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:

The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans

Nature Medicine volume 29, pages 1750–1759 (2023)Cite this article

Subjects

Abstract

Exposure to environmental pollution influences respiratory health. The role of the airway microbial ecosystem underlying the interaction of exposure and respiratory health remains unclear. Here, through a province-wide chronic obstructive pulmonary disease surveillance program, we conducted a population-based survey of bacterial (n = 1,651) and fungal (n = 719) taxa and metagenomes (n = 1,128) from induced sputum of 1,651 household members in Guangdong, China. We found that cigarette smoking and higher PM2.5 concentration were associated with lung function impairment through the mediation of bacterial and fungal communities, respectively, and that exposure was associated with an enhanced inter-kingdom microbial interaction resembling the pattern seen in chronic obstructive pulmonary disease. Enrichment of Neisseria was associated with a 2.25-fold increased risk of high respiratory symptom burden, coupled with an elevation in Aspergillus, in association with occupational pollution. We developed an individualized microbiome-based health index, which covaried with exposure, respiratory symptoms and diseases, with potential generalizability to global datasets. Our results may inform environmental risk prevention and guide interventions that harness airway microbiome.

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: Overview of the airway microbiome and its geographic variation.
Fig. 2: Mediation of the airway microbiome between exposure and health outcomes.
Fig. 3: Interaction of the airway microbiome between occupational pollution and respiratory symptoms.
Fig. 4: Airway microbiome health index and its associations with exposure, health outcomes and diseases.
Fig. 5: Microbial interactome among healthy, pre-COPD and COPD individuals.

Similar content being viewed by others

Data availability

The raw 16S rRNA gene and ITS sequencing data have been deposited in the European Genome-phenome Archive under EGAS00001006720 and EGAS00001006721, respectively. The raw metagenomic sequencing data have been deposited in the Genome Sequence Archive in the National Genomics Data Center (https://ngdc.cncb.ac.cn/), Beijing Institute of Genomics (BIG), Chinese Academy of Sciences, under accession HRA003343 (BioProject accession: PRJCA012829), for controlled access, to abide by the Human Genetic Resources Administration of China regulation. The processed microbiome data tables are available in GitHub under https://github.com/wangzlab/population_airway_microbiome. Reference genomes and databases used in this study include the human genome GRCh38 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/), SILVA (https://www.arb-silva.de/), UNITE (https://unite.ut.ee/), VFDB (http://www.mgc.ac.cn/VFs/), Kraken 2 standard database (https://benlangmead.github.io/aws-indexes/k2) and KEGG (https://www.genome.jp/kegg/).

Code availability

The computer codes for main analyses in this study are deposited in GitHub under https://github.com/wangzlab/population_airway_microbiome/.

References

  1. Briggs, D. Environmental pollution and the global burden of disease. Br. Med. Bull. 68, 1–24 (2003).

    CAS  PubMed  Google Scholar 

  2. Oliveri, D. & Scoditti, E. Impact of environmental factors on lung defences. Eur. Res. Rev. 14, 51–56 (2005).

    Google Scholar 

  3. Menzies, D. et al. Respiratory symptoms, pulmonary function, and markers of inflammation among bar workers before and after a legislative ban on smoking in public places. JAMA 296, 1742–1748 (2006).

    CAS  PubMed  Google Scholar 

  4. Viegi, G. et al. Indoor air pollution and airway disease. Int. J. Tuberc. Lung Dis. 8, 1401–1415 (2004).

    CAS  PubMed  Google Scholar 

  5. Yi, X., Gao, J. & Wang, Z. The human lung microbiome—a hidden link between microbes and human health and diseases. iMeta 1, e33 (2022).

    Google Scholar 

  6. Whiteside, S. A., McGinniss, J. E. & Collman, R. G. The lung microbiome: progress and promise. J. Clin. Invest. https://doi.org/10.1172/JCI150473 (2021).

  7. Moffatt, M. F. & Cookson, W. O. The lung microbiome in health and disease. Clin. Med. 17, 525–529 (2017).

    Google Scholar 

  8. Dickson, R. P., Erb-Downward, J. R. & Huffnagle, G. B. The role of the bacterial microbiome in lung disease. Expert Rev. Respir. Med. 7, 245–257 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Gacesa, R. et al. Environmental factors shaping the gut microbiome in a Dutch population. Nature 604, 732–739 (2022).

