GDVII Theiler’s murine encephalomyelitis virus. c. Representative flow cytometry histograms of protein expression of lung ILC2 in age-matched SPF (blue) and CoH (red) mice at 2wk and >2mo. Vertical gray lines are added to aid in comparison of expression among groups.

Extended Data Fig. 2 Example flow cytometry gating.

Representative flow plots to illustrate gating strategy of immune cells in the lungs. a, Neutrophil, eosinophil, and alveolar macrophage gating. b, γδ T cell, CD4+ T cell, CD8+ T cell, and NK cell gating. c, ILC2 and B cell gating.

Extended Data Fig. 3 A. alternata response is susceptible to inhibition by microbial factors.

Eosinophils (CD45+ SiglecF+ CD11b+ CD11c− cells) were identified by being in the lung parenchyma or airways by being unlabeled by a fluorescently tagged anti-CD45 antibody injected intravenously three minutes before euthanasia. Shown are B6 mice treated 24 hours prior with intranasal PBS or A. alternata (Alt) or HpARI and Alt. Numbers in plots represent percent of cells in the gate. b, Number of eosinophils in the lungs and airways (i.v. CD45−) 24 hours after indicated intranasal treatment. Mice were B6 (white circles) or B6xBALB/c IL-5WT/venus (IL-5v F1, light blue squares). Bar graph shows mean +SD of log-transformed values. c, Representative flow plots of IL-5 venus expression in IL-5v F1 lung ILC2. d, IL-5 venus gMFI of IL-5 venus+ lung ILC2. e-f, ST2 (e) and CD25 (f) of B6 and IL-5v F1 mice. gMFIs from multiple experiments normalized to PBS-treated group set to 100. b and e, f, Pooled from one B6 and one IL-5v F1 experiment (n = 5−9/group). d, Data from one experiment in IL-5v F1 mice (n = 2-3/group). Bar graphs show mean +SD. Each symbol represents a mouse. P values were determined with one-way ANOVA with Tukey’s multiple comparisons test; n.s. p ≥ 0.05. Source Data contains exact P-values and group sizes.

Source data

Extended Data Fig. 4 ILC2 numbers and CD4+ T cell data from two-week cohoused mice.

Flow analysis 24 hours after intranasal PBS or Alt treatment. Mice were B6 (white) or IL-5v F1 (light blue) and were cohoused with pet store mice for approximately two weeks (2wk) or were age-matched SPF controls. a, f, Data in Figures 2a and 2d are presented again, with samples from each experiment indicated with a different symbol/color combination so that experiment-to-experiment variation can be visualized. b, Number of lung ILC2. c, d, IL-5 venus expression within lung (i.v. CD45-) CD4+ T cells of IL-5v F1 SPF and 2wk CoH mice 24 hours after PBS or Alt treatment. c, Representative flow plots of IL-5 venus expression in lung CD4+ T cells. d, Percent IL-5 venus+ and e, normalized IL-5 venus gMFI of IL-5 venus+ lung CD4+ T cells. b, Pooled from four B6 experiments and two IL-5v F1 experiments (n = 12–14/group). c-e, Pooled from two IL-5v F1 experiments (n = 4-6/group). Numbers in plots represent percent of cells in the gate. Bar graphs show mean +SD. g, Mice were treated with intranasal PBS or papain every other day three times and analyzed 24 hours after the last treatment and are from the same experiments described in Fig. 2g-j. Number of lung i.v.- CD4+ T cells in 2wk or >2mo CoH B6 mice or age-matched SPF controls. Bar graphs show mean +SD of log-transformed values. Each symbol represents a mouse. P values were determined with a 2-way ANOVA with Tukey’s multiple comparisons test; n.s. p ≥ 0.05. Source Data contains exact P-values and group sizes.

