Se-methylselenocysteine inhibits phosphatidylinositol 3-kinase activity of mouse mammary epithelial tumor cells in vitro

INTRODUCTION: Se-methylselenocysteine (MSC), a naturally occurring selenium compound, is a promising chemopreventive agent against in vivo and in vitro models of carcinogen-induced mouse and rat mammary tumorigenesis. We have demonstrated previously that MSC induces apoptosis after a cell growth arrest in S phase in a mouse mammary epithelial tumor cell model (TM6 cells) in vitro. The present study was designed to examine the involvement of the phosphatidylinositol 3-kinase (PI3-K) pathway in TM6 tumor model in vitro after treatment with MSC. METHODS: Synchronized TM6 cells treated with MSC and collected at different time points were examined for PI3-K activity and Akt phosphorylation along with phosphorylations of Raf, MAP kinase/ERK kinase (MEK), extracellular signal-related kinase (ERK) and p38 mitogen-activated protein kinase (MAPK). The growth inhibition was determined with a [(3)H]thymidine incorporation assay. Immunoblotting and a kinase assay were used to examine the molecules of the survival pathway. RESULTS: PI3-K activity was inhibited by MSC followed by dephosphorylation of Akt. The phosphorylation of p38 MAPK was also downregulated after these cells were treated with MSC. In parallel experiments MSC inhibited the Raf–MEK–ERK signaling pathway. CONCLUSION: These studies suggest that MSC blocks multiple signaling pathways in mouse mammary tumor cells. MSC inhibits cell growth by inhibiting the activity of PI3-K and its downstream effector molecules in mouse mammary tumor cells in vitro

Gradient Descent Optimization in Gene Regulatory Pathways

BACKGROUND: Gene Regulatory Networks (GRNs) have become a major focus of interest in recent years. Elucidating the architecture and dynamics of large scale gene regulatory networks is an important goal in systems biology. The knowledge of the gene regulatory networks further gives insights about gene regulatory pathways. This information leads to many potential applications in medicine and molecular biology, examples of which are identification of metabolic pathways, complex genetic diseases, drug discovery and toxicology analysis. High-throughput technologies allow studying various aspects of gene regulatory networks on a genome-wide scale and we will discuss recent advances as well as limitations and future challenges for gene network modeling. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms. METHODOLOGY: In the present article, we introduce a new method for identifying a set of optimal gene regulatory pathways by using structural equations as a tool for modeling gene regulatory networks. The method, first of all, generates data on reaction flows in a pathway. A set of constraints is formulated incorporating weighting coefficients. Finally the gene regulatory pathways are obtained through optimization of an objective function with respect to these weighting coefficients. The effectiveness of the present method is successfully tested on ten gene regulatory networks existing in the literature. A comparative study with the existing extreme pathway analysis also forms a part of this investigation. The results compare favorably with earlier experimental results. The validated pathways point to a combination of previously documented and novel findings. CONCLUSIONS: We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. The present method has been successful in deriving the optimal regulatory pathways for all the regulatory networks considered. The biological significance and applicability of the optimal pathways has also been discussed. Finally the usefulness of the present method on genetic engineering is depicted with an example