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      Precision microbiome restoration of bile acid-mediated resistance to Clostridium difficile

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          The gastrointestinal tracts of mammals are colonized by hundreds of microbial species that contribute to health, including colonization resistance against intestinal pathogens 1 . Many antibiotics destroy intestinal microbial communities and increase susceptibility to intestinal pathogens 2 . Among these, Clostridium difficile, a major cause of antibiotic-induced diarrhea, greatly increases morbidity and mortality in hospitalized patients 3 . Which intestinal bacteria provide resistance to C. difficile infection and their in vivo inhibitory mechanisms remain unclear. By treating mice with different antibiotics that result in distinct microbiota changes and lead to varied susceptibility to C. difficile, we correlated loss of specific bacterial taxa with development of infection. Mathematical modeling augmented by microbiota analyses of hospitalized patients identified resistance-associated bacteria common to mice and humans. Using these platforms, we determined that Clostridium scindens, a bile acid 7-dehydroxylating intestinal bacterium, is associated with resistance to C. difficile infection and, upon administration, enhances resistance to infection in a secondary bile acid-dependent fashion. Using a workflow involving mouse models, clinical studies, metagenomic analyses and mathematical modeling, we identified a probiotic candidate that corrects a clinically relevant microbiome deficiency. These findings have implications for rational design of targeted antimicrobials as well as microbiome-based diagnostics and therapeutics for individuals at risk for C. difficile infection.

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          Most cited references 43

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          QIIME allows analysis of high-throughput community sequencing data.

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            Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities.

            mothur aims to be a comprehensive software package that allows users to use a single piece of software to analyze community sequence data. It builds upon previous tools to provide a flexible and powerful software package for analyzing sequencing data. As a case study, we used mothur to trim, screen, and align sequences; calculate distances; assign sequences to operational taxonomic units; and describe the alpha and beta diversity of eight marine samples previously characterized by pyrosequencing of 16S rRNA gene fragments. This analysis of more than 222,000 sequences was completed in less than 2 h with a laptop computer.
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              Is Open Access

              UCHIME improves sensitivity and speed of chimera detection

              Motivation: Chimeric DNA sequences often form during polymerase chain reaction amplification, especially when sequencing single regions (e.g. 16S rRNA or fungal Internal Transcribed Spacer) to assess diversity or compare populations. Undetected chimeras may be misinterpreted as novel species, causing inflated estimates of diversity and spurious inferences of differences between populations. Detection and removal of chimeras is therefore of critical importance in such experiments. Results: We describe UCHIME, a new program that detects chimeric sequences with two or more segments. UCHIME either uses a database of chimera-free sequences or detects chimeras de novo by exploiting abundance data. UCHIME has better sensitivity than ChimeraSlayer (previously the most sensitive database method), especially with short, noisy sequences. In testing on artificial bacterial communities with known composition, UCHIME de novo sensitivity is shown to be comparable to Perseus. UCHIME is >100× faster than Perseus and >1000× faster than ChimeraSlayer. Contact: Availability: Source, binaries and data: Supplementary information: Supplementary data are available at Bioinformatics online.

                Author and article information

                [1 ]Infectious Diseases Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
                [2 ]Lucille Castori Center for Microbes, Inflammation and Cancer, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
                [3 ]Bone Marrow Transplant Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
                [4 ]Immunology, Sloan-Kettering Institute, New York, New York 10065, USA
                [5 ]Computational Biology Programs, Sloan-Kettering Institute, New York, New York 10065, USA
                [6 ]Genomics, Sloan-Kettering Institute, New York, New York 10065, USA
                [7 ]Molecular Microbiology Core Laboratories, Sloan-Kettering Institute, New York, New York 10065, USA
                [8 ]Donald B. and Catherine C. Marron Cancer Metabolism Center, Sloan-Kettering Institute, New York, New York 10065, USA
                [9 ]Department of Biology, University of Massachusetts Dartmouth, N. Dartmouth, MA 02747, USA
                27 February 2015
                22 October 2014
                8 January 2015
                08 July 2015
                : 517
                : 7533
                : 205-208
                25337874 4354891 10.1038/nature13828 NIHMS625849



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