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Multi-omics analyses of airway host–microbe interactions in chronic obstructive pulmonary disease identify potential therapeutic interventions

Nature Microbiology volume 7, pages 1361–1375 (2022)Cite this article

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Abstract

The mechanistic role of the airway microbiome in chronic obstructive pulmonary disease (COPD) remains largely unexplored. We present a landscape of airway microbe–host interactions in COPD through an in-depth profiling of the sputum metagenome, metabolome, host transcriptome and proteome from 99 patients with COPD and 36 healthy individuals in China. Multi-omics data were integrated using sequential mediation analysis, to assess in silico associations of the microbiome with two primary COPD inflammatory endotypes, neutrophilic or eosinophilic inflammation, mediated through microbial metabolic interaction with host gene expression. Hypotheses of microbiome–metabolite–host interaction were identified by leveraging microbial genetic information and established metabolite–human gene pairs. A prominent hypothesis for neutrophil-predominant COPD was altered tryptophan metabolism in airway lactobacilli associated with reduced indole-3-acetic acid (IAA), which was in turn linked to perturbed host interleukin-22 signalling and epithelial cell apoptosis pathways. In vivo and in vitro studies showed that airway microbiome-derived IAA mitigates neutrophilic inflammation, apoptosis, emphysema and lung function decline, via macrophage–epithelial cell cross-talk mediated by interleukin-22. Intranasal inoculation of two airway lactobacilli restored IAA and recapitulated its protective effects in mice. These findings provide the rationale for therapeutically targeting microbe–host interaction in COPD.

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Fig. 1: Airway metagenome profiles for patients with COPD and healthy controls.
Fig. 2: Airway metabolome, host transcriptome and proteome profiles for patients with COPD and healthy controls.
Fig. 3: Overview of microbiome–metabolite–host interaction in COPD.
Fig. 4: Top links for microbiome–metabolite–host interaction in COPD.
Fig. 5: IAA ameliorates lung function decline, neutrophilic inflammation and apoptosis.
Fig. 6: IAA inhibits apoptosis through macrophage–epithelial cross-talk via IL-22.

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Data availability

The raw metagenomic data for the human cohort have been deposited in the European Genome-phenome Archive (EGA) under EGAS00001006398 for controlled access. The raw transcriptomic data for the human cohort have been deposited in the Chinese National Gene Bank Nucleotide Sequence Archive (CNSA, https://db.cngb.org/cnsa/) under CNP0001954 for controlled access. The raw metabolomic data for the human cohort have been deposited in EBI MetaboLights under MTBLS4017. The raw 16S rRNA gene and transcriptome data for the murine experiments have been deposited in NCBI GenBank under PRJNA852659 and PRJNA852891. The processed human multi-omic data and metadata tables have been deposited in Figshare (https://doi.org/10.6084/m9.figshare.19126655). The processed mouse omic data tables have been deposited in Figshare (https://doi.org/10.6084/m9.figshare.18738428). Reference genomes and databases used in this study include the human genome GRCh38 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.26/), mouse genome GRCm38 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.20/), KEGG (https://www.genome.jp/kegg/), HMDB (https://hmdb.ca/), METLIN (https://metlin.scripps.edu/), MetaCyc (https://metacyc.org/), and STITCH (http://stitch.embl.de/).

Code availability

The computer codes necessary for generating key results in this work have been deposited as in GitHub under https://github.com/wangzlab/COPD_multiomics.

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Acknowledgements

This work was supported by the National Key R&D Programme of China (2017YFC1310600) to R.C., the National Natural Science Foundation of China (31970112, 32170109) to Z.W., the Science and Technology Foundation of Guangdong Province (2019A1515011395) to Z.W., the Shenzhen Science Technology and Innovative Commission (SZSTI) (KCXFZ202002011008256) to L.W. and the Medical Scientific Research Foundation of Guangdong Province (C2019001) to R.C.

