Project description:The rate of probiotic usage by pregnant women in the US and Canada ranges from 1.3 to 3.6 %. Probiotic supplements are available without a prescription and have gained currency in treating a variety of ailment ranging from reducing risk of constipation, diarrhea, other gastrointestinal conditions, eczema, pre-term birth, and prevent adverse pregnancy outcomes, including gestational diabetes mellitus (GDM) and depression/anxiety. Three possible mechanisms by which maternal probiotic supplementation might influence the placenta are through 1) directly impacting possible bacteria residing in the placenta (placenta microbiome), 2) altering bacterial metabolites produced by gut microbiota within the mother that induce placental changes, and 3) maternal probiotics might affect the composition of the bacteria within the maternal gut that affects her immune cells and their responses to the heterologous placenta. For the second potential mechanism, bacterial metabolites that might influence placenta include short chain fatty acids (SCFAs), polyamines (PAs), and Vitamins B9 (Folic Acid) and 12 (Cobalamin), among others. This project aims to determine the effects maternal probiotic supplementation in mice might have on the fetal placenta. With the number of women taking over probiotic supplements increasing, further research is needed to determine how these bioactive agents may affect the placenta and health of the offspring.
Project description:Probiotic bacteria, specific representatives of bacterial species that are a common part of the human microbiota, are proposed to deliver health benefits to the consumer by modulation of intestinal function via largely unknown molecular mechanisms. To explore in vivo mucosal responses of healthy adults to probiotics, we obtained transcriptomes in an intervention study following a double-blind placebo-controlled cross-over design. In the mucosa of the proximal small intestine of healthy volunteers, probiotic strains from the species Lactobacillus acidophilus, L. casei and L. rhamnosus each induced differential gene regulatory networks and pathways in the human mucosa. Comprehensive analyses revealed that these transcriptional networks regulate major basal mucosal processes, and uncovered remarkable similarity to response profiles obtained for specific bioactive molecules and drugs. This study elucidates how intestinal mucosa of healthy humans perceive different probiotics and provides avenues for rationally designed tests of clinical applications. Keywords: mucosal response of healthy adult humans to lactic acid bacteria
Project description:Probiotic bacteria, specific representatives of bacterial species that are a common part of the human microbiota, are proposed to deliver health benefits to the consumer by modulation of intestinal function via largely unknown molecular mechanisms. To explore in vivo mucosal responses of healthy adults to probiotics, we obtained transcriptomes in an intervention study following a double-blind placebo-controlled cross-over design. In the mucosa of the proximal small intestine of healthy volunteers, probiotic strains from the species Lactobacillus acidophilus, L. casei and L. rhamnosus each induced differential gene regulatory networks and pathways in the human mucosa. Comprehensive analyses revealed that these transcriptional networks regulate major basal mucosal processes, and uncovered remarkable similarity to response profiles obtained for specific bioactive molecules and drugs. This study elucidates how intestinal mucosa of healthy humans perceive different probiotics and provides avenues for rationally designed tests of clinical applications. Keywords: mucosal response of healthy adult humans to lactic acid bacteria This study was set up according to a randomised double-blind cross-over placebo-controlled design. It contains transcriptional profiles from biopsies from 7 healthy individuals after oral intake of three different Lactobacillus species or placebo control. In total, this study includes data from 7 individuals x 4 treatments=28 arrays.