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      Dietary Modulation of Gut Microbiota Contributes to Alleviation of Both Genetic and Simple Obesity in Children

      research-article
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      EBioMedicine
      Elsevier
      Prader–Willi syndrome, Obesity, Gut microbiota, Metagenomics, Metabolomics, Genome interaction network

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          Abstract

          Gut microbiota has been implicated as a pivotal contributing factor in diet-related obesity; however, its role in development of disease phenotypes in human genetic obesity such as Prader–Willi syndrome (PWS) remains elusive. In this hospitalized intervention trial with PWS (n = 17) and simple obesity (n = 21) children, a diet rich in non-digestible carbohydrates induced significant weight loss and concomitant structural changes of the gut microbiota together with reduction of serum antigen load and alleviation of inflammation. Co-abundance network analysis of 161 prevalent bacterial draft genomes assembled directly from metagenomic datasets showed relative increase of functional genome groups for acetate production from carbohydrates fermentation. NMR-based metabolomic profiling of urine showed diet-induced overall changes of host metabotypes and identified significantly reduced trimethylamine N-oxide and indoxyl sulfate, host-bacteria co-metabolites known to induce metabolic deteriorations. Specific bacterial genomes that were correlated with urine levels of these detrimental co-metabolites were found to encode enzyme genes for production of their precursors by fermentation of choline or tryptophan in the gut. When transplanted into germ-free mice, the pre-intervention gut microbiota induced higher inflammation and larger adipocytes compared with the post-intervention microbiota from the same volunteer. Our multi-omics-based systems analysis indicates a significant etiological contribution of dysbiotic gut microbiota to both genetic and simple obesity in children, implicating a potentially effective target for alleviation.

          Research in context

          Poorly managed diet and genetic mutations are the two primary driving forces behind the devastating epidemic of obesity-related diseases. Lack of understanding of the molecular chain of causation between the driving forces and the disease endpoints retards progress in prevention and treatment of the diseases. We found that children genetically obese with Prader–Willi syndrome shared a similar dysbiosis in their gut microbiota with those having diet-related obesity. A diet rich in non-digestible but fermentable carbohydrates significantly promoted beneficial groups of bacteria and reduced toxin-producers, which contributes to the alleviation of metabolic deteriorations in obesity regardless of the primary driving forces.

          Highlights

          • Genetic and simple obesity in children shared a similar dysbiotic gut microbiota.

          • A diet rich in non-digestible carbohydrates significantly improved gut microbiota and alleviated genetic and simple obesity.

          • Specific bacterial genomes for producing obesity-related metabolites were identified.

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          Most cited references29

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          The influence of diet on the gut microbiota.

          Diet is a major factor driving the composition and metabolism of the colonic microbiota. The amount, type and balance of the main dietary macronutrients (carbohydrates, proteins and fats) have a great impact on the large intestinal microbiota. The human colon contains a dense population of bacterial cells that outnumber host cells 10-fold. Bacteroidetes, Firmicutes and Actinobacteria are the three major phyla that inhabit the human large intestine and these bacteria possess a fascinating array of enzymes that can degrade complex dietary substrates. Certain colonic bacteria are able to metabolise a remarkable variety of substrates whilst other species carry out more specialised activities, including primary degradation of plant cell walls. Microbial metabolism of dietary carbohydrates results mainly in the formation of short chain fatty acids and gases. The major bacterial fermentation products are acetate, propionate and butyrate; and the production of these tends to lower the colonic pH. These weak acids influence the microbial composition and directly affect host health, with butyrate the preferred energy source for the colonocytes. Certain bacterial species in the colon survive by cross-feeding, using either the breakdown products of complex carbohydrate degradation or fermentation products such as lactic acid for growth. Microbial protein metabolism results in additional fermentation products, some of which are potentially harmful to host health. The current 'omic era promises rapid progress towards understanding how diet can be used to modulate the composition and metabolism of the gut microbiota, allowing researchers to provide informed advice, that should improve long-term health status. Copyright © 2012 Elsevier Ltd. All rights reserved.
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            Serum indoxyl sulfate is associated with vascular disease and mortality in chronic kidney disease patients.

