Molecular Determinants Of Disease Research Articles

Development of a New Tomato Torrado Virus-Based Vector Tagged with GFP for Monitoring Virus Movement in Plants.

Green fluorescent protein (GFP)-tagged viruses are basic research tools widely applied in studies concerning molecular determinants of disease during virus infection. Here, we described a new generation of genetically stable infectious clones of tomato torrado virus isolate Kra (ToTVpJL-Kra) that could infect Nicotiana benthamiana and Solanum lycopersicum. Importantly, a modified variant of the viral RNA2—with inserted sGFP (forming, together with virus RNA1, into ToTVpJL-KraGFP)—was engineered as well. RNA2 of ToTVpJL-KraGFP was modified by introducing an additional open reading frame (ORF) of sGFP flanked with an amino acid-coding sequence corresponding to the putative virus protease recognition site. Our further analysis revealed that sGFP-tagged ToTV-Kra was successfully passaged by mechanical inoculation and spread systemically in plants. Therefore, the clone might be applied in studying the in vivo cellular, tissue, and organ-level localization of ToTV during infection. By performing whole-plant imaging, followed by fluorescence and confocal microscopy, the presence of the ToTVpJL-KraGFP-derived fluorescence signal was confirmed in infected plants. All this information was verified by sGFP-specific immunoprecipitation and western blot analysis. The molecular biology of the torradovirus-plant interaction is still poorly characterized; therefore, the results obtained here opened up new possibilities for further research. The application of sGFP-tagged virus infectious clones and their development method can be used for analyzing plant-virus interactions in a wide context of plant pathology.

Genomics of brain tumor imaging.

Imaging genomics combines imaging-defined phenotypes with molecular determinants of disease. Recent studies have examined the relationship between MRI-derived feature sets and gene expression in gliomas, including glioblastoma (GBM). Several groups have identified correlations between the expression of particular molecularly defined oncogenic pathways in GBM and malignant phenotypes on MRI. The combination of clinical, genetic, and imaging data has improved prognostic modeling and has identified potential therapeutic targets. Many challenges remain in fully leveraging the associations between such large datasets, but even current methodology shows promise in helping to craft individually tailored treatments to patients with brain tumors and other diseases.

Learning a cost function for microscope image segmentation.

Quantitative analysis of microscopy images is increasingly important in clinical researchers' efforts to unravel the cellular and molecular determinants of disease, and for pathological analysis of tissue samples. Yet, manual segmentation and measurement of cells or other features in images remains the norm in many fields. We report on a new system that aims for robust and accurate semi-automated analysis of microscope images. A user interactively outlines one or more examples of a target object in a training image. We then learn a cost function for detecting more objects of the same type, either in the same or different images. The cost function is incorporated into an active contour model, which can efficiently determine optimal boundaries by dynamic programming. We validate our approach and compare it to some standard alternatives on three different types of microscopic images: light microscopy of blood cells, light microscopy of muscle tissue sections, and electron microscopy cross-sections of axons and their myelin sheaths.

Personalized Medicine in Diabetes

Multiple genes that are associated with the risk of developing diabetes or the risk of diabetes complications have been identified by candidate gene analysis and genomewide scanning. These molecular markers, together with clinical data and findings from proteomics, metabolomics, pharmacogenetics, and other methods, lead to a consideration of the extent to which personalized approaches can be applied to the treatment of diabetes mellitus. Known genes that cause monogenic subtypes of diabetes are reviewed, and several examples are discussed in which the genotype of an individual with diabetes can direct considerations of preferred choices for glycemic therapy. The extent of characterization of polygenic determinants of type 1 and type 2 diabetes is summarized, and the potential for using this information in personalized management of glycemia and complications in diabetes is discussed. The application and current limitations of proteomic and metabolomic methods in elucidating diabetes heterogeneity is reviewed. There is established heterogeneity in the determinants of diabetes and the risk of diabetes complications. Understanding the basis of this heterogeneity provides an opportunity for personalizing prevention and treatment strategies according to individual patient clinical and molecular characteristics. There is evidence-based support for benefits from a personalized approach to diabetes care in patients with certain monogenic forms of diabetes. It is anticipated that strategies for individualized treatment decisions in the more common forms of diabetes will emerge with expanding knowledge of polygenic factors and other molecular determinants of disease.