Bioorthogonal non-canonical amino acid tagging reveals translationally active subpopulations of the cystic fibrosis lung microbiota

Culture-independent profiling of cystic fibrosis (CF) lung microbiota has revealed a more complex and dynamic bacterial community than previously appreciated. Yet, microbiome studies have provided few mechanistic insights into the polymicrobial basis of lung disease progression. Deciphering the specific contributions of individual taxa to CF pathogenesis requires a comprehensive understanding of their in situ ecophysiology. However, a major caveat of conventional molecular-based profiling (i.e. 16S rRNA gene sequencing) is that it does not capture metabolic activity nor does it differentiate between active, dormant and dead biomass. To overcome this limitation, we applied bioorthogonal non-canonical amino acid tagging (BONCAT), a ‘click’ chemistry-based metabolic labeling approach, to characterize the translational activity of CF microbiota. Using BONCAT-based fluorescent imaging on sputum collected from stable CF subjects, we found that only a subset of bacterial cells are translationally active and exhibit heterogeneous activity in situ. We also combined BONCAT with fluorescent activated cell sorting (FACS) and 16S rRNA gene sequencing to assign taxonomy to the active subpopulation, and revealed that most dominant taxa detected by conventional approaches are indeed translationally active. On average, only ~12-18% of cells per sample were BONCAT-labeled, suggesting a heterogeneous growth strategy widely employed by most respiratory microbiota. BONCAT-based characterization of sputum bacterial communities and differentiating translationally active populations from those that are dormant adds to our evolving understanding of the polymicrobial basis of CF lung disease progression. We propose that the continued development and application of this approach to the study of the CF microbial community dynamics can help guide patient-specific therapeutic strategies targeting active bacterial populations that are most likely to be susceptible.