Comparative analysis of the alveolar microbiome in COPD, ECOPD, Sarcoidosis, and ILD patients to identify respiratory illnesses specific microbial signatures

Research output: Contribution to journalJournal articleResearchpeer-review

  • Shashank Gupta
  • Malini Shariff
  • Gaura Chaturvedi
  • Agrima Sharma
  • Nitin Goel
  • Monika Yadav
  • Martin S. Mortensen
  • Sørensen, Søren Johannes
  • Mitali Mukerji
  • Nar Singh Chauhan

Studying respiratory illness-specific microbial signatures and their interaction with other micro-residents could provide a better understanding of lung microbial ecology. Each respiratory illness has a specific disease etiology, however, so far no study has revealed disease—specific microbial markers. The present study was designed to determine disease-specific microbial features and their interactions with other residents in chronic obstructive pulmonary diseases (stable and exacerbated), sarcoidosis, and interstitial lung diseases. Broncho-alveolar lavage samples (n = 43) were analyzed by SSU rRNA gene sequencing to study the alveolar microbiome in these diseases. A predominance of Proteobacteria followed by Firmicutes, Bacteroidetes, Actinobacteria, and Fusobacteria was observed in all the disease subsets. Shannon diversity was significantly higher in stable COPD when compared to exacerbated chronic obstructive pulmonary disease (ECOPD) (p = 0.0061), and ILD patient samples (p = 0.037). The lung microbiome of the patients with stable COPD was more diverse in comparison to ECOPD and ILD patients (p < 0.001). Lefse analysis identified 40 disease—differentiating microbial features (LDA score (log10) > 4). Species network analysis indicated a significant correlation (p < 0.05) of diseases specific microbial signature with other lung microbiome members. The current study strengthens the proposed hypothesis that each respiratory illness has unique microbial signatures. These microbial signatures could be used as diagnostic markers to differentiate among various respiratory illnesses.

Original languageEnglish
Article number3963
JournalScientific Reports
Volume11
Number of pages11
ISSN2045-2322
DOIs
Publication statusPublished - 2021

ID: 258374900