Abstract

Microaspiration is a common phenomenon in healthy subjects, but its frequency is increased in chronic inflammatory airway diseases, and its role in inflammatory and immune phenotypes is unclear. We have previously demonstrated that acellular bronchoalveolar lavage samples from half of the healthy people examined are enriched with oral taxa (here called pneumotypeSPT) and this finding is associated with increased numbers of lymphocytes and neutrophils in bronchoalveolar lavage. Here, we have characterized the inflammatory phenotype using a multi-omic approach. By evaluating both upper airway and acellular bronchoalveolar lavage samples from 49 subjects from three cohorts without known pulmonary disease, we observed that pneumotypeSPT was associated with a distinct metabolic profile, enhanced expression of inflammatory cytokines, a pro-inflammatory phenotype characterized by elevated Th-17 lymphocytes and, conversely, a blunted alveolar macrophage TLR4 response. The cellular immune responses observed in the lower airways of humans with pneumotypeSPT indicate a role for the aspiration-derived microbiota in regulating the basal inflammatory status at the pulmonary mucosal surface.

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References

  1. 1.

    et al. Topographical continuity of bacterial populations in the healthy human respiratory tract. Am. J. Respir. Crit. Care Med. 184, 957–963 (2011).

  2. 2.

    et al. Analysis of the lung microbiome in the ‘healthy’ smoker and in COPD. PLoS ONE 6, e16384 (2011).

  3. 3.

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

  4. 4.

    et al. Comparison of the respiratory microbiome in healthy non-smokers and smokers. Am. J. Respir. Crit. Care Med. 187, 1067–1075 (2013).

  5. 5.

    , & Quantitative aspiration during sleep in normal subjects. Chest 111, 1266–1272 (1997).

  6. 6.

    et al. Laryngeal penetration and aspiration in individuals with stable COPD. Respirology 16, 269–275 (2011).

  7. 7.

    et al. Role of gastroesophageal reflux symptoms in exacerbations of COPD. Chest 130, 1096–1101 (2006).

  8. 8.

    & Understanding bacteriophage specificity in natural microbial communities. Viruses 5, 806–823 (2013).

  9. 9.

    et al. Airway microbiome dynamics in exacerbations of chronic obstructive pulmonary disease. J. Clin. Microbiol. 52, 2813–2823 (2014).

  10. 10.

    & Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, e1002687 (2012).

  11. 11.

    et al. Signal transducer and activator of transcription-3 (Stat3) plays a critical role in implantation via progesterone receptor in uterus. FASEB J. 27, 2553–2563 (2013).

  12. 12.

    & The carboxyl-terminal region of STAT3 controls gene induction by the mouse haptoglobin promoter. J. Biol. Chem. 272, 14571–14579 (1997).

  13. 13.

    , & Smad proteins suppress CCAAT/enhancer-binding protein (C/EBP) β- and STAT3-mediated transcriptional activation of the haptoglobin promoter. J. Biol. Chem. 276, 24719–24725 (2001).

  14. 14.

    et al. Principles of interleukin (IL)-6-type cytokine signalling and its regulation. Biochem. J. 374, 1–20 (2003).

  15. 15.

    , , , & Pyruvate kinase M2 facilitates colon cancer cell migration via the modulation of STAT3 signalling. Cell. Signal. 26, 1853–1862 (2014).

  16. 16.

    et al. Loss of phosphatase and tensin homolog (PTEN) induces leptin-mediated leptin gene expression: feed-forward loop operating in the lung. J. Biol. Chem. 288, 29821–29835 (2013).

  17. 17.

    , , & Pharyngeal aspiration in normal adults and patients with depressed consciousness. Am. J. Med. 64, 564–568 (1978).

  18. 18.

    et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).

  19. 19.

    et al. Rethinking ‘enterotypes’. Cell Host Microbe 16, 433–437 (2014).

  20. 20.

    et al. Widespread colonization of the lung by Tropheryma whipplei in HIV infection. Am. J. Respir. Crit. Care Med. 187, 1110–1117 (2013).

  21. 21.

    , , , & The lung microbiome in moderate and severe chronic obstructive pulmonary disease. PLoS ONE 7, e47305 (2012).

  22. 22.

    et al. The lung tissue microbiome in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 185, 1073–1080 (2012).

  23. 23.

    et al. Lung microbiota and bacterial abundance in patients with bronchiectasis when clinically stable and during exacerbation. Am. J. Respir. Crit. Care Med. 187, 1118–1126 (2013).

  24. 24.

    et al. Is there a relationship between obstructive sleep apnea and gastroesophageal reflux disease? Clin. Gastroenterol. Hepatol. 2, 761–768 (2004).

  25. 25.

    et al. Obstructive sleep apnea syndrome may be a significant cause of gastroesophageal reflux disease in older people. J. Am. Geriatr. Soc. 47, 1273–1274 (1999).

  26. 26.

    , , & Prevalence of gastroesophageal reflux symptoms in asthma. Chest 109, 316–322 (1996).

  27. 27.

    , & Gastroesophageal reflux in patients with cystic fibrosis. J. Pediatr. 106, 223–227 (1985).

