Skip to main content

Thank you for visiting 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.

Multi-omic meta-analysis identifies functional signatures of airway microbiome in chronic obstructive pulmonary disease


The interaction between airway microbiome and host in chronic obstructive pulmonary disease (COPD) is poorly understood. Here we used a multi-omic meta-analysis approach to characterize the functional signature of airway microbiome in COPD. We retrieved all public COPD sputum microbiome datasets, totaling 1640 samples from 16S rRNA gene datasets and 26 samples from metagenomic datasets from across the world. We identified microbial taxonomic shifts using random effect meta-analysis and established a global classifier for COPD using 12 microbial genera. We inferred the metabolic potentials for the airway microbiome, established their molecular links to host targets, and explored their effects in a separate meta-analysis on 1340 public human airway transcriptome samples for COPD. 29.6% of differentially expressed human pathways were predicted to be targeted by microbiome metabolism. For inferred metabolite–host interactions, the flux of disease-modifying metabolites as predicted from host transcriptome was generally concordant with their predicted metabolic turnover in microbiome, suggesting a synergistic response between microbiome and host in COPD. The meta-analysis results were further validated by a pilot multi-omic study on 18 COPD patients and 10 controls, in which airway metagenome, metabolome, and host transcriptome were simultaneously characterized. 69.9% of the proposed “microbiome-metabolite–host” interaction links were validated in the independent multi-omic data. Butyrate, homocysteine, and palmitate were the microbial metabolites showing strongest interactions with COPD-associated host genes. Our meta-analysis uncovered functional properties of airway microbiome that interacted with COPD host gene signatures, and demonstrated the possibility of leveraging public multi-omic data to interrogate disease biology.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: The multi-omic meta-analysis pipeline for the COPD airway microbiome.
Fig. 2: Statistical meta-analysis of the COPD airway microbiome datasets.
Fig. 3: Metabolic inference of the COPD airway microbiome.
Fig. 4: Microbiome metabolites target genes in COPD host transcriptome signature.
Fig. 5: Validation of “microbiome-metabolite–host” interaction links in the independent COPD multi-omic cohort.

Data availability

The processed public 16S rRNA gene, metagenomics, and host transcriptome data are available on Figshare ( The raw multi-omic data for the pilot cohort has been deposited in the Chinese National Gene Bank Nucleotide Sequence Archive (CNSA) under accession code CNP0000837.

Code availability

The key computer codes are on GitHub ( or in the supplementary document.


  1. 1.

    Lopez AD, Shibuya K, Rao C, Mathers CD, Hansell AL, Held LS, et al. Chronic obstructive pulmonary disease: current burden and future projections. Eur Respir J. 2006;27:397–412.

    CAS  PubMed  Google Scholar 

  2. 2.

    Taraseviciene-Stewart L, Douglas IS, Nana-Sinkam PS, Lee JD, Tuder RM, Nicolls MR, et al. Is alveolar destruction and emphysema in chronic obstructive pulmonary disease an immune disease? Proc Am Thorac Soc. 2006;3:687–90.

    CAS  PubMed  Google Scholar 

  3. 3.

    Pragman AA, Kim HB, Reilly CS, Wendt C, Isaacson RE. The lung microbiome in moderate and severe chronic obstructive pulmonary disease. PLoS ONE. 2012;7:e47305.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Einarsson GG, Comer DM, McIlreavey L, Parkhill J, Ennis M, Tunney MM, et al. Community dynamics and the lower airway microbiota in stable chronic obstructive pulmonary disease, smokers and healthy non-smokers. Thorax. 2016;71:795–803.

    CAS  PubMed  Google Scholar 

  5. 5.

    Huang YJ, Sethi S, Murphy T, Nariya S, Boushey HA, Lynch SV. Airway microbiome dynamics in exacerbations of chronic obstructive pulmonary disease. J Clin Microbiol. 2014;52:2813–23.

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    Wang Z, Bafadhel M, Haldar K, Spivak A, Mayhew D, Miller BE, et al. Lung microbiome dynamics in COPD exacerbations. Eur Respir J. 2016;47:1082–92.

    PubMed  Google Scholar 

  7. 7.

    Wang Z, Singh R, Miller BE, Tal-Singer R, Van Horn S, Tomsho L, et al. Sputum microbiome temporal variability and dysbiosis in chronic obstructive pulmonary disease exacerbations: an analysis of the COPDMAP study. Thorax. 2018;73:331–8.

