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      Disease-associated gut microbiome and metabolome changes in patients with chronic obstructive pulmonary disease

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          Abstract

          Chronic obstructive pulmonary disease (COPD) is the third commonest cause of death globally, and manifests as a progressive inflammatory lung disease with no curative treatment. The lung microbiome contributes to COPD progression, but the function of the gut microbiome remains unclear. Here we examine the faecal microbiome and metabolome of COPD patients and healthy controls, finding 146 bacterial species differing between the two groups. Several species, including Streptococcus sp000187445, Streptococcus vestibularis and multiple members of the family Lachnospiraceae, also correlate with reduced lung function. Untargeted metabolomics identifies a COPD signature comprising 46% lipid, 20% xenobiotic and 20% amino acid related metabolites. Furthermore, we describe a disease-associated network connecting Streptococcus parasanguinis_B with COPD-associated metabolites, including N-acetylglutamate and its analogue N-carbamoylglutamate. While correlative, our results suggest that the faecal microbiome and metabolome of COPD patients are distinct from those of healthy individuals, and may thus aid in the search for biomarkers for COPD.

          Abstract

          Chronic obstructive pulmonary disease (COPD) is a progressing disease, with lung but not gut microbiota implicated in its etiology. Here the authors compare the stool from patients with COPD and healthy controls to find specific gut bacteria and metabolites associated with active disease, thereby hinting at a potential role for the gut microbiome in COPD.

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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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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              Fast and accurate short read alignment with Burrows–Wheeler transform

              Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Contributors
                philip.hansbro@uts.edu.au
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 November 2020
                18 November 2020
                2020
                : 11
                Affiliations
                [1 ]GRID grid.1003.2, ISNI 0000 0000 9320 7537, Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, , The University of Queensland, ; Brisbane, QLD Australia
                [2 ]GRID grid.266842.c, ISNI 0000 0000 8831 109X, Priority Research Centre for Healthy Lungs, Hunter Medical Research Institute, and The University of Newcastle, ; Newcastle, NSW Australia
                [3 ]Thoracic Research Centre, Faculty of Medicine, The University of Queensland, and Department of Thoracic Medicine, The Prince Charles Hospital, Brisbane, QLD Australia
                [4 ]Centre for Inflammation, Centenary Institute & University of Technology Sydney, School of Life Sciences, Faculty of Science, Sydney, NSW Australia
                Article
                19701
                10.1038/s41467-020-19701-0
                7676259
                33208745
                2f057a96-84d0-4692-8682-3b976392d87c
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                Funding
                Funded by: Prince Charles Hospital Foundation Innovations Grants Prince(INN2019-24). Charles Hospital Foundation (RF2017-05)
                Funded by: Prince Charles Hospital Foundation Innovations Grants to IAY (INN2018-30)
                Funded by: FundRef https://doi.org/10.13039/501100000925, Department of Health | National Health and Medical Research Council (NHMRC);
                Award ID: 1059238
                Award ID: 1059238
                Award ID: 1079187
                Award ID: 1175134
                Award Recipient :
                Funded by: Felicity and Michael Thomson Rainbow foundation
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                antimicrobial responses,chronic inflammation,microbiome,allergy
                Uncategorized
                antimicrobial responses, chronic inflammation, microbiome, allergy

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