Author information

Author notes

  1. These authors contributed equally: Zhengzheng Yan, Boxuan Chen, Yuqiong Yang, Xinzhu Yi, Mingyuan Wei.

Authors and Affiliations

  1. Institute of Ecological Sciences, South China Normal University, Guangzhou, China

    Zhengzheng Yan, Boxuan Chen, Xinzhu Yi, Mingyuan Wei, Jingyuan Gao, Weijie Liang, Xiaomin Liu, Junhao Yang, Wensheng Shu & Zhang Wang

  2. Department of Anesthesiology, Nanfang Hospital, Guangdong Provincial Key Laboratory of Proteomics, Southern Medical University, Guangzhou, China

    Zhengzheng Yan

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

    Yuqiong Yang, Zhenyu Liang, Fengyan Wang & Weijuan Shi

  4. Department of Pediatrics, Scripps Institution of Oceanography, University of California San Diego, La Jolla, CA, USA

    Gertrude Ecklu-Mensah, Mary M. Buschmann & Jack A. Gilbert

  5. Department of Laboratory Medicine, The First Affiliated Hospital of Xiamen University, Xiamen, China

    Haiyue Liu

  6. Institute of Statistics and Big Data, Renmin University of China, Beijing, China

    Wei Ma

  7. Key Laboratory of Shenzhen Respiratory Diseases, Institute of Shenzhen Respiratory Diseases, Shenzhen People’s Hospital, Shenzhen, China

    Dandan Chen, Lingwei Wang & Rongchang Chen

  8. Department of Medicine, Firestone Institute of Respiratory Health at St. Joseph’s Healthcare, McMaster University, Hamilton, Ontario, Canada

    Martin R. Stampfli

  9. Microbiome Research Centre, St. George and Sutherland Clinical School, UNSW, Sydney, New South Wales, Australia

    Pan Li & Emad M. El-Omar

  10. School of Traditional Chinese Medicine, Southern Medical University, Guangzhou, China

    Shenhai Gong

  11. Department of Obstetrics and Gynecology, First People’s Hospital of Foshan, Foshan, China

    Xia Chen

  12. Center for Advanced Biotechnology and Medicine, Rutgers University, New Brunswick, NJ, USA

    Martin J. Blaser

  13. Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China

    Hongwei Zhou

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Contributions

Z.W. conceived the study design. Y.Y., H.L., Z.L., F.W., D.C., L.W. and W. Shi contributed to clinical sample and data collection. X.Y., W.M., J.G., J.Y. and Z.W. performed statistical and bioinformatic analyses. Z.Y., B.C., M.W., X.L. and W.L. performed in vivo and in vitro experiments. G.E.-M., M.M.B., P.L., S.G., X.C., W. Shu, M.R.S., E.M.E.-O., J.A.G., M.J.B., H.Z. and R.C. assisted in data analyses and interpretation. H.Z., R.C. and Z.W. co-supervised the study. Z.W. drafted the manuscript. All authors provided critical revisions and approved the final manuscript.

Corresponding authors

Correspondence to Hongwei Zhou, Rongchang Chen or Zhang Wang.

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Competing interests

The authors declare no competing interests.

Peer review

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Nature Microbiology thanks Michael Weiden, Katrine Whiteson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 The overall workflow integrating human multi-omics, in vivo murine model experiments and in vitro cellular assays to elucidate airway microbe-host interactions in COPD.

a) Induced sputum was collected from COPD patients and healthy controls from Guangzhou (discovery cohort) and Shenzhen (validation cohort), China. Demographic and clinical metadata including age, gender, smoking history, medication history, spirometry, sputum and blood differential cell counts were obtained. b) Induced sputum was subject to simultaneous characterization of metagenome (n = 135), metabolome (n = 129), host transcriptome (n = 130) and proteome (n = 59). The multi-omic profiles were processed by a combination of knowledge-driven and data-driven dimensionality reduction to generate KEGG and co-abundance modules, which were then filtered by COPD-association testing. To identify microbial-metabolite-host interaction links, a sequential mediation analysis was performed along the metagenome-metabolome-transcriptome-proteome axis for modules associated with neutrophilic or eosinophilic inflammation, respectively. Cross-omic biological links were then identified from pairs of modules with significant mediation effects, utilizing microbial genetic information, metabolite-target pairs and canonical pathways from existing databases. c) The selected microbiome-metabolite-host interaction hypothesis was tested using a murine model of emphysema and in vitro cell assays to elucidate molecular and cellular mechanisms. Lung function, airway inflammation, tissue destruction, and apoptosis were measured as key endpoints in the in vivo and in vitro experiments.

