Abstract
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.
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Data availability
The processed public 16S rRNA gene, metagenomics, and host transcriptome data are available on Figshare (https://doi.org/10.6084/m9.figshare.12199436). 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 (https://github.com/wangzlab/COPD_metaanalysis/) or in the supplementary document.
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Acknowledgements
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.
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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.
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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.
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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). https://doi.org/10.1038/s41396-020-0727-y
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DOI: https://doi.org/10.1038/s41396-020-0727-y
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