Chronic Melatonin Administration Reduced Oxidative Damage and Cellular Senescence in the Hippocampus of a Mouse Model of Down Syndrome

Previous studies have demonstrated that melatonin administration improves spatial learning and memory and hippocampal long-term potentiation in the adult Ts65Dn (TS) mouse, a model of Down syndrome (DS). This functional benefit of melatonin was accompanied by protection from cholinergic neurodegeneration and the attenuation of several hippocampal neuromorphological alterations in TS mice. Because oxidative stress contributes to the progression of cognitive deficits and neurodegeneration in DS, this study evaluates the antioxidant effects of melatonin in the brains of TS mice. Melatonin was administered to TS and control mice from 6 to 12 months of age and its effects on the oxidative state and levels of cellular senescence were evaluated. Melatonin treatment induced antioxidant and antiaging effects in the hippocampus of adult TS mice. Although melatonin administration did not regulate the activities of the main antioxidant enzymes (superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase, and glutathione S-transferase) in the cortex or hippocampus, melatonin decreased protein and lipid oxidative damage by reducing the thiobarbituric acid reactive substances (TBARS) and protein carbonyls (PC) levels in the TS hippocampus due to its ability to act as a free radical scavenger. Consistent with this reduction in oxidative stress, melatonin also decreased hippocampal senescence in TS animals by normalizing the density of senescence-associated â-galactosidase positive cells in the hippocampus. These results showed that this treatment attenuated the oxidative damage and cellular senescence in the brain of TS mice and support the use of melatonin as a potential therapeutic agent for age-related cognitive deficits and neurodegeneration in adults with DS

Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin