Feature selection of the respiratory microbiota associated with asthma

Abstract

Abstract The expanding development of data mining and statistical learning techniques have enriched recent efforts to understand and identify metagenomics biomarkers in airways diseases. In contribution to the growing microbiota research in respiratory contexts, this study aims to characterize respiratory microbiota in asthmatic patients (pediatrics and adults) in comparison to healthy controls, to explore the potential of microbiota as a biomarker for asthma diagonosis and prediction. Analysis of 16 S-ribosomal RNA gene sequences reveals that respiratory microbial composition and diversity are significantly different between asthmatic and healthy subjects. Phylum Proteobacteria represented the predominant bacterial communities in asthmatic patients in comparison to healthy subjects. In contrast, a higher abundance of Moraxella and Alloiococcus was more prevalent in asthmatic patients compared to healthy controls. Using a machine learning approach, 57 microbial markers were identified and used to characterize notable microbiota composition differences between the groups. Among the selected OTUs, Moraxella and Corynebacterium genera were found to be more enriched on the pediatric asthmatics (p-values < 0.01). In the era of precision medicine, the discovery of the respiratory microbiota associated with asthma can lead to valuable applications for individualized asthma care.

Keywords

• Asthma
• Respiratory microbiota
• Metagenomics
• Machine learning