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      The fungal airway microbiome in cystic fibrosis and non-cystic fibrosis bronchiectasis

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          Highlights

          • The prevalence of fungal disease is increasing in CF and non-CF bronchiectasis.

          • Effective management of fungal disease requires an understanding of the mycobiome.

          • Culture methods alone are inadequate for the accurate diagnosis of fungal disease.

          • Our study provides a framework to characterize fungal airway disease using NGS.

          • NGS can improve detection and clinical management of fungal infections.

          Abstract

          Background

          The prevalence of fungal disease in cystic fibrosis (CF) and non-CF bronchiectasis is increasing and the clinical spectrum is widening. Poor sensitivity and a lack of standard diagnostic criteria renders interpretation of culture results challenging. In order to develop effective management strategies, a more accurate and comprehensive understanding of the airways fungal microbiome is required. The study aimed to use DNA sequences from sputum to assess the load and diversity of fungi in adults with CF and non-CF bronchiectasis.

          Methods

          Next generation sequencing of the ITS2 region was used to examine fungal community composition (n = 176) by disease and underlying clinical subgroups including allergic bronchopulmonary aspergillosis, chronic necrotizing pulmonary aspergillosis, non-tuberculous mycobacteria, and fungal bronchitis. Patients with no known active fungal disease were included as disease controls.

          Results

          ITS2 sequencing greatly increased the detection of fungi from sputum. In patients with CF fungal diversity was lower, while burden was higher than those with non-CF bronchiectasis. The most common operational taxonomic unit (OTU) in patients with CF was Candida parapsilosis (20.4%), whereas in non-CF bronchiectasis sputum Candida albicans (21.8%) was most common. CF patients with overt fungal bronchitis were dominated by Aspergillus spp. , Exophiala spp., Candida parapsilosis or Scedosporium spp.

          Conclusion

          This study provides a framework to more accurately characterize the extended spectrum of fungal airways diseases in adult suppurative lung diseases.

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          Most cited references36

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data

            Background The analysis of microbial communities through DNA sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data. Results Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research. Conclusions The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
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              Associations between species and groups of sites: indices and statistical inference

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                Author and article information

                Contributors
                Journal
                J Cyst Fibros
                J Cyst Fibros
                Journal of Cystic Fibrosis
                Elsevier
                1569-1993
                1873-5010
                1 March 2021
                March 2021
                : 20
                : 2
                : 295-302
                Affiliations
                [a ]Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London SW3 6NP, UK
                [b ]National Heart and Lung Institute, Imperial College, London SW3 6LY, UK
                Author notes
                [* ]Corresponding author. w.cookson@ 123456imperial.ac.uk
                [1]

                Joint First Authors.

                [2]

                Joint Senior Authors.

                Article
                S1569-1993(20)30163-6
                10.1016/j.jcf.2020.05.013
                8048771
                32540174
                c68ab7f5-5ced-42fc-b18b-5ad8ab0f1fdd
                © 2020 The Authors. Published by Elsevier B.V. on behalf of European Cystic Fibrosis Society.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 20 February 2020
                : 28 May 2020
                : 28 May 2020
                Categories
                Article

                Genetics
                mycobiome,fungal airway disease,filamentous fungi,chronic airway disease
                Genetics
                mycobiome, fungal airway disease, filamentous fungi, chronic airway disease

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