Source data

Extended Data Fig. 5 BALF and lung cytokine measurements.

a, b, e, f, B6 mice (white circles) or c-d, BALB/c mice (pink diamonds) were cohoused with pet store mice for at least two months (>2mo CoH) or were age-matched SPF controls. Alt (a-d), recombinant IL-33 (rIL-33, e, f) or control PBS were given intranasally to mice and lungs and bronchoalveolar lavage fluid (BALF) were collected 4.5 hours later. IL-5 and IL-13 in the lung homogenates (c, d) and BALF (a-b, e, f) were detected by ELISA and lung concentrations were normalized to the amount of total protein in the samples. a, b pooled from three experiments (n = 6–10/group). c-d pooled from four experiments (n = 6-12/group). e, f pooled from three experiments (n = 5–12/group). Bar graphs show mean +SD. Each symbol represents a mouse. P values were determined with a 2-way ANOVA with Tukey’s multiple comparisons test; n.s. p ≥ 0.05. Source Data contains exact P-values and group sizes.

Source data

Extended Data Fig. 6 Fecal microbiota analysis.

a, Principal coordinates analysis (PCoA) of Bray-Curtis distances of mouse fecal samples from cohoused Cages 2 and 3 (Cage 1 PCoA in Fig. 5b). SPF samples represented by circles are control SPF mice used in analysis of all cages and SPF samples represented by the symbols used for cohoused mice in each graph are from those same mice before cohousing. b, Distributions of abundant genera in mouse fecal samples from Cages 2 and 3. Less abundant genera accounted for less than 2.5% of the community among all samples. c, Distributions of abundant genera in mouse fecal samples from SPF control mice collected on day 0 of cohousing and 60 days later. Less abundant genera accounted for less than 2.5% of the community among all samples.

Extended Data Fig. 7 Serum cytokines and Mx1-GFP expression in cohoused mice.

a, Serum cytokine and chemokine levels of B6 mice days 0 and 60 after cohousing with a pet store mouse, (n = 8). These are from the same data in Fig. 5d. Bar graphs show mean +SD. P values were determined with a two-tailed Wilcoxon matched-pairs signed rank test; n.s. p ≥ 0.05. b, Representative flow plots of Mx1-GFP expression in blood CD8+ T cells from wild-type B6 and Mx1gfp mice either housed in SPF conditions or after seven days of cohousing. Numbers in plots represent percent of cells in the gate. c, d, Percent Mx1-GFP+ of the indicated cell populations in the spleens of (c) 2wk or (d) >2mo CoH mice or age-matched controls. e, f, Percent Mx1-GFP+ of the indicated cell populations in the lungs of (e) 2wk or (f) >2mo CoH mice or age-matched controls. c-f, Bar graphs show mean +SD. Each symbol represents a mouse. P values were determined with a Student’s t-test (two-tailed). When groups had unequal variance the t-test was conducted with Welch’s correction; n.s. p ≥ 0.05. g, Representative flow plots of Mx1-GFP expression in lung ILC2 from wild-type Mx1gfp mice 24 hours after intranasal treatment with PBS or Alt, with the indicated group treated with intranasal poly(I:C) 24 hours before Alt treatment. Numbers in plots represent percent of cells in the gate. Source Data contains exact P-values and group sizes.

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical source data and exact P values.

Source Data Fig. 2

Statistical source data and exact P values.

Source Data Fig. 3

Statistical source data and exact P values.

Source Data Fig. 4

Statistical source data and exact P values.

Source Data Fig. 6

Statistical source data and exact P values.

Source Data Fig. 7

Statistical source data and exact P values.

Source Data Extended Data Fig. 3

Statistical source data and exact P values.

Source Data Extended Data Fig. 4

Statistical source data and exact P values.

Source Data Extended Data Fig. 5

Statistical source data and exact P values.

Source Data Extended Data Fig. 7

Statistical source data and exact P values.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Block, K.E., Iijima, K., Pierson, M.J. et al. Physiological microbial exposure transiently inhibits mouse lung ILC2 responses to allergens. Nat Immunol 23, 1703–1713 (2022). https://doi.org/10.1038/s41590-022-01350-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41590-022-01350-8

This article is cited by

Search

Advanced 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