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      Associations between host gene expression, the mucosal microbiome, and clinical outcome in the pelvic pouch of patients with inflammatory bowel disease

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          Abstract

          Background

          Pouchitis is common after ileal pouch-anal anastomosis (IPAA) surgery for ulcerative colitis (UC). Similar to inflammatory bowel disease (IBD), both host genetics and the microbiota are implicated in its pathogenesis. We use the IPAA model of IBD to associate mucosal host gene expression with mucosal microbiomes and clinical outcomes. We analyze host transcriptomic data and 16S rRNA gene sequencing data from paired biopsies from IPAA patients with UC and familial adenomatous polyposis. To achieve power for a genome-wide microbiome-transcriptome association study, we use principal component analysis for transcript and clade reduction, and identify significant co-variation between clades and transcripts.

          Results

          Host transcripts co-vary primarily with biopsy location and inflammation, while microbes co-vary primarily with antibiotic use. Transcript-microbe associations are surprisingly modest, but the most strongly microbially-associated host transcript pattern is enriched for complement cascade genes and for the interleukin-12 pathway. Activation of these host processes is inversely correlated with Sutterella, Akkermansia, Bifidobacteria, and Roseburia abundance, and positively correlated with Escherichia abundance.

          Conclusions

          This study quantifies the effects of inflammation, antibiotic use, and biopsy location upon the microbiome and host transcriptome during pouchitis. Understanding these effects is essential for basic biological insights as well as for well-designed and adequately-powered studies. Additionally, our study provides a method for profiling host-microbe interactions with appropriate statistical power using high-throughput sequencing, and suggests that cross-sectional changes in gut epithelial transcription are not a major component of the host-microbiome regulatory interface during pouchitis.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-015-0637-x) contains supplementary material, which is available to authorized users.

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          Most cited references 45

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          Singular value decomposition for genome-wide expression data processing and modeling.

          We describe the use of singular value decomposition in transforming genome-wide expression data from genes x arrays space to reduced diagonalized "eigengenes" x "eigenarrays" space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.
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            What is principal component analysis?

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              Host-derived nitrate boosts growth of E. coli in the inflamed gut.

              Changes in the microbial community structure are observed in individuals with intestinal inflammatory disorders. These changes are often characterized by a depletion of obligate anaerobic bacteria, whereas the relative abundance of facultative anaerobic Enterobacteriaceae increases. The mechanisms by which the host response shapes the microbial community structure, however, remain unknown. We show that nitrate generated as a by-product of the inflammatory response conferred a growth advantage to the commensal bacterium Escherichia coli in the large intestine of mice. Mice deficient in inducible nitric oxide synthase did not support the growth of E. coli by nitrate respiration, suggesting that the nitrate generated during inflammation was host-derived. Thus, the inflammatory host response selectively enhances the growth of commensal Enterobacteriaceae by generating electron acceptors for anaerobic respiration.
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                Author and article information

                Contributors
                xmorgan@hsph.harvard.edu
                btk2102@gmail.com
                levi.waldron@hunter.cuny.edu
                atyler@mtsinai.on.ca
                timothyltickle@gmail.com
                rmilgrom@mtsinai.on.ca
                jstempak@mtsinai.on.ca
                dgevers@broadinstitute.org
                xavier@molbio.mgh.harvard.edu
                msilverberg@mtsinai.on.ca
                chuttenh@hsph.harvard.edu
                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                8 April 2015
                8 April 2015
                2015
                : 16
                : 1
                Affiliations
                [ ]Department of Biostatistics, Harvard T. H. Chan School of Public Health, 655 Huntington Ave, Boston, MA 02115 USA
                [ ]The Broad Institute of MIT and Harvard, 415 Main St, Cambridge, MA 02142 USA
                [ ]Mount Sinai Hospital, Zane Cohen Centre for Digestive Diseases, University of Toronto, 600 University Ave, Toronto, ON M5G 1X5 Canada
                [ ]City University of New York School of Public Health, Hunter College, 2180 3rd Ave Rm 538, New York, NY 10035-4003 USA
                Article
                637
                10.1186/s13059-015-0637-x
                4414286
                25887922
                © Morgan et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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.

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                Research
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                © The Author(s) 2015

                Genetics

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