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      Gut Microbiome Profiles Are Associated With Type 2 Diabetes in Urban Africans

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          Abstract

          Gut dysbiosis has been associated with several disease outcomes including diabetes in human populations. Currently, there are no studies of the gut microbiome composition in relation to type 2 diabetes (T2D) in Africans. Here, we describe the profile of the gut microbiome in non-diabetic adults (controls) and investigate the association between gut microbiota and T2D in urban West Africans. Gut microbiota composition was determined in 291 Nigerians (98 cases, 193 controls) using fecal 16S V4 rRNA gene sequencing done on the Illumina MiSeq platform. Data analysis of operational taxonomic units (OTU) was conducted to describe microbiome composition and identify differences between T2D and controls. The most abundant phyla were Firmicutes, Actinobacteria, and Bacteroidetes. Clostridiaceae, and Peptostreptococcaceaea were significantly lower in cases than controls ( p < 0.001). Feature selection analysis identified a panel of 18 OTUs enriched in cases that included Desulfovibrio piger, Prevotella, Peptostreptococcus, and Eubacterium. A panel of 17 OTUs that was enriched in the controls included Collinsella, Ruminococcus lactaris, Anaerostipes, and Clostridium. OTUs with strain-level annotation showing the largest fold-change included Cellulosilyticum ruminicola (log 2FC = −3.1; p = 4.2 × 10 −5), Clostridium paraputrificum (log 2FC = −2.5; p = 0.005), and Clostridium butyricum (log 2FC = −1.76; p = 0.01), all lower in cases. These findings are notable because supplementation with Clostridium butyricum and Desulfovibrio piger has been shown to improve hyperglycemia and reduce insulin resistance in murine models. This first investigation of gut microbiome and diabetes in urban Africans shows that T2D is associated with compositional changes in gut microbiota highlighting the possibility of developing strategies to improve glucose control by modifying bacterial composition in the gut.

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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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              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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                Author and article information

                Contributors
                Journal
                Front Cell Infect Microbiol
                Front Cell Infect Microbiol
                Front. Cell. Infect. Microbiol.
                Frontiers in Cellular and Infection Microbiology
                Frontiers Media S.A.
                2235-2988
                25 February 2020
                2020
                : 10
                : 63
                Affiliations
                [1] 1Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health , Bethesda, MD, United States
                [2] 2Department of Epidemiology and Public Health and Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine , Baltimore, MD, United States
                [3] 3Center for Bioethics and Research , Ibadan, Nigeria
                [4] 4Institute of Human Virology, University of Maryland School of Medicine , Baltimore, MD, United States
                Author notes

                Edited by: Shai Bel, Bar-Ilan University, Israel

                Reviewed by: Junguk Hur, University of North Dakota, United States; Chenlu Zhang, UT Southwestern Medical Center, United States

                *Correspondence: Charles N. Rotimi rotimic@ 123456mail.nih.gov

                This article was submitted to Microbiome in Health and Disease, a section of the journal Frontiers in Cellular and Infection Microbiology

                †These authors have contributed equally to this work

                Article
                10.3389/fcimb.2020.00063
                7052266
                32158702
                026f85d0-1f14-4de8-bc59-d67bc4608f55
                Copyright © 2020 Doumatey, Adeyemo, Zhou, Lei, Adebamowo, Adebamowo and Rotimi.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 12 September 2019
                : 07 February 2020
                Page count
                Figures: 5, Tables: 5, Equations: 0, References: 58, Pages: 13, Words: 8435
                Categories
                Cellular and Infection Microbiology
                Original Research

                Infectious disease & Microbiology
                gut microbiome,type 2 diabetes,urban africans,16s v4 rrna sequencing,microbial composition

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