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      A core gut microbiome in obese and lean twins

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          Abstract

          The human distal gut harbors a vast ensemble of microbes (the microbiota) that provide us with important metabolic capabilities, including the ability to extract energy from otherwise indigestible dietary polysaccharides 16. Studies of a small number of unrelated, healthy adults have revealed substantial diversity in their gut communities, as measured by sequencing 16S rRNA genes 68, yet how this diversity relates to function and to the rest of the genes in the collective genomes of the microbiota (the gut microbiome) remains obscure. Studies of lean and obese mice suggest that the gut microbiota affects energy balance by influencing the efficiency of calorie harvest from the diet, and how this harvested energy is utilized and stored 35. To address the question of how host genotype, environmental exposures, and host adiposity influence the gut microbiome, we have characterized the fecal microbial communities of adult female monozygotic and dizygotic twin pairs concordant for leanness or obesity, and their mothers. Analysis of 154 individuals yielded 9,920 near full-length and 1,937,461 partial bacterial 16S rRNA sequences, plus 2.14 gigabases from their microbiomes. The results reveal that the human gut microbiome is shared among family members, but that each person’s gut microbial community varies in the specific bacterial lineages present, with a comparable degree of co-variation between adult monozygotic and dizygotic twin pairs. However, there was a wide array of shared microbial genes among sampled individuals, comprising an extensive, identifiable ‘core microbiome’ at the gene, rather than at the organismal lineage level. Obesity is associated with phylum-level changes in the microbiota, reduced bacterial diversity, and altered representation of bacterial genes and metabolic pathways. These results demonstrate that a diversity of organismal assemblages can nonetheless yield a core microbiome at a functional level, and that deviations from this core are associated with different physiologic states (obese versus lean).

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          Most cited references 39

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          Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB.

          A 16S rRNA gene database (http://greengenes.lbl.gov) addresses limitations of public repositories by providing chimera screening, standard alignment, and taxonomic classification using multiple published taxonomies. It was found that there is incongruent taxonomic nomenclature among curators even at the phylum level. Putative chimeras were identified in 3% of environmental sequences and in 0.2% of records derived from isolates. Environmental sequences were classified into 100 phylum-level lineages in the Archaea and Bacteria.
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            An obesity-associated gut microbiome with increased capacity for energy harvest.

            The worldwide obesity epidemic is stimulating efforts to identify host and environmental factors that affect energy balance. Comparisons of the distal gut microbiota of genetically obese mice and their lean littermates, as well as those of obese and lean human volunteers have revealed that obesity is associated with changes in the relative abundance of the two dominant bacterial divisions, the Bacteroidetes and the Firmicutes. Here we demonstrate through metagenomic and biochemical analyses that these changes affect the metabolic potential of the mouse gut microbiota. Our results indicate that the obese microbiome has an increased capacity to harvest energy from the diet. Furthermore, this trait is transmissible: colonization of germ-free mice with an 'obese microbiota' results in a significantly greater increase in total body fat than colonization with a 'lean microbiota'. These results identify the gut microbiota as an additional contributing factor to the pathophysiology of obesity.
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              Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences.

              In 2001 and 2002, we published two papers (Bioinformatics, 17, 282-283, Bioinformatics, 18, 77-82) describing an ultrafast protein sequence clustering program called cd-hit. This program can efficiently cluster a huge protein database with millions of sequences. However, the applications of the underlying algorithm are not limited to only protein sequences clustering, here we present several new programs using the same algorithm including cd-hit-2d, cd-hit-est and cd-hit-est-2d. Cd-hit-2d compares two protein datasets and reports similar matches between them; cd-hit-est clusters a DNA/RNA sequence database and cd-hit-est-2d compares two nucleotide datasets. All these programs can handle huge datasets with millions of sequences and can be hundreds of times faster than methods based on the popular sequence comparison and database search tools, such as BLAST.
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                Author and article information

                Journal
                0410462
                6011
                Nature
                Nature
                0028-0836
                1476-4687
                24 October 2008
                30 November 2008
                22 January 2009
                22 July 2009
                : 457
                : 7228
                : 480-484
                Affiliations
                [1 ]Center for Genome Sciences, Washington University School of Medicine, St Louis MO 63108, USA
                [2 ]Department of Psychiatry, Washington University School of Medicine, St Louis MO 63108, USA
                [3 ]Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
                [4 ]Department of Chemistry and Biochemistry, University of Colorado, Boulder, CO 80309, USA
                [5 ]CNRS, UMR6098 Marseille, France
                [6 ]Josephine Bay Paul Center, Marine Biological Laboratory, Woods Hole, MA 02543, USA
                [7 ]Environmental Genomics Core Facility, University of South Carolina, Columbia, SC 29208, USA
                [8 ]Department of Chemistry and Biochemistry and the Advanced Center for Genome Technology, University of Oklahoma, Norman, OK 73019, USA
                [9 ]454 Life Sciences, Branford, CT 06405, USA.
                Author notes
                [* ]Correspondence and requests for materials should be addressed to J.I.G. ( jgordon@ 123456wustl.edu )
                Article
                nihpa74182
                10.1038/nature07540
                2677729
                19043404
                Funding
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Institute of Child Health & Human Development : NICHD
                Funded by: National Institute on Alcohol Abuse and Alcoholism : NIAAA
                Funded by: National Institute of Environmental Health Sciences : NIEHS
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases : NIDDK
                Award ID: T32 GM065103-07 ||GM
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Institute of Child Health & Human Development : NICHD
                Funded by: National Institute on Alcohol Abuse and Alcoholism : NIAAA
                Funded by: National Institute of Environmental Health Sciences : NIEHS
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases : NIDDK
                Award ID: R01 HD049024-01 ||HD
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Institute of Child Health & Human Development : NICHD
                Funded by: National Institute on Alcohol Abuse and Alcoholism : NIAAA
                Funded by: National Institute of Environmental Health Sciences : NIEHS
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases : NIDDK
                Award ID: R01 AA009022-10 ||AA
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Institute of Child Health & Human Development : NICHD
                Funded by: National Institute on Alcohol Abuse and Alcoholism : NIAAA
                Funded by: National Institute of Environmental Health Sciences : NIEHS
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases : NIDDK
                Award ID: P50 ES012742-049001 ||ES
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Institute of Child Health & Human Development : NICHD
                Funded by: National Institute on Alcohol Abuse and Alcoholism : NIAAA
                Funded by: National Institute of Environmental Health Sciences : NIEHS
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases : NIDDK
                Award ID: P30 DK056341-08 ||DK
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Institute of Child Health & Human Development : NICHD
                Funded by: National Institute on Alcohol Abuse and Alcoholism : NIAAA
                Funded by: National Institute of Environmental Health Sciences : NIEHS
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases : NIDDK
                Award ID: P30 DK056341-07 ||DK
                Funded by: National Institute of General Medical Sciences : NIGMS
                Funded by: National Institute of Child Health & Human Development : NICHD
                Funded by: National Institute on Alcohol Abuse and Alcoholism : NIAAA
                Funded by: National Institute of Environmental Health Sciences : NIEHS
                Funded by: National Institute of Diabetes and Digestive and Kidney Diseases : NIDDK
                Award ID: P01 DK078669-01 ||DK
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