Functional lower airways genomic profiling of the microbiome to capture active microbial metabolism.

Rationale Microbiome studies of the lower airway 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 host9s 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. Measurements We extended our microbiota characterisation beyond 16S rRNA gene sequencing with the use of whole genome (WGS) and RNA metatranscriptome sequencing. Short chain fatty acids (SCFA) 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. Main Results Functional evaluations of the lower airway microbiota using inferred metagenome, WGS and metatranscriptome 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 SCFAs levels were compared with WGS and metatranscriptome. 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 identify metabolically active organisms capable of producing metabolites with immunomodulatory capacity such as SCFAs.