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Biology subjects

Seguin, R. P.

Publications and source records attributed to Seguin, R. P..

2 recordsLinked to original sources

Oral Exposure to Benzalkonium Chlorides in Male and Female Mice Reveals Sex-Dependent Alteration of the Gut Microbiome and Bile Acid Profile

Benzalkonium chlorides (BACs) are commonly used disinfectants in a variety of consumer and food-processing settings, and the COVID-19 pandemic has led to increased usage of BACs. The prevalence of BACs raises the concern that BAC exposure could disrupt the gastrointestinal microbiota, thus interfering with the beneficial functions of the microbes. We hypothesize that BAC exposure can alter the gut microbiome diversity and composition, which will disrupt bile acid homeostasis along the gut-liver axis. In this study, male and female mice were exposed orally to d7-C12- and d7-C16-BACs at 120 {micro}g/g/day for one week. UPLC-MS/MS analysis of liver, blood, and fecal samples of BAC-treated mice demonstrated the absorption and metabolism of BACs. Both parent BACs and their metabolites were detected in all exposed samples. Additionally, 16S rRNA sequencing was carried out on the bacterial DNA isolated from the cecum intestinal content. For female mice, and to a lesser extent in males, we found that treatment with either d7-C12- or d7-C16-BAC led to decreased alpha diversity and differential composition of gut bacteria with notably decreased actinobacteria phylum. Lastly, through a targeted bile acid quantitation analysis, we observed decreases in secondary bile acids in BAC-treated mice, which was more pronounced in the female mice. This finding is supported by decreases in bacteria known to metabolize primary bile acids into secondary bile acids, such as the families of Ruminococcaceae and Lachnospiraceae. Together, these data signify the potential impact of BACs on human health through disturbance of the gut microbiome and gut-liver interactions.

pharmacology and toxicology↗

High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites

Drug metabolite identification is a bottleneck of drug metabolism studies. Ion mobility-mass spectrometry (IM-MS) enables the measurement of collision cross section (CCS), a unique physical property related to an ions gas-phase size and shape, which can be used to increase the confidence in the identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we present the production of a large-scale database of drug and drug metabolite CCS values, assembled using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors. This novel prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers for the first time.

bioinformatics↗