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:

Immune impacts of fire smoke exposure

Abstract

Exposure to fire smoke has become a global health concern and is associated with increased morbidity and mortality. There is a lack of understanding of the specific immune mechanisms involved in smoke exposure, with preventive and targeted interventions needed. After exposure to fire smoke, which includes PM2.5, toxic metals and perfluoroalkyl and polyfluoroalkyl substances, epidemiology-based studies have demonstrated increases in respiratory (for example, asthma exacerbation), cardiac (for example, myocardial infarction, arrhythmias), neurological (for example, stroke) and pregnancy-related (for example, low birthweight, premature birth) outcomes. However, mechanistic studies exploring how smoke exposure disrupts cellular homeostasis are lacking. Therefore, we collected blood from smoke-exposed individuals (n = 31) and age-matched and sex-matched non-smoke-exposed controls (n = 29), and investigated these complex interactions using a single-cell exposomic approach based on both methylation and mass cytometry. Overall, our data demonstrated a strong association between smoke exposure and methylation at 133 disease-relevant gene loci, while immunophenotyping showed increased homing and activation biomarkers. We developed an application of mass cytometry to analyze single-cell/metal binding and found, for example, increased levels of mercury in dead cells and cadmium in the live and dead cell populations. Moreover, mercury levels were associated with years of smoke exposure. Several epigenetic sites across multiple chromosomes were associated with individual toxic metal isotopes in single immune cells. Our methods for detecting the effect of smoke exposure at the single-cell level and the study results may help to determine the timing of exposure and identify specific molecular targets that could be modified to prevent and manage exposure to smoke.

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

Access options

Buy this article

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

Fig. 1: Schematic workflow of the multi-omics-based study of smoke-exposed versus non-smoke-exposed cohorts.
Fig. 2: DNA methylation changes associated with smoke exposure and PFAS levels.
Fig. 3: CD4+ T cell differential analysis.
Fig. 4: Single-cell toxic metal profiling.
Fig. 5: Epigenetic modifications versus toxic metal profiling.

Similar content being viewed by others

Data availability

The DNA methylation and mass cytometry datasets (including manually gated live single cells and cell subsets) generated during the current study are available via the Harvard Dataverse data repository at https://doi.org/10.7910/DVN/5PWZJM. Source data are provided with this paper.

Code availability

The R code needed to reproduce the results of the study is available at https://github.com/knadeaulab/Smoke_Exposure_Study.

References

  1. Wu, Y. et al. Synergistic aircraft and ground observations of transported wildfire smoke and its impact on air quality in New York City during the summer 2018 LISTOS campaign. Sci. Total Environ. 773, 145030 (2021).

    Article  CAS  PubMed  Google Scholar 

  2. Childs, M. L. et al. Daily local-level estimates of ambient wildfire smoke PM2.5 for the contiguous US. Environ. Sci. Technol. 56, 13607–13621 (2022).

    Article  CAS  PubMed  Google Scholar 

  3. Chen, G. et al. Mortality risk attributable to wildfire-related PM2.5 pollution: a global time series study in 749 locations. Lancet Planet Health 5, e579–e587 (2021).

    Article  PubMed  Google Scholar 

  4. Demers, P. A. et al. Carcinogenicity of occupational exposure as a firefighter. Lancet Oncol. 23, 985–986 (2022).

    Article  PubMed  Google Scholar 

  5. Gu, W. et al. Particulate polycyclic aromatic hydrocarbons and metals, DNA methylation and DNA methyltransferase among middle-school students in China. Sci. Total Environ. 926, 172087 (2024).

    Article  CAS  PubMed  Google Scholar 

  6. Martin, E. M. & Fry, R. C. Environmental influences on the epigenome: exposure-associated DNA methylation in human populations. Annu. Rev. Public Health 39, 309–333 (2018).

