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      Outgrowth of the Bacterial Airway Microbiome after Rhinovirus Exacerbation of Chronic Obstructive Pulmonary Disease

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          Abstract

          Rhinovirus infection is followed by significantly increased frequencies of positive, potentially pathogenic sputum cultures in chronic obstructive pulmonary disease (COPD). However, it remains unclear whether these represent de novo infections or an increased load of organisms from the complex microbial communities (microbiome) in the lower airways. To investigate the effect of rhinovirus infection on the airway bacterial microbiome. Subjects with COPD (n = 14) and healthy control subjects with normal lung function (n = 17) were infected with rhinovirus. Induced sputum was collected at baseline before rhinovirus inoculation and again on Days 5, 15, and 42 after rhinovirus infection and DNA was extracted. The V3-V5 region of the bacterial 16S ribosomal RNA gene was amplified and pyrosequenced, resulting in 370,849 high-quality reads from 112 of the possible 124 time points. At 15 days after rhinovirus infection, there was a sixfold increase in 16S copy number (P = 0.007) and a 16% rise in numbers of proteobacterial sequences, most notably in potentially pathogenic Haemophilus influenzae (P = 2.7 × 10(-20)), from a preexisting community. These changes occurred only in the sputum microbiome of subjects with COPD and were still evident 42 days after infection. This was in contrast to the temporal stability demonstrated in the microbiome of healthy smokers and nonsmokers. After rhinovirus infection, there is a rise in bacterial burden and a significant outgrowth of Haemophilus influenzae from the existing microbiota of subjects with COPD. This is not observed in healthy individuals. Our findings suggest that rhinovirus infection in COPD alters the respiratory microbiome and may precipitate secondary bacterial infections.

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          Most cited references41

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          QIIME allows analysis of high-throughput community sequencing data.

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            Search and clustering orders of magnitude faster than BLAST.

            Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. 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. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
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              Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

              The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
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                Author and article information

                Journal
                American Journal of Respiratory and Critical Care Medicine
                Am J Respir Crit Care Med
                American Thoracic Society
                1073-449X
                1535-4970
                November 15 2013
                November 15 2013
                : 188
                : 10
                : 1224-1231
                Affiliations
                [1 ]National Heart and Lung Institute, Imperial College, London, United Kingdom
                [2 ]Centre for Respiratory Infection, London, United Kingdom; and
                [3 ]Imperial College Healthcare NHS Trust, London, United Kingdom
                Article
                10.1164/rccm.201302-0341OC
                3863728
                23992479
                a008b453-f259-4a3b-b593-810079ab50de
                © 2013
                History

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