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      Defining Dysbiosis for a Cluster of Chronic Diseases

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      Scientific Reports
      Springer Science and Business Media LLC

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

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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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              QIIME allows analysis of high-throughput community sequencing data.

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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Scientific Reports
                Sci Rep
                Springer Science and Business Media LLC
                2045-2322
                December 2019
                September 09 2019
                December 2019
                : 9
                : 1
                Article
                10.1038/s41598-019-49452-y
                336d699a-df02-482a-b949-4a5698323113
                © 2019

                https://creativecommons.org/licenses/by/4.0

                https://creativecommons.org/licenses/by/4.0

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