    CAS  PubMed  Google Scholar 

  10. Fouladi, F. et al. Air pollution exposure is associated with the gut microbiome as revealed by shotgun metagenomic sequencing. Environ. Int. 138, 105604 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Adar, S. D., Huffnagle, G. B. & Curtis, J. L. The respiratory microbiome: an underappreciated player in the human response to inhaled pollutants? Ann. Epidemiol. 26, 355–359 (2016).

    PubMed  PubMed Central  Google Scholar 

  12. Man, W. H., de Steenhuijsen Piters, W. A. & Bogaert, D. The microbiota of the respiratory tract: gatekeeper to respiratory health. Nat. Rev. Microbiol. 15, 259–270 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. World Health Organization. Chronic obstructive pulmonary disease (COPD). World Health Organization http://www.who.int/respiratory/copd/en/ (2021).

  14. Lopez, A. D. et al. Chronic obstructive pulmonary disease: current burden and future projections. Eur. Respir. J. 27, 397–412 (2006).

    CAS  PubMed  Google Scholar 

  15. Wang, C. et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet 391, 1706–1717 (2018).

    PubMed  Google Scholar 

  16. Johannessen, A. et al. Post-bronchodilator spirometry reference values in adults and implications for disease management. Am. J. Respir. Crit. Care Med. 173, 1316–1325 (2006).

    PubMed  Google Scholar 

  17. Pauwels, R. A. et al. Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease. NHLBI/WHO Global Initiative for Chronic Obstructive Lung Disease (GOLD) Workshop summary. Am. J. Respir. Crit. Care Med. 163, 1256–1276 (2001).

    CAS  PubMed  Google Scholar 

  18. Fazleen, A. & Wilkinson, T. Early COPD: current evidence for diagnosis and management. Ther. Adv. Respir. Dis. 14, 1753466620942128 (2020).

    PubMed  PubMed Central  Google Scholar 

  19. Han, M. K. et al. From GOLD 0 to pre-COPD. Am. J. Respir. Crit. Care Med. 203, 414–423 (2021).

    PubMed  PubMed Central  Google Scholar 

  20. Aho, V. T. E. et al. The microbiome of the human lower airways: a next-generation sequencing perspective. World Allergy Organ J. 8, 23 (2015).

    PubMed  PubMed Central  Google Scholar 

  21. Haldar, K. et al. The sputum microbiome is distinct between COPD and health, independent of smoking history. Respir. Res. 21, 183 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Lim, M. Y. et al. Analysis of the association between host genetics, smoking and sputum microbiota in healthy humans. Sci. Rep. 6, 23745 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Zheng, X. Y. et al. Effects of cigarette smoking and biomass fuel on lung function and respiratory symptoms in middle-aged adults and the elderly in Guangdong province, China: a cross-sectional study. Indoor Air 30, 860–871 (2020).

    CAS  PubMed  Google Scholar 

  24. Rodriguez, R. L., Gunturu, S., Tiedje, J. M., Cole, J. R. & Konstantinidis, K. T. Nonpareil 3: fast estimation of metagenomic coverage and sequence diversity. mSystems https://doi.org/10.1128/mSystems.00039-18 (2018).

  25. Gupta, V. K. et al. A predictive index for health status using species-level gut microbiome profiling. Nat. Commun. 11, 4635 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Xiao, L., Zhang, F. & Zhao, F. Large-scale microbiome data integration enables robust biomarker identification. Nat. Comput. Sci. https://doi.org/10.1038/s43588-022-00247-8 (2022).

  27. He, Y. et al. Regional variation limits applications of healthy gut microbiome reference ranges and disease models. Nat. Med. 24, 1532–1535 (2018).

    CAS  PubMed  Google Scholar 

  28. Gupta, V. K., Paul, S. & Dutta, C. Geography, ethnicity or subsistence-specific variations in human microbiome composition and diversity. Front. Microbiol. 8, 1162 (2017).

    PubMed  PubMed Central  Google Scholar 

  29. Liu, X. et al. Smoking related environmental microbes affecting the pulmonary microbiome in Chinese population. Sci. Total Environ. 829, 154652 (2022).

