Crandall and Perez-Losada Publish Article on Pediatric Asthma in "Microbiome"

October 11, 2018

Dr. Keith Crandall, Director of the Computational Biology Institute (CBI) and Professor in the Department of Epidemiology and Biostatistics (DEB), and Dr. Marcos Perez-Losada, member of the CBI and Assistant Professor in DEB, co-authored an article, "Pediatric asthma comprises different phenotypic clusters with unique nasal microbiotas," published in Microbiome on October 4th, 2018.

Pediatric asthma is the most common chronic childhood disease in the USA, currently affecting ~ 7 million children. This heterogeneous syndrome is thought to encompass various disease phenotypes of clinically observable characteristics, which can be statistically identified by applying clustering approaches to patient clinical information. Extensive evidence has shown that the airway microbiome impacts both clinical heterogeneity and pathogenesis in pediatric asthma. Yet, so far, airway microbiotas have been consistently neglected in the study of asthma phenotypes. Here, we couple extensive clinical information with 16S rRNA high-throughput sequencing to characterize the microbiota of the nasal cavity in 163 children and adolescents clustered into different asthma phenotypes.

Our clustering analyses identified three statistically distinct phenotypes of pediatric asthma. Four core OTUs of the pathogenic genera Moraxella, Staphylococcus, Streptococcus, and Haemophilus were present in at least 95% of the studied nasal microbiotas. Phyla (Proteobacteria, Actinobacteria, and Bacteroidetes) and genera (Moraxella, Corynebacterium, Dolosigranulum, and Prevotella) abundances, community composition, and structure varied significantly (0.05 < P ≤ 0.0001) across asthma phenotypes and one of the clinical variables (preterm birth). Similarly, microbial networks of co-occurrence of bacterial genera revealed different bacterial associations across asthma phenotypes.

This study shows that children and adolescents with different clinical characteristics of asthma also show different nasal bacterial profiles, which is indicative of different phenotypes of the disease. Our work also shows how clinical and microbial information could be integrated to validate and refine asthma classification systems and develop biomarkers of disease.