  28. 28.

    et al. Prevalence of gastroesophageal reflux disease in patients with nontuberculous mycobacterial lung disease. Chest 131, 1825–1830 (2007).

  29. 29.

    et al. Smoking is associated with shortened airway cilia. PLoS ONE 4, e8157 (2009).

  30. 30.

    , & Ozone exposure alters tracheobronchial mucociliary function in humans. J. Appl. Phys 63, 996–1002 (1987).

  31. 31.

    et al. Triglyceride-rich lipoprotein lipolysis releases neutral and oxidized FFAs that induce endothelial cell inflammation. J. Lipid Res. 50, 204–213 (2009).

  32. 32.

    et al. Specific microbiota direct the differentiation of IL-17-producing T-helper cells in the mucosa of the small intestine. Cell Host Microbe 4, 337–349 (2008).

  33. 33.

    et al. Microbiota regulates immune defense against respiratory tract influenza A virus infection. Proc. Natl Acad. Sci. USA 108, 5354–5359 (2011).

  34. 34.

    et al. Cell-associated bacteria in the human lung microbiome. Microbiome 2, 28 (2014).

  35. 35.

    et al. Comparison of whole and acellular bronchoalveolar lavage to oral wash microbiomes. Should acellular bronchoalveolar lavage be the standard? Ann. Am. Thorac. Soc. 11, S72–S73 (2014).

  36. 36.

    et al. Spatial variation in the healthy human lung microbiome and the adapted island model of lung biogeography. Ann. Am. Thorac. Soc. 12, 821–830 (2015).

  37. 37.

    et al. The treatment-naive microbiome in new-onset Crohn's disease. Cell Host Microbe 15, 382–392 (2014).

  38. 38.

    et al. Topographic diversity of the respiratory tract mycobiome and alteration in HIV and lung disease. Am. J. Respir. Crit. Care Med. 191, 932–942 (2015).

  39. 39.

    et al. Case studies of the spatial heterogeneity of DNA viruses in the cystic fibrosis lung. Am. J. Respir. Cell Mol. Biol. 46, 127–131 (2012).

  40. 40.

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

  41. 41.

    et al. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7, 335–336 (2010).

  42. 42.

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

  43. 43.

    , , & Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

  44. 44.

    et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics 26, 266–267 (2010).

  45. 45.

    , , , & UniFrac: an effective distance metric for microbial community comparison. ISME J. 5, 169–172 (2011).

  46. 46.

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

  47. 47.

    et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnol. 31, 814–821 (2013).

  48. 48.

    , , & STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124 (2014).

  49. 49.

    et al. Identification of novel viruses using VirusHunter—an automated data analysis pipeline. PLoS ONE 8, e78470 (2013).

  50. 50.

    et al. Pharmacometabolomics reveals racial differences in response to atenolol treatment. PLoS ONE 8, e57639 (2013).

  51. 51.

    et al. Quality control for plant metabolomics: reporting MSI-compliant studies. Plant J. 53, 691–704 (2008).

  52. 52.

    , , & MetaboAnalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res. 43(W1), W251–W257 (2015).

  53. 53.

    et al. Regulatory T cells attenuate mycobacterial stasis in alveolar and blood-derived macrophages from patients with tuberculosis. Am. J. Respir. Crit. Care Med. 187, 1249–1258 (2013).

  54. 54.

    et al. A network-based analysis of systemic inflammation in humans. Nature 437, 1032–1037 (2005).

  55. 55.

    , & Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19, 368–375 (2003).

  56. 56.

    et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).

  57. 57.

    et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).

  58. 58.

    et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

Download references

Acknowledgements

Research support funding was provided by the National Institute of Allergy and Infectious Diseases (NIAID) K23 AI102970 (to L.N.S.); the National Heart, Lung and Blood Institute (NHLBI) R01 HL125816 (to S.B.K.); NIAID K24 AI080298 (to M.D.W.); the Clinical and Translational Science Institute (CTSI) grant no. UL1 TR000038; the Early Detection Research Network (EDRN) 5U01CA086137-13; the Diane Belfer Program for Human Microbial Ecology; the Michael Saperstein Scholarship Fund; the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) R01DK090989; the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS) UH2 AR57506; NIAID U01AI111598; NHLBI R01 HL090339; NHLBI K24 HL123342 (to A.M.); NHLBI U01 HL098962 (to A.M. and E.G.); NHLBI K24HL123342; NHLBI K24 HL087713 (to L.H.); NIAID and the National Cancer Institute (NCI) UO1-AI-35042; 5-MO1-RR-00722 from the General Clinical Research Center (GCRC); UL1TR000124 from the University of California Los Angeles Clinical and Translational Research Center (UCLA CTRC); NIAID UO1-AI-35043; NIAID UO1-AI-37984; NIAID UO1-AI-35039; NIAID UO1-AI-35040; NIAID UO1-AI-37613; NIAID UO1-AI-35041 (Multicenter AIDS Cohort); NIAID and the National Institute of Child Health and Human Development (NICHHD) UO1-AI-35004; NIAID UO1-AI-31834; NIAID UO1-AI-34994; NIAID UO1-AI-34989; NIAID UO1-AI-34993; NIAID UO1-AI-42590; NICHHD UO1-HD-32632; the Women's Interagency HIV Study (WIHS); NHLBI U01-HL098957 and NHLBI R01-HL113252 (to R.G.C.).

