Gut Microbiota Composition Modifies Fecal Metabolic Profiles in Mice

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• 作者：Ying Zhao ; Junfang Wu ; Jia V. Li ; Ning-Yi Zhou ; Huiru Tang ; Yulan Wang
• 刊名：Journal of Proteome Research
• 出版年：2013
• 出版时间：June 7, 2013
• 年：2013
• 卷：12
• 期：6
• 页码：2987-2999
• 全文大小：723K
• 年卷期：v.12,no.6(June 7, 2013)
• ISSN：1535-3907

文摘

The gut microbiome is known to be extensively involved in human health and disease. In order to reveal the metabolic relationship between host and microbiome, we monitored recovery of the gut microbiota composition and fecal profiles of mice after gentamicin and/or ceftriaxone treatments. This was performed by employing ¹H nuclear magnetic resonance (NMR)-based metabonomics and denaturing gradient gel electrophoresis (DGGE) fingerprint of gut microbiota. The common features of fecal metabolites postantibiotic treatment include decreased levels of short chain fatty acids (SCFAs), amino acids and primary bile acids and increased oligosaccharides, d-pinitol, choline and secondary bile acids (deoxycholic acid). This suggests suppressed bacterial fermentation, protein degradation and enhanced gut microbial modification of bile acids. Barnesiella, Prevotella, and Alistipes levels were shown to decrease as a result of the antibiotic treatment, whereas levels of Bacteroides, Enterococcus and Erysipelotrichaceae incertae sedis, and Mycoplasma increased after gentamicin and ceftriaxone treatment. In addition, there was a strong correlation between fecal profiles and levels of Bacteroides, Barnesiella, Alistipes and Prevotella. The integration of metabonomics and gut microbiota profiling provides important information on the changes of gut microbiota and their impact on fecal profiles during the recovery after antibiotic treatment. The correlation between gut microbiota and fecal metabolites provides important information on the function of bacteria, which in turn could be important in optimizing therapeutic strategies, and developing potential microbiota-based disease preventions and therapeutic interventions.

Keywords:

gut microbiota; antibiotics; NMR; PCR-DGGE; fecal extracts; metabonomics