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Features of the bronchial bacterial microbiome associated with atopy, asthma, and responsiveness to inhaled corticosteroid treatment

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TLDR
Even in subjects with mild steroid‐naive asthma, differences in the bronchial microbiome are associated with immunologic and clinical features of the disease, suggesting possible microbiome targets for future approaches to asthma treatment or prevention.
Abstract
Background Compositional differences in the bronchial bacterial microbiota have been associated with asthma, but it remains unclear whether the findings are attributable to asthma, to aeroallergen sensitization, or to inhaled corticosteroid treatment. Objectives We sought to compare the bronchial bacterial microbiota in adults with steroid-naive atopic asthma, subjects with atopy but no asthma, and nonatopic healthy control subjects and to determine relationships of the bronchial microbiota to phenotypic features of asthma. Methods Bacterial communities in protected bronchial brushings from 42 atopic asthmatic subjects, 21 subjects with atopy but no asthma, and 21 healthy control subjects were profiled by using 16S rRNA gene sequencing. Bacterial composition and community-level functions inferred from sequence profiles were analyzed for between-group differences. Associations with clinical and inflammatory variables were examined, including markers of type 2–related inflammation and change in airway hyperresponsiveness after 6 weeks of fluticasone treatment. Results The bronchial microbiome differed significantly among the 3 groups. Asthmatic subjects were uniquely enriched in members of the Haemophilus , Neisseria , Fusobacterium , and Porphyromonas species and the Sphingomonodaceae family and depleted in members of the Mogibacteriaceae family and Lactobacillales order. Asthma-associated differences in predicted bacterial functions included involvement of amino acid and short-chain fatty acid metabolism pathways. Subjects with type 2–high asthma harbored significantly lower bronchial bacterial burden. Distinct changes in specific microbiota members were seen after fluticasone treatment. Steroid responsiveness was linked to differences in baseline compositional and functional features of the bacterial microbiome. Conclusion Even in subjects with mild steroid-naive asthma, differences in the bronchial microbiome are associated with immunologic and clinical features of the disease. The specific differences identified suggest possible microbiome targets for future approaches to asthma treatment or prevention.

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Determinants of the Nasal Microbiome: Pilot Study of Effects of Intranasal Medication Use

TL;DR: Administration of intranasal mometasone furoate or mupirocin resulted in shifts in microbial diversity that persisted to some degree following treatment cessation, and these effects persisted at least 2 weeks beyond cessation of treatment.
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The dynamic lung microbiome in health and disease

TL;DR: Natalini et al. as discussed by the authors provide an overview of lower airway microbial dynamics in health and disease and discuss future work that is required to uncover novel therapeutic targets to improve lung health.
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Microbiome in Mechanisms of Asthma

TL;DR: The mechanisms that connect the lung and gut microbiota to asthma development and severity are reviewed, including direct anti-inflammatory action or induction of non-type 2 inflammation by certain bacterial colonies.
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Associations between respiratory infections and bacterial microbiome in student dormitories in Northern China

TL;DR: The first association study between the indoor microbiome and respiratory infections is presented, randomly selected students living in 86 dormitory rooms in Shanxi University were randomly selected to survey symptoms of infections.
References
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Robert C. Edgar
- 01 Oct 2010 - 
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Journal ArticleDOI

A new method for non-parametric multivariate analysis of variance

TL;DR: In this article, a non-parametric method for multivariate analysis of variance, based on sums of squared distances, is proposed. But it is not suitable for most ecological multivariate data sets.
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FLASH: Fast Length Adjustment of Short Reads to Improve Genome Assemblies

TL;DR: FLASH is a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short and when FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds.
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