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      The Impact of Microbiome and Microbiota-Derived Sodium Butyrate on Drosophila Transcriptome and Metabolome Revealed by Multi-Omics Analysis

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          Abstract

          The host microbiome plays an important role in regulating physiology through microbiota-derived metabolites during host-microbiome interactions. However, molecular mechanism underly host-microbiome interactions remains to be explored. In this study, we used Drosophila as the model to investigate the influence of microbiome and microbiota-derived metabolite sodium butyrate on host transcriptome and metabolome. We established both a sterile Drosophila model and a conventional Drosophila model to demonstrate the role of sodium butyrate. Using multi-omics analysis, we found that microbiome and sodium butyrate could impact host gene expression patterns in both the sterile Drosophila model and the conventional Drosophila model. The analysis of gut microbial using 16S rRNA sequencing showed sodium butyrate treatment also influenced Drosophila bacterial structures. In addition, Drosophila metabolites identified by ultra-high performance liquid chromatography-MS/MS were shown to be affected by sodium butyrate treatment with lipids as the dominant changed components. Our integrative analysis of the transcriptome, the microbiome, and the metabolome data identified candidate transcripts that are coregulated by sodium butyrate. Taken together, our results reveal the impact of the microbiome and microbiota-derived sodium butyrate on host transcriptome and metabolome, and our work provides a better understanding of host-microbiome interactions at the molecular level with multi-omics data.

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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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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              Cutadapt removes adapter sequences from high-throughput sequencing reads

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Metabolites
                Metabolites
                metabolites
                Metabolites
                MDPI
                2218-1989
                06 May 2021
                May 2021
                : 11
                : 5
                : 298
                Affiliations
                [1 ]Guangdong Provincial Key Laboratory of Insect Developmental Biology and Applied Technology, Institute of Insect Science and Technology, School of Life Sciences, South China Normal University, Guangzhou 510631, China; 2019022488@ 123456m.scnu.edu.cn (F.Z.); liuxin136865812@ 123456163.com (X.L.); luoluo.wang@ 123456scnu.edu.cn (L.W.); huangj@ 123456scnu.edu.cn (J.H.)
                [2 ]State Key Laboratory of Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou 510275, China; liubd6@ 123456mail2.sysu.edu.cn (B.L.); liyan226@ 123456mail2.sysu.edu.cn (Y.L.)
                Author notes
                [†]

                These authors have contributed equally to this work.

                Author information
                https://orcid.org/0000-0003-3162-0109
                https://orcid.org/0000-0002-6797-9319
                https://orcid.org/0000-0002-3000-6441
                Article
                metabolites-11-00298
                10.3390/metabo11050298
                8148185
                34066348
                3b7ddb23-0be4-445d-9f3a-0bae58f31107
                © 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 ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 19 March 2021
                : 20 April 2021
                Categories
                Article

                drosophila,microbiome,transcriptome,metabolome
                drosophila, microbiome, transcriptome, metabolome

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