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Culturing of Human Nasal Epithelial Cells at the Air Liquid Interface
Published on: October 8, 2013
Pediatric asthma comprises different phenotypic clusters with unique nasal microbiotas
Marcos Pérez-Losada1,2,3, Kayla J Authelet4, Claire E Hoptay4
1Computational Biology Institute, Milken Institute School of Public Health,, George Washington University, Innovation Hall, Suite 305, 45085 University Drive, Ashburn, VA, 20147, USA. mlosada323@gmail.com.
Insights
Pediatric asthma presents distinct nasal bacterial profiles linked to specific disease phenotypes. This research integrates clinical and microbial data to refine asthma classification and identify potential biomarkers.
Area of Science:
- Microbiology
- Pediatrics
- Genomics
Background:
- Pediatric asthma affects 7 million US children, exhibiting significant clinical heterogeneity.
- The airway microbiome's role in asthma pathogenesis is established but understudied in relation to phenotypes.
- Previous research has largely overlooked airway microbiota in asthma phenotype studies.
Purpose of the Study:
- To investigate the relationship between nasal microbiota composition and distinct pediatric asthma phenotypes.
- To explore the potential of integrating clinical and microbial data for improved asthma classification.
- To identify microbial biomarkers associated with pediatric asthma.
Main Methods:
- Clustering analysis of clinical information from 163 children and adolescents with asthma.
- 16S rRNA high-throughput sequencing to characterize nasal cavity microbiota.
- Statistical analysis of microbial abundance, composition, and co-occurrence networks across asthma phenotypes.
Main Results:
- Three distinct pediatric asthma phenotypes were identified through clustering.
- Specific bacterial genera (Moraxella, Staphylococcus, Streptococcus, Haemophilus) were prevalent across samples.
- Significant variations in microbial phyla and genera abundances and community structure were observed across asthma phenotypes and preterm birth status.
Conclusions:
- Children with different asthma phenotypes exhibit unique nasal bacterial profiles.
- Integrating clinical and microbial data can refine asthma classification systems.
- Nasal microbiota variations may serve as biomarkers for pediatric asthma phenotypes.
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.
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