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      Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort

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          Abstract

          Background

          The gut microbiome is a complex and metabolically active community that directly influences host phenotypes. In this study, we profile gut microbiota using 16S rRNA gene sequencing in 531 well-phenotyped Finnish men from the Metabolic Syndrome In Men (METSIM) study.

          Results

          We investigate gut microbiota relationships with a variety of factors that have an impact on the development of metabolic and cardiovascular traits. We identify novel associations between gut microbiota and fasting serum levels of a number of metabolites, including fatty acids, amino acids, lipids, and glucose. In particular, we detect associations with fasting plasma trimethylamine N-oxide (TMAO) levels, a gut microbiota-dependent metabolite associated with coronary artery disease and stroke. We further investigate the gut microbiota composition and microbiota–metabolite relationships in subjects with different body mass index and individuals with normal or altered oral glucose tolerance. Finally, we perform microbiota co-occurrence network analysis, which shows that certain metabolites strongly correlate with microbial community structure and that some of these correlations are specific for the pre-diabetic state.

          Conclusions

          Our study identifies novel relationships between the composition of the gut microbiota and circulating metabolites and provides a resource for future studies to understand host–gut microbiota relationships.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13059-017-1194-2) contains supplementary material, which is available to authorized users.

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          Most cited references 16

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          Intestinal Short Chain Fatty Acids and their Link with Diet and Human Health

          The colon is inhabited by a dense population of microorganisms, the so-called “gut microbiota,” able to ferment carbohydrates and proteins that escape absorption in the small intestine during digestion. This microbiota produces a wide range of metabolites, including short chain fatty acids (SCFA). These compounds are absorbed in the large bowel and are defined as 1-6 carbon volatile fatty acids which can present straight or branched-chain conformation. Their production is influenced by the pattern of food intake and diet-mediated changes in the gut microbiota. SCFA have distinct physiological effects: they contribute to shaping the gut environment, influence the physiology of the colon, they can be used as energy sources by host cells and the intestinal microbiota and they also participate in different host-signaling mechanisms. We summarize the current knowledge about the production of SCFA, including bacterial cross-feedings interactions, and the biological properties of these metabolites with impact on the human health.
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            Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice.

            Obesity is a highly heritable disease driven by complex interactions between genetic and environmental factors. Human genome-wide association studies (GWAS) have identified a number of loci contributing to obesity; however, a major limitation of these studies is the inability to assess environmental interactions common to obesity. Using a systems genetics approach, we measured obesity traits, global gene expression, and gut microbiota composition in response to a high-fat/high-sucrose (HF/HS) diet of more than 100 inbred strains of mice. Here we show that HF/HS feeding promotes robust, strain-specific changes in obesity that are not accounted for by food intake and provide evidence for a genetically determined set point for obesity. GWAS analysis identified 11 genome-wide significant loci associated with obesity traits, several of which overlap with loci identified in human studies. We also show strong relationships between genotype and gut microbiota plasticity during HF/HS feeding and identify gut microbial phylotypes associated with obesity. Copyright © 2013 Elsevier Inc. All rights reserved.
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              Microbiome of prebiotic-treated mice reveals novel targets involved in host response during obesity

              The gut microbiota is involved in metabolic and immune disorders associated with obesity and type 2 diabetes. We previously demonstrated that prebiotic treatment may significantly improve host health by modulating bacterial species related to the improvement of gut endocrine, barrier and immune functions. An analysis of the gut metagenome is needed to determine which bacterial functions and taxa are responsible for beneficial microbiota–host interactions upon nutritional intervention. We subjected mice to prebiotic (Pre) treatment under physiological (control diet: CT) and pathological conditions (high-fat diet: HFD) for 8 weeks and investigated the production of intestinal antimicrobial peptides and the gut microbiome. HFD feeding significantly decreased the expression of regenerating islet-derived 3-gamma (Reg3g) and phospholipase A2 group-II (PLA2g2) in the jejunum. Prebiotic treatment increased Reg3g expression (by ∼50-fold) and improved intestinal homeostasis as suggested by the increase in the expression of intectin, a key protein involved in intestinal epithelial cell turnover. Deep metagenomic sequencing analysis revealed that HFD and prebiotic treatment significantly affected the gut microbiome at different taxonomic levels. Functional analyses based on the occurrence of clusters of orthologous groups (COGs) of proteins also revealed distinct profiles for the HFD, Pre, HFD-Pre and CT groups. Finally, the gut microbiota modulations induced by prebiotics counteracted HFD-induced inflammation and related metabolic disorders. Thus, we identified novel putative taxa and metabolic functions that may contribute to the development of or protection against the metabolic alterations observed during HFD feeding and HFD-Pre feeding.
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                Author and article information

                Contributors
                (372) 737 4039 , elin.org@ut.ee
                yuna.blum@gmail.com
                Silva.Kasela@ut.ee
                mehrabi.m@gmail.com
                johanna.kuusisto@kuh.fi
                antti.kangas@computationalmedicine.fi
                pasi.soininen@uef.fi
                WANGZ2@ccf.org
                mika.ala-korpela@computationalmedicine.fi
                HAZENS@ccf.org
                markku.laakso@uef.fi
                (310) 825-1359 , jlusis@mednet.ucla.edu
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                13 April 2017
                13 April 2017
                2017
                : 18
                Affiliations
                [1 ]GRID grid.19006.3e, Department of Medicine, , University of California, Los Angeles, ; Los Angeles, CA 90095 USA
                [2 ]GRID grid.10939.32, Estonian Genome Centre, , University of Tartu, ; Tartu, 51010 Estonia
                [3 ]GRID grid.10939.32, , Institute of Molecular and Cell Biology, University of Tartu, ; Tartu, 51010 Estonia
                [4 ]GRID grid.9668.1, , Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, ; Kuopio, Finland
                [5 ]GRID grid.410705.7, , Kuopio University Hospital, ; Kuopio, Finland
                [6 ]GRID grid.10858.34, Computational Medicine, Faculty of Medicine, , University of Oulu and Biocenter Oulu, ; Oulu, Finland
                [7 ]GRID grid.9668.1, NMR metabolomics Laboratory, , School of Pharmacy, University of Eastern Finland, ; Kuopio, Finland
                [8 ]GRID grid.239578.2, Department of Cellular and Molecular Medicine, , Cleveland Clinic, ; Cleveland, OH 44195 USA
                [9 ]GRID grid.5337.2, Computational Medicine, School of Social and Community Medicine, , University of Bristol and Medical Research Council Integrative Epidemiology Unit at the University of Bristol, ; Bristol, UK
                [10 ]GRID grid.19006.3e, Department of Human Genetics, , University of California, Los Angeles, ; Los Angeles, CA 90095 USA
                [11 ]GRID grid.19006.3e, Department of Microbiology, Immunology and Molecular Genetics, , University of California, Los Angeles, ; Los Angeles, CA 90095 USA
                Article
                1194
                10.1186/s13059-017-1194-2
                5390365
                © The Author(s). 2017

                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.

                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: HL028481
                Award ID: HL30568
                Award ID: DK094311
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004963, Seventh Framework Programme;
                Award ID: FP7-MC-IOF grant no 330381
                Award Recipient :
                Categories
                Research
                Custom metadata
                © The Author(s) 2017

                Genetics

                type 2 diabetes, host-microbiota interactions, tmao, metabolic traits, serum metabolites

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