Features of the bronchial bacterial microbiome associated with atopy, asthma, and responsiveness to inhaled corticosteroid treatment
University of California, San Francisco1, University of Michigan2, Washington University in St. Louis3, Pennsylvania State University4, Brigham and Women's Hospital5, University of Arizona6, Jewish Hospital7, Northwestern University8, University of Chicago9, University of Wisconsin-Madison10, University of Pittsburgh11, Duke University12, Wake Forest University13
TL;DR: 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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TL;DR: FastTree as mentioned in this paper uses sequence profiles of internal nodes in the tree to implement neighbor-joining and uses heuristics to quickly identify candidate joins, then uses nearest-neighbor interchanges to reduce the length of the tree.
Abstract: Gene families are growing rapidly, but standard methods for inferring phylogenies do not scale to alignments with over 10,000 sequences. We present FastTree, a method for constructing large phylogenies and for estimating their reliability. Instead of storing a distance matrix, FastTree stores sequence profiles of internal nodes in the tree. FastTree uses these profiles to implement neighbor-joining and uses heuristics to quickly identify candidate joins. FastTree then uses nearest-neighbor interchanges to reduce the length of the tree. For an alignment with N sequences, L sites, and a different characters, a distance matrix requires O(N^2) space and O(N^2 L) time, but FastTree requires just O( NLa + N sqrt(N) ) memory and O( N sqrt(N) log(N) L a ) time. To estimate the tree's reliability, FastTree uses local bootstrapping, which gives another 100-fold speedup over a distance matrix. For example, FastTree computed a tree and support values for 158,022 distinct 16S ribosomal RNAs in 17 hours and 2.4 gigabytes of memory. Just computing pairwise Jukes-Cantor distances and storing them, without inferring a tree or bootstrapping, would require 17 hours and 50 gigabytes of memory. In simulations, FastTree was slightly more accurate than neighbor joining, BIONJ, or FastME; on genuine alignments, FastTree's topologies had higher likelihoods. FastTree is available at http://microbesonline.org/fasttree.
2,436 citations
TL;DR: This review aims to analyze how the lung and gut microbiota influence each other and may impact on respiratory diseases, with a specific attention on inter-kingdom microbial crosstalks which are able to shape local or long-reached host responses within the GLA.
Abstract: The gut and lungs are anatomically distinct, but potential anatomic communications and complex pathways involving their respective microbiota have reinforced the existence of a gut-lung axis (GLA). Compared to the better-studied gut microbiota, the lung microbiota, only considered in recent years, represents a more discreet part of the whole microbiota associated to human hosts. While the vast majority of studies focused on the bacterial component of the microbiota in healthy and pathological conditions, recent works have highlighted the contribution of fungal and viral kingdoms at both digestive and respiratory levels. Moreover, growing evidence indicates the key role of inter-kingdom crosstalks in maintaining host homeostasis and in disease evolution. In fact, the recently emerged GLA concept involves host-microbe as well as microbe-microbe interactions, based both on localized and long-reaching effects. GLA can shape immune responses and interfere with the course of respiratory diseases. In this review, we aim to analyze how the lung and gut microbiota influence each other and may impact on respiratory diseases. Due to the limited knowledge on the human virobiota, we focused on gut and lung bacteriobiota and mycobiota, with a specific attention on inter-kingdom microbial crosstalks which are able to shape local or long-reached host responses within the GLA.
325 citations
TL;DR: This review focuses on recently discovered connections between lung and gut microbiota, including bacteria, fungi, viruses, and archaea, and their influence on asthma.
271 citations
Cites background from "Features of the bronchial bacterial..."
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...On the other hand, a study in mild asthmatics not treated with ICSs has demonstrated a unique enrichment in members of the Haemophilus, Neisseria, Fusobacterium, and Porphyromonas species and the Sphingomonodaceae family with concurrent depletion in members of the Mogibacteriaceae family and Lactobacillales in their airways (Durack et al., 2017) ....
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TL;DR: The lungs, previously thought to be sterile, are now known to harbor a unique microbiota and, additionally, to be influenced by microbial signals from distal body sites, such as the intestine.