    CAS  PubMed  Google Scholar 

  30. Pfeiffer, S. et al. Different responses of the oral, nasal and lung microbiomes to cigarette smoke. Thorax 77, 191–195 (2022).

    PubMed  Google Scholar 

  31. Segal, L. N. et al. Enrichment of the lung microbiome with oral taxa is associated with lung inflammation of a TH17 phenotype. Nat. Microbiol .1, 16031 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Segal, L. N. et al. Enrichment of lung microbiome with supraglottic taxa is associated with increased pulmonary inflammation. Microbiome 1, 19 (2013).

    PubMed  PubMed Central  Google Scholar 

  33. Liu, J. W. et al. Epidemiological correlation of pulmonary Aspergillus infections with ambient pollutions and influenza A (H1N1) in Southern Taiwan. J. Fungi https://doi.org/10.3390/jof7030227 (2021).

  34. Liu, P. Y. et al. Associations of PM2.5 and aspergillosis: ambient fine particulate air pollution and population‑based big data linkage analyses. J. Ambient Intell. Humaniz. Comput. https://doi.org/10.1007/s12652-018-0852-x (2018).

  35. Li, L. et al. Neisseria species as pathobionts in bronchiectasis. Cell Host Microbe 30, 1311–1327 (2022).

    CAS  PubMed  Google Scholar 

  36. Wang, C. et al. Microbial risk score for capturing microbial characteristics, integrating multi-omics data and predicting disease risk. Microbiome 10, 121 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Kim, W. J. & Lee, C. Y. Environmental exposures and chronic obstructive pulmonary disease. Mol. Cell Toxicol. 13, 251–255 (2017).

    CAS  Google Scholar 

  38. Mac Aogain, M. et al. Integrative microbiomics in bronchiectasis exacerbations. Nat. Med. 27, 688–699 (2021).

    Google Scholar 

  39. Wang, Z. et al. Inflammatory endotype-associated airway microbiome in chronic obstructive pulmonary disease clinical stability and exacerbations: a multicohort longitudinal analysis. Am. J. Respir. Crit. Care Med. 203, 1488–1502 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Pattaroni, C. et al. Early life inter-kingdom interactions shape the immunological environment of the airways. Microbiome 10, 34 (2022).

    PubMed  PubMed Central  Google Scholar 

  41. Narayana, J. K., Mac Aogain, M., Ali, N., Tsaneva-Atanasova, K. & Chotirmall, S. H. Similarity network fusion for the integration of multi-omics and microbiomes in respiratory disease. Eur. Respir. J. https://doi.org/10.1183/13993003.01016-2021 (2021).

  42. Chen, L. et al. The long-term genetic stability and individual specificity of the human gut microbiome. Cell 184, 2302–2315 (2021).

    CAS  PubMed  Google Scholar 

  43. Jiang, Z. et al. The gut microbiota–bile acid axis links the positive association between chronic insomnia and cardiometabolic diseases. Nat. Commun. 13, 3002 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Chen, L. et al. Influence of the microbiome, diet and genetics on inter-individual variation in the human plasma metabolome. Nat. Med. 28, 2333–2343 (2022).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Nguyen, L. D., Viscogliosi, E. & Delhaes, L. The lung mycobiome: an emerging field of the human respiratory microbiome. Front. Microbiol. 6, 89 (2015).

    PubMed  PubMed Central  Google Scholar 

  46. Carney, S. M. et al. Methods in lung microbiome research. Am. J. Respir. Cell Mol. Biol. 62, 283–299 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Choi, S. et al. Lung virome: new potential biomarkers for asthma severity and exacerbation. J. Allergy Clin. Immunol. 148, 1007–1015 (2021).

    CAS  PubMed  Google Scholar 

  48. Manor, O. et al. Health and disease markers correlate with gut microbiome composition across thousands of people. Nat. Commun. 11, 5206 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Feng, J. et al. Increasing prevalence and burden of bronchiectasis in urban Chinese adults, 2013–2017: a nationwide population-based cohort study. Respir. Res 23, 111 (2022).

    PubMed  PubMed Central  Google Scholar 

  50. Huang, K. et al. Prevalence, risk factors, and management of asthma in China: a national cross-sectional study. Lancet 394, 407–418 (2019).