    Article  PubMed  Google Scholar 

  7. Wallace, D. R. et al. Toxic-metal-induced alteration in miRNA expression profile as a proposed mechanism for disease development. Cells 9, 901 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Guo, X. et al. Associations of blood levels of trace elements and heavy metals with metabolic syndrome in Chinese male adults with microRNA as mediators involved. Environ. Pollut. 248, 66–73 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Manić, L. et al. Epigenetic mechanisms in metal carcinogenesis. Toxicol. Rep. 9, 778–787 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Liu, J. C. & Peng, R. D. The impact of wildfire smoke on compositions of fine particulate matter by ecoregion in the Western US. J. Expo. Sci. Environ. Epidemiol. 29, 765–776 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. Lopez, A. M., Pacheco, J. L. & Fendorf, S. Metal toxin threat in wildland fires determined by geology and fire severity. Nat. Commun. 14, 8007 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Burke, M. et al. The contribution of wildfire to PM2.5 trends in the USA. Nature 622, 761–766 (2023).

    Article  CAS  PubMed  Google Scholar 

  13. From Wildfire Smoke to PFAS: Innovative EPA Scientists Address Longstanding Research Gaps https://www.epa.gov/sciencematters/wildfire-smoke-pfas-innovative-epa-scientists-address-longstanding-research-gaps (United States Environmental Protection Agency, 2022).

  14. Wee, S. Y. & Aris, A. Z. Revisiting the “forever chemicals”, PFOA and PFOS exposure in drinking water. NPJ Clean Water 6, 57 (2023).

    Article  CAS  Google Scholar 

  15. van den Dungen, M. W., Murk, A. J., Kampman, E., Steegenga, W. T. & Kok, D. E. Association between DNA methylation profiles in leukocytes and serum levels of persistent organic pollutants in Dutch men. Environ. Epigenet. 3, dvx001 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Kishi, R. et al. Importance of two birth cohorts (n = 20,926 and n = 514): 15 years’ experience of the Hokkaido Study on Environment and Children’s Health: malformation, development and allergy. Nihon Eiseigaku Zasshi 73, 164–177 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Liu, P., Yang, F., Wang, Y. & Yuan, Z. Perfluorooctanoic acid (PFOA) exposure in early life increases risk of childhood adiposity: a meta-analysis of prospective cohort studies. Int. J. Environ. Res. Public Health 15, 2070 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Morin, A. et al. A functional genomics pipeline to identify high-value asthma and allergy CpGs in the human methylome. J. Allergy Clin. Immunol. 151, 1609–1621 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Goodrich, J. M. et al. Repeat measures of DNA methylation in an inception cohort of firefighters. Occup. Environ. Med. 79, 656–663 (2022).

    Article  PubMed  Google Scholar 

  20. Liu, Y. et al. Gestational perfluoroalkyl substance exposure and DNA methylation at birth and 12 years of age: a longitudinal epigenome-wide association study. Environ. Health Perspect. 130, 37005 (2022).

    Article  CAS  PubMed  Google Scholar 

  21. Slütter, B., Pewe, L. L., Kaech, S. M. & Harty, J. T. Lung airway-surveilling CXCR3hi memory CD8+ T cells are critical for protection against influenza A virus. Immunity 39, 939–948 (2013).

    Article  PubMed  Google Scholar 

  22. Deiuliis, J. A. et al. Pulmonary T cell activation in response to chronic particulate air pollution. Am. J. Physiol. Lung Cell. Mol. Physiol. 302, L399–L409 (2012).

    Article  CAS  PubMed  Google Scholar 

  23. Townley, R. G. & Agrawal, S. CRTH2 antagonists in the treatment of allergic responses involving TH2 cells, basophils, and eosinophils. Ann. Allergy Asthma Immunol. 109, 365–374 (2012).