Extended Data Fig. 2 Taxonomic and functional profiles of the airway microbiome in COPD and controls.

a) The total bacterial load for the 106 samples with available genomic DNA specimens, based on 16S rRNA gene qPCR assays (n = 106). Each sample was run in duplicate. The Ct value cannot be determined (ND) for the four reagent controls. 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. Significance was determined using two-sided Wilcoxon rank-sum test. b) The fold-changes of differential species-level taxa in COPD and controls using reads-based taxonomy are correlated with those using bin-based taxonomy. Significance was determined using two-sided Wilcoxon rank-sum test, adjusting for multiple comparisons. c) Association of differential species-level taxa and KEGG metagenome modules with sputum neutrophil and eosinophil percentages. The X-axis and Y-axis denote minus log10 (FDR P-value) times directionality of correlation for neutrophil and eosinophil, respectively. The full ordination space is divided into colored area for associations with neutrophil only, eosinophil only, neutrophil and eosinophil, and neither (FDR < 0.1).

Extended Data Fig. 3 Metabolome and host multi-omic profiles in COPD and controls.

Association of differential a) metabolome modules, b) host transcriptome modules, c) sputum proteome features and d) serum proteome features with sputum neutrophil and eosinophil percentages. Significance was determined using two-sided Wilcoxon rank-sum test, adjusting for multiple comparisons. The X-axis and Y-axis denote minus log10 (FDR P-value) times directionality of correlation for neutrophil and eosinophil, respectively. The full ordination space is divided into colored area for associations with neutrophil only, eosinophil only, neutrophil and eosinophil, and neither (FDR < 0.1).

Extended Data Fig. 4 Sequential mediation analysis for multi-omic data integration.

a) The number of paired MetaG-MetaB, MetaB-HostT, HostT-HostP (sputum protein: Spuprot, serum protein: Serprot) modules with or without significant mediation effect for neutrophil and eosinophil inflammation, respectively. b) The proportion of mediation effect is indicated for paired MetaG-MetaB (NEU = 1838, EOS = 248, PNEUvsEOS<2.2e-16), MetaB-HostT (NEU = 2806, EOS = 320, PNEUvsEOS <2.2e-16), HostT-HostP (sputum protein: Spuprot, NEU = 1530, EOS = 455, PNEUvsEOS<2.2e-16, serum protein: Serprot, NEU = 305, EOS = 105, PNEUvsEOS=2.0e-7) modules for neutrophil and eosinophil inflammation, respectively. Significance was determined using two-sided Wilcoxon rank-sum test. c) The comparison between the mediation effects in the forward (that is MetaG-MetaB-NEU) and reverse (that is MetaG-NEU-MetaB) mediation analyses (MetaG-MetaB-NEU = 66, MetaB-HostT-NEU = 136, MetaG-MetaB-EOS = 17, MetaB-HostT-EOS = 38). Significance was determined using paired sample, two-sided Wilcoxon rank-sum test. 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. d) Schematic illustration of sequential mediation analysis for MetaG, MetaB, HostT modules and HostP features in association with neutrophilic or eosinophilic COPD. The top three and top one MetaG-MetaB-HostT links are shown for neutrophilic or eosinophilic COPD, respectively. Each node represents an omic module/feature or host phenotype. The Spearman correlation’s rho is indicated between the nodes. The directionality of mediation analysis is indicated in red arrows. Blue arrows indicate that the linked metabolites in the MetaB modules are the products of the MetaG modules. The proportion of mediation effect is indicated in red. e) The top metagenomic species (MGS)-derived microbial species contributing to the four MetaG modules (P00380, M00044, P00250, and P00564) and their KOs, as identified in leave-one-species-out analysis. The full bacterial species names for the acronyms are: Aap: Aggregatibacter aphrophilus; Aba: Acinetobacter baumannii; Cgi: Capnocytophaga gingivalis; Eco: Escherichia coli; Hinf: Haemophilus influenzae; Lbg: Lachnospiraceae bacterium GAM79; Lbo: Lachnospiraceae bacterium oral taxon 500; Lsa: Lactobacillus salivarius; Mca: Moraxella catarrhalis; Mdi: Mogibacterium diversum; Nme: Nesseria meningtidis; Nmu: Neisseria mucosa; Nsu: Neisseria subflava; Pae: Pseudomonas aeruginosa; Pgi: Porphyromonas gingivalis; Pje: Prevotella jejuni; Pin: Prevotella intermedia; Pme: Prevotella melaninogenica; Pmi: Parvimonas micra; Rmu: Rothia mucilaginosa; Ppu: Pseudomonas putita; Rma: Ralstonia mannitolylitica; Rsh: Ruania sp. HY168; Sac: Saccharibacteria; Smi: Streptococcus mitis; Spn: Streptococcus pneumoniae; Ssa: Streptococcus salivarius; Vat: Veillonella atypica.

