Benchmark evaluation of true single molecular sequencing to determine cystic fibrosis airway microbiome diversity

Abstract

Cystic fibrosis (CF) is an autosomal recessive disease associated with recurrent lung infections that can lead to morbidity and mortality. The impact of antibiotics for treatment of acute pulmonary exacerbations on the CF airway microbiome remains unclear with prior studies giving conflicting results and being limited by their use of 16S ribosomal RNA sequencing. Our primary objective was to validate the use of true single molecular sequencing (tSMS) and PathoScope in the analysis of the CF airway microbiome. Three control samples were created with differing amounts of Burkholderia cepacia, Pseudomonas aeruginosa, and Prevotella melaninogenica, three common bacteria found in cystic fibrosis lungs. Paired sputa were also obtained from three study participants with CF before and > 6 days after initiation of antibiotics. Antibiotic resistant B. cepacia and P. aeruginosa were identified in concurrently obtained respiratory cultures. Direct sequencing was performed using tSMS, and filtered reads were aligned to reference genomes from NCBI using PathoScope and Kraken and unique clade-specific marker genes using MetaPhlAn. A total of 180-518 K of 6-12 million filtered reads were aligned for each sample. Detection of known pathogens in control samples was most successful using PathoScope. In the CF sputa, alpha diversity measures varied based on the alignment method used, but similar trends were found between pre- and post-antibiotic samples. PathoScope outperformed Kraken and MetaPhlAn in our validation study of artificial bacterial community controls and also has advantages over Kraken and MetaPhlAn of being able to determine bacterial strains and the presence of fungal organisms. PathoScope can be confidently used when evaluating metagenomic data to determine CF airway microbiome diversity.