Siddharth S Madapoosi1, Charmion Cruickshank-Quinn2, Kristopher Opron1, John R Erb-Downward1, Lesa A Begley1, Gen Li3, Igor Barjaktarevic4, R Graham Barr5,6, Alejandro P Comellas7, David J Couper8, Christopher B Cooper4, Christine M Freeman1, MeiLan K Han1, Robert J Kaner9, Wassim Labaki1, Fernando J Martinez9, Victor E Ortega10, Stephen P Peters10, Robert Paine11, Prescott Woodruff12, Jeffrey L Curtis1,13, Gary B Huffnagle1,14, Kathleen A Stringer15, Russell P Bowler16,17, Charles R Esther18,19, Nichole Reisdorph2, Yvonne J Huang1,20. 1. Division of Pulmonary and Critical Care Medicine, Department of Medicine. 2. Department of Pharmaceutical Sciences, University of Colorado, Anschutz Campus, Aurora, Colorado. 3. Department of Biostatistics, School of Public Health. 4. University of California at Los Angeles, Los Angeles, California. 5. Department of Medicine and. 6. Department of Epidemiology, Columbia University Medical Center, New York, New York. 7. University of Iowa, Iowa City, Iowa. 8. Department of Biostatistics. 9. Weill Cornell Medical College, New York, New York. 10. Wake Forest School of Medicine, Wake Forest University, Winston-Salem, North Carolina. 11. University of Utah, Salt Lake City, Utah. 12. University of California at San Francisco, San Francisco, California. 13. Medical Service, VA Ann Arbor Healthcare System, Ann Arbor, Michigan. 14. Department of Molecular, Cellular and Developmental Biology. 15. Department of Clinical Pharmacy, College of Pharmacy, and. 16. School of Medicine, University of Colorado, Aurora, Colorado; and. 17. Department of Medicine, National Jewish Health, Denver, Colorado. 18. Division of Pediatric Pulmonology, and. 19. Marsico Lung Institute, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina. 20. Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan.
Abstract
Rationale: Chronic obstructive pulmonary disease (COPD) is variable in its development. Lung microbiota and metabolites collectively may impact COPD pathophysiology, but relationships to clinical outcomes in milder disease are unclear. Objectives: Identify components of the lung microbiome and metabolome collectively associated with clinical markers in milder stage COPD. Methods: We analyzed paired microbiome and metabolomic data previously characterized from bronchoalveolar lavage fluid in 137 participants in the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), or (GOLD [Global Initiative for Chronic Obstructive Lung Disease Stage 0-2). Datasets used included 1) bacterial 16S rRNA gene sequencing; 2) untargeted metabolomics of the hydrophobic fraction, largely comprising lipids; and 3) targeted metabolomics for a panel of hydrophilic compounds previously implicated in mucoinflammation. We applied an integrative approach to select features and model 14 individual clinical variables representative of known associations with COPD trajectory (lung function, symptoms, and exacerbations). Measurements and Main Results: The majority of clinical measures associated with the lung microbiome and metabolome collectively in overall models (classification accuracies, >50%, P < 0.05 vs. chance). Lower lung function, COPD diagnosis, and greater symptoms associated positively with Streptococcus, Neisseria, and Veillonella, together with compounds from several classes (glycosphingolipids, glycerophospholipids, polyamines and xanthine, an adenosine metabolite). In contrast, several Prevotella members, together with adenosine, 5'-methylthioadenosine, sialic acid, tyrosine, and glutathione, associated with better lung function, absence of COPD, or less symptoms. Significant correlations were observed between specific metabolites and bacteria (Padj < 0.05). Conclusions: Components of the lung microbiome and metabolome in combination relate to outcome measures in milder COPD, highlighting their potential collaborative roles in disease pathogenesis.
Rationale: Chronic obstructive pulmonary disease (COPD) is variable in its development. Lung microbiota and metabolites collectively may impact COPD pathophysiology, but relationships to clinical outcomes in milder disease are unclear. Objectives: Identify components of the lung microbiome and metabolome collectively associated with clinical markers in milder stage COPD. Methods: We analyzed paired microbiome and metabolomic data previously characterized from bronchoalveolar lavage fluid in 137 participants in the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), or (GOLD [Global Initiative for Chronic Obstructive Lung Disease Stage 0-2). Datasets used included 1) bacterial 16S rRNA gene sequencing; 2) untargeted metabolomics of the hydrophobic fraction, largely comprising lipids; and 3) targeted metabolomics for a panel of hydrophilic compounds previously implicated in mucoinflammation. We applied an integrative approach to select features and model 14 individual clinical variables representative of known associations with COPD trajectory (lung function, symptoms, and exacerbations). Measurements and Main Results: The majority of clinical measures associated with the lung microbiome and metabolome collectively in overall models (classification accuracies, >50%, P < 0.05 vs. chance). Lower lung function, COPD diagnosis, and greater symptoms associated positively with Streptococcus, Neisseria, and Veillonella, together with compounds from several classes (glycosphingolipids, glycerophospholipids, polyamines and xanthine, an adenosine metabolite). In contrast, several Prevotella members, together with adenosine, 5'-methylthioadenosine, sialic acid, tyrosine, and glutathione, associated with better lung function, absence of COPD, or less symptoms. Significant correlations were observed between specific metabolites and bacteria (Padj < 0.05). Conclusions: Components of the lung microbiome and metabolome in combination relate to outcome measures in milder COPD, highlighting their potential collaborative roles in disease pathogenesis.