The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Lung microbiome dynamics in COPD exacerbations
Open accessAbstract
Increasing evidence suggests that the lung microbiome plays an important role in chronic obstructive pulmonary disease (COPD) severity. However, the dynamics of the lung microbiome during COPD exacerbations and its potential role in disease aetiology remain poorly understood. We completed a longitudinal 16S ribosomal RNA survey of the lung microbiome on 476 sputum samples collected from 87 subjects with COPD at four visits defined as stable state, exacerbation, 2 weeks post-therapy and 6 weeks recovery. Our analysis revealed a dynamic lung microbiota where changes appeared to be associated with exacerbation events and indicative of specific exacerbation phenotypes. Antibiotic and steroid treatments appear to have differential effects on the lung microbiome. We depict a microbial interaction network for the lung microbiome and suggest that perturbation of a few bacterial operational taxonomic units, in particular Haemophilus spp., could greatly impact the overall microbial community structure. Furthermore, several serum and sputum biomarkers, in particular sputum interleukin-8, appear to be highly correlated with the structure and diversity of the microbiome. Our study furthers the understanding of lung microbiome dynamics in COPD patients and highlights its potential as a biomarker, and possibly a target, for future respiratory therapeutics.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
QIIME allows analysis of high-throughput community sequencing data
Caporaso, J. G., Kuczynski, J., Stombaugh, J., Bittinger, K., Bushman, F. D., Costello, E. K., Fierer, N., Peña, A. G., Goodrich, J. K., Gordon, J. I., et al. (2010) · Nature Methods · doi:10.1038/nmeth.f.303 · Cited by 34,887
pROC: an open-source package for R and S+ to analyze and compare ROC curves
Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.-C., & Müller, M. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-77 · Cited by 13,697
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
Reagent and laboratory contamination can critically impact sequence-based microbiome analyses
Salter, S. J., Cox, M. J., Turek, E. M., Calus, S. T., Cookson, W. O., Moffatt, M. F., Turner, P., Parkhill, J., Loman, N. J., & Walker, A. W. (2014) · BMC Biology · doi:10.1186/s12915-014-0087-z · Cited by 3,463
The keystone-pathogen hypothesis
Hajishengallis, G., Darveau, R. P., & Curtis, M. A. (2012) · Nature Reviews Microbiology · doi:10.1038/nrmicro2873 · Cited by 1,790
Microbial co-occurrence relationships in the human microbiome
Faust, K., Sathirapongsasuti, J. F., Izard, J., Segata, N., Gevers, D., Raes, J., & Huttenhower, C. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002606 · Cited by 1,527
Characterization of the oral fungal microbiome (mycobiome) in healthy individuals
Ghannoum, M. A., Jurevic, R. J., Mukherjee, P. K., Cui, F., Sikaroodi, M., Naqvi, A., & Gillevet, P. M. (2010) · PLoS Pathogens · doi:10.1371/journal.ppat.1000713 · Cited by 1,150
Molyneaux, P. L., Mallia, P., Cox, M. J., Footitt, J., Willis-Owen, S. A. G., Homola, D., Trujillo-Torralbo, M.-B., Elkin, S., Kon, O. M., Cookson, W. O. C., et al. (2013) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201302-0341oc · Cited by 390
Airway microbiome dynamics in exacerbations of chronic obstructive pulmonary disease
Huang, Y. J., Sethi, S., Murphy, T., Nariya, S., Boushey, H. A., & Lynch, S. V. (2014) · Journal of Clinical Microbiology · doi:10.1128/jcm.00035-14 · Cited by 323
Cited by
Microbiome effects on immunity, health and disease in the lung
Shukla, S. D., Budden, K. F., Neal, R., & Hansbro, P. M. (2017) · Clinical & Translational Immunology · doi:10.1038/cti.2017.6 · Cited by 287
Mechanistic insight into the function of the microbiome in lung diseases
Ubags, N. D. J., & Marsland, B. J. (2017) · European Respiratory Journal · doi:10.1183/13993003.02467-2016 · Cited by 109
Healy, C., Munoz-Wolf, N., Strydom, J., Faherty, L., Williams, N. C., Kenny, S., Donnelly, S. C., & Cloonan, S. M. (2021) · Respiratory Research · doi:10.1186/s12931-021-01722-y · Cited by 78
The microbiome-metabolome crosstalk in the pathogenesis of respiratory fungal diseases
Gonçalves, S. M., Lagrou, K., Duarte-Oliveira, C., Maertens, J. A., Cunha, C., & Carvalho, A. (2017) · Virulence · doi:10.1080/21505594.2016.1257458 · Cited by 32
Metadata
- Authors
- Wang, Z., Bafadhel, M., Haldar, K., Spivak, A., Mayhew, D., Miller, B. E., Tal-Singer, R., Johnston, S. L., Ramsheh, M. Y., Barer, M. R., Brightling, C. E., & Brown, J. R.
- Venue
- European Respiratory Journal
- Publisher
- European Respiratory Society (ERS)
- Published
- 2016-02-25
- Volume
- 47
- Issue
- 4
- Pages
- 1082-1092
- ISSN
- 0903-1936 (ISSN-L)
- 1399-3003
- OpenAlex
- W2333373066
- Cited by
- 459
- References
- 11
- FWCI
- 29.93
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Lung microbiome dynamics in COPD exacerbations. https://app.cochat.ai/papers/10.1183/13993003.01406-2015
- The paper
- Wang, Z., Bafadhel, M., Haldar, K., Spivak, A., Mayhew, D., Miller, B. E., Tal-Singer, R., Johnston, S. L., Ramsheh, M. Y., Barer, M. R., Brightling, C. E., & Brown, J. R. (2016). Lung microbiome dynamics in COPD exacerbations. European Respiratory Journal. https://doi.org/10.1183/13993003.01406-2015
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.