AbstractCulture-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 theirin situecophysiology. 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. Using BONCAT-based fluorescent imaging on sputum collected from stable CF subjects, we reveal that only a subset of bacteria are translationally active. We also combined BONCAT with fluorescent activated cell sorting (FACS) and 16S rRNA gene sequencing to assign taxonomy to the active subpopulation and found that the most dominant taxa are indeed translationally active. On average, only ∼12-18% of bacterial cells were BONCAT labeled, suggesting a heterogeneous growth strategy widely employed by most airway microbiota. Differentiating translationally active populations from those that are dormant adds to our evolving understanding of the polymicrobial basis of CF lung disease and may help guide patient-specific therapeutic strategies targeting active bacterial populations that are most likely to be susceptible.
WS19-6 Bioorthogonal non-canonical amino acid tagging reveals translationally active subpopulations of the cystic fibrosis lung microbiota