Structure-based design, synthesis, and biological evaluation of dihydroquinazoline-derived potent β-secretase inhibitors

Structure-based design, synthesis, and biological evaluation of a series of dihydroquinazoline-derived β-secretase inhibitors incorporating thiazole and pyrazole-derived P2-ligands are described. We have identified inhibitor 4f which has shown potent enzyme inhibitory (K(i)=13nM) and cellular (IC(50)=21nM in neuroblastoma cells) assays. A model of 4f was created based upon the X-ray structure of 3a-bound β-secretase. The model suggested possible interactions in the active site.     

RNA-binding protein HuD controls insulin translation

Although expression of the mammalian RNA-binding protein HuD was considered to be restricted to neurons, we report that HuD is present in pancreatic β cells, where its levels are controlled by the insulin receptor pathway. We found that HuD associated with a 22-nucleotide segment of the 5' untranslated region (UTR) of preproinsulin (Ins2) mRNA. Modulating HuD abundance did not alter Ins2 mRNA levels, but HuD overexpression decreased Ins2 mRNA translation and insulin production, and conversely, HuD silencing enhanced Ins2 mRNA translation and insulin production. Following treatment with glucose, HuD rapidly dissociated from Ins2 mRNA and enabled insulin biosynthesis. Importantly, HuD-knockout mice displayed higher insulin levels in pancreatic islets, while HuD-overexpressing mice exhibited lower insulin levels in islets and in plasma. In sum, our results identify HuD as a pivotal regulator of insulin translation in pancreatic β cells.     

Data-driven analysis approach for biomarker discovery using molecular-profiling technologies.

High-throughput molecular-profiling technologies provide rapid, efficient and systematic approaches to search for biomarkers. Supervised learning algorithms are naturally suited to analyse a large amount of data generated using these technologies in biomarker discovery efforts. The study demonstrates with two examples a data-driven analysis approach to analysis of large complicated datasets collected in high-throughput technologies in the context of biomarker discovery. The approach consists of two analytic steps: an initial unsupervised analysis to obtain accurate knowledge about sample clustering, followed by a second supervised analysis to identify a small set of putative biomarkers for further experimental characterization. By comparing the most widely applied clustering algorithms using a leukaemia DNA microarray dataset, it was established that principal component analysis-assisted projections of samples from a high-dimensional molecular feature space into a few low dimensional subspaces provides a more effective and accurate way to explore visually and identify data structures that confirm intended experimental effects based on expected group membership. A supervised analysis method, shrunken centroid algorithm, was chosen to take knowledge of sample clustering gained or confirmed by the first step of the analysis to identify a small set of molecules as candidate biomarkers for further experimentation. The approach was applied to two molecular-profiling studies. In the first study, PCA-assisted analysis of DNA microarray data revealed that discrete data structures exist in rat liver gene expression and correlated with blood clinical chemistry and liver pathological damage in response to a chemical toxicant diethylhexylphthalate, a peroxisome-proliferator-activator receptor agonist. Sixteen genes were then identified by shrunken centroid algorithm as the best candidate biomarkers for liver damage. Functional annotations of these genes revealed roles in acute phase response, lipid and fatty acid metabolism and they are functionally relevant to the observed toxicities. In the second study, 26 urine ions identified from a GC/MS spectrum, two of which were glucose fragment ions included as positive controls, showed robust changes with the development of diabetes in Zucker diabetic fatty rats. Further experiments are needed to define their chemical identities and establish functional relevancy to disease development.