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. 2018 Oct 4;6(1):179.
doi: 10.1186/s40168-018-0564-7.

Pediatric asthma comprises different phenotypic clusters with unique nasal microbiotas

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Pediatric asthma comprises different phenotypic clusters with unique nasal microbiotas

Marcos Pérez-Losada et al. Microbiome. .

Abstract

Background: 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.

Results: 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.

Conclusions: 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.

Keywords: 16S rRNA; Asthma; Microbiome; Nose; Phenotype.

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Conflict of interest statement

Ethics approval and consent to participate

All participants in this study were part of The AsthMaP-2 Project. AsthMaP-2 and the work presented here was approved by the Children’s National Medical Center Institutional Review Board. Written consent was obtained from all independent participants or their legal guardians using the IRB-approved informed consent documents.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures

Fig. 1
Fig. 1
Microbial profiles (mean relative proportions) of most abundant (> 1%) phyla and genera in the nasal microbiomes of children and adolescents belonging to three different asthma phenotypic clusters (APCs)
Fig. 2
Fig. 2
Network analyses of microbial co-occurrences in the nasal microbiomes of children and adolescents belonging to three different asthma phenotypic clusters (APCs). Different network edge colors were used for each phenotype. Thin edges correspond to probabilities of 0.90, while thick edges correspond to probabilities of 0.95. Only bacterial genera involved in a network are displayed

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References

    1. Freishtat RJ, Watson AM, Benton AS, Iqbal SF, Pillai DK, Rose MC, Hoffman EP. Asthmatic airway epithelium is intrinsically inflammatory and mitotically dyssynchronous. Am J Respir Cell Mol Biol. 2011;44(6):863–869. doi: 10.1165/rcmb.2010-0029OC. - DOI - PMC - PubMed
    1. Stemmy EJ, Benton AS, Lerner J, Alcala S, Constant SL, Freishtat RJ. Extracellular cyclophilin levels associate with parameters of asthma in phenotypic clusters. J Asthma. 2011;48(10):986–993. doi: 10.3109/02770903.2011.623334. - DOI - PMC - PubMed
    1. National Institute of Allergy and Infectious Diseases. NIAID Strategic Plan 2013. 2013. Available from: http://www.niaid.nih.gov/about/whoweare/planningpriorities/Pages/default.... Last accessed: 6 Jan 2015.
    1. Barnes KC, Grant AV, Hansel NN, Gao P, Dunston GM. African Americans with asthma: genetic insights. Proc Am Thorac Soc. 2007;4(1):58–68. doi: 10.1513/pats.200607-146JG. - DOI - PMC - PubMed
    1. Ferrante SC, Nadler EP, Pillai DK, Hubal MJ, Wang Z, Wang JM, Gordish-Dressman H, Koeck E, Sevilla S, Wiles AA, et al. Adipocyte-derived exosomal miRNAs: a novel mechanism for obesity-related disease. Pediatr Res. 2015;77(3):447–454. doi: 10.1038/pr.2014.202. - DOI - PMC - PubMed

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