PT - JOURNAL ARTICLE AU - Conan Y. Zhao AU - Yiqi Hao AU - Yifei Wang AU - John J. Varga AU - Arlene A. Stecenko AU - Joanna B. Goldberg AU - Sam P. Brown TI - Microbiome data enhances predictive models of lung function in people with CF AID - 10.1101/656066 DP - 2019 Jan 01 TA - bioRxiv PG - 656066 4099 - http://biorxiv.org/content/early/2019/05/31/656066.short 4100 - http://biorxiv.org/content/early/2019/05/31/656066.full AB - The polymicrobial context of chronic infection has received increasing attention due to widespread use of microbiome sequencing technology. However, clinical microbiology analysis of infection samples in hospitals continues to focus only on established human pathogens. This disconnect between diverse ‘infection microbiomes’ and limited clinical microbiology profiling leaves open the possibility that important risk markers are being unexploited during infection management. To address this disconnect, we focus on lung infections in people with Cystic Fibrosis (CF). A cohort of CF patients (N=77) were recruited for this study. We collected health information (age, BMI, lung function) and clinical microbiology records for each patient. We also collected sputum samples during a period of clinical stability, and determined lung microbiome compositions through 16S rDNA sequencing. We use a regularized linear regression algorithm (ElasticNet) to select informative features to predict lung function. We find that models including whole microbiome quantitation outperform models trained on pathogen quantitation alone, with or without the inclusion of patient metadata. Our most predictive models retain key pathogens as negative predictors (Pseudomonas, Achromobacter) along with established correlates of CF disease state (age, BMI, CF related diabetes). In addition, our models select specific non-pathogen taxa (Fusobacterium, Rothia) as positive predictors of lung health. Our analysis does not address causality, leaving open whether these non-pathogen taxa are playing an active role in promoting lung health (e.g. by suppressing pathogens), or are simply informative biomarkers of patient health (orthogonal to age, BMI, etc). Our results support a reconsideration of clinical microbiology pipelines to ensure the provision of the most informative data to guide clinical practice.