    PubMed  Google Scholar 

  51. Fang, L. et al. Chronic obstructive pulmonary disease in China: a nationwide prevalence study. Lancet Respir. Med. 6, 421–430 (2018).

    PubMed  PubMed Central  Google Scholar 

  52. Asher, M. I. et al. International study of asthma and allergies in childhood (ISAAC): rationale and methods. Eur. Respir. J. 8, 483–491 (1995).

    CAS  PubMed  Google Scholar 

  53. Standards for the diagnosis and care of patients with chronic obstructive pulmonary disease (COPD) and asthma. This official statement of the American Thoracic Society was adopted by the ATS Board of Directors, November 1986. Am. Rev. Respir. Dis. https://doi.org/10.1164/ajrccm/136.1.225 (1987).

  54. Jones, P. W. et al. Development and first validation of the COPD Assessment Test. Eur. Respir. J. 34, 648–654 (2009).

    CAS  PubMed  Google Scholar 

  55. Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).

    CAS  PubMed  Google Scholar 

  56. Wei, J. et al. Estimating 1-km-resolution PM2.5 concentrations across China using the space-time random forest approach. Remote Sens. Environ. https://doi.org/10.1016/j.rse.2019.111221 (2019).

  57. Bafadhel, M. et al. Profiling of sputum inflammatory mediators in asthma and chronic obstructive pulmonary disease. Respiration 83, 36–44 (2012).

    CAS  PubMed  Google Scholar 

  58. Bafadhel, M. et al. Acute exacerbations of chronic obstructive pulmonary disease: identification of biologic clusters and their biomarkers. Am. J. Respir. Crit. Care Med. 184, 662–671 (2011).

    PubMed  Google Scholar 

  59. Bolyen, E. et al. Reproducible, interactive, scalable, and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Clarke, E. L. et al. Sunbeam: an extensible pipeline for analyzing metagenomic sequencing experiments. Microbiome 7, 46 (2019).

    PubMed  PubMed Central  Google Scholar 

  62. Marcel, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Google Scholar 

  63. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Saenz, J. S. et al. Oral administration of antibiotics increased the potential mobility of bacterial resistance genes in the gut of the fish Piaractus mesopotamicus. Microbiome 7, 24 (2019).

    PubMed  PubMed Central  Google Scholar 

  65. Beghini, F. et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife https://doi.org/10.7554/eLife.65088 (2021).

  66. Yan, Z. et al. Multi-omics analyses of airway host–microbe interactions in chronic obstructive pulmonary disease identify potential therapeutic interventions. Nat. Microbiol. 7, 1361–1375 (2022).

    CAS  PubMed  Google Scholar 

  67. Yin, X. et al. ARGs-OAP v2.0 with an expanded SARG database and Hidden Markov Models for enhancement characterization and quantification of antibiotic resistance genes in environmental metagenomes. Bioinformatics 34, 2263–2270 (2018).

    CAS  PubMed  Google Scholar 

  68. Yi, X. et al. Inflammatory endotype-associated airway resistome in chronic obstructive pulmonary disease. Microbiol. Spectr. 10, e0259321 (2022).

    PubMed  Google Scholar 

  69. Wood, D. E., Lu, J. & Langmead, B. Improved metagenomic analysis with Kraken 2. Genome Biol. 20, 257 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. Kaminski, J. et al. High-specificity targeted functional profiling in microbial communities with ShortBRED. PLoS Comput. Biol. 11, e1004557 (2015).

    PubMed  PubMed Central  Google Scholar 

  71. Chen, L. et al. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 33, D325–D328 (2005).

    CAS  PubMed  Google Scholar 

  72. Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-7 https://CRAN.R-project.org/package=vegan (2020).

  73. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach for multiple testing. J. R. Stat. Soc. B 57, 289–300 (1995).

    Google Scholar 

  74. Tingley, D., Yamamoto, T., Hirose, K., Keele, L. & Imai, K. mediation: R package for causal mediation analysis. J. Stat. Softw. 59, 1–38 (2014).

