Gut Microbiota Characteristics in Mice with Antibiotic-Associated Diarrhea

Abstract

Background: Antibiotic-associated diarrhea (AAD), defined as diarrhea that occurs in association with the administration of antibiotics and without another clear etiology, is one of the most commonly adverse drug events of antibiotics therapy. We established a diarrhea model induced by gentamycin and cefradine to investigate the microbiota characteristics in the intestinal lumen of mice with AAD and provide insights into noteworthy bacteria related to gentamicin and cefradine-associated diarrhea. Results: The number of OTUs in the model group and the normal group was 983 and 2107, respectively, and 872 identical OTUs were shared between two groups. Species richness and species diversity of intestinal microbe were altered by antibiotics administration. PCoA showed a clear separation between AAD and health control. The dominant phyla of AAD mice were Firmicutes (52.63%) and Proteobacteria (46.37%). Among the genus with top 20 abundance, the relative abundance of 7 genera, Ruminococcus, Blautia, Enterococcus, Eubacterium, Clostridium, Coprococcus, and Aerococcus, were enriched in the model group. Based upon the LEfSe analysis, Enterococcus, Eubacterium, Ruminococcus, and Blautia were identified as potential biomarker for AAD. Conclusions: The bacterial diversity of the intestinal lumen was diminished after gentamicin and cefradine administration. The alterations in the abundance and composition of gut microbiota further led to the dysfunction of gut microbiota. More specifically, gentamicin and cefradine significantly increased the abundance of the opportunistic pathogens, of which Enterococcus and Clostridium were the most prominent and most worthy of attention.

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“…The Tukey's HSD test was used to evaluate α-indices among three groups and Welch's t test was used between two groups. βdiversity analysis was performed by the VennDiagram package 30 of the R project which used to analyze element distribution, while the Vegan package in the R project was utilized for principal component analysis (PCA), and PCoA (principal coordinates analysis) 31 and NMDS (non-metric multi-dimensional scaling) 32 of Bray-Curtis distances were calculated for multivariate statistical techniques.…”