Faculty Project Proposal Summary
Calendar Year 2014 Program
Faculty Name & Department/School: Stephen Ramsey – dual appointment in
Department of Biomedical Sciences (College of Veterinary Medicine)
School of Electrical Engineering and Computer Science (College of Engineering)
Research Project Title:
Drug repurposing for cardiovascular disease treatment – a bioinformatic approach
The main pathologic condition underlying cardiovascular disease (which is the leading cause of death) is atherosclerosis, an inflammatory disease in which arteries develop lipid-rich lesions called plaque. In inbred laboratory mouse models of atherosclerosis, plaque regression can be induced through genetic recombination-induced lipid lowering as well as through administration of lipid-lowering biologics. In mouse, in vivo lipid lowering appears to trigger the emigration of macrophages (key cells of the innate immune system) from the plaque. Despite intense interest in the macrophage as a potential cellular target for prevention or treatment of atherosclerosis, the intracellular signaling interactions that connect changing lipid levels to macrophage directional motility are poorly understood, and this has limited the yield of new macrophage-specific molecular targets in pathways activated by lipoproteins. Despite these hurdles, transcriptome profiling data have been acquired in plaque macrophages in two mouse models of plaque regression, enabling direct insight into the molecular changes in macrophages in response to lipid lowering. At the same time, the Connectivity Map (CMap) project has amassed a database of transcriptome responses of a promyelocytic-type cell line (HL60) to over 1,300 FDA-approved drugs. In this summer project, the student will computationally screen the CMap 2.0 database using a novel similarity scoring method to rank FDA-approved drugs based on the global similarity of HL60 transcriptome responses (to the drugs), to plaque macrophage responses during plaque regression. Such an approach has recently found an unexpected role for an antiseizure drug in the treatment of IBD (Sirota et al., Science Translational Medicine, 2011). Top-scoring compounds from the analysis will then be more comprehensively evaluated as candidate therapeutics based on the literature on their mechanisms-of-action for their indicated medical uses. This project is an important first step towards future targeted functional studies to assess promising candidate compounds for potential to elicit beneficial cellular responses in vitro and in vivo.
What skills will students obtain in this project?
(1) microarray data analysis
(2) quantitative methods for data-mining
(3) gene functional annotation and pathway annotation enrichment analysis
(4) bioinformatics programming
(5) keeping an electronic lab notebook
(6) PubMed literature research
The lab PI (Dr. Ramsey) will provide instruction in all of these areas.
Student research tasks:
The student will computationally analyze microarray transcriptome profiling data (already acquired in previous experiments) for plaque macrophage responses to in vivo lipid lowering, in two models of plaque regression. The in vivo macrophage transcriptional responses will be quantitatively compared with responses of HL60 cells in the CMap 2.0 database to ~1,300 FDA-approved compounds, to identify candidate drugs that elicit transcriptional responses in HL60 cells that most closely resemble macrophage responses to in vivo lipid lowering. Student will write simple analysis programs in a suitable programming language (such as Python, R, or MATLAB), validate correct functioning of the analysis programs, graphically depict results from analysis programs in a heat-map or related data visualization method, and record research activities in an electronic lab notebook. Student will also document research progress through PowerPoint slides.
Number of hours per week expected of student: 10
Download student application HERE
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