Luteolin Reduces Zinc-Induced Tau Phosphorylation at Ser262/356 in an ROS-Dependent Manner in SH-SY5Y Cells

In brain, excess zinc alters the metabolism of amyloid precursor protein, leading to ??-amyloid protein deposition, one of the hallmarks of Alzheimer??s disease (AD) pathology. Recently, it has been reported that zinc accelerates in vitro tau fibrillization, another hallmark of AD. In the current study, we examined the effect of high-concentration zinc on tau phosphorylation in human neuroblastoma SH-SY5Y cells. We found that incubation of cells with zinc resulted in abnormal tau phosphorylation at Ser262/356. Moreover, the current study has investigated whether luteolin (Lu), a bioflavonoid, could decrease zinc-induced tau hyperphosphorylation and its underlying mechanisms. Using Western blot and protein phosphatase activity assay, activities of tau kinases and phosphatase were investigated. Our data suggest (1) that zinc induces tau hyperphosphorylation at Ser262/356 epitope and (2) that Lu efficiently attenuates zinc-induced tau hyperphosphorylation through not only its antioxidant action but also its regulation of the phosphorylation/dephosphorylation system.     

FastGCN: A GPU Accelerated Tool for Fast Gene Co-Expression Networks

Gene co-expression networks comprise one type of valuable biological networks. Many methods and tools have been published to construct gene co-expression networks; however, most of these tools and methods are inconvenient and time consuming for large datasets. We have developed a user-friendly, accelerated and optimized tool for constructing gene co-expression networks that can fully harness the parallel nature of GPU (Graphic Processing Unit) architectures. Genetic entropies were exploited to filter out genes with no or small expression changes in the raw data preprocessing step. Pearson correlation coefficients were then calculated. After that, we normalized these coefficients and employed the False Discovery Rate to control the multiple tests. At last, modules identification was conducted to construct the co-expression networks. All of these calculations were implemented on a GPU. We also compressed the coefficient matrix to save space. We compared the performance of the GPU implementation with those of multi-core CPU implementations with 16 CPU threads, single-thread C/C++ implementation and single-thread R implementation. Our results show that GPU implementation largely outperforms single-thread C/C++ implementation and single-thread R implementation, and GPU implementation outperforms multi-core CPU implementation when the number of genes increases. With the test dataset containing 16,000 genes and 590 individuals, we can achieve greater than 63 times the speed using a GPU implementation compared with a single-thread R implementation when 50 percent of genes were filtered out and about 80 times the speed when no genes were filtered out.