The effects of cycled inhaled aztreonam on the cystic fibrosis (CF) lung microbiome.

BACKGROUND: To improve clinical outcomes, cystic fibrosis (CF) patients with chronic Pseudomonas aeruginosa infections are prescribed inhaled anti-pseudomonal antibiotics. Although, a diverse microbial community exists within CF airways, little is known about how the CF microbiota influences patient outcomes. We hypothesized that organisms within the CF microbiota are affected by inhaled-antibiotics and baseline microbiome may be used to predict therapeutic response. METHODS: Adults with chronic P. aeruginosa infection from four clinics were observed during a single 28-day on/off inhaled-aztreonam cycle. Patients performed serial sputum collection, CF-respiratory infection symptom scores (CRISS), and spirometry. Patients achieving a decrease of ≥2 CRISS by day 28 were categorized as subjective responders (SR). The airway microbiome was defined by Illumina MiSeq analysis of the 16S rRNA gene. RESULTS: Thirty-seven patients (median 37.4 years and FEV1 44% predicted) were enrolled. No significant cohort-wide changes in the microbiome were observed between on/off AZLI cycles in either alpha- or beta-diversity metrics. However, at an individual level shifts were apparent. Twenty-one patients (57%) were SR and fourteen patients did not subjectively respond. While alpha-diversity metrics did not associate with response, patients who did not subjectively respond had a higher abundance of Staphylococcus and Streptococcus, and lower abundance of Haemophilus. CONCLUSIONS: The CF microbiome is relatively resilient to AZLI perturbations. However, associated changes were observed at the individual patient level. The relative abundance of key "off-target" organisms associated with subjective improvements suggesting that the microbiome may be used as a tool to predict patient response - potentially improving outcomes.

Longitudinal sampling of the lung microbiota in individuals with cystic fibrosis.

Cystic fibrosis (CF) manifests in the lungs resulting in chronic microbial infection. Most morbidity and mortality in CF is due to cycles of pulmonary exacerbations-episodes of acute inflammation in response to the lung microbiome-which are difficult to prevent and treat because their cause is not well understood. We hypothesized that longitudinal analyses of the bacterial component of the CF lung microbiome may elucidate causative agents within this community for pulmonary exacerbations. In this study, 6 participants were sampled thrice-weekly for up to one year. During sampling, sputum, and data (antibiotic usage, spirometry, and symptom scores) were collected. Time points were categorized based on relation to exacerbation as Stable, Intermediate, and Treatment. Retrospectively, a subset of were interrogated via 16S rRNA gene sequencing. When samples were examined categorically, a significant difference between the lung microbiota in Stable, Intermediate, and Treatment samples was observed in a subset of participants. However, when samples were examined longitudinally, no correlations between microbial composition and collected data (antibiotic usage, spirometry, and symptom scores) were observed upon exacerbation onset. In this study, we identified no universal indicator within the lung microbiome of exacerbation onset but instead showed that changes to the CF lung microbiome occur outside of acute pulmonary episodes and are patient-specific.

The effects of inhaled aztreonam on the cystic fibrosis lung microbiome.

BACKGROUND: Aztreonam lysine for inhalation (AZLI) is an inhaled antibiotic used to treat chronic Pseudomonas aeruginosa infection in CF. AZLI improves lung function and quality of life, and reduces exacerbations-improvements attributed to its antipseudomonal activity. Given the extremely high aztreonam concentrations achieved in the lower airways by nebulization, we speculate this may extend its spectrum of activity to other organisms. As such, we sought to determine if AZLI affects the CF lung microbiome and whether community constituents can be used to predict treatment responsiveness. METHODS: Patients were included if they had chronic P. aeruginosa infection and repeated sputum samples collected before and after AZLI. Sputum DNA was extracted, and the V3-hypervariable region of the 16S ribosomal RNA (rRNA) gene amplified and sequenced. RESULTS: Twenty-four patients naïve to AZLI contributed 162 samples. The cohort had a median age of 37.1 years, and a  median FEV1 of 44% predicted. Fourteen patients were a priori defined as responders for achieving ≥3% FEV1 improvement following initiation. No significant changes in alpha diversity were noted following AZLI. Furthermore, beta diversity demonstrated clustering with respect to patients, but had no association with AZLI use. However, we did observe a decline in the relative abundance of several individual operational taxonomic units (OTUs) following AZLI initiation suggesting that specific sub-populations of organisms may be impacted. Patients with higher abundance of Staphylococcus and anaerobic organisms including Prevotella and Fusobacterium were less likely to respond to therapy. CONCLUSIONS: Results from our study suggest potential alternate/additional mechanisms by which AZLI functions. Moreover, our study suggests that the CF microbiota may be used as a biomarker to predict patient responsiveness to therapy suggesting the microbiome may be harnessed for the personalization of therapies.