Systematic Analysis of the Association between Gut Flora and Obesity through High-Throughput Sequencing and Bioinformatics Approaches

Chih-Min Chiu, Wei Chih Huang, Shun Long Weng, Han-Chi Tseng, Chao Liang, Wei-Chi Wang, Ting Yang, Tzu Ling Yang, Chen-Tsung Weng, Tzu-Hao Chang, Hsien-Da Huang

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Abstract

Eighty-one stool samples from Taiwanese were collected for analysis of the association between the gut flora and obesity. The supervised analysis showed that the most, abundant genera of bacteria in normal samples (from people with a body mass index (BMI) <= 24) were Bacteroides (27.7%), Prevotella (19.4%), Escherichia (12%), Phascolarctobacterium(3.9%), and Eubacterium (3.5%). The most abundant genera of bacteria in case samples (with a BMI >= 27) were Bacteroides (29%), Prevotella (21%), Escherichia (7.4%), Megamonas (5.1%), and Phascolarctobacterium (3.8%). A principal coordinate analysis (PCoA) demonstrated that normal samples were clustered more compactly than case samples. An unsupervised analysis demonstrated that bacterial communities in the gut were clustered into two main groups: N-like and OB-like groups. Remarkably, most normal samples (78%) were clustered in the N-like group, and most case samples (81%) were clustered in the OB-like group (Fisher's P value = 1.61E-07). The results showed that bacterial communities in the gut were highly associated with obesity. This is the first study in Taiwan to investigate the association between human gut flora and obesity, and the results provide new insights into the correlation of bacteria with the rising trend in obesity.
Original languageEnglish
JournalBioMed Research International
DOIs
StatePublished - 2014

Keywords

  • SHEWANELLA-PUTREFACIENS; INTESTINAL MICROBIOTA; METABOLIC-DISORDERS; DIET; RISK; NOV.; RECLASSIFICATION; METAGENOME; DIVERSITY; PATTERNS

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    Chiu, C-M., Huang, W. C., Weng, S. L., Tseng, H-C., Liang, C., Wang, W-C., Yang, T., Yang, T. L., Weng, C-T., Chang, T-H., & Huang, H-D. (2014). Systematic Analysis of the Association between Gut Flora and Obesity through High-Throughput Sequencing and Bioinformatics Approaches. BioMed Research International. https://doi.org/10.1155/2014/906168