Alya A Heirali1, Nicole Acosta1, Douglas G Storey2, Matthew L Workentine3, Ranjani Somayaji4, Isabelle Laforest-Lapointe5, Winnie Leung6, Bradley S Quon7, Yves Berthiaume8, Harvey R Rabin4, Barbara J Waddell1, Laura Rossi9, Michael G Surette10, Michael D Parkins11. 1. Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada. 2. Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada; Department of Biological Sciences, University of Calgary, Calgary, AB, Canada. 3. Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada. 4. Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada; Department of Medicine, University of Calgary, Calgary, AB, Canada. 5. Departments of Physiology & Pharmacology, University of Calgary, Calgary, AB, Canada; Department of Pediatrics, University of Calgary, Alberta, Canada. 6. Department of Medicine, University of Alberta, Edmonton, AB, Canada. 7. Department of Medicine and Centre for Heart Lung Innovation, University of British Columbia, Vancouver, BC, Canada. 8. Institut de recherches cliniques de Montreal and Department of Medicine, Universite de Montreal, QB, Canada. 9. Department of Medicine, McMaster University, Hamilton, ON, Canada. 10. Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada; Department of Medicine, McMaster University, Hamilton, ON, Canada. 11. Department of Microbiology, Immunology and Infectious Diseases, University of Calgary, Calgary, AB, Canada; Department of Medicine, University of Calgary, Calgary, AB, Canada. Electronic address: mdparkin@ucalgary.ca.
Abstract
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.
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.
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