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      Genomic characterization of the uncultured Bacteroidales family S24-7 inhabiting the guts of homeothermic animals

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          Abstract

          Background

          Our view of host-associated microbiota remains incomplete due to the presence of as yet uncultured constituents. The Bacteroidales family S24-7 is a prominent example of one of these groups. Marker gene surveys indicate that members of this family are highly localized to the gastrointestinal tracts of homeothermic animals and are increasingly being recognized as a numerically predominant member of the gut microbiota; however, little is known about the nature of their interactions with the host.

          Results

          Here, we provide the first whole genome exploration of this family, for which we propose the name “ Candidatus Homeothermaceae,” using 30 population genomes extracted from fecal samples of four different animal hosts: human, mouse, koala, and guinea pig. We infer the core metabolism of “ Ca. Homeothermaceae” to be that of fermentative or nanaerobic bacteria, resembling that of related Bacteroidales families. In addition, we describe three trophic guilds within the family, plant glycan (hemicellulose and pectin), host glycan, and α-glucan, each broadly defined by increased abundance of enzymes involved in the degradation of particular carbohydrates.

          Conclusions

          Ca. Homeothermaceae” representatives constitute a substantial component of the murine gut microbiota, as well as being present within the human gut, and this study provides important first insights into the nature of their residency. The presence of trophic guilds within the family indicates the potential for niche partitioning and specific roles for each guild in gut health and dysbiosis.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s40168-016-0181-2) contains supplementary material, which is available to authorized users.

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          Most cited references64

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          Dietary Fiber-Induced Improvement in Glucose Metabolism Is Associated with Increased Abundance of Prevotella.

          The gut microbiota plays an important role in human health by interacting with host diet, but there is substantial inter-individual variation in the response to diet. Here we compared the gut microbiota composition of healthy subjects who exhibited improved glucose metabolism following 3-day consumption of barley kernel-based bread (BKB) with those who responded least to this dietary intervention. The Prevotella/Bacteroides ratio was higher in responders than non-responders after BKB. Metagenomic analysis showed that the gut microbiota of responders was enriched in Prevotella copri and had increased potential to ferment complex polysaccharides after BKB. Finally, germ-free mice transplanted with microbiota from responder human donors exhibited improved glucose metabolism and increased abundance of Prevotella and liver glycogen content compared with germ-free mice that received non-responder microbiota. Our findings indicate that Prevotella plays a role in the BKB-induced improvement in glucose metabolism observed in certain individuals, potentially by promoting increased glycogen storage.
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            Immunoglobulin A coating identifies colitogenic bacteria in inflammatory bowel disease.

            Specific members of the intestinal microbiota dramatically affect inflammatory bowel disease (IBD) in mice. In humans, however, identifying bacteria that preferentially affect disease susceptibility and severity remains a major challenge. Here, we used flow-cytometry-based bacterial cell sorting and 16S sequencing to characterize taxa-specific coating of the intestinal microbiota with immunoglobulin A (IgA-SEQ) and show that high IgA coating uniquely identifies colitogenic intestinal bacteria in a mouse model of microbiota-driven colitis. We then used IgA-SEQ and extensive anaerobic culturing of fecal bacteria from IBD patients to create personalized disease-associated gut microbiota culture collections with predefined levels of IgA coating. Using these collections, we found that intestinal bacteria selected on the basis of high coating with IgA conferred dramatic susceptibility to colitis in germ-free mice. Thus, our studies suggest that IgA coating identifies inflammatory commensals that preferentially drive intestinal disease. Targeted elimination of such bacteria may reduce, reverse, or even prevent disease development. Copyright © 2014 Elsevier Inc. All rights reserved.
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              A new generation of homology search tools based on probabilistic inference.

              Many theoretical advances have been made in applying probabilistic inference methods to improve the power of sequence homology searches, yet the BLAST suite of programs is still the workhorse for most of the field. The main reason for this is practical: BLAST's programs are about 100-fold faster than the fastest competing implementations of probabilistic inference methods. I describe recent work on the HMMER software suite for protein sequence analysis, which implements probabilistic inference using profile hidden Markov models. Our aim in HMMER3 is to achieve BLAST's speed while further improving the power of probabilistic inference based methods. HMMER3 implements a new probabilistic model of local sequence alignment and a new heuristic acceleration algorithm. Combined with efficient vector-parallel implementations on modern processors, these improvements synergize. HMMER3 uses more powerful log-odds likelihood scores (scores summed over alignment uncertainty, rather than scoring a single optimal alignment); it calculates accurate expectation values (E-values) for those scores without simulation using a generalization of Karlin/Altschul theory; it computes posterior distributions over the ensemble of possible alignments and returns posterior probabilities (confidences) in each aligned residue; and it does all this at an overall speed comparable to BLAST. The HMMER project aims to usher in a new generation of more powerful homology search tools based on probabilistic inference methods.
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                Author and article information

                Contributors
                p.hugenholtz@uq.edu.au
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                7 July 2016
                7 July 2016
                2016
                : 4
                : 36
                Affiliations
                [ ]Australian Centre for Ecogenomics, School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Australia
                [ ]Priority Research Centre for Healthy Lungs, The University of Newcastle and Hunter Medical Research Institute, Newcastle, Australia
                [ ]QFAB Bioinformatics, The University of Queensland, Brisbane, Australia
                [ ]Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
                [ ]Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia
                [ ]Microbial Biology and Metagenomics, The University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Australia
                Article
                181
                10.1186/s40168-016-0181-2
                4936053
                27388460
                f785e4e6-a95d-4aa3-885c-2e6da7d47941
                © The Author(s). 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 31 March 2016
                : 23 June 2016
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: Program Grant APP1071822
                Funded by: FundRef http://dx.doi.org/10.13039/501100000925, National Health and Medical Research Council;
                Award ID: Project Grant APP1059239
                Funded by: Lung Foundation Australia (AU)
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                gut microbiome,s24-7,homeothermaceae,population genomes,metagenomics,comparative genomics

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