Genetic variants in protein kinase C zeta gene and type 2 diabetes risk: a case-control study of a Chinese Han population
DIABETES-METABOLISM RESEARCH AND REVIEWS
Authors: Qin, Lei; Zhou, Ling; Wu, Xueji; Cheng, Jinluo; Wang, Jingsong; Du, Yunfeng; Hu, Jihong; Shen, Moyu; Zhou, Jinyi
Abstract
Background Previous investigations of the protein kinase C zeta (PRKCZ) gene, a susceptibility factor for type 2 diabetes mellitus (T2DM), have focussed on its single nucleotide polymorphisms (SNPs). In a departure from these earlier studies, we tested six tagging markers of PRKCZ based on the Hapmap database to evaluate their association with T2DM. Methods PCR-RFLP was performed to test the genotypes in type 2 diabetes patients (n = 343) and normal controls (n = 429). Insulin-related indexes were calculated from levels of fasting plasma insulin (FPI) and fasting plasma glucose (FPG). Results The variant genotypes, rs3753242TT, rs2503706CT, rs2503706CT/ TT and rs262654TT were associated with T2DM. And the significance remained for rs3753242 TT (p < 0.001) after the false discovery rate (FDR) correction. There was a doubling in the prevalence of the TTGCCC haplotype in the case subjects (14.2%) compared with the control subjects (6.9%) [OR (95% CI) = 2.19(1.49-3.22)]. In the control group, none of the haplotypes studied was associated with insulin-related indexes. Conclusions The PRKCZ gene variants associated with the development of T2DM in this study must be investigated in a larger population to reveal any potential effects on metabolism. Copyright (C) 2008 John Wiley & Sons, Ltd.
Systems Biology Approach to Transplant Tolerance: Proof of Concept Experiments Using RNA Interference (RNAi) to Knock Down Hub Genes in Jurkat and HeLa Cells In Vitro
JOURNAL OF SURGICAL RESEARCH
Authors: Lwin, Wint Wah; Park, Ken; Wauson, Matthew; Gao, Qin; Finn, Patricia W.; Perkins, David; Khanna, Ajai
Abstract
Background. Systems biology is gaining importance in studying complex systems such as the functional interconnections of human genes [1]. To investigate the molecular interactions involved in T cell immune responses, we used databases of physical gene-gene interactions to constructed molecular interaction networks (interconnections) with R language algorithms. This helped to identify highly interconnected "hub'' genes AT(1) P5C1, IL6ST, PRKCZ, MYC, FOS, JUN, and MAPK1. We hypothesized that suppression of these hub genes in the gene network would result in significant phenotypic effects on T cells and examined this in vitro. The molecular interaction networks were then analyzed and visualized with Cytoscape. Materials and Methods. Jurkat and HeLa cells were transfected with siRNA for the selected hub genes. Cell proliferation was measured using ATP luminescence and BrdU labeling, which were measured 36, 72, and 96 h after activation. Results. Following T cell stimulation, we found a significant decrease in ATP production (P < 0.05) when the hub genes ATP5C1 and PRKCZ were knocked down using siRNA transfection, whereas no difference in ATP production was observed in siRNA transfected HeLa cells. However, HeLa cells showed a significant (P < 0.05) decrease in cell proliferation when the genes MAPK1, IL6ST, ATP5C1, JUN, and FOS were knocked down. Conclusion. In both Jurkat and HeLa cells, targeted gene knockdown using siRNA showed decreased cell proliferation and ATP production in both Jurkat and HeLa cells. However, Jurkat T cells and HELA cells use different hub genes to regulate activation responses. This experiment provides proof of principle of applying siRNA knockdown of T cell hub genes to evaluate their proliferative capacity and ATP production. This novel concept outlines a systems biology approach to identify hub genes for targeted therapeutics. Published by Elsevier Inc.