Sputum Bacterial Metacommunities in Distinguishing Heterogeneity in Respiratory Health and Disease

Jiyeon Si; Yongbin Choi; Jeroen Raes; Gwangpyo Ko; Hyun Ju You

doi:10.3389/fmicb.2022.719541

Sputum Bacterial Metacommunities in Distinguishing Heterogeneity in Respiratory Health and Disease

Front Microbiol. 2022 Mar 31:13:719541. doi: 10.3389/fmicb.2022.719541. eCollection 2022.

Authors

Jiyeon Si^{1

2

3}, Yongbin Choi^{2

3}, Jeroen Raes^{4

5}, Gwangpyo Ko^{2

3

6

7}, Hyun Ju You^{2

3

6

8}

Affiliations

¹ Medical Science Research Institute, School of Medicine, Sungkyunkwan University (SKKU), Suwon, South Korea.
² Department of Environmental Health Sciences, Graduate School of Public Health, Seoul National University, Seoul, South Korea.
³ Center for Human and Environmental Microbiome, Seoul National University, Seoul, South Korea.
⁴ Department of Microbiology and Immunology, Rega Institute for Medical Research, Leuven, Belgium.
⁵ VIB-KU Leuven Center for Microbiology, Leuven, Belgium.
⁶ Bio-MAX/N-Bio, Seoul National University, Seoul, South Korea.
⁷ KobioLabs, Seoul, South Korea.
⁸ Institute of Health and Environment, Seoul National University, Seoul, South Korea.

Abstract

Background and objective: Cluster-based analysis, or community typing, has been attempted as a method for studying the human microbiome in various body niches with the aim of reducing variations in the bacterial composition and linking the defined communities to host health and disease. In this study, we have presented the bacterial subcommunities in the healthy and the diseased population cohorts and have assessed whether these subcommunities can distinguish different host health conditions.

Methods: We performed community typing analysis on the sputum microbiome dataset obtained from a healthy Korean twin-family cohort (n = 202) and an external chronic obstructive pulmonary disease (COPD) cohort (n = 324) and implemented a networks analysis to investigate the associations of bacterial metacommunities with host health parameters and microbial interactions in disease.

Results: The analysis of the sputum microbiome of a healthy Korean cohort revealed high levels of interindividual variation, which was driven by two dominant bacteria: Neisseria and Prevotella. Community typing of the cohort samples identified three metacommunities, namely, Neisseria 1 (N1), Neisseria 2 (N2), and Prevotella (P), each of which showed different functional potential and links to host traits (e.g., triglyceride levels, waist circumference, and levels of high-sensitivity C-reactive protein). In particular, the Prevotella-dominant metacommunity showed a low-community diversity, which implies an adverse health association. Network analysis of the healthy twin cohort illustrated co-occurrence of Prevotella with pathogenic anaerobic bacteria; this bacterial cluster was negatively associated with high-density lipoproteins but positively correlated with waist circumference, blood pressure, and pack-years. Community typing of the external COPD cohort identified three sub-metacommunities: one exclusively comprising healthy subjects (HSs) and the other two (CS1 and CS2) comprising patients. The two COPD metacommunities, CS1 and CS2, showed different abundances of specific pathogens, such as Serratia and Moraxella, as well as differing functional potential and community diversity. Network analysis of the COPD cohort showed enhanced bacterial coexclusions in the CS metacommunities when compared with HS metacommunity.

Conclusion: Overall, our findings point to a potential association between pulmonary Prevotella and host health and disease, making it possible to implement community typing for the diagnosis of heterogenic respiratory disease.

Keywords: COPD metacommunity in sputum microbiome; Prevotella; community typing; inflammation; metacommunity; network; sputum microbiome.