The small intestinal microbiota has recently been implicated in contributing to metabolic disease. We previously demonstrated that diets rich in saturated milk fat have a particularly strong impact on the small bowel microbiota as opposed to more distal gastrointestinal (GI) regions. However, the impact of antibiotics and diet on the small bowel microbiota has not been clearly demonstrated. Thus, we sought to determine how diet and antibiotics interact in modulating the regional landscape of the gut microbiota. We conducted a study using male mice on a high fat (HF) or a low fat (LF) diet (n = 15/group) that received either water control (n = 5/diet), rifaximin, (nonabsorbable broad-spectrum antibiotic; n = 5/diet) or an antibiotic cocktail consisting of metronidazole, cefoperazone, vancomycin, and neomycin (Abx cocktail; n = 5/diet). 16S rRNA sequencing was performed on mucosal scrapings collected from the small intestine and cecum, as well as on stool samples. Interestingly, antibiotics had a significant effect on community composition throughout the small intestine, cecum and stool, whereas diet significantly affected only the jejunum and cecum microbiota. The antibiotic cocktail, regardless of diet, was most effective in increasing cecum size, reducing body fat percentage, and plasma lipid levels. Altogether, this study reveals a selective and divergent regional alteration of the gut microbiota by diet and antibiotics.

Background Understanding sources of microbial contamination in outpatient rehabilitation (REHAB) clinics is important to patients and healthcare providers. Purpose The purpose of this study was to characterize the microbiome of an outpatient REHAB clinic and examine relationships between clinic factors and contamination. Methods Forty commonly contacted surfaces in an outpatient REHAB clinic were observed for frequency of contact and swiped using environmental sample collection kits. Surfaces were categorized based on frequency of contact and cleaning and surface type. Total bacterial and fungal load was assessed using primer sets specific for the 16S rRNA and ITS genes, respectively. Bacterial samples were sequenced using the Illumina system and analyzed using Illumina-utils, Minimum Entropy Decomposition, QIIME2 (for alpha and beta diversity), LEfSe and ANCOM-BC for taxonomic differential abundance and ADONIS to test for differences in beta diversity (p<0.05). Results Porous surfaces had more bacterial DNA compared to non-porous surfaces (median non-porous = 0.0016ng/μL, 95%CI = 0.0077–0.00024ng/μL, N = 15; porous = 0.0084 ng/μL, 95%CI = 0.0046–0.019 ng/μL, N = 18. p = 0.0066,DNA. Samples clustered by type of surface with non-porous surfaces further differentiated by those contacted by hand versus foot. ADONIS two-way ANOVA showed that the interaction of porosity and contact frequency (but neither alone) had a significant effect on 16S communities (F = 1.7234, R2 = 0.0609, p = 0.032). Discussion Porosity of surfaces and the way they are contacted may play an underestimated, but important role in microbial contamination. Additional research involving a broader range of clinics is required to confirm results. Results suggest that surface and contact-specific cleaning and hygiene measures may be needed for optimal sanitization in outpatient REHAB clinics.