UNIVERSITY OF WISCONSIN SYSTEM

The significance and nature of this collaboration fits very well with the thematic areas applying genomics and other high throughput technologies, translating basic science discoveries into new and better treatment, using science to enable health care reform, and reinvigorating the biomedical community. Diabetes predominantly affects the microvascular circulation of the retina and results in a range of microvascular structural changes that are unique to this tissue. The clinical course of diabetic retinopathy is classified into stages reflecting the progression of the disease and the prognostic risk of blindness. It is assumed that the full spectrum of diabetic retinal microangiopathy arises from a continuum, and that one stage of the disease is likely to lead to another, more advanced stage with a greater risk for blindness. These stages include "no retinopathy", "nonproliferative", and (mild, moderate, severe) "proliferative" retinopathy. Two of the earliest histopathological lesions, diffuse thickening of the basement membrane and selective loss of pericytes, go undetected by routine fundus examinations. Our hypothesis is that these changes have significant impact on retinal vascular hemodynamic and oxygenation, which can be detected non-invasively, utilizing a novel multimodal imaging methodology developed by our group. Here we have assembled a team of investigators to take full advantage of the frontiers of computational, engineering, biological, and biochemical analysis to address a major clinical concern, namely detection of early retinopathies associated with diabetes. As a proof of concept, we purpose to further develop and adopt a multimodal retinal functional imaging, complemented with various high throughput biochemical and computational analysis for study of early diabetic changes in novel mouse models of diabetes developed by our group. We will utilize metabolomic changes of the retina and serum prepared from animals with different duration of diabetes, as well as gene expression profiling of retinal samples, and retinal vascular cells under normal and high glucose, to further identify specific early changes during diabetes. Our group has developed and has the expertise to design and implement unique algorithm for large data set analysis and modeling based on specific genotypic and phenotypic variations. In addition, we have computational expertise to improve our imaging capabilities and qualities. Our team will be able to successfully implement the research proposed here and integrate our findings in a meaningful way which could be readily adapted for diagnosis and treatment strategies in humans. Detection and identification of the molecular and cellular bases of early changes that progress to advance stages of the disease will allow for a better diagnosis, prevention and treatment modalities.

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