    PubMed  Google Scholar 

  8. 8.

    Budden KF, Shukla SD, Rehman SF, Bowerman KL, Keely S, Hugenholtz P, et al. Functional effects of the microbiota in chronic respiratory disease. Lancet Respir Med. 2019;7:907–20.

    PubMed  Google Scholar 

  9. 9.

    Dickson RP, Martinez FJ, Huffnagle GB. The role of the microbiome in exacerbations of chronic lung diseases. Lancet. 2014;384:691–702.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Cui L, Morris A, Huang L, Beck JM, Twigg HL 3rd, von Mutius E, et al. The microbiome and the lung. Ann Am Thorac Soc. 2014;11:S227–232.

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Castaner O, Goday A, Park YM, Lee SH, Magkos F, Shiow STE, et al. The gut microbiome profile in obesity: a systematic review. Int J Endocrinol. 2018;2018:4095789.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Ren L, Zhang R, Rao J, Xiao Y, Zhang Z, Yang B, et al. Transcriptionally active lung microbiome and its association with bacterial biomass and host inflammatory status. mSystems. 2018;3:e00199–18.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Segal LN, Clemente JC, Tsay JC, Koralov SB, Keller BC, Wu BG, et al. Enrichment of the lung microbiome with oral taxa is associated with lung inflammation of a Th17 phenotype. Nat Microbiol. 2016;1:16031.

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Segal LN, Clemente JC, Wu BG, Wikoff WR, Gao Z, Li Y, et al. Randomised, double-blind, placebo-controlled trial with azithromycin selects for anti-inflammatory microbial metabolites in the emphysematous lung. Thorax. 2017;72:13–22.

    PubMed  Google Scholar 

  15. 15.

    Duvallet C, Gibbons SM, Gurry T, Irizarry RA, Alm EJ. Meta-analysis of gut microbiome studies identifies disease-specific and shared responses. Nat Commun. 2017;8:1784.

    PubMed  PubMed Central  Google Scholar 

  16. 16.

    Wirbel J, Pyl PT, Kartal E, Zych K, Kashani A, Milanese A, et al. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat Med. 2019;25:679–89.

    CAS  PubMed  Google Scholar 

  17. 17.

    Thomas AM, Manghi P, Asnicar F, Pasolli E, Armanini F, Zolfo M, et al. Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation. Nat Med. 2019;25:667–78.

    CAS  PubMed  Google Scholar 

  18. 18.

    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet C, Al-Ghalith GA, et al. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. PeerJ Prepr. 2018;6:e27295v2.

    Google Scholar 

  19. 19.

    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13:581–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Langille MG, Zaneveld J, Caporaso JG, McDonald D, Knights D, Reyes JA, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31:814–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Marcel M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–12.

    Google Scholar 

  22. 22.

    Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–9.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015;12:902–3.

    CAS  Google Scholar 

  24. 24.

    Abubucker S, Segata N, Goll J, Schubert AM, Izard J, Cantarel BL, et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput Biol. 2012;8:e1002358.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.

    PubMed  PubMed Central  Google Scholar 

  26. 26.

    Wang X, Kang DD, Shen K, Song C, Lu S, Chang LC, et al. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics. 2012;28:2534–6.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Marot G, Foulley JL, Mayer CD, Jaffrezic F. Moderated effect size and P-value combinations for microarray meta-analyses. Bioinformatics. 2009;25:2692–9.

    CAS  PubMed  Google Scholar 

  28. 28.

    Zhou G, Stevenson MM, Geary TG, Xia J. Comprehensive transcriptome meta-analysis to characterize host immune responses in helminth infections. PLoS Negl Trop Dis. 2016;10:e0004624.

    PubMed  PubMed Central  Google Scholar 

  29. 29.

    Gibbons SM, Duvallet C, Alm EJ. Correcting for batch effects in case-control microbiome studies. PLoS Comput Biol. 2018;14:e1006102.

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Frank E, Hall M, Witten I, The WEKA Workbench. Online appendix for “Data mining: practical machine learning tools and techniques”. 4th ed. San Francisco, CA: Morgan Kaufmann; 2016.

  31. 31.