    Google Scholar 

  75. Wang, D. D. et al. The gut microbiome modulates the protective association between a Mediterranean diet and cardiometabolic disease risk. Nat. Med. 27, 333–343 (2021).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Hunter, D. J. Gene–environment interactions in human diseases. Nat. Rev. Genet. 6, 287–298 (2005).

    CAS  PubMed  Google Scholar 

  77. Shi, Y. et al. A genome-wide association study identifies new susceptibility loci for non-cardia gastric cancer at 3q13.31 and 5p13.1. Nat. Genet. 43, 1215–1218 (2011).

    CAS  PubMed  Google Scholar 

  78. Kuhn, M. caret: Classification and regression training. R package version 6.0-88 https://CRAN.R-project.org/package=caret (2021).

  79. Wang, Z. et al. A refined view of airway microbiome in chronic obstructive pulmonary disease at species and strain levels. Front. Microbiol. 11, 1758 (2020).

    PubMed  PubMed Central  Google Scholar 

  80. Durack, J. et al. Bacterial biogeography of adult airways in atopic asthma. Microbiome 6, 104 (2018).

    PubMed  PubMed Central  Google Scholar 

  81. Liu, H. et al. Airway bacterial and fungal microbiome in chronic obstructive pulmonary disease. Med. Microecol. 7, 100035 (2021).

  82. Liu, H. Y. et al. The interactions of airway bacterial and fungal communities in clinically stable Asthma. Front. Microbiol. 11, 1647 (2020).

    PubMed  PubMed Central  Google Scholar 

  83. Engel, M. et al. Influence of lung CT changes in chronic obstructive pulmonary disease (COPD) on the human lung microbiome. PLoS ONE 12, e0180859 (2017).

    PubMed  PubMed Central  Google Scholar 

  84. Pang, Z. et al. Airway microbiome in different inflammatory phenotypes of Asthma: a cross-sectional study in Northeast China. Int. J. Med. Sci. 16, 477–485 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Wang, Z. et al. Airway host–microbiome interactions in chronic obstructive pulmonary disease. Respir. Res. 20, 113 (2019).

    PubMed  PubMed Central  Google Scholar 

  86. Lu, H. et al. Alterations of the human lung and gut microbiomes in non-small cell lung carcinomas and distant metastasis. Microbiol. Spectr. 9, e0080221 (2021).

    PubMed  Google Scholar 

  87. Tiew, P. Y. et al. A high-risk airway mycobiome is associated with frequent exacerbation and mortality in COPD. Eur. Respir. J. https://doi.org/10.1183/13993003.02050-2020 (2021).

  88. Sala, C. et al. Multicenter analysis of sputum microbiota in tuberculosis patients. PLoS ONE 15, e0240250 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJournal, Complex Systems 1695, https://igraph.org/ (2006).

  90. Wen, T. et al. ggClusterNet: an R package for microbiome network analysis and modularity-based multiple network layouts. iMeta https://doi.org/10.1002/imt2.32 (2022).

  91. Bastian, M., Heymann, S. & Jacomy, M. Gephi: an open source software for exploring and manipulating networks. In Third International AAAI Conference on Weblogs and Social Media (2009).

  92. Kurtz, Z. D. et al. Sparse and compositionally robust inference of microbial ecological networks. PLoS Comput. Biol. 11, e1004226 (2015).

    PubMed  PubMed Central  Google Scholar 

  93. Schieber, T. A. et al. Quantification of network structural dissimilarities. Nat. Commun. 8, 13928 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Wang, Z. et al. Multi-omic meta-analysis identifies functional signatures of airway microbiome in chronic obstructive pulmonary disease. ISME J. 14, 2748–2765 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the working staff from the local center for disease control and prevention, and the public health practitioners from the local health station or community health service center, for their organization and contribution to the study. We thank all participants for their valuable contribution to the study. This work was supported by the National Key R&D Program of China (2022YFA1304300 to Z.W.), the National Natural Science Foundation of China (31970112 and 32170109 to Z.W., 41907211 to X.Y., 82202629 to H. Liu and 82171931 to Z.X.), the National Natural Science Foundation of China Outstanding Youth Fund (82222001 to W.-j.G.), the Science and Technology Foundation of Guangdong Province (2019A1515011395 to Z.W., 2021B1212030007 to J.S. and 2023A1515012328 to X.-y.Z.), the Guangzhou Science and Technology Plan (2023B03J0407 and 202102010372 to W.-j.G.), the Zhongnanshan Medical Foundation of Guangdong Province (ZNSA-2020013 to W.-j.G.) and the plan on enhancing scientific research in Guangzhou Medical University (no. YX2022022 to W.-j.G).

