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Eating a "Westernized" diet high in fat and sugar leads to weight gain and numerous health problems, including the development of type 2 diabetes mellitus (T2DM). Rodent studies have shown that resveratrol supplementation reduces blood glucose levels, preserves β-cells in islets of Langerhans, and improves insulin action. Although rodent models are helpful for understanding β-cell biology and certain aspects of T2DM pathology, they fail to reproduce the complexity of the human disease as well as that of nonhuman primates. Rhesus monkeys were fed a standard diet (SD), or a high-fat/high-sugar diet in combination with either placebo (HFS) or resveratrol (HFS+Resv) for 24 months, and pancreata were examined before overt dysglycemia occurred. Increased glucose-stimulated insulin secretion and insulin resistance occurred in both HFS and HFS+Resv diets compared with SD. Although islet size was unaffected, there was a significant decrease in β-cell and an increase in α-cells containing glucagon and glucagon-like peptide 1 with HFS diets. Islets from HFS+Resv monkeys were morphologically similar to SD. HFS diets also resulted in decreased expression of essential β-cell transcription factors forkhead box O1 (FOXO1), NKX6-1, NKX2-2, and PDX1, which did not occur with resveratrol supplementation. Similar changes were observed in human islets where the effects of resveratrol were mediated through Sirtuin 1. These findings have implications for the management of humans with insulin resistance, prediabetes, and diabetes. [ABSTRACT FROM AUTHOR]

As pharmacological data sets become increasingly large and complex, new visual analysis and filtering programs are needed to aid their appreciation. One of the most commonly used methods for visualizing biological data is the Venn diagram. Currently used Venn analysis software often presents multiple problems to biological scientists, in that only a limited number of simultaneous data sets can be analyzed. An improved appreciation of the connectivity between multiple, highly-complex datasets is crucial for the next generation of data analysis of genomic and proteomic data streams. We describe the development of VENNTURE, a program that facilitates visualization of up to six datasets in a user-friendly manner. This program includes versatile output features, where grouped data points can be easily exported into a spreadsheet. To demonstrate its unique experimental utility we applied VENNTURE to a highly complex parallel paradigm, i.e. comparison of multiple G protein-coupled receptor drug dose phosphoproteomic data, in multiple cellular physiological contexts. VENNTURE was able to reliably and simply dissect six complex data sets into easily identifiable groups for straightforward analysis and data output. Applied to complex pharmacological datasets, VENNTURE's improved features and ease of analysis are much improved over currently available Venn diagram programs. VENNTURE enabled the delineation of highly complex patterns of dose-dependent G protein-coupled receptor activity and its dependence on physiological cellular contexts. This study highlights the potential for such a program in fields such as pharmacology, genomics, and bioinformatics. [ABSTRACT FROM AUTHOR]