Our results indicated that the composition of microbial community could play a role in emotional change in mentally physically healthy adults.

No abstract

Inflammation is highly associated with colon carcinogenesis. Epigenetic mechanisms could play an important role in the initiation and progression of colon cancer. Curcumin, a dietary phytochemical, shows promising effects in suppressing colitis-associated colon cancer in azoxymethane-dextran sulfate sodium (AOM-DSS) mice. However, the potential epigenetic mechanisms of curcumin in colon cancer remain unknown. In this study, the anticancer effect of curcumin in suppressing colon cancer in an 18-week AOM-DSS colon cancer mouse model was confirmed. We identified lists of differentially expressed and differentially methylated genes in pairwise comparisons and several pathways involved in the potential anticancer effect of curcumin. These pathways include LPS/IL-1-mediated inhibition of RXR function, Nrf2-mediated oxidative stress response, production of NO and ROS in macrophages and IL-6 signaling. Among these genes, Tnf stood out with decreased DNA CpG methylation of Tnf in the AOM-DSS group and reversal of the AOM-DSS induced Tnf demethylation by curcumin. These observations in Tnf methylation correlated with increased and decreased Tnf expression in RNA-seq. The functional role of DNA methylation of Tnf was further confirmed by in vitro luciferase transcriptional activity assay. In addition, the DNA methylation level in a group of inflammatory genes was decreased in the AOM+DSS group but restored by curcumin and was validated by pyrosequencing. This study shows for the first time epigenomic changes in DNA CpG methylation in the inflammatory response from colitis-associated colon cancer and the reversal of their CpG methylation changes by curcumin. Future clinical epigenetic studies with curcumin in inflammation-associated colon cancer would be warranted.

This paper develops a ridge procedure for structural equation modeling (SEM) with ordinal and continuous data by modeling polychoric/polyserial/product-moment correlation matrix R. Rather than directly fitting R, the procedure fits a structural model to Ra = R + aI by minimizing the normal-distribution-based discrepancy function, where a > 0. Statistical properties of the parameter estimates are obtained. Four statistics for overall model evaluation are proposed. Empirical results indicate that the ridge procedure for SEM with ordinal data has better convergence rate, smaller bias, smaller mean square error and better overall model evaluation than the widely used maximum likelihood procedure.