Extended Data Fig. 5 Antibiotic depletion of lung or gut microbiota in mice.

a) Barplot shows reduced bacterial load in mouse lung but not cecum when the antibiotics were intranasally administered (n = 5). b) Intranasal delivery of antibiotics resulted in significantly decreased load of Lactobacillus in the lung but not cecum in mice (n = 5). c) Intranasal delivery of antibiotics did not significantly alter the level of indole-3-acetic acid (IAA) in mouse serum (n = 5). d) Intragastrical and/or drinking water delivery of antibiotics resulted in significantly decreased total bacterial load in cecum but not lung in mice (n = 5). e) Intragastrical and/or drinking water delivery of antibiotics resulted in significantly decreased Lactobacillus load in cecum but not lung in mice (n = 5). f) Intragastrical and/or drinking water delivery of antibiotics did not significantly alter the level of IAA in mouse BALF (n = 5). Barplots are presented as mean ± SD. Significance was determined using two-sided Student’s t-test. Exact P-values are provided in Source Data. *** P < 0.001, ** P < 0.01, * P < 0.05, + P < 0.1.

Extended Data Fig. 6 Airway microbiome-derived indole-3-acetic acid (IAA) ameliorated lung function decline, neutrophilic inflammation and apoptosis.

a) Barplot shows non-significantly increased forced vital capacity (FVC) by IAA in emphysema murine model (n = 5). b) Barplots shows decreased airway TNF-α, IL-1β, IL-6 and IL-17A levels by IAA in the emphysema murine model (n = 5). c) Barplot shows reduced IAA levels in the emphysema murine model (n = 5). d) Barplots show decreased emphysema, as measured by mean linear intercept and destruction index by IAA in the emphysema murine model (n = 5). e) Barplots for area of collagen deposition (μm2) per basement membrane perimeter (μm) and representative images of collagen deposition show decreased collagen deposition by IAA in the emphysema murine model (n = 5). f) Barplot shows increased CCND1 (Cyclin D1) mRNA level by IAA in the emphysema murine model (n = 5). g) Barplot shows increased macrophage over neutrophil percentage in BALF by IAA in the emphysema murine model (n = 5). h) Barplot shows decreased neutrophilic elastase by IAA in the emphysema murine model. Barplots are presented as mean ± SD. Significance was determined using two-sided Student’s t-test. Exact P-values are provided in Source Data. *** P < 0.001, ** P < 0.01, * P < 0.05, + P < 0.1.

