Literature DB >> 26036145

Use of T-RFLP and seven restriction enzymes to compare the faecal microbiota of obese and lean Japanese healthy men.

T Kobayashi1,2, T Osaki3, S Oikawa1.   

Abstract

The composition of the intestinal microbiota of 92 healthy Japanese men was measured following consumption of identical meals for 3 days; terminal restriction fragment length polymorphisms were then used to analyse the DNA content of their faeces. The obtained operational taxonomic units (OTUs) were further analysed using seven restriction enzymes: 516f-BslI and -HaeIII, 27f-MspI and -AluI, and 35f-HhaI, -MspI and -AluI. Subjects were classified by their body mass index (BMI) as lean (<18.5) or obese (>25.0). OTUs were then analysed using data mining software. Pearson correlation coefficients on data mining results indicated only a weak relationship between BMI and OTU diversity. Specific OTUs attributed to lean and obese subjects were further examined by data mining with six groups of enzymes and closely related accession numbers for lean and obese subjects were successfully narrowed down. 16S rRNA sequences showed Bacillus spp., Erysipelothrix spp. and Holdemania spp. to be present among 30 bacterial candidates related to the lean group. Fifteen candidates were classified Firmicutes, one was classified as Chloroflexi, and the others were not classified. 45 Microbacteriaceae, 11 uncultured Actinobacterium, and 3 other families were present among the 119 candidate OTUs related to obesity. We conclude that the presence of Firmicutes and Actinobacteria may be related to the BMI of the subject.

Entities:  

Keywords:  body mass index; data mining analysis; decision tree; human intestinal microbiota; operational taxonomic unit; restriction enzyme

Mesh:

Substances:

Year:  2015        PMID: 26036145     DOI: 10.3920/BM2014.0147

Source DB:  PubMed          Journal:  Benef Microbes        ISSN: 1876-2883            Impact factor:   4.205


  5 in total

1.  Characterization of gut microbiota profiles in coronary artery disease patients using data mining analysis of terminal restriction fragment length polymorphism: gut microbiota could be a diagnostic marker of coronary artery disease.

Authors:  Takuo Emoto; Tomoya Yamashita; Toshio Kobayashi; Naoto Sasaki; Yushi Hirota; Tomohiro Hayashi; Anna So; Kazuyuki Kasahara; Keiko Yodoi; Takuya Matsumoto; Taiji Mizoguchi; Wataru Ogawa; Ken-Ichi Hirata
Journal:  Heart Vessels       Date:  2016-04-28       Impact factor: 2.037

2.  Food withdrawal alters the gut microbiota and metabolome in mice.

Authors:  Xiaojiao Zheng; Kejun Zhou; Yunjing Zhang; Xiaolong Han; Aihua Zhao; Jiajian Liu; Chun Qu; Kun Ge; Fengjie Huang; Brenda Hernandez; Herbert Yu; Jun Panee; Tianlu Chen; Weiping Jia; Wei Jia
Journal:  FASEB J       Date:  2018-04-05       Impact factor: 5.834

3.  Influence of dairy by-product waste milk on the microbiomes of different gastrointestinal tract components in pre-weaned dairy calves.

Authors:  Y F Deng; Y J Wang; Y Zou; A Azarfar; X L Wei; S K Ji; J Zhang; Z H Wu; S X Wang; S Z Dong; Y Xu; D F Shao; J X Xiao; K L Yang; Z J Cao; S L Li
Journal:  Sci Rep       Date:  2017-03-10       Impact factor: 4.379

4.  Polygonatum odoratum Polysaccharides Modulate Gut Microbiota and Mitigate Experimentally Induced Obesity in Rats.

Authors:  Yan Wang; Yanquan Fei; Lirui Liu; Yunhua Xiao; Yilin Pang; Jinhe Kang; Zheng Wang
Journal:  Int J Mol Sci       Date:  2018-11-13       Impact factor: 5.923

Review 5.  Numerical analyses of intestinal microbiota by data mining.

Authors:  Toshio Kobayashi; Akira Andoh
Journal:  J Clin Biochem Nutr       Date:  2018-01-11       Impact factor: 3.114

  5 in total

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