The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.
Features of the bronchial bacterial microbiome associated with atopy, asthma, and responsiveness to inhaled corticosteroid treatment
Open accessAbstract
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. Graphical abstract Figure. No Caption available.
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Cites
A new method for non-parametric multivariate analysis of variance
Anderson, M. J. (2001) · Austral Ecology · doi:10.1111/j.1442-9993.2001.01070.pp.x · Cited by 13,828
Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
DeSantis, T. Z., Hugenholtz, P., Larsen, N., Rojas, M., Brodie, E. L., Keller, K., Huber, T., Dalevi, D., Hu, P., & Andersen, G. L. (2006) · Applied and Environmental Microbiology · doi:10.1128/aem.03006-05 · Cited by 10,891
Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample
Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Lozupone, C. A., Turnbaugh, P. J., Fierer, N., & Knight, R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000080107 · Cited by 9,711
Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences
Langille, M. G. I., Zaneveld, J., Caporaso, J. G., McDonald, D., Knights, D., Reyes, J. A., Clemente, J. C., Burkepile, D. E., Vega Thurber, R. L., Knight, R., et al. (2013) · Nature Biotechnology · doi:10.1038/nbt.2676 · Cited by 9,116
Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms
Caporaso, J. G., Lauber, C. L., Walters, W. A., Berg-Lyons, D., Huntley, J., Fierer, N., Owens, S. M., Betley, J., Fraser, L., Bauer, M., et al. (2012) · The ISME Journal · doi:10.1038/ismej.2012.8 · Cited by 8,839
Conservation evaluation and phylogenetic diversity
Faith, D. P. (1992) · Biological Conservation · doi:10.1016/0006-3207(92)91201-3 · Cited by 5,693
FastTree: Computing Large Minimum Evolution Trees with Profiles instead of a Distance Matrix
Price, M. N., Dehal, P. S., & Arkin, A. P. (2009) · Molecular Biology and Evolution · doi:10.1093/molbev/msp077 · Cited by 5,602
Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible
McMurdie, P. J., & Holmes, S. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003531 · Cited by 2,921
Gut Microbiota Metabolism of Dietary Fiber Influences Allergic Airway Disease and Hematopoiesis
Trompette, A., Gollwitzer, E. S., Yadava, K., Sichelstiel, A. K., Sprenger, N., Ngom-Bru, C., Blanchard, C., Junt, T., Nicod, L. P., Harris, N. L., et al. (2014) · Nature Medicine · doi:10.1038/nm.3444 · Cited by 2,862
Development and validation of a questionnaire to measure asthma control
Juniper, E. F., O′byrne, P. M., Guyatt, G. H., Ferrie, P. J., & King, D. R. (1999) · European Respiratory Journal · doi:10.1034/j.1399-3003.1999.14d29.x · Cited by 2,473
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Wypych, T. P., Wickramasinghe, L. C., & Marsland, B. J. (2019) · Nature Immunology · doi:10.1038/s41590-019-0451-9 · Cited by 611
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Natalini, J. G., Singh, S., & Segal, L. N. (2023) · Nature Reviews Microbiology · doi:10.1038/s41579-022-00821-x · Cited by 523
Huang, Y. J., Marsland, B. J., Bunyavanich, S., O'Mahony, L., Leung, D. Y. M., Muraro, A., & Fleisher, T. A. (2017) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.02.007 · Cited by 330
Self-citationTaylor, S. L., Leong, L. E. X., Choo, J. M., Wesselingh, S., Yang, I. A., Upham, J. W., Reynolds, P. N., Hodge, S., James, A. L., Jenkins, C., et al. (2018) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.03.044 · Cited by 314
Metadata
- Authors
- Durack, J., Lynch, S. V., Nariya, S., Bhakta, N. R., Beigelman, A., Castro, M., Dyer, A.-M., Israel, E., Kraft, M., Martin, R. J., Mauger, D. T., Rosenberg, S. R., Sharp-King, T., White, S. R., Woodruff, P. G., Avila, P. C., Denlinger, L. C., Holguin, F., Lazarus, S. C., Lugogo, N., Moore, W. C., Peters, S. P., Que, L., Smith, L. J., Sorkness, C. A., Wechsler, M. E., Wenzel, S. E., Boushey, H. A., & Huang, Y. J.
- Venue
- Journal of Allergy and Clinical Immunology
- Publisher
- Elsevier BV
- Published
- 2017-07
- Volume
- 140
- Issue
- 1
- Pages
- 63-75
- ISSN
- 0091-6749 (ISSN-L)
- OpenAlex
- W2552795086
- Cited by
- 320
- References
- 51
- FWCI
- 16.88
- Type
- journal article
- Language
- en
- Open access
- green
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- Durack, J., Lynch, S. V., Nariya, S., Bhakta, N. R., Beigelman, A., Castro, M., Dyer, A.-M., Israel, E., Kraft, M., Martin, R. J., Mauger, D. T., Rosenberg, S. R., Sharp-King, T., White, S. R., Woodruff, P. G., Avila, P. C., Denlinger, L. C., Holguin, F., Lazarus, S. C., . . . Huang, Y. J. (2017). Features of the bronchial bacterial microbiome associated with atopy, asthma, and responsiveness to inhaled corticosteroid treatment. Journal of Allergy and Clinical Immunology. https://doi.org/10.1016/j.jaci.2016.08.055
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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