Obesity (Silver Spring, Md.) · 2017
Dysbiosis Signatures of Gut Microbiota Along the Sequence from Healthy, Young Patients to Those with Overweight and Obesity.
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Gao Renyuan, Zhu Cuiling, Li Hao, Yin Mingming, Pan Cheng, Huang Linsheng, Kong Cheng, Wang Xingchun, Zhang Yi, Qu Shen, Qin Huanlong
Аннотация
OBJECTIVE: To investigate the gut microbiota in healthy volunteers (HVs), patients with overweight (OW), and patients with obesity (OB), including those with acanthosis nigricans (AN) or without AN (N-AN).
METHODS: Microbial 16S rRNA genes were examined by using pyrosequencing technology and analyzed by using bioinformatics methods.
RESULTS: Subjects in the OW and OB groups showed severe disturbances in glycemic control, lipid profile, and inflammatory markers (all P < 0.05); patients with AN had worse metabolic status (P < 0.001) and a lower diversity of microbiota (P < 0.05). The OB and HV groups showed totally different gut microbiota composition. In the OB group, beneficial microbiotas including Bifidobacterium (0.01% vs. 0.05%, false discovery rate [FDR] = 4.27*10-5 ), anti-inflammatory Faecalibacterium (6.70% vs. 13.82%, FDR = 0.010), and butyrate-producing Ruminococcaceae were significantly decreased, whereas Bacillus (0.58% vs. 0.04%, FDR = 0.013) and potential opportunistic pathogens such as Fusobacterium (1.44% vs. 0.11%, FDR < 0.01) and Escherichia-Shigella (6.01% vs. 0.76%, FDR = 0.041) had outgrown dramatically. Function prediction revealed a significant increase in lipopolysaccharide biosynthesis proteins and bacterial invasion of epithelial cell-associated genes and a significant decrease in glucose and essential amino acid-related genes.
CONCLUSIONS: Gut microbiotas and their functions were significantly changed in obesity. More prospective studies on association and causality between microbiota and obesity are imperative and might contribute to the prevention, diagnosis, and treatment of obesity.
METHODS: Microbial 16S rRNA genes were examined by using pyrosequencing technology and analyzed by using bioinformatics methods.
RESULTS: Subjects in the OW and OB groups showed severe disturbances in glycemic control, lipid profile, and inflammatory markers (all P < 0.05); patients with AN had worse metabolic status (P < 0.001) and a lower diversity of microbiota (P < 0.05). The OB and HV groups showed totally different gut microbiota composition. In the OB group, beneficial microbiotas including Bifidobacterium (0.01% vs. 0.05%, false discovery rate [FDR] = 4.27*10-5 ), anti-inflammatory Faecalibacterium (6.70% vs. 13.82%, FDR = 0.010), and butyrate-producing Ruminococcaceae were significantly decreased, whereas Bacillus (0.58% vs. 0.04%, FDR = 0.013) and potential opportunistic pathogens such as Fusobacterium (1.44% vs. 0.11%, FDR < 0.01) and Escherichia-Shigella (6.01% vs. 0.76%, FDR = 0.041) had outgrown dramatically. Function prediction revealed a significant increase in lipopolysaccharide biosynthesis proteins and bacterial invasion of epithelial cell-associated genes and a significant decrease in glucose and essential amino acid-related genes.
CONCLUSIONS: Gut microbiotas and their functions were significantly changed in obesity. More prospective studies on association and causality between microbiota and obesity are imperative and might contribute to the prevention, diagnosis, and treatment of obesity.