    Caspi R, Foerster H, Fulcher CA, Hopkinson R, Ingraham J, Kaipa P, et al. MetaCyc: a multiorganism database of metabolic pathways and enzymes. Nucleic Acids Res. 2006;34:D511–516.

    CAS  PubMed  Google Scholar 

  32. 32.

    Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44:D380–384.

    CAS  PubMed  Google Scholar 

  33. 33.

    Consortium GT. The genotype-tissue expression (GTEx) project. Nat Genet. 2013;45:580–5.

    Google Scholar 

  34. 34.

    Larsen PE, Collart FR, Field D, Meyer F, Keegan KP, Henry CS, et al. Predicted Relative Metabolomic Turnover (PRMT): determining metabolic turnover from a coastal marine metagenomic dataset. Micro Inf Exp. 2011;1:4.

    Google Scholar 

  35. 35.

    Noecker C, Eng A, Srinivasan S, Theriot CM, Young VB, Jansson JK, et al. Metabolic model-based integration of microbiome taxonomic and metabolomic profiles elucidates mechanistic links between ecological and metabolic variation. mSystems. 2016;1:e00013–15.

    Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Wang Z, Arat S, Magid-Slav M, Brown JR. Meta-analysis of human gene expression in response to Mycobacterium tuberculosis infection reveals potential therapeutic targets. BMC Syst Biol. 2018;12:3.

    PubMed  PubMed Central  Google Scholar 

  37. 37.

    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Liao Y, Smyth GK, Shi W. The subread aligner: fast, accurate and scalable read mapping by seed-and-vote. Nucleic Acids Res. 2013;41:e108.

    PubMed  PubMed Central  Google Scholar 

  39. 39.

    Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27.

    PubMed  PubMed Central  Google Scholar 

  40. 40.

    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

    PubMed  PubMed Central  Google Scholar 

  41. 41.

    Bafadhel M, McCormick M, Saha S, McKenna S, Shelley M, Hargadon B, et al. Profiling of sputum inflammatory mediators in asthma and chronic obstructive pulmonary disease. Respiration. 2012;83:36–44.

    CAS  PubMed  Google Scholar 

  42. 42.

    Dunn WB, Broadhurst D, Begley P, Zelena E, Francis-McIntyre S, Anderson N, et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat Protoc. 2011;6:1060–83.

    CAS  PubMed  Google Scholar 

  43. 43.

    Wishart DS, Feunang YD, Marcu A, Guo AC, Liang K, Vazquez-Fresno R, et al. HMDB 4.0: the human metabolome database for 2018. Nucleic Acids Res. 2018;46:D608–D617.

    CAS  PubMed  Google Scholar 

  44. 44.

    Guijas C, Montenegro-Burke JR, Domingo-Almenara X, Palermo A, Warth B, Hermann G, et al. METLIN: a technology platform for identifying knowns and unknowns. Anal Chem. 2018;90:3156–64.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 1999;27:29–34.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46.

    Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 2018;46:W486–94.

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Noecker C, Chiu HC, McNally CP, Borenstein E. Defining and evaluating microbial contributions to metabolite variation in microbiome-metabolome association studies. mSystems. 2019;4:e00579–19.

    Article  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Sze MA, Schloss PD. Looking for a signal in the noise: revisiting obesity and the microbiome. mBio. 2016;7:e01018–16.

    Article  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Ramasamy A, Mondry A, Holmes CC, Altman DG. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med. 2008;5:e184.

    PubMed  PubMed Central  Google Scholar 

  50. 50.

    Michaeloudes C, Kuo CH, Haji G, Finch DK, Halayko AJ, Kirkham P, et al. Metabolic re-patterning in COPD airway smooth muscle cells. Eur Respir J. 2017;50:1700202.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Zhou BR, Zhang JA, Zhang Q, Permatasari F, Xu Y, Wu D, et al. Palmitic acid induces production of proinflammatory cytokines interleukin-6, interleukin-1beta, and tumor necrosis factor-alpha via a NF-kappaB-dependent mechanism in HaCaT keratinocytes. Mediators Inflamm. 2013;2013:530429.

    PubMed  PubMed Central  Google Scholar 

  52. 52.

    Sato Y, Fujimoto S, Mukai E, Sato H, Tahara Y, Ogura K, et al. Palmitate induces reactive oxygen species production and beta-cell dysfunction by activating nicotinamide adenine dinucleotide phosphate oxidase through Src signaling. J Diabetes Investig. 2014;5:19–26.