Extended Data Fig. 7 The roles of indole-3-acetic acid (IAA) were mediated through AhR and IL-22.

a) Barplot shows a non-significantly reversed trend of lung function in response to IAA when AhR was inhibited (n = 5). b–e) lung histopathological score and representative H and E stained lung sections (b, n = 5, arrows indicate inflammatory infiltrates), TUNEL assay for apoptosis (c, n = 5), immuno-fluorescence (d, n = 5) and western blots (d, n = 3) for cleaved caspase-3, expression of TNF-α, IL-1β, IL-6, and IL-17A (e, n = 5) in the lung of emphysema mice treated with or without IAA, and with or without anti-IL-22 neutralizing antibody. f) Flow cytometry shows increased numbers of IL-22+ interstitial macrophages (F4/80 + CD11c + CD11b + IL-22 + ) by IAA in the emphysema murine model (n = 5). Barplots are presented as mean ± SD. Significance was determined using two-sided Student’s t-test. Exact P-values are provided in Source Data. *** P < 0.001, ** P < 0.01, * P < 0.05, + P < 0.1.

Extended Data Fig. 8 Depletion of lung macrophages abrogated the anti-inflammatory and anti-apoptotic effects of indole-3-acetic acid (IAA).

a-b) Flow cytometry shows successful depletion of lung macrophages by intratracheal instillation of dichloromethylene-diphosphonate liposomes (n = 5). c) Depletion of lung macrophages abolished IAA’s protective effects on lung function decline (n = 5). d) Barplot of lung histopathological score and representative H and E stained lung sections show abolished effects of IAA on lung injury upon depletion of lung macrophages (n = 5). e) TUNEL assays and representative fluorescence images show abolished effects of IAA on tissue apoptosis upon depletion of lung macrophages (n = 5). TUNEL-positive cells are indicated in white arrows. Barplots are presented as mean ± SD. Significance was determined using two-sided Student’s t-test. Exact P-values are provided in Source Data. *** P < 0.001, ** P < 0.01, * P < 0.05, + P < 0.1.

Extended Data Fig. 9 The phenotypic effects of indole-3-acetic acid (IAA) were validated in a cigarette smoke (CS)-induced COPD murine model.

a) Barplot shows ameliorated lung function decline by IAA in CS-induced COPD murine model (n = 5). b) Barplot of lung histopathological score and representative H and E stained lung sections show reduced tissue injury by IAA in CS-induced COPD murine model (n = 5). c) Barplots show decreased emphysema, as measured by mean linear intercept and destruction index by IAA in CS-induced COPD murine model (n = 5). d) Barplots show decreased TNF-α, IL-1β, IL-6 and IL-17A levels by IAA in CS-induced COPD murine model (n = 5). e) TUNEL assay and representative fluorescence images show decreased apoptotic rates by IAA in CS-induced COPD murine model (n = 5). TUNEL-positive cells are indicated in white arrows. Barplots are presented as mean ± SD. Significance was determined using two-sided Student’s t-test. Exact P-values are provided in Source Data. *** P < 0.001, ** P < 0.01, * P < 0.05, + P < 0.1.

Extended Data Fig. 10 Intranasal inoculation of Lactobacillus salivarius and Lactobacillus oris restored indole-3-acetic acid (IAA) and recapitulated its effects.

a) Barplot shows mitigated lung functional decline when inoculating L. oris (LO), L. salivarius (LS), and L. oris+L. salivarius (LO + LS) (n = 5). b) Barplot of lung histopathological score and representative H and E stained lung sections show significantly alleviated tissue injury when inoculating LO, LS and LO + LS (n = 5). Arrows indicate inflammatory infiltrates. c) TUNEL assays and representative fluorescence images show decreased apoptotic rates when inoculating LO, LS and LO + LS (n = 5). TUNEL-positive cells are indicated in white arrows. d) Barplots show reduced levels of TNF-α, IL-1β, IL-6 and IL-17A when inoculating LO, LS and LO + LS (n = 5). Barplots are presented as mean ± SD. Significance was determined using two-sided Student’s t-test. Exact P-values are provided in Source Data. *** P < 0.001, ** P < 0.01, * P < 0.05, + P < 0.1.

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Yan, Z., Chen, B., Yang, Y. 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). https://doi.org/10.1038/s41564-022-01196-8

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