Gene mapping

Diabetic retinopathy is an ischemic retinal neovascular disease causing vision loss among adults. The studies presented involve the design and testing of a gene therapy vector to inhibit retinal revascularization, similar to that found in diabetic retinopathy. Gene therapy has proven to be an effective method to introduce therapeutic proteins to treat retinal diseases. Targeting a specific cell type and expression of therapeutic proteins according to the tissue microenvironment should have an advantage over traditional gene therapy by avoiding unwanted transgene expression. Hypoxia plays a significant role in the pathophysiology of many retinal ischemic diseases. Retinal Mèuller cells provide structural and functional support to retinal neurons, as well as playing a significant role in retinal neovascularization. Targeting Mèuller cells may be an effective strategy to prevent retinal neovascularization under pathological conditions. ... The hypoxia regulated, glial specific vector successfully reduced the abnormal neovascularization in the periphery by 93% and reduced the central vasobliterated area by 90%. A substantial amount of exogenous endostatin was produced in the retinas of P17 OIR mice. A significant increase in human endostatin protein and reduced vascular endothelial growth factor (VEGF) were identified by Western blot and ELISA, respectively. These findings suggest hypoxia-regulated, glial cell-specific scAAV mediated gene expression may be useful to prevent blindness found in devastating retinal diseases involving neovascularization.

In this thesis, we propose to discover co-regulated genes using microarray expression data, as well as providing visualization functionalities for domain experts to study relationships among discovered co-regulated genes. To discover co-regulated genes, we first use existing gene selection methods to select a small portion of genes which are relevant to the target diseases, on which we build an ordered similarity matrix by using nearest neighbor based similarity assessment criteria. We then apply a threshold based clustering algorithm named Spectral Clustering to the matrix to obtain a number of clusters. The genes which are clustered together in one cluster represent a group of co-regulated genes and to visualize them, we use Java Swings as the tool and develop a visualization platform which provides functionalities for domain experts to study relationships between different groups of co-regulated genes; study internal structures within each group of genes, and investigate details of each individual gene and of course for gene function prediction. Results are analyzed based on microarray expression datasets collected from brain tumor, lung cancers and leukemia samples.