    CAS  PubMed  Google Scholar 

  53. 53.

    Kanter JE, Kramer F, Barnhart S, Averill MM, Vivekanandan-Giri A, Vickery T, et al. Diabetes promotes an inflammatory macrophage phenotype and atherosclerosis through acyl-CoA synthetase 1. Proc Natl Acad Sci USA. 2012;109:E715–724.

    CAS  PubMed  Google Scholar 

  54. 54.

    Seemungal TA, Lun JC, Davis G, Neblett C, Chinyepi N, Dookhan C, et al. Plasma homocysteine is elevated in COPD patients and is related to COPD severity. Int J Chron Obstruct Pulmon Dis. 2007;2:313–21.

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Moshal KS, Sen U, Tyagi N, Henderson B, Steed M, Ovechkin AV, et al. Regulation of homocysteine-induced MMP-9 by ERK1/2 pathway. Am J Physiol Cell Physiol. 2006;290:C883–891.

    CAS  PubMed  Google Scholar 

  56. 56.

    Crane JK, Mongiardo KM. Pro-inflammatory effects of uric acid in the gastrointestinal tract. Immunol Investig. 2014;43:255–66.

    CAS  Google Scholar 

  57. 57.

    Biljak VR, Rumora L, Cepelak I, Pancirov D, Popovic-Grle S, Soric J, et al. Glutathione cycle in stable chronic obstructive pulmonary disease. Cell Biochem Funct. 2010;28:448–53.

    CAS  PubMed  Google Scholar 

  58. 58.

    Trompette A, Gollwitzer ES, Pattaroni C, Lopez-Mejia IC, Riva E, Pernot J, et al. Dietary fiber confers protection against flu by shaping Ly6c(-) patrolling monocyte hematopoiesis and CD8(+) T cell metabolism. Immunity. 2018;48:992–1005.e1008.

    CAS  PubMed  Google Scholar 

  59. 59.

    Mao K, Chen S, Chen M, Ma Y, Wang Y, Huang B, et al. Nitric oxide suppresses NLRP3 inflammasome activation and protects against LPS-induced septic shock. Cell Res. 2013;23:201–12.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Wang H, Liu Y, Shi H, Wang X, Zhu H, Pi D, et al. Aspartate attenuates intestinal injury and inhibits TLR4 and NODs/NF-kappaB and p38 signaling in weaned pigs after LPS challenge. Eur J Nutr. 2017;56:1433–43.

    CAS  PubMed  Google Scholar 

  61. 61.

    Qin Q, Xu X, Wang X, Wu H, Zhu H, Hou Y, et al. Glutamate alleviates intestinal injury, maintains mTOR and suppresses TLR4 and NOD signaling pathways in weanling pigs challenged with lipopolysaccharide. Sci Rep. 2018;8:15124.

    PubMed  PubMed Central  Google Scholar 

  62. 62.

    Wang Y, LeCao KA. Managing batch effects in microbiome data. Brief Bioinform. 2019.

  63. 63.

    Ditz B, Christenson S, Rossen J, Brightling C, Kerstjens HAM, van den Berge M, et al. Sputum microbiome profiling in COPD: beyond singular pathogen detection. Thorax. 2020;75:338–44.

    PubMed  PubMed Central  Google Scholar 

Download references


This work was supported by the National Key R&D Program of China (2017YFC1310600) funded to RC, and the National Natural Science Foundation of China (31970112) and the Science and Technology Foundation of Guangdong Province (2019A1515011395) funded to ZW. Funders had no role in study design, collection, analysis and interpretation of data, and in writing the manuscript.

Author information




ZW conceived and designed the study. ZW, ZY and HL acquired data, developed data analysis workflows and interpreted the data. YY, ZL, FW collected clinical samples. BEM, RTS, MRS, CEB provided clinical insights to data interpretation. XY and JL assisted in the statistical analysis. ZW drafted the article. BEM, RTS, MRS, HZ, CEB, JRB, MW, WS, and RC provided critical revisions to the article. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zhang Wang.

Ethics declarations

Conflict of interest

BEM, RTS and JRB were employees and shareholders in GlaxoSmithKline PLC at the time of this study. Other authors have no conflict of interest to declare.

Additional information

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

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Further reading


Quick links