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      Comparing microbiota profiles in induced and spontaneous sputum samples in COPD patients

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          Abstract

          Background

          Induced and spontaneous sputum are used to evaluate the airways microbiota. Whether the sputum types can be used interchangeably in microbiota research is unknown. Our aim was to compare microbiota in induced and spontaneous sputum from COPD patients sampled during the same consultation.

          Methods

          COPD patients from Bergen, Norway, were followed between 2006/2010, examined during the stable state and exacerbations. 30 patients delivered 36 sample pairs. DNA was extracted by enzymatic and mechanical lysis methods. The V3-V4 region of the 16S rRNA gene was PCR-amplified and prepared for paired-end sequencing. Illumina Miseq System was used for sequencing, and Quantitative Insights Into Microbial Ecology (QIIME) and Stata were used for bioinformatics and statistical analyses.

          Results

          Approximately 4 million sequences were sorted into 1004 different OTUs and further assigned to 106 different taxa. Pair-wise comparison of both taxonomic composition and beta-diversity revealed significant differences in one or both parameters in 1/3 of sample pairs. Alpha-diversity did not differ. Comparing abundances for each taxa identified, showed statistically significant differences between the mean abundances in induced versus spontaneous samples for 15 taxa when disease state was considered. This included potential pathogens like Haemophilus and Moraxella.

          Conclusion

          When studying microbiota in sputum samples one should take into consideration how samples are collected and avoid the usage of both induced and spontaneous sputum in the same study.

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          Most cited references20

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          16S ribosomal DNA sequence analysis of a large collection of environmental and clinical unidentifiable bacterial isolates.

          Some bacteria are difficult to identify with phenotypic identification schemes commonly used outside reference laboratories. 16S ribosomal DNA (rDNA)-based identification of bacteria potentially offers a useful alternative when phenotypic characterization methods fail. However, as yet, the usefulness of 16S rDNA sequence analysis in the identification of conventionally unidentifiable isolates has not been evaluated with a large collection of isolates. In this study, we evaluated the utility of 16S rDNA sequencing as a means to identify a collection of 177 such isolates obtained from environmental, veterinary, and clinical sources. For 159 isolates (89.8%) there was at least one sequence in GenBank that yielded a similarity score of > or =97%, and for 139 isolates (78.5%) there was at least one sequence in GenBank that yielded a similarity score of > or =99%. These similarity score values were used to defined identification at the genus and species levels, respectively. For isolates identified to the species level, conventional identification failed to produce accurate results because of inappropriate biochemical profile determination in 76 isolates (58.7%), Gram staining in 16 isolates (11.6%), oxidase and catalase activity determination in 5 isolates (3.6%) and growth requirement determination in 2 isolates (1.5%). Eighteen isolates (10.2%) remained unidentifiable by 16S rDNA sequence analysis but were probably prototype isolates of new species. These isolates originated mainly from environmental sources (P = 0.07). The 16S rDNA approach failed to identify Enterobacter and Pantoea isolates to the species level (P = 0.04; odds ratio = 0.32 [95% confidence interval, 0.10 to 1.14]). Elsewhere, the usefulness of 16S rDNA sequencing was compromised by the presence of 16S rDNA sequences with >1% undetermined positions in the databases. Unlike phenotypic identification, which can be modified by the variability of expression of characters, 16S rDNA sequencing provides unambiguous data even for rare isolates, which are reproducible in and between laboratories. The increase in accurate new 16S rDNA sequences and the development of alternative genes for molecular identification of certain taxa should further improve the usefulness of molecular identification of bacteria.
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            Advancing our understanding of the human microbiome using QIIME.

            High-throughput DNA sequencing technologies, coupled with advanced bioinformatics tools, have enabled rapid advances in microbial ecology and our understanding of the human microbiome. QIIME (Quantitative Insights Into Microbial Ecology) is an open-source bioinformatics software package designed for microbial community analysis based on DNA sequence data, which provides a single analysis framework for analysis of raw sequence data through publication-quality statistical analyses and interactive visualizations. In this chapter, we demonstrate the use of the QIIME pipeline to analyze microbial communities obtained from several sites on the bodies of transgenic and wild-type mice, as assessed using 16S rRNA gene sequences generated on the Illumina MiSeq platform. We present our recommended pipeline for performing microbial community analysis and provide guidelines for making critical choices in the process. We present examples of some of the types of analyses that are enabled by QIIME and discuss how other tools, such as phyloseq and R, can be applied to expand upon these analyses. © 2013 Elsevier Inc. All rights reserved.
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              The role of the bacterial microbiome in lung disease.

              Novel culture-independent techniques have recently demonstrated that the lower respiratory tract, historically considered sterile in health, contains diverse communities of microbes: the lung microbiome. Increasing evidence supports the concept that a distinct microbiota of the lower respiratory tract is present both in health and in various respiratory diseases, although the biological and clinical significance of these findings remains undetermined. In this article, the authors review and synthesize published reports of the lung microbiota of healthy and diseased subjects, discuss trends of microbial diversity and constitution across disease states, and look to the extrapulmonary microbiome for hypotheses and future directions for study.
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                Author and article information

                Contributors
                stangedal@gmail.com
                marianne.aanerud@med.uib.no
                rune.gronseth@k2.uib.no
                christine.drengenes@helse-bergen.no
                harald.wiker@uib.no
                per.bakke@uib.no
                tomas.eagan@uib.no
                Journal
                Respir Res
                Respir. Res
                Respiratory Research
                BioMed Central (London )
                1465-9921
                1465-993X
                29 August 2017
                29 August 2017
                2017
                : 18
                : 164
                Affiliations
                [1 ]ISNI 0000 0000 9753 1393, GRID grid.412008.f, Department of Thoracic Medicine, , Haukeland University Hospital, ; Bergen, Norway
                [2 ]ISNI 0000 0004 1936 7443, GRID grid.7914.b, Department of Clinical Science, Faculty of Medicine and Dentistry, , University of Bergen, ; Bergen, Norway
                [3 ]ISNI 0000 0000 9753 1393, GRID grid.412008.f, Department of Microbiology, , Haukeland University Hospital, ; Bergen, Norway
                Author information
                http://orcid.org/0000-0001-8060-5306
                Article
                645
                10.1186/s12931-017-0645-3
                5576328
                28851370
                aa4a0a94-20d9-4387-a5f5-9122a9f528b1
                © The Author(s). 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 16 January 2017
                : 20 August 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100006475, Bergens Forskningsstiftelse;
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Respiratory medicine
                copd,sputum,microbiota,high-throughput sequencing
                Respiratory medicine
                copd, sputum, microbiota, high-throughput sequencing

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