| Literature DB >> 33176677 |
Pauline Tirelle1,2, Jonathan Breton1,2,3, Gaëtan Riou2,4, Pierre Déchelotte1,2,3, Moïse Coëffier1,2,3, David Ribet5,6,7.
Abstract
BACKGROUND: The use of animal models with depleted intestinal microbiota has recently increased thanks to the huge interest in the potential role of these micro-organisms in human health. In particular, depletion of gut bacteria using antibiotics has recently become popular as it represents a low cost and easy alternative to germ-free animals. Various regimens of antibiotics are used in the literature, which differ in composition, dose, length of treatment and mode of administration. In order to help investigators in choosing the most appropriate protocol for their studies, we compared here three modes of antibiotic delivery to deplete gut bacteria in C57Bl/6 mice. We delivered one of the most frequently used combination of antibiotics (a mix of ampicillin, neomycin, metronidazole and vancomycin) either ad libitum in drinking water or by oral gavage once or twice per day.Entities:
Keywords: Antibiotics; Antifungals; Dysbiosis; Escherichia; Fecal bacteria; Gut microbiome; Gut microbiota; Intestinal microbiome; Intestinal microbiota; Mouse model
Mesh:
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Year: 2020 PMID: 33176677 PMCID: PMC7657353 DOI: 10.1186/s12866-020-02018-9
Source DB: PubMed Journal: BMC Microbiol ISSN: 1471-2180 Impact factor: 3.605
Fig. 1Effect of antibiotics on fecal bacterial density and fecal moisture content in mice. a Flow cytometry gating strategy for quantification of bacterial densities. Acquisition plots for fecal samples from untreated mice (no ATB; top) and mice treated with antibiotics by oral gavage twice a day during 1 week (2xG-ATB; bottom) are represented. A first gate was defined based on green fluorescence/SSC-A channels to exclude debris or background events (gate 1). A secondary gating was performed on events from gate 1 to count bacteria and to exclude events with low green fluorescence and high red auto-fluorescence intensities. The volume analyzed by the flow cytometer for each sample was determined by quantifying the number of calibrating beads detected in gate 2. b Flow cytometry-based quantification of bacterial density in mice feces during antibiotic treatment (means ± SEMs, n = 12–15; Kruskal-Wallis test with Dunn’s correction). c Flow cytometry-based quantification of bacterial density in mice feces during recovery from antibiotic treatment (means ± SEMs, n = 9–10; Kruskal-Wallis test with Dunn’s correction). d Moisture content in mice feces during antibiotic treatment (values are expressed as fold-change compared to untreated mice; means ± SEMs, n = 12–15; one-way ANOVA with Holm-Sidak’s correction). e Moisture content in mice feces during recovery from antibiotic treatment (values are expressed as fold-change compared to untreated mice; means ± SEMs, n = 8–10; one-way ANOVA with Holm-Sidak’s correction). Labeled plots without a common letter differ; P < 0.05; *, P < 0.05 versus “no ATB” group; **, P < 0.01; ***, P < 0.001; NS, not significant
Fig. 2Fecal abundance of different bacterial and fungal taxa. Relative quantification of different bacterial and fungal taxa in mouse feces after 4, 7 or 12 days of treatment, as determined by qPCR analysis (values are expressed as fold-change of the mean abundance in untreated mice and represented as whisker plots with minimum and maximum values, n = 4–5 per group; Labeled plots without a common letter differ; P < 0.05, one-way ANOVA with Tukey’s correction)
Fig. 3Effect of antibiotics on mouse gut microbiota composition. Principal components analyses of mice treated or not with antibiotics during 4, 7 or 12 days, based on the quantification of nine bacterial and fungal taxa by qPCR
Fig. 4Effect of antibiotics on body composition in mice. Fat and lean masses at Day 7 and Day 26 (values are expressed as percentage of body weight; means ± SEMs, n = 9–10; Labeled means without a common letter differ, P < 0.05; Kruskal-Wallis test with Dunn’s correction)