Author information

Author notes

  1. These authors contributed equally: Lifeng Lin, Xinzhu Yi, Haiyue Liu, Ruilin Meng.

Authors and Affiliations

  1. Guangdong Provincial Center for Disease Control and Prevention, Guangzhou, China

    Lifeng Lin, Ruilin Meng, Yanjun Xu, Chuan Li, Ye Wang, Ni Xiao, Xue-yan Zheng & Jiufeng Sun

  2. Institute of Ecological Sciences, School of Life Sciences, South China Normal University, Guangzhou, China

    Xinzhu Yi, Xiaomin Liu, Junhao Yang, Wensheng Shu & Zhang Wang

  3. Xiamen Key Laboratory of Genetic Testing, Department of Laboratory Medicine, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China

    Haiyue Liu

  4. School of Public Health, Sun Yat-sen University, Guangzhou, China

    Saiqiang Li

  5. State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute for Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

    Huimin Li & Wei-jie Guan

  6. Xiamen Key Laboratory of Cardiac Electrophysiology, Department of Cardiology, Xiamen Institute of Cardiovascular Diseases, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China

    Zuheng Liu

  7. Department of Radiology, Panyu Central Hospital, Guangzhou, China

    Zhiming Xiang

  8. Department of Thoracic Surgery, Guangzhou Institute for Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China

    Wei-jie Guan

Authors
  1. Lifeng Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xinzhu Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haiyue Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ruilin Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Saiqiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaomin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Junhao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yanjun Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Chuan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ye Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ni Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Huimin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zuheng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Zhiming Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Wensheng Shu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Wei-jie Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Xue-yan Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Jiufeng Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Zhang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.L., R.M., W.-j.G., X.-y.Z., J.S. and Z.W. conceived the study. Y.X., C.L., Y.W., N.X. and H. Li contributed to sample and data collection. H. Liu, S.L., X.L. and Z.L. contributed to sample processing and sequencing. X.Y., J.Y. and Z.W. performed statistical and bioinformatic analyses. Z.X. and W.S. assisted in data analyses and interpretation. Z.W. and W.-j.G. drafted the paper. All authors provided critical revisions and approved the final paper.

Corresponding authors

Correspondence to Lifeng Lin, Wei-jie Guan, Xue-yan Zheng, Jiufeng Sun or Zhang Wang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Andrew Kau, Leopoldo Segal and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Alison Farrell, 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.

Extended data

Extended Data Fig. 1 Flowchart of sputum collection, quality control and microbiome analyses.

A total of 3,915 individuals were initially approached. 3,820 individuals completed the questionnaire. Of them, 3,737 individuals had body measurement (height and weight), and 3,424 individuals further had acceptable spirometry measurement after quality control (grade C or above). 907 individuals with post-bronchodilator FEV1 below 35% predicted, unwilling or unable to participate, or unable to produce sputum after induction were excluded, leading to 2,517 sputum samples. Of them, 84 individuals who reported the antibiotic use within one month and 782 individuals who yielded low quality (<0.2 g) sputum or yielded sputum that did not pass quality control were excluded, leading to 1,651 sputum samples stored and transported for further microbiome analysis. Genomic DNA was extracted from the 1,651 samples for 16S rRNA gene and metagenomic sequencing. For 719 samples with remaining genomic DNA and yielded ITS amplicon of adequate quality, ITS sequencing was further performed. For 1,651 sputum samples with metagenomic data, 1,128 had microbial coverage >70% as estimated by Nonpareil software, and were retained for further analyses.

Extended Data Fig. 2 The overview of the airway microbiome profiles.

The overview of bacterial taxonomic profiles based on 16S rRNA gene amplicon data (N = 1,651), fungal taxonomic profiles based on ITS amplicon data (N = 719), and bacterial taxonomic and functional (KEGG categories, ARGs and VFs) profiles based on metagenomic data (N = 1,128).

