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      Duodenal Metatranscriptomics to Define Human and Microbial Functional Alterations Associated with Severe Obesity: A Pilot Study

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          Abstract

          Obesity is a multifactorial disorder, and the gut microbiome has been suggested to contribute to its onset. In order to better clarify the role of the microbiome in obesity, we evaluated the metatranscriptome in duodenal biopsies from a cohort of 23 adult severely obese and lean control subjects using next generation sequencing. Our aim was to provide a general picture of the duodenal metatranscriptome associated with severe obesity. We found altered expressions of human and microbial genes in the obese compared to lean subjects, with most of the gene alterations being present in the carbohydrate, protein, and lipid metabolic pathways. Defects were also present in several human genes involved in epithelial intestinal cells differentiation and function, as well as in the immunity/inflammation pathways. Moreover, the microbial taxa abundance inferred by our transcriptomic data differed in part from the data that we previously evaluated by 16S rRNA in 13/23 individuals of our cohort, particularly concerning the Firmicutes and Proteobacteria phyla abundances. In conclusion, our pilot study provides the first taxonomic and functional characterization of duodenal microbiota in severely obese subjects and lean controls. Our findings suggest that duodenal microbiome and human genes both play a role in deregulating metabolic pathways, likely affecting energy metabolism and thus contributing to the obese phenotype.

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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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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              Fast gapped-read alignment with Bowtie 2.

              As the rate of sequencing increases, greater throughput is demanded from read aligners. The full-text minute index is often used to make alignment very fast and memory-efficient, but the approach is ill-suited to finding longer, gapped alignments. Bowtie 2 combines the strengths of the full-text minute index with the flexibility and speed of hardware-accelerated dynamic programming algorithms to achieve a combination of high speed, sensitivity and accuracy.
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                Author and article information

                Journal
                Microorganisms
                Microorganisms
                microorganisms
                Microorganisms
                MDPI
                2076-2607
                17 November 2020
                November 2020
                : 8
                : 11
                : 1811
                Affiliations
                [1 ]Institute for High Performance Computing and Networking (ICAR), National Research Council (CNR), 80131 Naples, Italy; ilaria.granata@ 123456icar.cnr.it
                [2 ]Department of Molecular Medicine and Medical Biotechnologies, University of Naples Federico II, 80131 Naples, Italy; carmela.nardelli@ 123456unina.it
                [3 ]CEINGE Biotecnologie Avanzate S.C.a R.L., 80131 Naples, Italy; dargenio@ 123456ceinge.unina.it
                [4 ]Task Force on Microbiome Studies, University of Naples Federico II, 80131 Naples, Italy
                [5 ]Department of Human Sciences and Quality of Life Promotion, San Raffaele Open University, 00166 Rome, Italy
                [6 ]Department of Medicine and Surgery, University of Salerno, 84084 Salerno, Italy; salvytra@ 123456libero.it (S.T.); vpilone@ 123456unisa.it (V.P.)
                [7 ]Department of Clinical Medicine and Surgery, University of Naples Federico II, 80131 Naples, Italy; debora.compare@ 123456unina.it (D.C.); gerardoantoniopio.nardone@ 123456unina.it (G.N.)
                [8 ]Department of Economics and Law, University of Cassino and Southern Lazio, 03043 Cassino, Italy; mario.guarracino@ 123456unicas.it
                Author notes
                [* ]Correspondence: sacchett@ 123456unina.it
                [†]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-9540-3405
                https://orcid.org/0000-0001-9273-3698
                https://orcid.org/0000-0003-2870-8134
                Article
                microorganisms-08-01811
                10.3390/microorganisms8111811
                7698607
                33213098
                f0894ae2-a631-4f30-9295-d9a96df96dac
                © 2020 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/).

                History
                : 22 October 2020
                : 16 November 2020
                Categories
                Article

                metatranscriptomics,microbial-host duodenal transcriptome,human obesity,metabolism

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