    Article  CAS  PubMed  Google Scholar 

  24. Chen, G. et al. Inhibition of CRTH2-mediated Th2 activation attenuates pulmonary hypertension in mice. J. Exp. Med. 215, 2175–2195 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Oyesola, O. O. et al. The prostaglandin D2 receptor CRTH2 promotes IL-33-induced ILC2 accumulation in the lung. J. Immunol. 204, 1001–1011 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Spits, H. & Mjösberg, J. Heterogeneity of type 2 innate lymphoid cells. Nat. Rev. Immunol. 22, 701–712 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Hilvering, B. et al. Synergistic activation of pro-inflammatory type-2 CD8+ T lymphocytes by lipid mediators in severe eosinophilic asthma. Mucosal Immunol. 11, 1408–1419 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Hinks, T. S. C., Hoyle, R. D. & Gelfand, E. W. CD8+ Tc2 cells: underappreciated contributors to severe asthma. Eur. Respir. Rev. 28, 190092 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Esensten, J. H., Helou, Y. A., Chopra, G., Weiss, A. & Bluestone, J. A. CD28 costimulation: from mechanism to therapy. Immunity 44, 973–988 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Glinos, D. A. et al. Genomic profiling of T-cell activation suggests increased sensitivity of memory T cells to CD28 costimulation. Genes Immun. 21, 390–408 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Collier, J. L., Weiss, S. A., Pauken, K. E., Sen, D. R. & Sharpe, A. H. Not-so-opposite ends of the spectrum: CD8+ T cell dysfunction across chronic infection, cancer and autoimmunity. Nat. Immunol. 22, 809–819 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Radziszewska, A., Moulder, Z., Jury, E. C. & Ciurtin, C. CD8+ T cell phenotype and function in childhood and adult-onset connective tissue disease. Int. J. Mol. Sci. 23, 11431 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mulliken, J. S. et al. Risk of systemic fungal infections after exposure to wildfires: a population-based, retrospective study in California. Lancet Planet Health 7, e381–e386 (2023).

    Article  PubMed  Google Scholar 

  34. Zhou, X. et al. Excess of COVID-19 cases and deaths due to fine particulate matter exposure during the 2020 wildfires in the United States. Sci. Adv. 7, eabi8789 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Landguth, E. L. et al. The delayed effect of wildfire season particulate matter on subsequent influenza season in a mountain west region of the USA. Environ. Int. 139, 105668 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Singh, S. P., Zhang, H. H., Foley, J. F., Hedrick, M. N. & Farber, J. M. Human T cells that are able to produce IL-17 express the chemokine receptor CCR6. J. Immunol. 180, 214–221 (2008).

    Article  CAS  PubMed  Google Scholar 

  37. Gómez-Melero, S. & Caballero-Villarraso, J. CCR6 as a potential target for therapeutic antibodies for the treatment of inflammatory diseases. Antibodies 12, 30 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Liu, H. & Rohowsky-Kochan, C. Regulation of IL-17 in human CCR6+ effector memory T cells. J. Immunol. 180, 7948–7957 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Starner, T. D., Barker, C. K., Jia, H. P., Kang, Y. & McCray, P. B. Jr. CCL20 is an inducible product of human airway epithelia with innate immune properties. Am. J. Respir. Cell Mol. Biol. 29, 627–633 (2003).

    Article  CAS  PubMed  Google Scholar 

  40. Li, Y.-J. et al. Correction: progress of CCL20-CCR6 in the airways: a promising new therapeutic target. J. Inflamm. 22, 2 (2025).

    Article  Google Scholar 

  41. Zuniga, E. I. & Harker, J. A. T-cell exhaustion due to persistent antigen: quantity not quality? Eur. J. Immunol. 42, 2285–2289 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Webster, J. P., Kane, T. J., Obrist, D., Ryan, J. N. & Aiken, G. R. Estimating mercury emissions resulting from wildfire in forests of the Western United States. Sci. Total Environ. 568, 578–586 (2016).

    Article  CAS  PubMed  Google Scholar 

  43. McKee, A. S. & Fontenot, A. P. Interplay of innate and adaptive immunity in metal-induced hypersensitivity. Curr. Opin. Immunol. 42, 25–30 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mou, Y. et al. Environmental pollutants induce NLRP3 inflammasome activation and pyroptosis: roles and mechanisms in various diseases. Sci. Total Environ. 900, 165851 (2023).

    Article  CAS  PubMed  Google Scholar 

  45. Vergilio, C. S., Carvalho, C. E. V. & Melo, E. J. T. Mercury-induced dysfunctions in multiple organelles leading to cell death. Toxicol. In Vitro 29, 63–71 (2015).

    Article  CAS  PubMed  Google Scholar 

  46. Isley, C. F. & Taylor, M. P. Atmospheric remobilization of natural and anthropogenic contaminants during wildfires. Environ. Pollut. 267, 115400 (2020).

