High Resolution Analysis of Longitudinal Samples from Cystic Fibrosis Patients

INTRODUCTION: Cystic Fibrosis (CF) is a genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) protein. It affects many organs in the body, but is characterized by mucus... [ view full abstract ]

INTRODUCTION: Cystic Fibrosis (CF) is a genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) protein. It affects many organs in the body, but is characterized by mucus accumulation in the lungs and poor clearance thereof. The quality of life for patients with CF is low, primarily because of debilitating pulmonary infections that are difficult to eradicate due to the static mucoid environment. In the long term, the lungs become chronically colonized by a diverse and refractory microbiota that necessitates frequent antibiotic treatment. The antibiotic regimens are generally not targeted and typically are administered at an advanced stage of disease. Aside from undesirable side effects from the drugs, antibiotic resistant forms of disease can and frequently will emerge due to long-term use whereby complicating treatment in subsequent infections. The purpose of this study is to better understand the CF pulmonary microbiome over time and determine if emerging pathogens and antibiotic resistance can be detected prior to exacerbation. In addition to 16S and whole metagenome profiling of samples, we utilize high resolution methods, including a personalized amplicon sequencing assay and Whole Genome Focused Array SNP typing (WG-FAST). In combination, these methods allow for the identification of antibiotic resistance elements, virulence factors, and clade-specific markers corresponding to species/strains of interest.

METHODS: After obtaining consent, adult subjects (>/= 18 y.o.) were enrolled in a year-long observational study. The study involved monthly sputa collections from up to 13 patients over the course of one year. The sputa samples were analyzed for 16S rDNA and Whole MetaGenome Sequence (WMGS) community analysis. The sputa samples were evaluated through TGEN’s amplicon sequencing analysis pipeline (ASAP), profiling the personalized microbial community in these samples through amplicon sequencing. WMGS data was analyzed with WGFAST to determine where on a robust SNP phylogeny that sample falls.

RESULTS: 16S analysis revealed high relative abundances of many of the common pathogens associated with CF, including Pseudomonas, Streptococcus, Staphylococcus, Haemophilus, and Enterobacteriaceae with variation among patients and among timepoints within patients. Analysis of metagenomic data through GOTTCHA and WGFAST revealed overlapping patterns with the 16S, but was able to resolve to species and strain level. Amplicon sequencing revealed two CF patients with MRSA (SCCmec type II) and another patient with MSSA. Other species targets such as for Achromobacter, Haemophilus and Streptococcus are currently under development. Analysis of the fungal community is ongoing. 

DISCUSSION: Newer techniques for analysis of the CF microbiome have the potential of not only describing the microbiological environment in the CF lung but also distinguish susceptibilities and personalize treatments and therapies in a timely fashion.