20: NASOPHARYNX MICROBIOME COMPOSITION VARIES OVER TIME IN PEDIATRIC ASTHMA. Issue 3 (23rd February 2016)

• Record Type: Journal Article
• Authors: Perez-Losada, M; Goldstein, A; Alamri, L; Crandall, KA; Freishtat, RJ
• Is Part Of: Journal of investigative medicine. Volume 64:Issue 3(2016)
• Page Start: 815
• Page End: 815
• Publication Date: 2016-02-23
• Subjects: Abdomen; Clinical medicine -- Periodicals; Medicine -- Research -- Periodicals; Medicine; Research -- United States; Clinical medicine; Medicine -- Research; Periodicals; 616.075
• DOI: 10.1136/jim-2016-000080.36
• Languages: English
• ISSNs: 1081-5589

Abstract

Abstract : Purpose of Study: The application of next-generation sequencing (NGS) technology has shown that microbial communities in the respiratory airways (i.e., the microbiome) play a significant role in the onset, development and severity of asthma. However, little is known about their temporal dynamics (i.e., microbial succession), which poses a significant obstacle to identifying pulmotypes of disease and assessing inter-patient variation. Here, we couple NGS and 16S rRNA data to characterize the nasopharynx microbiome of children with asthma and determine its stability over time. Methods Used: We collected nasal washes from 40 children with asthma enrolled in the AsthMaP-2 Project from two consecutive visits, six months apart. Total DNA was extracted and sequenced for the 16S-V4 rRNA gene region (∼250 bp) using the MySeq Illumina platform. Reads were analyzed in Mothur using the SILVAv119 reference database. Alpha diversity metrics and phylogenetic and count-base distance community indexes of beta diversity were compared across samples and time points. PCoA and NJ clustering analysis were used to assess community relatedness. Differences in alpha diversity and OTU abundance between sample pairs across time points were also compared. Summary of Results: A mean of 27, 479 clean 16S sequences corresponding to an average of 173 OTUs were sequenced and detected per sample, respectively. Representatives of Moraxella, Corynebacterium, Prevotella, Staphylococcus, Alloiococcus, Streptococcus, Peptoniphilus, Fusobacterium, and Haemophilus accounted for 36 to 99% of the reads across samples. These genera have been previously found in the nasopharynx of asthmatic and healthy children. A total of 61 OTUs from these genera were present in at least 50% of the samples (i.e., the nasal core microbiome). Significant differences in core microbiome composition were detected between sample pairs, but no directional trend (increase or decrease) was observed across sample pairs. Samples were randomly ordinated and did not cluster together. Conclusions: Our analysis of nasal microbiomes in 40 asthmatic children revealed significant differences in composition within individuals over six months. Future cross-sectional microbiome studies need to be aware of short span temporal dynamics in nasal microbiota.