Abstract
Respiratory infection in cystic fibrosis (CF) is polymicrobial, but standard sputum microbiology does not account for the lung microbiome or detect changes in microbial diversity associated with disease. As a clinically applicable CF microbiome surveillance scheme, total sputum nucleic acids isolated by a standard high-throughput robotic method for accredited viral diagnosis were profiled for bacterial diversity using ribosomal intergenic spacer analysis (RISA) PCR. Conventional culture and RISA were performed on 200 paired sputum samples from 93 CF adults; pyrosequencing of the 16S rRNA gene was applied to 59 patients to systematically determine bacterial diversity. Compared to the microbiology data, RISA profiles clustered into two groups: the emerging nonfermenting Gram-negative organisms (eNFGN) and Pseudomonas groups. Patients who were culture positive for Burkholderia, Achromobacter, Stenotrophomonas, and Ralstonia clustered within the eNFGN group. Pseudomonas group RISA profiles were associated with Pseudomonas aeruginosa culture-positive patients. Sequence analysis confirmed the abundance of eNFGN genera and Pseudomonas within these respective groups. Low bacterial diversity was associated with severe lung disease (P < 0.001) and the presence of Burkholderia (P < 0.001). An absence of Streptococcus (P < 0.05) occurred in individuals with lung function in the lowest quartile. In summary, nucleic acids isolated from CF sputum can serve as a single template for both molecular virology and bacteriology, with a RISA PCR rapidly detecting the presence of dominant eNFGN pathogens or P. aeruginosa missed by culture (11% of cases). We provide guidance for how this straightforward CF microbiota profiling scheme may be adopted by clinical laboratories.