(663f) Metabolic Modeling of Cystic Fibrosis Airway Communities Predicts Mechanisms of Pathogen Dominance
In this paper, we utilized 16S rRNA gene amplicon library sequencing data from three published studies to develop a 17-species bacterial community model for predicting species abundances in CF airway communities. The 16S rRNA gene sequence data covers 75 distinct sputum samples from 46 adult CF patients, and captures the heterogeneity of CF polymicrobial infections with respect to taxonomic diversity and the prevalence of pathogens including Pseudomonas, Streptococcus, Burkholderia, Achromobacter and Enterobacteriaceae. The in silico community model was used to predict when each pathogen may dominate the polymicrobial infection by using the 16S rRNA gene sequence data to restrict which pathogens were present in the simulated community. By randomly varying the availability of host-derived nutrients, the model was used to simulate sample-by-sample heterogeneity of community compositions across patients and to understand how metabolite cross-feeding enhanced pathogen abundances. To our knowledge, this study represents the first attempt to metabolically model the CF airway bacterial community rather than model the individual metabolism of common CF pathogens.