• Lonzo Suhr posted an update 4 months ago

    Several studies have investigated PD associated with individual drugs. In the present study, we systematically studied PD of drug-related genes by simultaneously considering all reported DR genes. This integrative approach may help clarify the inconsistent genetic features of drug response associated with PD. Furthermore, our findings will improve the study and prediction of drug responses that differ among populations due to genetic stratification. To investigate the biological differences between the HD and LD gene groups, we performed a GO analysis and a pathway analysis using the Database for Annotation, Visualization and SAG 21k Integrated Discovery v6.7 functional annotation tool. Annotated genes from each group were used as the input, while a list of whole genes in DAVID with at least one annotation in the analyzing categories was used as the background. For the GO analysis, the following three categories were selected: biological process, molecular function, and cellular component. For the pathway analysis, the Kyoto Encyclopedia of Genes and Genomes pathway was used. Additional GO and pathway analyses were performed in a similar manner in order to compare genes in the HD gene group to those in the DR gene group. In this case, the DR HD gene group was used as the input for analysis, and the DR gene group was used as the background. To correct for multiple tests, we used the hypergeometric test from Benjamini-Hochberg’s method. Fold enrichments, defined as the ratios of proportions between the input and background, were calculated for each term. Terms with Benjamini-Hochberg’s q-values of 0.05 or lower were considered significant. PD is important for understanding differences in drug responses among populations. However, PD often refers to the distance between two different subpopulations; therefore, several studies have investigated approaches for averaging the PD of each SNP. For instance, the impact of SNP ascertainment on estimating the distance between subpopulations has already been reported. In contrast, the present study identified population-specific pharmacogenomics variants. We did not focus on identifying average distances using all SNPs; rather, we used each SNP to identify population-specific pharmacogenomics variants. As a result, our results described the impact of sample ascertainment on different measures of PD for each SNP. In addition, the present study investigated PD of genes in the PharmGKB database, while several previous studies have focused on genes related to individual drugs. This approach enabled us to more systematically study PD of DR genes by considering all reported DR genes from PharmGKB. In conclusion, the present study describes an approach for assessing PD associated with multiple drugs using a database. Therefore, the integrated approach may identify valid genetic features different from the background gene list. We validated results from other systematic analyses. Moreover, our approach allows the possibility of improving the results. DR genes that are unknown or newly reported were not included in the present study. Thus, our approach may be limited in its ability to interpret the population-specific difference in drug response or efficacy caused by genetic divergence. However, this method remains convincing, because our statistical analyses revealed high specificity and sensitivity robust to sample size. Furthermore, we obtained significant differences from other DR genes in the PharmGKB database, and our approach thus represents a systematic method for identifying valid population-specific pharmacogenomics variants. Human immunodeficiency virus protease inhibitors are the major components of highly active anti-retroviral therapy and have been successfully used to control disease progression in HIV-1 patients. However, the decline in morbidity and mortality has been clouded by the emergence of a number of metabolic derangements. The prevalence of dyslipidemia in patients receiving HIV PIs is more than 50%, which significantly increases the risk of cardiovascular disease. Although cellular/molecular mechanisms underlying HIV PIinduced CVD remain to be fully elucidated, sufficient evidence suggests that lipid accumulation, inflammation, and activation of endoplasmic reticulum stress are all involved in HIV PIinduced cardiovascular complications and metabolic syndromes. Several mechanisms including modulation of AMPdependent protein kinase activity and regulation of tyrosine kinase, Akt and NF-kB signaling are identified to be associated with the beneficial effects of berberine on improvement of obesity-associated lipid dysregulation and inhibition of vascular and intestinal inflammation. Our previous study also indicated that inhibition of ER stress by BBR represents a key mechanism by which this molecule prevents the HIV PI-induced inflammatory response.