            As a major component of uremic syndrome, cardiovascular disease is largely responsible for the high mortality observed in chronic kidney disease (CKD). Preclinical studies have evidenced an association between serum levels of indoxyl sulfate (IS, a protein-bound uremic toxin) and vascular alterations. The aim of this study is to investigate the association between serum IS, vascular calcification, vascular stiffness, and mortality in a cohort of CKD patients. One-hundred and thirty-nine patients (mean +/- SD age: 67 +/- 12; 60% male) at different stages of CKD (8% at stage 2, 26.5% at stage 3, 26.5% at stage 4, 7% at stage 5, and 32% at stage 5D) were enrolled. Baseline IS levels presented an inverse relationship with renal function and a direct relationship with aortic calcification and pulse wave velocity. During the follow-up period (605 +/- 217 d), 25 patients died, mostly because of cardiovascular events (n = 18). In crude survival analyses, the highest IS tertile was a powerful predictor of overall and cardiovascular mortality (P = 0.001 and 0.012, respectively). The predictive power of IS for death was maintained after adjustment for age, gender, diabetes, albumin, hemoglobin, phosphate, and aortic calcification. The study presented here indicates that IS may have a significant role in the vascular disease and higher mortality observed in CKD patients.
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              Statistical total correlation spectroscopy: an exploratory approach for latent biomarker identification from metabolic 1H NMR data sets.

              We describe here the implementation of the statistical total correlation spectroscopy (STOCSY) analysis method for aiding the identification of potential biomarker molecules in metabonomic studies based on NMR spectroscopic data. STOCSY takes advantage of the multicollinearity of the intensity variables in a set of spectra (in this case 1H NMR spectra) to generate a pseudo-two-dimensional NMR spectrum that displays the correlation among the intensities of the various peaks across the whole sample. This method is not limited to the usual connectivities that are deducible from more standard two-dimensional NMR spectroscopic methods, such as TOCSY. Moreover, two or more molecules involved in the same pathway can also present high intermolecular correlations because of biological covariance or can even be anticorrelated. This combination of STOCSY with supervised pattern recognition and particularly orthogonal projection on latent structure-discriminant analysis (O-PLS-DA) offers a new powerful framework for analysis of metabonomic data. In a first step O-PLS-DA extracts the part of NMR spectra related to discrimination. This information is then cross-combined with the STOCSY results to help identify the molecules responsible for the metabolic variation. To illustrate the applicability of the method, it has been applied to 1H NMR spectra of urine from a metabonomic study of a model of insulin resistance based on the administration of a carbohydrate diet to three different mice strains (C57BL/6Oxjr, BALB/cOxjr, and 129S6/SvEvOxjr) in which a series of metabolites of biological importance can be conclusively assigned and identified by use of the STOCSY approach.
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                Author and article information

                Contributors
                Journal
                EBioMedicine
                EBioMedicine
                EBioMedicine
                Elsevier
                2352-3964
                10 July 2015
                August 2015
                10 July 2015
                : 2
                : 8
                : 966-982
                Affiliations
                [a ]State Key Laboratory of Microbial Metabolism and Ministry of Education Key Laboratory of Systems Biomedicine, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
                [b ]Medical Genetic Centre and Maternal and Children Metabolic-Genetic Key Laboratory, Guangdong Women and Children Hospital, Guangzhou, Guangdong 510010, China
                [c ]CAS Key Laboratory of Magnetic Resonance in Biological Systems, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Centre for Magnetic Resonance, Wuhan Institute of Physics and Mathematics, Chinese Academy of Sciences, Wuhan 430071, China
                [d ]Department of Laboratory Animal Science, College of Basic Medical Sciences, Third Military Medical University, Chongqing 400038, China
                [e ]Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, United Kingdom
                [f ]Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
                [g ]Institut National de la Recherche Agronomique, 78350 Jouy en Josas, France
                [h ]Institut of cardiometabolism and Nutrition, Pitié-Salpêtrière, Paris, France
                [i ]Whistler Center for Carbohydrate Research, Department of Food Science, Purdue University, 745 Agriculture Mall Drive, West Lafayette, IN 47907, USA
                [j ]Department of Microbiology and Molecular Biology, Brigham Young University, Provo, UT, USA
                [k ]School of Biological and Biomedical Sciences, Durham University, South Road, Durham DH1 3LE, UK
                [l ]Shanghai-MOST Key Laboratory for Disease and Health Genomics, Shang Biochip Company, Shanghai 201203, China
                Author notes
                [1]

                Contributed equally to this work.

                Article
                S2352-3964(15)30064-5
                10.1016/j.ebiom.2015.07.007
                4563136
                26425705
                547559ba-1769-482d-8004-98c435ad2272
                © 2015 The Authors. Published by Elsevier B.V.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 29 April 2015
                : 2 July 2015
                : 2 July 2015
                Categories
                Original Article

                prader–willi syndrome,obesity,gut microbiota,metagenomics,metabolomics,genome interaction network

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