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      Defining Dysbiosis for a Cluster of Chronic Diseases

      research-article
      1 , 2 , 2 , 3 ,
      Scientific Reports
      Nature Publishing Group UK
      Metagenomics, Microbial ecology

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          Abstract

          The prevalence of many chronic diseases has increased over the last decades. It has been postulated that dysbiosis driven by environmental factors such as antibiotic use is shifting the microbiome in ways that increase inflammation and the onset of chronic disease. Dysbiosis can be defined through the loss or gain of bacteria that either promote health or disease, respectively. Here we use multiple independent datasets to determine the nature of dysbiosis for a cluster of chronic diseases that includes urinary stone disease (USD), obesity, diabetes, cardiovascular disease, and kidney disease, which often exist as co-morbidities. For all disease states, individuals exhibited a statistically significant association with antibiotics in the last year compared to healthy counterparts. There was also a statistically significant association between antibiotic use and gut microbiota composition. Furthermore, each disease state was associated with a loss of microbial diversity in the gut. Three genera, Bacteroides, Prevotella, and Ruminococcus, were the most common dysbiotic taxa in terms of being enriched or depleted in disease populations and was driven in part by the diversity of operational taxonomic units (OTUs) within these genera. Results of the cross-sectional analysis suggest that antibiotic-driven loss of microbial diversity may increase the risk for chronic disease. However, longitudinal studies are needed to confirm the causative effect of diversity loss for chronic disease risk.

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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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            Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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              QIIME allows analysis of high-throughput community sequencing data.

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                Author and article information

                Contributors
                millera25@ccf.org
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                9 September 2019
                9 September 2019
                2019
                : 9
                : 12918
                Affiliations
                [1 ]ISNI 0000 0001 0675 4725, GRID grid.239578.2, Lerner College of Medicine, , Cleveland Clinic, ; Cleveland, OH USA
                [2 ]ISNI 0000 0001 0675 4725, GRID grid.239578.2, Glickman Urological and Kidney Institute, , Cleveland Clinic, ; Cleveland, OH USA
                [3 ]ISNI 0000 0001 0675 4725, GRID grid.239578.2, Department of Inflammation and Immunity, Lerner Research Institute, , Cleveland Clinic, ; Cleveland, OH USA
                Author information
                http://orcid.org/0000-0001-9010-330X
                Article
                49452
                10.1038/s41598-019-49452-y
                6733864
                31501492
                336d699a-df02-482a-b949-4a5698323113
                © The Author(s) 2019

                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/.

                History
                : 20 May 2019
                : 21 August 2019
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                © The Author(s) 2019

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                metagenomics,microbial ecology
                Uncategorized
                metagenomics, microbial ecology

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