Physiologic Genomics of Pulmonary Hypertension & RVH

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DESCRIPTION (provided by applicant): Recent breakthroughs in molecular biotechnology are allowing simultaneous measurements of thousands of gene products in a single experiment. Understanding the complexity of such datasets requires a thorough training in both traditional molecular biology and computational sciences. The primary goal of this mentored development award is to provide the Principal Investigator (PI) the training and environmental support necessary to develop into an independent computational molecular biologist in the field of cardiopulmonary biology. This multidisciplinary award will support the PI's training in the disciplines of physiology, molecular biology, computational biology and statistics, and allow him to combine these tools to investigate the molecular basis of pulmonary hypertension and right ventricular remodeling. The primary scientific goal of this project is to discover several key genetic pathways in the pathogenesis of pulmonary hypertension and right ventricular hypertrophy (RVH) by applying rigorous statistical methods to data generated from global gene expression profiling. Two concurrent but interdependent Specific Aims will be pursued. Aim 1 will identify, characterize and confirm a set of temporally and functionally related genes that are crucial in the development and regression of pulmonary hypertension and RVH in mice. Different mouse models of pulmonary hypertension and RVH will be compared to search for common genetic pathways during both the progression and regression of these processes. Additionally, the hypothesis that pulmonary vascular and right ventricular remodeling share several common molecular pathways will be tested. This approach will uniquely provide global insights into the complex molecular processes activated during vascular and ventricular remodeling. Aim 2 will allow the PI to develop and improve computational methods to normalize raw data, determine statistically significant gene expression, and apply clustering strategies to microarray-generated datasets. The results of investigations undertaken in Aim 2 will be applied directly to data generated in Aim 1. Pursuit of a Master's of Science in Computational Biology will further enhance the PI's ability to successfully merge complex mathematical models with datasets resulting from large-scale gene expression profiling. Ultimately, this mentored research career award will allow the PI to develop into an independent computational molecular biologist and pursue an academic research career in the physiologic genomics of the cardiopulmonary system.