Development of a gut microbiota model for the analysis of bacterial modifications of xenobiotics

The human gut microbiota influences host physiology by metabolizing xenobiotics, such as drugs, dietary additives, and environmental contaminants. To enable standardized assessment of microbial xenobiotic metabolism, we established a defined bacterial colon model from pooled human fecal samples, by preparing clean bacterial cell suspensions using density gradient centrifugation. The bacterial cell fraction remained structurally intact, allowing long-term preservation at -70°C with glycerol as a cryoprotectant. The bacteria were fully reactivated without compromising cellular integrity. Glycerol as a disturbing substrate was removed by washing steps. The system preserved microbial diversity, enzymatic activity, and metabolic functionality over 24 hours under anaerobic conditions. Unlike fecal-based models, the model was free from non-cell contaminants, which could cause unpredictable interactions between test compounds and reactive components within the fecal matrix. Enzymatic assays demonstrated hydrolytic, reductive, and proteolytic activities comparable to native feces. 16S rRNA gene sequencing confirmed taxonomic stability and compositional shifts in response to different substrates. A fiber-rich substrate proved to be optimal for maintaining the bacterial composition over 24 hours. We further applied the model to investigate the microbial biotransformation of selected model xenobiotics using high-performance liquid chromatography and mass spectrometry, revealing substrate-specific metabolite formation. A versatile addition of food ingredients, dietary supplements, pharmaceuticals, and environmental chemicals is possible to analyze their effects on the composition of the microbiota, its enzymatic activity, and the excretion of metabolites and end products. Together, this bacterial colon model provides a reproducible, high-throughput capable and ethically viable platform for pharmacomicrobiomic studies and next-generation risk assessment.