Abstract: The revolution in microbiota research over the past decade has provided invaluable knowledge about the function of the microbial species that inhabit the human body. It has become widely accepted that these microorganisms, collectively called 'the microbiota', engage in networks of interactions with each other and with the host that aim to benefit both the microbial members and the mammalian members of this unique ecosystem. The lungs, previously thought to be sterile, are now known to harbor a unique microbiota and, additionally, to be influenced by microbial signals from distal body sites, such as the intestine. Here we review the role of the lung and gut microbiotas in respiratory health and disease and highlight the main pathways of communication that underlie the gut-lung axis.
264 citations
TL;DR: A review of progress in approaches used to manipulate the microbiome will be addressed, identifying what has and has not worked to serve as a baseline for future directions to intervene in allergic disease development, progression, or both.
Abstract: PRACTALL is a joint initiative of the American Academy of Allergy, Asthma & Immunology and the European Academy of Allergy and Clinical Immunology to provide shared evidence-based recommendations on cutting-edge topics in the field of allergy and immunology. PRACTALL 2017 is focused on what has been established regarding the role of the microbiome in patients with asthma, atopic dermatitis, and food allergy. This is complemented by outlining important knowledge gaps regarding its role in allergic disease and delineating strategies necessary to fill these gaps. In addition, a review of progress in approaches used to manipulate the microbiome will be addressed, identifying what has and has not worked to serve as a baseline for future directions to intervene in allergic disease development, progression, or both.
255 citations
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TL;DR: An overview of the analysis pipeline and links to raw data and processed output from the runs with and without denoising are provided.
Abstract: Supplementary Figure 1 Overview of the analysis pipeline. Supplementary Table 1 Details of conventionally raised and conventionalized mouse samples. Supplementary Discussion Expanded discussion of QIIME analyses presented in the main text; Sequencing of 16S rRNA gene amplicons; QIIME analysis notes; Expanded Figure 1 legend; Links to raw data and processed output from the runs with and without denoising.
28,911 citations
TL;DR: UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters and offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets.
Abstract: Motivation: Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification.
Results: UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets.
Availability: Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch
Contact: [email protected]
Supplementary information:Supplementary data are available at Bioinformatics online.
17,301 citations
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.
Abstract: Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, however, are too stringent in their assumptions for most ecological multivariate data sets. Non-parametric methods, based on permutation tests, are preferable. This paper describes a new non-parametric method for multivariate analysis of variance, after McArdle and Anderson (in press). It is given here, with several applications in ecology, to provide an alternative and perhaps more intuitive formulation for ANOVA (based on sums of squared distances) to complement the description pro- vided by McArdle and Anderson (in press) for the analysis of any linear model. It is an improvement on previous non-parametric methods because it allows a direct additive partitioning of variation for complex models. It does this while maintaining the flexibility and lack of formal assumptions of other non-parametric methods. The test- statistic is a multivariate analogue to Fisher's F-ratio and is calculated directly from any symmetric distance or dissimilarity matrix. P-values are then obtained using permutations. Some examples of the method are given for tests involving several factors, including factorial and hierarchical (nested) designs and tests of interactions.
12,328 citations
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.
Abstract: Motivation: Next-generation sequencing technologies generate very large numbers of short reads. Even with very deep genome coverage, short read lengths cause problems in de novo assemblies. The use of paired-end libraries with a fragment size shorter than twice the read length provides an opportunity to generate much longer reads by overlapping and merging read pairs before assembling a genome.
Results: We present FLASH, a fast computational tool to extend the length of short reads by overlapping paired-end reads from fragment libraries that are sufficiently short. We tested the correctness of the tool on one million simulated read pairs, and we then applied it as a pre-processor for genome assemblies of Illumina reads from the bacterium Staphylococcus aureus and human chromosome 14. FLASH correctly extended and merged reads >99% of the time on simulated reads with an error rate of <1%. With adequately set parameters, FLASH correctly merged reads over 90% of the time even when the reads contained up to 5% errors. When FLASH was used to extend reads prior to assembly, the resulting assemblies had substantially greater N50 lengths for both contigs and scaffolds.
Availability and Implementation: The FLASH system is implemented in C and is freely available as open-source code at http://www.cbcb.umd.edu/software/flash.
Contact: moc.liamg@cogam.t
9,827 citations
TL;DR: A 16S rRNA gene database (http://greengenes.lbl.gov) was used to provide chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies as mentioned in this paper.
Abstract: A 16S rRNA gene database (http://greengenes.lbl.gov) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria.
9,593 citations