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      Intestinal Metaproteomics Reveals Host-Microbiota Interactions in Subjects at Risk for Type 1 Diabetes

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          Abstract

          OBJECTIVE

          Dysbiosis of the gut microbiota has been linked to disease pathogenesis in type 1 diabetes, yet the functional consequences to the host of this dysbiosis are unknown. We investigated the functional interactions between the microbiota and the host associated with type 1 diabetes disease risk.

          RESEARCH DESIGN AND METHODS

          We performed a cross-sectional analysis of stool samples from subjects with recent-onset type 1 diabetes ( n = 33), islet autoantibody–positive subjects ( n = 17), low-risk autoantibody-negative subjects ( n = 29), and healthy subjects ( n = 22). Metaproteomic analysis was used to identify gut- and pancreas-derived host and microbial proteins, and these data were integrated with sequencing-based microbiota profiling.

          RESULTS

          Both human (host-derived) proteins and microbial-derived proteins could be used to differentiate new-onset and islet autoantibody–positive subjects from low-risk subjects. Significant alterations were identified in the prevalence of host proteins associated with exocrine pancreas output, inflammation, and mucosal function. Integrative analysis showed that microbial taxa associated with host proteins involved in maintaining function of the mucous barrier, microvilli adhesion, and exocrine pancreas were depleted in patients with new-onset type 1 diabetes.

          CONCLUSIONS

          These data support that patients with type 1 diabetes have increased intestinal inflammation and decreased barrier function. They also confirmed that pancreatic exocrine dysfunction occurs in new-onset type 1 diabetes and show for the first time that this dysfunction is present in high-risk individuals before disease onset. The data identify a unique type 1 diabetes–associated signature in stool that may be useful as a means to monitor disease progression or response to therapies aimed at restoring a healthy microbiota.

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          Most cited references22

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          Is Open Access

          mixOmics: An R package for ‘omics feature selection and multiple data integration

          The advent of high throughput technologies has led to a wealth of publicly available ‘omics data coming from different sources, such as transcriptomics, proteomics, metabolomics. Combining such large-scale biological data sets can lead to the discovery of important biological insights, provided that relevant information can be extracted in a holistic manner. Current statistical approaches have been focusing on identifying small subsets of molecules (a ‘molecular signature’) to explain or predict biological conditions, but mainly for a single type of ‘omics. In addition, commonly used methods are univariate and consider each biological feature independently. We introduce mixOmics, an R package dedicated to the multivariate analysis of biological data sets with a specific focus on data exploration, dimension reduction and visualisation. By adopting a systems biology approach, the toolkit provides a wide range of methods that statistically integrate several data sets at once to probe relationships between heterogeneous ‘omics data sets. Our recent methods extend Projection to Latent Structure (PLS) models for discriminant analysis, for data integration across multiple ‘omics data or across independent studies, and for the identification of molecular signatures. We illustrate our latest mixOmics integrative frameworks for the multivariate analyses of ‘omics data available from the package.
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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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              The Immune Response to Prevotella Bacteria in Chronic Inflammatory Disease.

              The microbiota plays a central role in human health and disease by shaping immune development, immune responses, metabolism, and protecting from invading pathogens. Technical advances that allow comprehensive characterization of microbial communities by genetic sequencing have sparked the hunt for disease modulating bacteria. Emerging studies in humans have linked increased abundance of Prevotella species at mucosal sites to localized and systemic disease, including periodontitis, bacterial vaginosis, rheumatoid arthritis, metabolic disorders, and low-grade systemic inflammation. Intriguingly, Prevotella abundance is reduced within the lung microbiota of asthma and COPD. Increased Prevotella abundance is associated with augmented Th17-mediated mucosal inflammation, which is in line with the marked capacity of Prevotella in driving Th17 immune responses in vitro. Studies indicate, that Prevotella predominantly activate TLR2 leading to production of Th17-polarizing cytokines by antigen presenting cells, including IL-23 and IL-1. Furthermore, Prevotella stimulate epithelial cells to produce IL-8, IL-6 and CCL20, which can promote mucosal Th17 immune responses and neutrophil recruitment. Prevotella-mediated mucosal inflammation leads to systemic dissemination of inflammatory mediators, bacteria, and bacterial products, which in turn may affect systemic disease outcomes. Studies in mice support a causal role of Prevotella as colonization experiments promote clinical and inflammatory features of human disease. When compared to strict commensal bacteria, Prevotella exhibit increased inflammatory properties as demonstrated by augmented release of inflammatory mediators from immune cells and various stromal cells. These findings indicate that some Prevotella strains may be clinically important pathobionts that can participate in human disease by promoting chronic inflammation. This article is protected by copyright. All rights reserved.
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                Author and article information

                Journal
                Diabetes Care
                Diabetes Care
                diacare
                dcare
                Diabetes Care
                Diabetes Care
                American Diabetes Association
                0149-5992
                1935-5548
                October 2018
                12 August 2018
                : 41
                : 10
                : 2178-2186
                Affiliations
                [1] 1The University of Queensland Diamantina Institute, The University of Queensland, Translational Research Institute, Brisbane, Queensland, Australia
                [2] 2Translational Research Institute, Brisbane, Queensland, Australia
                [3] 3Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO
                Author notes
                Corresponding author: Emma E. Hamilton-Williams, e.hamiltonwilliams@ 123456uq.edu.au .

                P.G.G. and J.A.M. contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-4452-6974
                http://orcid.org/0000-0003-4892-3691
                Article
                0777
                10.2337/dc18-0777
                6150433
                30100563
                8c6dcf96-5753-4ec9-998f-4d606980cb2e
                © 2018 by the American Diabetes Association.

                Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at http://www.diabetesjournals.org/content/license.

                History
                : 10 April 2018
                : 16 July 2018
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 40, Pages: 9
                Funding
                Funded by: JDRF, DOI http://dx.doi.org/10.13039/100000901;
                Award ID: PDF-2014-222-A-N
                Award ID: 2-SRA-2015-306-Q-R
                Award ID: 2-2013-34
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases, DOI http://dx.doi.org/10.13039/100000062;
                Award ID: U01-DK-85509
                Categories
                0407
                Pathophysiology/Complications

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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