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Glycogen synthase kinase 3 (GSK3) is a serine/threonine kinase which in the presence of ATP in its ATP-binding pocket transfers a phosphate to a primed substrate. GSK3β is an isoform of GSK3 which has been projected as a potent therapeutic target in human diseases including cancers and metabolic syndrome. Incidentally, cardiovascular disease is a common cause of non-cancer related deaths in prostate cancer (PCa) patients, mainly due to the effects of androgen-deprivation therapy (ADT), a mainstay for PCa treatment. Several small molecular inhibitors of GSK3 are either ATP-competitive (bind to the ATP-binding pocket), or non-ATP-competitive inhibitors (binding to the substrate-binding site of the enzyme). In this study, 2-β-D-glucopyranosyl-1,3,6,7-tetrahydroxy-9H-xanthen-9-one (βDGT), a natural xanthonoid present in many plant species, is reported to bind to the ATP-binding pocket of GSK3β and inhibit its activity, as demonstrated by the molecular docking and molecular dynamics simulation analysis and experimental validation in vitro . A comparison of the binding affinities with five known ATP-competitive inhibitors of GSK3β suggested similarity in binding site residues in the ATP-binding pocket of the enzyme. The optimum inhibitory concentration of the xanthonoid as determined by the luminescent kinase assay was 200 µM. The study envisages the use of βDGT as a natural ATP-competitive inhibitor of GSK3β and implicates its use in PCa patients on ADT, a cardiovascular disease risk, and other pathological conditions where GSK3 inhibition may be clinically important. HighlightsGSK3β is a multifaceted kinase known for its role in cancers, cardiovascular, and other diseases.In this study, βDGT, a xanthonoid, is reported to bind to the ATP-binding pocket of GSK3β.A comparison of βDGT binding with 5 known ATP-competitive inhibitors of GSK3β suggested the involvement of residues at the ATP binding site.The binding site analysis suggested an ATP-competitive mechanism of enzyme inhibition.Study envisages the use of βDGT as a natural ATP-competitive inhibitor of GSK3β and implicates its use in prostate cancer patients on androgen-deprivation therapy, a cardiovascular disease risk, and other pathological conditions.Communicated by Ramaswamy H. Sarma.

Complex disease networks can be studied successfully using network theoretical approach which helps in finding key disease genes and associated disease modules. We studied prostate cancer (PCa) protein-protein interaction (PPI) network constructed from patients' gene expression datasets and found that the network exhibits hierarchical scale free topology which lacks centrality lethality rule. Knockout experiments of the sets of leading hubs from the network leads to transition from hierarchical (HN) to scale free (SF) topology affecting network integration and organization. This transition, HN → SF, due to removal of significant number of the highest degree hubs, leads to relatively decrease in information processing efficiency, cost effectiveness of signal propagation, compactness, clustering of nodes and energy distributions. A systematic transition from a diassortative PCa PPI network to assortative networks after the removal of top 50 hubs then again reverting to disassortativity nature on further removal of the hubs was also observed indicating the dominance of the largest hubs in PCa network intergration. Further, functional classification of the hubs done by using within module degrees and participation coefficients for PCa network, and leading hubs knockout experiments indicated that kinless hubs serve as the basis of establishing links among constituting modules and heterogeneous nodes to maintain network stabilization. We, then, checked the essentiality of the hubs in the knockout experiment by performing Fisher's exact test on the hubs, and showed that removal of kinless hubs corresponded to maximum lethality in the network. However, excess removal of these hubs essentially may cause network breakdown., (Copyright © 2020. Published by Elsevier Inc.)

Identification of key regulators and regulatory pathways is an important step in the discovery of genes involved in cancer. Here, we propose a method to identify key regulators in prostate cancer (PCa) from a network constructed from gene expression datasets of PCa patients. Overexpressed genes were identified using BioXpress, having a mutational status according to COSMIC, followed by the construction of PCa Interactome network using the curated genes. The topological parameters of the network exhibited power law nature indicating hierarchical scale-free properties and five levels of organization. Highest degree hubs (k ≥ 65) were selected from the PCa network, traced, and 19 of them was identified as novel key regulators, as they participated at all network levels serving as backbone. Of the 19 hubs, some have been reported in literature to be associated with PCa and other cancers. Based on participation coefficient values most of these are connector or kinless hubs suggesting significant roles in modular linkage. The observation of non-monotonicity in the rich club formation suggested the importance of intermediate hubs in network integration, and they may play crucial roles in network stabilization. The network was self-organized as evident from fractal nature in topological parameters of it and lacked a central control mechanism.