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      Comparison of Microbiomes from Different Niches of Upper and Lower Airways in Children and Adolescents with Cystic Fibrosis

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          Abstract

          Changes in the airway microbiome may be important in the pathophysiology of chronic lung disease in patients with cystic fibrosis. However, little is known about the microbiome in early cystic fibrosis lung disease and the relationship between the microbiomes from different niches in the upper and lower airways. Therefore, in this cross-sectional study, we examined the relationship between the microbiome in the upper (nose and throat) and lower (sputum) airways from children with cystic fibrosis using next generation sequencing. Our results demonstrate a significant difference in both α and β-diversity between the nose and the two other sampling sites. The nasal microbiome was characterized by a polymicrobial community while the throat and sputum communities were less diverse and dominated by a few operational taxonomic units. Moreover, sputum and throat microbiomes were closely related especially in patients with clinically stable lung disease. There was a high inter-individual variability in sputum samples primarily due to a decrease in evenness linked to increased abundance of potential respiratory pathogens such as Pseudomonas aeruginosa. Patients with chronic Pseudomonas aeruginosa infection exhibited a less diverse sputum microbiome. A high concordance was found between pediatric and adult sputum microbiomes except that Burkholderia was only observed in the adult cohort. These results indicate that an adult-like lower airways microbiome is established early in life and that throat swabs may be a good surrogate in clinically stable children with cystic fibrosis without chronic Pseudomonas aeruginosa infection in whom sputum sampling is often not feasible.

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          Lung infections associated with cystic fibrosis.

          While originally characterized as a collection of related syndromes, cystic fibrosis (CF) is now recognized as a single disease whose diverse symptoms stem from the wide tissue distribution of the gene product that is defective in CF, the ion channel and regulator, cystic fibrosis transmembrane conductance regulator (CFTR). Defective CFTR protein impacts the function of the pancreas and alters the consistency of mucosal secretions. The latter of these effects probably plays an important role in the defective resistance of CF patients to many pathogens. As the modalities of CF research have changed over the decades from empirical histological studies to include biophysical measurements of CFTR function, the clinical management of this disease has similarly evolved to effectively address the ever-changing spectrum of CF-related infectious diseases. These factors have led to the successful management of many CF-related infections with the notable exception of chronic lung infection with the gram-negative bacterium Pseudomonas aeruginosa. The virulence of P. aeruginosa stems from multiple bacterial attributes, including antibiotic resistance, the ability to utilize quorum-sensing signals to form biofilms, the destructive potential of a multitude of its microbial toxins, and the ability to acquire a mucoid phenotype, which renders this microbe resistant to both the innate and acquired immunologic defenses of the host.
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            Dirichlet Multinomial Mixtures: Generative Models for Microbial Metagenomics

            We introduce Dirichlet multinomial mixtures (DMM) for the probabilistic modelling of microbial metagenomics data. This data can be represented as a frequency matrix giving the number of times each taxa is observed in each sample. The samples have different size, and the matrix is sparse, as communities are diverse and skewed to rare taxa. Most methods used previously to classify or cluster samples have ignored these features. We describe each community by a vector of taxa probabilities. These vectors are generated from one of a finite number of Dirichlet mixture components each with different hyperparameters. Observed samples are generated through multinomial sampling. The mixture components cluster communities into distinct ‘metacommunities’, and, hence, determine envirotypes or enterotypes, groups of communities with a similar composition. The model can also deduce the impact of a treatment and be used for classification. We wrote software for the fitting of DMM models using the ‘evidence framework’ (http://code.google.com/p/microbedmm/). This includes the Laplace approximation of the model evidence. We applied the DMM model to human gut microbe genera frequencies from Obese and Lean twins. From the model evidence four clusters fit this data best. Two clusters were dominated by Bacteroides and were homogenous; two had a more variable community composition. We could not find a significant impact of body mass on community structure. However, Obese twins were more likely to derive from the high variance clusters. We propose that obesity is not associated with a distinct microbiota but increases the chance that an individual derives from a disturbed enterotype. This is an example of the ‘Anna Karenina principle (AKP)’ applied to microbial communities: disturbed states having many more configurations than undisturbed. We verify this by showing that in a study of inflammatory bowel disease (IBD) phenotypes, ileal Crohn's disease (ICD) is associated with a more variable community.
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              Evaluation of a new definition for chronic Pseudomonas aeruginosa infection in cystic fibrosis patients.