    Article  CAS  PubMed  Google Scholar 

  47. Cui, Z.-G., Ahmed, K., Zaidi, S. F. & Muhammad, J. S. Ins and outs of cadmium-induced carcinogenesis: mechanism and prevention. Cancer Treat. Res. Commun. 27, 100372 (2021).

    Article  PubMed  Google Scholar 

  48. Faroon, O. et al. Toxicological Profile for Cadmium (Agency for Toxic Substances and Disease Registry, 2012).

  49. Driller, R. et al. Metal-triggered conformational reorientation of a self-peptide bound to a disease-associated HLA-B*27 subtype. J. Biol. Chem. 294, 13269–13279 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Chiossone, L., Dumas, P.-Y., Vienne, M. & Vivier, E. Natural killer cells and other innate lymphoid cells in cancer. Nat. Rev. Immunol. 18, 671–688 (2018).

    Article  CAS  PubMed  Google Scholar 

  51. Baliu-Piqué, M. et al. Short lifespans of memory T-cells in bone marrow, blood, and lymph nodes suggest that T-cell memory is maintained by continuous self-renewal of recirculating cells. Front. Immunol. 9, 2054 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Choi, J. E. et al. Heavy metal exposure linked to metabolic syndrome in Korean male firefighters: FRESH cohort cross-sectional analysis. Sci. Rep. 13, 14016 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Allonneau, A. et al. Lead contamination among Paris Fire Brigade firefighters who fought the Notre Dame Cathedral fire in Paris. Int. J. Hyg. Environ. Health 233, 113707 (2021).

    Article  CAS  PubMed  Google Scholar 

  54. Leary, D. B., Takazawa, M., Kannan, K. & Khalil, N. Perfluoroalkyl substances and metabolic syndrome in firefighters: a pilot study. J. Occup. Environ. Med. 62, 52–57 (2020).

    Article  CAS  PubMed  Google Scholar 

  55. Liang, S. et al. Sensei: how many samples to tell a change in cell type abundance? BMC Bioinformatics 23, 2 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Thomas, M. F. et al. Single-cell transcriptomic analyses reveal distinct immune cell contributions to epithelial barrier dysfunction in checkpoint inhibitor colitis. Nat. Med. 30, 1349–1362 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. De Mel, S. et al. Single cell multi-omic profiling of multiple myeloma with t(4;14) finds an immune microenvironment gene signature that correlates with clinical outcomes. Blood 138, 2653 (2021).

    Article  Google Scholar 

  58. Li, J. et al. Integrated multi-omics single cell atlas of the human retina. Preprint at Res. Sq. https://doi.org/10.21203/rs.3.rs-3471275/v1 (2023).

  59. Rodriguez-Meira, A. et al. Single-cell multi-omics identifies chronic inflammation as a driver of TP53-mutant leukemic evolution. Nat. Genet. 55, 1531–1541 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Aguilera, J. et al. Granzymes, IL-16, and poly(ADP-ribose) polymerase 1 increase during wildfire smoke exposure. J. Allergy Clin. Immunol. Glob. 2, 100093 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Barros, B., Oliveira, M. & Morais, S. Firefighters’ occupational exposure: contribution from biomarkers of effect to assess health risks. Environ. Int. 156, 106704 (2021).

    Article  CAS  PubMed  Google Scholar 

  62. Kaushik, A. et al. CD8+ T cell differentiation status correlates with the feasibility of sustained unresponsiveness following oral immunotherapy. Nat. Commun. 13, 6646 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Guidelines on Drawing Blood: Best Practices in Phlebotomy (World Health Organization, 2010).

  64. Fuss, I. J., Kanof, M. E., Smith, P. D. & Zola, H. Isolation of whole mononuclear cells from peripheral blood and cord blood. Curr. Protoc. Immunol. https://doi.org/10.1002/0471142735.im0701s85 (2009).

  65. Leipold, M. D. & Maecker, H. T. Phenotyping of live human PBMC using CyTOFTM mass cytometry. Bio Protoc. 5, e1382 (2015).

    Article  PubMed  Google Scholar 

  66. Van Gassen, S. et al. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636–645 (2015).

    Article  PubMed  Google Scholar 

  67. Hahne, F., Gopalakrishnan, N., Khodabakhshi, A.H., Wong, C. & Lee, K. flowStats: Statistical methods for the analysis of flow cytometry data, R package version 4.16.0 https://bioconductor.org/packages/flowStats (2024).