The authors also thank H.W. Virgin (Washington University School of Medicine), S. Stone, S. Fong, A. Malki and S. Tokman (University of California San Francisco (UCSF)), C. Kessinger, N. Leo, D. Camp, M.P. George, L. Lucht, M. Gingo, R. Hoffman, M. Fitzpatrick, J. Ries, A. Clarke (Pittsburgh) and J. Dermand and E. Kleerup (UCLA).

Computing was partially supported by the Department of Scientific Computing at the Icahn School of Medicine at Mount Sinai.

Some of the Pittsburgh LHMP data in this manuscript were collected by the Multicenter AIDS Cohort Study (MACS) with centres (Principal Investigators) at UCLA (R. Detels, U01-AI35040); University of Pittsburgh (C. Rinaldo, U01-AI35041); the Center for Analysis and Management of MACS, Johns Hopkins University Bloomberg School of Public Health (L. Jacobson, UM1-AI35043). MACS is funded primarily by NIAID, with additional cofunding from the NCI. Targeted supplemental funding for specific projects was also provided by the National Heart, Lung, and Blood Institute (NHLBI), and the National Institute on Deafness and Communication Disorders (NIDCD). MACS data collection was also supported by UL1-TR000424 Johns Hopkins University Clinical and Translational Science Awards (JHU CTSA, https://statepi.jhsph.edu/macs/macs.html). The contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH).

Some of the Pittsburgh LHMP data in this manuscript were collected by the WIHS. WIHS (principal investigators): U01-AI-103408; Connie Wofsy Women's HIV Study, Northern California (R. Greenblatt, B. Aouizerat and P. Tien). The WIHS is funded primarily by NIAID, with additional cofunding from the Eunice Kennedy Shriver NICHD, the NCI, the National Institute on Drug Abuse (NIDA) and the National Institute on Mental Health (NIMH). WIHS data collection was also supported by UL1-TR000004 (UCSF CTSA).

Author information

Affiliations

  1. Division of Pulmonary, Critical Care and Sleep Medicine, New York University School of Medicine, New York, USA

    • Leopoldo N. Segal
    • , Jun-Chieh J. Tsay
    • , Benjamin G. Wu
    • , Yonghua Li
    • , William N. Rom
    • , Daniel H. Sterman
    •  & Michael D. Weiden
  2. Department of Medicine, New York University School of Medicine, New York, New York, USA

    • Leopoldo N. Segal
    • , Jun-Chieh J. Tsay
    • , Benjamin G. Wu
    • , Yonghua Li
    • , William N. Rom
    • , Daniel H. Sterman
    • , Martin J. Blaser
    •  & Michael D. Weiden
  3. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, USA

    • Jose C. Clemente
    •  & Nan Shen
  4. Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, USA

    • Jose C. Clemente
  5. Department of Pathology, New York University School of Medicine, New York, New York, USA

    • Sergei B. Koralov
  6. Division of Pulmonary and Critical Care Medicine, The Ohio State University, Columbus, Ohio, USA

    • Brian C. Keller
    •  & Phillip Diaz
  7. Department of Biology, Center for Genomics & Systems Biology, College of Global Public Health, New York University, New York, New York, USA

    • Elodie Ghedin
  8. Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pennsylvania, USA

    • Alison Morris
  9. Department of Medicine, University of California San Francisco, San Francisco, California, USA

    • Laurence Huang
  10. Department of Molecular and Cellular Biology & Genome Center, University of California, Davis, California, USA

    • William R. Wikoff
  11. Center for Public Health Research, FISABIO, Valencia, Spain

    • Carles Ubeda
    •  & Alejandro Artacho
  12. Department of Medicine and Microbiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania, USA

    • Ronald G. Collman

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Contributions

L.N.S., J.C.C., M.J.B. and M.D.W. conceived and designed the study. L.N.S., J.J.T., A.M., L.H., P.D. and W.R.W. acquired the data. L.N.S., J.C.C, J.J.T., S.B.K., B.G.W., Y.L., N.S., W.R.W., C.U., A.A., B.C.K., R.G.C., M.J.B. and M.D.W. analysed and interpreted the data. L.N.S., J.C.C., J.J.T., S.B.K., E.G., A.M., P.D., L.H., W.R.W., B.C.K., W.N.R., D.H.S., R.G.C., M.J.B. and M.D.W. drafted or revised the article. L.N.S., J.C.C., J.J.T., S.B.K., B.G.W., Y.L., N.S., E.G., A.M., P.D., L.H., W.R.W., C.U., A.A., B.C.K., W.N.R., D.H.S., R.G.C., M.J.B. and M.D.W. approved the final manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding authors

Correspondence to Leopoldo N. Segal or Michael D. Weiden.

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DOI

https://doi.org/10.1038/nmicrobiol.2016.31