              Patients were defined each successive month as either 'chronic' when more than 50% of the preceding 12 months were PA culture positive, 'intermittent' when < or =50% of the preceding 12 months were PA culture positive, 'free of PA', with no growth of PA for the previous 12 months, having previously been PA culture positive, or 'never infected', when PA had never been cultured. Cross-sectional analysis of 146 children attending the Leeds Regional Cystic Fibrosis Centre was performed to assess relationship between the new definition and clinical scores and investigations. The response variable was regressed on age and sex and the residuals analysed using the Kruskal-Wallis test. The 'chronic' group (18% of patients) had significantly worse Shwachman-Kulczycki (SK) and Northern chest X-ray scores, and % predicted FEV(1) values than the 'free' (28%) or 'never' (20%) categories (P<0.004). The 'intermittent' group (34%) had a significantly higher SK score than the 'chronic' group (P<0.0001), and a significantly lower % predicted FEV(1) value than the 'free' or 'never' groups (P<0.0003). 'Chronic' patients were significantly associated with a positive, and 'never' patients with a negative, PA antibody result (P<0.001). The validity and importance of identifying these four subgroups is demonstrated. Previous definitions may over-estimate the prevalence of chronic infection.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                28 January 2015
                2015
                : 10
                : 1
                : e0116029
                Affiliations
                [1 ]Dept. of Infectious Diseases—Medical Microbiology and Hygiene, University Hospital Heidelberg, Heidelberg, Germany
                [2 ]Department of Translational Pulmonology, University Hospital Heidelberg, Heidelberg, Germany
                [3 ]Div. of Pediatric Pulmonology & Allergology and Cystic Fibrosis Center, Dept. of Pediatrics, University Hospital Heidelberg, Heidelberg, Germany
                [4 ]Translational Lung Research Center Heidelberg (TLRC), Member of the German Center for Lung Research (DZL), Heidelberg, Germany
                [5 ]Institute for Medical Informatics and Biometry, Technical University Dresden, Dresden, Germany
                [6 ]CF & Airways Microbiology Group, Queen’s University Belfast, Belfast, United Kingdom
                [7 ]School of Pharmacy, Queen’s University Belfast, Belfast, United Kingdom
                [8 ]Centre for Infection & Immunity, School of Medicine, Dentistry & Biomedical Science, Queen’s University Belfast, Belfast, United Kingdom
                Ghent University, BELGIUM
                Author notes

                Competing Interests: This work was partly supported by a grant from Gilead. This does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials. Gilead had no impact on design, realization and interpretation of this study or on manuscript preparation. Regarding the conflict of interest policy the authors also indicate that coauthors Michael Tunney and Lars Kaderali are members of the PLOS One Editorial Board. Stuart Elborn acted as consultant for Novartis and Gilead.

                Conceived and designed the experiments: SB MT SE MM AD. Performed the experiments: SB SG MW JP MS DC LK GE. Analyzed the data: SB SG MW MS DC LK GE MT SE MM AD. Contributed reagents/materials/analysis tools: DC LK GE MT SE. Wrote the paper: SB SG MW DC LK GE MT SE MM AD.

                Article
                PONE-D-14-43347
                10.1371/journal.pone.0116029
                4309611
                25629612
                feba47ce-5e82-49bf-afc0-8bd32249b5a3
                Copyright @ 2015

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

                History
                : 26 September 2014
                : 1 December 2014
                Page count
                Figures: 7, Tables: 2, Pages: 19
                Funding
                This study was supported in part by a grant to MAM, JSE, MT and GE from the European Commission (Seventh Framework Programme Project No. 603038, CFMatters). This study was further supported by a grant from Gilead to AD and MAM. This does not alter our adherence to PLOS ONE policies on sharing data and materials. Gilead had no impact on design, realization and interpretation of this study or on manuscript preparation.
                Categories
                Research Article
                Custom metadata
                One FASTA file for the microbiome data is available from the MG-RAST database ( http://metagenomics.anl.gov/) with accession number 4603051.3.

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                Uncategorized

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