  68. McInnes, L., Healy, J. & Melville, J. UMAP: uniform manifold approximation and projection for dimension reduction. Preprint at https://arxiv.org/abs/1802.03426 (2018).

  69. Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).

    Article  CAS  PubMed  Google Scholar 

  70. Scrucca, L., Fraley, C., Murphy, T. B. & Raftery, A. E. Model-Based Clustering, Classification, and Density Estimation Using mclust in R (CRC Press, 2023).

  71. Hansen, K. D. IlluminaHumanMethylationAllergymanifest. GitHub https://github.com/hansenlab/IlluminaHumanMethylationAllergymanifest (2023).

  72. Xu, Z., Niu, L. & Taylor, J. A. The ENmix DNA methylation analysis pipeline for Illumina BeadChip and comparisons with seven other preprocessing pipelines. Clin. Epigenetics 13, 216 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Du, P., Kibbe, W. A. & Lin, S. M. lumi: a pipeline for processing Illumina microarray. Bioinformatics 24, 1547–1548 (2008).

    Article  CAS  PubMed  Google Scholar 

  74. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  75. McLean, C. Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Zhou, Y. et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun. 10, 1523 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Makowski, D., Ben-Shachar, M. S. & Lüdecke, D. bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J. Open Source Softw. 4, 1541 (2019).

    Article  Google Scholar 

  78. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  PubMed  Google Scholar 

  79. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Use R!) (Springer, 2009).

  80. Blighe. K, Rana, S. & Lewis, M. EnhancedVolcano: publication-ready volcano plots with enhanced colouring and labeling, R package version 1.22.0 https://bioconductor.org/packages/EnhancedVolcano (2024).

  81. Kolde, R. pheatmap: Pretty Heatmaps, R package version 1.0.12 https://cran.r-project.org/web/packages/pheatmap/index.html (2019).

Download references

Acknowledgements

We thank the firefighters for their service and commitment to health research and the San Francisco Cancer Prevention Foundation for their support. The following sources of funding were used: AIRHEALTH PPG P01HL152953 (K.C.N.); the San Francisco Cancer Prevention Foundation (K.C.N.); Pregnancy R01 ES032253 (K.C.N.); the Asthma and Allergic Diseases Cooperative Research Center (K.C.N.); grant no. U19AI167903 (K.C.N.); T32 (E. Simonin); and the Keck Foundation (M. Burke). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

Authors

Contributions

M.M.J. conceived the study design, managed all aspects of the study, performed the data collection and developed and wrote the manuscript. A.K. designed and executed the data analysis workflow and interpretation, created the figures and wrote the manuscript. O.A.K. contributed to sample collection and processing, and performed the mass cytometry, data analysis and manuscript development. E.Smith assisted with the study design, firefighter recruitment and performed the data collection. Y.P. and L.B. performed the mass cytometry. S.A. and J.A. performed the data collection. X.Z. contributed to data analysis and results interpretation. E. Simonin, J.A., A.F., M.C., O.B., R.S.C., E.P., M.S., C.A.A., M. Burke and M. Bondy reviewed the manuscript. M.A. and C.A.A. supervised the mass cytometry and provided critical revisions of the manuscript. K.C.N. conceived the study design, supervised all aspects of the study and wrote and finalized the manuscript. All authors reviewed and approved the manuscript.

Corresponding author

Correspondence to Kari C. Nadeau.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Liam Messin, in collaboration with the Nature Medicine team.

Additional information

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

Supplementary information

Supplementary Information

Supplementary Figs. 1–19 and Tables 1–4.

Reporting Summary

Supplementary Table 1

Supplementary Tables 1–4 as a single Excel sheet.

Supplementary Data 1

Source data for Supplementary Figs. 1–19.

Supplementary Data 1

Computational data.

Supplementary Data 2

Computational data.

Supplementary Data 3

Computational data.

Supplementary Data 4

Computational data.

Supplementary Data 5

Computational data.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

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

Johnson, M.M., Kaushik, A., Kline, O.A. et al. Immune impacts of fire smoke exposure. Nat Med (2025). https://doi.org/10.1038/s41591-025-03777-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41591-025-03777-6

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