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      Analysis of gut microbiota in patients with Williams–Beuren Syndrome reveals dysbiosis linked to clinical manifestations

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          Abstract

          Williams–Beuren syndrome (WBS) is a multisystem genetic disease caused by the deletion of a region of 1.5–1.8 Mb on chromosome 7q11.23. The elastin gene seems to account for several comorbidities and distinct clinical features such including cardiovascular disease, connective tissue abnormalities, growth retardation, and gastrointestinal (GI) symptoms. Increasing evidence points to alterations in gut microbiota composition as a primary or secondary cause of some GI or extra-intestinal characteristics. In this study, we performed the first exploratory analysis of gut microbiota in WBS patients compared to healthy subjects (CTRLs) using 16S rRNA amplicon sequencing, by investigating the gut dysbiosis in relation to diseases and comorbidities. We found that patients with WBS have significant dysbiosis compared to age-matched CTRLs, characterized by an increase in proinflammatory bacteria such as Pseudomonas, Gluconacetobacter and Eggerthella, and a reduction of anti-inflammatory bacteria including Akkermansia and Bifidobacterium. Microbial biomarkers associated with weight gain, GI symptoms and hypertension were identified. Gut microbiota profiling could represent a new tool that characterise intestinal dysbiosis to complement the clinical management of these patients. In particular, the administration of microbial-based treatments, alongside traditional therapies, could help in reducing or preventing the burden of these symptoms and improve the quality of life of these patients.

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          DADA2: High resolution sample inference from Illumina amplicon data

          We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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            Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2

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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
                federica.delchierico@opbg.net
                lorenza.putignani@opbg.net
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                16 June 2023
                16 June 2023
                2023
                : 13
                : 9797
                Affiliations
                [1 ]GRID grid.414125.7, ISNI 0000 0001 0727 6809, Immunology, Rheumatology and Infectious Diseases Research Area, , Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, ; Rome, Italy
                [2 ]GRID grid.414125.7, ISNI 0000 0001 0727 6809, Genetics and Rare Diseases Research Division and Medical Genetics Department, , Bambino Gesù Children’s Hospital, IRCCS, ; Rome, Italy
                [3 ]GRID grid.414125.7, ISNI 0000 0001 0727 6809, Translational Cytogenomics Research Unit, Bambino Gesù Children’s Hospital, IRCCS, ; Rome, Italy
                [4 ]GRID grid.414125.7, ISNI 0000 0001 0727 6809, Research Unit of Diagnostical and Management Innovations, , Bambino Gesù Children’s Hospital, IRCCS, ; Rome, Italy
                [5 ]GRID grid.26790.3a, ISNI 0000 0004 1936 8606, Crohn’s and Colitis Center, Division of Digestive Health and Liver Diseases, Department of Medicine, , University of Miami, Miller School of Medicine, ; Miami, FL USA
                [6 ]GRID grid.414125.7, ISNI 0000 0001 0727 6809, Scientific Directorate, , Bambino Gesù Children’s Hospital, IRCCS, ; Rome, Italy
                [7 ]GRID grid.414125.7, ISNI 0000 0001 0727 6809, Unit of Microbiology and Diagnostic Immunology, Unit of Microbiomics and Immunology, Rheumatology and Infectious Diseases Research Area, , Unit of Human Microbiome, Bambino Gesù Children’s Hospital, IRCCS, ; Rome, Italy
                Article
                36704
                10.1038/s41598-023-36704-1
                10275996
                37328513
                99e8ef98-046c-4752-86aa-65b0f28cb482
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 February 2023
                : 8 June 2023
                Funding
                Funded by: Italian Ministry of Health
                Funded by: Associazione Italiana Sindrome di Williams
                Categories
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                © Springer Nature Limited 2023

                Uncategorized
                genetics,microbiology,diseases,gastroenterology,medical research,molecular medicine
                Uncategorized
                genetics, microbiology, diseases, gastroenterology, medical research, molecular medicine

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