Extended Data Fig. 3 Overview of sputum metagenomic sequencing.

a) Nonpareil curves of all samples showing the estimated average coverage as a function of sequencing effort. For each curve, the hollow circle represents the actual sequencing effort for that sample and its corresponding estimated average coverage. Samples with estimated microbial coverage below 70% are colored in blue. b) Barplot showing the correlation coefficient between the shared genera identified from 16S rRNA gene-based amplicon data (relative abundance > =1e-4) and metagenomic data. Significant correlations were found in 45 out of 51 genera (P < 0.001, Pearson correlation). c) A phylogenetic diagram showing the bacterial genera identified in amplicon or metagenomic data. Shown from the outside inward are the presence or absence of the genus in metagenomic data, in 16S rRNA gene-based amplicon data, and its corresponding phylum. A greater number of genera were identified in amplicon than metagenomic data. d) Histogram showing the distribution on the proportion of viral reads of the overall microbial community for all 1,128 samples. e) Piechart showing the top 10 most abundant viral species in the observed viral community across all 1,128 samples. f) Rarefaction plot showing the number of viral species detected with the increment of sequencing depth. The exact P-values for comparisons in b) are provided in Supplementary Table 18. *** P < 0.001, ** P < 0.01, * P < 0.05, + P < 0.1.

Extended Data Fig. 4 Association between host and environmental factors with all microbiome profiles in all individuals adjusting for district, and in individuals within each district and sub-district.

Associations with P < 0.1 (adonis) are shown in red. The strength of the color is proportional to statistical significance. Only the factors with available results for all comparisons (that is non-constant within all sub-districts) are shown.

Extended Data Fig. 5 Volcano plots showing the association of microbiome features with exposure factors and health outcomes.

The microbiome features showing the associations with exposure factors and health outcomes at nominal significance (P < 0.05) are colored by their feature types (amplicon-derived bacterial genera, fungal genera, KOs, ARGs and VFs). P-values were obtained by associating each microbiome feature with the exposure factor or health outcome in general linear model, with district, age, sex, BMI, and medication use adjusted as confounders.

Extended Data Fig. 6 Airway disease-associated multi-kingdom microbiome signature.

a) Heatmaps showing the association of bacterial and fungal genera with the general health (without any reported diseases), airway health (without airway diseases), and airway diseases. The differential bacterial and fungal genera between airway health and diseases at P-value threshold of 0.25 are shown. Only airway diseases present in at least 10 individuals in the analysis set are shown. Associations with P < 0.25 are colored according to their z-scores, and associations at nominal significance (P < 0.05) are further marked with plus and minus for their directionality. P-values were obtained by associating each microbiome feature with health or diseases in general linear model, with district, age, sex, BMI, and medication use adjusted as covariates. The differential bacterial and fungal genera demonstrated overall consistent directionality of associations with the diverse airway diseases. b) Comparison of Pearson correlations between the comorbidities of the airway diseases (top right), and the predicted values of diseases based on a set of disease-specific random forest models (with 5-fold cross-validation) constructed using bacterial and fungal taxonomic profiles (bottom left), respectively. A greater inter-disease correlation was observed for the predicted values of diseases based on the microbiome than their actual comorbidities, suggesting the possible existence of a pan-disease microbiome signature across the diverse airway diseases.

Extended Data Fig. 7 The airway microbiome healthy index.

