Functional lower airways genomic profiling of the microbiome to capture active microbial metabolism. Issue 1 (29th July 2021)

• Record Type: Journal Article
• Authors: Sulaiman, Imran; Wu, Benjamin G.; Li, Yonghua; Tsay, Jun-Chieh; Sauthoff, Maya; Scott, Adrienne S.; Ji, Kun; Koralov, Sergei B.; Weiden, Michael; Clemente, Jose C.; Jones, Drew; Huang, Yvonne J.; Stringer, Kathleen A.; Zhang, Lingdi; Geber, Adam; Banakis, Stephanie; Tipton, Laura; Ghedin, Elodie; Segal, Leopoldo N.
• Is Part Of: European respiratory journal. Volume 58:Issue 1(2021)
• Publication Date: 2021-07-29
• Subjects: Respiratory organs -- Diseases -- Periodicals; Respiration -- Periodicals; 616.2
• DOI: 10.1183/13993003.03434-2020
• Languages: English
• ISSNs: 0903-1936

Abstract

Background: Microbiome studies of the lower airways based on bacterial 16S rRNA gene sequencing assess microbial community structure but can only infer functional characteristics. Microbial products, such as short-chain fatty acids (SCFAs), in the lower airways have significant impact on the host's immune tone. Thus, functional approaches to the analyses of the microbiome are necessary. Methods: Here we used upper and lower airway samples from a research bronchoscopy smoker cohort. In addition, we validated our results in an experimental mouse model. We extended our microbiota characterisation beyond 16S rRNA gene sequencing with the use of whole-genome shotgun (WGS) and RNA metatranscriptome sequencing. SCFAs were also measured in lower airway samples and correlated with each of the sequencing datasets. In the mouse model, 16S rRNA gene and RNA metatranscriptome sequencing were performed. Results: Functional evaluations of the lower airway microbiota using inferred metagenome, WGS and metatranscriptome data were dissimilar. Comparison with measured levels of SCFAs shows that the inferred metagenome from the 16S rRNA gene sequencing data was poorly correlated, while better correlations were noted when SCFA levels were compared with WGS and metatranscriptome data. Modelling lower airway aspiration with oral commensals in a mouse model showed that the metatranscriptome most efficiently captures transient active microbial metabolism, which was overestimated by 16S rRNA gene sequencing. Conclusions: Functional characterisation of the lower airway microbiota through metatranscriptome data identifies metabolically active organisms capable of producing metabolites with immunomodulatory capacity, such as SCFAs. This study shows that both whole-genome shotgun and RNA metatranscriptome sequencing can be done on lower airway samples and can provide valuable information on bacterial function https://bit.ly/3hNmZfi