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

Culture-independent studies of cystic fibrosis lung microbiota have provided few mechanistic insights into the polymicrobial basis of disease. Deciphering the specific contributions of individual taxa to CF pathogenesis requires a comprehensive understanding of their in situ ecophysiology. Towards this end, we applied bioorthogonal non-canonical amino acid tagging (BONCAT), a 'click' chemistry-based metabolic labeling approach, to quantify and visualize translational activity among CF microbiota. We used BONCAT-based fluorescent imaging on sputum collected from CF subjects to test our hypothesis that only a subset of bacteria are translationally active in vivo. We report that the percentage of BONCAT-labeled (i.e. translationally active) bacterial cells varies substantially between clinically stable patients (6-56 percent), suggesting a range of metabolic activity among bacterial communities in the CF airways. We also combined BONCAT with fluorescent activated cell sorting (FACS) and 16S rRNA gene sequencing to assign taxonomy to the active subpopulation. While many dominant taxa were indeed translationally "active", the majority of bacterial species detected by conventional molecular profiling were also represented in the dormant subpopulation. Together, these data suggest a heterogeneous growth strategy widely employed by most airway microbiota in which only a subpopulation is actively growing in situ. Differentiating translationally active populations from those that are dormant (or dead) adds to our evolving understanding of the polymicrobial basis of CF lung disease and may help guide patient-specific treatment strategies targeting active bacterial populations that are most likely to be susceptible to antibiotic therapy.