Network Topology Reveals Key Cardiovascular Disease Genes

The structure of protein-protein interaction (PPI) networks has already been successfully used as a source of new biological information. Even though cardiovascular diseases (CVDs) are a major global cause of death, many CVD genes still await discovery. We explore ways to utilize the structure of the human PPI network to find important genes for CVDs that should be targeted by drugs. The hope is to use the properties of such important genes to predict new ones, which would in turn improve a choice of therapy. We propose a methodology that examines the PPI network wiring around genes involved in CVDs. We use the methodology to identify a subset of CVD-related genes that are statistically significantly enriched in drug targets and “driver genes.” We seek such genes, since driver genes have been proposed to drive onset and progression of a disease. Our identified subset of CVD genes has a large overlap with the Core Diseasome, which has been postulated to be the key to disease formation and hence should be the primary object of therapeutic intervention. This indicates that our methodology identifies “key” genes responsible for CVDs. Thus, we use it to predict new CVD genes and we validate over 70% of our predictions in the literature. Finally, we show that our predicted genes are functionally similar to currently known CVD drug targets, which confirms a potential utility of our methodology towards improving therapy for CVDs.     

Highly Selective Anti-Cancer Activity of Cholesterol-Interacting Agents Methyl-β-Cyclodextrin and Ostreolysin A/Pleurotolysin B Protein Complex on Urothelial Cancer Cells

Cholesterol content can vary distinctly between normal and cancer cells, with elevated levels in cancer cells. Here, we investigated cholesterol sequestration with methyl-β-cyclodextrin (MCD), and pore-formation with the ostreolysin A/pleurotolysin B (OlyA/PlyB) protein complex that binds to cholesterol/sphingomyelin-rich membrane domains. We evaluated the effects on viability of T24 invasive and RT4 noninvasive human urothelial cancer cells and normal porcine urothelial (NPU) cells. Cholesterol content strongly correlated with cancerous transformation, as highest in the T24 high-grade invasive urothelial cancer cells, and lowest in NPU cells. MCD treatment induced prominent cell death of T24 cells, whereas OlyA/PlyB treatment resulted in greatly decreased viability of the RT4 low-grade noninvasive carcinoma cells. Biochemical and transmission electron microscopy analyses revealed that MCD and OlyA/PlyB induce necrotic cell death in these cancer cells, while viability of NPU cells was not significantly affected by either treatment. We conclude that MCD is more toxic for T24 high-grade invasive urothelial cancer cells, and OlyA/PlyB for RT4 low-grade noninvasive urothelial cancer cells, and neither is toxic for NPU cells. The cholesterol and cholesterol/sphingomyelin-rich membrane domains in urothelial cancer cells thus constitute a selective therapeutic target for elimination of urothelial cancer cells.     

Optimized Null Model for Protein Structure Networks

Much attention has recently been given to the statistical significance of topological features observed in biological networks. Here, we consider residue interaction graphs (RIGs) as network representations of protein structures with residues as nodes and inter-residue interactions as edges. Degree-preserving randomized models have been widely used for this purpose in biomolecular networks. However, such a single summary statistic of a network may not be detailed enough to capture the complex topological characteristics of protein structures and their network counterparts. Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them. The RIGs are derived from a structurally diverse protein data set at various distance cut-offs and for different groups of interacting atoms. We compare the network structure of RIGs to several random graph models. We show that 3-dimensional geometric random graphs, that model spatial relationships between objects, provide the best fit to RIGs. We investigate the relationship between the strength of the fit and various protein structural features. We show that the fit depends on protein size, structural class, and thermostability, but not on quaternary structure. We apply our model to the identification of significantly over-represented structural building blocks, i.e., network motifs, in protein structure networks. As expected, choosing geometric graphs as a null model results in the most specific identification of motifs. Our geometric random graph model may facilitate further graph-based studies of protein conformation space and have important implications for protein structure comparison and prediction. The choice of a well-fitting null model is crucial for finding structural motifs that play an important role in protein folding, stability and function. To our knowledge, this is the first study that addresses the challenge of finding an optimized null model for RIGs, by comparing various RIG definitions against a series of network models.     

Inhibition of copper-induced lipid peroxidation by sinapic acid and its derivatives in correlation to their effect on the membrane structural properties

The efficiencies of sinapic acid and its derivatives syringic acid, syringaldehyde, three sinapoyl esters (ethyl, propyl, butyl sinapates), 4-vinylsyringol and sinapine were investigated for prevention of lipid peroxidation in correlation with their interactions with model lipid membrane systems. Significant antioxidant activities of propyl and butyl sinapates were seen by fluorimetric assay in phosphatidylcholine liposomes as model membrane using C11-BODIPY^581/591 lipophilic fluorescent probe. The sinapic acid esters also had the highest impact on membrane structural properties, as observed by differential scanning calorimetry and fluorescence polarisation measurements. The greatest protection of phospholipids from peroxidation by these esters correlated well with their polarity and insertion into the lipid bilayer.     

Cathepsin X cleaves the C-terminal dipeptide of alpha- and gamma-enolase and impairs survival and neuritogenesis of neuronal cells

The cysteine carboxypeptidase cathepsin X has been recognized as an important player in degenerative processes during normal aging and in pathological conditions. In this study we identify isozymes alpha- and gamma-enolases as targets for cathepsin X. Cathepsin X sequentially cleaves C-terminal amino acids of both isozymes, abolishing their neurotrophic activity. Neuronal cell survival and neuritogenesis are, in this way, regulated, as shown on pheochromocytoma cell line PC12. Inhibition of cathepsin X activity increases generation of plasmin, essential for neuronal differentiation and changes the length distribution of neurites, especially in the early phase of neurite outgrowth. Moreover, cathepsin X inhibition increases neuronal survival and reduces serum deprivation induced apoptosis, particularly in the absence of nerve growth factor. On the other hand, the proliferation of cells is decreased, indicating induction of differentiation. Our study reveals enolase isozymes as crucial neurotrophic factors that are regulated by the proteolytic activity of cathepsin X.     

Expression of cell adhesion molecule,Gicerin/CD146 during the formation of heart and in the cardiac hypertrophy

Molecular and Cellular Biochemistry - Gicerin/CD146 is a cell adhesion molecule which belongs to the immunoglobulin (Ig) superfamily. We have reported the existence of gicerin/CD146 in the nervous...