The effects of cycled inhaled aztreonam on the cystic fibrosis (CF) lung microbiome.
J Cyst Fibros. 2019 Mar 08;:
Authors: Heirali AA, Acosta N, Storey DG, Workentine ML, Somayaji R, Laforest-Lapointe I, Leung W, Quon BS, Berthiaume Y, Rabin HR, Waddell BJ, Rossi L, Surette MG, Parkins MD
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.
PMID: 30857926 [PubMed – as supplied by publisher]