a) AUC and accuracy of AMHI calculated based on the functional features (KOs, ARGs and VFs) in distinguishing airway health and disease status. b) Violin plots showing a significant decrease of AMHI (using amplicon-based bacterial and fungal genera) in disease over healthy individuals across all 6 districts (Wilcoxon rank-sum test, two-sided). The number of individuals is indicated in the parenthesis. c) Violin plots showing the association of AMHI with airway symptoms among airway healthy individuals only. For respiratory symptoms, the P-values were obtained in comparison with the no symptom group using Wilcoxon rank-sum test (two-sided). The number of individuals in each group is indicated in the parenthesis. Exact P-values (top to bottom): 0.0727, 0.538, 0.0330, 0.316, and 0.00388. d) The interaction effects of AMHI with biofuel exposure, second-hand smoking, and occupational pollution on their effects on the high respiratory symptom burden (CAT > = 10). Shown are the estimate and P-value of the interaction term in the general linear model, and the increased odds of having a high symptom burden in exposure to occupational pollution with one unit decrement of AMHI (Δ odds). e) The top KMs, ARGs and VFs correlated with AMHI. For display purpose, KMs with FDR < 0.005 in association with AMHI are shown. ARGs and VFs with FDR < 0.05 are shown. f) Violin plots showing an extrapolation of AMHI to 5 external sputum microbiome datasets on healthy smokers and non-smokers. For each dataset, the relative AMHI scores in smokers normalized to the average and standard deviation of non-smokers are shown in the violin plot. P-value was obtained using Wilcoxon rank-sum test (two-sided). Exact P-values (left to right): 0.0436, 0.0576, 0.00354, 0.235, and 0.00856. The numbers of smokers and non-smokers are indicated for each dataset. For the boxplots within the violin plots, the central line indicates the median. The lower and upper hinges indicate the first and third quartiles. The lower and upper whiskers extend from the hinge to the smallest and largest values no further than 1.5 * inter-quartile range from the hinge. *** P < 0.001, ** P < 0.01, * P < 0.05, + P < 0.1.

Extended Data Fig. 8 Microbial interactome among healthy, Pre-COPD and COPD individuals.

a) Receiver operating characteristic (ROC) curves for classifying health versus Pre-COPD, Pre-COPD versus COPD, and health versus COPD, using AMHI and the differential microbiome features (P < 0.05). The area under curve is shown for each ROC curve. b) The number of edges and nodes for the networks of healthy, Pre-COPD and COPD individuals, built using different cutoffs for Spearman’s rho (0.2 to 0.6) and with P < 0.05. Also shown are the number of edges (bacterial-bacterial, bacterial-fungal, and fungal-fungal) and number of nodes (bacteria or fungi) for the networks of healthy, Pre-COPD and COPD individuals, built using all samples (rho>0.4, P < 0.05), size-balanced samples (rho>0.4, P < 0.05), and using SpeicEasi algorithm. c) The alpha diversity measurements (Shannon and observed ASVs) for bacterial and fungal communities in healthy (n = 496), Pre-COPD (n = 141) and COPD (n = 83) individuals. P-values were obtained using Wilcoxon rank-sum test (two-sided). For the boxplots within the violin plots, the central line indicates the median. The lower and upper hinges indicate the first and third quartiles. The lower and upper whiskers extend from the hinge to the smallest and largest values no further than 1.5 * inter-quartile range from the hinge.

Extended Data Fig. 9 Exposure-associated microbial interactome.

a) The interaction networks of bacterial (amplicon data-derived) and fungal genera among healthy individuals without any exposure or airway symptoms, and individuals experiencing each exposure factor, and individuals with documented airway symptoms. Each node represents a bacterial (hollow circle) or fungal genus (solid circle) colored by its associated network module. Each edge represents a significant correlation between pairs of taxa (absolute Spearman’s rho>0.4, P < 0.05). b) The network dissimilarity index D from the network of healthy individuals (without exposure or airway symptoms), for the network of Pre-COPD individuals (gray), and the networks of participants experiencing individual exposure factors (green) and participants with airway symptoms (purple). Also shown are the module sizes, number of edges, number of nodes, and strength of correlation (absolute correlation coefficient, mean ± SD) for the network of healthy individuals (number of correlations: nbac-bac=4,753, nbac-fun=28,518, nfun-fun=42,915) and the networks associated with individual exposure factors (number of correlations: biofuel: nbac-bac=4,753, nbac-fun=28,322, nfun-fun=41,616; occupational pollution: nbac-bac=4,753, nbac-fun=28,224, nfun-fun=41,327; smoking: nbac-bac=4,753, nbac-fun=28,126, nfun-fun=41,041; second hand smoking: nbac-bac=4,753, nbac-fun=28,126, nfun-fun=41,041) and respiratory symptoms (nbac-bac=4,753, nbac-fun=27,734, nfun-fun=39,903).

Extended Data Table 1 List of host and environmental factors
Full size table

Supplementary information

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

Lin, L., Yi, X., Liu, H. et al. The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans. Nat Med 29, 1750–1759 (2023). https://doi.org/10.1038/s41591-023-02424-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-023-02424-2

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