Mo1808 Post-Partum Antibiotic Treatment Disturbs Development of the Intestinal Microbiota

Growing interest in the gut microbiome has revealed links between gut microbiome composition and many diseases including not only infections but also obesity, diabetes and inflammatory bowel disease. The microbiome composition can undergo changes as a result of interactions between gut microbiome and exogenous agents. However, the impact of commonly used pharmaceuticals on the gut microbiome has not been well characterized. In this study, we characterized the impact of 24 common pharmaceuticals on gut microbiome compositional shift in mice and in a human fecal culture system. Mice were dosed with each optimized pharmaceutical compound daily for five consecutive days. Fecal samples were collected on day -1, 0, 4 and 5 of the administration. For the human fecal culture system, human stool samples were collected and immediately stored at -80 degree C. After resuspension of the sample in reduced medium, fecal cultures were incubated with individual compounds for 24 hours in an anaerobic chamber. DNA was extracted, then the V4 region of 16S rRNA gene was amplified by PCR and sequenced using the Illumina MiSeq. A negative binomial Wald test was used to detect microbial taxa difference. Principal component analysis exhibits greatest shift in mice microbiome by antimicrobial drugs such as Azithromycin, Amoxicillin and Metronidazole as well as Type 2 diabetes drugs such as Acarbose and Metformin between day 0 and day 5. Permutational multivariate analysis of variance using Euclidean distance exhibited greatest significant microbiome shift by Acarbose, Amoxicillin and Metformin (p 0.1). These results suggest different impact of administration of antimicrobial agents (e.g. protein synthesis inhibitor) and modulation of glucose availability to the intestinal microbiome. Similar microbiome shifts were also observed in the anaerobic human fecal culture system. Understanding the impact of pharmaceuticals to the gut microbiome could ultimately be useful in improving predictions of the therapeutic intervention outcomes on variety of individuals.