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      Anti-Acid Drug Treatment Induces Changes in the Gut Microbiome Composition of Hemodialysis Patients

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          Abstract

          Anti-acid drugs, proton pump inhibitor (PPI) and histamine-2 blocker (H 2-blocker), are commonly prescribed to treat gastrointestinal disorders. These anti-acid drugs alter gut microbiota in the general population, but their effects are not known in hemodialysis patients. Hence, we investigated the microbiota composition in hemodialysis patients treated with PPIs or H 2-blocker. Among 193 hemodialysis patients, we identified 32 H 2-blocker users, 23 PPI users, and 138 no anti-acid drug subjects. Fecal samples were obtained to analyze the gut microbiome using 16S RNA amplicon sequencing. Differences in the microbial composition of the H 2-blocker users, PPI users, and controls were assessed using linear discriminant analysis effect size and the random forest algorithm. The species richness or evenness (α-diversity) was similar among the three groups, whereas the inter-individual diversity (β-diversity) was different between H 2-blocker users, PPI users, and controls. Hemodialysis patients treated with H 2-blocker and PPIs had a higher microbial dysbiosis index than the controls, with a significant increase in the genera Provetella 2, Phascolarctobacterium, Christensenellaceae R-7 group, and Eubacterium oxidoreducens group in H 2-blocker users, and Streptococcus and Veillonella in PPI users. In addition, compared to the H 2-blocker users, there was a significant enrichment of the genera Streptococcus in PPI users, as confirmed by the random forest analysis and the confounder-adjusted regression model. In conclusion, PPIs significantly changed the gut microbiota composition in hemodialysis patients compared to H 2-blocker users or controls. Importantly, the Streptococcus genus was significantly increased in PPI treatment. These findings caution against the overuse of PPIs.

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          Most cited references 48

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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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            Metagenomic biomarker discovery and explanation

            This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at http://huttenhower.sph.harvard.edu/lefse/.
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              Random forest: a classification and regression tool for compound classification and QSAR modeling.

              A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.
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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Microorganisms
                Microorganisms
                microorganisms
                Microorganisms
                MDPI
                2076-2607
                30 January 2021
                February 2021
                : 9
                : 2
                Affiliations
                [1 ]Department of Family Medicine, Kaohsiung Medical University Hospital, Kaohsiung 807, Taiwan; 960254@ 123456kmuh.org.tw (Y.-T.L.); kinkipag@ 123456gmail.com (Y.-S.C.)
                [2 ]Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
                [3 ]Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan; chiuyiwen@ 123456kmu.edu.tw (Y.-W.C.); mechku@ 123456kmu.edu.tw (M.-C.K.)
                [4 ]Division of Nephrology, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, and School of Medicine, Tzu Chi University, Hualien 970, Taiwan; water_h2o_6@ 123456hotmail.com (T.-Y.L.); szuchun.hung@ 123456gmail.com (S.-C.H.)
                [5 ]Department of Internal Medicine, National Taiwan University College of Medicine, Taipei 106, Taiwan; poyu.liu@ 123456gmail.com
                [6 ]Division of Nephrology, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan
                [7 ]Department of Microbiology and Immunology, Kaohsiung Medical University, Kaohsiung 807, Taiwan; wchung@ 123456kmu.edu.tw
                [8 ]Faculty of Renal Care, College of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
                [9 ]Institute of Biomedical Informatics, Medical College, National Yang-Ming University, Taipei 112, Taiwan; dr.wu.taiwan@ 123456gmail.com
                [10 ]Division of Translational Research, Department of Medical Research Taipei Veterans General Hospital, Taipei 112, Taiwan
                [11 ]Department of Public Health, China Medical University, Taichung 406, Taiwan
                [12 ]National Institute of Cancer Research, National Health Research Institutes, Miaoli 350, Taiwan
                Author notes
                [* ]Correspondence: 970392@ 123456kmuh.org.tw ; Tel.: +886-7-3121101
                Article
                microorganisms-09-00286
                10.3390/microorganisms9020286
                7910989
                33573326
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

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