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      Population-level analysis of gut microbiome variation.

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          Abstract

          Fecal microbiome variation in the average, healthy population has remained under-investigated. Here, we analyzed two independent, extensively phenotyped cohorts: the Belgian Flemish Gut Flora Project (FGFP; discovery cohort; N = 1106) and the Dutch LifeLines-DEEP study (LLDeep; replication; N = 1135). Integration with global data sets (N combined = 3948) revealed a 14-genera core microbiota, but the 664 identified genera still underexplore total gut diversity. Sixty-nine clinical and questionnaire-based covariates were found associated to microbiota compositional variation with a 92% replication rate. Stool consistency showed the largest effect size, whereas medication explained largest total variance and interacted with other covariate-microbiota associations. Early-life events such as birth mode were not reflected in adult microbiota composition. Finally, we found that proposed disease marker genera associated to host covariates, urging inclusion of the latter in study design.

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          Most cited references 16

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          Individuality in gut microbiota composition is a complex polygenic trait shaped by multiple environmental and host genetic factors.

          In vertebrates, including humans, individuals harbor gut microbial communities whose species composition and relative proportions of dominant microbial groups are tremendously varied. Although external and stochastic factors clearly contribute to the individuality of the microbiota, the fundamental principles dictating how environmental factors and host genetic factors combine to shape this complex ecosystem are largely unknown and require systematic study. Here we examined factors that affect microbiota composition in a large (n = 645) mouse advanced intercross line originating from a cross between C57BL/6J and an ICR-derived outbred line (HR). Quantitative pyrosequencing of the microbiota defined a core measurable microbiota (CMM) of 64 conserved taxonomic groups that varied quantitatively across most animals in the population. Although some of this variation can be explained by litter and cohort effects, individual host genotype had a measurable contribution. Testing of the CMM abundances for cosegregation with 530 fully informative SNP markers identified 18 host quantitative trait loci (QTL) that show significant or suggestive genome-wide linkage with relative abundances of specific microbial taxa. These QTL affect microbiota composition in three ways; some loci control individual microbial species, some control groups of related taxa, and some have putative pleiotropic effects on groups of distantly related organisms. These data provide clear evidence for the importance of host genetic control in shaping individual microbiome diversity in mammals, a key step toward understanding the factors that govern the assemblages of gut microbiota associated with complex diseases.
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            Low counts of Faecalibacterium prausnitzii in colitis microbiota.

            The intestinal microbiota is suspected to play a role in colitis and particularly in inflammatory bowel disease (IBD) pathogenesis. The aim was to compare the fecal microbiota composition of patients with colitis to that of healthy subjects (HS). fecal samples from 22 active Crohn's disease (A-CD) patients, 10 CD patients in remission (R-CD), 13 active ulcerative colitis (A-UC) patients, 4 UC patients in remission (R-UC), 8 infectious colitis (IC) patients, and 27 HS were analyzed by quantitative real-time polymerase chain reaction (PCR) targeting the 16S rRNA gene. Bacterial counts were transformed to logarithms (Log(10) CFU) for statistical analysis. Bacteria of the phylum Firmicutes (Clostridium leptum and Clostridium coccoides groups) were less represented in A-IBD patients (9.7; P = 0.004) and IC (9.4; P = 0.02), compared to HS (10.8). Faecalibacterium prausnitzii species (a major representative of the C. leptum group) had lower counts in A-IBD and IC patients compared to HS (8.8 and 8.3 versus 10.4; P = 0.0004 and P = 0.003). The Firmicutes/Bacteroidetes ratio was lower in A-IBD (1.3; P = 0.0001) and IC patients (0.4; P = 0.002). Compared to HS, Bifidobacteria were less represented in A-IBD and IC (7.9 and 7.7 versus 9.2; P = 0.001 and P = 0.01). The fecal microbiota of patients with IBD differs from that of HS. The phylum Firmicutes and particularly the species F. prausnitzii, are underrepresented in A-IBD patients as well as in IC patients. These bacteria could be crucial to gut homeostasis since lower counts of F. prausnitzii are consistently associated with a reduced protection of the gut mucosa.
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              Meta-analyses of human gut microbes associated with obesity and IBD.

              Recent studies have linked human gut microbes to obesity and inflammatory bowel disease, but consistent signals have been difficult to identify. Here we test for indicator taxa and general features of the microbiota that are generally consistent across studies of obesity and of IBD, focusing on studies involving high-throughput sequencing of the 16S rRNA gene (which we could process using a common computational pipeline). We find that IBD has a consistent signature across studies and allows high classification accuracy of IBD from non-IBD subjects, but that although subjects can be classified as lean or obese within each individual study with statistically significant accuracy, consistent with the ability of the microbiota to experimentally transfer this phenotype, signatures of obesity are not consistent between studies even when the data are analyzed with consistent methods. The results suggest that correlations between microbes and clinical conditions with different effect sizes (e.g. the large effect size of IBD versus the small effect size of obesity) may require different cohort selection and analysis strategies.
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                Author and article information

                Journal
                Science
                Science (New York, N.Y.)
                1095-9203
                0036-8075
                Apr 29 2016
                : 352
                : 6285
                Affiliations
                [1 ] KU Leuven-University of Leuven, Department of Microbiology and Immunology, Leuven, Belgium. VIB, Center for the Biology of Disease, Leuven, Belgium.
                [2 ] KU Leuven-University of Leuven, Department of Microbiology and Immunology, Leuven, Belgium. VIB, Center for the Biology of Disease, Leuven, Belgium. Vrije Universiteit Brussel, Faculty of Sciences and Bioengineering Sciences, Microbiology Unit, Brussels, Belgium.
                [3 ] Institute of Chemical Biology and Fundamental Medicine SB RAS, Novosibirsk, Russia. Novosibirsk State University, Novosibirsk, Russia.
                [4 ] University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, Netherlands.
                [5 ] VIB, Center for the Biology of Disease, Leuven, Belgium. Vrije Universiteit Brussel, Faculty of Sciences and Bioengineering Sciences, Microbiology Unit, Brussels, Belgium.
                [6 ] University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, Netherlands. Top Institute Food and Nutrition, Wageningen, Netherlands.
                [7 ] University of Groningen, University Medical Center Groningen, Department of Genetics, 9700 RB Groningen, Netherlands. University of Groningen, University Medical Center Groningen, Department of Pediatrics, 9700 RB Groningen, Netherlands.
                [8 ] KU Leuven-University of Leuven, Department of Microbiology and Immunology, Leuven, Belgium. KU Leuven-University Hospitals Leuven, Department of General Internal Medicine, Leuven, Belgium.
                [9 ] KU Leuven-University of Leuven, Department of Microbiology and Immunology, Leuven, Belgium. VIB, Center for the Biology of Disease, Leuven, Belgium. Vrije Universiteit Brussel, Faculty of Sciences and Bioengineering Sciences, Microbiology Unit, Brussels, Belgium. jeroen.raes@kuleuven.be.
                Article
                352/6285/560
                10.1126/science.aad3503
                27126039
                Copyright © 2016, American Association for the Advancement of Science.

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