Strain-Resolved Dynamics of the Lung Microbiome in Patients with Cystic Fibrosis.

In cystic fibrosis, dynamic and complex communities of microbial pathogens and commensals can colonize the lung. Cultured isolates from lung sputum reveal high inter- and intraindividual variability in pathogen strains, sequence variants, and phenotypes; disease progression likely depends on the precise combination of infecting lineages. Routine clinical protocols, however, provide a limited overview of the colonizer populations. Therefore, a more comprehensive and precise identification and characterization of infecting lineages could assist in making corresponding decisions on treatment. Here, we describe longitudinal tracking for four cystic fibrosis patients who exhibited extreme clinical phenotypes and, thus, were selected from a pilot cohort of 11 patients with repeated sampling for more than a year. Following metagenomics sequencing of lung sputum, we find that the taxonomic identity of individual colonizer lineages can be easily established. Crucially, even superficially clonal pathogens can be subdivided into multiple sublineages at the sequence level. By tracking individual allelic differences over time, an assembly-free clustering approach allows us to reconstruct multiple lineage-specific genomes with clear structural differences. Our study showcases a culture-independent shotgun metagenomics approach for longitudinal tracking of sublineage pathogen dynamics, opening up the possibility of using such methods to assist in monitoring disease progression through providing high-resolution routine characterization of the cystic fibrosis lung microbiome.IMPORTANCE Cystic fibrosis patients frequently suffer from recurring respiratory infections caused by colonizing pathogenic and commensal bacteria. Although modern therapies can sometimes alleviate respiratory symptoms by ameliorating residual function of the protein responsible for the disorder, management of chronic respiratory infections remains an issue. Here, we propose a minimally invasive and culture-independent method to monitor microbial lung content in patients with cystic fibrosis at minimal additional effort on the patient's part. Through repeated sampling and metagenomics sequencing of our selected cystic fibrosis patients, we successfully classify infecting bacterial lineages and deconvolute multiple lineage variants of the same species within a given patient. This study explores the application of modern computational methods for deconvoluting lineages in the cystic fibrosis lung microbiome, an environment known to be inhabited by a heterogeneous pathogen population that complicates management of the disorder.

Sputum DNA sequencing in cystic fibrosis: non-invasive access to the lung microbiome and to pathogen details.

BACKGROUND: Cystic fibrosis (CF) is a life-threatening genetic disorder, characterized by chronic microbial lung infections due to abnormally viscous mucus secretions within airways. The clinical management of CF typically involves regular respiratory-tract cultures in order to identify pathogens and to guide treatment. However, culture-based methods can miss atypical or slow-growing microbes. Furthermore, the isolated microbes are often not classified at the strain level due to limited taxonomic resolution. RESULTS: Here, we show that untargeted metagenomic sequencing of sputum DNA can provide valuable information beyond the possibilities of culture-based diagnosis. We sequenced the sputum of six CF patients and eleven control samples (including healthy subjects and chronic obstructive pulmonary disease patients) without prior depletion of human DNA or cell size selection, thus obtaining the most unbiased and comprehensive characterization of CF respiratory tract microbes to date. We present detailed descriptions of the CF and healthy lung microbiome, reconstruct near complete pathogen genomes, and confirm that the CF lungs consistently exhibit reduced microbial diversity. Crucially, the obtained genomic sequences enabled a detailed identification of the exact pathogen strain types, when analyzed in conjunction with existing multi-locus sequence typing databases. We also detected putative pathogenicity islands and indicators of antibiotic resistance, in good agreement with independent clinical tests. CONCLUSIONS: Unbiased sputum metagenomics provides an in-depth profile of the lung pathogen microbiome, which is complementary to and more detailed than standard culture-based reporting. Furthermore, functional and taxonomic features of the dominant pathogens, including antibiotics resistances, can be deduced-supporting accurate and non-invasive clinical diagnosis.